Digital twin platform construction system and method

Abstract

The present application provides a digital twin platform construction system and method thereof, which relates to the field of data processing technology, and includes: an attribute acquisition module, a collection module and a construction module, wherein: the attribute acquisition module is used to obtain the data attributes of each data to be collected corresponding to the target to be constructed, so as to obtain a data attribute vector list; the collection module is used to obtain a target collection method list according to the data attribute vector list; the construction module is used to collect the data to be collected corresponding to the target to be constructed according to the target collection method list, so as to construct a digital twin platform corresponding to the target to be constructed. The digital twin platform obtained by the present application has higher accuracy and effectiveness, and its actual value in various industry applications is greater.

Description

Background Art

[0002] With the acceleration of digital transformation, data twin technology is playing an increasingly important role in many industries, such as manufacturing, smart city construction, transportation, and energy management. Data twin aims to create a virtual digital model of a physical entity or system to achieve real-time mapping, analysis, and optimization of the entire life cycle of the physical entity, thereby providing strong support for decision-making.

[0003] One of the key foundations for building data twins is data collection. Accurate and appropriate data collection is crucial for the data twin model to truly and effectively reflect the state of the physical entity and perform its intended functions.

[0004] On the one hand, most data twin construction platforms are basically based on the same data collection accuracy to obtain the required data. Whether it is for simple mechanical parts monitoring scenarios, or complex large-scale industrial systems or city-level infrastructure monitoring scenarios, unified accuracy standards are often used for data collection. For example, in a factory workshop that contains both small sensor devices and large and complex electromechanical equipment, when building a data twin model, the existing platform may uniformly collect the operating parameters (temperature, pressure, speed, etc.) of the equipment at fixed time intervals and accuracy (such as collecting data every 5 minutes with an accuracy of two decimal places). However, different equipment has different requirements for data collection accuracy due to its own structure, operating characteristics and importance in the entire production system. For some critical high-precision processing equipment with extremely high accuracy requirements, such a fixed collection accuracy may make it impossible to capture subtle but critical operating status changes, affecting the subsequent early prediction of equipment failures and process optimization decisions; and for some relatively simple auxiliary equipment with relatively stable operating status, too high collection accuracy will cause data redundancy, increase the burden of data storage and transmission, and waste unnecessary resources.

[0005] On the other hand, the existing data twin construction platform mainly relies on the experience of operators to make choices when determining the data collection method. Operators subjectively decide which data collection method to use based on past similar projects or their own accumulated work experience, such as choosing wired transmission or wireless transmission to collect data, determining the installation location of sensors and the specific sampling frequency. However, the limitation of experience is that it lacks comprehensive and scientific consideration of specific data characteristics and application scenarios. Taking the data twin construction of smart city transportation system as an example, if the collection method of traffic flow data is determined only by experience, the impact of factors such as traffic congestion characteristics of different sections, traffic flow changes, and road surrounding environment on data collection may be ignored. At some intersections with large traffic volume and complex traffic conditions, the collected data may not accurately reflect the real traffic conditions due to improper collection methods (such as unreasonable sensor layout, too low collection frequency, etc.), which makes the constructed data twin model unable to provide accurate and effective decision-making basis for traffic command and dispatch, affecting the operation efficiency and intelligent management level of the entire urban transportation system.

[0006] In summary, the existing data twin construction platforms are basically based on the same data collection accuracy and rely on experience to determine the collection method. They do not fully consider the specific data characteristics and the corresponding collection accuracy and appropriate collection methods. This greatly limits the accuracy and effectiveness of the data twin model and its actual value in various industry applications. Therefore, there is an urgent need for a data twin construction platform and related technologies that can overcome the above-mentioned defects to optimize the data collection link and improve the overall performance of data twins. Summary of the invention

[0007] In response to the above technical problems, the present application provides a digital twin platform construction system that at least partially solves the problems existing in the prior art.

[0008] In a first aspect of the present application, a digital twin platform construction system is provided, the system comprising: an attribute acquisition module, a collection module and a construction module, wherein:

[0009] The attribute acquisition module is used to acquire the data attribute of each data to be collected corresponding to the target to be constructed, so as to obtain a data attribute vector list; wherein the target to be constructed has a corresponding BIM model; the BIM model contains a number of data corresponding to the target to be constructed; the data to be collected is the data corresponding to the target to be constructed that is not extracted from the BIM model; each data to be collected has a corresponding preset data accuracy level;

[0010] The acquisition module is used to obtain the target acquisition method corresponding to each to-be-acquired data in the data attribute vector list to obtain a target acquisition method list; wherein the target acquisition method is one of several acquisition methods; each acquisition method has a corresponding acquisition attribute vector and a corresponding acquisition accuracy level; the target acquisition method is determined according to the data attributes of the to-be-acquired data, the acquisition attribute vector corresponding to each acquisition method, the preset data accuracy level corresponding to the to-be-acquired data, and the acquisition accuracy level corresponding to each acquisition method;

[0011] The construction module is used to collect the data to be collected corresponding to the target to be constructed according to the target collection method list, so as to build a digital twin platform corresponding to the target to be constructed.

[0012] In a second aspect of the present application, a method for constructing a digital twin platform is provided, the method comprising:

[0013] Acquire data attributes of each data to be collected corresponding to the target to be constructed, so as to obtain a data attribute vector list; wherein the target to be constructed has a corresponding BIM model; the BIM model contains a number of data corresponding to the target to be constructed; the data to be collected is data corresponding to the target to be constructed that is not extracted from the BIM model; each data to be collected has a corresponding preset data accuracy level;

[0014] Obtaining a target acquisition method corresponding to each to-be-collected data in the data attribute vector list to obtain a target acquisition method list; wherein the target acquisition method is one of several acquisition methods; each acquisition method has a corresponding acquisition attribute vector and a corresponding acquisition accuracy level; the target acquisition method is determined according to the data attributes of the to-be-collected data, the acquisition attribute vector corresponding to each acquisition method, the preset data accuracy level corresponding to the to-be-collected data, and the acquisition accuracy level corresponding to each acquisition method;

[0015] According to the target collection method list, collect the data to be collected corresponding to the target to be constructed to build a digital twin platform corresponding to the target to be constructed.

[0016] This application has at least the following beneficial effects:

[0017] The digital twin platform construction system provided in the present application first uses the attribute acquisition module to obtain the data attributes of each data to be collected corresponding to the target to be constructed, that is, to obtain the data characteristics and properties of the data to be collected. The target to be constructed has a corresponding BIM model, and the BIM model does not cover the above-mentioned information to be collected of the target to be constructed. Therefore, a number of data to be collected are obtained, and then the data attributes of each data to be collected are obtained, and each data attribute is encoded to obtain the data attribute vector of each data to be collected. In addition, when constructing a digital twin platform for the target to be constructed, some data have a large accuracy requirement, and some data have a low accuracy requirement. If the same acquisition standard is used, the accuracy and restoration degree of the obtained digital twin platform are low. Therefore, in the present application, each data to be collected has a corresponding preset data accuracy level. Here, the higher the preset data accuracy level, the higher the acquisition accuracy requirement corresponding to the data, and vice versa. Furthermore, the collection method corresponding to each data to be collected is determined according to the collection attribute vector corresponding to each collection method, the collection accuracy level, the data attribute vector of each data to be collected, and the preset data accuracy level of each data to be collected. Both the data attributes of the data to be collected and the accuracy requirements of the data to be collected are considered. In this way, a more suitable collection method is determined for each data to be collected, so that the collection efficiency is high and the data accuracy obtained is more in line with the accuracy requirements of the data to be collected. The final digital twin platform has higher accuracy and effectiveness, and its actual value in various industry applications is greater. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] 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.

[0019] Figure 1 A structural block diagram of a digital twin platform construction system provided in an embodiment of the present application;

[0020] Figure 2 A flowchart of a method for constructing a digital twin platform provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] 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.

[0022] 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.

[0023] 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.

[0024] Please refer to Figure 1 As shown, an embodiment of the present application provides a digital twin platform construction system 100, the system comprising: an attribute acquisition module 110, a collection module 120 and a construction module 130; wherein,

[0025] The attribute acquisition module 110 is used to obtain the data attributes of each data to be collected corresponding to the target to be constructed, so as to obtain a data attribute vector list; wherein the target to be constructed has a corresponding BIM model; the BIM model contains a number of data corresponding to the target to be constructed; the data to be collected is the data corresponding to the target to be constructed that has not been extracted from the BIM model; each data to be collected has a corresponding preset data accuracy level.

[0026] Specifically, the data attribute vector list Y=(Y1, Y2, ..., Y i , …, Y n ); i=1, 2, ..., n; wherein n is the number of data to be collected that are not extracted from the BIM model corresponding to the target to be constructed; Y iIt is the data attribute vector of the i-th data to be collected that is not extracted from the BIM model corresponding to the target to be constructed; each data to be collected has a corresponding preset data accuracy level.

[0027] Among them, the target to be constructed can be targets of different sizes such as factories, cities, and schools. As an example: the target to be constructed is a factory, and the data to be collected can be machine tool size, equipment A temperature, forklift trajectory, etc. Each piece of collected data has corresponding data attributes, wherein data attributes include type attributes, association attributes, dynamic attributes, spatiotemporal attributes, etc. The data attributes corresponding to each piece of data to be collected can describe the characteristics and features of the data to be collected. Among them, as an example: the type attribute of the machine tool size data corresponds to the description of the equipment appearance parameters; the association attribute corresponds to the description of the equipment capacity and equipment ID; the dynamic attribute corresponds to the description of non-changing data; the spatiotemporal attribute corresponds to the description of the location of the machine tool and its positional relationship with other equipment. According to the data attributes of each piece of data to be collected, the characteristics and features of the data to be collected can be known.

[0028] The basic attributes include identification attributes, time attributes, space attributes, etc.

[0029] It should be noted that the target to be constructed in this embodiment has a corresponding BIM model, and the above BIM model is a building information model, Building Information Modeling. BIM model is a comprehensive management system based on a digital three-dimensional model, which integrates various relevant information in the entire life cycle of a construction project (from planning and design, construction to operation and maintenance). However, the BIM model does not cover the above-mentioned information to be collected of the target to be constructed. Therefore, a number of data to be collected are obtained, and then the data attributes of each data to be collected are obtained, and each data attribute is encoded to obtain a data attribute vector of each data to be collected.

[0030] In addition, when constructing a digital twin platform for the target to be constructed, some data have higher accuracy requirements, while others have lower accuracy requirements. As an example: when the target to be constructed is a factory, the collection accuracy requirements for equipment geometry data, production operation data, equipment operation data, and other data are relatively high. On the contrary, the collection accuracy requirements for data such as the green area in ordinary areas of the factory are relatively low. If the same collection standard is used, the accuracy and restoration degree of the obtained digital twin platform will be low, which is not conducive to monitoring the production situation in the factory.

[0031] Therefore, in this embodiment, each data to be collected has a corresponding preset data accuracy level. Here, the higher the preset data accuracy level is, the higher the collection accuracy requirement corresponding to the data is, and vice versa.

[0032] In an exemplary embodiment of the present application, the attribute acquisition module 110 further includes an area identification acquisition unit and an attribute acquisition unit, wherein:

[0033] The area identification acquisition unit is used to obtain the area range of each preset functional area corresponding to the target to be constructed according to each preset functional area corresponding to the target to be constructed, so as to obtain a functional area range list.

[0034] Specifically, the function area range list G=(G1, G2, ..., G x , …, G y );x=1,2,…,y;wherein y is the number of preset functional areas corresponding to the target to be constructed; G x The area range of the xth preset functional area corresponding to the target to be built.

[0035] Here, the target to be constructed has a plurality of corresponding preset functional areas. For example, if the target to be constructed is a factory, the corresponding preset functional areas may be a production area, a living area, an entertainment area, etc. Each preset functional area has a corresponding regional range. The above regional range can be determined based on a factory map, etc., or can be manually demarcated. The regional range corresponding to each preset functional area does not overlap. y preset functional areas constitute a large area corresponding to the target to be constructed.

[0036] As an example: G x The data format is a set of coordinate points of longitude and latitude coordinates obtained every 100 meters on the boundary line of the x-th preset functional area corresponding to the target to be constructed.

[0037] The attribute acquisition unit is used to acquire the data attributes of the data to be collected corresponding to each preset functional area according to the functional area range list, so as to obtain a data attribute vector list.

[0038] Here, the data attributes of the data to be collected corresponding to each preset functional area are obtained respectively, that is, the data attribute vector list Y=(Y1, Y2, ..., Y i , …, Y n ).

[0039] The acquisition module 120 is used to obtain the target acquisition method corresponding to each data to be collected in the data attribute vector list to obtain a target acquisition method list; wherein the target acquisition method is one of several acquisition methods; each acquisition method has a corresponding acquisition attribute vector and a corresponding acquisition accuracy level; the target acquisition method is determined based on the data attributes of the data to be collected, the acquisition attribute vector corresponding to each acquisition method, the preset data accuracy level of the data to be collected, and the acquisition accuracy level corresponding to each acquisition method.

[0040] Specifically, according to Y, we get the target acquisition method list C=(C1, C2, ..., C i, …, C n ), where C i Y i corresponding target acquisition method; each acquisition method has a corresponding acquisition attribute vector and a corresponding acquisition accuracy level; C i According to Y i , the acquisition attribute vector corresponding to each acquisition method, Y i The corresponding preset data accuracy level and the collection accuracy level corresponding to each collection method are determined.

[0041] There may be multiple collection methods for each data to be collected, and different collection methods have different adaptability to the data to be collected. For example, in terms of accuracy, the accuracy of the data to be collected by different collection methods may be different. Each collection method has a corresponding collection attribute vector and a corresponding collection accuracy level. The collection attribute vector here is the attribute vector corresponding to the data collected by the collection method, and the collection accuracy level is the collection accuracy level corresponding to the data collected by the collection method.

[0042] In this embodiment, the collection method corresponding to each data to be collected is determined based on the collection attribute vector corresponding to each collection method, the collection accuracy level, the data attribute vector of each data to be collected, and the preset data accuracy level of each data to be collected. Both the data attributes of the data to be collected and the accuracy requirements of the data to be collected are taken into consideration. In this way, a more suitable collection method is determined for each data to be collected, so that the collection efficiency is high and the obtained data accuracy is more in line with the accuracy requirements of the data to be collected.

[0043] It should be noted that each collection method has a corresponding method identifier, which can be in the form of numbers, letters, or a combination of numbers and letters, and the method identifier is used to uniquely identify each collection method. The above target collection method list C contains the method identifier of the target collection method corresponding to each data, and the target collection method is the collection method that is most suitable for the data selected from a large number of collection methods.

[0044] As an example: C i The value is 10987, where 10987 is the method identifier corresponding to manual collection.

[0045] The construction module 130 is used to collect the data to be collected corresponding to the target to be constructed according to the target collection method list, so as to construct a digital twin platform corresponding to the target to be constructed.

[0046] In summary, the data collected according to the above method and the digital twin platform constructed are more accurate and effective, and the actual value it can play in various industry applications is greater.

[0047] In an exemplary embodiment of the present application, the acquisition module includes:

[0048] The acquisition unit is used to acquire an acquisition attribute vector corresponding to each of the plurality of acquisition methods.

[0049] The clustering unit is used to cluster a number of acquisition attribute vectors to obtain a number of acquisition attribute clustering clusters, so as to obtain an acquisition attribute clustering cluster list; wherein each acquisition attribute clustering cluster has a corresponding center vector.

[0050] Specifically, collect the attribute cluster list J=(J1, J2, ..., J j , …, J m );j=1,2,…,m;where m is the number of collection attribute clusters; J j is the cluster identifier of the i-th collection attribute clustering cluster; each collection attribute clustering cluster has a corresponding center vector.

[0051] Here, the matching degree between any two acquisition attribute vectors in each acquisition attribute cluster is greater than a preset matching degree threshold, that is, each acquisition attribute cluster contains a collection attribute vector set of a type of acquisition method. In this embodiment, an acquisition attribute cluster can be a plurality of acquisition methods corresponding to one or more data to be collected; each acquisition attribute cluster has a corresponding center vector. The above center vector is the average vector of the acquisition attribute vectors of all acquisition methods included in the acquisition attribute cluster.

[0052] A target determination unit is used to obtain a target cluster corresponding to each to-be-collected data according to the data attribute vector list and the collection attribute cluster list; wherein the matching degree between the data attribute of any to-be-collected data and the center vector of the corresponding target cluster is greater than the matching degree between the data attribute of the to-be-collected data and the center vector of any collection attribute cluster except the target cluster;

[0053] Here, the type of collection method corresponding to the target cluster corresponding to the data to be collected is the type of collection method that best matches the data attribute corresponding to the data to be collected, that is, it may be a type of method that can collect the data to be collected. i The degree of match between the center vector of the corresponding target cluster can be calculated by Euclidean distance, cosine distance, etc.

[0054] The acquisition unit is used to obtain the target acquisition method corresponding to each data to be collected according to the acquisition attribute vector of each acquisition method in the target cluster corresponding to each data to be collected, the acquisition accuracy level corresponding to each acquisition method in the target cluster corresponding to each data to be collected, the data attribute vector corresponding to each data to be collected and the preset data accuracy level corresponding to each data to be collected, so as to generate a target acquisition method list.

[0055] Specifically, the target acquisition method list C=(C1, C2, ..., C i , …, C n ).

[0056] Here, the target acquisition method is obtained through the following subunits:

[0057] The attribute matching degree obtaining subunit is used to obtain the attribute matching degree between each data attribute vector in the data attribute vector list and each collection attribute vector in the target cluster corresponding to each to-be-collected data, so as to obtain an attribute matching degree list set.

[0058] Attribute matching list set SP = (SP1, SP2, ..., SP i ,…,SP n ), where SP i Y i Corresponding attribute matching list, SP i =(SP i,1 , S.P. i,2 ,…,SP i,a ,…,SP i,f(i) ); a=1, 2,…, f(i); f(i) is Y i The number of acquisition methods contained in the corresponding target cluster; SP i,a Y i The corresponding target cluster contains the a-th collection attribute vector and Y i The degree of match between them.

[0059] Here, the attribute matching degree between each data attribute vector in Y and each collection attribute vector in the target cluster corresponding to each data to be collected is obtained, that is, the collection method with the highest matching degree is determined in the corresponding target cluster for each data to be collected. Here, the higher the matching degree, the higher the adaptability and collection efficiency of the collection method when collecting the data to be collected, and vice versa.

[0060] As an example: Y i The corresponding target cluster contains several acquisition attribute vectors, each of which uniquely corresponds to a collection method, namely Y i The corresponding target cluster contains several collection methods. Here, Y i Each acquisition method contained in the corresponding target cluster can be used to i The corresponding method for collecting data to be collected, but Y i Each acquisition method contained in the corresponding target cluster is Y i The corresponding data to be collected have different adaptability when collecting data. Therefore, obtaining Y iThe matching degree between the acquisition attribute vector corresponding to each acquisition method contained in the corresponding target cluster is used to represent Y i Select the most suitable collection method from a variety of collection methods.

[0061] It should be noted that each collection method here may be based on relevant information or historical data, and the collection efficiency may be different. The collection efficiency here may include collection difficulty, collection time, etc.

[0062] In addition, the attribute matching degree in this embodiment can be calculated by Euclidean distance, cosine distance, etc., which will not be described in detail here.

[0063] The precision matching acquisition subunit is used to obtain a precision matching list set according to the preset data precision level corresponding to each data attribute vector in the data attribute vector list and the collection precision level corresponding to each collection method in the target cluster corresponding to each data to be collected; wherein, DP i,a Meet the following conditions: DP i,a =1-|CE i,a -YE i |CE i,a <1; YE i <1;DP i,a is the data attribute vector Y of the i-th data to be collected contained in the data attribute vector list i The corresponding target cluster contains the ath acquisition accuracy level CE i,a With Y i The corresponding preset data accuracy level is YE i The degree of match between them.

[0064] Specifically, the precision matching list set DP=(DP1, DP2, ..., DP i , …, DP n ), where DP i Y i The corresponding precision matching list, DP i =(DP i,1 , DP i,2 , …, DP i,a , …, DP i,f(i) );DP i,a Y i The corresponding target cluster contains the ath acquisition accuracy level CE i,a With Y i The corresponding preset data accuracy level is YE i The matching degree between DP i,a =1-|CE i,a -YE i |CE i,a <1; YEi <1.

[0065] Here, |CE i,a -YE i | indicates Y i The corresponding target cluster contains the ath acquisition accuracy level CE i,a With Y i The corresponding preset data accuracy level is YE i The absolute value of the difference between the two. The larger the value, the more incompatible the accuracy levels between the two are, that is, one accuracy level is high and the other is low. If the preset data accuracy level of the data to be collected is high, and the corresponding collection method has a low collection accuracy level, the collected data may have low accuracy and fail to meet the collection requirements. On the contrary, if the preset data accuracy level of the data to be collected is low, and the corresponding collection method has a high collection accuracy level, the collected data may have high accuracy, wasting related equipment or human resources. Therefore, |CE i,a -YE i The smaller | is, the more Y i The corresponding target cluster contains the ath acquisition accuracy level CE i,a With Y i The corresponding preset data accuracy level is YE i The smaller the absolute value of the difference between , the more suitable the acquisition accuracy level of the acquisition method corresponding to the preset data accuracy level of the data to be acquired is. i,a =1-|CE i,a -YE i |The larger it is, the more suitable the collection accuracy level of the collection method is for the preset data accuracy level of the data to be collected, and vice versa.

[0066] As an example: CE i,a =0.8; YE i =0.2; then DP i,a =1-|0.8-0.2|=0.4=40%.

[0067] The comprehensive matching degree acquisition subunit is used to obtain the comprehensive matching degree between each acquisition method contained in the target cluster corresponding to the data attribute vector of each data to be collected and the corresponding data attribute vector of the data to be collected according to the attribute matching degree list set and the accuracy matching degree list set, so as to generate a comprehensive matching degree list; wherein, ZP i,a =SP i,a ×DP i,a ; ZP i,a Y i The corresponding target cluster contains the a-th collection method and Y i The comprehensive matching degree between i,a Yi The corresponding target cluster contains the a-th collection attribute vector and Y i The degree of match between them.

[0068] Specifically, the comprehensive matching list ZP=(ZP1, ZP2, ..., ZP i , …, ZP n ), where ZP i Y i The corresponding comprehensive matching list, ZP i =(ZP i,1 , ZP i,2 , …, ZP i,a , …, ZP i,f(i) ); ZP i,a Y i The corresponding target cluster contains the a-th collection method and Y i The comprehensive matching degree between ZP i,a =SP i,a ×DP i,a .

[0069] In this embodiment, ZP is obtained i,a =SP i,a ×DP i,a , that is, the comprehensive matching degree is proportional to the attribute matching degree and the precision matching degree respectively. The higher the comprehensive matching degree in this embodiment, the higher the attribute adaptability and precision adaptability between the corresponding acquisition method and the data to be processed. That is, the accuracy of the data obtained after acquisition by the corresponding acquisition method is just right, and the efficiency of the acquisition method is also high. It is the most suitable acquisition method.

[0070] The collection subunit is used to obtain the target collection method corresponding to each data to be collected according to the comprehensive matching degree list to generate a target collection method list; wherein the target collection method is the collection method with the highest comprehensive matching degree between the data attribute vector of the data to be collected and the target cluster corresponding to the data to be collected.

[0071] Specifically, the target acquisition method list C=(C1, C2, ..., C i , …, C n ), where C i Y i The corresponding target cluster contains the same i The collection method with the highest comprehensive match between them.

[0072] Here, the collection method with the highest comprehensive matching degree is determined as the target collection method of the corresponding data to be processed.

[0073] In an exemplary embodiment of the present application, each data to be collected has a preset functional area; each preset functional area has a corresponding preset regional accuracy level; i Meet the following conditions:

[0074] YE i =HE i ×KE i ;

[0075] Among them, HE i Y i The basic data accuracy level corresponding to the data to be collected, KE i Y i The preset regional accuracy level of the preset functional area to which the corresponding data to be collected belongs; i <1; KE i <1.

[0076] In this embodiment, each preset functional area has a corresponding preset area accuracy level. As an example: when the target to be constructed is a factory, preset functional area one is the living area, and preset functional area two is the working area. Then the preset area accuracy level corresponding to preset functional area one is lower than that of preset functional area two.

[0077] Therefore, in this embodiment, YE i It is not only related to the accuracy level of the basic data, but also to the accuracy level of the preset functional area to which the corresponding data to be collected belongs, which further improves the accuracy of subsequent accuracy level matching. In turn, it improves the accuracy of the digital twin platform.

[0078] Please refer to Figure 2 As shown, the embodiment of the present application provides a method for constructing a digital twin platform. For a detailed description of the method, please refer to the above system embodiment, which will not be repeated here. The method includes the following steps:

[0079] Step S100, obtaining data attributes of each data to be collected corresponding to the target to be constructed, so as to obtain a data attribute vector list; wherein the target to be constructed has a corresponding BIM model; the BIM model contains a number of data corresponding to the target to be constructed; the data to be collected is the data corresponding to the target to be constructed that has not been extracted from the BIM model; each data to be collected has a corresponding preset data accuracy level.

[0080] Step S200, obtain the target acquisition method corresponding to each data to be collected in the data attribute vector list to obtain a target acquisition method list; wherein the target acquisition method is one of several acquisition methods; each acquisition method has a corresponding acquisition attribute vector and a corresponding acquisition accuracy level; the target acquisition method is determined based on the data attributes of the data to be collected, the acquisition attribute vector corresponding to each acquisition method, the preset data accuracy level of the data to be collected, and the acquisition accuracy level corresponding to each acquisition method.

[0081] Step S300: Collect the data to be collected corresponding to the target to be constructed according to the target collection method list to construct a digital twin platform corresponding to the target to be constructed.

[0082] In an exemplary embodiment of the present application, step S100 further includes the following steps:

[0083] According to each preset functional area corresponding to the target to be constructed, the area range of each preset functional area corresponding to the target to be constructed is obtained to obtain a functional area range list.

[0084] According to the function area range list, the data attributes of the data to be collected corresponding to each preset function area are obtained to obtain a data attribute vector list.

[0085] In an exemplary embodiment of the present application, step S200 further includes the following steps:

[0086] Obtaining a collection attribute vector corresponding to each of several collection methods;

[0087] Clustering is performed on a number of acquisition attribute vectors to obtain a number of acquisition attribute clustering clusters, so as to obtain an acquisition attribute clustering cluster list; wherein each acquisition attribute clustering cluster has a corresponding center vector.

[0088] According to the data attribute vector list and the collection attribute clustering cluster list, the target clustering cluster corresponding to each data to be collected is obtained; wherein, the matching degree between the data attribute of any data to be collected and the central vector of the corresponding target clustering cluster is greater than the matching degree between the data attribute of the data to be collected and the central vector of any collection attribute clustering cluster except the target clustering cluster.

[0089] According to the acquisition attribute vector of each acquisition method in the target cluster corresponding to each data to be collected, the acquisition accuracy level corresponding to each acquisition method in the target cluster corresponding to each data to be collected, the data attribute vector corresponding to each data to be collected and the preset data accuracy level corresponding to each data to be collected, the target acquisition method corresponding to each data to be collected is obtained to generate a target acquisition method list.

[0090] In an exemplary embodiment of the present application, the target acquisition method corresponding to each data to be collected is obtained according to the acquisition attribute vector of each acquisition method in the target cluster corresponding to each data to be collected, the acquisition accuracy level corresponding to each acquisition method in the target cluster corresponding to each data to be collected, the data attribute vector corresponding to each data to be collected, and the preset data accuracy level corresponding to each data to be collected, so as to generate a target acquisition method list, including:

[0091] The attribute matching degree between each data attribute vector in the data attribute vector list and each collection attribute vector in the target cluster corresponding to each to-be-collected data is obtained to obtain an attribute matching degree list set.

[0092] According to the preset data accuracy level corresponding to each data attribute vector in the data attribute vector list and the acquisition accuracy level corresponding to each acquisition method in the target cluster corresponding to each data to be collected, the accuracy matching list set is obtained; wherein DP i,a Meet the following conditions: DP i,a =1-|CE i,a -YE i |CE i,a <1; YE i <1;DP i,a is the data attribute vector Y of the i-th data to be collected contained in the data attribute vector list i The corresponding target cluster contains the ath acquisition accuracy level CE i,a With Y i The corresponding preset data accuracy level is YE i The degree of match between them.

[0093] According to the attribute matching list set and the precision matching list set, the comprehensive matching degree between each acquisition method contained in the target cluster corresponding to the data attribute vector of each data to be collected and the corresponding data attribute vector of the data to be collected is obtained to generate a comprehensive matching degree list; wherein, ZP i,a =SP i,a ×DP i,a ; ZP i,a Y i The corresponding target cluster contains the a-th collection method and Y i The comprehensive matching degree between i,a Y i The corresponding target cluster contains the a-th collection attribute vector and Y i The degree of match between them.

[0094] According to the comprehensive matching degree list, the target collection method corresponding to each data to be collected is obtained to generate a target collection method list; wherein the target collection method is the collection method with the highest comprehensive matching degree between the data attribute vector of the data to be collected and the target cluster corresponding to the data to be collected.

[0095] In the exemplary embodiment of the present application, each data to be collected has a corresponding preset functional area; each preset functional area has a corresponding preset regional accuracy level; i Meet the following conditions:

[0096] YE i =HE i ×KE i ;

[0097] Among them, HE i Y i The basic data accuracy level corresponding to the data to be collected, KE i Y i The preset regional accuracy level of the preset functional area to which the corresponding data to be collected belongs; i <1; KE i <1.

[0098] In an exemplary embodiment of the present application, an electronic device capable of implementing the above method is also provided.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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 digital twin platform construction system, characterized in that: The system includes an attribute acquisition module, a collection module and a construction module, wherein: The attribute acquisition module is used to acquire the data attribute of each data to be collected corresponding to the target to be constructed, so as to obtain a data attribute vector list; wherein the target to be constructed has a corresponding BIM model; the BIM model contains a number of data corresponding to the target to be constructed; the data to be collected is the data corresponding to the target to be constructed that is not extracted from the BIM model; each data to be collected has a corresponding preset data accuracy level; The acquisition module is used to obtain the target acquisition method corresponding to each to-be-acquired data in the data attribute vector list to obtain a target acquisition method list; wherein the target acquisition method is one of several acquisition methods; each acquisition method has a corresponding acquisition attribute vector and a corresponding acquisition accuracy level; the target acquisition method is determined according to the data attributes of the to-be-acquired data, the acquisition attribute vector corresponding to each acquisition method, the preset data accuracy level corresponding to the to-be-acquired data, and the acquisition accuracy level corresponding to each acquisition method; A construction module is used to collect the data to be collected corresponding to the target to be constructed according to the target collection method list, so as to construct a digital twin platform corresponding to the target to be constructed; The acquisition module comprises: An acquisition unit, used for acquiring an acquisition attribute vector corresponding to each of the plurality of acquisition methods; A clustering unit, used for clustering a number of acquisition attribute vectors to obtain a number of acquisition attribute clustering clusters, so as to obtain an acquisition attribute clustering cluster list; wherein each acquisition attribute clustering cluster has a corresponding center vector; A target determination unit is used to obtain a target cluster corresponding to each to-be-collected data according to the data attribute vector list and the collection attribute cluster list; wherein the matching degree between the data attribute of any to-be-collected data and the center vector of the corresponding target cluster is greater than the matching degree between the data attribute of the to-be-collected data and the center vector of any collection attribute cluster except the target cluster; The acquisition unit is used to obtain the target acquisition method corresponding to each data to be collected according to the acquisition attribute vector of each acquisition method in the target cluster corresponding to each data to be collected, the acquisition accuracy level corresponding to each acquisition method in the target cluster corresponding to each data to be collected, the data attribute vector corresponding to each data to be collected and the preset data accuracy level corresponding to each data to be collected, so as to generate a target acquisition method list.

2. The digital twin platform construction system according to claim 1, characterized in that: The attribute acquisition module includes: The area identification acquisition unit is used to obtain the area range of each preset functional area corresponding to the target to be constructed according to each preset functional area corresponding to the target to be constructed, so as to obtain a functional area range list; The attribute acquisition unit is used to acquire the data attributes of the data to be collected corresponding to each preset functional area according to the functional area range list, so as to obtain a data attribute vector list.

3. The digital twin platform construction system according to claim 1, characterized in that: The acquisition unit comprises: The attribute matching degree obtaining subunit is used to obtain the attribute matching degree between each data attribute vector in the data attribute vector list and each collection attribute vector in the target cluster corresponding to each to-be-collected data, so as to obtain an attribute matching degree list set; The precision matching acquisition subunit is used to obtain a precision matching list set according to the preset data precision level corresponding to each data attribute vector in the data attribute vector list and the collection precision level corresponding to each collection method in the target cluster corresponding to each data to be collected; wherein, DP i,a Meet the following conditions: DP i,a =1-|CE i,a -YE i |CE i,a <1; YE i <1;DP i,a is the data attribute vector Y of the i-th data to be collected contained in the data attribute vector list i The corresponding target cluster contains the ath acquisition accuracy level CE i,a With Y i The corresponding preset data accuracy level is YE i The degree of match between The comprehensive matching degree acquisition subunit is used to obtain the comprehensive matching degree between each acquisition method contained in the target cluster corresponding to the data attribute vector of each data to be collected and the corresponding data attribute vector of the data to be collected according to the attribute matching degree list set and the accuracy matching degree list set, so as to generate a comprehensive matching degree list; wherein, ZP i,a =SP i,a ×DP i,a ; ZP i,a Y i The corresponding target cluster contains the a-th collection method and Y i The comprehensive matching degree between i,a Y i The corresponding target cluster contains the a-th collection attribute vector and Y i The degree of match between The collection subunit is used to obtain the target collection method corresponding to each data to be collected according to the comprehensive matching degree list to generate a target collection method list; wherein the target collection method is the collection method with the highest comprehensive matching degree between the data attribute vector of the data to be collected and the target cluster corresponding to the data to be collected.

4. The digital twin platform construction system according to claim 3, characterized in that: Each data to be collected has a preset functional area; each preset functional area has a corresponding preset regional accuracy level; i Meet the following conditions: YE i =HE i ×KE i ; Among them, HE i Y i The basic data accuracy level corresponding to the data to be collected, KE i Y i The preset regional accuracy level of the preset functional area to which the corresponding data to be collected belongs; i <1; KE i <1.

5. A method for constructing a digital twin platform, characterized in that: The method comprises: Acquire data attributes of each data to be collected corresponding to the target to be constructed, so as to obtain a data attribute vector list; wherein the target to be constructed has a corresponding BIM model; the BIM model contains a number of data corresponding to the target to be constructed; the data to be collected is data corresponding to the target to be constructed that is not extracted from the BIM model; each data to be collected has a corresponding preset data accuracy level; Obtaining a target acquisition method corresponding to each to-be-collected data in the data attribute vector list to obtain a target acquisition method list; wherein the target acquisition method is one of several acquisition methods; each acquisition method has a corresponding acquisition attribute vector and a corresponding acquisition accuracy level; the target acquisition method is determined according to the data attributes of the to-be-collected data, the acquisition attribute vector corresponding to each acquisition method, the preset data accuracy level corresponding to the to-be-collected data, and the acquisition accuracy level corresponding to each acquisition method; According to the target collection method list, collect the data to be collected corresponding to the target to be built, so as to build a digital twin platform corresponding to the target to be built; The step of obtaining the target acquisition method corresponding to each to-be-collected data in the data attribute vector list to obtain a target acquisition method list includes: Obtaining a collection attribute vector corresponding to each of the plurality of collection methods; Clustering a number of acquisition attribute vectors to obtain a number of acquisition attribute clustering clusters, so as to obtain an acquisition attribute clustering cluster list; wherein each acquisition attribute clustering cluster has a corresponding center vector; According to the data attribute vector list and the collection attribute cluster list, the target cluster corresponding to each to-be-collected data is obtained; wherein the matching degree between the data attribute of any to-be-collected data and the center vector of the corresponding target cluster is greater than the matching degree between the data attribute of the to-be-collected data and the center vector of any collection attribute cluster except the target cluster; According to the acquisition attribute vector of each acquisition method in the target cluster corresponding to each data to be collected, the acquisition accuracy level corresponding to each acquisition method in the target cluster corresponding to each data to be collected, the data attribute vector corresponding to each data to be collected and the preset data accuracy level corresponding to each data to be collected, the target acquisition method corresponding to each data to be collected is obtained to generate a target acquisition method list.

6. The method for constructing a digital twin platform according to claim 5, characterized in that: The step of obtaining the data attributes of each data to be collected corresponding to the target to be constructed to obtain a data attribute vector list includes: According to each preset functional area corresponding to the target to be constructed, the area range of each preset functional area corresponding to the target to be constructed is obtained to obtain a functional area range list; According to the function area range list, the data attributes of the data to be collected corresponding to each preset function area are obtained to obtain a data attribute vector list.

7. The method for constructing a digital twin platform according to claim 5, characterized in that: The target acquisition method corresponding to each data to be collected is obtained according to the acquisition attribute vector of each acquisition method in the target cluster corresponding to each data to be collected, the acquisition accuracy level corresponding to each acquisition method in the target cluster corresponding to each data to be collected, the data attribute vector corresponding to each data to be collected, and the preset data accuracy level corresponding to each data to be collected, so as to generate a target acquisition method list, including: Obtaining the attribute matching degree between each data attribute vector in the data attribute vector list and each collection attribute vector in the target cluster corresponding to each to-be-collected data, so as to obtain an attribute matching degree list set; According to the preset data accuracy level corresponding to each data attribute vector in the data attribute vector list and the acquisition accuracy level corresponding to each acquisition method in the target cluster corresponding to each data to be collected, the accuracy matching list set is obtained; wherein DP i,a Meet the following conditions: DP i,a =1-|CE i,a -YE i |CE i,a <1; YE i <1;DP i,a is the data attribute vector Y of the i-th data to be collected contained in the data attribute vector list i The corresponding target cluster contains the ath acquisition accuracy level CE i,a With Y i The corresponding preset data accuracy level is YE i The degree of match between According to the attribute matching list set and the precision matching list set, the comprehensive matching degree between each acquisition method contained in the target cluster corresponding to the data attribute vector of each data to be collected and the corresponding data attribute vector of the data to be collected is obtained to generate a comprehensive matching degree list; wherein, ZP i,a =SP i,a ×DP i,a ; ZP i,a Y i The corresponding target cluster contains the a-th collection method and Y i The comprehensive matching degree between i,a Y i The corresponding target cluster contains the a-th collection attribute vector and Y i The degree of match between According to the comprehensive matching degree list, the target collection method corresponding to each data to be collected is obtained to generate a target collection method list; wherein the target collection method is the collection method with the highest comprehensive matching degree between the data attribute vector of the data to be collected and the target cluster corresponding to the data to be collected.

8. The method for constructing a digital twin platform according to claim 7, characterized in that: Each data to be collected has a preset functional area; each preset functional area has a corresponding preset regional accuracy level; i Meet the following conditions: YE i =HE i ×KE i ; Among them, HE i Y i The basic data accuracy level corresponding to the data to be collected, KE i Y i The preset regional accuracy level of the preset functional area to which the corresponding data to be collected belongs; i <1; KE i <1.

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