Digital twin target category determination method and device, medium and electronic equipment

Abstract

The invention provides a digital twin target category determination method and device, a medium and electronic equipment, and relates to the technical field of digital twin, and the method comprises the steps: collecting basic data of each preset region of an actual target corresponding to a digital twin target, so as to obtain a basic data list set S; according to the S and an attribute classification model, obtaining an attribute expected value list set X; according to the X, obtaining a target category influence value Z corresponding to the digital twin target; and determining an interval according to the Z and a preset category, and obtaining a category corresponding to the digital twin target. According to the method, the computing power is saved, the computing time is shortened, and the computing precision is improved. In consideration of different requirements of different types of degrees for each ability, different weights are set for the ability, and the obtained evaluation result is more objective and more accurate.

Description

Background Art

[0002] Digital twin is a technical means to map, interact and deeply integrate physical entities with their virtual digital models in real time. It is widely used in many fields such as manufacturing, smart cities, transportation, energy management, etc. By building a digital twin platform, enterprises and organizations can achieve all-round digital presentation, simulation analysis and optimized decision support for physical assets, processes, systems, etc. With the rapid development of digital twin technology, more and more physical organizations have built their own digital twin platforms, aiming to improve operational efficiency, reduce costs, and improve product quality with its powerful functions.

[0003] The digital twin platform has a variety of capability interfaces, each of which is used to achieve certain functions. Different application scenarios, different developers, and different users have different understandings and factors to consider when evaluating the quality of the digital twin platform. For example, manufacturing companies may be more concerned about the accuracy and effectiveness of the digital twin platform in production process simulation and equipment failure prediction; while managers in the smart city field focus on the accuracy and timeliness of the platform in urban resource allocation and emergency response simulation. The existing evaluation methods for digital twin platforms lack unified standards, making it difficult to conduct objective and fair horizontal comparisons between digital twin platforms, and it is not conducive to users accurately judging the actual value of the platform they are using and finding directions for improvement.

[0004] In view of the many shortcomings in the above-mentioned existing technologies, how to establish a scientific, comprehensive and objective evaluation system and method to accurately evaluate the digital twin platform that has been built has become an important technical problem that needs to be solved urgently. Summary of the invention

[0005] In response to the above technical problems, the present application provides a digital twin target category determination method, device, medium and electronic device, which at least partially solve the problems existing in the prior art.

[0006] In a first aspect of the present application, a method for determining a digital twin target category is provided, the method comprising the following steps: S100, collecting basic data of each preset area of ​​the actual target corresponding to the digital twin target to obtain a basic data list set S=(S1, S2, ..., S i , …, S n ), i=1, 2, ..., n, where n is the number of preset areas; S i is the basic data list corresponding to the i-th preset area; S200, based on S and the attribute classification model, obtain the attribute expected value list set X=(X1, X2, …, X i , …, X n), where X i is the attribute expected value list corresponding to the i-th preset area; X i =(X i，1 , X i，2 , …, X i，j , …, X i，m ); j = 1, 2, ..., m; m is the number of attributes corresponding to the digital twin target; X i，j is the expected value of the jth attribute corresponding to the i-th preset area; S300, based on X, obtain the target category impact value Z=Σ corresponding to the digital twin target m j=1 (G j ×N j ); where N j is the attribute matching degree corresponding to the jth attribute of the digital twin target; and N j It is determined based on the target attribute category and actual attribute category of the jth attribute of the digital twin target; G j is the comprehensive expected value corresponding to the jth attribute of the digital twin target; G j =Σ n i=1 (X i，j ×M i ); M i is the area matching degree corresponding to the i-th preset area; S400, determine the interval according to Z and the preset category, and obtain the category corresponding to the digital twin target.

[0007] In a second aspect of the present application, a digital twin target category determination device is provided, the device comprising: The acquisition unit is used to collect the basic data of each preset area of ​​the actual target corresponding to the digital twin target to obtain a basic data list set S=(S1, S2, ..., S i , …, S n ), i=1, 2, ..., n, where n is the number of preset areas; S i is the basic data list corresponding to the i-th preset area; The expected value determination unit is used to obtain the attribute expected value list set X=(X1, X2, …, X i , …, X n ), where X i is the attribute expected value list corresponding to the i-th preset area; X i =(X i，1 , X i，2 , …, X i，j , …, X i，m); j = 1, 2, ..., m; m is the number of attributes corresponding to the digital twin target; X i，j is the expected value of the jth attribute corresponding to the i-th preset area; The influence value determination unit is used to obtain the target category influence value Z=Σ corresponding to the digital twin target according to X. m j=1 (G j ×N j ); where N j is the attribute matching degree corresponding to the jth attribute of the digital twin target; and N j It is determined based on the target attribute category and actual attribute category of the jth attribute of the digital twin target; G j is the comprehensive expected value corresponding to the jth attribute of the digital twin target; G j =Σ n i=1 (X i，j ×M i ); M i is the area matching degree corresponding to the i-th preset area; The category determination unit determines the interval according to Z and the preset category to obtain the category corresponding to the digital twin target.

[0008] In a third aspect of the present application, a non-transitory computer-readable storage medium is provided, in which at least one instruction or at least one program is stored, and the at least one instruction or at least one program is loaded and executed by a processor to implement the aforementioned digital twin target category determination method.

[0009] In a fourth aspect of the present application, an electronic device is provided, comprising a processor and the above-mentioned non-transitory computer-readable storage medium.

[0010] This application has at least the following beneficial effects: The digital twin target category determination method provided in this application obtains the basic data of the actual target corresponding to the constructed digital twin target, obtains the actual target's demand for each attribute (capability) required to construct the digital twin target, and then evaluates the constructed digital twin target according to the actual practical degree of each attribute (capability) in the constructed digital twin target and the demand degree (actual use degree that should be achieved) for each attribute (capability) required to construct the digital twin target. On the one hand, it takes into account that different types of digital twin cities have different demands for different attributes (capabilities). By dividing the actual target into smaller areas, it is possible to judge the demand degree of the actual target for each attribute (capability), and then obtain the comprehensive demand degree for each capability of the entire city, which saves computing power, reduces computing time, and improves computing accuracy. Taking into account the different demands of different types of degrees for each capability, different weights are set for them, and the evaluation results obtained are more objective and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A flow chart of a method for determining a digital twin target category provided in an embodiment of the present application; Figure 2 A structural block diagram of a digital twin target category determination device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0014] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0015] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present application, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0016] Please refer to Figure 1 As shown, an embodiment of the present application provides a method for determining a digital twin target category, the method comprising the following steps: S100, collecting basic data of each preset area of ​​the actual target corresponding to the digital twin target to obtain a basic data list set S=(S1, S2, ..., S i , …, S n ), i=1, 2, ..., n, where n is the number of preset areas; S i It is the basic data list corresponding to the i-th preset area.

[0017] Specifically, in this implementation, the digital twin target is the digital twin city that has been built, which includes the application layer, the capability component layer and the twin layer, where the capability component layer is the various capabilities required to build the digital twin city, including: IoT perception and control capabilities, 3D model building capabilities, visualization rendering capabilities, data fusion supply capabilities, spatiotemporal analysis and calculation capabilities, simulation and deduction capabilities, virtual-reality fusion and interaction capabilities, self-learning and self-optimization capabilities, and mass innovation and expansion application capabilities. These capabilities are used to process and analyze the collected data.

[0018] It should be noted that for different digital twin cities, the degree of demand for each capability is different due to different demand focuses. For example, for heavy transportation cities and heavy industrial cities, the demand for capabilities when building digital twin cities is different. The demand for capabilities is reflected in the capability level. Each capability has a corresponding level. The higher the level, the more mature and complete the capability is, and the higher the application frequency is.

[0019] In this embodiment, the actual target corresponding to the digital twin target is the actual city corresponding to the digital twin city. In the construction of the digital twin platform, data collection is the basis. Here, the actual city corresponding to the digital twin city is divided into several preset areas. Here, the division of the preset areas can be divided according to the preset area shape to obtain a grid-shaped area, wherein the area close to the edge of the city may not fill the corresponding grid-shaped area. It can also be divided according to other area division methods. Then, according to each preset area that has been divided, data collection is performed separately. To obtain a basic data list set S. Here, it can be understood that the data included in the basic data list is all the data to be collected in the actual city corresponding to the digital twin city.

[0020] S200, based on S and the attribute classification model, obtain the attribute expected value list set X=(X1, X2, …, X i , …, X n ), where X i is the attribute expected value list corresponding to the i-th preset area; X i =(X i，1 , X i，2 , …, X i，j , …, X i，m ); j = 1, 2, ..., m; m is the number of attributes corresponding to the digital twin target; X i，j is the expected value of the jth attribute corresponding to the ith preset area.

[0021] Specifically, the attribute classification model is used to obtain the required value of each type of capability for the input data of each preset area, that is, the attribute expected value. i Encode to get the corresponding feature vector, and then input the attribute classification model to get X i =(X i，1 , X i，2 , …, X i，j , …, X i，m ); where X i，jThe attribute expected value of the jth attribute corresponding to the ith preset area is the degree of demand for the jth capability required for building a digital twin city in the ith preset area. Since the data characteristics and features of each preset area are different, the corresponding output demand for each capability is different. This reflects the corresponding capability demand degree of each preset area in building a digital twin city.

[0022] S300, based on X, obtain the target category impact value Z=Σ corresponding to the digital twin target m j=1 (G j ×N j ); where N j is the attribute matching degree corresponding to the jth attribute of the digital twin target; and N j It is determined based on the target attribute category and actual attribute category of the jth attribute of the digital twin target; G j is the comprehensive expected value corresponding to the jth attribute of the digital twin target; G j =Σ n i=1 (X i，j ×M i ); M i is the area matching degree corresponding to the i-th preset area.

[0023] Specifically, first, according to the actual demand degree of each preset area of ​​the actual city for each capability required for building the digital twin city, the demand degree of the actual city for each capability when building the digital twin city is obtained. Here, each preset area has a corresponding regional matching degree, and the regional matching degree indicates the importance of the area to the actual city compared with other areas. If the importance is greater, the proportion is greater when calculating the demand degree of the actual city for each capability when building the digital twin city, which means that the corresponding weight (regional matching degree) of the area is higher. In this way, the final demand degree of the actual city for each capability when building the digital twin city takes into account the importance of each preset area, and the obtained demand degree of the actual city for each capability when building the digital twin city is more accurate.

[0024] Furthermore, after obtaining the degree of demand for each capability of the actual city when constructing the digital twin city, in order to accurately evaluate the constructed digital twin city, it is necessary to evaluate whether the application of each capability of the constructed digital twin city is appropriate based on the actual application of each capability in the constructed digital twin city (actual attribute category) and the degree of demand for each capability of the actual city corresponding to the constructed digital twin city obtained by collecting data (target attribute category). In this embodiment, the target category impact value Z=Σ mj=1 (G j ×N j ); N j It is determined based on the target attribute category and actual attribute category of the jth attribute of the digital twin target. j Assign weights to the actual demand for each capability required for building a digital twin city in each preset area of ​​the actual city, so that the target category impact value obtained by the constructed digital twin city is more accurate. Here, if the actual attribute category of a certain attribute (capability) is equal to or greater than the target attribute category, it means that when building a digital twin city, the application degree of the attribute (capability) has reached the standard, and the functions that can be achieved based on the attribute (capability) are sufficiently accurate and complete. As an example, for the spatiotemporal analysis function interface, the corresponding actual attribute category is the fifth category (wherein the categories include five categories: the first category, the second category, the third category, the fourth category and the fifth category). The higher the corresponding category, the higher the frequency of use and the accuracy of use of the capability. The demand level for the spatiotemporal analysis function interface when building a digital twin city obtained by collecting the basic data of the actual city through steps S100 to S300 of this application is level four. Therefore, it can be seen that the degree of use of the spatiotemporal analysis function interface in the constructed digital twin city should be higher than that of the spatiotemporal analysis function interface, that is, the degree of use reaches the standard. On the contrary, if the actual attribute category of a certain attribute (capability) is smaller than the target attribute category, it means that the application level of this attribute (capability) has not met the standard when constructing the digital twin city. Then, the function that can be achieved by this attribute (capability) in the constructed digital twin city may not be perfect enough.

[0025] In this embodiment, the target category impact value is determined by the total demand level of each attribute (capability) of the digital twin city, and a corresponding weight is assigned to each attribute (capability). The weight is obtained based on the actual application of each capability in the constructed digital twin city (actual attribute category) and the demand level of each capability in the actual city corresponding to the constructed digital twin city obtained by collecting data (target attribute category) when constructing the digital twin city. The obtained target category impact value takes into account the actual usage level of each attribute (capability) and more accurately reflects the actual usage of each attribute (capability) in the constructed digital twin city.

[0026] S400, determine the interval according to Z and the preset category, and obtain the category corresponding to the digital twin target.

[0027] Specifically, the preset category determination interval is used to rate the target category impact value. As an example, the preset category determination interval is: 0-30 for the first category, 30-60 for the second category, 60-80 for the third category, and 80-100 for the fourth category. According to the above method for determining the target category impact value, the larger the target category impact value, the higher the score of the digital twin target, the higher its effect, and the more reasonable and accurate the use of each capability. Conversely, the smaller the target category impact value, the lower the score of the digital twin target, the worse the simulation effect of the actual city, and the use of one or some capabilities may not be accurate enough. Therefore, the larger the corresponding preset category determination interval, the better the effect of the digital twin target.

[0028] In this embodiment, by obtaining the basic data of the actual target corresponding to the constructed digital twin target, the actual target's demand for each attribute (capability) required to construct the digital twin target is obtained, and then the constructed digital twin target is evaluated according to the actual practical degree of each attribute (capability) in the constructed digital twin target and the demand degree (actual use degree to be achieved) for each attribute (capability) required to construct the digital twin target. On the one hand, considering that different types of digital twin cities have different demands for different attributes (capabilities), the actual target is divided into smaller areas to judge the demand degree of each attribute (capability) of the actual target, and then the comprehensive demand degree for each capability of the entire city is obtained, which saves computing power, reduces computing time, and improves computing accuracy. Taking into account the different demands of different types of degrees for each capability, different weights are set for them, and the evaluation results obtained are more objective and accurate.

[0029] In an exemplary embodiment of the present application, N j Follow these steps to determine: S310, according to G j And the preset attribute category determines the interval, and G j Corresponding target attribute category MG j .

[0030] S320, obtaining the actual attribute category JG of the jth attribute corresponding to the digital twin target j .

[0031] S330, if JG j ≥MG j , then N j =1.

[0032] S340, if JG j <MG j , then N j =JG j / MGj .

[0033] In this embodiment, if JG j ≥MG j , that is, the actual attribute category is equal to or greater than the target attribute category, which means that when building a digital twin city, the application degree of this attribute (capability) has reached the standard, and the functions that can be achieved based on this attribute (capability) are accurate and complete enough. At this time, the corresponding weight is set to 1. On the contrary, if JG j <MG j , if the actual attribute category is smaller than the target attribute category, it means that the application degree of this attribute (capability) is not up to standard when building the digital twin city. Then, the function that can be realized by this attribute (capability) corresponding to the constructed digital twin city may not be perfect. At this time, the corresponding weight is less than 1, and the more the actual attribute category is smaller than the target attribute category, the smaller the corresponding weight is, which leads to a lower score for this attribute (capability), which will further lead to a lower score for the digital twin target.

[0034] In an exemplary embodiment of the present application, M i Follow these steps to determine: S350, obtaining the number of floating population in each preset area to obtain a floating population list L=(L1, L2, ..., L i , …, L n ), where L i is the number of floating population corresponding to the i-th preset area.

[0035] S360, according to L, get M i ; Among them, M i Meet the following conditions: M i =L i / MAX(L); MAX() is the preset maximum value determination function.

[0036] In this embodiment, the regional matching degree of each preset area is determined according to the number of floating population in each preset area. Here, the more floating population there is, the more important the area is, and the higher the corresponding weight is. i =L i / MAX(L), that is, the weight of the preset area with the largest floating population is 1, which is the most important compared with other preset areas. For other preset areas, the weights are all less than 1. The more floating population there is, the greater the weight. This is used as the regional matching degree of each preset area, which reflects the importance of each preset area, making the obtained comprehensive expected value more accurate.

[0037] In an exemplary embodiment of the present application, after step S100, the method further includes: S500, obtaining m attribute classification sub-models corresponding to the digital twin target; wherein each attribute classification sub-model has corresponding attributes; and the attributes corresponding to any two attribute classification sub-models are different.

[0038] S600, based on S and m attribute classification sub-models, obtain a list set of expected values ​​of key attributes X'=(X1', X2', ..., X i ', ..., X n '); where X i ' is the expected value list of key attributes corresponding to the i-th preset area; X i '=(X i，1 ', X i，2 ', ..., X i，j ', ..., X i，m ');X i，j ' is the expected value of the key attribute of the jth attribute corresponding to the ith preset area obtained according to the jth attribute classification sub-model.

[0039] S700, according to X', obtain the target category impact value Z'=Σ m j=1 (G j '×N j ); where G j ' is the key comprehensive expected value corresponding to the jth attribute of the digital twin target; G j '=Σ n i=1 (X i，j '×M i ).

[0040] S800, determine the interval according to Z' and the preset category, and obtain the category corresponding to the digital twin target.

[0041] In this embodiment, there are multiple classification models, that is, a corresponding attribute classification sub-model is trained for each attribute (capability). Compared with using only one attribute classification model to obtain the degree of demand for each attribute (capability), the attribute classification sub-model obtained by vertical field training for each attribute (capability) outputs a more accurate degree of demand for the attribute (capability).

[0042] It is understood that when using S i Input m attribute classification sub-models respectively and get X i '=(X i，1 ', X i，2 ', ..., X i，j ', ..., X i，m '). X i，j 'Then S iThe key attribute expected value of the jth attribute corresponding to the ith preset area obtained by inputting the jth attribute classification sub-model.

[0043] In an exemplary embodiment of the present application, the preset area can be determined according to the following steps: S110, obtaining a population density distribution map corresponding to the actual target; wherein the pixel value of each pixel point in the population density distribution map represents the population density of the physical area corresponding to the pixel point.

[0044] S120, segmenting the population density distribution map according to a superpixel segmentation algorithm to obtain a plurality of preset sub-regions; wherein the difference between any two pixel values ​​in each preset sub-region is less than a preset first pixel value difference threshold.

[0045] Specifically, the superpixel algorithm aims to divide the image into multiple relatively uniform small areas (superpixels) with similar features. Compared with traditional pixel-level processing, it can greatly reduce the amount of data processed by subsequent algorithms while retaining the main structure and feature information of the image. Therefore, the difference between any two pixel values ​​in each preset sub-area is less than the preset first pixel value difference threshold. Further, in this embodiment, the superpixel algorithm can be a SLIC algorithm, which is Simple Linear Iterative Clustering.

[0046] S130, merging regions according to a watershed algorithm to obtain a plurality of preset intermediate regions; wherein the difference between any two pixel values ​​in each preset intermediate region is less than a preset second pixel value difference threshold; and the second pixel value difference threshold is greater than the first pixel value difference threshold.

[0047] Specifically, the watershed algorithm first constructs a color difference gradient map (virtual terrain), that is, calculates the color difference between superpixels: traverse each superpixel area and calculate the color difference between it and the adjacent superpixel area. The color distance calculation formula in the corresponding color mode can also be used (such as the Euclidean distance calculation method based on the difference of each color component in the RGB mode). This color difference value is used as the basis for measuring the "height difference" between superpixels. Just like the height change of terrain is constructed based on grayscale values ​​in the traditional watershed algorithm, the height of virtual terrain is constructed based on color difference. Secondly, a gradient map is generated: according to the color difference calculation results between superpixels, a corresponding "gradient value" (i.e., color difference value) is assigned to each pixel point on the boundary of the superpixel, thereby constructing a gradient map reflecting the drastic degree of color change between superpixel areas. A large gradient value indicates a large color change, which is equivalent to a steep slope in the terrain. A small gradient value indicates a gentle color change, similar to a flat land in the terrain. This gradient map will be used as a "virtual terrain" for subsequent simulation of water flow convergence. After that, the seed area (initial area) is marked, and then the seed area is selected based on the superpixel features, that is, from the existing superpixel areas, some are selected as seed areas for marking according to the area size, color uniformity and other characteristics of the superpixel. For example, an area threshold can be set to mark the superpixel areas with an area greater than the threshold and relatively small internal color difference (measured by calculating statistical indicators such as the variance of pixel colors within the superpixel), and they are given different marking values ​​(such as using different integers to identify different seed areas). These marking values ​​will guide the division and expansion of the area in the subsequent simulation of water flow. Finally, based on the constructed color difference gradient map (virtual terrain), the water flow convergence process is simulated according to the principle of the watershed algorithm. Starting from the marked seed area, let the "water flow" (which can be understood as the expansion trend of the area) flow along the direction of small color difference (that is, similar color), and gradually divide different connected areas. In this process, since the seed area has been marked and the water flow tends to flow along the direction of similar color, it will naturally move in the direction of dividing pixels with similar colors together and separating them from adjacent areas with large color differences, eventually forming multiple connected areas.

[0048] S140, obtaining the area of ​​each preset middle area.

[0049] S150, if the middle area Y is preset p The area of ​​the region is smaller than the preset middle area threshold, then the p Each adjacent preset middle area, to obtain the adjacent area list J = (J p，1 , J p，2 , …, J p，e , …, J p，f ), e=1, 2, ..., f, where f is thep The number of adjacent preset intermediate areas; J p，e For Y p The region identifier Y of the adjacent e-th preset middle region e .

[0050] S160, according to J, obtain the average pixel value absolute difference list BYC p =(BYC p，1 , BYC p，2 ,…,BYC p，e ,…,BYC p，f ); Among them, BYC p，e Y p With Y e The average absolute difference of pixel values ​​between p，e =|BY e -BY P | BY e Y e The corresponding area average pixel value; BY P Y p The corresponding area average pixel value.

[0051] S170, if BYC p，e =MIN(BYC p ), and BYC p，e If the average pixel value difference is less than the preset threshold, Y p With Y e Perform regional fusion to obtain n preset regions; wherein MIN() is a preset minimum value determination function.

[0052] Specifically, after the watershed algorithm, there may still be some small connected areas that do not meet the final requirements, and they need to be merged into adjacent large connected areas to further optimize the segmentation results. Then, the adjacent middle area of ​​the preset middle area with a smaller area is obtained, and if the average pixel value difference threshold between the two is the smallest and less than the preset average pixel value difference threshold, the fusion is performed.

[0053] In this embodiment, n preset areas are obtained based on population density. There is a certain difference in population density between any two preset areas. Each preset area is formed based on population density. Compared with the more uniform physical areas divided manually, the method for determining the preset areas provided in this embodiment can better reflect the population distribution, and then assign different regional matching degrees to them, which can better reflect the importance of each preset area, so that the final evaluation result is more accurate and objective.

[0054] Please refer to Figure 2 As shown, an embodiment of the present application provides a digital twin target category determination device 100, the device comprising: The collection unit 110 is used to collect basic data of each preset area of ​​the actual target corresponding to the digital twin target to obtain a basic data list set S=(S1, S2, ..., S i , …, S n ), i=1, 2, ..., n, where n is the number of preset areas; S i It is the basic data list corresponding to the i-th preset area.

[0055] The expected value determination unit 120 is used to obtain the attribute expected value list set X=(X1, X2, ..., X i , …, X n ), where X i is the attribute expected value list corresponding to the i-th preset area; X i =(X i，1 , X i，2 , …, X i，j , …, X i，m ); j = 1, 2, ..., m; m is the number of attributes corresponding to the digital twin target; X i，j is the expected value of the jth attribute corresponding to the ith preset area.

[0056] The influence value determination unit 130 is used to obtain the target category influence value Z corresponding to the digital twin target according to X=Σ m j=1 (G j ×N j ); where N j is the attribute matching degree corresponding to the jth attribute of the digital twin target; and N j It is determined based on the target attribute category and actual attribute category of the jth attribute of the digital twin target; G j is the comprehensive expected value corresponding to the jth attribute of the digital twin target; G j =Σ n i=1 (X i，j ×M i ); M i is the area matching degree corresponding to the i-th preset area.

[0057] The category determination unit 140 determines the interval according to Z and the preset category to obtain the category corresponding to the digital twin target.

[0058] An embodiment of the present application also provides a computer program product, which includes program code. When the program product is run on an electronic device, the program code is used to enable the electronic device to execute the steps of the method described above in this specification according to various exemplary embodiments of the present application.

[0059] In addition, although the steps of the method in the present application are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0060] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solution according to the implementation method of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation method of the present application.

[0061] In an exemplary embodiment of the present application, an electronic device capable of implementing the above method is also provided.

[0062] Those skilled in the art will appreciate that various aspects of the present application may be implemented as a system, method or program product. Therefore, various aspects of the present application may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to as "circuit", "module" or "system" herein.

[0063] The electronic device according to this embodiment of the present application is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0064] The electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: the at least one processor mentioned above, the at least one storage device mentioned above, and a bus connecting different system components (including storage devices and processors).

[0065] The storage stores program codes, which can be executed by the processor, so that the processor executes the steps described in the above “Exemplary Method” section of this specification according to various exemplary embodiments of the present application.

[0066] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read only memory (ROM).

[0067] The storage may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0068] The bus may represent one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0069] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may communicate with one or more devices that enable a user to interact with the electronic device, and / or may communicate with any device (e.g., routers, modems, etc.) that enables the electronic device to communicate with one or more other computing devices. This communication may be performed through an input / output (I / O) interface. In addition, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter. As shown in the figure, the network adapter communicates with other modules of the electronic device through a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0070] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solution according to the implementation method of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation method of the present application.

[0071] In an exemplary embodiment of the present application, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present application can also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary implementations of the present application described in the above "Exemplary Method" section of the present specification.

[0072] The program product may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0073] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0074] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0075] Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0076] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0077] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0078] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for determining a digital twin target category, characterized in that: The method comprises: S100, collecting basic data of each preset area of ​​the actual target corresponding to the digital twin target to obtain a basic data list set S=(S1, S2, ..., S i , …, S n ), i=1, 2, ..., n, where n is the number of preset areas; S i is the basic data list corresponding to the i-th preset area; S200, based on S and the attribute classification model, obtain the attribute expected value list set X=(X1, X2, …, X i , …, X n ), where X i is the attribute expected value list corresponding to the i-th preset area; X i =(X i，1 , X i，2 , …, X i，j , …, X i，m ); j = 1, 2, ..., m; m is the number of attributes corresponding to the digital twin target; X i，j is the expected value of the jth attribute corresponding to the i-th preset area; S300, based on X, obtain the target category impact value Z=Σ corresponding to the digital twin target m j=1 (G j ×N j ); where N j is the attribute matching degree corresponding to the jth attribute of the digital twin target; and N j It is determined based on the target attribute category and actual attribute category of the jth attribute of the digital twin target; G j is the comprehensive expected value corresponding to the jth attribute of the digital twin target; G j =Σ n i=1 (X i，j ×M i ); M i is the area matching degree corresponding to the i-th preset area; S400, determine the interval according to Z and the preset category, and obtain the category corresponding to the digital twin target.

2. The method for determining the target category of a digital twin according to claim 1, characterized in that: N j Follow these steps to determine: S310, according to G j And the preset attribute category determines the interval, and G j Corresponding target attribute category MG j ; S320, obtaining the actual attribute category JG of the jth attribute corresponding to the digital twin target j ; S330, if JG j ≥MG j , then N j =1.

3. The method for determining the target category of a digital twin according to claim 2, characterized in that: After step S320, the method further includes: S340, if JG j <MG j , then N j =JG j / MG j .

4. The method for determining the target category of a digital twin according to claim 1, characterized in that: M i Follow these steps to determine: S350, obtaining the number of floating population in each preset area to obtain a floating population list L=(L1, L2, ..., L i , …, L n ), where L i is the number of floating population corresponding to the i-th preset area; S360, according to L, get M i ; Among them, M i Meet the following conditions: M i =L i / MAX(L); MAX() is the preset maximum value determination function.

5. The method for determining the target category of a digital twin according to claim 1, characterized in that: After step S100, the method further includes: S500, obtaining m attribute classification sub-models corresponding to the digital twin target; wherein each attribute classification sub-model has corresponding attributes; and the attributes corresponding to any two attribute classification sub-models are different; S600, based on S and m attribute classification sub-models, obtain a list set of expected values ​​of key attributes X'=(X1', X2', ..., X i ', ..., X n '); where X i ' is the expected value list of key attributes corresponding to the i-th preset area; X i '=(X i，1 ', X i，2 ', ..., X i，j ', ..., X i，m ');X i，j ' is the expected value of the key attribute of the jth attribute corresponding to the i-th preset area obtained according to the j-th attribute classification sub-model; S700, according to X', obtain the target category impact value Z'=Σ m j=1 (G j '×N j ); where G j ' is the key comprehensive expected value corresponding to the jth attribute of the digital twin target; G j '=Σ n i=1 (X i，j '×M i ); S800, determine the interval according to Z' and the preset category, and obtain the category corresponding to the digital twin target.

6. The method for determining the digital twin target category according to claim 1, characterized in that: The preset area is determined according to the following steps: S110, obtaining a population density distribution map corresponding to the actual target; wherein the pixel value of each pixel point in the population density distribution map represents the population density of the physical area corresponding to the pixel point; S120, segmenting the population density distribution map according to a superpixel segmentation algorithm to obtain a plurality of preset sub-regions; wherein the difference between any two pixel values ​​in each preset sub-region is less than a preset first pixel value difference threshold; S130, performing region merging according to a watershed algorithm to obtain a plurality of preset intermediate regions; wherein the difference between any two pixel values ​​in each preset intermediate region is less than a preset second pixel value difference threshold; and the second pixel value difference threshold is greater than the first pixel value difference threshold; S140, obtaining the area of ​​each preset middle area; S150, if the middle area Y is preset p The area of ​​the region is smaller than the preset middle area threshold, then the p Each adjacent preset middle area, to obtain the adjacent area list J = (J p，1 , J p，2 , …, J p，e , …, J p，f ), e=1, 2, ..., f, where f is the p The number of adjacent preset intermediate areas; J p，e For Y p The region identifier Y of the adjacent e-th preset middle region e ; S160, according to J, obtain the average pixel value absolute difference list BYC p =(BYC p，1 , BYC p，2 ,…,BYC p，e ,…,BYC p，f ); Among them, BYC p，e Y p With Y e The average absolute difference of pixel values ​​between p，e =|BY e -BY P | BY e Y e The corresponding area average pixel value; BY P Y p The corresponding area average pixel value; S170, if BYC p，e =MIN(BYC p ), and BYC p，e If the average pixel value difference is less than the preset threshold, Y p With Y e Perform regional fusion to obtain n preset regions; wherein MIN() is a preset minimum value determination function.

7. The method for determining the digital twin target category according to claim 6, characterized in that: After step S110, the method further includes: S001, normalize the pixel values ​​of the population density distribution map.

8. A digital twin target category determination device, characterized in that: The device comprises: The acquisition unit is used to collect the basic data of each preset area of ​​the actual target corresponding to the digital twin target to obtain a basic data list set S=(S1, S2, ..., S i , …, S n ), i=1, 2, ..., n, where n is the number of preset areas; S i is the basic data list corresponding to the i-th preset area; The expected value determination unit is used to obtain the attribute expected value list set X=(X1, X2, …, X i , …, X n ), where X i is the attribute expected value list corresponding to the i-th preset area; X i =(X i，1 , X i，2 , …, X i，j , …, X i，m ); j = 1, 2, ..., m; m is the number of attributes corresponding to the digital twin target; X i，j is the expected value of the jth attribute corresponding to the i-th preset area; The influence value determination unit is used to obtain the target category influence value Z=Σ corresponding to the digital twin target according to X. m j=1 (G j ×N j ); where N j is the attribute matching degree corresponding to the jth attribute of the digital twin target; and N j It is determined based on the target attribute category and actual attribute category of the jth attribute of the digital twin target; G j is the comprehensive expected value corresponding to the jth attribute of the digital twin target; G j =Σ n i=1 (X i，j ×M i ); M i is the area matching degree corresponding to the i-th preset area; The category determination unit determines the interval according to Z and the preset category to obtain the category corresponding to the digital twin target.

9. A non-transitory computer-readable storage medium, characterized in that: The storage medium stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the method as claimed in any one of claims 1 to 7.

10. An electronic device, characterized in that: The invention comprises a processor and the non-transitory computer-readable storage medium as claimed in claim 9.

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