Method, device, equipment, system and chip for mapping pipeline network data terminal

Abstract

The present application provides a method, device, equipment, system and chip for mapping pipeline network data terminals. The method includes: obtaining coordinates corresponding to vector maps, real-time flow meter reading data and GIS nodes; vector marking the vector map to obtain spatial information corresponding to the real-time flow meter reading data; performing distributed calculation on the real-time flow meter reading data to obtain calculation results; sorting the calculation results to obtain the three GIS nodes closest to the spatial information of each real-time flow meter reading data; performing fuzzy search of the term weights on the three nearest GIS nodes, obtaining a GIS node with the highest weight confidence and mapping it with the corresponding real-time flow meter reading data. The method can quickly, real-time and accurately map flow meter reading data and GIS nodes, so as to facilitate simulation of the pipeline network and calculation of topological data.

Description

Technical Field

[0001] The present application relates to the field of smart city management technology, and in particular to methods, devices, equipment, systems and chips for pipe network data terminal mapping. Background Art

[0002] Gas, water and heat pipe networks are important components of urban infrastructure, and their safe, efficient and intelligent management is the basis for smart city operations. Flow meter reading data records the usage of each user and is key information in pipe network operation and management. It can be used to evaluate the operation status of the pipe network, optimize resource allocation, formulate charging strategies and ensure public safety.

[0003] Geographic Information System (GIS) technology is a key technology for mapping flow meter reading data to specific geographical locations. Existing terminal mapping technologies include manual mapping, connection matching using the public ID of the two, and incorporation of meter reading data into the map database. When the amount of data is large, the error rate and missed detection rate of manual mapping are high; the use scenarios of connection matching are greatly restricted, and the problems of missing ID and unmatched ID cannot be solved; the incorporation of meter reading data into the map database will cause a large number of isolated points, and the time cost of processing isolated points is high. Existing technologies cannot map and match data in real time, efficiently and accurately, so how to map the data of the pipe network terminal has become an urgent problem to be solved.

[0004] Based on this, the present application provides methods, devices, equipment, systems and chips for pipeline data terminal mapping to achieve real-time, efficient and accurate mapping of pipeline terminal data, and provide support for smart city operations. Summary of the invention

[0005] The purpose of this application is to provide a method, device, equipment, system and chip for pipeline network data terminal mapping, which can quickly, real-time and accurately map flow meter reading data and GIS nodes to facilitate simulation of the pipeline network and calculation of topological data.

[0006] The purpose of this application is achieved by the following technical solutions:

[0007] In a first aspect, the present application provides a method for mapping a pipe network data terminal, which dynamically acquires flow meter reading data and maps it with a GIS node in real time, the method comprising:

[0008] Obtain vector maps, real-time flow meter reading data and coordinates corresponding to GIS nodes;

[0009] Based on the real-time flow meter reading data, vector marking is performed on the vector map to obtain the spatial information corresponding to the real-time flow meter reading data;

[0010] Based on the spatial information corresponding to the real-time flow meter reading data and the coordinates corresponding to the GIS node, the real-time flow meter reading data is subjected to distributed calculation to obtain a calculation result, wherein the calculation result is the distance from the spatial information of each piece of real-time flow meter reading data to the GIS node:

[0011] Sorting the calculation results to obtain three GIS nodes closest to the spatial information of each piece of the real-time flow meter reading data;

[0012] The three nearest GIS nodes are respectively subjected to a fuzzy search of the term weights, and a GIS node with the highest weight confidence is obtained and mapped with the corresponding real-time flow meter reading data.

[0013] The beneficial effect of the technical solution is that the method significantly improves the accuracy and efficiency of traffic monitoring and management by dynamically acquiring flow meter reading data and mapping it with GIS nodes in real time. First, the coordinates of the vector map, real-time flow meter reading data and GIS nodes are integrated to ensure the timeliness and accuracy of the data. By vector marking the vector map, the real-time flow meter reading data can be effectively associated with the spatial information, forming a comprehensive geographic information data foundation. This integration of spatial information provides a solid foundation for subsequent analysis and decision-making, and enhances the visualization ability of the system. Secondly, distributed computing is performed to make the distance calculation process between each flow meter reading data and the GIS node efficient and fast. This real-time computing capability enables the system to respond to traffic changes in a timely manner, ensuring the efficiency and accuracy of data processing. By sorting the calculation results, the method can quickly find the three nearest GIS nodes, which greatly shortens the response time and improves the real-time nature of traffic monitoring. Furthermore, a weighted fuzzy search is performed on these three GIS nodes to obtain the node with the highest confidence, ensuring the reliability of the mapping results. This weight-based fuzzy search mechanism helps to consider multiple factors in node selection and improves the accuracy and rationality of the final selection. By mapping the optimal GIS node with the corresponding real-time flow meter reading data, the system can realize flexible resource allocation in a dynamic environment. This method can quickly, real-time and accurately map flow meter reading data and GIS nodes to facilitate the simulation of the pipe network and the calculation of topological data, promoting the development of intelligent pipe network management.

[0014] In some optional implementations, obtaining the vector map includes:

[0015] Load real-time geographic information from the geographic information service platform;

[0016] Get vector maps by accessing map services.

[0017] The beneficial effects of this technical solution are as follows: First, loading real-time geographic information from the geographic information service platform ensures the timeliness and accuracy of the data, providing a solid foundation for subsequent flow meter reading data analysis. This real-time nature helps to reflect environmental changes in a timely manner and support more accurate decision-making. Secondly, by accessing the map service to obtain vector maps, the system's visualization capabilities are improved. Vector maps clearly display geographic features, making it easier for users to intuitively understand data distribution and changes. This integration not only optimizes flow monitoring management, but also improves resource utilization efficiency and reduces acquisition costs. It can not only meet basic map information browsing, but also speed up web page access.

[0018] In some optional implementations, the vector map is vector-marked based on the real-time flow meter reading data to obtain spatial information corresponding to the real-time flow meter reading data, including:

[0019] Analyze the flow location information from the real-time flow meter reading data;

[0020] Performing vector marking in the vector map based on the traffic location information to obtain vector marking coordinates;

[0021] Determine whether the coordinates corresponding to the GIS node are of Mercator type;

[0022] If the coordinates corresponding to the GIS node are of Mercator type, no operation is performed;

[0023] If the coordinates corresponding to the GIS node are not of Mercator type, Mercator transformation is performed on the coordinates corresponding to the GIS node;

[0024] Based on the node coordinates and vector marker coordinates corresponding to the GIS, spatial information corresponding to the real-time flow meter reading data is obtained.

[0025] The beneficial effects of the technical solution are as follows: first, the method analyzes the real-time flow meter reading data and extracts the flow location information therefrom. This process combines the flow data with the specific geographical location, laying the foundation for subsequent spatial analysis, and accurate flow location information helps to achieve more effective resource monitoring and management. Based on the extracted flow location information, vector marking is performed in the vector map to obtain the vector marking coordinates. This process ensures the visualization of flow data in the geographic information system, which facilitates the intuitive understanding of the flow distribution. It is determined whether the coordinates corresponding to the GIS node are of Mercator type. If it is of Mercator type, no conversion is required to maintain the consistency and stability of the data; if not, Mercator conversion is performed to ensure the consistency of the coordinate format. This step improves the flexibility and compatibility of the system, so that data from different coordinate systems can be effectively integrated. Based on the GIS node coordinates and the vector marking coordinates, the spatial information corresponding to the real-time flow meter reading data is obtained, which provides important support for subsequent data analysis and decision-making, and can more accurately reflect the flow status and changes. Through efficient vector marking and coordinate conversion, the method improves the accuracy and operability of real-time flow monitoring, and provides strong data support for intelligent pipe network management.

[0026] In some optional implementations, the acquiring of spatial information corresponding to the real-time flow meter reading data based on the coordinates corresponding to the GIS node and the vector marker coordinates includes:

[0027] Performing Mercator transformation on the vector marker coordinates;

[0028] Get the offset of the coordinates corresponding to the GIS node;

[0029] Based on the offset of the coordinates corresponding to the GIS node, the coordinates of the vector markers after Mercator transformation are offset to obtain the vector projection;

[0030] Based on vector projection, spatial information corresponding to the real-time flow meter reading data is obtained.

[0031] The beneficial effect of this technical solution is that by performing Mercator transformation on the vector marker coordinates, all data are processed under a unified coordinate system, thereby effectively eliminating the errors caused by different coordinate systems. This step is crucial to improving the consistency and accuracy of data, ensuring that subsequent spatial analysis can be based on reliable basic data. Next, the method obtains the offset of the corresponding coordinates of the GIS node. This process helps to understand the spatial relationship between the GIS node and the actual flow location information, and provides the necessary basis for subsequent coordinate adjustment. Based on the obtained offset, the coordinates of the vector marker coordinates after Mercator transformation are offset to obtain an accurate vector projection. This adjustment ensures the precise positioning of the flow data on the map, so that the real-time flow meter reading data can more realistically reflect its position in the geographic space. Finally, based on the obtained vector projection, the spatial information corresponding to the real-time flow meter reading data is extracted. This integration not only enhances the visualization of the data, but also provides a solid basis for subsequent flow analysis and decision-making, supporting more accurate resource management and scheduling.

[0032] In some optional implementations, based on the spatial information corresponding to the real-time flow meter reading data and the coordinates corresponding to the GIS node, the real-time flow meter reading data is distributedly calculated to obtain a calculation result, and the calculation result is the distance from each real-time flow meter reading data to the GIS node, including:

[0033] Performing a traversal search operation on the real-time flow meter reading data to obtain a traversal result;

[0034] Based on the traversal results, the spatial information corresponding to the real-time flow meter reading data and the coordinates corresponding to the GIS node, the distance from each piece of the real-time flow meter reading data to the GIS node is calculated to obtain a calculation result.

[0035] The beneficial effect of this technical solution is that: first, the real-time flow meter reading data is traversed and searched to obtain comprehensive traversal results. This process ensures the detailed analysis of all flow data and lays a good foundation for subsequent calculations. Based on the traversal results, the spatial information of each flow meter reading data and the coordinates of the GIS node, the distance between each flow data and each GIS node is calculated one by one. This calculation not only reveals the relationship between flow data and geographic location, but also can effectively identify the closest GIS node, thereby providing important information for subsequent resource allocation and decision-making processes.

[0036] In some optional implementations, the step of performing a fuzzy search of the term weights on the three nearest GIS nodes, obtaining a GIS node with the highest weight confidence, and mapping it to the real-time flow meter reading data includes:

[0037] Get the field information corresponding to the GIS node;

[0038] Using the entry weight model, the field information corresponding to the GIS node is weighted to obtain the weight evaluation result;

[0039] Based on the weight evaluation results, the fuzzy matching algorithm is used to calculate the weighted reliability of the field information corresponding to the GIS node;

[0040] Based on the weight evaluation result, the weight reliability of the field information corresponding to the GIS node and the coordinates corresponding to the GIS node, a GIS node with the highest weight reliability is obtained and mapped to the real-time flow meter reading data.

[0041] The beneficial effect of this technical solution is that: first, the field information corresponding to the GIS node is obtained to provide a basis for subsequent data processing. Next, the field information is weighted and evaluated using the term weight model to obtain the weight evaluation results. This process helps identify the factors that have the greatest impact on flow monitoring by quantifying the importance of each field, and weights the terms in each text field, thereby more accurately reflecting the importance of the terms in the text. Based on the weight evaluation results, the fuzzy matching algorithm is used to calculate the weight reliability of the GIS node field information. This step ensures that the diversity and uncertainty of field information can be considered in practical applications, providing a more accurate basis for subsequent node selection. Combining the weight evaluation results, the weight reliability of the field information and the coordinates of the GIS node, the system can identify the GIS node with the highest weight reliability and effectively map it with the real-time flow meter reading data. It not only improves the accuracy of data matching, but also greatly simplifies the management process of meter reading data, providing a solid data foundation for subsequent data analysis, decision support, gas volume assignment, pipe network simulation and other work.

[0042] In a second aspect, the present application provides a device for pipe network data terminal mapping, the device comprising a processor, and the processor is configured to implement the following steps:

[0043] Information acquisition module, used to obtain vector maps, real-time flow meter reading data and coordinates corresponding to GIS nodes;

[0044] A vector marking module is used to mark the vector map based on the real-time flow meter reading data to obtain the spatial information corresponding to the real-time flow meter reading data;

[0045] The distance calculation module is used to perform distributed calculation on the real-time flow meter reading data based on the spatial information corresponding to the real-time flow meter reading data and the coordinates corresponding to the GIS node to obtain a calculation result, wherein the calculation result is the distance from each real-time flow meter reading data to the GIS node:

[0046] A distance matching module is used to sort the calculation results to obtain three GIS nodes closest to each piece of real-time flow meter reading data;

[0047] The fuzzy search module is used to perform fuzzy search of the term weights on the three nearest GIS nodes respectively, obtain a GIS node with the highest weight confidence and map it with the corresponding real-time flow meter reading data.

[0048] In some optional implementations, the processor is configured to obtain the vector map in the following manner:

[0049] Load real-time geographic information from the geographic information service platform;

[0050] Get vector maps by accessing map services.

[0051] In some optional implementations, the processor is configured to perform vector marking on the vector map based on the real-time flow meter reading data in the following manner to obtain spatial information corresponding to the real-time flow meter reading data:

[0052] Analyze the flow location information from the real-time flow meter reading data;

[0053] Performing vector marking in the vector map based on the traffic location information to obtain vector marking coordinates;

[0054] Determine whether the coordinates corresponding to the GIS node are of Mercator type;

[0055] If the coordinates corresponding to the GIS node are of Mercator type, no operation is performed;

[0056] If the coordinates corresponding to the GIS node are not of Mercator type, Mercator transformation is performed on the coordinates corresponding to the GIS node;

[0057] Based on the node coordinates and vector marker coordinates corresponding to the GIS, spatial information corresponding to the real-time flow meter reading data is obtained.

[0058] In some optional implementations, the processor is configured to obtain spatial information corresponding to the real-time flow meter reading data based on the coordinates corresponding to the GIS node and the vector marker coordinates in the following manner:

[0059] Performing Mercator transformation on the vector marker coordinates;

[0060] Get the offset of the coordinates corresponding to the GIS node;

[0061] Based on the offset of the coordinates corresponding to the GIS node, the coordinates of the vector markers after Mercator transformation are offset to obtain the vector projection;

[0062] Based on vector projection, spatial information corresponding to the real-time flow meter reading data is obtained.

[0063] In some optional implementations, the processor is configured to perform distributed computing on the real-time flow meter reading data based on the spatial information corresponding to the real-time flow meter reading data and the coordinates corresponding to the GIS node in the following manner to obtain a computing result, wherein the computing result is the distance from each piece of real-time flow meter reading data to the GIS node:

[0064] Performing a traversal search operation on the real-time flow meter reading data to obtain a traversal result;

[0065] Based on the traversal results, the spatial information corresponding to the real-time flow meter reading data and the coordinates corresponding to the GIS node, the distance from each piece of the real-time flow meter reading data to the GIS node is calculated to obtain a calculation result.

[0066] In some optional implementations, the processor is configured to perform a fuzzy search of the term weights on the three nearest GIS nodes respectively in the following manner, obtain a GIS node with the highest weight confidence and map it with the real-time flow meter reading data:

[0067] Get the field information corresponding to the GIS node;

[0068] Using the entry weight model, the field information corresponding to the GIS node is weighted to obtain the weight evaluation result;

[0069] Based on the weight evaluation results, the fuzzy matching algorithm is used to calculate the weighted reliability of the field information corresponding to the GIS node;

[0070] Based on the weight evaluation result, the weight reliability of the field information corresponding to the GIS node and the coordinates corresponding to the GIS node, a GIS node with the highest weight reliability is obtained and mapped to the real-time flow meter reading data.

[0071] In a third aspect, the present application provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program.

[0072] In a fourth aspect, the present application provides a system for pipe network data terminal mapping, the system comprising:

[0073] The above electronic equipment.

[0074] In a fifth aspect, the present application provides a chip storing a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] The present application is further described below in conjunction with the accompanying drawings and implementation methods.

[0076] Figure 1 A flow chart of a method for pipe network data terminal mapping provided in an embodiment of the present application is shown.

[0077] Figure 2 A schematic diagram of a process for obtaining spatial information corresponding to real-time flow meter reading data provided by an embodiment of the present application is shown.

[0078] Figure 3 A schematic diagram of a process for obtaining real-time flow meter reading data mapping provided in an embodiment of the present application is shown.

[0079] Figure 4 A schematic diagram of the structure of spatial information corresponding to real-time flow meter reading data provided in an embodiment of the present application is shown.

[0080] Figure 5 A structural block diagram of a device for pipe network data terminal mapping provided in an embodiment of the present application is shown.

[0081] Figure 6 A structural framework diagram of an electronic device provided in an embodiment of the present application is shown.

[0082] Figure 7 A structural schematic diagram of a system for pipe network data terminal mapping provided in an embodiment of the present application is shown.

[0083] Figure 8 A schematic diagram of the structure of a program product provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0084] Below, the embodiments of the present application are further described in conjunction with the accompanying drawings and specific implementation methods. It should be noted that, under the premise of no conflict, the embodiments or technical features described below can be arbitrarily combined to form a new implementation method.

[0085] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can represent: a, b, c, a and b, a and c, b and c, a and b and c, where a, b and c can be single or multiple. It is worth noting that "at least one" can also be interpreted as "one or more items".

[0086] It should also be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any implementation or design scheme described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other implementations or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0087] Method Embodiment

[0088] See also Figure 1 , Figure 1 A flow chart of a method for pipe network data terminal mapping provided in an embodiment of the present application is shown.

[0089] The embodiment of the present application provides a method for mapping a pipe network data terminal, which dynamically acquires flow meter reading data and maps it with a GIS node in real time, the method comprising:

[0090] Step S101: obtaining vector maps, real-time flow meter reading data and coordinates corresponding to GIS nodes;

[0091] Step S102: Based on the real-time flow meter reading data, vector marking is performed on the vector map to obtain spatial information corresponding to the real-time flow meter reading data;

[0092] Step S103: Based on the spatial information corresponding to the real-time flow meter reading data and the coordinates corresponding to the GIS node, the real-time flow meter reading data is subjected to distributed calculation to obtain a calculation result, wherein the calculation result is the distance from the spatial information of each piece of real-time flow meter reading data to the GIS node;

[0093] Step S104: sorting the calculation results to obtain three GIS nodes closest to the spatial information of each piece of the real-time flow meter reading data;

[0094] Step S105: Perform a fuzzy search of the term weights on the three nearest GIS nodes respectively, obtain a GIS node with the highest weight confidence, and map it with the corresponding real-time flow meter reading data.

[0095] Therefore, this method significantly improves the accuracy and efficiency of traffic monitoring and management by dynamically acquiring traffic meter reading data and mapping it with GIS nodes in real time. First, the vector map, real-time traffic meter reading data and the coordinates of GIS nodes are integrated to ensure the timeliness and accuracy of the data. By vector marking the vector map, the real-time traffic meter reading data can be effectively associated with spatial information, forming a comprehensive geographic information data foundation. This integration of spatial information provides a solid foundation for subsequent analysis and decision-making, and enhances the visualization capability of the system.

[0096] Secondly, distributed computing is performed to make the distance calculation process between each flow meter reading data and the GIS node efficient and fast. This real-time computing capability enables the system to respond to flow changes in a timely manner, ensuring the efficiency and accuracy of data processing. By sorting the calculation results, this method can quickly find the three nearest GIS nodes, which greatly shortens the response time and improves the real-time performance of flow monitoring.

[0097] Furthermore, a weighted fuzzy search is performed on these three GIS nodes to obtain the node with the highest confidence, ensuring the reliability of the mapping results. This weight-based fuzzy search mechanism helps to consider multiple factors in node selection and improves the accuracy and rationality of the final selection. By mapping the optimal GIS node with the corresponding real-time flow meter reading data, the system can achieve flexible resource allocation in a dynamic environment. This method can quickly, real-time and accurately map flow meter reading data and GIS nodes to facilitate the simulation of the pipeline network and the calculation of topological data, promoting the development of intelligent pipeline network management.

[0098] The embodiment of the present application does not limit the number of GIS nodes, which may be, for example, 1, 2, 3, 4, 5, 10, 50, 80, 200, 500, etc.

[0099] In the embodiment of the present application, the attributes of the GIS node include attribute information such as the node ID, location name, node type, coordinates, etc. The node types generally include source points, users, pressure regulating stations, valves, plugs, etc.

[0100] In the embodiment of the present application, flow meter reading data in gas, water, and heat refers to a data table containing attribute information such as ID, company address, company name, street, community, contract number, flow value, etc.

[0101] In some optional implementations, a distributed computing analysis tool may be used to perform distributed computing on the real-time flow meter reading data to obtain computing results, and the distributed computing analysis tool may be Elasticsearch.

[0102] In the embodiment of the present application, Elasticsearch (ES for short) is an open source, highly scalable, distributed full-text search engine built on Apache Lucene, and is also the core of the entire Elastic Stack technology stack. The characteristics of Elasticsearch are: Distributed architecture: Elasticsearch is a distributed database that allows multiple servers to work together, can be expanded to hundreds of servers, and can be searched even if it processes PB-level data, providing a near real-time search experience. High availability: Through the sharding and replica mechanism, Elasticsearch ensures high availability and fault tolerance of data. Each shard can have zero or more replicas, and each data node in the cluster can host one or more shards, and coordinate and process various operations; load rebalancing and routing are automatically completed. Flexible data model: Elasticsearch does not need to predefine a strict table structure, but uses a dynamic schema or mapping, which can automatically identify data types and structures based on document content. Elasticsearch is often used in scenarios such as content search, product search (such as e-commerce websites), and document search. Elasticsearch provides fast and accurate full-text search, supports complex queries such as fuzzy matching, phrase search, term weight, etc., and is suitable for various scenarios that require real-time search, analysis, and storage of large-scale data.

[0103] In some optional implementations, after obtaining the vector map, the real-time traffic meter reading data is vector-marked in the vector map according to the company address, name, community and other data to obtain the spatial information (latitude and longitude) corresponding to the real-time traffic meter reading data; the GIS nodes in the format of the postgresql database or SHP file are read and converted into JSON format, and the JSON format data is inserted into the Elasticsearch search engine.

[0104] In some optional implementations, obtaining the vector map includes:

[0105] Load real-time geographic information from the geographic information service platform;

[0106] Get vector maps by accessing map services.

[0107] Therefore, first of all, real-time geographic information is loaded from the geographic information service platform to ensure the timeliness and accuracy of the data, providing a solid foundation for subsequent flow meter reading data analysis. This real-time nature helps to reflect environmental changes in a timely manner and support more accurate decision-making.

[0108] Secondly, by accessing the map service to obtain vector maps, the system's visualization capabilities are improved. Vector maps clearly display geographic features, allowing users to intuitively understand data distribution and changes. This integration not only optimizes traffic monitoring management, but also improves resource utilization efficiency and reduces acquisition costs. It can not only meet basic map information browsing, but also speed up web page access.

[0109] In an embodiment of the present application, the geographic information service platform may be TiandiTu.

[0110] In the real-time example of this application, the data structure of the vector map is: the vector map is composed of geometric objects such as points, lines, and surfaces, and each object has specific coordinates and attributes. Scaling and resolution: Vector maps can be scaled losslessly, and the details and clarity of the map will not be lost no matter how many times it is magnified. Interactivity: Vector maps are usually highly interactive, and users can click, drag, and query specific objects on the map. File size: Vector map files of the same area are usually smaller than static image files of the same quality, because vector data only stores geometric objects and attributes, not color information for each pixel. Rendering: Vector maps need to be dynamically rendered on the client or server side, and the display content can be updated in real time based on user operations.

[0111] See also Figure 2 , Figure 2 A schematic diagram of a process for obtaining spatial information corresponding to real-time flow meter reading data provided by an embodiment of the present application is shown.

[0112] In some optional implementations, the vector map is vector-marked based on the real-time flow meter reading data to obtain spatial information corresponding to the real-time flow meter reading data, including:

[0113] Step S201: Analyze flow location information from real-time flow meter reading data;

[0114] Step S202: performing vector marking in the vector map based on the flow location information to obtain vector marking coordinates;

[0115] Step S203: Determine whether the coordinates corresponding to the GIS node are of Mercator type;

[0116] Step S204: If the coordinates corresponding to the GIS node are of Mercator type, no operation is performed;

[0117] Step S205: If the coordinates corresponding to the GIS node are not of Mercator type, Mercator transformation is performed on the coordinates corresponding to the GIS node;

[0118] Step S206: Based on the node coordinates and vector marker coordinates corresponding to the GIS, the spatial information corresponding to the real-time flow meter reading data is obtained.

[0119] Therefore, first, the method analyzes the real-time flow meter reading data and extracts flow location information from it. This process combines flow data with specific geographical locations, laying the foundation for subsequent spatial analysis. Accurate flow location information helps to achieve more effective resource monitoring and management.

[0120] Based on the extracted flow location information, vector marking is performed in the vector map to obtain the vector marking coordinates. This process ensures the visualization of flow data in the geographic information system, which facilitates the intuitive understanding of flow distribution. Determine whether the coordinates corresponding to the GIS node are of Mercator type.

[0121] If it is a Mercator type, no conversion is required to maintain data consistency and stability; if not, Mercator conversion is performed to ensure consistency in the coordinate format. This step improves the flexibility and compatibility of the system, allowing data from different coordinate systems to be effectively integrated. Based on GIS node coordinates and vector marker coordinates, the spatial information corresponding to real-time flow meter reading data is obtained, which provides important support for subsequent data analysis and decision-making, and can more accurately reflect flow status and changes.

[0122] This method improves the accuracy and operability of real-time flow monitoring through efficient vector marking and coordinate conversion, and provides strong data support for intelligent pipe network management.

[0123] In some optional implementations, it is necessary to convert the vector marker coordinates and the node coordinates corresponding to the GIS into Mercator coordinates. By adjusting the offset of the node coordinates corresponding to the GIS, the area can be mapped to the center or a suitable position of the coordinate system, so as to facilitate subsequent calculations and analysis. The Mercator transformation is a conformal cylindrical projection method, which is characterized in that the angle between any two points on the projected map is equal to the angle between them on the earth's surface.

[0124] In some optional implementations, the acquiring of spatial information corresponding to the real-time flow meter reading data based on the coordinates corresponding to the GIS node and the vector marker coordinates includes:

[0125] Performing Mercator transformation on the vector marker coordinates;

[0126] Get the offset of the coordinates corresponding to the GIS node;

[0127] Based on the offset of the coordinates corresponding to the GIS node, the coordinates of the vector markers after Mercator transformation are offset to obtain the vector projection;

[0128] Based on vector projection, spatial information corresponding to the real-time flow meter reading data is obtained.

[0129] Therefore, by performing Mercator transformation on the vector marker coordinates, all data can be processed in a unified coordinate system, thus effectively eliminating the errors caused by different coordinate systems. This step is crucial to improving the consistency and accuracy of the data and ensuring that subsequent spatial analysis can be based on reliable basic data.

[0130] Obtaining the offset of the corresponding coordinates of the GIS node helps to understand the spatial relationship between the GIS node and the actual flow location information, and provides the necessary basis for subsequent coordinate adjustment. Based on the obtained offset, the coordinates of the vector markers after Mercator transformation are offset to obtain accurate vector projection. This adjustment ensures the precise positioning of the flow data on the map, so that the real-time flow meter reading data can more truly reflect its position in the geographic space.

[0131] Based on the obtained vector projection, the spatial information corresponding to the real-time flow meter reading data is extracted. This integration not only enhances the visualization of the data, but also provides a solid basis for subsequent flow analysis and decision-making, supporting more accurate resource management and scheduling.

[0132] In some optional implementations, based on the spatial information corresponding to the real-time flow meter reading data and the coordinates corresponding to the GIS node, the real-time flow meter reading data is distributedly calculated to obtain a calculation result, and the calculation result is the distance from each real-time flow meter reading data to the GIS node, including:

[0133] Performing a traversal search operation on the real-time flow meter reading data to obtain a traversal result;

[0134] Based on the traversal results, the spatial information corresponding to the real-time flow meter reading data and the coordinates corresponding to the GIS node, the distance from each piece of the real-time flow meter reading data to the GIS node is calculated to obtain a calculation result.

[0135] Therefore, the real-time flow meter reading data is first traversed and searched to obtain comprehensive traversal results. This process ensures the detailed analysis of all flow data and lays a good foundation for subsequent calculations.

[0136] Based on the traversal results, the spatial information of each flow meter reading data and the coordinates of the GIS nodes, the distance between each flow data and each GIS node is calculated one by one. This calculation not only reveals the relationship between flow data and geographic location, but also effectively identifies the closest GIS node, thus providing important information for subsequent resource allocation and decision-making processes.

[0137] In the embodiment of the present application, flow meter reading data is usually closely related to the geographical location, such as the flow monitoring points of gas, water, and heat pipe networks. GIS nodes also contain data closely related to geographical locations. GIS nodes can solve the distance between them through geometric calculations and spatial analysis algorithms to find the nearest distance matching result.

[0138] In some optional implementations, the distance calculation matching steps are as follows: Step 1: traverse the real-time flow meter reading data with spatial information, insert each data into the Elasticsearch calculation engine and calculate. Step 2: Sort according to the distance from each real-time flow meter reading data to all GIS nodes. Step 3: Select the first three GIS nodes with the smallest distance.

[0139] See also Figure 3 , Figure 3 A schematic diagram of a process for obtaining real-time flow meter reading data mapping provided in an embodiment of the present application is shown.

[0140] In some optional implementations, the step of performing a fuzzy search of the term weights on the three nearest GIS nodes, obtaining a GIS node with the highest weight confidence, and mapping it to the real-time flow meter reading data includes:

[0141] Step S301: Obtain field information corresponding to a GIS node;

[0142] Step S302: using the term weight model, weight evaluation is performed on the field information corresponding to the GIS node to obtain a weight evaluation result;

[0143] Step S303: Based on the weight evaluation result, the weighted reliability of the field information corresponding to the GIS node is calculated using a fuzzy matching algorithm;

[0144] Step S304: Based on the weight evaluation result, the weight reliability of the field information corresponding to the GIS node and the coordinates corresponding to the GIS node, a GIS node with the highest weight reliability is obtained and mapped with the real-time flow meter reading data.

[0145] Therefore, we first obtain the field information corresponding to the GIS node to provide a basis for subsequent data processing. Next, we use the term weight model to evaluate the weight of these field information to obtain the weight evaluation results. This process helps identify the most influential factors for traffic monitoring by quantifying the importance of each field, and evaluates the weight of the terms in each text field, thereby more accurately reflecting the importance of the terms in the text.

[0146] Based on the weight evaluation results, the fuzzy matching algorithm is used to calculate the weighted reliability of the GIS node field information. This step ensures that the diversity and uncertainty of field information can be considered in practical applications, providing a more accurate basis for subsequent node selection.

[0147] Combining the weight evaluation results, the weight confidence of the field information and the coordinates of the GIS nodes, the system can identify the GIS nodes with the highest weight confidence and effectively map them with the real-time flow meter reading data. This not only improves the accuracy of data matching, but also greatly simplifies the management process of meter reading data, providing a solid data foundation for subsequent data analysis, decision support, gas volume assignment, pipe network simulation and other work.

[0148] In the embodiment of the present application, the weighted confidence is an indicator that evaluates the service's understanding ability and similarity of two calculated text addresses. This score is usually calculated based on multiple factors and algorithms, and is intended to reflect the accuracy and completeness of the service in processing, parsing addresses, and calculating the similarity of two text addresses. The following are some common calculation methods and considerations: 1. Accuracy: Correctness of address parsing: The service can correctly parse and identify various parts of the address, such as street name, house number, city, state / province, zip code, etc. 2. Similarity: Similarity refers to the degree of similarity between two objects. In address parsing, similarity is used to measure the similarity between two addresses, thereby helping with fuzzy matching and error handling. A common method is cosine similarity: convert the address into a word frequency vector, calculate the cosine similarity of the two vectors, and measure the similarity by calculating the cosine value of the angle between the two vectors 3. Completeness: The completeness of the address information: The service can parse out all necessary parts of the address, such as street, city, state / province, zip code, etc. Handling of missing information: How the service handles incomplete or partially missing address information, and whether it can be supplemented by context or other information. 4. Consistency: Address format consistency: The service can unify addresses of different formats into a standard format. Address standardization: The service can convert special characters and garbled characters contained in some addresses into standard addresses that the service can recognize. 5. Robustness: Error handling: How the service handles incorrect address information, such as spelling errors, grammatical errors, etc. Fuzzy matching: The service can handle fuzzy or ambiguous address information, such as partial matching, approximate matching, etc.

[0149] The weighted reliability calculation formula is as follows:

[0150] We have the following features and their weights:

[0151] Accuracy (A): weight 0.25

[0152] Similarity (S): weight 0.3

[0153] Completeness (C): Weight 0.2

[0154] Consistency (U): weight 0.15

[0155] Robustness (R): weight 0.1

[0156] The formula can be expressed as: Weighted confidence = 0.25A + 0.3S + 0.2C + 0.15U + 0.1R

[0157] Among them, the value range of A, S, C, U, and R is 0 to 100.

[0158] The final calculated weight confidence ranges from 0 to 100. The larger the score, the better the weight confidence of the two text addresses.

[0159] In the embodiment of the present application, term weight fuzzy search is an efficient information retrieval technology. Term weight refers to the importance or influence that the search engine gives to each term in a document when evaluating the relevance of a document to a query term. The higher the weight, the greater the importance of the term in the document, and the greater its contribution to the relevance of the document to the query term. In the management scenario of flow meter reading data, each flow meter reading contains key information such as user information, address, meter reading, etc. In order to accurately map this data with the nodes in the geographic information system (GIS), traditional methods often rely on exact matching, which is inadequate when the address information is incomplete or there are errors. Term weight fuzzy search can solve this problem well.

[0160] In some optional implementations, first, by constructing a term weight model, the terms in each text field are weighted and evaluated, so as to more accurately reflect the importance of the terms in the text. Using the fuzzy matching algorithm, the system will calculate the confidence of it and the name and address fields of the three GIS nodes screened in the previous step. Weight confidence is an indicator to measure the degree of matching. By comprehensively considering multiple factors such as term weight, matching length, and location, it can more accurately evaluate which GIS node is most matched with the meter reading data. Finally, the system will select an optimal GIS point for mapping for each meter reading data based on the maximum confidence value. This mapping method not only improves the accuracy of data matching, but also greatly simplifies the management process of meter reading data, providing a solid data foundation for subsequent data analysis, decision support, gas volume assignment, pipeline network simulation and other work.

[0161] See also Figure 4 , Figure 4 A schematic diagram of the structure of spatial information corresponding to real-time flow meter reading data provided in an embodiment of the present application is shown.

[0162] In a specific embodiment, first, obtain the vector map, real-time flow meter reading data and the coordinates corresponding to the GIS node. By connecting to the geographic information service platform, load the real-time geographic information from the geographic information service platform, and access its map service to obtain the latest vector map. At the same time, dynamically collect real-time flow meter reading data, including the flow information of each terminal and its corresponding location information. The steps of obtaining the vector map are: using the real-time geographic information of the geographic information service platform loaded by JavaScript API, access the static map (map) service of the geographic information service platform to obtain the vector map.

[0163] Next, based on the real-time traffic meter reading data, the vector map is vector-marked, that is, the name, address, traffic, etc. in the real-time traffic meter reading data are vector-marked in the vector map to extract the spatial information corresponding to the traffic data. Analyze the location information in the traffic data and mark it on the vector map to form the corresponding vector mark coordinates. Then, determine the coordinate type of the GIS node. If it is a Mercator type, do not do anything; if not, perform a Mercator transformation on the coordinates of the GIS node to ensure coordinate consistency. Mercator transformation is to convert the coordinates of the GIS node (i.e., longitude and latitude) into Mercator coordinates. Perform a preset coordinate offset on the vector mark to obtain a projection and coordinate system that is consistent with the coordinate offset of the GIS node, thereby obtaining the spatial information corresponding to the real-time traffic meter reading data (such as Figure 4 ).

[0164] Then, Elasticsearch is used to perform distributed computing on real-time flow meter reading data and calculate the distance from each flow meter reading data to the GIS node. In this step, a traversal search operation is first performed to obtain the traversal results of all flow data. Based on the traversal results, the spatial information corresponding to the flow data, and the coordinates of the GIS nodes, the distance from each flow data to each GIS node is calculated respectively, and the calculation results are sorted to obtain the three GIS nodes closest to each flow data.

[0165] Next, perform a fuzzy search of the term weights on the three nearest GIS nodes. First, obtain the field information corresponding to the GIS node, and use the term weight model to perform a weight assessment on the field information to obtain the weight assessment result. Based on the weight assessment result, apply the fuzzy matching algorithm to calculate the weight reliability of the field information corresponding to the GIS node. First, by constructing a term weight model, perform a weight assessment on the field information corresponding to each GIS node, so as to more accurately reflect the importance of the field information in the text. Using the fuzzy matching algorithm, the system will perform a weight reliability calculation on it and the name and address fields of the three GIS nodes screened out in the previous step.

[0166] Finally, based on the weight evaluation results, the weight confidence of the GIS node field information, and the coordinates of the GIS node, the GIS node with the highest weight confidence is selected and mapped with the corresponding real-time traffic meter reading data. The real-time traffic meter reading data with spatial information is traversed, and each data is inserted into the Elasticsearch computing engine to optimize the field information corresponding to the GIS node: ensure that the fields in the document are correctly segmented and indexed, and assign appropriate weights to them; use Elasticsearch's built-in TF-IDF-based scoring formula and term weight mechanism for fuzzy search matching; each real-time traffic meter reading data is matched with a GIS node with the highest weight confidence from the three GIS node data to map it. After this mapping process is completed, the system records the mapping results for subsequent use.

[0167] Device Embodiment

[0168] See also Figure 5 , Figure 5 A structural block diagram of a device for pipe network data terminal mapping provided in an embodiment of the present application is shown.

[0169] The embodiment of the present application also provides a device for mapping a pipe network data terminal, and its specific implementation method is consistent with the implementation method and the technical effect achieved in the above method embodiment, and some contents will not be repeated here.

[0170] The present application provides a device for pipe network data terminal mapping, the device comprising a processor, and the processor is configured to implement the following steps:

[0171] The information acquisition module 101 is used to obtain the vector map, real-time flow meter reading data and the coordinates corresponding to the GIS nodes;

[0172] A vector marking module 102 is used to mark the vector map based on the real-time flow meter reading data to obtain the spatial information corresponding to the real-time flow meter reading data;

[0173] The distance calculation module 103 is used to perform distributed calculation on the real-time flow meter reading data based on the spatial information corresponding to the real-time flow meter reading data and the coordinates corresponding to the GIS node to obtain a calculation result, wherein the calculation result is the distance from each real-time flow meter reading data to the GIS node:

[0174] A distance matching module 104 is used to sort the calculation results to obtain three GIS nodes closest to each piece of real-time flow meter reading data;

[0175] The fuzzy search module 105 is used to perform a fuzzy search of the term weights on the three nearest GIS nodes respectively, obtain a GIS node with the highest weight confidence and map it with the corresponding real-time flow meter reading data.

[0176] In some optional implementations, the processor is further configured to perform slice division on the pipe network in the following manner to obtain a slice division result, wherein the slice division result is used to indicate a slice area and a level corresponding to the pipe network, wherein the level includes one or more slice areas:

[0177] Divide the pipe network into multiple areas;

[0178] Adjacent areas are merged to obtain multiple levels, and the levels are connected by one or more pressure regulating stations.

[0179] In some optional implementations, the processor is configured to obtain the vector map in the following manner:

[0180] Load real-time geographic information from the geographic information service platform;

[0181] Get vector maps by accessing map services.

[0182] In some optional implementations, the processor is configured to perform vector marking on the vector map based on the real-time flow meter reading data in the following manner to obtain spatial information corresponding to the real-time flow meter reading data:

[0183] Analyze the flow location information from the real-time flow meter reading data;

[0184] Performing vector marking in the vector map based on the traffic location information to obtain vector marking coordinates;

[0185] Determine whether the coordinates corresponding to the GIS node are of Mercator type;

[0186] If the coordinates corresponding to the GIS node are of Mercator type, no operation is performed;

[0187] If the coordinates corresponding to the GIS node are not of Mercator type, Mercator transformation is performed on the coordinates corresponding to the GIS node;

[0188] Based on the node coordinates and vector marker coordinates corresponding to the GIS, spatial information corresponding to the real-time flow meter reading data is obtained.

[0189] In some optional implementations, the processor is configured to obtain spatial information corresponding to the real-time flow meter reading data based on the coordinates corresponding to the GIS node and the vector marker coordinates in the following manner:

[0190] Performing Mercator transformation on the vector marker coordinates;

[0191] Get the offset of the coordinates corresponding to the GIS node;

[0192] Based on the offset of the coordinates corresponding to the GIS node, the coordinates of the vector markers after Mercator transformation are offset to obtain the vector projection;

[0193] Based on vector projection, spatial information corresponding to the real-time flow meter reading data is obtained.

[0194] In some optional implementations, the processor is configured to perform distributed computing on the real-time flow meter reading data based on the spatial information corresponding to the real-time flow meter reading data and the coordinates corresponding to the GIS node in the following manner to obtain a computing result, wherein the computing result is the distance from each piece of real-time flow meter reading data to the GIS node:

[0195] Performing a traversal search operation on the real-time flow meter reading data to obtain a traversal result;

[0196] Based on the traversal results, the spatial information corresponding to the real-time flow meter reading data and the coordinates corresponding to the GIS node, the distance from each piece of the real-time flow meter reading data to the GIS node is calculated to obtain a calculation result.

[0197] In some optional implementations, the processor is configured to perform a fuzzy search of the term weights on the three nearest GIS nodes respectively in the following manner, obtain a GIS node with the highest weight confidence and map it with the real-time flow meter reading data:

[0198] Get the field information corresponding to the GIS node;

[0199] Using the entry weight model, the field information corresponding to the GIS node is weighted to obtain the weight evaluation result;

[0200] Based on the weight evaluation results, the fuzzy matching algorithm is used to calculate the weighted reliability of the field information corresponding to the GIS node;

[0201] Based on the weight evaluation result, the weight reliability of the field information corresponding to the GIS node and the coordinates corresponding to the GIS node, a GIS node with the highest weight reliability is obtained and mapped to the real-time flow meter reading data.

[0202] Device Embodiment

[0203] An embodiment of the present application also provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program. The specific implementation method is consistent with the implementation method and the technical effect achieved in the above method embodiment, and some contents will not be repeated here.

[0204] See also Figure 6 , Figure 6A structural framework diagram of an electronic device provided in an embodiment of the present application is shown.

[0205] The electronic device includes at least one memory 210, at least one processor 220, and a bus 230 connecting different platform systems.

[0206] The memory 210 may include a readable medium in the form of a volatile memory, such as a random access memory (RAM) 211 and / or a cache memory 212 , and may further include a read-only memory (ROM) 213 .

[0207] The memory 210 also stores a computer program, which can be executed by the processor 220, so that the processor 220 implements the steps of any of the above methods.

[0208] The memory 210 may also include a utility 214 having at least one program module 215, such program module 215 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0209] Accordingly, the processor 220 may execute the above-mentioned computer program and may execute the utility 214 .

[0210] The processor 220 may be implemented by one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs), or other electronic components.

[0211] Bus 230 may be a local bus representing one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or any of a variety of bus architectures.

[0212] The electronic device may also communicate with one or more external devices 240 such as keyboards, pointing devices, Bluetooth devices, etc., and may also communicate with one or more devices that can interact with the electronic device, and / or communicate with any device (such as a router, a modem, etc.) that enables the electronic device to communicate with one or more other computing devices. Such communication may be performed through the input and output interface 250. In addition, the electronic device may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and / or a public network, such as the Internet) through a network adapter 260. The network adapter 260 may communicate with other modules of the electronic device through the bus 230. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0213] System Example

[0214] See also Figure 7 , Figure 7 A structural schematic diagram of a system for pipe network data terminal mapping provided in an embodiment of the present application is shown.

[0215] The embodiment of the present application also provides a system for pipe network data terminal mapping, the system comprising:

[0216] The above electronic equipment.

[0217] Medium Embodiment

[0218] An embodiment of the present application also provides a chip, which stores a computer program. When the computer program is executed by a processor, the steps of any of the above methods are implemented. The specific implementation method is consistent with the implementation method and the technical effect achieved in the above method embodiment, and some contents will not be repeated here.

[0219] See also Figure 8 , Figure 8 A schematic diagram of the structure of a program product provided in an embodiment of the present application is shown.

[0220] The program product is used to implement any of the above methods. The program product can use a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited to this. In the embodiment of the present application, the readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The program product can use any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0221] The chip may include a data signal propagated in the baseband or as part of a carrier wave, wherein a readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium may also be any readable medium, which may send, propagate, or transmit a program for use by an instruction execution system, an apparatus, or a device or used in combination with it. The program code contained on the readable storage medium may be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above. The program code for performing the operation of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and also conventional procedural programming languages ​​such as C language, Python language, or similar programming languages. The program code may be executed entirely on a user computing device, partially on a user device, as an independent software package, partially on a user computing device, partially on a remote computing device, or entirely on a remote computing device or server. Where a remote computing device is involved, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0222] This application is explained from the perspectives of purpose of use, effectiveness, progress and novelty, and has met the functional enhancement and usage requirements emphasized by the Patent Law. The above description and drawings of this application are only the preferred embodiments of this application, and are not intended to limit this application. Therefore, all structures, devices, features, etc. that are similar or identical to this application, that is, all equivalent replacements or modifications made in accordance with the scope of the patent application of this application, should fall within the scope of protection of the patent application of this application.

Claims

1. A method for mapping a pipe network data terminal, characterized in that: Dynamically acquire flow meter reading data and map it with GIS nodes in real time, the method comprising: Obtain vector maps, real-time flow meter reading data and coordinates corresponding to GIS nodes; Based on the real-time flow meter reading data, vector marking is performed on the vector map to obtain the spatial information corresponding to the real-time flow meter reading data; Based on the real-time flow meter reading data, the vector map is vector-marked to obtain the spatial information corresponding to the real-time flow meter reading data, including: Analyze the flow location information from the real-time flow meter reading data; Performing vector marking in the vector map based on the traffic location information to obtain vector marking coordinates; Determine whether the coordinates corresponding to the GIS node are of Mercator type; If the coordinates corresponding to the GIS node are of Mercator type, no operation is performed; If the coordinates corresponding to the GIS node are not of Mercator type, Mercator transformation is performed on the coordinates corresponding to the GIS node; Based on the coordinates corresponding to the GIS nodes and the vector marker coordinates, obtaining the spatial information corresponding to the real-time flow meter reading data; Based on the coordinates corresponding to the GIS nodes and the vector marker coordinates, spatial information corresponding to the real-time flow meter reading data is obtained, including: Performing Mercator transformation on the vector marker coordinates; Get the offset of the coordinates corresponding to the GIS node; Based on the offset of the coordinates corresponding to the GIS node, the coordinates of the vector markers after Mercator transformation are offset to obtain the vector projection; Based on vector projection, obtaining spatial information corresponding to the real-time flow meter reading data; Based on the spatial information corresponding to the real-time flow meter reading data and the coordinates corresponding to the GIS node, Elasticsearch is used to perform distributed calculation on the real-time flow meter reading data to obtain a calculation result, wherein the calculation result is the distance from the spatial information of each real-time flow meter reading data to the GIS node; Sorting the calculation results to obtain three GIS nodes closest to the spatial information of each piece of the real-time flow meter reading data; Performing a fuzzy search of the term weights on the three nearest GIS nodes respectively, obtaining a GIS node with the highest weight confidence and mapping it with the corresponding real-time flow meter reading data; Performing a fuzzy search of the term weights on the three nearest GIS nodes respectively, obtaining a GIS node with the highest weight confidence and mapping it with the real-time flow meter reading data, including: Obtain the field information corresponding to the GIS node, insert each piece of data into the Elasticsearch computing engine, optimize the field information corresponding to the GIS node, ensure that the fields in the document are correctly segmented and indexed, and assign appropriate weights to them; Using the term weight model, the field information corresponding to the GIS node is weighted based on the TF-IDF scoring formula and the term weight mechanism to obtain the weight evaluation result; Based on the weight evaluation results, the fuzzy matching algorithm is used to calculate the weighted reliability of the field information corresponding to the GIS node; Based on the weight evaluation result, the weight reliability of the field information corresponding to the GIS node and the coordinates corresponding to the GIS node, a GIS node with the highest weight reliability is obtained and mapped to the real-time flow meter reading data.

2. The method for pipe network data terminal mapping according to claim 1, characterized in that: The step of obtaining a vector map includes: Load real-time geographic information from the geographic information service platform; Get vector maps by accessing map services.

3. The method for pipe network data terminal mapping according to claim 1, characterized in that: Based on the spatial information corresponding to the real-time flow meter reading data and the coordinates corresponding to the GIS node, the real-time flow meter reading data is subjected to distributed calculation to obtain a calculation result, wherein the calculation result is the distance from each real-time flow meter reading data to the GIS node, including: Performing a traversal search operation on the real-time flow meter reading data to obtain a traversal result; Based on the traversal results, the spatial information corresponding to the real-time flow meter reading data and the coordinates corresponding to the GIS node, the distance from each piece of the real-time flow meter reading data to the GIS node is calculated to obtain a calculation result.

4. A device for mapping a pipe network data terminal, characterized in that: The device for mapping the pipe network data terminal includes: Information acquisition module, used to obtain vector maps, real-time flow meter reading data and coordinates corresponding to GIS nodes; A vector marking module is used to mark the vector map based on the real-time flow meter reading data to obtain the spatial information corresponding to the real-time flow meter reading data; Based on the real-time flow meter reading data, the vector map is vector-marked to obtain the spatial information corresponding to the real-time flow meter reading data, including: Analyze the flow location information from the real-time flow meter reading data; Performing vector marking in the vector map based on the traffic location information to obtain vector marking coordinates; Determine whether the coordinates corresponding to the GIS node are of Mercator type; If the coordinates corresponding to the GIS node are of Mercator type, no operation is performed; If the coordinates corresponding to the GIS node are not of Mercator type, Mercator transformation is performed on the coordinates corresponding to the GIS node; Based on the coordinates corresponding to the GIS nodes and the vector marker coordinates, obtaining the spatial information corresponding to the real-time flow meter reading data; Based on the coordinates corresponding to the GIS nodes and the vector marker coordinates, spatial information corresponding to the real-time flow meter reading data is obtained, including: Performing Mercator transformation on the vector marker coordinates; Get the offset of the coordinates corresponding to the GIS node; Based on the offset of the coordinates corresponding to the GIS node, the coordinates of the vector markers after Mercator transformation are offset to obtain the vector projection; Based on vector projection, obtaining spatial information corresponding to the real-time flow meter reading data; A distance calculation module is used to perform distributed calculation on the real-time flow meter reading data using Elasticsearch based on the spatial information corresponding to the real-time flow meter reading data and the coordinates corresponding to the GIS node to obtain a calculation result, wherein the calculation result is the distance from each real-time flow meter reading data to the GIS node; A distance matching module is used to sort the calculation results to obtain three GIS nodes closest to each piece of real-time flow meter reading data; A fuzzy search module is used to perform a fuzzy search of the term weights on the three nearest GIS nodes respectively, obtain a GIS node with the highest weight confidence, and map it with the corresponding real-time flow meter reading data; Performing a fuzzy search of the term weights on the three nearest GIS nodes respectively, obtaining a GIS node with the highest weight confidence and mapping it with the real-time flow meter reading data, including: Obtain the field information corresponding to the GIS node, insert each piece of data into the Elasticsearch computing engine, optimize the field information corresponding to the GIS node, ensure that the fields in the document are correctly segmented and indexed, and assign appropriate weights to them; Using the term weight model, the field information corresponding to the GIS node is weighted based on the TF-IDF scoring formula and the term weight mechanism to obtain the weight evaluation result; Based on the weight evaluation results, the fuzzy matching algorithm is used to calculate the weighted reliability of the field information corresponding to the GIS node; Based on the weight evaluation result, the weight reliability of the field information corresponding to the GIS node and the coordinates corresponding to the GIS node, a GIS node with the highest weight reliability is obtained and mapped to the real-time flow meter reading data.

5. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the steps of implementing the method according to any one of claims 1 to 3.

6. A system for pipe network data terminal mapping, characterized in that: The system for pipe network data terminal mapping includes: The electronic device as claimed in claim 5.

7. A chip, characterized in that: The chip stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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