CN118261544A

CN118261544A - Commercial hyperdigital management method and system

Info

Publication number: CN118261544A
Application number: CN202410380512.9A
Authority: CN
Inventors: 张喜明; 金垚; 钱波
Original assignee: Hangzhou Yuanwulian Technology Co ltd
Current assignee: Hangzhou Yuanwulian Technology Co ltd
Priority date: 2024-03-30
Filing date: 2024-03-30
Publication date: 2024-06-28

Abstract

The application discloses a business super digital management method and system, and relates to the technical field of data management. In the method, a super business area is divided to obtain a plurality of inventory management subareas and commodity settlement subareas; acquiring commodity labels and initial inventory data corresponding to various types of commodities in each inventory management subarea; acquiring a commodity image of a first subarea, and acquiring the commodity weight corresponding to each type of commodity; judging whether inventory change type commodities exist or not based on the image analysis result and the weight analysis result; when the commodity label corresponding to the commodity of the inventory variation type is detected in the commodity settlement subarea in a preset first time period under the condition that the commodity of the inventory variation type exists, first inventory variation data are obtained; first corrected inventory data corresponding to various types of goods is obtained based on the respective initial inventory data and the first inventory variation data. By implementing the technical scheme of the application, the accuracy of inventory data statistics can be effectively improved.

Description

Commercial hyperdigital management method and system

Technical Field

The application relates to the technical field of data management, in particular to a business super-digital management method and system.

Background

The super market refers to the combination of a market and a supermarket, combines the two forms, is a large retail business unit generally, provides various commodities ranging from basic living necessities to household appliances, clothes and the like, and meets diversified shopping requirements of consumers. In the current super business operation, inventory management is a complex and key link, and is directly related to super business operation management. Traditional inventory management is mainly used for managing inventory through manual inspection and periodical inventory, and meanwhile, a traditional ERP system is used for data recording and processing. However, due to the large number of commodities in the commercial process, the traditional method relies on manual inventory and periodical update of inventory data, and data delay and error record are very easy to occur when inventory data are counted, so that the inventory data are inaccurate.

Therefore, how to improve the accuracy of inventory statistics is a technical problem to be solved.

Disclosure of Invention

The application provides a business super-digital management method and a system, which can effectively improve the accuracy of inventory data statistics.

In a first aspect, the present application provides a method for super-digital management of a business, the method comprising: dividing the super-integral business area to obtain a plurality of inventory management subareas and a commodity settlement subarea; acquiring commodity labels and initial inventory data corresponding to various types of commodities in each inventory management subarea; acquiring first sub-area commodity images corresponding to all types of commodities in each inventory management sub-area acquired by each camera in real time, and acquiring commodity weights corresponding to each type of commodity in each inventory management sub-area acquired by each weight sensor in real time, wherein the cameras and the weight sensors are arranged in each inventory management sub-area; analyzing the commodity images of each first subarea to obtain an image analysis result; analyzing the weights of the commodities corresponding to the various types of commodities to obtain a weight analysis result; judging whether inventory variation type commodities exist or not based on the image analysis result and the weight analysis result; when the commodity label corresponding to the commodity of the stock change type is detected in the commodity settlement subarea in a preset first time period under the condition that the commodity of the stock change type exists, first stock change data are obtained; and obtaining first corrected inventory data corresponding to various types of commodities based on the initial inventory data and the first inventory change data.

By adopting the technical scheme, a plurality of inventory management subareas and a commodity settlement subarea are obtained by dividing the super-integral business area, so that more accurate management of super-business inventory space is realized; the commodity labels and initial inventory data corresponding to various types of commodities in each inventory management subarea are obtained, so that the comprehensiveness and initial accuracy of inventory information are ensured, and basic data are provided for subsequent inventory management and adjustment; the images and the weights of the commodities in the inventory management subarea acquired by the cameras and the weight sensors are acquired in real time, so that dynamic monitoring of commodity states is realized, and the real-time performance and the accuracy of inventory management are improved; the image analysis result is obtained by analyzing the commodity image of the first subarea, and the weight analysis result is obtained by analyzing the weight corresponding to the commodity, so that two data dimensions of vision and weight are effectively combined, and the accuracy of inventory change identification is improved; judging whether inventory change type commodities exist or not based on the images and weight analysis results, so that inventory change is rapidly and accurately identified; when the commodity label corresponding to the commodity of the inventory variation type is detected in the commodity settlement subarea within the preset first time period under the condition that the commodity of the inventory variation type exists, the first inventory variation data is obtained, so that the timely and accurate recording of the inventory variation data is ensured, and the first correction inventory data corresponding to the commodity of various types is obtained based on the initial inventory data and the first inventory variation data, so that the timely correction and updating of the inventory data are realized, and the accuracy of the inventory data statistics is further effectively improved.

Optionally, the analyzing the commodity image of each first sub-area to obtain an image analysis result specifically includes: determining a starting image and a stopping image based on a plurality of continuous first sub-region commodity images corresponding to the inventory management sub-regions; performing image analysis on the initial image and the termination image, and judging whether a difference exists between the initial image and the termination image; when no difference exists between the initial image and the termination image, determining the image analysis result as a first result; and when the difference exists between the starting image and the ending image, determining the image analysis result as a second result.

By adopting the technical scheme, the initial image and the termination image are determined based on a plurality of continuous first sub-region commodity images corresponding to each inventory management sub-region, so that accurate monitoring of commodity storage state change is realized, and an intuitive visual basis is provided for inventory change analysis. And judging whether the difference exists between the initial image and the termination image or not by carrying out image analysis on the initial image and the termination image, thereby effectively identifying the change of the stock.

Optionally, the analyzing the weights of the commodities corresponding to the various types of commodities to obtain a weight analysis result specifically includes: analyzing the weights of the commodities corresponding to the various types of commodities, and determining the variation errors of the weights of the commodities corresponding to the various types of commodities in a preset second time period; judging whether the variation error is in a preset error range or not; when the variation error is within a preset error range, determining the gravimetric analysis result as a third result; and when the variation error is not in the preset error range, determining the gravimetric analysis result as a fourth result.

By adopting the technical scheme, the change errors of the commodity weights corresponding to the commodities of various types in the preset second time period are determined by analyzing the commodity weights corresponding to the commodities of various types, so that accurate monitoring of commodity weight change is realized, and important quantitative data is provided for inventory change. By judging whether the variation error is in the preset error range, false alarm and missing alarm are effectively reduced, and the accuracy of inventory management is improved.

Optionally, the determining whether the inventory change type commodity exists based on the image analysis result and the weight analysis result specifically includes: determining that the inventory variation type commodity is not present when the image analysis result is the first result and the gravimetric analysis result is the third result; when the image analysis result is the second result and the gravimetric analysis result is the fourth result, it is determined that the inventory variation type commodity is present.

Optionally, when the inventory change type commodity exists, in a preset first period of time, when a commodity label corresponding to the inventory change type commodity is detected in the commodity settlement sub-area, first inventory change data is obtained, which specifically includes: determining a first inventory management subarea corresponding to the inventory change type commodity when the inventory change type commodity exists; when the commodity label corresponding to the inventory change type commodity is detected in the commodity settlement subarea, determining the commodity quantity corresponding to the inventory change type commodity; and obtaining the first inventory change data based on the commodity label corresponding to the inventory change type commodity, the first inventory management subarea and the commodity quantity corresponding to the inventory change type commodity.

Optionally, after the determining whether the inventory variation type commodity exists based on the image analysis result and the weight analysis result, the method further includes: when the commodity label corresponding to the commodity of the inventory change type is not detected in the commodity settlement subarea in the first preset time period under the condition that the commodity of the inventory change type exists, determining a first inventory management subarea corresponding to the commodity of the inventory change type; determining a plurality of second inventory management sub-areas proximate to the first inventory management sub-area; acquiring second sub-region commodity images corresponding to all types of commodities in each second inventory management sub-region, wherein the second sub-region commodity images are acquired by cameras arranged in each second inventory management sub-region; obtaining second inventory variation data based on the second sub-region commodity image; and obtaining second corrected inventory data corresponding to various types of commodities based on the initial inventory data and the second inventory change data.

By adopting the technical scheme, when the commodity label corresponding to the inventory change type commodity is not detected in the commodity settlement sub-area in the first preset time period under the condition that the inventory change type commodity exists, the first inventory management sub-area corresponding to the inventory change type commodity is determined, so that the initial positioning of the uncompleted and misplaced commodity is realized, and key information is provided for further inventory management and commodity tracking. By determining a plurality of second inventory management sub-areas similar to the first inventory management sub-areas, the range for searching for misplaced goods is enlarged, the possibility that misplaced goods are found and corrected is improved, and the accuracy and the completeness of inventory are optimized. The second sub-region commodity images corresponding to all types of commodities in each second inventory management sub-region are acquired by the cameras arranged in each second inventory management sub-region, so that misplaced commodities are further accurately identified and positioned by utilizing visual information, and the efficiency and the accuracy of inventory change identification are improved; the second inventory change data is obtained based on the second sub-region commodity image, so that accurate record of inventory change caused by misplaced commodities is realized, basis is provided for correction and update of inventory data, and the second correction inventory data corresponding to various types of commodities is obtained based on each initial inventory data and the second inventory change data, so that timely update and accuracy of inventory information are ensured, inventory errors caused by misplaced commodities are reduced, and further the accuracy of inventory data statistics is effectively improved.

Optionally, the obtaining second inventory variation data based on the second sub-region commodity image specifically includes: acquiring an inventory change type commodity image corresponding to the inventory change type commodity; acquiring inventory change type commodity characteristics corresponding to the inventory change type commodity based on the inventory change type commodity image; extracting a plurality of commodity features of the second subarea based on the commodity image of the second subarea; judging whether commodity characteristics matched with the inventory variation type commodity characteristics exist in the commodity characteristics of the second subareas or not; when the commodity characteristics matched with the inventory change type commodity characteristics exist in the plurality of second sub-area commodity characteristics, determining a second inventory management sub-area where the inventory change type commodity is currently located; and obtaining the second inventory change data based on the commodity label corresponding to the inventory change type commodity and the second inventory management subarea.

In a second aspect of the present application, there is provided a quotient hyperdigital management system, the system including a region dividing module, an acquiring module, and a processing module; the regional division module is used for dividing the super-integral region of the commodity to obtain a plurality of inventory management subregions and a commodity settlement subregion; the acquisition module is used for acquiring commodity labels and initial inventory data corresponding to various types of commodities in each inventory management subarea; the acquisition module is further used for acquiring first sub-area commodity images corresponding to all types of commodities in each inventory management sub-area acquired by each camera in real time, and acquiring commodity weights corresponding to each type of commodity in each inventory management sub-area acquired by each weight sensor in real time, wherein the cameras and the weight sensors are arranged in each inventory management sub-area; the processing module is used for analyzing the commodity images of the first subareas to obtain an image analysis result; the processing module is also used for analyzing the weights of the commodities corresponding to the various types of commodities to obtain a weight analysis result; the processing module is further used for judging whether inventory variation type commodities exist or not based on the image analysis result and the weight analysis result; the processing module is further configured to obtain first inventory variation data when a commodity label corresponding to the inventory variation type commodity is detected in the commodity settlement sub-area within a preset first time period when the inventory variation type commodity exists; the processing module is further configured to obtain first corrected inventory data corresponding to various types of commodities based on the initial inventory data and the first inventory variation data.

In a third aspect the application provides an electronic device comprising a processor, a memory for storing instructions, a user interface and a network interface for communicating to other devices, the processor being arranged to execute the instructions stored in the memory to cause the electronic device to perform a method according to any of the first aspects of the application.

In a fourth aspect of the application a computer readable storage medium is provided, storing a computer program capable of being loaded by a processor and performing a method according to any of the first aspects of the application.

In summary, one or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:

1. Dividing the super-commodity whole area to obtain a plurality of inventory management subareas and a commodity settlement subarea, so that more accurate management of super-commodity inventory space is realized; the commodity labels and initial inventory data corresponding to various types of commodities in each inventory management subarea are obtained, so that the comprehensiveness and initial accuracy of inventory information are ensured, and basic data are provided for subsequent inventory management and adjustment; the images and the weights of the commodities in the inventory management subarea acquired by the cameras and the weight sensors are acquired in real time, so that dynamic monitoring of commodity states is realized, and the real-time performance and the accuracy of inventory management are improved; the image analysis result is obtained by analyzing the commodity image of the first subarea, and the weight analysis result is obtained by analyzing the weight corresponding to the commodity, so that two data dimensions of vision and weight are effectively combined, and the accuracy of inventory change identification is improved; judging whether inventory change type commodities exist or not based on the images and weight analysis results, so that inventory change is rapidly and accurately identified; when the commodity label corresponding to the commodity of the inventory variation type is detected in the commodity settlement subarea within the preset first time period under the condition that the commodity of the inventory variation type exists, the first inventory variation data is obtained, so that the timely and accurate recording of the inventory variation data is ensured, and the first correction inventory data corresponding to the commodity of various types is obtained based on the initial inventory data and the first inventory variation data, so that the timely correction and updating of the inventory data are realized, and the accuracy of the inventory data statistics is further effectively improved.

2. When the commodity label corresponding to the commodity of the inventory variation type is not detected in the commodity settlement subarea in a first preset time period under the condition that the commodity of the inventory variation type exists, a first inventory management subarea corresponding to the commodity of the inventory variation type is determined, so that the preliminary positioning of the unqualified and misplaced commodity is realized, and key information is provided for further inventory management and commodity tracking. By determining a plurality of second inventory management sub-areas similar to the first inventory management sub-areas, the range for searching for misplaced goods is enlarged, the possibility that misplaced goods are found and corrected is improved, and the accuracy and the completeness of inventory are optimized. The second sub-region commodity images corresponding to all types of commodities in each second inventory management sub-region are acquired by the cameras arranged in each second inventory management sub-region, so that misplaced commodities are further accurately identified and positioned by utilizing visual information, and the efficiency and the accuracy of inventory change identification are improved; the second inventory change data is obtained based on the second sub-region commodity image, so that accurate record of inventory change caused by misplaced commodities is realized, basis is provided for correction and update of inventory data, and the second correction inventory data corresponding to various types of commodities is obtained based on each initial inventory data and the second inventory change data, so that timely update and accuracy of inventory information are ensured, inventory errors caused by misplaced commodities are reduced, and further the accuracy of inventory data statistics is effectively improved.

Drawings

FIG. 1 is a schematic flow chart of a business super-digital management method according to an embodiment of the present application;

FIG. 2 is a second flow chart of a business super-digital management method according to the embodiment of the application;

FIG. 3 is a schematic diagram of a business super-digital management system according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Reference numerals illustrate: 400. an electronic device; 401. a processor; 402. a communication bus; 403. a user interface; 404. a network interface; 405. a memory.

Detailed Description

In order that those skilled in the art will better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments.

In describing embodiments of the present application, words such as "for example" or "for example" are used to mean serving as examples, illustrations, or descriptions. Any embodiment or design described herein as "such as" or "for example" in embodiments of the application should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "or" for example "is intended to present related concepts in a concrete fashion.

In the description of embodiments of the application, the term "plurality" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating an indicated technical feature. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.

The application provides a business super-digital management method, and referring to fig. 1, one of flow diagrams of the business super-digital management method provided by the embodiment of the application is shown. The method comprises the steps S11-S18, wherein the steps are as follows:

step S11: dividing the super-integral business area to obtain a plurality of inventory management subareas and a commodity settlement subarea.

Specifically, in the technical scheme, the super-integral area is divided to obtain a plurality of inventory management subareas and a commodity settlement subarea, and the step aims to improve the efficiency and the precision of inventory monitoring through optimized management in space.

In the implementation of this step, first, detailed analysis of the overall layout of the business process is required, including customer flow paths, product classification and layout, and existing security monitoring facility layout. Based on the analysis results, the super-market space is first divided into different commodity areas, such as food, beverage, clothing, household appliances and other commodity areas, by combining super-market sales data and customer purchasing behavior patterns. The various commodity areas are divided into fine management units of a single goods shelf, namely inventory management subareas. For example, if there are three shelves in the beverage product area, namely shelf a, shelf B, and shelf C, then shelf a, shelf B, and shelf C are each one inventory management sub-area. After the multiple inventory management subareas are obtained, cameras and weight sensors are deployed in each inventory management subarea, so that real-time monitoring of inventory and variation of the commodities in the area is realized.

In addition, a special commodity settlement subarea is separately divided, and a bar code scanner and an RFID reader are arranged in the commodity settlement subarea and are used for processing the settlement process of all commodities, so that the accurate data input system of each transaction is ensured.

Step S12: and acquiring commodity labels and initial inventory data corresponding to various types of commodities in each inventory management subarea.

The specific implementation of the step involves the labeling treatment and the initial inventory data entry of all the commodities in the super business. First, each item is assigned a unique identifier, i.e., item tags, using bar code or RFID (radio frequency identification) technology, which contain basic information about the item, such as name, category, price, vendor, etc. Next, the initial inventory data for each item within each inventory management sub-area is collected by a scanning device, such as a bar code scanner or RFID reader, and these data are entered into the super-merchant inventory management system.

Step S13: and acquiring commodity images of first subareas corresponding to all types of commodities in each inventory management subarea acquired by each camera in real time, and acquiring commodity weights corresponding to each type of commodity in each inventory management subarea acquired by each weight sensor in real time, wherein the cameras and the weight sensors are arranged in each inventory management subarea.

Specifically, a dynamic and real-time inventory monitoring mechanism is provided for the business process by acquiring the commodity image and commodity weight of the first sub-area corresponding to all types of commodities in each inventory management sub-area in real time. The implementation of the step is to realize the immediate capturing and analysis of the commodity inventory status, thereby effectively managing the inventory, reducing the inventory error and improving the accuracy and efficiency of inventory management. By arranging cameras and weight sensors in each inventory management subarea, the commodity superstaff can monitor inventory change of commodities in real time, discover and solve inventory problems in time, and further optimize inventory management flow.

The implementation of the step firstly involves the installation of high definition cameras and accurate weight sensors in each inventory management sub-area. The camera is used for capturing visual images of the commodity in real time, and the weight sensor is used for measuring and recording weight information of the commodity. These devices require connection to a super management system in order to transmit the captured image and weight data in real time. Through advanced image recognition technology and data analysis algorithm, the super management system can automatically recognize the commodity in the image, monitor the adding or removing condition of the commodity, and analyze the weight change of the commodity according to the data provided by the weight sensor, so as to update the inventory information in real time.

Step S14: and analyzing the commodity images of each first subarea to obtain an image analysis result.

Specifically, in step S14, the commodity images of the first sub-areas are analyzed to capture the access dynamics of the commodity by visual data, thereby realizing the real-time monitoring of the inventory change. This step is performed to automatically identify and record the change in the merchandise, including the addition, movement or removal of the merchandise, using image recognition techniques. The method is introduced to improve the automation degree and the accuracy of inventory management and reduce the frequency and the error of manual inventory.

The implementation of step S14 involves several key links. First, commodity images are captured in real time by using the high-definition cameras disposed in the respective inventory management sub-areas in step S13. Next, the captured image data is transmitted to a management system, and the image is subjected to depth analysis based on an image processing algorithm and a machine learning algorithm such as a Convolutional Neural Network (CNN), thereby identifying commodity information and a change state in the image.

In step S14, in particular, it is determined whether or not the commodity has changed within a certain period of time by comparing the continuously captured commodity images, i.e., the start image and the end image. If no significant difference is found between the starting image and the ending image, the system will determine that "no change" has occurred (first result). In contrast, if a significant difference is found, such as the addition or removal of merchandise, the system determines that "change" (second outcome). The process is automatically completed through an algorithm, so that the judging speed and accuracy are greatly improved.

Next, a specific embodiment of step S14 will be further described.

In one possible implementation, step S14 specifically includes the following steps:

A start image and a stop image are determined based on a plurality of consecutive first sub-region commodity images corresponding to each inventory management sub-region.

In particular, it is desirable to ensure that cameras deployed in each inventory management sub-area are able to continuously and stably capture high definition commodity images. The time window for analysis is then set by the management system, such as automatically selecting one image as the starting image at regular intervals (e.g., every minute), the next image as the ending image, or the length of the time window is set as desired. The system will automatically record the time stamps and associated metadata for the two images and store the images in a database for subsequent analysis.

And carrying out image analysis on the initial image and the termination image, and judging whether a difference exists between the initial image and the termination image.

In the implementation of this step, the initial image and the final image need to be preprocessed by using an image processing technology, for example, the image is resized, the image is enhanced, and the accuracy of the image analysis is ensured. The two images are then analyzed using image recognition techniques, such as by a deep learning model such as Convolutional Neural Network (CNN). These models can identify the merchandise in the image and compare the differences in the merchandise in the starting and ending images, such as an increase or decrease in the number of merchandise, the appearance of new merchandise, or the disappearance of original merchandise. In this process, the system automatically calculates and identifies changes between images, and determines if there is a significant difference.

When there is no difference between the start image and the end image, determining the image analysis result as a first result. When there is a difference between the start image and the end image, determining the image analysis result as a second result.

In particular, when the system determines through image analysis that no significant difference is found between the starting image and the ending image, this indicates that no addition, removal, or significant movement of the merchandise of the inventory management subregion has occurred during the period of time of the analysis. In this case, the system will not make adjustments to the inventory data because there is no sign of inventory variation. The effect of implementing this step is to maintain the stability of the current inventory status, avoid unnecessary inventory adjustment operations, and reduce management confusion that may be caused by false alarms.

When the system recognizes that there is a difference between the start image and the end image, it is determined that an inventory change has occurred. Such a change may be the goods being purchased, restocked, or otherwise moved. In this case, the system needs to further analyze the specific case of the discrepancy and update the inventory data according to the nature of the discrepancy. For example, if it is detected that an item has disappeared from the shelf without appearing in the termination image, the system may record it as a sale or removal and reduce the inventory count accordingly. The implementation of the step has the effects of ensuring the dynamic updating and the accuracy of the inventory data and providing timely and accurate inventory information for the business process.

Step S15: and analyzing the weights of the commodities corresponding to the various types of commodities to obtain a weight analysis result.

Specifically, step S15 is directed to performing in-depth analysis of commodity weights corresponding to various types of commodities to obtain a weight analysis result. This step is based on real-time monitoring of the weight of the commodity for each type of commodity in the inventory management sub-area, with the aim of identifying dynamic changes in inventory, including the addition or removal of commodities, through changes in weight. The main purpose of this step is to use the weight change as another monitoring dimension of the inventory change, thereby supplementing the blind spots that may exist in the image analysis and improving the accuracy and timeliness of inventory change monitoring.

Implementing step S15 involves first installing accurate weight sensors in each inventory management sub-area that can capture and record the total weight of the items on the shelves in real time. Weight data for each sub-area is then collected periodically by the management system and analyzed in real time. The system determines whether a substantial change in weight exists by comparing the weight change over a predetermined period of time. If no weight change is found within a predetermined error range, the system determines that there is no inventory change (third result); conversely, if the weight change is outside of the predetermined error range, the system recognizes that the inventory has changed (fourth outcome).

Next, a specific embodiment of step S15 will be further described.

In one possible implementation, step S15 specifically includes the following steps:

and analyzing the weights of the commodities corresponding to the commodities of various types, and determining the variation errors of the weights of the commodities corresponding to the commodities of various types in a preset second time period.

Embodying this step involves installing high precision weight sensors in each inventory management sub-area and ensuring that these sensors can capture and record the total weight of the items on the shelves in real time. These weight data are collected by the management system periodically, in particular during a preset second period of time, such as every minute, to capture weight changes over a short period of time.

And judging whether the variation error is in a preset error range.

In the implementation of this step, it is first necessary to determine a reasonable weight change error range corresponding to various types of commodities, which may be different from commodity to commodity, based on historical data and actual operation experience. For example, for some lighter weight and more expensive items, the error range may be set smaller; while for heavier and less expensive bulk goods, the error range may be relatively large. And then analyzing the collected weight data through the management system, calculating the weight change error of each commodity in a preset second time period, and comparing the weight change error with a preset error range.

And when the variation error is within a preset error range, determining the gravimetric analysis result as a third result. And when the variation error is not within the preset error range, determining the gravimetric analysis result as a fourth result.

Specifically, when the system analysis determines that the variation error of the commodity weight is within a preset reasonable range, the variation is considered to be normal fluctuation, and no obvious sign of stock increase or decrease exists. In this case, the system does not make adjustments to the inventory data. The effect of this step is to reduce unnecessary inventory adjustments due to normal weight fluctuations or small scale errors.

When the system analysis determines that the variation error in the weight of the commodity exceeds the preset error range, this generally means that an actual inventory change occurs, such as a commodity purchase or restocking. In this case, the system will record this change as an inventory change, updating the inventory data accordingly. The effect of implementing this step is to ensure the real-time and accuracy of the inventory information, enabling the manufacturer to respond to actual inventory changes in time.

Step S16: and judging whether the inventory change type commodity exists or not based on the image analysis result and the weight analysis result.

In particular, all types of inventory variations may not be captured completely accurately due to individual image analysis or weight monitoring, e.g., movement of certain merchandise may not be easily captured visually or weight changes are too small to be detected. Therefore, in step S16, based on the image analysis result and the weight analysis result obtained in the foregoing steps, the task of the system is to integrate the data of these two dimensions to determine whether or not there is a commodity of the stock change type. This step is performed to enhance the accuracy of inventory variation determinations using two complementary monitoring techniques, image analysis and weight monitoring.

In one possible implementation, step S16 specifically includes the following steps:

And when the image analysis result is the first result and the gravimetric analysis result is the third result, determining that the inventory variation type commodity is not present. And when the image analysis result is the second result and the weight analysis result is the fourth result, determining that the inventory change type commodity exists.

Embodying step S16 involves first collecting and processing the image analysis results (first or second results) and the gravimetric analysis results (third or fourth results) from the inventory management sub-area. Then, the system integrates the data of the two dimensions through a preset logic rule. If the image analysis result shows that the variation does not occur (first result), and the weight analysis result also shows that the weight variation is within a preset error range (third result), the system judges that the inventory variation type commodity does not exist. In contrast, if the image analysis result shows that a change has occurred (second result) while the weight analysis result shows that the weight change has exceeded the preset error range (fourth result), the system determines that an inventory change type commodity exists.

Step S17: when the commodity label corresponding to the commodity of the inventory change type is detected in the commodity settlement subarea in a preset first time period under the condition that the commodity of the inventory change type exists, first inventory change data are obtained.

Since correctly recording and analyzing the data in the commodity settlement process is a key to ensuring the accuracy of the inventory, step S17 focuses on detecting the commodity label corresponding to the commodity of the inventory variation type in the commodity settlement sub-area for a preset first period of time when the commodity of the inventory variation type exists, so as to obtain the first inventory variation data, thereby ensuring that the commodity is correctly recorded and tracked in the flow from the inventory management sub-area through the settlement sub-area.

In one possible implementation, step S17 specifically includes the following steps:

And when the inventory change type commodity exists, determining a first inventory management subarea corresponding to the inventory change type commodity.

In particular, first, information about the commodity at settlement is captured using a commodity identification technique (such as a bar code scanner or RFID reader) deployed in the commodity settlement sub-area. The system then determines, from the captured merchandise information, the inventory management sub-area in which the merchandise was originally located by a database query.

And when the commodity label corresponding to the inventory change type commodity is detected in the commodity settlement subarea, determining the commodity quantity corresponding to the inventory change type commodity.

In particular, the method is implemented by first capturing information of each commodity passing through the settlement point, including a commodity label, by using a high-efficiency commodity identification technology such as a bar code scanner or an RFID reader in the commodity settlement sub-area. The system will then automatically count the type and quantity of the settled items based on the captured item tag information. In this process, the system needs to be able to process and identify a large number of different types of merchandise tags without error.

The first inventory change data is obtained based on the commodity label corresponding to the inventory change type commodity, the first inventory management subarea and the commodity quantity corresponding to the inventory change type commodity.

Specifically, first, the specific information of each commodity captured through the commodity settlement sub-area identification technology in the previous step, such as a commodity label, is combined with the original inventory management sub-area information of the commodity. The system then integrates this information with the actual sales volume of the good to form a detailed inventory change data record.

Step S18: first corrected inventory data corresponding to various types of goods is obtained based on the respective initial inventory data and the first inventory variation data.

Specifically, first, the system needs to collect and aggregate initial inventory data within all relevant inventory management sub-areas, including basic information about the type, quantity, and location of the items. Subsequently, the first inventory change data obtained in the previous step is combined, and the inventory change data is applied to the initial inventory data to calculate and adjust, so as to generate corrected inventory data.

For example, a customer purchases one bottle of cola and two bottles of mineral water in a beverage commodity area, wherein the cola is placed on a shelf a, the mineral water is placed on a shelf B, the initial stock of cola on the shelf a is x bottles, and the initial stock of mineral water on the shelf B is y bottles. When the customer completes the payment in the merchandise settlement sub-area, the first revised inventory data displayed by the system may be in the form of: subtracting one bottle of cola from a shelf A in the super beverage commodity area, wherein the rest of cola is x-1 bottles; two bottles of mineral water are deducted from a shelf B in the super beverage commodity area, and the rest of mineral water in the shelf B is y-2 bottles.

In a possible implementation manner, after step S17, referring to fig. 2, a second flow chart of a business super-digital management method provided by the embodiment of the present application is shown. The method further comprises the steps of S21-S25:

Step S21: and when the commodity label corresponding to the commodity with the inventory change type is not detected in the commodity settlement subarea in a preset first time period under the condition that the commodity with the inventory change type exists, determining a first inventory management subarea corresponding to the commodity with the inventory change type.

Specifically, in step S21, the scenario faced is when there is an inventory change type commodity, i.e., a commodity is not intended to be restored to its original position after purchase by the customer. At this time, the commodity labels corresponding to the inventory change type commodities are not detected in the commodity settlement sub-area within the preset first time period. Therefore, it is desirable to track specific inventory management sub-areas corresponding to these items using the item tags and data in the inventory management system.

Step S22: a plurality of second inventory management sub-areas proximate to the first inventory management sub-area is determined.

Step S22 is directed to determining a plurality of second inventory management sub-areas that are proximate to the first inventory management sub-area. In the event that the customer does not want to restore the merchandise correctly after purchase, this step is performed in order to expand the search area to more accurately locate and restore the merchandise to its correct inventory location.

Implementation of step S22 involves first using the map of the business process and the data in the inventory management system to analyze and determine which inventory management sub-areas are adjacent to the first inventory management sub-area where the inventory change occurred. In analyzing a plurality of second inventory management sub-areas that are similar to the first inventory management sub-area, specific layout features of the business process, such as connections of channels, similarity of commodity categories, etc., need to be considered to ensure that the selected second inventory management sub-area is logically closely related to the first inventory management sub-area. The system then marks these adjacent inventory management sub-areas as potential inventory misplacement areas for use by subsequent inventory reconciliation and inventory restoration operations.

Step S23: and acquiring second sub-area commodity images corresponding to all types of commodities in each second inventory management sub-area, wherein the second sub-area commodity images are acquired by cameras arranged in each second inventory management sub-area.

Specifically, the specific implementation method of step S23 is similar to the specific implementation method of step S13 for obtaining the first sub-area commodity image corresponding to all types of commodities in each inventory management sub-area collected by each camera, so that redundant description is omitted here.

Step S24: and obtaining second inventory variation data based on the second sub-region commodity image.

The core objective of step S24 is to obtain second inventory variation data based on the second sub-region commodity image. This step is performed to ensure that inventory information inconsistencies caused by customer misplacement can be identified and handled in time, and that those items that are not restored in situ are correctly identified in the second inventory management sub-area and inventory data updated accordingly.

The implementation of step S24 involves several key links. First, the system needs to analyze the merchandise image in the second inventory management sub-area acquired from step S23 in detail. This process typically involves advanced image recognition techniques, such as using artificial intelligence and machine learning algorithms to identify the items in the image and match the known inventory change type items. The system then integrates the identified inventory change type items and their quantity information to form second inventory change data reflecting actual inventory change conditions resulting from customer misplacement behavior.

Next, a specific embodiment of step S24 will be further described.

In one possible implementation, step S24 specifically includes the following steps:

And acquiring an inventory change type commodity image corresponding to the inventory change type commodity.

Specifically, the inventory change type commodity image corresponding to the inventory change type commodity needs to ensure that details of the commodity, such as labels, packages and other characteristic information of the commodity, can be clearly captured so as to facilitate subsequent image recognition and matching work.

And obtaining the inventory change type commodity characteristics corresponding to the inventory change type commodity based on the inventory change type commodity image.

In particular, the system first analyzes the inventory change type merchandise image obtained in the previous step using image processing and analysis techniques, such as machine learning or deep learning algorithms, including but not limited to applying image recognition algorithms to identify specific features and patterns in the merchandise image, such as bar codes, brand marks, colors, shapes, etc. The system then encodes and stores these extracted feature data to facilitate subsequent merchandise matching and identification operations.

And extracting a plurality of commodity features of the second subarea based on the commodity images of the second subarea.

In the specific implementation of this step, first, the commodity images in the second sub-area collected in step S23 are used to analyze the images by using image processing and analysis techniques, such as machine learning or deep learning models, so as to extract key commodity feature information. In this process, the image needs to be preprocessed, such as resizing, enhancing contrast, etc., to improve the accuracy of feature extraction. The system then encodes and stores the extracted commodity features in a sorted manner, providing base data for subsequent commodity matching and identification.

And judging whether the commodity characteristics matched with the inventory change type commodity characteristics exist in the commodity characteristics of the plurality of second subareas.

In particular, the system first uses the inventory change type commodity features and the second sub-region commodity features extracted in the previous steps to compare and match the features through image recognition and pattern matching techniques, such as machine learning algorithms. In this process, the system will evaluate and identify items in the second sub-region commodity characteristics database that match the known inventory variation type commodity characteristics. To improve the accuracy of the matching, multiple feature dimensions, including but not limited to shape, color, size, and label information, need to be considered

And when the commodity characteristics matched with the inventory change type commodity characteristics exist in the plurality of second sub-area commodity characteristics, determining a second inventory management sub-area in which the inventory change type commodity is currently located.

Specifically, once the matching of the features of the merchandise is successful, the system will automatically record specific location information of the second inventory management sub-area where the successfully matched merchandise is located. This process requires accurate understanding of the super-internal layout of the merchant and accurate marking of the location of the merchandise, ensuring that the specific location of the misplaced merchandise can be quickly and accurately found. The system then synchronizes the location information with the super inventory management system, and updates the inventory status and location information of the corresponding merchandise to reflect its current actual location.

And obtaining second inventory change data based on the commodity label corresponding to the inventory change type commodity and the second inventory management subarea.

Specifically, this step is performed to ensure that the merchant can accurately record and update the inventory status of the merchandise whose position is changed due to the misplaced behavior of the customer, and therefore, the merchandise tag corresponding to the merchandise of the inventory change type is combined with the second inventory management sub-area to obtain the second inventory change data, and the inventory record is ensured to accurately reflect the actual status and position of each merchandise.

Note that, when there is no commodity feature matching the inventory change type commodity feature in the plurality of second sub-area commodity features, it is described that the inventory change type commodity misplaced by the customer cannot be identified in these specific second inventory management sub-areas. In this case, it is necessary to re-evaluate the first inventory management sub-area and other potential areas around it, taking into account whether there are uncovered areas or areas where misplaced items may be brought further by customers. Based on the re-evaluation results, the inventory data is updated to reflect the failure to identify misplaced items in the second sub-area, and these items are marked as missing or their inventory status is adjusted to maintain the accuracy of the inventory records.

Step S25: and obtaining second corrected inventory data corresponding to various types of commodities based on the initial inventory data and the second inventory change data.

Specifically, first, based on all of the misplaced commodity cases identified and processed in the previous step, the generated second inventory change data is collected and summarized, including the inventory increase or decrease cases that occur due to the misplacement of the commodity. And then, the second inventory change data are applied to the initial inventory data to obtain second corrected inventory data corresponding to various types of commodities so as to adjust the quantity and position information of the misplaced commodities and ensure that the inventory records accurately reflect the actual state and position of each commodity.

It should be noted that, this scheme can also cover all key areas in the business through the high accuracy camera system of deployment, catches commodity's removal and customer's activity condition in real time. The video data is analyzed by using an image recognition technology, areas with frequent movement pins and frequent customer activities are identified, and the areas are marked. And by combining an inventory management system, the inventory plans of the areas are adjusted, the inventory frequency is increased, and the accuracy of inventory data is ensured. And customer behavior data such as walking track, stay time and the like are collected based on the high-precision camera system. Consumer characteristics of the consumer are identified in combination with consumer purchase history and preference analysis. Based on the data, the sales promotion placement strategy of the commodity is optimized, such as placing the commodity with high attention in the area with large customer flow, and the commodity classification and display mode is adjusted according to the customer preference.

Referring to fig. 3, a schematic structural diagram of a quotient super-digital management system provided by an embodiment of the present application is shown, where the system includes a region dividing module, an obtaining module and a processing module; the regional division module is used for dividing the super-integral region to obtain a plurality of inventory management subareas and a commodity settlement subarea; the acquisition module is used for acquiring commodity labels and initial inventory data corresponding to various types of commodities in each inventory management subarea; the acquisition module is also used for acquiring the first sub-area commodity images corresponding to all types of commodities in each inventory management sub-area acquired by each camera in real time and acquiring the commodity weight corresponding to each type of commodity in each inventory management sub-area acquired by each weight sensor in real time, wherein the cameras and the weight sensors are arranged in each inventory management sub-area; the processing module is used for analyzing the commodity images of each first subarea to obtain an image analysis result; the processing module is also used for analyzing the weights of the commodities corresponding to various types of commodities to obtain a weight analysis result; the processing module is also used for judging whether inventory variation type commodities exist or not based on the image analysis result and the weight analysis result; the processing module is further used for obtaining first inventory change data when the commodity label corresponding to the inventory change type commodity is detected in the commodity settlement subarea in a preset first time period under the condition that the inventory change type commodity exists; the processing module is also used for obtaining first corrected inventory data corresponding to various types of commodities based on the initial inventory data and the first inventory change data.

In one possible implementation, the processing module is further configured to determine a start image and a stop image based on a plurality of consecutive first sub-region commodity images corresponding to each inventory management sub-region; the processing module is also used for carrying out image analysis on the initial image and the termination image and judging whether the difference exists between the initial image and the termination image; the processing module is further used for determining an image analysis result as a first result when no difference exists between the initial image and the termination image; and the processing module is also used for determining the image analysis result as a second result when the difference exists between the initial image and the termination image.

In a possible implementation manner, the processing module is further configured to analyze weights of the commodities corresponding to the various types of commodities, and determine a variation error of the weights of the commodities corresponding to the various types of commodities in a preset second period of time; the processing module is also used for judging whether the change error is in a preset error range; the processing module is further used for determining that the weight analysis result is a third result when the change error is in a preset error range; and the processing module is also used for determining that the weight analysis result is a fourth result when the change error is not in the preset error range.

In one possible implementation, the processing module is further configured to determine that the inventory variation type commodity is not present when the image analysis result is the first result and the gravimetric analysis result is the third result; and the processing module is also used for determining that the inventory change type commodity exists when the image analysis result is the second result and the weight analysis result is the fourth result.

In a possible implementation manner, the processing module is further configured to determine, when an inventory change type commodity exists, a first inventory management sub-area corresponding to the inventory change type commodity; the processing module is also used for determining the quantity of the commodities corresponding to the inventory change type commodities when the commodity label corresponding to the inventory change type commodities is detected in the commodity settlement subarea; the processing module is further configured to obtain first inventory change data based on the commodity label corresponding to the inventory change type commodity, the first inventory management sub-area, and the commodity quantity corresponding to the inventory change type commodity.

In a possible implementation manner, the processing module is further configured to determine, when the commodity label corresponding to the inventory change type commodity is not detected in the commodity settlement sub-area within a first preset period of time in the case where the inventory change type commodity exists, a first inventory management sub-area corresponding to the inventory change type commodity; the processing module is also used for determining a plurality of second inventory management subareas which are close to the first inventory management subareas; the acquisition module is also used for acquiring second sub-area commodity images corresponding to all types of commodities in each second inventory management sub-area acquired by the cameras arranged in each second inventory management sub-area; the processing module is also used for obtaining second inventory variation data based on the commodity image of the second subarea; the processing module is also used for obtaining second corrected inventory data corresponding to various types of commodities based on the initial inventory data and the second inventory change data.

In one possible implementation manner, the acquiring module is further configured to acquire an inventory change type commodity image corresponding to the inventory change type commodity; the processing module is also used for obtaining inventory change type commodity characteristics corresponding to the inventory change type commodity based on the inventory change type commodity image; the processing module is also used for extracting a plurality of commodity features of the second subarea based on the commodity images of the second subarea; the processing module is also used for judging whether commodity characteristics matched with inventory variation type commodity characteristics exist in the commodity characteristics of the plurality of second subareas; the processing module is further used for determining a second inventory management subarea where the inventory change type commodity is currently located when commodity features matched with the inventory change type commodity features exist in the commodity features of the plurality of second subareas; the processing module is further configured to obtain second inventory variation data based on the commodity label corresponding to the inventory variation type commodity and the second inventory management sub-area.

It should be noted that: in the device provided in the above embodiment, when implementing the functions thereof, only the division of the above functional modules is used as an example, in practical application, the above functional allocation may be implemented by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to implement all or part of the functions described above. In addition, the embodiments of the apparatus and the method provided in the foregoing embodiments belong to the same concept, and specific implementation processes of the embodiments of the method are detailed in the method embodiments, which are not repeated herein.

The application also discloses electronic equipment. Referring to fig. 4, fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. The electronic device 400 may include: at least one processor 401, at least one network interface 404, a user interface 403, a memory 405, and at least one communication bus 402.

Wherein communication bus 402 is used to enable connected communications between these components.

The user interface 403 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 403 may further include a standard wired interface and a standard wireless interface.

The network interface 404 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.

Wherein the processor 401 may include one or more processing cores. The processor 401 connects the various parts within the entire server using various interfaces and lines, performs various functions of the server and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 405, and invoking data stored in the memory 405. Alternatively, the processor 401 may be implemented in at least one hardware form of digital signal processing (DigitalSignalProcessing, DSP), field programmable gate array (Field-ProgrammableGateArray, FPGA), programmable logic array (ProgrammableLogicArray, PLA). The processor 401 may integrate one or a combination of several of a central processor (CentralProcessingUnit, CPU), an image processor (GraphicsProcessingUnit, GPU), a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 401 and may be implemented by a single chip.

The memory 405 may include a random access memory (RandomAccessMemory, RAM) or a Read-only memory (Read-only memory). Optionally, the memory 405 includes a non-transitory computer readable medium (non-transitorycomputer-readablestoragemedium). Memory 405 may be used to store instructions, programs, code sets, or instruction sets. The memory 405 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-described various method embodiments, etc.; the storage data area may store data or the like involved in the above respective method embodiments. The memory 405 may also optionally be at least one storage device located remotely from the aforementioned processor 401. Referring to fig. 4, an operating system, a network communication module, a user interface module, and an application program may be included in the memory 405 as a computer readable storage medium.

In the electronic device 400 shown in fig. 4, the user interface 403 is mainly used as an interface for providing input for a user, and obtains data input by the user; and processor 401 may be used to invoke an application stored in memory 405 that, when executed by one or more processors 401, causes electronic device 400 to perform the method as in one or more of the embodiments described above. It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all of the preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.

In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, such as a division of units, merely a division of logic functions, and there may be additional divisions in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some service interface, device or unit indirect coupling or communication connection, electrical or otherwise.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in whole or in part in the form of a software product stored in a memory, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method of the various embodiments of the present application. And the aforementioned memory includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a magnetic disk or an optical disk.

The above are merely exemplary embodiments of the present disclosure and are not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure.

This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a scope and spirit of the disclosure being indicated by the claims.

Claims

1. A method for business super-digital management, the method comprising:

dividing the super-integral business area to obtain a plurality of inventory management subareas and a commodity settlement subarea;

Acquiring commodity labels and initial inventory data corresponding to various types of commodities in each inventory management subarea;

Acquiring first sub-area commodity images corresponding to all types of commodities in each inventory management sub-area acquired by each camera in real time, and acquiring commodity weights corresponding to each type of commodity in each inventory management sub-area acquired by each weight sensor in real time, wherein the cameras and the weight sensors are arranged in each inventory management sub-area;

analyzing the commodity images of each first subarea to obtain an image analysis result;

Analyzing the weights of the commodities corresponding to the various types of commodities to obtain a weight analysis result;

judging whether inventory variation type commodities exist or not based on the image analysis result and the weight analysis result;

When the commodity label corresponding to the commodity of the stock change type is detected in the commodity settlement subarea in a preset first time period under the condition that the commodity of the stock change type exists, first stock change data are obtained;

And obtaining first corrected inventory data corresponding to various types of commodities based on the initial inventory data and the first inventory change data.

2. The method according to claim 1, wherein the analyzing the commodity image of each first sub-area to obtain an image analysis result specifically includes:

Determining a starting image and a stopping image based on a plurality of continuous first sub-region commodity images corresponding to the inventory management sub-regions;

performing image analysis on the initial image and the termination image, and judging whether a difference exists between the initial image and the termination image;

when no difference exists between the initial image and the termination image, determining the image analysis result as a first result;

And when the difference exists between the starting image and the ending image, determining the image analysis result as a second result.

3. The method according to claim 2, wherein the analyzing the weights of the commodities corresponding to the various types of commodities to obtain the weight analysis result specifically comprises:

Analyzing the weights of the commodities corresponding to the various types of commodities, and determining the variation errors of the weights of the commodities corresponding to the various types of commodities in a preset second time period;

Judging whether the variation error is in a preset error range or not;

when the variation error is within a preset error range, determining the gravimetric analysis result as a third result;

And when the variation error is not in the preset error range, determining the gravimetric analysis result as a fourth result.

4. A method according to claim 3, wherein said determining whether an inventory change type commodity exists based on said image analysis result and said weight analysis result comprises:

Determining that the inventory variation type commodity is not present when the image analysis result is the first result and the gravimetric analysis result is the third result;

When the image analysis result is the second result and the gravimetric analysis result is the fourth result, it is determined that the inventory variation type commodity is present.

5. The method according to claim 1, wherein when the commodity label corresponding to the inventory change type commodity is detected in the commodity settlement sub-area within a preset first period of time in the case where the inventory change type commodity exists, obtaining first inventory change data specifically includes:

Determining a first inventory management subarea corresponding to the inventory change type commodity when the inventory change type commodity exists;

When the commodity label corresponding to the inventory change type commodity is detected in the commodity settlement subarea, determining the commodity quantity corresponding to the inventory change type commodity;

And obtaining the first inventory change data based on the commodity label corresponding to the inventory change type commodity, the first inventory management subarea and the commodity quantity corresponding to the inventory change type commodity.

6. The method according to claim 1, wherein after said determining whether or not there is an inventory change type commodity based on said image analysis result and said weight analysis result, the method further comprises:

When the commodity label corresponding to the commodity of the inventory change type is not detected in the commodity settlement subarea in the first preset time period under the condition that the commodity of the inventory change type exists, determining a first inventory management subarea corresponding to the commodity of the inventory change type;

determining a plurality of second inventory management sub-areas proximate to the first inventory management sub-area;

acquiring second sub-region commodity images corresponding to all types of commodities in each second inventory management sub-region, wherein the second sub-region commodity images are acquired by cameras arranged in each second inventory management sub-region;

Obtaining second inventory variation data based on the second sub-region commodity image;

and obtaining second corrected inventory data corresponding to various types of commodities based on the initial inventory data and the second inventory change data.

7. The method according to claim 6, wherein the obtaining second inventory variation data based on the second sub-region commodity image specifically includes:

acquiring an inventory change type commodity image corresponding to the inventory change type commodity;

Acquiring inventory change type commodity characteristics corresponding to the inventory change type commodity based on the inventory change type commodity image;

extracting a plurality of commodity features of the second subarea based on the commodity image of the second subarea;

judging whether commodity characteristics matched with the inventory variation type commodity characteristics exist in the commodity characteristics of the second subareas or not;

When the commodity characteristics matched with the inventory change type commodity characteristics exist in the plurality of second sub-area commodity characteristics, determining a second inventory management sub-area where the inventory change type commodity is currently located;

and obtaining the second inventory change data based on the commodity label corresponding to the inventory change type commodity and the second inventory management subarea.

8. A business super digital management system, the system comprising: the device comprises a region dividing module, an acquisition module and a processing module;

the regional division module is used for dividing the super-integral region of the commodity to obtain a plurality of inventory management subregions and a commodity settlement subregion;

the acquisition module is used for acquiring commodity labels and initial inventory data corresponding to various types of commodities in each inventory management subarea;

The acquisition module is further used for acquiring first sub-area commodity images corresponding to all types of commodities in each inventory management sub-area acquired by each camera in real time, and acquiring commodity weights corresponding to each type of commodity in each inventory management sub-area acquired by each weight sensor in real time, wherein the cameras and the weight sensors are arranged in each inventory management sub-area;

The processing module is used for analyzing the commodity images of the first subareas to obtain an image analysis result;

The processing module is also used for analyzing the weights of the commodities corresponding to the various types of commodities to obtain a weight analysis result;

The processing module is further used for judging whether inventory variation type commodities exist or not based on the image analysis result and the weight analysis result;

The processing module is further configured to obtain first inventory variation data when a commodity label corresponding to the inventory variation type commodity is detected in the commodity settlement sub-area within a preset first time period when the inventory variation type commodity exists;

The processing module is further configured to obtain first corrected inventory data corresponding to various types of commodities based on the initial inventory data and the first inventory variation data.

9. An electronic device comprising a processor (401), a memory (405), a user interface (403) and a network interface (404), the memory (405) being configured to store instructions, the user interface (403) and the network interface (404) being configured to communicate to other devices, the processor (401) being configured to execute the instructions stored in the memory (405) to cause the electronic device (400) to perform the method according to any of claims 1-7.

10. A computer readable storage medium storing instructions which, when executed, perform the method of any one of claims 1-7.