CN119089558B - AI-based historical building digital data processing method and system - Google Patents
AI-based historical building digital data processing method and system Download PDFInfo
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Abstract
本发明公开了一种基于AI的历史建筑数字化数据处理方法及系统，该方法包括：通过传感器获取目标历史建筑的实时传感信息；根据所述实时传感信息，基于建模算法，生成所述目标历史建筑对应的建筑数字模型；基于神经网络算法，对所述建筑数字模型进行缺失补全，得到补全后的数字模型；基于神经网络算法，根据所述补全后的数字模型以及所述实时传感信息，预测所述目标历史建筑的风险位置和风险类型。可见，本发明能够提高历史建筑的数字化模型的建模效率和建模精度，并实现更加智能化的历史建筑的风险监控，减少监控成本，提高监控效果。
The present invention discloses a method and system for processing digital data of historical buildings based on AI, the method comprising: obtaining real-time sensing information of a target historical building through a sensor; generating a digital building model corresponding to the target historical building based on a modeling algorithm according to the real-time sensing information; completing missing parts of the digital building model based on a neural network algorithm to obtain a completed digital model; predicting the risk location and risk type of the target historical building based on the completed digital model and the real-time sensing information based on a neural network algorithm. It can be seen that the present invention can improve the modeling efficiency and modeling accuracy of the digital model of historical buildings, and realize more intelligent risk monitoring of historical buildings, reduce monitoring costs, and improve monitoring effects.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an AI-based historical building digital data processing method and system.
Background
Along with the development of protection requirements of historical buildings such as ancient buildings or ancient cultural relics and frescoes and the like and the improvement of digital technologies, more and more historical building protection mechanisms begin to adopt digital technologies to realize data acquisition and risk analysis of the historical buildings, wherein how to improve modeling accuracy and risk monitoring timeliness of the historical buildings becomes an important technical problem. However, in the prior art, when the digitization of the historical building is realized, the modeling precision and the risk monitoring capability are not considered to be improved by combining an AI algorithm, so that the precision of a digitization model is insufficient, the aesthetic degree or the ornamental value of the digitization model for the user is also lost, and the risk monitoring cannot be effectively realized. It can be seen that the prior art has defects and needs to be solved.
Disclosure of Invention
The technical problem to be solved by the invention is to provide the AI-based historical building digital data processing method and system, which can improve the modeling efficiency and modeling precision of a digital model of a historical building, realize more intelligent risk monitoring of the historical building, reduce the monitoring cost and improve the monitoring effect.
In order to solve the technical problem, the first aspect of the invention discloses an AI-based historical building digital data processing method, which comprises the following steps:
Acquiring real-time sensing information of a target historical building through a sensor;
generating a building digital model corresponding to the target historical building based on a modeling algorithm according to the real-time sensing information;
based on a neural network algorithm, carrying out missing complementation on the building digital model to obtain a complemented digital model;
Based on a neural network algorithm, predicting the risk position and the risk type of the target historical building according to the completed digital model and the real-time sensing information.
As an optional implementation manner, in the first aspect of the present invention, the real-time sensing information includes image data, acoustic wave information, ultrasonic wave reflection information, and light reflection information.
As an optional implementation manner, in the first aspect of the present invention, the generating, based on a modeling algorithm, a building digital model corresponding to the target historical building according to the real-time sensing information includes:
acquiring a preset historical digital model corresponding to the target historical building;
generating a real-time three-dimensional model corresponding to the target historical building based on a three-dimensional modeling algorithm according to the real-time sensing information;
And updating the historical digital model according to the real-time three-dimensional model to obtain a building digital model corresponding to the target historical building.
In a first aspect of the present invention, updating the historical digital model according to the real-time three-dimensional model to obtain a building digital model corresponding to the target historical building includes:
Aligning the real-time three-dimensional model and the historical digital model into the same coordinate system;
Determining a first model portion of the real-time three-dimensional model and a second model portion of the historical digital model in spatial locations of any of the coordinate systems that simultaneously include the real-time three-dimensional model and the historical digital model;
Calculating an image difference degree and a three-dimensional difference degree between the first model part and the second model part;
Calculating a weighted sum average value of the image difference degree and the three-dimensional difference degree to obtain a position updating parameter corresponding to the space position;
Determining all the spatial positions of which the position updating parameters are larger than a first parameter threshold as updating positions;
And replacing the model part corresponding to the historical digital model corresponding to each updated position with the model part corresponding to the position in the real-time three-dimensional model based on the historical digital model to obtain a building digital model corresponding to the target historical building.
As an optional implementation manner, in the first aspect of the present invention, the performing, based on a neural network algorithm, deletion complement on the building digital model to obtain a complemented digital model includes:
framing the building digital model based on a plurality of viewpoints and a plurality of angles to obtain a plurality of framing images;
inputting each view finding image into a preset missing identification neural network model to obtain an image missing part corresponding to each view finding image;
identifying a missing model part in the building digital model according to the image missing parts corresponding to all the view finding images;
Predicting the model shape of each missing model part according to a historical digital model record corresponding to the target historical building and a preset structure prediction algorithm to obtain a predicted shape result;
And carrying out deletion complementation on each missing model part of the building digital model according to the predicted shape result to obtain a complemented digital model.
As an optional implementation manner, in the first aspect of the present invention, the identifying, according to the image missing portions corresponding to all the viewfinder images, a missing model portion in the building digital model includes:
Determining a plurality of possible missing parts in the building digital model based on a corresponding relation between a preset model part and missing possibility, wherein the possible missing parts are building connecting part parts, building inner bearing parts, building invisible surface parts or building low-visibility surface parts;
for each possible missing part, calculating the occurrence times of the possible missing part in the image missing parts corresponding to all the view finding images;
and if the occurrence number is larger than a preset number threshold, determining the possible missing part as a missing model part.
In a first aspect of the present invention, the predicting, according to the history digital model record corresponding to the target history building and a preset structure prediction algorithm, the model shape of each missing model part to obtain a predicted shape result includes:
Determining a plurality of nearby model parts corresponding to the missing model part for each missing model part, wherein the nearby model parts are model parts with the distance between the nearby model parts and the missing model part being smaller than a preset distance threshold value;
Inputting all the nearby model parts and the corresponding model positions into a trained shape prediction neural network to obtain a predicted model shape corresponding to the missing model part, wherein the shape prediction neural network is obtained by training a training data set comprising a plurality of training nearby model parts and corresponding specific region shape labels;
And determining the prediction model shapes corresponding to all the missing model parts as prediction shape results.
As an optional implementation manner, in the first aspect of the present invention, the predicting, based on the neural network algorithm and the digital model after completion and the real-time sensing information, a risk location and a risk type of the target historical building includes:
inputting the real-time sensing information corresponding to each missing model part in the completed digital model to a risk prediction neural network model corresponding to the corresponding data type so as to obtain risk prediction probability and risk prediction type corresponding to each missing model part;
Inputting the real-time sensing information corresponding to each updated position in the completed digital model into a risk prediction neural network model corresponding to the corresponding data type to obtain risk prediction probability and risk prediction type corresponding to each updated position;
And determining all risk prediction types with the risk prediction probability larger than a preset probability threshold as risk types of the target historical building, and determining the corresponding missing model part or the updating position as a corresponding risk position.
The second aspect of the embodiment of the invention discloses an AI-based historical building digital data processing system, which comprises:
The acquisition module is used for acquiring real-time sensing information of the target historical building through the sensor;
The generation module is used for generating a building digital model corresponding to the target historical building based on a modeling algorithm according to the real-time sensing information;
the complementing module is used for carrying out missing complementing on the building digital model based on a neural network algorithm to obtain a complemented digital model;
and the prediction module is used for predicting the risk position and the risk type of the target historical building according to the completed digital model and the real-time sensing information based on a neural network algorithm.
As an alternative embodiment, in the second aspect of the present invention, the real-time sensing information includes image data, acoustic wave information, ultrasonic wave reflection information, and light reflection information.
As an optional implementation manner, in the second aspect of the present invention, the generating module generates, based on a modeling algorithm, a building digital model corresponding to the target historical building according to the real-time sensing information, where the specific manner includes:
acquiring a preset historical digital model corresponding to the target historical building;
generating a real-time three-dimensional model corresponding to the target historical building based on a three-dimensional modeling algorithm according to the real-time sensing information;
And updating the historical digital model according to the real-time three-dimensional model to obtain a building digital model corresponding to the target historical building.
In a second aspect of the present invention, the generating module updates the historical digital model according to the real-time three-dimensional model to obtain a concrete mode of the building digital model corresponding to the target historical building, where the concrete mode includes:
Aligning the real-time three-dimensional model and the historical digital model into the same coordinate system;
Determining a first model portion of the real-time three-dimensional model and a second model portion of the historical digital model in spatial locations of any of the coordinate systems that simultaneously include the real-time three-dimensional model and the historical digital model;
Calculating an image difference degree and a three-dimensional difference degree between the first model part and the second model part;
Calculating a weighted sum average value of the image difference degree and the three-dimensional difference degree to obtain a position updating parameter corresponding to the space position;
Determining all the spatial positions of which the position updating parameters are larger than a first parameter threshold as updating positions;
And replacing the model part corresponding to the historical digital model corresponding to each updated position with the model part corresponding to the position in the real-time three-dimensional model based on the historical digital model to obtain a building digital model corresponding to the target historical building.
In a second aspect of the present invention, the supplementing module performs missing supplementation on the building digital model based on a neural network algorithm, to obtain a specific mode of the digital model after supplementation, which includes:
framing the building digital model based on a plurality of viewpoints and a plurality of angles to obtain a plurality of framing images;
inputting each view finding image into a preset missing identification neural network model to obtain an image missing part corresponding to each view finding image;
identifying a missing model part in the building digital model according to the image missing parts corresponding to all the view finding images;
Predicting the model shape of each missing model part according to a historical digital model record corresponding to the target historical building and a preset structure prediction algorithm to obtain a predicted shape result;
And carrying out deletion complementation on each missing model part of the building digital model according to the predicted shape result to obtain a complemented digital model.
In a second aspect of the present invention, as an optional implementation manner, the complement module identifies a specific manner of the missing model portion in the building digital model according to the image missing portions corresponding to all the viewfinder images, including:
Determining a plurality of possible missing parts in the building digital model based on a corresponding relation between a preset model part and missing possibility, wherein the possible missing parts are building connecting part parts, building inner bearing parts, building invisible surface parts or building low-visibility surface parts;
for each possible missing part, calculating the occurrence times of the possible missing part in the image missing parts corresponding to all the view finding images;
and if the occurrence number is larger than a preset number threshold, determining the possible missing part as a missing model part.
In a second aspect of the present invention, as an optional implementation manner, the completing module predicts a model shape of each missing model portion according to a history digital model record corresponding to the target history building and a preset structure prediction algorithm, so as to obtain a specific mode of a predicted shape result, where the specific mode includes:
Determining a plurality of nearby model parts corresponding to the missing model part for each missing model part, wherein the nearby model parts are model parts with the distance between the nearby model parts and the missing model part being smaller than a preset distance threshold value;
Inputting all the nearby model parts and the corresponding model positions into a trained shape prediction neural network to obtain a predicted model shape corresponding to the missing model part, wherein the shape prediction neural network is obtained by training a training data set comprising a plurality of training nearby model parts and corresponding specific region shape labels;
And determining the prediction model shapes corresponding to all the missing model parts as prediction shape results.
As an optional implementation manner, in the second aspect of the present invention, the predicting module predicts, based on a neural network algorithm, a risk location and a risk type of the target historical building according to the completed digital model and the real-time sensing information, where the specific manner includes:
inputting the real-time sensing information corresponding to each missing model part in the completed digital model to a risk prediction neural network model corresponding to the corresponding data type so as to obtain risk prediction probability and risk prediction type corresponding to each missing model part;
Inputting the real-time sensing information corresponding to each updated position in the completed digital model into a risk prediction neural network model corresponding to the corresponding data type to obtain risk prediction probability and risk prediction type corresponding to each updated position;
And determining all risk prediction types with the risk prediction probability larger than a preset probability threshold as risk types of the target historical building, and determining the corresponding missing model part or the updating position as a corresponding risk position.
In a third aspect, the invention discloses another AI-based historical building digitized data processing system, the system comprising:
a memory storing executable program code;
a processor coupled to the memory;
The processor invokes the executable program code stored in the memory to perform some or all of the steps in the AI-based historical building digital data processing method disclosed in the first aspect of the invention.
A fourth aspect of the invention discloses a computer storage medium storing computer instructions that, when invoked, are operable to perform part or all of the steps of the AI-based historical building digitization data processing method disclosed in the first aspect of the invention.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
According to the invention, the building digital model corresponding to the target historical building can be generated based on the modeling algorithm according to the real-time sensing information, then the building digital model is subjected to missing complementation based on the neural network algorithm, and the risk position and the risk type of the target historical building are accurately predicted, so that the modeling efficiency and the modeling precision of the digital model of the historical building can be improved, more intelligent risk monitoring of the historical building is realized, the monitoring cost is reduced, and the monitoring effect is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a historical building digital data processing method based on AI according to an embodiment of the invention.
FIG. 2 is a schematic diagram of an AI-based historian digital data processing system in accordance with an embodiment of the present invention.
FIG. 3 is a schematic diagram of another AI-based historian digital data processing system in accordance with an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, apparatus, article, or device that comprises a list of steps or elements is not limited to the list of steps or elements but may, in the alternative, include other steps or elements not expressly listed or inherent to such process, method, article, or device.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The invention discloses an AI-based historical building digital data processing method and system, which can generate a building digital model corresponding to a target historical building based on a modeling algorithm according to real-time sensing information, then carry out missing completion on the building digital model based on a neural network algorithm, and accurately predict the risk position and the risk type of the target historical building, so that the modeling efficiency and the modeling precision of the historical building digital model can be improved, more intelligent historical building risk monitoring can be realized, the monitoring cost is reduced, and the monitoring effect is improved. The following will describe in detail.
Example 1
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for processing AI-based historical building digital data according to an embodiment of the invention. The AI-based historical building digitized data processing method described in fig. 1 can be applied to a data processing device/data processing server (wherein the server comprises a local processing server or a cloud processing server). As shown in fig. 1, the AI-based historical building digitized data processing method may include the following operations:
101. And acquiring real-time sensing information of the target historical building through a sensor.
Specifically, in a specific embodiment, sensors such as a camera, a sound sensor, an ultrasonic reflection sensor and a light reflection sensor are arranged around and in an immovable relic, a historical building and a traditional landscape building, and an ancient tree name wood, and the sensor network is arranged on the building to monitor the structural health, environmental change and other information of the building in real time, and the AI algorithm is utilized to analyze and predict the data, so that potential safety hazards of the building can be found in time and early warning can be sent to an information management platform, for example, the camera is controlled to photograph the historical building at the frequency of 1 time per hour and update the historical building to the information management platform, the multi-mode capability of the AI model is utilized to identify the current situation of the historical building, and if damage risks such as breakage, cracks and the like occur, the early warning is sent to the information management platform.
102. And generating a building digital model corresponding to the target historical building based on a modeling algorithm according to the real-time sensing information.
103. And carrying out missing complementation on the building digital model based on a neural network algorithm to obtain a complemented digital model.
104. Based on a neural network algorithm, predicting the risk position and risk type of the target historical building according to the completed digital model and the real-time sensing information.
Specifically, in order to realize the scheme, various databases can be further established, the completed digital models are integrated into the databases to be used for displaying more visual historical building digital models for users, wherein the basic space information database comprises information such as digital line drawing arrangement and storage, government map database, internet map database, image database, digital elevation model, administrative division, place name address and the like, the historical building thematic database realizes checking, scanning, extracting, storing and the like on historical building thematic data and data such as immovable cultural relics, historical buildings, traditional style buildings, ancient tree famous trees and the like, establishes a historical building database, and particularly, the historical building thematic data requirement comprises thematic data related to data general investigation, spatially positions various levels and types of immovable cultural relics information, and marks the same on an electronic map, so that the information can be intuitively and vividly inquired on the electronic map. The historical culture famous city protection database comprises a historical culture famous city planning and related achievements when the region is a historical culture famous city, and mainly comprises data such as famous city protection framework planning, city area historical culture protection planning, historical culture famous town, historical culture protection planning, historical culture street block, historical feature block protection planning, historical culture famous city protection line and the like. Optionally, other databases may include data of immovable cultural relics, historical and traditional landscape architecture, live-action images of ancient tree names, VR panorama, laser point cloud, live-action three-dimensional model, and the like.
Based on the specific implementation scheme, the spatial distribution, the photos, the characteristics and the attributes of the immovable cultural relics, the historical architecture, the traditional style architecture and the ancient tree famous trees can be comprehensively expressed on the basis of various databases.
In order to concretely realize the technical scheme, a field collection applet is developed to face the collection personnel of the historical building outside industry, can collect cultural relic information in different modes such as photos, characters, voices and handbooks, record the current geographic position, and synchronously display the cultural relic information on a map of a collection mobile phone, so that support is provided for the collection personnel to check the collection effect. The field collection applet is mainly used for collecting historical building thematic data. The field acquisition applet comprises modules for creating tasks, loading tasks, map views, track recording, exiting, uploading data, loading a general list, and the like, wherein the map views comprise functions of adding acquisition points, adding photos, inquiring acquisition points, adding new bookmarks, inquiring bookmarks, measuring distances, enlarging and reducing, positioning, and the like.
Meanwhile, a data management subsystem is developed, and the main task is to manage the comprehensive database, and mainly comprises the functions of high-precision data coordinate conversion, census data management, census data warehouse management, basic space information data warehouse management, other novel mapping data warehouse management, real-time data warehouse management of camera acquisition data, data editing maintenance, data resource catalog management, symbol library management, metadata management, data updating, data backup management and the like.
Meanwhile, an information management subsystem is developed and used for carrying out digital management functions such as display, management, analysis, statistics and the like on the historical building special data formed after the data management subsystem is subjected to warehouse entry inspection. The system mainly provides services for daily management departments of historical buildings, comprises comprehensive management and diversified display of immovable cultural relics, historical buildings and traditional landscape buildings, and has the main functions of a common GIS function, heritage information editing, heritage information display, AI real-time early warning, archive management, printout and the like.
Therefore, the embodiment of the invention can generate the building digital model corresponding to the target historical building based on the modeling algorithm according to the real-time sensing information, then carry out missing complementation on the building digital model based on the neural network algorithm, and accurately predict the risk position and the risk type of the target historical building, so that the modeling efficiency and the modeling precision of the digital model of the historical building can be improved, more intelligent risk monitoring of the historical building can be realized, the monitoring cost is reduced, and the monitoring effect is improved.
As an alternative embodiment, in the above steps, the real-time sensing information includes image data, acoustic wave information, ultrasonic wave reflection information, and light reflection information.
Therefore, through the optional embodiment, the content of the real-time sensing information is limited, the structural characteristics of the historical building can be fully represented, the subsequent modeling is more accurate, the modeling efficiency and the modeling precision of the digital model of the historical building are improved in an auxiliary mode, the risk monitoring of the historical building is more intelligent, the monitoring cost is reduced, and the monitoring effect is improved.
As an optional embodiment, in the step, generating, based on the modeling algorithm, a building digital model corresponding to the target historical building according to the real-time sensing information includes:
Acquiring a preset historical digital model corresponding to a target historical building;
Generating a real-time three-dimensional model corresponding to the target historical building based on a three-dimensional modeling algorithm according to the real-time sensing information;
and updating the historical digital model according to the real-time three-dimensional model to obtain a building digital model corresponding to the target historical building.
Therefore, through the optional embodiment, the real-time three-dimensional model corresponding to the target historical building can be generated based on the modeling algorithm according to the real-time sensing information so as to update the historical digital model to obtain the building digital model corresponding to the target historical building, and the building digital model is subsequently used for missing completion and risk analysis, so that the modeling efficiency and modeling precision of the digital model of the historical building are improved in an auxiliary manner, more intelligent risk monitoring of the historical building is realized, the monitoring cost is reduced, and the monitoring effect is improved.
As an optional embodiment, in the step, updating the historical digital model according to the real-time three-dimensional model to obtain a building digital model corresponding to the target historical building includes:
Aligning the real-time three-dimensional model and the historical digital model to the same coordinate system;
determining a first model part of the real-time three-dimensional model and a second model part of the historical digital model in a spatial position of any one of the coordinate systems comprising the real-time three-dimensional model and the historical digital model simultaneously;
calculating an image difference degree and a three-dimensional difference degree between the first model part and the second model part;
calculating a weighted sum average value of the image difference degree and the three-dimensional difference degree to obtain a position updating parameter corresponding to the space position;
Determining spatial positions where all the position updating parameters are larger than a first parameter threshold as updating positions;
And replacing the model part corresponding to the historical digital model corresponding to each updated position with the model part corresponding to the position in the real-time three-dimensional model based on the historical digital model to obtain the building digital model corresponding to the target historical building.
It can be seen that, through the above-mentioned alternative embodiment, the update necessity of the position can be determined by calculating the image difference degree and the three-dimensional difference degree of the model part in the spatial position including the real-time three-dimensional model and the historical digital model simultaneously, and the part with high necessity is updated and replaced to obtain the building digital model, and then used for missing complement and risk analysis, so as to assist in realizing the modeling efficiency and modeling precision of the digital model of the historical building, realizing the more intelligent risk monitoring of the historical building, reducing the monitoring cost and improving the monitoring effect.
As an optional embodiment, in the step, based on a neural network algorithm, missing complement is performed on the building digital model, so as to obtain a complemented digital model, which includes:
framing the building digital model based on a plurality of viewpoints and a plurality of angles to obtain a plurality of framing images;
Inputting each view finding image into a preset missing identification neural network model to obtain an image missing part corresponding to each view finding image;
identifying a missing model part in the building digital model according to the image missing parts corresponding to all the view finding images;
Predicting the model shape of each missing model part according to a historical digital model record corresponding to the target historical building and a preset structure prediction algorithm to obtain a predicted shape result;
and carrying out missing complementation on each missing model part of the building digital model according to the predicted shape result to obtain a complemented digital model.
Therefore, through the optional embodiment, the part with the structure missing in the image can be identified through the neural network model based on the framing image obtained by framing of multiple angles and multiple viewpoints, and the shape of the missing part is predicted based on the history model record to complement, so that the modeling efficiency and the modeling precision of the digital model of the history building are improved in an auxiliary manner, the more intelligent risk monitoring of the history building is realized, the monitoring cost is reduced, and the monitoring effect is improved.
As an alternative embodiment, in the step, identifying the missing model portion in the building digital model according to the image missing portions corresponding to all the viewfinder images includes:
determining a plurality of possible missing parts in the building digital model based on the corresponding relation between the preset model part and the missing possibility, wherein the possible missing parts are optionally building connection parts, building inner bearing parts, building invisible surface parts or building low-visibility surface parts;
For each possible missing part, calculating the occurrence times of the possible missing part in the image missing parts corresponding to all the view finding images;
And if the occurrence number is greater than a preset number threshold, determining the possible missing part as a missing model part.
Therefore, through the above optional embodiment, after the possible missing part is determined, the missing model part is determined based on the counting mode, so that the determining efficiency of the missing model part is effectively improved, the modeling efficiency and the modeling precision of the digital model of the historical building are improved in an auxiliary manner, the more intelligent risk monitoring of the historical building is realized, the monitoring cost is reduced, and the monitoring effect is improved.
As an optional embodiment, in the step, predicting the model shape of each missing model portion according to the historical digital model record corresponding to the target historical building and a preset structure prediction algorithm to obtain a predicted shape result, including:
For each missing model part, determining a plurality of nearby model parts corresponding to the missing model part, wherein the nearby model parts are optional model parts with the distance between the nearby model parts and the missing model part being smaller than a preset distance threshold value;
Inputting all the nearby model parts and the corresponding model positions into a trained shape prediction neural network to obtain a predicted model shape corresponding to the missing model part, wherein the shape prediction neural network is obtained by training a training data set comprising a plurality of training nearby model parts and corresponding specific region shape labels;
And determining the prediction model shapes corresponding to all the missing model parts as prediction shape results.
Therefore, through the optional embodiment, the shape prediction neural network obtained through training based on the historical digital model record corresponding to the target historical building can accurately predict the predicted model shape corresponding to the missing model part according to the nearby model part and the corresponding model position, so that the modeling efficiency and the modeling precision of the digital model of the historical building are improved, more intelligent risk monitoring of the historical building is realized, the monitoring cost is reduced, and the monitoring effect is improved.
As an optional embodiment, in the step, based on the neural network algorithm, predicting the risk location and risk type of the target historical building according to the completed digital model and the real-time sensing information includes:
inputting the real-time sensing information corresponding to each missing model part in the completed digital model to a risk prediction neural network model corresponding to the corresponding data type so as to obtain risk prediction probability and risk prediction type corresponding to each missing model part;
Inputting the real-time sensing information corresponding to each updated position in the completed digital model into a risk prediction neural network model corresponding to the corresponding data type to obtain risk prediction probability and risk prediction type corresponding to each updated position;
And determining all risk prediction types with risk prediction probabilities larger than a preset probability threshold as risk types of the target historical building, and determining corresponding missing model parts or updated positions as corresponding risk positions.
Therefore, according to the above-mentioned alternative embodiment, the risk identification monitoring can be preferentially performed based on the missing part and the updated part of the model determined in the previous step, and the missing or updated change is large due to the existence of new building damage accident in the parts, so that the risk monitoring of the history building can be more intelligently realized, the monitoring cost is reduced, and the monitoring effect is improved.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of an AI-based historical building digital data processing system according to an embodiment of the present invention. Wherein the AI-based historian building digital data processing system described in fig. 2 can be applied in a data processing device/data processing server (wherein the server comprises a local processing server or a cloud processing server). As shown in fig. 2, the AI-based historian building digital data processing system can include:
an acquisition module 201, configured to acquire real-time sensing information of a target historical building through a sensor.
The generating module 202 is configured to generate a building digital model corresponding to the target historical building based on the modeling algorithm according to the real-time sensing information.
And the complementing module 203 is configured to perform missing complementing on the building digital model based on a neural network algorithm, so as to obtain a complemented digital model.
The prediction module 204 is configured to predict a risk location and a risk type of the target historical building according to the completed digital model and the real-time sensing information based on the neural network algorithm.
Therefore, the embodiment of the invention can generate the building digital model corresponding to the target historical building based on the modeling algorithm according to the real-time sensing information, then carry out missing complementation on the building digital model based on the neural network algorithm, and accurately predict the risk position and the risk type of the target historical building, so that the modeling efficiency and the modeling precision of the digital model of the historical building can be improved, more intelligent risk monitoring of the historical building can be realized, the monitoring cost is reduced, and the monitoring effect is improved.
As an alternative embodiment, the real-time sensed information includes image data, acoustic information, ultrasonic reflection information, and light reflection information.
Therefore, through the optional embodiment, the content of the real-time sensing information is limited, the structural characteristics of the historical building can be fully represented, the subsequent modeling is more accurate, the modeling efficiency and the modeling precision of the digital model of the historical building are improved in an auxiliary mode, the risk monitoring of the historical building is more intelligent, the monitoring cost is reduced, and the monitoring effect is improved.
As an optional embodiment, the generating module generates the building digital model corresponding to the target historical building based on the modeling algorithm according to the real-time sensing information, and the specific mode comprises the following steps:
Acquiring a preset historical digital model corresponding to a target historical building;
Generating a real-time three-dimensional model corresponding to the target historical building based on a three-dimensional modeling algorithm according to the real-time sensing information;
and updating the historical digital model according to the real-time three-dimensional model to obtain a building digital model corresponding to the target historical building.
Therefore, through the optional embodiment, the real-time three-dimensional model corresponding to the target historical building can be generated based on the modeling algorithm according to the real-time sensing information so as to update the historical digital model to obtain the building digital model corresponding to the target historical building, and the building digital model is subsequently used for missing completion and risk analysis, so that the modeling efficiency and modeling precision of the digital model of the historical building are improved in an auxiliary manner, more intelligent risk monitoring of the historical building is realized, the monitoring cost is reduced, and the monitoring effect is improved.
As an optional embodiment, the generating module updates the historical digital model according to the real-time three-dimensional model to obtain a specific mode of the building digital model corresponding to the target historical building, and the specific mode comprises the following steps:
Aligning the real-time three-dimensional model and the historical digital model to the same coordinate system;
determining a first model part of the real-time three-dimensional model and a second model part of the historical digital model in a spatial position of any one of the coordinate systems comprising the real-time three-dimensional model and the historical digital model simultaneously;
calculating an image difference degree and a three-dimensional difference degree between the first model part and the second model part;
calculating a weighted sum average value of the image difference degree and the three-dimensional difference degree to obtain a position updating parameter corresponding to the space position;
Determining spatial positions where all the position updating parameters are larger than a first parameter threshold as updating positions;
And replacing the model part corresponding to the historical digital model corresponding to each updated position with the model part corresponding to the position in the real-time three-dimensional model based on the historical digital model to obtain the building digital model corresponding to the target historical building.
It can be seen that, through the above-mentioned alternative embodiment, the update necessity of the position can be determined by calculating the image difference degree and the three-dimensional difference degree of the model part in the spatial position including the real-time three-dimensional model and the historical digital model simultaneously, and the part with high necessity is updated and replaced to obtain the building digital model, and then used for missing complement and risk analysis, so as to assist in realizing the modeling efficiency and modeling precision of the digital model of the historical building, realizing the more intelligent risk monitoring of the historical building, reducing the monitoring cost and improving the monitoring effect.
As an optional embodiment, the complement module performs missing complement on the building digital model based on a neural network algorithm, and the specific mode of obtaining the complemented digital model includes:
framing the building digital model based on a plurality of viewpoints and a plurality of angles to obtain a plurality of framing images;
Inputting each view finding image into a preset missing identification neural network model to obtain an image missing part corresponding to each view finding image;
identifying a missing model part in the building digital model according to the image missing parts corresponding to all the view finding images;
Predicting the model shape of each missing model part according to a historical digital model record corresponding to the target historical building and a preset structure prediction algorithm to obtain a predicted shape result;
and carrying out missing complementation on each missing model part of the building digital model according to the predicted shape result to obtain a complemented digital model.
Therefore, through the optional embodiment, the part with the structure missing in the image can be identified through the neural network model based on the framing image obtained by framing of multiple angles and multiple viewpoints, and the shape of the missing part is predicted based on the history model record to complement, so that the modeling efficiency and the modeling precision of the digital model of the history building are improved in an auxiliary manner, the more intelligent risk monitoring of the history building is realized, the monitoring cost is reduced, and the monitoring effect is improved.
As an optional embodiment, the complement module identifies a specific mode of the missing model part in the building digital model according to the image missing parts corresponding to all the viewfinder images, including:
determining a plurality of possible missing parts in the building digital model based on the corresponding relation between the preset model part and the missing possibility, wherein the possible missing parts are optionally building connection parts, building inner bearing parts, building invisible surface parts or building low-visibility surface parts;
For each possible missing part, calculating the occurrence times of the possible missing part in the image missing parts corresponding to all the view finding images;
And if the occurrence number is greater than a preset number threshold, determining the possible missing part as a missing model part.
Therefore, through the above optional embodiment, after the possible missing part is determined, the missing model part is determined based on the counting mode, so that the determining efficiency of the missing model part is effectively improved, the modeling efficiency and the modeling precision of the digital model of the historical building are improved in an auxiliary manner, the more intelligent risk monitoring of the historical building is realized, the monitoring cost is reduced, and the monitoring effect is improved.
As an optional embodiment, the completing module predicts the model shape of each missing model part according to the historical digital model record corresponding to the target historical building and a preset structure prediction algorithm, and a specific mode for obtaining a predicted shape result includes:
For each missing model part, determining a plurality of nearby model parts corresponding to the missing model part, wherein the nearby model parts are optional model parts with the distance between the nearby model parts and the missing model part being smaller than a preset distance threshold value;
Inputting all the nearby model parts and the corresponding model positions into a trained shape prediction neural network to obtain a predicted model shape corresponding to the missing model part, wherein the shape prediction neural network is obtained by training a training data set comprising a plurality of training nearby model parts and corresponding specific region shape labels;
And determining the prediction model shapes corresponding to all the missing model parts as prediction shape results.
Therefore, through the optional embodiment, the shape prediction neural network obtained through training based on the historical digital model record corresponding to the target historical building can accurately predict the predicted model shape corresponding to the missing model part according to the nearby model part and the corresponding model position, so that the modeling efficiency and the modeling precision of the digital model of the historical building are improved, more intelligent risk monitoring of the historical building is realized, the monitoring cost is reduced, and the monitoring effect is improved.
As an optional embodiment, the predicting module predicts the risk position and the risk type of the target historical building according to the completed digital model and the real-time sensing information based on the neural network algorithm, and includes:
inputting the real-time sensing information corresponding to each missing model part in the completed digital model to a risk prediction neural network model corresponding to the corresponding data type so as to obtain risk prediction probability and risk prediction type corresponding to each missing model part;
Inputting the real-time sensing information corresponding to each updated position in the completed digital model into a risk prediction neural network model corresponding to the corresponding data type to obtain risk prediction probability and risk prediction type corresponding to each updated position;
And determining all risk prediction types with risk prediction probabilities larger than a preset probability threshold as risk types of the target historical building, and determining corresponding missing model parts or updated positions as corresponding risk positions.
Therefore, according to the above-mentioned alternative embodiment, the risk identification monitoring can be preferentially performed based on the missing part and the updated part of the model determined in the previous step, and the missing or updated change is large due to the existence of new building damage accident in the parts, so that the risk monitoring of the history building can be more intelligently realized, the monitoring cost is reduced, and the monitoring effect is improved.
Example III
Referring to fig. 3, fig. 3 is a schematic diagram of yet another AI-based historical building digital data processing system according to an embodiment of the present invention. The AI-based historian building digital data processing system described in fig. 3 is applied in a data processing device/data processing server (where the server includes a local processing server or a cloud processing server). As shown in fig. 3, the AI-based historian building digital data processing system can include:
a memory 301 storing executable program code;
A processor 302 coupled with the memory 301;
Wherein the processor 302 invokes executable program code stored in the memory 301 for performing the steps of the AI-based historian digital data processing method as described in embodiment one.
Example IV
The embodiment of the invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps of the AI-based historical building digital data processing method described in the embodiment one.
Example five
The embodiment of the invention discloses a computer program product, which comprises a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute the steps of the AI-based historical building digital data processing method described in the embodiment.
The foregoing describes certain embodiments of the present disclosure, other embodiments being within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. Furthermore, the processes depicted in the accompanying drawings do not necessarily have to be in the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in one or more software and/or hardware elements when implemented in the present specification.
It will be appreciated by those skilled in the art that the present description may be provided as a method, system, or computer program product. Accordingly, the present specification embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description embodiments may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present description is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
Finally, it should be noted that the disclosure of the method and system for processing the digital data of the historical architecture based on AI in the embodiment of the present invention is only a preferred embodiment of the present invention, and is only for illustrating the technical scheme of the present invention, but not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art will understand that modifications may be made to the technical solutions described in the foregoing embodiments or equivalents may be substituted for some of the technical features thereof, and that these modifications or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention in essence of the corresponding technical solutions.
Claims (8)
1. An AI-based historical building digitized data processing method, the method comprising:
Acquiring real-time sensing information of a target historical building through a sensor;
according to the real-time sensing information, based on a modeling algorithm, generating a building digital model corresponding to the target historical building, including:
acquiring a preset historical digital model corresponding to the target historical building;
generating a real-time three-dimensional model corresponding to the target historical building based on a three-dimensional modeling algorithm according to the real-time sensing information;
Aligning the real-time three-dimensional model and the historical digital model into the same coordinate system;
Determining a first model portion of the real-time three-dimensional model and a second model portion of the historical digital model in spatial locations of any of the coordinate systems that simultaneously include the real-time three-dimensional model and the historical digital model;
Calculating an image difference degree and a three-dimensional difference degree between the first model part and the second model part;
Calculating a weighted sum average value of the image difference degree and the three-dimensional difference degree to obtain a position updating parameter corresponding to the space position;
Determining all the spatial positions of which the position updating parameters are larger than a first parameter threshold as updating positions;
Based on the historical digital model, replacing a model part corresponding to the historical digital model corresponding to each updated position with a model part corresponding to the position in the real-time three-dimensional model to obtain a building digital model corresponding to the target historical building;
based on a neural network algorithm, carrying out missing complementation on the building digital model to obtain a complemented digital model;
Based on a neural network algorithm, predicting the risk position and the risk type of the target historical building according to the completed digital model and the real-time sensing information.
2. The AI-based historical building digitized data processing method of claim 1 wherein the real-time sensory information comprises image data, acoustic information, ultrasonic reflection information, and light reflection information.
3. The AI-based historical building digitized data processing method of claim 1, wherein the neural network algorithm-based missing complement is performed on the building digital model to obtain a complemented digital model, comprising:
framing the building digital model based on a plurality of viewpoints and a plurality of angles to obtain a plurality of framing images;
inputting each view finding image into a preset missing identification neural network model to obtain an image missing part corresponding to each view finding image;
identifying a missing model part in the building digital model according to the image missing parts corresponding to all the view finding images;
Predicting the model shape of each missing model part according to a historical digital model record corresponding to the target historical building and a preset structure prediction algorithm to obtain a predicted shape result;
And carrying out deletion complementation on each missing model part of the building digital model according to the predicted shape result to obtain a complemented digital model.
4. The AI-based historical building digitized data processing method of claim 3, wherein said identifying missing model portions in said building digital model from image missing portions corresponding to all of said viewfinder images comprises:
Determining a plurality of possible missing parts in the building digital model based on a corresponding relation between a preset model part and missing possibility, wherein the possible missing parts are building connecting part parts, building inner bearing parts, building invisible surface parts or building low-visibility surface parts;
for each possible missing part, calculating the occurrence times of the possible missing part in the image missing parts corresponding to all the view finding images;
and if the occurrence number is larger than a preset number threshold, determining the possible missing part as a missing model part.
5. The AI-based historical building digitized data processing method of claim 3, wherein predicting the model shape of each missing model portion according to the historical digital model record corresponding to the target historical building and a preset structure prediction algorithm to obtain a predicted shape result comprises:
Determining a plurality of nearby model parts corresponding to the missing model part for each missing model part, wherein the nearby model parts are model parts with the distance between the nearby model parts and the missing model part being smaller than a preset distance threshold value;
Inputting all the nearby model parts and the corresponding model positions into a trained shape prediction neural network to obtain a predicted model shape corresponding to the missing model part, wherein the shape prediction neural network is obtained by training a training data set comprising a plurality of training nearby model parts and corresponding specific region shape labels;
And determining the prediction model shapes corresponding to all the missing model parts as prediction shape results.
6. The AI-based historical building digitized data processing method of claim 5 wherein said neural network-based algorithm predicts a risk location and a risk type for said target historical building from said completed digital model and said real-time sensory information, comprising:
inputting the real-time sensing information corresponding to each missing model part in the completed digital model to a risk prediction neural network model corresponding to the corresponding data type so as to obtain risk prediction probability and risk prediction type corresponding to each missing model part;
Inputting the real-time sensing information corresponding to each updated position in the completed digital model into a risk prediction neural network model corresponding to the corresponding data type to obtain risk prediction probability and risk prediction type corresponding to each updated position;
And determining all risk prediction types with the risk prediction probability larger than a preset probability threshold as risk types of the target historical building, and determining the corresponding missing model part or the updating position as a corresponding risk position.
7. An AI-based historical building digital data processing system, the system comprising:
The acquisition module is used for acquiring real-time sensing information of the target historical building through the sensor;
The generation module is used for generating a building digital model corresponding to the target historical building based on a modeling algorithm according to the real-time sensing information, and comprises the following steps:
acquiring a preset historical digital model corresponding to the target historical building;
generating a real-time three-dimensional model corresponding to the target historical building based on a three-dimensional modeling algorithm according to the real-time sensing information;
Aligning the real-time three-dimensional model and the historical digital model into the same coordinate system;
Determining a first model portion of the real-time three-dimensional model and a second model portion of the historical digital model in spatial locations of any of the coordinate systems that simultaneously include the real-time three-dimensional model and the historical digital model;
Calculating an image difference degree and a three-dimensional difference degree between the first model part and the second model part;
Calculating a weighted sum average value of the image difference degree and the three-dimensional difference degree to obtain a position updating parameter corresponding to the space position;
Determining all the spatial positions of which the position updating parameters are larger than a first parameter threshold as updating positions;
Based on the historical digital model, replacing a model part corresponding to the historical digital model corresponding to each updated position with a model part corresponding to the position in the real-time three-dimensional model to obtain a building digital model corresponding to the target historical building;
the complementing module is used for carrying out missing complementing on the building digital model based on a neural network algorithm to obtain a complemented digital model;
and the prediction module is used for predicting the risk position and the risk type of the target historical building according to the completed digital model and the real-time sensing information based on a neural network algorithm.
8. An AI-based historical building digital data processing system, the system comprising:
a memory storing executable program code;
a processor coupled to the memory;
The processor invokes the executable program code stored in the memory to perform the AI-based historian digital data processing method of any of claims 1-6.
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| CN118446520A (en) * | 2024-05-08 | 2024-08-06 | 中建八局发展建设有限公司 | Large-scale building fire prediction method, medium and system |
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