Digital twin-driven multi-source information intelligent decision-making system and method

Abstract

The invention relates to the technical field of community governance data processing, in particular to a digital twin-driven multi-source information intelligent decision system and method. The method comprises the steps of collecting community, building and facility operation data, generating a community three-dimensional virtual model, establishing a data corresponding relation between the virtual model and an entity, and performing data association; acquiring environment state data in real time, fusing the environment state data, and analyzing potential problems of community governance; generating a decision scheme according to the data analysis result, converting the decision scheme into an execution instruction, and sending the execution instruction to a corresponding execution main body; the system comprises a community virtual modeling module, a data fusion analysis module and a decision execution module. By means of the mode, real-time virtual mapping of the community space is achieved, community dynamic states are simulated and predicted in real time, and the decision support capacity is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of community governance data processing, in particular to a digital twin driven multi-source information intelligent decision-making system and method. BACKGROUND

[0002] With the acceleration of urbanization, the community as the basic unit of urban governance, its governance is increasingly difficult. The traditional community governance mode has been difficult to meet the increasingly complex community needs, and it is urgent to improve the governance capacity with the help of emerging technologies. Digital twin technology realizes real-time monitoring, analysis and optimization of physical entities by constructing virtual mapping of physical entities, and has been widely used in urban planning, intelligent manufacturing and other fields. Artificial intelligence technology has autonomous perception, decision-making and action ability, and can autonomously complete specific tasks in a virtual space environment, which provides the possibility for the intelligentization of community governance.

[0003] At present, some communities have begun to introduce information technology for governance, such as building community information systems and installing monitoring equipment. However, these systems are mostly independent, and data is difficult to interconnect. The existing digital solution integrates community population information, property information, security information, etc., which can realize digital management of the basic situation of the community, but lacks real-time virtual mapping of community space, cannot realize real-time simulation and prediction of community dynamics, relies on manual analysis and judgment, and has weak decision support capability. SUMMARY

[0004] The purpose of the present application is to provide a digital twin driven multi-source information intelligent decision-making system and method, which aims to solve the technical problems of lack of real-time virtual mapping of community space, inability to realize real-time simulation and prediction of community dynamics, reliance on manual analysis and judgment, and weak decision support capability in the prior art.

[0005] To achieve the above purpose, a digital twin driven multi-source information intelligent decision-making method is adopted, which comprises the following steps:

[0006] Collecting community, building and facility operation data, generating a three-dimensional virtual model of the community, and establishing a data correspondence relationship between the virtual model and the entity for data association;

[0007] Real-time acquisition of environmental state data, fusion of the environmental state data, and analysis of potential community governance problems;

[0008] Generating a decision-making scheme according to the data analysis results, converting the decision-making scheme into an execution instruction, and sending it to the corresponding execution subject.

[0009] In the step of collecting community, building and facility operation data, generating a three-dimensional virtual model of the community, and establishing a data correspondence relationship between the virtual model and the entity for data association:

[0010] Collecting geometry data of community, building and facility operation respectively, and integrating and preprocessing the geometry data;

[0011] Restoring and refining the building model according to the geometry data, and adding elements to generate a three-dimensional virtual model of the community;

[0012] Assigning a unique identifier to each element in the three-dimensional virtual model of the community, which corresponds to the actual entity.

[0013] After assigning a unique identifier to each element in the three-dimensional virtual model of the community, which corresponds to the actual entity:

[0014] Associating the collected community, building and facility operation data with the corresponding elements in the virtual model, and establishing a data table to store the associated information.

[0015] In the step of obtaining environmental state data in real time, fusing the environmental state data, and analyzing potential problems in community governance:

[0016] Deploying environmental sensors to collect environmental state data in real time and output multi-source heterogeneous data;

[0017] After preprocessing the multi-source heterogeneous data, use data fusion to fuse the measurement data of multiple sensors on the same environmental parameter;

[0018] Constructing association rules to analyze the association relationship between different environmental parameters and outputting frequently occurring parameter combinations and their association rules;

[0019] Respectively performing time series analysis and spatial distribution analysis on multi-source heterogeneous data, and outputting the analysis results.

[0020] After the step of constructing association rules to analyze the association relationship between different environmental parameters and outputting frequently occurring parameter combinations and their association rules:

[0021] Taking each environmental parameter as a node and the association relationship between parameters as an edge, draw an association map.

[0022] In the step of respectively performing time series analysis and spatial distribution analysis on multi-source heterogeneous data, and outputting the analysis results:

[0023] Respectively performing trend, periodicity and seasonality analysis on historical multi-source heterogeneous data, and based on the trend, periodicity and seasonality characteristics of historical data, establishing a prediction model to predict the data.

[0024] In the step of respectively performing time series analysis and spatial distribution analysis on multi-source heterogeneous data, and outputting the analysis results:

[0025] Conduct spatial pattern recognition and problem diagnosis in combination with geographic information and multi-source heterogeneous data.

[0026] In the step of generating a decision scheme according to the data analysis result, converting the decision scheme into an execution instruction, and sending the execution instruction to a corresponding execution subject:

[0027] Generating a decision scheme according to the data analysis result, and converting each measure in the scheme into an execution instruction; wherein the execution instruction includes operation content, an execution object, an execution time, and an execution standard.

[0028] In the step of generating a decision scheme according to the data analysis result, and converting each measure in the scheme into an execution instruction:

[0029] Sending the generated execution instruction to a corresponding execution subject through a communication channel.

[0030] The application also provides a digital twin driven multi-source information intelligent decision system, comprising a community virtual modeling module, a data fusion analysis module, and a decision execution module; wherein:

[0031] The community virtual modeling module is used to collect community, building, and facility operation data, generate a community three-dimensional virtual model, establish a data correspondence relationship between the virtual model and the entity, and perform data association.

[0032] The data fusion analysis module is used to acquire environmental state data in real time, fuse the environmental state data, and analyze potential community governance problems.

[0033] The decision execution module is used to generate a decision scheme according to the data analysis result, convert the decision scheme into an execution instruction, and send the execution instruction to a corresponding execution subject.

[0034] The digital twin driven multi-source information intelligent decision system and method of the application adopt the community virtual modeling module, the data fusion analysis module, and the decision execution module to perform the following steps: collecting community, building, and facility operation data, generating a community three-dimensional virtual model, establishing a data correspondence relationship between the virtual model and the entity, and performing data association; acquiring environmental state data in real time, fusing the environmental state data, and analyzing potential community governance problems; generating a decision scheme according to the data analysis result, converting the decision scheme into an execution instruction, and sending the execution instruction to a corresponding execution subject; through the above-mentioned manner, real-time virtual mapping of a community space is realized, community dynamics is simulated and predicted in real time, and decision support capability is improved. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only represent some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative effort.

[0036] Figure 1 is a step flow chart of the digital twin driven multi-source information intelligent decision method of the present application.

[0037] Figure 2 is a step flow chart of S100 of the present application.

[0038] Figure 3 is a step flow chart of S200 of the present application.

[0039] Figure 4 is a step flow chart of S300 of the present application.

[0040] Figure 5 is a structure principle diagram of the digital twin driven multi-source information intelligent decision system of the present application.

[0041] Figure 6 is a structure principle diagram of the electronic device of the present application.

[0042] 401-community virtual modeling module, 402-data fusion analysis module, 403-decision execution module. DETAILED DESCRIPTION

[0043] The exemplary embodiments will be described in detail herein with reference to the attached drawings. The following description is with reference to the drawings, in which like numerals represent like elements, unless otherwise described in the following description. The embodiments described in the following exemplary embodiments do not represent all the embodiments consistent with the present application.

[0044] The terms used in the present application are merely for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "an" and "the" used in the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein means and includes any or all possible combinations of one or more associated listed items.

[0045] It should be understood that, although the terms first, second, third, etc. can be employed in this application to describe various information, the information should not be limited to these terms. These terms are only used to distinguish one type of information from another type of information. For example, without departing from the scope of the present application, first information can also be referred to as second information, and similarly, second information can also be referred to as first information. Depending on the context, the word "if" as used herein can be interpreted as "when" or "upon" or "in response to determining".

[0046] Referring to Figures 1-4 The present application provides a digital twin driven multi-source information intelligent decision-making method, comprising the following steps:

[0047] S100: Collecting community, building, and facility operation data, generating a community three-dimensional virtual model, and establishing a data correspondence relationship between the virtual model and the entity for data association.

[0048] In this embodiment, community, building, and facility operation data are collected, a community three-dimensional virtual model is generated, and a data correspondence relationship between the virtual model and the entity is established for data association. The specific process is as follows:

[0049] S101: Collecting geometric data of community, building, and facility operation respectively, and integrating and preprocessing the geometric data;

[0050] S102: Restoring and refining the building model according to the geometric data, and adding elements to generate a community three-dimensional virtual model;

[0051] S103: Assigning a unique identifier to each element in the community three-dimensional virtual model, which corresponds to the actual entity;

[0052] S104: Associating the collected community, building, and facility operation data with the corresponding elements in the virtual model, and establishing a data table to store the associated information.

[0053] In the above process, geometric data of community, building, and facility operation are collected respectively; wherein community data collection: professional community geographic information collection equipment such as high-precision satellite positioning instrument, unmanned aerial vehicle, etc. is used to comprehensively collect information such as the boundary, road layout, and public area location of the community. At the same time, the community management department is used to obtain the demographic data of the community, including the number of residents, age distribution, occupation composition, and other basic information.

[0054] Building data collection: For various buildings in the community, a three-dimensional laser scanner is used to accurately scan the building's appearance, obtaining data such as the building's contour, facade details, etc. For the internal structure of the building, a handheld three-dimensional scanning device is used to collect detailed information such as room layout, door and window position, floor height, etc. In addition, collect planning and design drawings, construction records and other document materials of the building to supplement and improve the building data.

[0055] Facility operation data collection: Install sensors on various facilities in the community, such as water and electricity meter sensors to collect water and electricity usage data, elevator sensors to monitor elevator operation status, fault information, etc., and fire facility sensors to real-time feedback of key parameters such as pressure and electricity of fire equipment. Through Internet of Things technology, connect these sensors with data collection terminals to realize automatic collection and transmission of facility operation data.

[0056] Use three-dimensional modeling software such as 3D MAX, SketchUp, etc. to integrate and process the geometric data collected for the community, buildings and facilities. According to the building appearance scanning data, construct a three-dimensional model of the building, accurately restore the shape and facade features of the building; according to the internal structure scanning data, refine the room layout and internal facility position in the three-dimensional model. For the community environment, create a terrain model according to the geographic information collection data, and add roads, greenery and other elements to finally generate a complete three-dimensional virtual model of the community.

[0057] Assign a unique identifier to each element (building, facility, etc.) in the community's three-dimensional virtual model, which corresponds one-to-one with the actual entity. For example, generate a specific building number for each building, which is used for identification in both the virtual model and the management system of the actual building.

[0058] Correlate the collected various data (community basic data, building data, facility operation data) with the corresponding elements in the virtual model. Through the database management system, establish a data table to store these correlation information, so that when clicking on a building or facility in the virtual model, all related data can be quickly queried, realizing data intercommunication and dynamic correlation between the virtual model and the entity.

[0059] S200: Real-time acquisition of environmental state data, fusion of environmental state data, and analysis of potential problems in community governance.

[0060] In this embodiment, real-time acquisition of environmental state data, fusion of environmental state data, and analysis of potential problems in community governance. The specific process is as follows:

[0061] S201: Deploy environmental sensors to collect environmental state data in real time, output multi-source heterogeneous data;

[0062] S202: After preprocessing the multi-source heterogeneous data, the data fusion method is used to fuse the measurement data of multiple sensors on the same environmental parameter;

[0063] S203: Constructing association rules, analyzing the association relationship between different environmental parameters, and outputting the frequently occurring parameter combination and its association rule;

[0064] S204: Drawing an association graph with each environmental parameter as a node and the association relationship between parameters as an edge;

[0065] S205: Trend, periodicity, and seasonality analysis of historical multi-source heterogeneous data, based on the trend, periodicity, and seasonality characteristics of historical data, a prediction model is established to predict the data;

[0066] S206: Spatial pattern recognition and problem diagnosis combined with geographic information and multi-source heterogeneous data.

[0067] In the above process, various types of environmental sensors are widely deployed in the community, including air quality sensors (for monitoring PM2.5, PM10, sulfur dioxide, and other pollutant concentrations), temperature and humidity sensors (real-time acquisition of temperature and humidity data in the community), noise sensors (measurement of noise levels in different areas of the community), and water quality sensors (monitoring of water quality of rivers, lakes, or drinking water sources in the community, including pH, dissolved oxygen, heavy metal content, and other indicators). These sensors continuously collect environmental state data and transmit the data to the data center through wireless communication networks such as Wi-Fi, ZigBee, 4G / 5G, etc.

[0068] After the data center receives the environmental state data from various sensors, it uses data fusion algorithms to integrate and process multi-source heterogeneous data. Due to differences in data format, sampling frequency, and accuracy of different sensors, the data is first preprocessed, including data cleaning (removing noise data and outliers), data standardization (unifying data to the same dimension and range), etc. Then, weighted average, Kalman filtering, and other data fusion methods are used to fuse the measurement data of multiple sensors on the same environmental parameter, improving the accuracy and reliability of the data. For example, for air quality monitoring, the data of multiple air quality sensors is integrated to obtain more accurate overall air quality conditions in the community.

[0069] Constructing Association Rules: The community environment is a complex system, and different environmental parameters are interrelated and influence each other. For example, there is a certain relationship between air humidity and temperature. High temperature and high humidity environment may be more conducive to the growth of bacteria and mold, affecting the health of the community; air quality is also related to traffic flow. During the peak period, the increase in automobile exhaust emissions may lead to a decrease in air quality. Through the association rule mining algorithm in data mining technology (such as the Apriori algorithm), the association between different environmental parameters is analyzed, and the frequently occurring parameter combination and its association rule are found. For example, it is found that when the temperature is higher than 30℃ and the humidity is greater than 70%, the probability of bacterial growth in the community will significantly increase.

[0070] Drawing Association Graph: In order to more intuitively show the association between different environmental parameters, an association graph can be drawn. Each environmental parameter is taken as a node in the graph, and the association between parameters is taken as an edge. The thickness or color depth of the edge represents the strength of the association. Through the association graph, the complex relationship between various parameters in the community environment can be clearly seen, providing intuitive basis for in-depth analysis of potential problems in community governance. For example, it is found in the association graph that air quality, traffic flow and resident health complaints are closely related, and further analysis can speculate that traffic pollution may be an important factor leading to resident health problems.

[0071] Trend, periodicity, and seasonality analysis of historical multi-source heterogeneous data; among them:

[0072] Trend analysis calculates the long-term trend of data, which can use moving average method, exponential smoothing method, etc. Moving average method smooths data fluctuations by calculating the average value of data within a certain period, highlighting long-term trends. For example, calculating the 3-month moving average of air quality index (AQI) in the community in the past year can more clearly see the long-term trend of AQI. Exponential smoothing method gives more weight to recent data, which can more quickly reflect the trend of data change. By adjusting the smoothing coefficient, different types of data changes can be adapted. Linear regression model can also be used to fit the long-term trend line of data, and the slope of the regression equation can be used to determine whether the trend is rising, falling or stable. For example, linear regression analysis of the green coverage rate data in the community in the past few years shows that if the slope is positive, the green coverage rate is on the rise.

[0073] Periodic analysis, observe the periodic changes of data, such as daily, weekly, monthly or annual cycles. For example, community electricity consumption usually has daily periodicity, with higher electricity consumption during the day and lower at night; traffic flow may have weekly periodicity, with heavy traffic during weekdays and relatively less on weekends. Use Fourier analysis and other methods to decompose time series data into different frequency components, accurately identify the periodic characteristics in the data. By analyzing the amplitude and phase of each periodic component, understand the influence of different periods on the data.

[0074] Seasonal analysis, seasonal analysis mainly focuses on the change rule of data in different seasons of a year. For example, the number of tourists in a community may increase significantly during holidays and tourist peak seasons, while it is relatively small in the off-season; the incidence of some diseases may have obvious differences in different seasons. Calculate the seasonal index to measure the degree of seasonal fluctuation, the seasonal index is the ratio of the average value of a season to the annual average value. By comparing the seasonal indexes of different seasons, determine which seasons are peak or trough periods, and provide a basis for the rational allocation of community resources.

[0075] Forecasting analysis, based on the trend, periodicity and seasonality of historical data, establish a prediction model to predict future data. Common prediction models include autoregressive moving average model (ARMA), autoregressive integrated moving average model (ARIMA) and other models. Use historical data to train and estimate the parameters of the model, and evaluate the prediction accuracy and reliability of the model through model testing. For example, use mean square error (MSE), mean absolute error (MAE) and other indicators to measure the deviation between predicted values and actual values.

[0076] Collect the geographical basic data of the community, including vector data such as community boundaries, road networks, building distribution, and raster data such as topography, land use, etc. These data can be obtained through geographic information system (GIS) software or purchased from relevant departments. Ensure the accuracy and timeliness of geographic information data, update the data in a timely manner to reflect the latest changes in the community. For example, if there are new buildings or roads in the community, they need to be added to the geographic information database in a timely manner.

[0077] Correlate the community governance-related data to be analyzed (such as environmental quality data, safety hazard data, public service facility usage data, etc.) with geographic information data. Through geocoding technology, address information in the data can be converted into specific geographic coordinates, achieving accurate matching of data and space.

[0078] Integrate data from different sources and types to build a unified community spatial database. For example, integrate air quality monitoring data, noise monitoring data, garbage distribution data, etc. into a database for comprehensive analysis and visualization.

[0079] Spatial autocorrelation analysis is used to analyze the correlation of data in space and determine whether the data values of adjacent regions are similar. For example, by calculating Moran's I of air quality data, it is determined whether there is spatial clustering of air quality in the community. If the Moran's I is positive and significant, it means that regions with similar air quality tend to cluster in space; if it is negative and significant, it means that regions with large differences in air quality are adjacent in space.

[0080] Hot spot analysis identifies high-value and low-value aggregation areas (hot spots and cold spots) of data values in the community. For example, using the Getis-Ord Gi* statistic to conduct hot spot analysis on crime incident data in the community, find out the high-crime area, and provide the basis for strengthening security prevention and control.

[0081] Spatial interpolation is used when data is missing in some areas. The data of the surrounding known areas is used for spatial interpolation to estimate the data value of the missing area. Common spatial interpolation methods include inverse distance weighting interpolation, Kriging interpolation, etc. For example, for areas without air quality monitoring points, Kriging interpolation can be used to estimate the air quality of the surrounding monitoring points.

[0082] According to the results of spatial visualization and the conclusions of spatial statistical analysis, identify the spatial patterns and problems existing in the community. For example, it is found that the distribution of public service facilities (such as schools, hospitals, parks, etc.) in some areas of the community is uneven, resulting in inconvenience for some residents to enjoy public services; or some industrial areas are too close to residential areas, causing environmental pollution and safety risks.

[0083] Analyze the causes of the formation of spatial patterns, taking into account geographical, social, economic and other factors. For example, the uneven distribution of public service facilities may be due to historical planning reasons, land use restrictions, or insufficient funding.

[0084] S300: According to the data analysis results, generate a decision-making scheme, convert the decision-making scheme into an execution instruction, and send it to the corresponding execution subject.

[0085] In this embodiment, according to the data analysis results, a decision-making scheme is generated, the decision-making scheme is converted into an execution instruction, and the execution instruction is sent to the corresponding execution subject. The specific process is as follows:

[0086] S301: According to the data analysis results, generate a decision-making scheme, and convert each measure in the scheme into an execution instruction; wherein the execution instruction includes operation content, execution object, execution time and execution standard;

[0087] S302: Use a communication channel to send the generated execution instruction to the corresponding execution subject.

[0088] In the above process, according to the data analysis results, combined with the actual situation of the community, the resource status and the relevant policies and regulations, a targeted decision-making scheme is formulated. For example, if the analysis finds that the noise pollution in a certain area of the community is serious during a certain period, the decision-making scheme may include measures such as strengthening supervision of commercial activities during that period, setting up noise isolation facilities, adjusting traffic flow, etc.; if it is found that the air quality in the community is deteriorating, the decision-making scheme may involve measures such as strengthening the emission monitoring of surrounding industrial enterprises, promoting green travel methods, increasing the community greening area, etc. The decision-making scheme should clearly specify the specific goals, measures, implementation time and responsible subjects to ensure the operability and effectiveness of the scheme.

[0089] According to the generated decision-making scheme, the measures in the scheme are converted into specific execution instructions using a decision support system or a special instruction generation tool. The execution instructions should contain clear operation content, execution object, execution time and execution standard, etc. For example, for the measure of setting up noise isolation facilities, the execution instruction will specify the type, specification, installation location, installation time and acceptance standard of the isolation facilities; for the measure of strengthening supervision of commercial activities, the execution instruction will clearly specify the specific content of supervision (such as business hours, noise emission restrictions, etc.), supervision methods and responsible personnel, etc.

[0090] Through the community's internal management information system or a special communication channel, the generated execution instructions are accurately and correctly sent to the corresponding execution subjects. The execution subjects may include community property management departments, environmental protection law enforcement departments, traffic management departments, community volunteer organizations, etc. When sending the instructions, ensure that the execution subjects can timely receive and understand the instruction content, and at the same time establish an instruction feedback mechanism, requiring the execution subjects to regularly feedback the execution progress and results during the execution process, so that the community governance decision-making team can monitor and adjust the execution of the decision-making scheme in real time, and ensure that the community governance work can proceed smoothly according to the predetermined target.

[0091] Corresponding to the foregoing embodiments of the digital twin driven multi-source information intelligent decision-making method, the present application also provides embodiments of a digital twin driven multi-source information intelligent decision-making system.

[0092] Figure 5 is a block diagram of a digital twin driven multi-source information intelligent decision-making system according to an exemplary embodiment. Referring to Figure 5 , the system can include: a community virtual modeling module 401, a data fusion analysis module 402, a decision execution module 403; wherein:

[0093] The community virtual modeling module 401 is configured to collect community, building and facility operation data, generate a three-dimensional virtual model of the community, establish a data correspondence relationship between the virtual model and the entity, and perform data association.

[0094] The data fusion analysis module 402 is configured to acquire environmental state data in real time, fuse the environmental state data, and analyze potential problems in community governance.

[0095] The decision execution module 403 is configured to generate a decision scheme according to the data analysis result, convert the decision scheme into an execution instruction, and send the execution instruction to a corresponding execution subject.

[0096] In the embodiment, the community virtual modeling module 401 collects community, building, and facility operation data, generates a community three-dimensional virtual model, establishes a data correspondence relationship between the virtual model and the entity, and performs data correlation; the data fusion analysis module 402 acquires environmental state data in real time, fuses the environmental state data, and analyzes potential problems in community governance; the decision execution module 403 generates a decision scheme according to the data analysis result, converts the decision scheme into an execution instruction, and sends the execution instruction to a corresponding execution subject; through the above manner, real-time virtual mapping of a community space is realized, community dynamics is simulated and predicted in real time, and decision support capability is improved.

[0097] As to the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment related to the method, and will not be described in detail here.

[0098] For the system embodiment, since it basically corresponds to the method embodiment, the related parts can be referred to the part of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, that is, they can be located in one place or distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the present application. Those skilled in the art can understand and implement it without creative labor.

[0099] Correspondingly, the present application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the digital twin driven multi-source information intelligent decision method as described above. As Figure 6 As shown in the figure, a hardware structure diagram of a digital twin driven multi-source information intelligent decision system provided by an embodiment of the present application is in any device with data processing capability. In addition to Figure 6 In addition to the processor, the memory, and the network interface shown in the figure, any device with data processing capability in the embodiment can also include other hardware according to the actual function of the device with data processing capability, and this will not be described again.

[0100] Accordingly, the present application also provides a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the digital-twin-driven multi-source information intelligent decision-making method as described above. The computer readable storage medium can be an internal storage unit of any of the devices having data processing capability as described in any of the preceding embodiments, such as a hard disk or a memory. The computer readable storage medium can also be an external storage device, such as a plug-in hard disk, a smart media card (SMC), an SD card, a flash card, etc. equipped on the device. Further, the computer readable storage medium can include both the internal storage unit of any of the devices having data processing capability and the external storage device. The computer readable storage medium is used to store the computer program and other programs and data required by the devices having data processing capability, and can also be used to temporarily store data that has been output or will be output.

[0101] Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the present application cover any and all variations of the application that come within the scope of the general inventive concepts described herein and including all such variations as fall within the common general knowledge or customary practice of the art to which the application pertains.

[0102] It should be understood that the application is not limited to the precise construction that has been described above and shown in the accompanying drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the application.

Claims

1. A digital twin driven multi-source information intelligent decision-making method, characterized in that, The method comprises the following steps: Collecting community, building, and facility operation data, generating a three-dimensional virtual model of the community, establishing a data correspondence relationship between the virtual model and the entity, and performing data correlation; Real-time acquisition of environmental state data, fusion of the environmental state data, and analysis of potential problems in community governance; Generating a decision-making scheme based on the data analysis results, converting the decision-making scheme into an execution instruction, and sending the execution instruction to the corresponding execution subject.

2. The digital twin driven multi-source information intelligent decision-making method of claim 1, wherein, In the step of collecting community, building, and facility operation data, generating a three-dimensional virtual model of the community, and establishing a data correspondence relationship between the virtual model and the entity, and performing data correlation: Respectively collect the geometric data of community, building, and facility operation, and integrate and preprocess the geometric data; Restoring and refining the building model according to the geometric data, and adding elements to generate a three-dimensional virtual model of the community; Assigning a unique identifier to each element in the three-dimensional virtual model of the community, which corresponds to the actual entity.

3. The digital twin driven multi-source information intelligent decision-making method of claim 2, wherein, After the step of assigning a unique identifier to each element in the three-dimensional virtual model of the community, which corresponds to the actual entity: Correlate the collected community, building, and facility operation data with the corresponding elements in the virtual model, and establish a data table to store the correlation information.

4. The digital twin driven multi-source information intelligent decision-making method of claim 1, wherein, In the step of real-time acquisition of environmental state data, fusion of the environmental state data, and analysis of potential problems in community governance: Deploy environmental sensors to collect environmental state data in real time and output multi-source heterogeneous data; After preprocessing the multi-source heterogeneous data, use data fusion to fuse the measurement data of multiple sensors on the same environmental parameter; Constructing association rules, analyzing the association relationship between different environmental parameters, and outputting frequently occurring parameter combinations and their association rules; Respectively performing time series analysis and spatial distribution analysis on the multi-source heterogeneous data, and outputting the analysis results.

5. The digital twin driven multi-source information intelligent decision-making method of claim 4, wherein, After the step of constructing association rules, analyzing the association relationship between different environmental parameters, and outputting frequently occurring parameter combinations and their association rules: Draw an association graph with each environmental parameter as a node and the association relationship between parameters as an edge.

6. The digital twin driven multi-source information intelligent decision-making method of claim 4, wherein, In the step of respectively performing time series analysis and spatial distribution analysis on the multi-source heterogeneous data, and outputting the analysis results: Respectively perform trend, periodicity, and seasonality analysis on historical multi-source heterogeneous data, and based on the trend, periodicity, and seasonality characteristics of the historical data, establish a prediction model to predict the data.

7. The digital twin driven multi-source information intelligent decision-making method of claim 6, wherein, In the step of respectively performing time series analysis and spatial distribution analysis on the multi-source heterogeneous data, and outputting the analysis results: Combine geographic information and multi-source heterogeneous data to perform spatial pattern recognition and problem diagnosis.

8. The digital twin driven multi-source information intelligent decision-making method of claim 1, wherein, In the step of generating a decision-making scheme based on the data analysis results, converting the measures in the scheme into execution instructions, and sending the execution instructions to the corresponding execution subject: Generating a decision-making scheme based on the data analysis results, and converting each measure in the scheme into an execution instruction; wherein the execution instruction includes operation content, execution object, execution time, and execution standard.

9. The digital twin driven multi-source information intelligent decision-making method of claim 8, wherein, After the step of generating a decision-making scheme based on the data analysis results, and converting each measure in the scheme into an execution instruction: Use a communication channel to send the generated execution instruction to the corresponding execution subject.

10. A digital twin driven multi-source information intelligent decision system applied to the digital twin driven multi-source information intelligent decision method of claim 1, characterized in that, Comprise community virtual modeling module, data fusion analysis module, decision execution module;Among them: The community virtual modeling module is used to collect community, building, facility operation data, generate community three-dimensional virtual model, and establish the data correspondence relationship between virtual model and entity, and carry out data correlation; The data fusion analysis module is used to acquire environmental state data in real time, fuse environmental state data, and analyze potential problems of community governance; The decision execution module is used to generate decision scheme according to data analysis result, convert decision scheme into execution instruction, and send to corresponding execution subject.

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