Smart city digital twinning construction method, device, equipment and medium

Abstract

The invention discloses a smart city digital twinning construction method, device and equipment and a medium. The method comprises the following steps: receiving real city data sent by connected Internet of Things equipment, and preprocessing the real city data according to the equipment type of the Internet of Things equipment and a corresponding service demand to obtain twin city data stored in the smart city system; if a business instruction is received, carrying out resource integration and analysis on the twin city data according to the business instruction through a preset large language model, and generating a corresponding data analysis result; and displaying the data analysis result in a preset display page. By implementing the method provided by the invention, the problem that the smart city cannot effectively manage the city in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart cities, and in particular to a method, device, equipment and medium for constructing a digital twin of a smart city. Background Art

[0002] With the rapid development of technologies such as 5G, the Internet of Things, big data, urban information models and large models, the construction of smart cities has entered an advanced stage of "virtual-real symbiosis". IoT devices have been widely used in various fields of smart cities, generating massive amounts of data. How to efficiently manage these data, tap their potential value, and provide decision support for urban management has become a core issue. Although existing technologies have made progress in the field of digital twins of the Internet of Things in smart cities, they still face problems such as multi-system fragmentation, insufficient data value mining, and disconnection between data presentation and decision support. The decentralized data processing and analysis between systems make data circulation and sharing difficult, limiting overall operational efficiency. At the same time, the multi-dimensional association and deep mining of IoT data are insufficient, and the cross-domain data association, trend prediction and intelligent decision support capabilities are weak. In addition, it is difficult to integrate data anomaly detection with urban governance processes, and the application of artificial intelligence technology in early warning and decision-making assistance needs to be strengthened. In summary, the intelligence level of smart cities in existing technologies is limited and cannot effectively manage cities. Summary of the invention

[0003] The embodiments of the present invention provide a method, device, equipment and medium for constructing a digital twin of a smart city, aiming to solve the problem in the prior art that smart cities cannot effectively manage cities.

[0004] In the first aspect, an embodiment of the present invention provides a method for constructing a digital twin of a smart city, which is applied to a smart city system, and includes: receiving real city data sent by a connected Internet of Things device, and preprocessing the real city data according to the device type of the Internet of Things device and the corresponding business needs, and obtaining the twin city data stored in the smart city system; if a business instruction is received, integrating and analyzing the twin city data according to the business instruction through a preset large language model, and generating corresponding data analysis results; and displaying the data analysis results in a preset display page.

[0005] In the second aspect, an embodiment of the present invention also provides a smart city digital twin construction device, which is applied to a smart city system, and includes: a data processing unit, which is used to receive real city data sent by a connected Internet of Things device, and pre-process the real city data according to the device type of the Internet of Things device and the corresponding business needs, to obtain the twin city data stored in the smart city system; if an analysis unit is used to receive a business instruction, then the twin city data is integrated and analyzed by a preset large language model according to the business instruction, and corresponding data analysis results are generated; a display unit, which is used to display the data analysis results in a preset display page.

[0006] In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the computer program.

[0007] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a computer program, wherein the computer program includes program instructions, and the program instructions can implement the above method when executed by a processor.

[0008] The embodiment of the present invention provides a method, device, equipment and medium for constructing a digital twin of a smart city. The method includes: receiving real city data sent by a connected Internet of Things device, preprocessing the real city data according to the device type of the Internet of Things device and the corresponding business requirements, and obtaining the twin city data stored in the smart city system; if a business instruction is received, integrating and analyzing the twin city data according to the business instruction through a preset large language model, generating corresponding data analysis results; and displaying the data analysis results in a preset display page. The embodiment of the present invention obtains the real city data sent by different Internet of Things devices and processes the data to integrate the data, so as to facilitate information analysis and information mining based on the integrated data. After receiving the business instruction, the twin city data is automatically integrated and analyzed according to the query instruction, and the corresponding data analysis results are generated and displayed, which significantly reduces the time consumption of operators in data query and analysis and improves work efficiency. By obtaining multi-source data and performing data analysis and display, the time consumption of operators in data query and analysis is significantly reduced, work efficiency is improved, and the data governance level of smart cities can be effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0010] Figure 1 A schematic diagram of a process for constructing a digital twin of a smart city provided by an embodiment of the present invention; Figure 2 A schematic diagram of the first sub-process of the method for constructing a digital twin of a smart city provided in an embodiment of the present invention; Figure 3 A schematic diagram of a second sub-process of the method for constructing a digital twin of a smart city provided in an embodiment of the present invention; Figure 4 A schematic diagram of a third sub-process of the method for constructing a digital twin of a smart city provided in an embodiment of the present invention; Figure 5 A schematic diagram of a fourth sub-process of the method for constructing a digital twin of a smart city provided in an embodiment of the present invention; Figure 6 A schematic diagram of the fifth sub-process of the method for constructing a smart city digital twin provided in an embodiment of the present invention; Figure 7 A schematic diagram of the sixth sub-process of the smart city digital twin construction method provided by an embodiment of the present invention; Figure 8 A schematic block diagram of a smart city digital twin construction device provided by an embodiment of the present invention; Fig. 9 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0011] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0012] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0013] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0014] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0015] See also Figure 1 , Figure 1 A flow chart of a method for constructing a digital twin of a smart city provided in an embodiment of the present invention. The method for constructing a digital twin of a smart city in this embodiment can be applied to complex application scenarios in which multiple network element devices and systems in a smart city system work in collaboration, such as collaboration with meteorological sensors in an environmental monitoring system, collaboration with cameras and vehicle flow detection equipment in a traffic monitoring system, etc. The smart city system has a data perception layer, a data analysis layer, and an application layer. Real city data, such as environmental data, street light data, etc., is obtained through the data perception layer, and the data is pre-processed. The data analysis layer calls the processed data to generate corresponding data analysis results and displays them through the application layer. By adopting the method of the present invention, the data query and analysis time of operators in the corresponding system can be reduced, work efficiency can be improved, and the data governance level of smart cities can be effectively improved.

[0016] Figure 1 1 is a flow chart of a method for constructing a digital twin of a smart city provided by an embodiment of the present invention. As shown in the figure, the method includes the following steps S110-S130.

[0017] S110. Receive real city data sent by the connected IoT device, and pre-process the real city data according to the device type of the IoT device and the corresponding business requirements to obtain the twin city data stored in the smart city system.

[0018] In this embodiment, the connected IoT device is a device connected to the smart city system; the real city data is data obtained through a real environment. The real city data sent by the connected IoT device is received. Specifically, each IoT device (such as smart street lights, smart electric meters, water meters, gas meters, environmental monitoring equipment, etc.) is connected to the smart city system, and the data perception layer in the smart city system obtains the real city data transmitted by the IoT device through a wireless or wired network, for example, the city street light data sent by the smart street light through standard communication protocols such as NB, MQTT, CoAP, HTTP, etc. is obtained. The device type of the IoT device is the classification to which the IoT device belongs. For example, the type of the device directly connected to the smart city system is a direct connection type, and the type connected to the smart city system through other systems is a third-party type. The real city data is pre-processed according to the device type of the IoT device and the corresponding business requirements. For example, if the real city data is environmental monitoring data, its business requirement is to monitor environmental humidity, then the real city data is pre-processed by screening and cleaning, the corresponding environmental humidity data is obtained, and it is stored as twin city data in the smart city system. By pre-processing the real city data according to the device type of the Internet of Things device and the corresponding business needs to obtain the twin city data stored in the smart city system, the corresponding twin data of the real world is constructed in the city system to facilitate real-time monitoring and management of the real city world based on the twin data.

[0019] In one embodiment, if Figure 2 As shown, the step S110 includes steps S111 - S112 .

[0020] S111. If the real city data is data sent by a directly connected device, the real city data is parsed using a preset object model according to corresponding business requirements; S112. Convert the format of the parsed data according to a preset data format, obtain the twin city data and store it in a corresponding preset database.

[0021] In this embodiment, the device type includes a direct-connected device, which is an IoT device directly connected to the smart city system, such as a sensor, a smart camera, and the like. The system can directly obtain real city data through the IoT platform. The business demand refers to the demand for data in various application scenarios or systems in the smart city, such as temperature and humidity data required for environmental monitoring. Since the real city data is data sent by a device directly connected to the smart city system through a preset communication protocol, the format of the real city data at this time is relatively uniform. Therefore, the uploaded data can be parsed through a predefined preset object model. The preset object model is a data model used in the IoT platform to describe the function of a device or entity, which defines the attributes, services, and events of a device or entity. According to the corresponding business needs, the real city data is parsed through the preset object model. Specifically, the device information of the device is recorded in detail according to the asset template of the device, such as the device number, device name, city, region, street, and other information. And a database corresponding to the device is constructed according to the corresponding business needs. By performing data parsing, the corresponding data is obtained, the data is matched with the corresponding device information, and the data is converted into structured data, that is, the data format that conforms to the smart city system is stored in the corresponding database. By using the physical model to normalize the real city data, the format and structure of the data can be standardized, the accuracy and efficiency of data collection and storage can be effectively improved, and the problem of difficult data analysis can be avoided.

[0022] In one embodiment, if Figure 3 As shown, the step S110 includes steps S113-S114.

[0023] S113: If the real city data is data sent by a third-party device, the real city data is pre-processed by data cleaning and data classification using a preset large language model according to corresponding business requirements; S114, classifying and outputting the pre-processed real city data according to the manual review results to obtain the twin city data.

[0024] In this embodiment, the device type includes third-party devices, which refers to devices that are not provided within the project or system, and are devices that are connected to other systems. Since the business processes and data structures of different systems are different, the cleaning and classification of real city data at this time is time-consuming and labor-intensive, so when the real city data is data sent by a third-party device, the data is analyzed and processed by a preset large language model. The large language model refers to an AI model that can process and understand complex data structures. The preset large language model is trained to recognize, clean and classify data from different sources. Among them, the specific training process is not limited, and it is sufficient to effectively clean the data. Data cleaning refers to operations such as removing noise, errors, duplicates, etc. in the data to improve the quality and accuracy of the data. The data classification is to classify the cleaned data according to business needs, for example, environmental monitoring data is divided into categories such as air quality, water quality, and noise. According to the corresponding business needs, the real city data is pre-processed by the preset large language model for data cleaning and data classification, and the pre-processed real city data is classified and output according to the manual review results to obtain the twin city data. Specifically, according to the preset data cleaning template, the preset large language model generates a preliminary classification and cleaning plan, and the data analysts and business personnel confirm and optimize the manual review results. The preset language large model classifies and outputs the pre-processed real city data according to the manual review results. Specifically, the preset data cleaning template is: You will receive sensory data from different systems, which are used to build the digital assets of the smart city. Please follow the steps below to generate a preliminary data asset strategy: 1. Identify various types of real city data (such as environmental monitoring, traffic monitoring, energy management systems) and their corresponding business needs; 2. Automatically generate strategies for data cleaning, classification and storage.

[0025] -Data cleaning rules: remove duplicate data, fill in missing data, and standardize data formats.

[0026] -Data classification rules: Based on the business system, data is classified into "environmental data", "traffic data" and "energy data".

[0027] -Data storage rules: Find the corresponding object model according to the data type, and automatically clean and complete the relevant data.

[0028] The object model is as follows: {……} 3. Output preliminary strategies for data analysts to review and list the parts that need further confirmation.

[0029] Enter real city data: {……} Please classify the real city data provided according to the manual review results, and output the object model in JSON format. The classification is the first keyword of JSON, and only JSON is output.

[0030] Among them, there is no limitation on the content of the specific preset data cleaning template, and it is sufficient to guide the preset large language model to perform data cleaning and classification. At this time, the json field output in the model is the twin city data after cleaning, and the twin city data is stored, which can greatly reduce the workload of data classification and cleaning. By pre-processing the real city data through the preset large language model for data cleaning and data classification according to the corresponding business needs, the twin city data can be obtained to assist data analysts to efficiently assetize the data and significantly improve the efficiency of data governance.

[0031] In one embodiment, if Figure 4 As shown, the step S110 includes steps S1101-S1102.

[0032] S1101, performing data anomaly analysis on the twin city data using a preset anomaly analysis template in a preset large language model after each preset detection time interval; S1102: Determine whether the IoT device has an abnormality based on the data abnormality analysis result, and generate corresponding abnormality prompt information and maintenance suggestions based on the abnormal situation.

[0033] In this embodiment, the preset detection time refers to a fixed time interval set by the system, and the system will perform a data anomaly analysis task at every time interval. The setting of this time depends on many factors, such as the frequency of data updates, the importance of anomaly detection, and the availability of computing resources. The preset anomaly analysis template is an indicative model for guiding the large language model on how to analyze data and identify anomalies. Through the natural language processing function of the preset anomaly analysis template in the preset large language model, the operating status of the equipment can be judged based on the twin city data. For example, if the meter sensor frequently uploads abnormal data in a short period of time, the system will call up an abnormal alarm and trigger the work order management system to remind the operation and maintenance personnel to conduct further analysis and confirmation. Among them, the preset anomaly analysis template is: You will receive data from a certain category (such as an environmental monitoring system), including temperature, humidity, air quality, etc. Please perform data analysis and anomaly detection according to the following requirements: 1. Data source: <Enter data type here> 2. Anomaly detection criteria: <Fill in the business requirements here> 3. Analysis objectives: -Detect anomalies in the twin city data and record the time and specific value of the anomaly; -If an abnormality is found, an abnormality prompt message is generated and preliminary maintenance suggestions are provided.

[0034] 4. Output requirements: - Return abnormal prompt information, including abnormal type (temperature, humidity, air quality), abnormal value, and abnormal occurrence time; - Provide corresponding maintenance suggestions for each anomaly (such as "check whether the sensor is faulty", "check whether the equipment needs calibration").

[0035] The input data structure is as follows: <Enter the periodic data of this category here> Please analyze the above data, identify anomalies, and output detection results and suggestions.

[0036] The specific content of the preset abnormal analysis template is not limited, and it is sufficient to guide the preset large language model to identify abnormalities and output results and suggestions. The preset abnormal analysis template in the preset large language model is used to perform data abnormality analysis on the twin city data and generate corresponding abnormal prompt information and maintenance suggestions, so as to detect and remind abnormal situations in real time, improve the speed and accuracy of problem discovery, and reduce the workload of manual analysis.

[0037] In one embodiment, if Figure 5 As shown, step S1102 includes steps S1103-S1104.

[0038] S1103, determining whether a maintenance instruction generated by the abnormal prompt information is received; S1104: If received, send the abnormal prompt information and the maintenance suggestion to the corresponding maintenance platform according to the maintenance instruction.

[0039] In this embodiment, the maintenance instruction is an instruction generated by the operator on the visual screen according to the abnormal prompt information. Specifically, since the preset large language model detects that there is an abnormality in the twin city data, the system will pop up a reminder message with abnormal prompt information at the abnormal device on the visual large screen, wherein the reminder information has been associated with the work order transfer function. For example: if the air quality near a certain device is detected to be abnormal, a message is generated to notify the relevant sanitation personnel to verify. If the operator clicks on the abnormal prompt information and wants to perform corresponding maintenance, the smart city system will receive the maintenance instruction generated by the abnormal prompt information, and through the abnormal prompt information associated with the work order transfer function, the abnormal prompt information and maintenance suggestions will be transferred to the corresponding processing platform, such as the smart traffic management system, environmental monitoring platform, etc. By sending the abnormal prompt information and the maintenance suggestions to the corresponding maintenance platform according to the maintenance instruction, the intelligent integration of the twin city data and the system is realized, which significantly improves the operational efficiency of the smart city.

[0040] S120. If a business instruction is received, the twin city data is integrated and analyzed according to the business instruction through a preset large language model to generate corresponding data analysis results.

[0041] In this embodiment, the business instruction is a business instruction sent by the user to the data analysis layer through the application layer. For example, the business instruction is: extract one month of temperature and humidity sensor data for analysis. The data analysis layer will integrate and analyze the twin city data through a preset large language model to generate corresponding data analysis results. Specifically, the large language model will extract relevant data and information from the digital twin city according to the received business instructions. The model will integrate these data, remove redundant information, and extract valuable knowledge and patterns. Based on the results of resource integration and analysis, the large language model will generate data analysis results with corresponding data analysis reports or suggestions. These results may include an overview of the city's operating status, detailed information on problem diagnosis, predictions of future trends, etc. Reports and suggestions are usually presented in the form of natural language text, which is easy for city managers and decision makers to understand and use. By presetting a large language model to integrate and analyze the twin city data, corresponding data analysis results can be generated to provide strong support for city managers and decision makers.

[0042] In one embodiment, if Figure 6 As shown, step S120 includes steps S121-S122.

[0043] S121, inputting the business instruction into the preset large language model; S122. Traverse the corresponding resource database through the preset large language model to obtain corresponding business data, and generate the data analysis result through the preset instruction analysis template for the business data and business analysis requirements, wherein the business instruction carries the business analysis requirements.

[0044] In this embodiment, the data analysis layer in the smart city system inputs the business instruction into the preset large language model, traverses the corresponding resource database through the preset large language model to obtain the corresponding business data, and generates the data analysis result through the preset instruction analysis template for the business data and business analysis requirements, wherein the preset instruction analysis template is a template for guiding the large model to perform data analysis. For example, the business requirement in the business instruction is: "Abnormal air quality sensor in the area in the past three months", and the large language model will use the preset instruction analysis template to generate data analysis results according to the business analysis requirements carried in the business instruction. Business analysis requirements may include data aggregation methods (such as summation, average), data visualization forms (such as charts, reports), and specific data points or data relationships that need to be analyzed. The preset instruction analysis template is a predefined analysis framework or script that guides the large language model on how to process and display data according to business instructions and business analysis requirements. It is similar to the above template and is not given as an example here. By generating the data analysis results through the preset instruction analysis template for the business data and business analysis requirements, the time consumption of operators in data query and analysis is significantly reduced, and work efficiency is improved.

[0045] S130: Display the data analysis result on a preset display page.

[0046] In this embodiment, the data analysis results are displayed on a preset display page in the application layer through the data analysis layer. The preset display page is a pre-set page for displaying data and analysis results to users. The display page may include various elements, such as text boxes, chart areas, buttons, etc., which are used to organize and display data. The design of the display page is usually based on user experience principles to ensure that users can easily understand and use the displayed information. By displaying the data analysis results in a preset display page, users can quickly understand the results of the data analysis through this display page and make decisions or take actions accordingly.

[0047] In one embodiment, if Figure 7 As shown, step S130 includes steps S1301-S1302.

[0048] S1301. Generate a periodic report for each of the IoT devices through a preset large language model according to a preset periodic analysis template, preset analysis requirements, the twin city data, and the data analysis results; S1301. Display the periodic report through the preset display page.

[0049] In this embodiment, the preset periodic analysis template is a template that is pre-set to guide how the preset large language model generates corresponding feedback information, and there is no limitation on this. The preset analysis requirements are set according to the specific needs of urban management, indicating which data needs to be analyzed, which indicators to pay attention to, and the depth and breadth of the analysis. When the preset large language model generates periodic reports for each of the IoT devices according to the preset periodic analysis template, the preset analysis requirements, and the twin city data, the data analysis results in the historical data can be referred to. The trained large language model can generate corresponding periodic reports according to the preset periodic analysis template, the preset analysis requirements, the twin city data, and the data analysis results through the preset large language model, and display them. Generating periodic reports for each IoT device through the preset large language model can directly help platform operators complete daily data investigation and analysis, and use the analysis conclusions in the monthly analysis report, greatly reducing the workload of smart city operators.

[0050] Figure 8 is a schematic block diagram of a smart city digital twin construction device 200 provided in an embodiment of the present invention. Figure 8 As shown, corresponding to the above smart city digital twin construction method, the present invention also provides a smart city digital twin construction device. The smart city digital twin construction device includes a unit for executing the above smart city digital twin construction method, and the device can be configured in a desktop computer, tablet computer, laptop computer, etc. For details, please refer to Figure 8 The smart city digital twin construction device includes a data processing unit 210, an analysis unit 220, and a display unit 230.

[0051] The data processing unit 210 is used to receive the real city data sent by the connected Internet of Things device, and pre-process the real city data according to the device type of the Internet of Things device and the corresponding business requirements, and obtain the twin city data stored in the smart city system.

[0052] In one embodiment, the data processing unit 210 includes a parsing unit and a converting unit.

[0053] A parsing unit, configured to parse the real city data through a preset object model according to corresponding business requirements if the real city data is data sent by a directly connected device; A conversion unit is used to convert the format of the parsed data according to a preset data format, obtain the twin city data and store it in a corresponding preset database.

[0054] In one embodiment, the data processing unit 210 includes a cleaning unit and a classification unit.

[0055] A cleaning unit, configured to perform pre-processing operations of data cleaning and data classification on the real city data through a preset large language model according to corresponding business requirements if the real city data is data sent by a third-party device; The classification unit is used to classify and output the pre-processed real city data according to the manual review results to obtain the twin city data.

[0056] In one embodiment, the data processing unit 210 includes an abnormality analysis unit and a generation unit.

[0057] An anomaly analysis unit, used for performing data anomaly analysis on the twin city data by using a preset anomaly analysis template in a preset large language model after each preset detection time interval; The generating unit is used to determine whether the IoT device has an abnormality according to the data abnormality analysis result, and to generate corresponding abnormality prompt information and maintenance suggestions according to the abnormal situation.

[0058] In one embodiment, the data processing unit 210 includes a determining unit and a sending unit.

[0059] A judging unit, used to judge whether a maintenance instruction generated by the abnormal prompt information is received; A sending unit is used to send the abnormal prompt information and the maintenance suggestion to a corresponding maintenance platform according to the maintenance instruction if received.

[0060] The analysis unit 220 is used to receive a business instruction, and then integrate and analyze the twin city data according to the business instruction through a preset large language model to generate corresponding data analysis results.

[0061] In one embodiment, the analysis unit 220 includes an input unit and a traversal unit.

[0062] An input unit, used for inputting the business instruction into the preset large language model; The traversal unit is used to traverse the corresponding resource database through the preset large language model to obtain the corresponding business data, and generate the data analysis result through the preset instruction analysis template for the business data and business analysis requirements, wherein the business instruction carries the business analysis requirements.

[0063] The display unit 230 is used to display the data analysis result in a preset display page.

[0064] In one embodiment, the display unit 230 includes a configuration unit.

[0065] A report generation unit, configured to generate a periodic report for each of the IoT devices through a preset large language model according to a preset periodic analysis template, preset analysis requirements, the twin city data, and the data analysis results; The display subunit is used to display the periodic report through the preset display page.

[0066] It should be noted that technical personnel in the relevant field can clearly understand that the specific implementation process of the above-mentioned smart city digital twin construction device 200 and each unit can refer to the corresponding description in the aforementioned method embodiment, and for the convenience and conciseness of the description, it will not be repeated here.

[0067] The above-mentioned smart city digital twin construction device can be implemented in the form of a computer program, which can be used in Fig. 9 Runs on the computer device shown.

[0068] See also Fig. 9 , Fig. 9 5 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 500 may be a terminal or a server, wherein the terminal may be an electronic device with communication functions such as a smart phone, a tablet computer, a laptop computer, a desktop computer, a personal digital assistant, and a wearable device. The server may be an independent server or a server cluster composed of multiple servers.

[0069] See also Fig. 9 The computer device 500 includes a processor 502 , a memory and a network interface 505 connected via a system bus 501 , wherein the memory may include a non-volatile storage medium 503 and an internal memory 504 .

[0070] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions, which, when executed, can enable the processor 502 to execute a method for constructing a smart city digital twin.

[0071] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500 .

[0072] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a method for constructing a smart city digital twin.

[0073] The network interface 505 is used to communicate with other devices over the network. Fig. 9 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0074] The processor 502 is used to run a computer program 5032 stored in the memory to implement the steps of the above method.

[0075] It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0076] It can be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiment of the above method.

[0077] Therefore, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the steps of the above method.

[0078] The storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage media that can store program codes.

[0079] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0080] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0081] The steps in the method of the embodiment of the present invention can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present invention can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0082] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0083] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for constructing a digital twin of a smart city, characterized in that: The method is applied to a smart city system, and the method comprises: Receive real city data sent by the connected IoT device, and pre-process the real city data according to the device type of the IoT device and the corresponding business requirements to obtain the twin city data stored in the smart city system; If a business instruction is received, the twin city data is integrated and analyzed according to the business instruction through a preset large language model to generate corresponding data analysis results; The data analysis results are displayed on a preset display page.

2. The method according to claim 1, characterized in that The device type includes a directly connected device, and the step of preprocessing the real city data according to the device type of the Internet of Things device and the corresponding business requirements to obtain the twin city data stored in the smart city system includes: If the real city data is data sent by a directly connected device, the real city data is parsed through a preset object model according to corresponding business requirements; The parsed data is formatted according to a preset data format, and the twin city data is obtained and stored in a corresponding preset database.

3. The method according to claim 1, characterized in that The device type includes a third-party device, and the step of preprocessing the real city data according to the device type of the Internet of Things device and the corresponding business requirements to obtain the twin city data stored in the smart city system includes: If the real city data is data sent by a third-party device, the real city data is pre-processed by using a preset large language model to perform data cleaning and data classification according to corresponding business requirements; The pre-processed real city data is classified and output according to the manual review results to obtain the twin city data.

4. The method according to claim 1, characterized in that After the step of obtaining the twin city data stored in the smart city system, the method further comprises: After each preset detection time interval, performing data anomaly analysis on the twin city data using a preset anomaly analysis template in a preset large language model; Determine whether the IoT device has any abnormality based on the data anomaly analysis results, and generate corresponding abnormality prompt information and maintenance suggestions based on the abnormal situation.

5. The method according to claim 4, characterized in that After the step of generating corresponding abnormal prompt information and maintenance suggestions according to the abnormal situation, the following steps are included: Determining whether a maintenance instruction generated by the abnormal prompt information is received; If received, the abnormal prompt information and the maintenance suggestion are sent to the corresponding maintenance platform according to the maintenance instruction.

6. The method according to claim 1, characterized in that If a business instruction is received, the step of integrating and analyzing the twin city data according to the business instruction by using a preset large language model to generate corresponding data analysis results includes: Inputting the business instruction into the preset large language model; The corresponding resource database is traversed through the preset large language model to obtain the corresponding business data, and the business data and business analysis requirements are analyzed through a preset instruction analysis template to generate the data analysis result, wherein the business instruction carries the business analysis requirements.

7. The method according to claim 6, characterized in that After the step of displaying the data analysis result on a preset display page, the method further includes: Generate a periodic report for each of the IoT devices through a preset large language model according to a preset periodic analysis template, preset analysis requirements, the twin city data, and the data analysis results; The periodic report is displayed through the preset display page.

8. A smart city digital twin construction device, characterized in that: The device is applied to a smart city system, and the device comprises: A data processing unit, configured to receive real city data sent by a connected IoT device, and pre-process the real city data according to the device type of the IoT device and the corresponding business requirements, to obtain the twin city data stored in the smart city system; An analysis unit, configured to, upon receiving a business instruction, integrate and analyze the twin city data according to the business instruction using a preset large language model to generate corresponding data analysis results; The display unit is used to display the data analysis result in a preset display page.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the method according to any one of claims 1 to 7 can be implemented.

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