CN111833135B - Order data analysis method, device and electronic equipment - Google Patents
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Abstract
本申请提供了一种订单数据的分析方法、装置及电子设备，方法包括：在根据历史订单数据判断当前订单数据异常后，按照每个指标组合划分历史订单数据和当前订单数据，得到与每个指标组合对应的历史订单子数据和当前订单子数据；遍历每个指标组合对应的历史订单子数据和当前订单子数据，应用历史订单子数据判断当前订单子数据是否异常；对异常的当前子订单数据执行以下操作：判断当前订单子数据是否在历史订单子数据的第一置信区间内；如果不在，将当前遍历的指标组合中的子指标添加至当前订单数据的解释变量中。本申请通过多个指标组合对应的分析维度，对异常的当前订单数据进行分析，能够快速、准确的定位到导致数据异常的解释变量。
The present application provides an analysis method, device and electronic device for order data, the method comprising: after judging that the current order data is abnormal according to the historical order data, dividing the historical order data and the current order data according to each indicator combination, obtaining the historical order sub-data and the current order sub-data corresponding to each indicator combination; traversing the historical order sub-data and the current order sub-data corresponding to each indicator combination, applying the historical order sub-data to judge whether the current order sub-data is abnormal; performing the following operations on the abnormal current sub-order data: judging whether the current order sub-data is within the first confidence interval of the historical order sub-data; if not, adding the sub-indicator in the currently traversed indicator combination to the explanatory variables of the current order data. The present application analyzes the abnormal current order data through the analysis dimensions corresponding to multiple indicator combinations, and can quickly and accurately locate the explanatory variables that cause data abnormality.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method and an apparatus for analyzing order data, and an electronic device.
Background
When doing business analysis, a large amount of business information needs to be subjected to data analysis, mining and processing to determine the reasons for some data changes. For example, when determining the reason for the change in the order amount within a period of time, a worker often analyzes the change process of the order amount from the dimension of time according to its preset analysis dimension, so as to find the reason for the change. The analysis mode relies on manual fixed analysis dimensions and logic, the analysis and the mined data change reasons are single, and the data conclusion is not comprehensive and accurate.
Disclosure of Invention
Accordingly, the present application aims to provide a method, a device and an electronic device for analyzing order data, which can analyze abnormal current order data through analysis dimensions corresponding to a plurality of index combinations, and can rapidly and accurately locate interpretation variables causing data abnormality.
According to one aspect of the application, an analysis method of order data is provided, the method comprises the steps of judging whether current order data is abnormal according to historical order data, dividing the historical order data and the current order data according to each index combination to obtain historical order sub-data and current order sub-data corresponding to each index combination if the current order data is abnormal, traversing the historical order sub-data and the current order sub-data corresponding to each index combination, judging whether the current order sub-data is abnormal by applying the historical order sub-data, judging whether the current order sub-data is in a first confidence interval of the historical order sub-data or not according to the abnormal current sub-order data, calculating the historical order sub-data according to the first confidence interval through an interval estimation algorithm, and adding sub-indexes in the currently traversed index combination to interpretation variables of the current order data if the current order sub-data is not in the first confidence interval.
In some embodiments, the step of determining whether the current order data is abnormal according to the historical order data includes calculating a rate of change between the current order data and the historical order data, and determining that the current order data is abnormal if the rate of change is greater than a preset rate of change threshold.
In some embodiments, the step of determining whether the current order data is abnormal according to the historical order data includes calculating a second confidence interval corresponding to the historical order data through an interval estimation algorithm, and determining that the current order data is abnormal if the current order data is not in the second confidence interval.
In some embodiments, the step of determining whether the current order sub-data is abnormal by using the historical order sub-data includes calculating a sub-change rate of the current order sub-data and the historical order sub-data, and determining that the current order sub-data is abnormal if the sub-change rate is greater than a preset sub-change rate threshold.
In some embodiments, the step of dividing the historical order data according to each index combination includes dividing the historical order data according to service index groups to obtain first historical order data corresponding to each service index group, wherein each service index group comprises at least one service index, dividing the first historical order data according to sub-index groups contained in each service index group to obtain second historical order data corresponding to each sub-index group, and taking the second historical order data as historical order sub-data of index combinations corresponding to the service index group, wherein each sub-index group comprises at least one sub-index.
In some embodiments, after the step of judging whether the current order sub-data is abnormal by applying the historical order sub-data, the method further comprises the steps of calculating a third confidence interval corresponding to the sub-change rate of the plurality of current order sub-data through an interval estimation algorithm if the judging result is that the current order sub-data is abnormal, determining the current order sub-data which is not in the third confidence interval and corresponds to the sub-change rate as obvious abnormal order sub-data, taking the obvious abnormal order sub-data as the abnormal current order sub-data, and continuing to execute the step of judging whether the current order sub-data is in the first confidence interval of the historical order sub-data.
In some embodiments, before the step of determining whether the current order sub-data is within the first confidence interval of the historical order sub-data, the method further comprises the steps of calculating the mean value and the variance of the historical order sub-data respectively, and calculating the first confidence interval corresponding to the historical order sub-data based on the mean value and the variance of the historical order sub-data and a preset confidence interval coefficient.
In some embodiments, the method further comprises obtaining a first rate of change between the current order data and the historical order data and a second rate of change between the current order sub-data and the corresponding historical order sub-data that is not within the first confidence interval, and displaying the first rate of change, the second rate of change and interpretation variables corresponding to the current order data.
In some embodiments, if there are a plurality of current order sub-data not within the first confidence interval, the method further comprises ranking the plurality of interpretation variables corresponding to the current order data according to the degree of deviation of each current order sub-data from the corresponding first confidence interval.
In some embodiments, the current order data and the historical order data each include order quantity data, order duration data, or order amount data, the business indicia includes at least one of time, weather, and holidays, and the sub-indicia includes at least one of early peak, flat peak, late peak, sunny, cloudy, rainy, holidays, and non-holidays.
According to another aspect of the application, an analysis device of order data is provided, which comprises a data judging module, a data dividing module and an interpretation variable determining module, wherein the data judging module is used for judging whether current order data is abnormal according to historical order data, the data dividing module is used for dividing the historical order data and the current order data according to each index combination to obtain historical order sub-data and current order sub-data corresponding to each index combination if the current order data is abnormal, the index combination comprises at least one sub-index corresponding to at least one service index, the traversing module is used for traversing the historical order sub-data and the current order sub-data corresponding to each index combination, the historical order sub-data is used for judging whether the current order sub-data is abnormal or not according to the historical order sub-data, the interpretation variable determining module is used for executing the operation of judging whether the current order sub-data is in a first confidence interval of the historical order sub-data or not according to each index combination, the first confidence interval is calculated on the historical order sub-data through an interval estimation algorithm, and the sub-index in the current traversing index combination is added into interpretation variables of the current order data if the current order sub-data is not in the first confidence interval.
In some embodiments, the data judging module is further configured to calculate a rate of change between the current order data and the historical order data, and determine that the current order data is abnormal if the rate of change is greater than a preset rate of change threshold.
In some embodiments, the data judging module is further configured to calculate a second confidence interval corresponding to the historical order data through an interval estimation algorithm, and determine that the current order data is abnormal if the current order data is not in the second confidence interval.
In some embodiments, the traversing module is further configured to calculate a sub-rate of change between the current order sub-data and the historical order sub-data, and determine that the current order sub-data is abnormal if the sub-rate of change is greater than a preset sub-rate threshold.
In some embodiments, the data dividing module is further configured to divide the historical order data according to a service index group to obtain first historical order data corresponding to each service index group, where the service index group includes at least one service index, divide the first historical order data according to a sub-index group included in the service index group for the first historical order data corresponding to each service index group to obtain second historical order data corresponding to each sub-index group, and use the second historical order data as historical order sub-data of an index combination corresponding to the service index group, where the sub-index group includes at least one sub-index.
In some embodiments, the device further comprises a data screening module, wherein the data screening module is used for calculating a third confidence interval corresponding to the sub-change rate of the plurality of current order sub-data through an interval estimation algorithm if the judging result is that the plurality of current order sub-data are abnormal, determining the current order sub-data which is not in the third confidence interval and corresponds to the sub-change rate as obvious abnormal order sub-data, taking the obvious abnormal order sub-data as the abnormal current order sub-data, and continuing to execute the step of judging whether the current order sub-data is in the first confidence interval of the historical order sub-data.
In some embodiments, the device further comprises an interval estimation module, which is used for calculating the mean value and the variance of the historical order sub-data respectively, and calculating a first confidence interval corresponding to the historical order sub-data based on the mean value and the variance of the historical order sub-data and a preset confidence interval coefficient.
In some embodiments, the device further comprises a data display module, wherein the data display module is used for acquiring a first change rate between the current order data and the historical order data and a second change rate between the current order sub-data and the corresponding historical order sub-data, which are not in the first confidence interval, and displaying the first change rate, the second change rate and the interpretation variable corresponding to the current order data.
In some embodiments, if there are a plurality of current order sub-data not within the first confidence interval, the apparatus further comprises a ranking module for ranking the importance of the plurality of interpretation variables corresponding to the current order data according to the degree of deviation of each current order sub-data from the corresponding first confidence interval.
In some embodiments, the current order data and the historical order data each include order quantity data, order duration data, or order amount data, the business indicia includes at least one of time, weather, and holidays, and the sub-indicia includes at least one of early peak, flat peak, late peak, sunny, cloudy, rainy, holidays, and non-holidays.
According to another aspect of the application there is provided an electronic device comprising a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating over the bus when the electronic device is in operation, the processor executing the machine-readable instructions to perform the steps of any of the methods described above.
According to another aspect of the present application there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of any of the methods described above.
Based on any one of the aspects, the method comprises the steps of firstly carrying out overall judgment on current order data according to historical order data, dividing the historical order data and the current order data according to each index combination to obtain historical order sub-data and current order sub-data corresponding to each index combination if the current order data is abnormal, traversing the historical order sub-data and the current order sub-data corresponding to each index combination, judging whether the current order sub-data is abnormal by applying the historical order sub-data, judging whether the current order sub-data is in a first confidence interval of the historical order sub-data or not, wherein the first confidence interval is obtained by calculating the historical order sub-data through an interval estimation algorithm, and adding sub-indexes in the currently traversed index combination into interpretation variables of the current order data if the current order sub-data is not in the first confidence interval. According to the application, the abnormal current order data is analyzed through the analysis dimensions corresponding to the index combinations, so that the interpretation variable causing the data abnormality can be rapidly and accurately positioned.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an analysis system for order data according to an embodiment of the present application;
FIG. 2 is a flow chart illustrating a method for analyzing order data according to an embodiment of the present application;
FIG. 3 is a flow chart illustrating another method of analyzing order data provided by an embodiment of the present application;
FIG. 4 is a schematic diagram illustrating another method for analyzing order data according to an embodiment of the present application;
FIG. 5 is a schematic illustration showing explanatory variables in an analysis method of order data according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an analysis device for order data according to an embodiment of the present application;
FIG. 7 is a schematic diagram showing another configuration of an apparatus for analyzing order data according to an embodiment of the present application;
fig. 8 shows a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for the purpose of illustration and description only and are not intended to limit the scope of the present application. In addition, it should be understood that the schematic drawings are not drawn to scale. A flowchart, as used in this disclosure, illustrates operations implemented according to some embodiments of the present application. It should be understood that the operations of the flow diagrams may be implemented out of order and that steps without logical context may be performed in reverse order or concurrently. Moreover, one or more other operations may be added to or removed from the flow diagrams by those skilled in the art under the direction of the present disclosure.
In addition, the described embodiments are only some, but not all, embodiments of the application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.
In order to enable those skilled in the art to use the present disclosure, the following embodiments are presented in connection with a specific application scenario "analysis scenario of network about vehicle order data". It will be apparent to those having ordinary skill in the art that the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the application. While the present application is primarily described in terms of network vehicle order data, it should be understood that this is but one exemplary embodiment.
It should be noted that the term "comprising" will be used in embodiments of the application to indicate the presence of the features stated hereafter, but not to exclude the addition of other features.
Fig. 1 is a schematic diagram of an architecture of an analysis system 100 for order data according to an embodiment of the present application. For example, the analysis system 100 of order data may be an online transportation service platform for transportation services such as taxis, drive-up services, express, carpools, bus services, driver leases, or airlines services, or any combination thereof, or may be an online ordering platform for take-away ordering. The analysis system 100 for order data may include one or more of a server 110, a network 120, a service requester 130, and a database 140.
In some embodiments, server 110 may include a processor. The processor may process information and/or data related to the service request to perform one or more of the functions described in the present application. For example, the processor may determine the target vehicle providing the service based on a service request, such as a taxi request, obtained from the service request end 130.
In some embodiments, the device type corresponding to the service request end 130 may be a mobile device, for example, may include a smart home device, a wearable device, a smart mobile device, a virtual reality device, or an augmented reality device, and may also be a tablet computer, a laptop computer, or a built-in device in a motor vehicle, and so on.
In some embodiments, database 140 may be connected to network 120 to communicate with one or more components (e.g., server 110, service requester 130, etc.) in analysis system 100 of the order data. One or more components in the analysis system 100 of order data may access data or instructions stored in the database 140 via the network 120. In some embodiments, the database 140 may be directly connected to one or more components in the analysis system 100 of the order data, or the database 140 may be part of the server 110.
The method for analyzing order data according to the embodiment of the present application will be described in detail with reference to the description of the system 100 for analyzing order data shown in fig. 1.
Referring to fig. 2, a flow chart of an analysis method of order data according to an embodiment of the present application is shown, and the method may perform data analysis on the whole order data to determine the cause of the data abnormality, that is, explain the variable. The order data may include order data of the network about vehicle or take-out order data, and the method is described below by taking the order data of the network about vehicle service as an example, and the method may be executed by a server in the analysis system 100 of the order data, and the specific execution process includes the following steps:
Step S202, judging whether the current order data is abnormal or not according to the historical order data.
In a specific application, the current order data and the historical order data can be order quantity data, order amount data or order duration data. That is, the analysis variables of the order data may include a variety of order amounts, and order durations. Taking the analysis variable as the order quantity as an example, the current order data may be the order quantity in the day of the year, the order quantity in the current week, the order quantity in a month, etc., and the corresponding historical order data may be the order quantity in the previous week, the order quantity in the previous month, etc., which are not limited specifically herein. The following example illustrates the current order data as the amount of orders in the day today.
The comparison of the current order data and the historical order data is performed first to preliminarily determine whether the current order data is abnormal, and specific determination modes can be various, for example, a mode of comparing the change rate of the current order data and the historical order data with a preset change rate threshold value, or a mode of comparing the change rate of the current order data and the historical order data with a confidence interval corresponding to the historical order data, which is not described herein.
Step S204, if the current order data is abnormal, dividing the historical order data and the current order data according to each index combination to obtain the historical order sub-data and the current order sub-data corresponding to each index combination.
The index combination comprises at least one sub-index corresponding to at least one service index. The index combination can also be the dimension of data analysis, for example, the service index comprises time, weather, holiday and the like, each service index comprises a plurality of sub-indexes, for example, the time comprises early peak, flat peak and late peak, the weather comprises sunny day, yin and rainy day, the holiday comprises holiday and non-holiday, and the service index and the sub-indexes can be combined to obtain a plurality of combinations with different dimensions, for example, the index combination under the two service index combinations of weather and holiday can comprise six combinations of [ holiday, sunny ], [ holiday, yin ], [ holiday, rainy ], [ non-holiday, sunny ], [ non-holiday, yin ] and [ non-holiday, rainy ].
It should be noted that, at a minimum, the above index combination may include one sub-index of one service index, such as an early peak sub-index under a time service index.
After the abnormality of the current order data is judged, the historical order data and the current order data are divided according to each index combination, so that the corresponding historical order data and the corresponding slicing data of the current order data under each index combination are obtained, namely the corresponding historical order sub-data and the corresponding current order sub-data of each index combination.
Step S206, traversing the historical order sub-data and the current order sub-data corresponding to each index combination, and applying the historical order sub-data to judge whether the current order sub-data is abnormal.
In specific implementation, the traversing process can be implemented by using a KD tree query algorithm, that is, all analysis dimensions of the order data, that is, each index combination can be traversed.
In the traversing process of each index combination, according to the historical order sub-data and the current order sub-data of each index combination, whether the current order sub-data is abnormal or not is judged according to the historical order sub-data, and a specific judging mode can be that the change rate of the current order sub-data is compared with a preset change rate threshold value, and if the change rate exceeds the threshold value, the current order sub-data is determined to be abnormal. After all the index combinations are traversed, whether abnormal current order sub-data exist in the current order sub-data corresponding to each index combination of the current order data can be determined, and if so, the index combination corresponding to the abnormal current order sub-data can be further determined.
S208, judging whether the current order sub-data is in a first confidence interval of the historical order sub-data or not according to the abnormal current sub-order data, and if not, adding the sub-index in the index combination of the current traversal into an interpretation variable of the current order data.
The first confidence interval is calculated from the historical order sub-data through an interval estimation algorithm. And further judging whether the current order sub-data is in a first confidence interval corresponding to the historical order sub-data or not according to the abnormal current order sub-data, if so, indicating that the index combination is not the reason for causing the abnormality of the current order data, and if not, indicating that the index combination is the reason for causing the abnormality of the current order data, and adding the sub-index corresponding to the index combination into an interpretation variable of the current order data. If the abnormal current order sheet data is multiple, multiple interpretation variables corresponding to the current order sheet data can be obtained.
According to the method for analyzing the order data, firstly, overall judgment is conducted on the current order data according to the historical order data, if the current order data is abnormal, then the historical order data and the current order data are divided according to each index combination to obtain historical order sub-data and current order sub-data corresponding to each index combination, then the historical order sub-data and the current order sub-data corresponding to each index combination are traversed, whether the current order sub-data are abnormal or not is judged by applying the historical order sub-data, the abnormal current sub-order data are subjected to the following operation, whether the current order sub-data are in a first confidence interval of the historical order sub-data or not is judged, the first confidence interval is calculated on the historical order sub-data through an interval estimation algorithm, and if the current sub-index in the index combination is not in the first confidence interval, the sub-index in the current traversed index combination is added to an interpretation variable of the current order data. And the analysis dimension corresponding to the combination of the indexes is used for analyzing the abnormal current order data, so that the interpretation variable causing the data abnormality can be rapidly and accurately positioned.
To describe the above process of determining whether the current order data is abnormal in detail, two specific implementation methods are listed below:
The first method comprises the steps of calculating the change rate between the current order data and the historical order data, and determining that the current order data is abnormal if the change rate is larger than a preset change rate threshold.
For example, table 1 shows the amounts of orders corresponding to the current order data and the historical order data, respectively, the historical order data being the amounts of orders corresponding to the current order data being the amounts of orders of the day 2019.1.7, respectively, within six days 2019.1.1-2019.1.6.
TABLE 1
| 2019.1.1 | 2019.1.2 | 2019.1.3 | 2019.1.4 | 2019.1.5 | 2019.1.6 | 2019.1.7 |
| 31 | 35 | 37 | 31 | 32 | 38 | 30 |
The rate of change between the current order data and the historical order data is calculated based on the data shown in table 4, and the specific calculation mode may be (average of the current order amount-six-day historical order amount)/average of six-day historical order amount, or (current order amount-previous order amount)/previous order amount.
And after the change rate is calculated, comparing the calculated change rate with a preset change rate threshold, and if the calculated change rate is larger than the change rate threshold, determining that the current order data is abnormal. By the method, whether the tail of the current order quantity is abnormal or not can be judged rapidly.
And secondly, calculating a second confidence interval corresponding to the historical order data through an interval estimation algorithm, and determining that the current order data is abnormal if the current order data is not in the second confidence interval.
Based on the data shown in table 4, first, the mean and variance corresponding to the historical order data are calculated, for example:
mean value u= (31+35+37+31+32+38+30)/7=33.43;
Variance:
s＝[(31-33.43)2+(35-33.43)2+(37-33.43)2+(31-33.43)2+(32-33.43)2+(38-33.43)2+(30-33.43)2]/7＝8.82;
Assuming that the data obeys normal distribution, the coefficient of the 95% confidence interval is 1.96, and the second confidence interval is [33.43-8.82×1.96,33.43+8.82×1.96] = [16.14,50.71].
The current order data, that is, the amount of orders on 2019.1.7 days is 30, which is within the second confidence interval [16.14,50.71], so that the current order data is determined to be normal, and if the current order data is not within the second confidence interval, the current order data is indicated to be abnormal. This way the determination is relatively more accurate.
In this embodiment, all the processes of calculating the confidence interval, for example, the first confidence interval corresponding to the sub-data of the history order used later and the third confidence interval of the sub-change rate, may refer to the calculation process of the second confidence interval, which will not be described in detail later.
Similarly, the step of determining whether the current order sub-data is abnormal by applying the historical order sub-data may also be implemented by calculating and comparing the change rates, specifically, calculating the sub-change rate of the current order sub-data and the historical order sub-data, and then determining whether the sub-change rate is greater than a preset sub-change rate threshold, if so, determining that the current order sub-data is abnormal, which is not described herein.
To clearly illustrate the above steps of dividing the historical order data by each index combination, a specific example of application is listed below, as shown in FIG. 3:
Step S302, dividing the historical order data according to the business index groups to obtain first historical order data corresponding to each business index group.
Wherein the set of business indicators comprises at least one business indicator. For example, the historical order data is the order quantity corresponding to 1 month, 1 day, 1 month and 6 days each day, and is firstly divided according to the service index groups to obtain the first historical order quantity corresponding to each service index group, wherein the service index groups can be [ time ], [ weather ], [ time, weather ] or [ time, weather, holiday ] and the like.
Step S304, for the first historical order data corresponding to each business index group, dividing the first historical order data according to the sub-index groups included in the business index group to obtain second historical order data corresponding to each sub-index group, and taking the second historical order data as the historical order sub-data of the index combination corresponding to the business index group.
Wherein the sub-index group comprises at least one sub-index. For example, for the first historical order data corresponding to the time business index group, the second historical order data corresponding to each sub-index group is obtained by performing secondary division based on the sub-index groups, wherein the sub-index groups comprise early peak, flat peak and late peak. For the first historical order data corresponding to the [ time, weather ] business index group, carrying out secondary division based on sub index groups to obtain second historical order data corresponding to each sub index group, wherein the sub index groups comprise nine groups of [ fine, early peak ], [ fine, flat peak ], [ fine, late peak ], [ yin, early peak ], [ yin, flat peak ], [ yin, late peak ], [ rain, early peak ], [ rain, flat peak ] and [ rain, late peak ], and the like.
The dividing method of the current order data is the same as the dividing process of the historical order data, the total order quantity in the current order data can be divided into sub order quantities under each index combination, if an order meeting the condition is not placed under a certain index combination, the sub order quantity is 0, and the data with the sub order quantity of 0 is not considered.
Table 2 below shows the historical order sub-data and the current order sub-data (order amount of 1 month 7 days) corresponding to the three index combinations under the [ time ] business index combination, table 3 shows the historical order sub-data and the current order sub-data (only 5 columns are shown) corresponding to the nine index combinations under the [ time, weather ] business index combination, table 4 shows the historical order sub-data and the current order sub-data (only 5 columns are shown) corresponding to the eighteen index combinations under the [ time, weather, holiday ] business index combination:
TABLE 2
TABLE 3 Table 3
| Sunny and early peak | Sunny and flat peak | Peak in sunny and evening | Peak of yin and early | Yin, flat peak | |
| 1 Month and 1 day | X | X | X | X | X |
| 1 Month and 2 days | X | X | X | X | X |
| 1 Month and 3 days | X | X | X | X | X |
| ... | |||||
| 1 Month and 7 days | X | X | X | X | X |
TABLE 4 Table 4
After the historical order data are divided according to each index combination to obtain a plurality of historical order sub-data, the method can further comprise the following operation of calculating the mean value and the variance of the historical order sub-data respectively according to each historical order sub-data, and calculating a first confidence interval corresponding to the historical order sub-data based on the mean value and the variance of the historical order sub-data and a preset confidence interval coefficient. Therefore, a first confidence interval corresponding to each historical order sub-data is obtained, and the specific process refers to the interval estimation algorithm and is not repeated here.
Compared to the method for analyzing order data shown in fig. 2, the present embodiment further includes a process of screening the current order sub-data with significant anomalies after determining the current order sub-data with multiple anomalies, specifically referring to fig. 4, including the following steps:
step S402, judging whether the current order data is abnormal according to the historical order data.
In step S404, if the current order data is abnormal, the historical order data and the current order data are divided according to each index combination, so as to obtain the historical order sub-data and the current order sub-data corresponding to each index combination.
Step S406, traversing the historical order sub-data and the current order sub-data corresponding to each index combination, and applying the historical order sub-data to judge whether the current order sub-data is abnormal.
Step S408, if the abnormal current order sub-data is determined to be a plurality of, calculating a third confidence interval corresponding to the sub-change rate of the plurality of current order sub-data through the interval estimation algorithm.
And step S410, determining the current order sub-data which does not correspond to the sub-change rate in the third confidence interval as the obvious abnormal order sub-data, and taking the obvious abnormal order sub-data as the abnormal current order sub-data.
Step S412, the abnormal current sub-order data is processed by judging whether the current order sub-data is within the first confidence interval of the historical order sub-data, and if not, adding the sub-index in the index combination of the current traversal to the interpretation variable of the current order data.
The specific implementation process of step S402, step S404, step S406 and step S412 may be referred to the specific description of the steps in fig. 2, and will not be repeated here. In step S408 and step S410, first, a third confidence interval of sub-change rates corresponding to the current order sub-data of the plurality of anomalies is calculated, then, by comparing the change rate of the current order sub-data of each anomaly with the third confidence interval, the current order sub-data of anomalies corresponding to the change rate not in the third confidence interval is determined as the obvious anomaly order sub-data, and further, the obvious anomaly order sub-data is compared with the first confidence interval corresponding to the corresponding historical order sub-data, thereby, whether the index combination corresponding to the obvious anomaly order sub-data is the reason of the anomaly of the current order data is judged.
The method for screening the sub data of the remarkable abnormal order can reduce the workload of data analysis and can rapidly determine the interpretation variable corresponding to the abnormal current order data. In this case, since there are a plurality of abnormal order sub-data, there may be a plurality of obtained explanatory variables, and the importance of the plurality of explanatory variables may be sorted, that is, which sub-index in the index combination is the most direct cause of the abnormality of the current order data is looked at.
For example, importance ranking is performed on the plurality of interpretation variables corresponding to the current order data according to the deviation degree of each current order sub-data from the corresponding first confidence interval. The current order sub-data is abnormal current order sub-data, and the greater the deviation degree of the first confidence interval corresponding to the corresponding historical order sub-data, the more important influencing factors are the sub-indexes in the corresponding index combination, namely the more important explanatory variables corresponding to the abnormal current order data.
In addition, in order to intuitively reflect the reason for the abnormality of the current order data, the method may further include the steps of obtaining a first change rate between the current order data and the historical order data and a second change rate between the current order sub-data and the corresponding historical order sub-data, which are not in the first confidence interval, and displaying the first change rate, the second change rate and the interpretation variable corresponding to the current order data, as shown in fig. 5. The total current order data corresponds to 80% of order quantity increase rate, three corresponding explanatory variables are [ sunny, early peak ], [ early peak ] and [ holiday ], and the sub order increase rates of the order sub data in the three explanatory variables are 50%, 40% and 20% respectively. From this, the explanatory variables that cause the abnormality of the current order data are ranked in terms of importance from high to low as [ fine, early peak ], [ early peak ], and [ holiday ].
It should be noted that, the manner in which the data is displayed is various, and fig. 5 is merely an example, and the specific display form is not limited in particular.
According to the analysis method for the order data, provided by the embodiment of the application, the abnormal current order data can be analyzed through the analysis dimension corresponding to the combination of the multiple indexes, the interpretation variable causing the data abnormality can be rapidly and accurately positioned, the importance of the interpretation variable can be sequenced, and the final result can be displayed, so that a user can intuitively see the reason causing the data abnormality for subsequent processing.
Based on the above method embodiment, the embodiment of the application also provides an analysis device for order data, which is shown in fig. 6, and includes a data judging module 62, a data dividing module 64, a traversing module 66 and an interpretation variable determining module 68.
The system comprises a data judging module 62, a data dividing module 64 and an interpretation variable determining module 68, wherein the data judging module 62 is used for judging whether current order data is abnormal according to historical order data, the data dividing module 64 is used for dividing the historical order data and the current order data according to each index combination to obtain historical order sub-data and current order sub-data corresponding to each index combination if the current order data is abnormal, the index combination comprises at least one sub-index corresponding to at least one service index, the traversing module 66 is used for traversing the historical order sub-data and the current order sub-data corresponding to each index combination, the historical order sub-data is applied to judge whether the current order sub-data is abnormal or not, the interpretation variable determining module 68 is used for executing the following operation on the abnormal current sub-order data, wherein the first confidence interval is obtained by calculating the historical order sub-data through an interval estimation algorithm, and the sub-index in the currently traversed index combination is added into the interpretation variable of the current order data if the current order sub-data is not in the first confidence interval.
According to the analysis device for the order data, provided by the embodiment of the application, the abnormal current order data is analyzed through the analysis dimension corresponding to the combination of the multiple indexes, so that the interpretation variable causing the data abnormality can be rapidly and accurately positioned.
Fig. 7 shows a schematic structural diagram of another analysis apparatus for order data, which may further include a data filtering module 710, an interval estimating module 712, a data displaying module 714, and a sorting module 716, in addition to the data judging module 702, the data dividing module 704, the traversing module 706, and the interpretation variable determining module 708, which are similar to the previous embodiment.
The data filtering module 710 is configured to, if the determination result is that the current order sub-data is abnormal is multiple, calculate, by using an interval estimation algorithm, a third confidence interval corresponding to a sub-rate of change of the multiple current order sub-data, determine that the current order sub-data corresponding to a sub-rate of change that is not in the third confidence interval is significant abnormal order sub-data, and use the significant abnormal order sub-data as the current order sub-data, and continue to execute the step of determining whether the current order sub-data is in the first confidence interval of the historical order sub-data.
The interval estimation module 712 is configured to calculate a mean value and a variance of the historical order sub-data, and calculate a first confidence interval corresponding to the historical order sub-data based on the mean value and the variance of the historical order sub-data and a preset confidence interval coefficient.
The data display module 714 is configured to obtain a first rate of change between the current order data and the historical order data and a second rate of change between the current order sub-data and the corresponding historical order sub-data that are not in the first confidence interval, and display the first rate of change, the second rate of change, and interpretation variables corresponding to the current order data.
If there are a plurality of current order sub-data not within the first confidence interval, the ranking module 716 is configured to rank importance of a plurality of interpretation variables corresponding to the current order data according to a deviation degree of each current order sub-data from the corresponding first confidence interval.
In some embodiments, the data determination module 702 is further configured to calculate a rate of change between the current order data and the historical order data, and determine that the current order data is abnormal if the rate of change is greater than a preset rate of change threshold.
In some embodiments, the data determining module 702 is further configured to calculate a second confidence interval corresponding to the historical order data through an interval estimation algorithm, and determine that the current order data is abnormal if the current order data is not within the second confidence interval.
In some embodiments, the traversing module 706 is further configured to calculate a sub-rate of change of the current order sub-data and the historical order sub-data, and determine that the current order sub-data is abnormal if the sub-rate of change is greater than a preset sub-rate threshold.
In some embodiments, the data dividing module 704 is further configured to divide the historical order data according to a service index group to obtain first historical order data corresponding to each service index group, where the service index group includes at least one service index, divide the first historical order data according to a sub-index group included in the service index group for the first historical order data corresponding to each service index group to obtain second historical order data corresponding to each sub-index group, and use the second historical order data as historical order sub-data of an index combination corresponding to the service index group, where the sub-index group includes at least one sub-index.
In some embodiments, the current order data and the historical order data each include order quantity data, order duration data, or order amount data, the business indicia includes at least one of time, weather, and holidays, and the sub-indicia includes at least one of early peak, flat peak, late peak, sunny, cloudy, rainy, holidays, and non-holidays.
The modules may be connected or communicate with each other via wired or wireless connections. The wired connection may include a metal cable, optical cable, hybrid cable, or the like, or any combination thereof. The wireless connection may include a connection through a LAN, WAN, bluetooth, zigBee, or NFC, or any combination thereof. Two or more modules may be combined into a single module, and any one module may be divided into two or more units.
For ease of understanding, fig. 8 shows a schematic diagram of exemplary hardware and software components of an electronic device 800 that may implement the concepts of the present application, according to some embodiments of the present application. For example, processor 820 may be used on electronic device 800 and to perform functions in the present application.
The electronic device 800 may be a general purpose computer or a special purpose computer, both of which may be used to implement the order data analysis method of the present application. Although only one computer is shown, the functionality described herein may be implemented in a distributed fashion across multiple similar platforms for convenience to balance processing loads.
For example, electronic device 800 may include a network port 810 connected to a network, one or more processors 820 for executing program instructions, a communication bus 830, and various forms of storage media 840 such as magnetic disk, ROM, or RAM, or any combination thereof. By way of example, the computer platform may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application may be implemented in accordance with these program instructions. The electronic device 800 also includes an Input/Output (I/O) interface 850 between the computer and other Input/Output devices (e.g., keyboard, display screen).
For ease of illustration, only one processor is depicted in the electronic device 800. It should be noted, however, that the electronic device 800 of the present application may also include multiple processors, and thus, steps performed by one processor described in the present application may also be performed jointly by multiple processors or separately. For example, if the processor of the electronic device 800 performs steps a and B, it should be understood that steps a and B may also be performed by two different processors together or performed separately in one processor. For example, the first processor performs step a, the second processor performs step B, or the first processor and the second processor together perform steps a and B.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program executes the steps of the order data analysis method when being executed by a processor.
Specifically, the storage medium can be a general storage medium, such as a mobile disk, a hard disk, and the like, and when the computer program on the storage medium is run, the analysis method of the order data can be executed, so that the technical problems of single data analysis mode and incomplete and accurate data conclusion in the prior art are solved.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system and apparatus may refer to corresponding procedures in the method embodiments, and are not repeated in the present disclosure. In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, and the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, and for example, multiple modules 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 communication interface, indirect coupling or communication connection of devices or modules, electrical, mechanical, or other form.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple 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 functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. 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 a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. The storage medium includes various media capable of storing program codes such as a U disk, a mobile hard disk, a ROM, a RAM, a magnetic disk or an optical disk.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily appreciate variations or alternatives within the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (9)
1. A method of analyzing order data, comprising:
Judging whether the current order data is abnormal or not according to the historical order data;
Dividing the historical order data and the current order data according to each index combination if the current order data is abnormal, and obtaining historical order sub-data and current order sub-data corresponding to each index combination, wherein the index combination comprises at least one sub-index corresponding to at least one service index;
traversing the historical order sub-data and the current order sub-data corresponding to each index combination, and applying the historical order sub-data to judge whether the current order sub-data is abnormal or not;
The following operations are performed on the abnormal current sub-order data:
judging whether the current order sub-data is in a first confidence interval of the historical order sub-data or not, wherein the first confidence interval is obtained by calculating the historical order sub-data through an interval estimation algorithm;
If not, adding sub-indicators in the indicator combination currently traversed to an interpretation variable of the current order data, wherein the interpretation variable is used for representing the reason for data abnormality;
If there are a plurality of current order sub-data not within the first confidence interval, the method further comprises:
And according to the deviation degree of each current order sub-data and the corresponding first confidence interval, importance sorting is carried out on a plurality of interpretation variables corresponding to the current order data, and the greater the deviation degree of the current order sub-data and the first confidence interval is, the more important is the sub-index in the index combination corresponding to the current order sub-data.
2. The method of claim 1, wherein the step of determining whether the current order data is anomalous based on the historical order data comprises:
Calculating a second confidence interval corresponding to the historical order data through an interval estimation algorithm;
and if the current order data is not in the second confidence interval, determining that the current order data is abnormal.
3. The method of claim 1, wherein the step of applying the historical order sub-data to determine if the current order sub-data is anomalous comprises:
calculating the sub-change rate of the current order sub-data and the historical order sub-data;
And if the sub-change rate is larger than a preset sub-change rate threshold, determining that the current ordering sheet data is abnormal.
4. The method of claim 1, wherein the step of dividing the historical order data by each index combination comprises:
Dividing the historical order data according to service index groups to obtain first historical order data corresponding to each service index group, wherein the service index groups comprise at least one service index;
dividing the first historical order data according to the sub-index groups contained in the service index groups to obtain second historical order data corresponding to each sub-index group, and taking the second historical order data as historical order sub-data of index combinations corresponding to the service index groups, wherein the sub-index groups comprise at least one sub-index.
5. The method of claim 3, further comprising, after the step of applying the historical order sub-data to determine if the current order sub-data is anomalous:
If the judgment result is that the current order sub-data is abnormal, calculating a third confidence interval corresponding to the sub-change rate of the current order sub-data through the interval estimation algorithm;
Determining current order sub-data corresponding to the sub-change rate not in the third confidence interval as remarkable abnormal order sub-data;
And taking the remarkable abnormal order sub-data as the abnormal current order sub-data, and continuing to execute the step of judging whether the current order sub-data is in the first confidence interval of the historical order sub-data.
6. The method according to claim 1, wherein the method further comprises:
Acquiring a first change rate between the current order data and the historical order data and a second change rate between current order sub-data and corresponding historical order sub-data which are not in the first confidence interval;
and displaying the first change rate, the second change rate and the interpretation variable corresponding to the current order data.
7. An apparatus for analyzing order data, comprising:
The data judging module is used for judging whether the current order data is abnormal according to the historical order data;
The data dividing module is used for dividing the historical order data and the current order data according to each index combination if the current order data is abnormal, so as to obtain historical order sub-data and current order sub-data corresponding to each index combination, wherein the index combination comprises at least one sub-index corresponding to at least one service index;
The traversing module is used for traversing the historical order sub-data and the current order sub-data corresponding to each index combination, and judging whether the current order sub-data is abnormal or not by applying the historical order sub-data;
An explanatory variable determining module for performing the following operations on the abnormal current sub order data:
judging whether the current order sub-data is in a first confidence interval of the historical order sub-data or not, wherein the first confidence interval is obtained by calculating the historical order sub-data through an interval estimation algorithm;
If not, adding the sub-index in the index combination currently traversed to an interpretation variable of the current order data, wherein the interpretation variable is used for representing the reason for data abnormality
And if a plurality of current order sub-data are not in the first confidence interval, sorting importance of a plurality of interpretation variables corresponding to the current order data according to the deviation degree of each current order sub-data and the corresponding first confidence interval, wherein the greater the deviation degree of the current order sub-data and the first confidence interval is, the more important is the sub-index in the index combination corresponding to the current order sub-data.
8. An electronic device comprising a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium in communication over the bus when the electronic device is in operation, the processor executing the machine-readable instructions to perform the steps of the method of any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, performs the steps of the method according to any of claims 1 to 6.
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