Spatial data management system and method for large-scale real estate high-concurrency service

Abstract

The embodiment of the invention provides a spatial data management system and method for large-scale real estate high-concurrency service, and relates to the technical field of large-scale data processing technologies. The method comprises the following steps: acquiring platform node information and registration data; and calling a first platform node through a load balancing server based on the platform node information under the condition that the data high concurrency that the system resource for processing the registration data is less than a preset value is determined, so as to adjust the system resource. Through the method and the device, the problem that the system response is not timely is solved, and the effect of improving the system response efficiency is further achieved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of large-scale data processing technology. Specifically, the present invention relates to a spatial data management system and method for large-scale real estate high-concurrency services. Background Art

[0002] With the rapid development of information technology, the amount of real estate registration data has increased sharply. Especially in high-concurrency scenarios, higher requirements are put forward for data processing capabilities.

[0003] However, in the case of high concurrency of large-scale real estate registration spatial data, system performance problems will occur. Specifically, high concurrency will increase the load on the system, resulting in slower system response, service crashes, or unresponsiveness.

[0004] In response to the above problems, there is currently no good solution. Summary of the Invention

[0005] The embodiments of the present invention provide a spatial data management system and method for large-scale real estate high-concurrency services, so as to at least solve the problem of untimely system response in related technologies.

[0006] According to an embodiment of the present invention, there is provided a spatial data management method for large-scale real estate high-concurrency services, including:

[0007] Obtaining platform node information and registration data, wherein the platform node information is obtained through distributed deployment based on a distributed GIS system, the distributed GIS system includes multiple nodes, and the registration data includes real estate registration spatial data;

[0008] In the case of high data concurrency where the system resources determined for processing the registration data are less than a preset value, based on the platform node information, a first platform node is called through a load balancing server to adjust the system resources.

[0009] In an exemplary embodiment, after obtaining the registration data, the method further includes:

[0010] Performing data conversion on the registration data to obtain first registration data;

[0011] Performing data association on the first registration data to obtain a first database;

[0012] In the case of obtaining a real estate data processing instruction, based on the first database, system resources are called to perform real estate data processing to obtain a real estate GIS model map.

[0013] In an exemplary embodiment, invoking a first platform node by the load balancing server based on the platform node information includes:

[0014] Obtaining first load information of the first platform node;

[0015] When the first load information of the first platform node is the least loaded, distributing a resource load request to the first platform node to implement the invocation of the first platform node.

[0016] In an exemplary embodiment, after obtaining the load information of the first platform node, the method further includes:

[0017] Obtaining load information of all platform nodes, where the load information includes the first load information;

[0018] Constructing a load matrix based on the load information;

[0019] Performing a correlation calculation on the load matrix, and when the correlation calculation result does not meet a first condition, determining that the load information is abnormal.

[0020] According to another embodiment of the present invention, there is provided a spatial data management system for large-scale real estate high-concurrency services, including:

[0021] A user-side GIS for obtaining registration data, where the registration data includes real estate registration spatial data;

[0022] A platform node module for obtaining platform node information, where the platform node information is obtained by distributed deployment based on a distributed GIS system, and the distributed GIS system includes multiple nodes;

[0023] A resource adjustment module for, in a case of high data concurrency where it is determined that the system resources for processing the registration data are less than a preset value, invoking a first platform node based on the platform node information through a load balancing server to adjust the system resources.

[0024] In an exemplary embodiment, it further includes:

[0025] A data conversion module for, after obtaining the registration data, performing data conversion on the registration data to obtain first registration data;

[0026] A data association module for performing data association on the first registration data to obtain a first database;

[0027] A map generation module, which is configured to, when receiving an instruction for processing real estate data, call system resources based on the first database to process real estate data, so as to obtain a real estate GIS model map.

[0028] In an exemplary embodiment, the calling of the first platform node by the load balancing server based on the platform node information includes:

[0029] Obtaining first load information of the first platform node;

[0030] When the first load information of the first platform node is the least loaded, distributing a resource load request to the first platform node to implement the calling of the first platform node.

[0031] In an exemplary embodiment, the platform further includes:

[0032] A load collection module, which is configured to, after obtaining the load information of the first platform node, obtain the load information of all platform nodes, where the load information includes the first load information;

[0033] A matrix construction module, which is configured to construct a load matrix based on the load information;

[0034] An anomaly judgment module, which is configured to perform a correlation calculation on the load matrix, and determine that the load information is abnormal when the correlation calculation result does not meet the first condition.

[0035] According to another embodiment of the present invention, there is also provided a computer-readable storage medium, in which a computer program is stored, where the computer program is configured to execute the steps in any one of the above method embodiments when running.

[0036] According to another embodiment of the present invention, there is also provided an electronic device, including a memory and a processor, where a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0037] With the present invention, since multiple platform nodes are called and system tasks are assigned to these called platform nodes, the system response speed is accelerated. Therefore, the problem of untimely system response can be solved, and the effect of improving the system response efficiency can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flowchart of a spatial data management method for large-scale real estate high-concurrency services according to an embodiment of the present invention;

[0039] Figure 2It is the structural block diagram of the management system platform in a specific embodiment of the present invention;

[0040] Figure 3 It is the process block diagram of a specific embodiment of the present invention;

[0041] Figure 4 It is the structural block diagram of a spatial data management system for large-scale real estate high-concurrency services in an embodiment of the present invention. Specific implementation manners

[0042] Next, the technical solutions in the embodiments of the present application will be described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0043] Hereinafter, terms such as "first" and "second" are only used for descriptive purposes, and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", etc. may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0044] As used herein, "about", "substantially" or "approximately" includes the stated value and the average value within an acceptable deviation range of the specific value, where the acceptable deviation range is determined by those of ordinary skill in the art considering the measurement being discussed and the error associated with the measurement of the specific quantity (i.e., the limitations of the measurement system).

[0045] In this embodiment, a spatial data management method for large-scale real estate high-concurrency services is provided. Figure 1 It is the flowchart of a spatial data management method for large-scale real estate high-concurrency services according to an embodiment of the present invention. As Figure 1 shown, the process includes the following steps:

[0046] Step S11, obtaining platform node information and registration data, where the platform node information is obtained by distributed deployment based on a distributed GIS system, the distributed GIS system includes multiple nodes, and the registration data includes real estate registration spatial data;

[0047] In this embodiment, after obtaining the real estate spatial registration data, the real estate spatial registration data is converted into GIS map data according to requirements, and is displayed or otherwise processed through a three-dimensional model or a three-dimensional image.

[0048] Among them, distributed deployment based on a distributed GIS system means relying on the SuperMap distributed GIS technology system (where SuperMap refers to SuperMap Software), providing more available nodes through distributed deployment; at the same time, splitting GIS functions into microservices to achieve full microserviceization of map, 3D, big data, and AI functions, and elastic scaling on demand. Specifically, as Figure 2 shown, it includes edge GIS functions (including edge GIS front-end proxy, edge GIS service aggregation, edge spatial data distribution, edge spatial analysis and processing), cloud-native GIS functions (GIS microservices, GIS containerized deployment, GIS automated orchestration, and the above functions are all achieved through cluster deployment), distributed spatial analysis and processing functions (distributed spatial data processing, distributed spatial analysis, distributed spatial rendering), and distributed real estate spatial data storage and management functions (distributed SQL spatial database, distributed spatial file system, distributed NoSQL spatial database, spatial blockchain, etc.); specifically, render maps based on registration data, preset PostGIS data, and HBase data; or perform spatial queries, attribute queries, and conduct GIS analysis and processing operations such as measurement, coordinate conversion, spatial relationship, and spatial operation based on registration data, preset PostGIS data, and HBase data, making full use of the capabilities of edge devices to improve the overall analysis and calculation efficiency of GIS.

[0049] Step S12, in the case of high data concurrency where it is determined that the system resources for processing the registration data are less than the preset value, based on the platform node information, call the first platform node through the load balancing server to adjust the system resources.

[0050] In this embodiment, during the high data concurrency stage, the platform system resources will shrink sharply. At this time, calculate the registration data and the node resource situation, and then send the processing requirements of the registration data to the corresponding platform node according to the calculation results, so that the corresponding platform node processes the processing requirements, thereby ensuring that the processing requirements can be processed quickly. It should be noted that in order to meet the requirements of high-concurrency access, an Nginx load balancing server can be deployed in cooperation with a docker cluster and a redis sentinel mode to ensure reasonable resource allocation and maximize the hardware performance.

[0051] Among them, the essence of load balancing is to calculate the mean value, and the load for a period of time can be statistically calculated. For example, to calculate the load l a-x in the most recent x minutes, it can be calculated using the following formula:

[0052] l a-x = l a+x ×0.37 + n x ×0.63 (Formula 1)

[0053] where l a+x is the load from system startup to x minutes ago, and n x is the number of running tasks in the last x minutes.

[0054] In SuperMap iServer (a server GIS software platform), the weighted round-robin scheduling algorithm can be used. This algorithm obtains the GIS service with the minimum load through the load balancing algorithm and works for client requests as follows:

[0055]

[0056] where W i is the weight of the i-th node, L i is the load of the i-th node, and K is a constant used to adjust the relationship between the weight and the load.

[0057] Node allocation is performed through the load balancing server to ensure that the pressure of the application service is evenly distributed, improving the performance and stability of the application; specifically, freely allocate and manage the distributed deployment nodes. When resource tension or performance bottlenecks are detected, application resources can be quickly allocated to enable relevant platform nodes to quickly join the cluster, ensuring the normal stability of the map service and providing a highly scalable map support service.

[0058] In some application scenarios, the method of this application can be used in the provincial real estate registration platform to support the timely response of the registration departments in cities and counties within the province to the high access volume of real estate registration spatial data. Thus, in a high-concurrency scenario, when performing read and write operations on real estate spatial data, the real estate registration platform and the database can still respond relatively quickly, achieving data consistency for multiple users to perform read and write operations on the same data simultaneously.

[0059] Through the above steps, since node allocation is performed through the load balancing server to ensure that the pressure of the application service is evenly distributed, the problem of untimely response caused by high system pressure is solved, and the system response efficiency is improved.

[0060] In an optional embodiment, after obtaining the registration data, as Figure 3 shown, the method further includes:

[0061] Step S111: Perform data conversion on the registration data to obtain the first registration data;

[0062] Step S112: Perform data association on the first registration data to obtain the first database;

[0063] Step S113, when a real estate data processing instruction is obtained, based on the first database, call system resources to process real estate data to obtain a real estate GIS model map.

[0064] In this embodiment, real estate registration data may come from multiple different registration agencies and standards. Therefore, it is necessary to unify the standards, establish an information management platform with consistent calibers, and achieve data layer fusion, feature layer fusion, and decision layer fusion; this includes data conversion and data association to form a standardized basic database and construct a basic data resource library with mutual internal relationships.

[0065] Among them, data conversion can be achieved through ETL (Extract, Transform, Load) tools; data association includes associating the first registration data with geographic information system (GIS) data, historical data, or other relevant databases through SQL queries or NoSQL query languages in a database management system (DBMS) such as MySQL, PostgreSQL, or MongoDB to determine the relationships between data, such as ownership relationships, geographical location relationships, etc., and store these association results in the database to form the first database; map generation is to use GIS software or libraries, such as ArcGIS or QGIS, to convert the processed data into a GIS model, and then render the GIS model into a map to display the location, boundaries, and other relevant information of the real estate.

[0066] In an alternative embodiment, as Figure 3 shown, the calling of the first platform node based on the platform node information through the load balancing server includes:

[0067] Step S121, obtain the first load information of the first platform node;

[0068] Step S122, when the first load information of the first platform node is the least loaded, distribute the resource load request to the first platform node to achieve the calling of the first platform node.

[0069] In this embodiment, call the platform node with the least load to ensure that there are sufficient resources for data processing.

[0070] Among them, the first load information includes the current load situation of the first platform node, such as CPU occupancy, port occupancy, storage space, etc.; the distribution of the load request is achieved through the following steps:

[0071] 1. Use the least-connections load balancing algorithm:

[0072] According to the description in the search results, the Least Connection load balancing algorithm can assign requests to the server with the fewest connections. This algorithm is suitable for applications with significant differences in request processing times, ensuring balanced workload distribution and preventing individual servers from being overloaded.

[0073] 2. Implement a load balancing server:

[0074] An example of a load balancing server implemented based on the Go language:

[0075] Create a LeastConnectionLoadBalancer struct to store the mapping of server connection counts and a mutex to ensure concurrency safety. Add servers through the AddServer method and initialize the connection count to 0. The GetServerWithLeastConnections method is used to find the server with the fewest connections and increment the connection count of that server. The ReleaseConnection method is used to simulate the function of releasing a connection and reduce the connection count of the specified server.

[0076] 3. Configure the SuperMap iServer cluster service:

[0077] In the system configuration file (iserver-system.xml) of SuperMap iServer, through <clusterservice>Node configuration cluster service. Set <enabled>Set to true to enable the cluster service, <monitorperiod>Set the time interval for the cluster service to monitor the sub-nodes, <balancer>Set the load balancing algorithm used by the cluster service to calculate the GIS service load.

[0078] 4. Select the load balancing algorithm:

[0079] In <balancer>In the node, set the class name implementing the algorithm, such as com.supermap.services.cluster.WeightedRoundBalancer, which is the class name of the weighted round robin scheduling algorithm provided by SuperMap iServer. This algorithm can help the cluster service obtain the GIS service with the minimum load through the load balancing calculation result and work for the client requests. The relevant calculation formula can be obtained through the aforementioned formula 1-2.

[0080] 5. Implement load balancing:

[0081] When the system resources are less than the preset value, the cluster service of SuperMap iServer will automatically distribute the requests to the platform node with the minimum load, that is, the first platform node, according to the configured load balancing algorithm to ensure the response speed and availability of the service.

[0082] Through the above steps, when the load information of the first platform node is the minimum load, the resource load requests can be distributed to this node through the load balancing technology.

[0083] In an alternative embodiment, as Figure 3 shown, after obtaining the load information of the first platform node, the method further includes:

[0084] Step S114, obtain the load information of all platform nodes, where the load information includes the first load information;

[0085] Step S115, construct a load matrix based on the load information;

[0086] Step S116, perform a correlation calculation on the load matrix, and determine that the load information is abnormal when the correlation calculation result does not meet the first condition.

[0087] In this embodiment, since the node allocation is calculated based on the node load and its resource amount, it is necessary to confirm the load situation of the node to ensure that the resource amount can meet the subsequent processing requirements.

[0088] Among them, the correlation can be calculated through the Pearson correlation coefficient. When the correlation calculation result is within the preset threshold range (i.e., the aforementioned first condition), it is judged as normal, otherwise it is judged as abnormal; the construction of the load matrix can be achieved through the following steps:

[0089] S1. Define resource and load parameters

[0090] First, define the resource usage and load parameters for each node. Let \(n\) be the number of nodes and \(m\) be the number of resource types (such as CPU, memory, disk I / O, network bandwidth, etc.). For each node \(i\) and each resource \(j\), we define:

[0091] R ij : The utilization rate of resource \(j\) by node \(i\).

[0092] L ij : The load of resource \(j\) on node \(i\).

[0093] S2. Construct the load matrix

[0094] Construct an \(n\times m\) load matrix \(L\), where each element \(L\) ij represents the load of node \(i\) on resource \(j\):

[0095]

[0096] In an optional embodiment, after constructing the load matrix based on the load information, the method further includes:

[0097] Step S1151, obtain system resource information;

[0098] Step S1152, perform risk identification calculation according to the system resource information and the load matrix to obtain a risk identification.

[0099] In this embodiment, calculating the risk identification helps to identify which resources are overused or underutilized, so that resource reallocation and optimization can be carried out to improve resource utilization; at the same time, by continuously monitoring and calculating the node load and system resource information, the performance status of the system can be understood in real time, performance bottlenecks can be discovered in time, and the risk of system crashes can also be reduced by allocating more resources or reallocating tasks in a timely manner when the node load is too high; in particular, automated scaling operations can also be triggered according to the risk identification to adapt to changing load requirements.

[0100] Among them, the risk identification calculation includes the following steps:

[0101] 1. Calculate the comprehensive load of the node

[0102] To calculate the comprehensive load of each node, we can assign a weight \(w_j\) to each resource, and these weights reflect the impact degree of different resources on the system performance. Then, the comprehensive load \(S\) of each node i , can be expressed as:

[0103]

[0104] 2. Calculate the resource utilization rate identification

[0105] Resource utilization rate identifier U i It can be calculated based on the comprehensive load and resource utilization rate of the node, and can be specifically implemented through the following formula:

[0106]

[0107] Through Formula 5, the resource utilization rate of each node can be combined with its comprehensive load to determine the resource utilization rate identifier, where the weight w j can be adjusted according to the importance of the resource and its impact on performance.

[0108] Through the description of the above implementation manners, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation manner. Based on such an understanding, the technical solution of the present invention, in essence, or the part that makes a contribution to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions for causing a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the present invention.

[0109] In this embodiment, a spatial data management system for large-scale real estate high-concurrency services is also provided. This system is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0110] Figure 4 is a structural block diagram of a spatial data management system for large-scale real estate high-concurrency services according to an embodiment of the present invention. As Figure 4 shown, the system includes:

[0111] A client GIS 31, which is used to obtain registration data, and the registration data includes real estate registration spatial data;

[0112] A platform node module 32, which is used to obtain platform node information. Among them, the platform node information is obtained by distributed deployment based on a distributed GIS system, and the distributed GIS system includes multiple nodes;

[0113] A resource adjustment module 33, configured to, in the case of high data concurrency where the system resources for processing the registration data are determined to be less than a preset value, based on the platform node information, call a first platform node through a load balancing server to adjust the system resources.

[0114] In an alternative embodiment, it further includes:

[0115] A data conversion module, configured to, after obtaining the registration data, perform data conversion on the registration data to obtain first registration data;

[0116] A data association module, configured to perform data association on the first registration data to obtain a first database;

[0117] A map generation module, configured to, in the case of obtaining a real estate data processing instruction, based on the first database, call system resources to process real estate data to obtain a real estate GIS model map.

[0118] In an alternative embodiment, the calling of the first platform node through the load balancing server based on the platform node information includes:

[0119] Obtaining first load information of the first platform node;

[0120] In the case where the first load information of the first platform node is the least loaded, distributing a resource load request to the first platform node to achieve the calling of the first platform node.

[0121] In an alternative embodiment, the platform further includes:

[0122] A load collection module, configured to, after obtaining the load information of the first platform node, obtain the load information of all platform nodes, where the load information includes the first load information;

[0123] A matrix construction module, configured to construct a load matrix based on the load information;

[0124] An anomaly judgment module, configured to perform a correlation calculation on the load matrix, and in the case where the correlation calculation result does not meet a first condition, determine that the load information is abnormal.

[0125] It should be noted that the above-mentioned various modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to this: all the above modules are located in the same processor; or, the above-mentioned various modules are respectively located in different processors in any combination form.

[0126] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program, where the computer program is configured to execute the steps in any of the above method embodiments when running.

[0127] In an exemplary embodiment, the above computer-readable storage medium may include, but is not limited to: various media such as USB flash drives, read-only memories (ROM), random access memories (RAM), mobile hard disks, magnetic disks, or optical discs that can store computer programs.

[0128] Embodiments of the present invention also provide an electronic device including a memory and a processor, where the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any of the above method embodiments.

[0129] It should be noted that the data types of real estate registration spatial data include: basic geographic information elements and their corresponding real estate registration information elements.

[0130] Real estate registration information elements include: real estate units (such as land parcels, sea areas, houses, forests, and trees, etc.) and real estate rights (such as ownership: land ownership, house ownership, forest and tree ownership, etc., usufructuary rights: state-owned and collective construction land use rights, homestead use rights, land contract management rights, agricultural land use rights, sea area use rights, forest land use rights, etc.; security interests: mortgages).

[0131] Real estate units are distinguished from the geographical space and include: administrative regions, cadastral regions, cadastral sub-regions, land parcels, sea areas, houses, and other fixtures.

[0132] Real estate units are distinguished from the attributes associated with the geographical space and include: right holders and rights.

[0133] The operations of real estate registration include: mortgage registration, correction registration, advance notice registration, objection registration, seizure registration, application, acceptance, receipt, review, entry into the register, issuance of certificates, charging, and filing, etc.

[0134] In an exemplary embodiment, the above electronic device may further include a transmission device and input / output devices, where the transmission device is connected to the above processor, and the input / output devices are connected to the above processor.

[0135] Through the description of the above embodiments, those skilled in the art can clearly understand that for the convenience and brevity of description, only the division of the above functional modules is used as an example. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

[0136] In several embodiments provided in the present application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in electrical, mechanical or other forms.

[0137] The unit described as a separated component may or may not be physically separated. The component displayed as a unit may be a physical unit or multiple physical units, that is, it can be located in one place, or it can be distributed to multiple different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0138] In addition, each functional unit in various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0139] 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 readable storage medium. Based on this understanding, the technical solution of the embodiments of the present application, in essence, 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 software product is stored in a storage medium and includes several instructions for causing a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods in various embodiments of the present application. The aforementioned storage medium includes: USB flash drive, mobile hard disk, read only memory (ROM), random access memory (RAM), magnetic disk or optical disc and other various media that can store program codes.

[0140] The above content is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.< / balancer> < / balancer> < / monitorperiod> < / enabled> < / clusterservice>

Claims

1. A spatial data management method for large-scale real estate high-concurrency services, characterized in that: include: Acquire platform node information and registration data, wherein the platform node information is obtained through distributed deployment based on a distributed GIS system, the distributed GIS system includes multiple nodes, and the registration data includes real estate registration spatial data; In the case of high data concurrency where it is determined that the system resources used to process the registration data are less than a preset value, the first platform node is called through the load balancing server based on the platform node information to adjust the system resources.

2. The method according to claim 1, characterized in that: After obtaining the registration data, the method further includes: Performing data conversion on the registration data to obtain first registration data; Performing data association on the first registration data to obtain a first database; When the real estate data processing instruction is obtained, based on the first database, system resources are called to process the real estate data to obtain a real estate GIS model map.

3. The method according to claim 1, characterized in that: The calling the first platform node through the load balancing server based on the platform node information includes: Obtaining first load information of the first platform node; When the first load information of the first platform node indicates that the load is the smallest, the resource load request is distributed to the first platform node to implement the call to the first platform node.

4. The method according to claim 3, characterized in that After acquiring the load information of the first platform node, the method further includes: Acquire load information of all platform nodes, wherein the load information includes the first load information; constructing a load matrix based on the load information; A correlation calculation is performed on the load matrix, and when the correlation calculation result does not meet the first condition, it is determined that the load information is abnormal.

5. A spatial data management system for large-scale real estate high-concurrency services, characterized in that: include: A user-side GIS, used to obtain registration data, wherein the registration data includes real estate registration spatial data; A platform node module is used to obtain platform node information, wherein the platform node information is obtained based on distributed deployment of a distributed GIS system, and the distributed GIS system includes multiple nodes; The resource adjustment module is used to call the first platform node through the load balancing server based on the platform node information to adjust the system resources in the case of high data concurrency when it is determined that the system resources used to process the registration data are less than a preset value.

6. The platform according to claim 5, characterized in that Also includes: A data conversion module, configured to perform data conversion on the registration data after acquiring the registration data, so as to obtain first registration data; A data association module, used for performing data association on the first registration data to obtain a first database; The map generation module is used to call system resources to process the real estate data based on the first database when obtaining the real estate data processing instruction to obtain the real estate GIS model map.

7. The platform according to claim 5, characterized in that The calling the first platform node through the load balancing server based on the platform node information includes: Obtaining first load information of the first platform node; When the first load information of the first platform node indicates that the load is the smallest, the resource load request is distributed to the first platform node to implement the call to the first platform node.

8. The platform according to claim 7, characterized in that The platform also includes: A load acquisition module, configured to acquire load information of all platform nodes after acquiring the load information of the first platform node, wherein the load information includes the first load information; A matrix construction module, used for constructing a load matrix based on the load information; The abnormality judgment module is used to perform correlation calculation on the load matrix, and determine that the load information is abnormal when the correlation calculation result does not meet the first condition.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 4 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 4.

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