Map information service system for innovation and entrepreneurship

Abstract

The invention discloses an innovation and entrepreneurship map information service system, relates to the technical field of internet, and solves the technical problems that tasks cannot be reasonably allocated according to the actual performance of a server and the characteristics of a requested data volume, and the overall performance of the system is influenced. According to the invention, preprocessing operations such as coordinate conversion and projection conversion are carried out on map data at a server side, and a proper projection mode is selected according to a map application scene, so that the accuracy and applicability of the map data are ensured, and the data processing efficiency is improved; equipment is divided into high-performance equipment and low-performance equipment, for the high-performance equipment, real-time cloud rendering is adopted, cloud computing resources are fully utilized, and high-quality map information display is provided; for low-performance equipment, gradual rendering is adopted, rough graphs are rapidly presented at low resolution, then the quality is gradually improved, and it is guaranteed that good user experience can be provided on different equipment.

Description

Technical Field

[0001] The present invention relates to the field of Internet technology, and in particular to an innovation and entrepreneurship map information service system. Background Art

[0002] In the current wave of innovation and entrepreneurship, the innovation and entrepreneurship map information service system has gradually become an important tool for entrepreneurs to obtain resources and understand the market.

[0003] According to the patent application with publication number CN113486131A, an innovation and entrepreneurship map information service system is disclosed, including a data interface service module, a background data management module, a map information service module, a functional module and an entrepreneurial activity module. The background data management module includes type management, information management, event management and policy management. The map information service module includes an information brief column module and a location display module. The functional module includes a screening module and a quick positioning module. The system is carried and run through a web application server and a load balancing server, and provides user terminal information services for access through the network, thereby realizing real-time updating and fast and convenient acquisition of information of innovation and entrepreneurship units.

[0004] However, as entrepreneurs' demands for map information become increasingly complex and diverse, existing systems have exposed many deficiencies in data processing efficiency, reasonable resource allocation, and adaptability to different device performance. Some existing service systems are unable to reasonably allocate tasks based on the actual performance of the server and the characteristics of the requested data volume when processing user requests. There is no targeted allocation strategy for excessive and low-volume data, which can easily cause unbalanced server loads, with some server resources idle and some servers overloaded, affecting the overall performance of the system. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an innovation and entrepreneurship map information service system, which solves the problem of being unable to reasonably allocate tasks according to the actual performance of the server and the characteristics of the requested data volume, thus affecting the overall performance of the system.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an innovation and entrepreneurship map information service system, comprising:

[0007] The map information acquisition module is used to acquire the map data and the entrepreneur's operation data, and transmit the two to the regional block optimization processing module, and the entrepreneur's operation data is specifically represented as the map area corresponding to the click;

[0008] The regional block optimization processing module receives the map data transmitted by the map information acquisition module, divides the map data into blocks according to the rules, generates optimized data through data preprocessing, and then transmits it to the server request analysis module;

[0009] The server request analysis module is used to receive the request signal and classify it into a single quantity signal and a multi-type quantity signal, and for the single quantity signal, compare the requested data volume with a preset value to classify it into excess data and under-volume data;

[0010] Analyze the excess data, divide the excess data equally to obtain the equally distributed tasks, assign and sum the memory bandwidth and memory latency of the server to obtain the server performance value, determine the corresponding weight of the server, and perform weighted polling allocation on the equally distributed tasks according to the weight to generate request matching information;

[0011] For the analysis of low-volume data, the servers to be analyzed are screened based on the processing speed, and the servers to be analyzed with the largest idle computing resources are selected as the standard to generate request matching information, and the request matching information is transmitted to the comprehensive processing and analysis module;

[0012] For multiple types of quantity signals, the server computing resources are matched from large to small with the request time corresponding to the request signal from first to last, and request matching information is generated and transmitted to the comprehensive processing and analysis module at the same time;

[0013] The comprehensive processing and analysis module is used to analyze the device network and device performance, and assign values ​​based on the network speed and CPU processing speed. The two are summed up to get the effect score, and the devices are classified into high-performance devices and low-performance devices according to the scoring criteria. High-performance devices use real-time cloud rendering to obtain map information, and low-performance devices use progressive rendering to obtain map information, which is finally transmitted to the service management module.

[0014] The service management module is used to transmit the rendered map information to the client for entrepreneurs to view.

[0015] As a further solution of the present invention, the specific method in which the regional block optimization processing module generates optimized data through data preprocessing is:

[0016] The acquired map data is divided into blocks according to certain rules to obtain block division information. At the same time, a cache mechanism is set up on the user side and the server side. At the same time, data preprocessing is performed on the obtained block division information to obtain optimized data.

[0017] As a further solution of the present invention, the specific manner in which the server request analysis module classifies and obtains excess data and under-data is as follows:

[0018] Obtaining a request data volume corresponding to a single request signal, and comparing the request data volume with a request preset value, if the request data volume is greater than the request preset value, classifying the corresponding request data volume as excess data, otherwise classifying it as under-volume data;

[0019] Get all servers and label them as i, where i=1, 2, ..., j, where j represents the number of servers. Get the processing speed of the servers as Vi, and sort the servers from large to small according to the processing speed Vi.

[0020] As a further solution of the present invention, the specific manner in which the server request analysis module generates request matching information by analyzing low-volume data is as follows:

[0021] The server that meets the processing requirements is recorded as the server to be analyzed, and the satisfied processing here means that the corresponding processing speed Vi can meet the processing requirements of the current request data volume, and the idle computing resources corresponding to the server to be analyzed are obtained. At the same time, the server to be analyzed with the largest idle computing resources is selected as the standard to generate request matching information.

[0022] As a further solution of the present invention, the specific manner in which the server request analysis module generates request matching information by analyzing excess data is as follows:

[0023] Divide the excess data into a tasks, and calculate the performance value of each server i. First, obtain the memory bandwidth and memory latency of server i, assign corresponding values ​​to obtain the memory bandwidth assignment and memory latency assignment, add the two together to obtain the memory assignment, then obtain the processing latency of server i and assign a value to obtain the processing latency assignment. The memory assignment and processing latency assignment are added together to obtain the performance of server i.

[0024] Calculate the sum of the values ​​of memory assignment and processing delay assignment to obtain the performance value corresponding to server i, and determine the weight based on the calculated performance value. Then obtain the weight of server i, and perform weighted polling allocation on the obtained equal-share tasks based on the lowest weight, and generate request matching information at the same time.

[0025] As a further solution of the present invention, the specific manner in which the server request analysis module generates request matching information for multiple types of quantity signals is:

[0026] All request signals are obtained and labeled as a, where a=1, 2, ..., b, where b represents the number of request signals. At the same time, the computing resources corresponding to server i are obtained, and server i is sorted from large to small according to the computing resources. At the same time, the request time corresponding to the request signal a is matched with the sorted server i in sequence, and request matching information is generated. The generated request matching information is then transmitted to the comprehensive processing and analysis module.

[0027] As a further solution of the present invention, the specific method in which the comprehensive processing and analysis module analyzes the device network and device performance to obtain the effect score is:

[0028] The network status corresponding to the device is obtained, and the network status is represented by the corresponding periodic network speed. Specifically, the unit network transmission speed corresponding to the device in the time period t is obtained, and the average network transmission speed in the time period t is calculated, and the calculated average is used as the network value of the device;

[0029] Get the CPU processing speed of the device, and assign corresponding values ​​based on the CPU processing speed to get the corresponding performance value;

[0030] The obtained network value and performance value are summed to obtain the effect score corresponding to the device, and the effect score is processed at the same time.

[0031] As a further solution of the present invention, the specific manner in which the comprehensive processing and analysis module processes the effect score is as follows:

[0032] Match the effect score with the scoring criteria, and classify the devices into high-performance devices and low-performance devices, and analyze the two separately;

[0033] For high-performance devices obtained through classification, the obtained request matching information is rendered in real time on the cloud, and the processed map information is transmitted to the service management module;

[0034] For low-performance devices obtained through classification, progressive rendering is performed on the obtained request matching information, and the processed map information is transmitted to the service management module.

[0035] The present invention provides an innovation and entrepreneurship map information service system. Compared with the prior art, it has the following beneficial effects:

[0036] The present invention performs preprocessing operations such as coordinate conversion and projection transformation on map data on the server side, and selects a suitable projection method according to the map application scenario to ensure the accuracy and applicability of map data and improve data processing efficiency;

[0037] For a single quantity signal, the requested data volume is divided into excess data and under-volume data according to the comparison result with the preset value. For under-volume data, the server with the processing time less than the preset time and the largest idle computing resources is selected to improve resource utilization. For excess data, the weight is determined by calculating the server performance value, and the tasks are assigned by weighted polling based on the lowest weight to achieve load balancing and improve the overall utilization efficiency of server resources.

[0038] For multi-type quantity signals, we match the remaining request signals in order of server computing resources from large to small and request time from early to late, ensuring that requests are reasonably allocated and improving the system's ability to handle complex requests.

[0039] The comprehensive processing and analysis module analyzes the device network and performance, calculates the effect score and matches it with the scoring criteria, and divides the devices into high-performance and low-performance devices. For high-performance devices, real-time cloud rendering is used to make full use of cloud computing resources to provide high-quality map information display; for low-performance devices, progressive rendering is used to quickly present rough graphics at low resolution, and then gradually improve the quality to ensure that a good user experience can be provided on different devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a block diagram of the system principle of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] For example, see Figure 1 This application provides an innovative and entrepreneurial map information service system, including a map information acquisition module, a regional block optimization processing module, a server request analysis module, a comprehensive processing analysis module and a service management output module, and according to Figure 1 It can be known that the above functional modules are electrically connected in a unidirectional manner.

[0043] A map information acquisition module is used to acquire map data and entrepreneur's operation data, and transmit the two to the regional block optimization processing module, wherein the entrepreneur's operation data is specifically represented by the corresponding clicked map area.

[0044] The regional block optimization processing module is used to block the acquired map data according to certain rules. Specifically, the certain rules here include regular grid division (for example, in a city-level fine map application, in order to accurately locate each store on the street, the grid can be set to a side length of 50 square of meters), based on administrative divisions (for example, when displaying the distribution of tourist attractions across the country, the scenic spot data and geographic information of each province are divided into their own corresponding map blocks, which is convenient for users to query and browse by province. If a detailed map is made for a certain province, the city-level or county-level administrative divisions can be used as the basis for block division to further refine data management) and based on geographical features (for example, the map data of the Yangtze River Basin is divided into two different map blocks on the south bank and the north bank of the Yangtze River with the Yangtze River as the boundary. When studying the ecological environment of the Yangtze River Basin, the ecological data and land use data of the south and north banks can be stored and managed separately. At the same time, by establishing cross-regional associations, it is ensured that the data can be presented completely and accurately when conducting an overall analysis of the basin), block division information is obtained, and a cache mechanism is set on the user side and the server side. Specifically, after a user visits a block of data for the first time, it will be cached. The next time you access a map block, you can directly read it from the cache without having to obtain it from the server again. When a user accesses a map block for the first time, the system automatically caches the data in the local storage of the user's device, such as the memory of a mobile phone or the hard disk of a computer. The server also sets a cache mechanism to store frequently accessed map block data in the cache. At the same time, the obtained block division information is preprocessed to obtain optimized data, and the data preprocessing includes coordinate conversion, projection transformation and other operations on the map data. Different geographic data sources may use different coordinate systems, such as the common WGS84, GCJ-02, etc. For example, when making a global map, the Mercator projection may be used to meet the needs of accurate representation of direction and distance in fields such as navigation and aviation; when making a local area map, the Gauss-Krüger projection may be used to ensure the shape and area accuracy of the map within a small range. For example, when making a detailed city planning map for a city planning department, the Gauss-Krüger projection is used to accurately project the geographic information of the earth's surface onto a plane, making it easier for planners to perform operations such as area measurement and distance calculation. The data preprocessing here is performed on the server side, and then the optimized data is transmitted to the server request analysis module.

[0045] A server request analysis module, which is used to analyze the obtained optimization data and analyze the obtained request signals at the same time. First, the request signals are obtained, and the number of request signals is determined to generate a single quantity signal and a multi-type quantity signal, and the two are analyzed respectively;

[0046] Analyze the generated single quantity signal to obtain the request data volume corresponding to the single request signal, and the request data volume here is represented by the map data volume corresponding to the map area clicked by the entrepreneur, and compare the request data volume with the request preset value, and the specific value of the request preset value is set by the operator. If the request data volume is greater than the request preset value, the corresponding request data volume is classified as excess data, otherwise, if the request data volume is less than the request preset value, the corresponding request data volume is classified as low data;

[0047] Then, all servers corresponding to the server are obtained and labeled as i, where i=1, 2, ..., j, where j represents the number of servers. At the same time, the processing speed corresponding to the server is obtained and recorded as Vi, and the servers are sorted from large to small according to the processing speed Vi;

[0048] For the low-volume data obtained by classification, obtain a server that meets the processing requirements as the server to be analyzed, and here the processing requirements are represented by the corresponding processing speed Vi being able to meet the processing requirements of the current request data volume, which is specifically represented by the processing time. Select a server whose processing time is less than a preset time, and the specific value of the preset time is set by the operator. Obtain the idle computing resources corresponding to the server to be analyzed, and select the server to be analyzed with the largest idle computing resources as the standard to generate request matching information;

[0049] For the excess data obtained by classification, the excess data is divided into a tasks, and the specific value of a is set by the operator. At the same time, all servers i are obtained, and the performance value of server i is calculated. The specific calculation method is:

[0050] Get the memory bandwidth and memory latency corresponding to server i, where memory bandwidth = memory frequency × memory bit width ÷ 8. The memory frequency of a server is 3200MHz and the bit width is 64 bits, then its memory bandwidth = 3200 × 64 ÷ 8 = 25600MB / s. The memory latency can be obtained through the memory test tool. At the same time, the obtained memory bandwidth and memory latency are assigned corresponding values ​​to obtain the memory bandwidth assignment and memory latency assignment. The assignment here is to match the two with the corresponding assignment evaluation intervals, for example: less than 10000MB / s is assigned 1, 10000-20000MB / s is assigned 2, and more than 20000MB / s is assigned 3; the assignment evaluation interval of memory latency is: more than 80ns is assigned 1, 50-80ns is assigned 2, and less than 50ns is assigned 3, and the sum of the two values ​​is recorded as the memory assignment of server i. Then get the processing delay corresponding to server i, perform the same assignment on the processing delay, and obtain the processing delay assignment. For example, after a large number of data According to the analysis, the evaluation interval of the processing delay is: 1 for a value higher than 50ms, 2 for 30-50ms, and 3 for a value lower than 30ms. The sum of the values ​​of the memory value and the processing delay value is calculated to obtain the performance value corresponding to server i, and the weight is determined according to the calculated performance value. The weight here is determined according to the corresponding performance value to determine the performance effect of the server, and the performance effect specifically includes three levels: strong performance, general performance, and weak performance. For example, in a system that comprehensively considers multiple aspects of performance such as CPU, memory, storage, and network, assuming that the full score of the performance value is 10 points, when the performance value is in the range of 8-10 points, it can be determined that the server performance is strong and is assigned a weight of 3; the performance value is between 5-7 points, corresponding to general performance, and is assigned a weight of 2; if the performance value is lower than 5 points, it indicates that the server performance is weak, and is assigned a weight of 1. Then the weight of server i is obtained, and the average task obtained is weighted polling allocation based on the lowest weight, and request matching information is generated at the same time;

[0051] Specifically, enhanced round-robin allocation is represented by allocation based on different weights of servers. For example, server 1 has strong performance, and its weight is set to 3; server 2 has average performance, and its weight is set to 2; server 3 has slightly weaker performance, and its weight is set to 1. Then, when allocating requests, the probability that server 1 is allocated a request is three times that of server 3. Overall, requests are allocated according to the weight ratio to achieve more reasonable load balancing.

[0052] The generated request matching information is then transmitted to the comprehensive processing and analysis module.

[0053] The generated multi-type quantity signals are analyzed, all request signals are obtained and labeled as a, and a=1, 2, ..., b, where b represents the quantity corresponding to the request signal. At the same time, the computing resources corresponding to server i are obtained, and server i is sorted from large to small according to the computing resources. At the same time, the request time corresponding to the request signal a is matched with the sorted server i in sequence, and request matching information is generated. The matching here is to match the server in sequence from first to last according to the request time. At the same time, for matching the remaining request signals, rotation matching is performed. Specifically, the request signal corresponding to the earliest request time in the remaining request signals is matched with the server with the largest computing resources, and then matching is performed in this type. Then the generated request matching information is transmitted to the comprehensive processing and analysis module.

[0054] A comprehensive processing and analysis module, which is used to analyze the obtained request matching information, calculate the corresponding effect score by analyzing the device performance of the display device, and the effect score is analyzed from two aspects: network and performance;

[0055] The specific method of analyzing the network of the device is to obtain the network status corresponding to the device, and the network status is represented by the corresponding periodic network speed. Specifically, the unit network transmission speed corresponding to the device in the time period t is obtained, and the average value of the network transmission speed in the time period t is calculated, and the calculated average value is used as the network value of the device;

[0056] The specific method of analyzing the performance of the device is to obtain the CPU processing speed corresponding to the device, and to perform corresponding assignment based on the CPU processing speed to obtain the corresponding performance value, and the CPU assignment here is based on the assignment range obtained by analyzing a large amount of market data;

[0057] The network value and performance value are summed to obtain the effect score of the device. The effect score is matched with the scoring standard. The scoring standard here is a numerical range obtained through a large amount of market research. The devices are classified into high-performance devices and low-performance devices, and the two are analyzed separately.

[0058] For high-performance devices obtained through classification, the requested matching information is rendered in real time on the cloud, and the processed map information is transmitted to the service management module. Specifically, the rendering task is uploaded to the cloud server, rendered using the powerful computing resources of the cloud, and then the rendered video stream is transmitted to the local device for display in real time.

[0059] For low-performance devices classified, the requested matching information is progressively rendered, and the processed map information is transmitted to the service management module. Specifically, the progressive rendering method is used to quickly present the general graphic content in a low-resolution and low-quality form, and then gradually load higher-resolution and more detailed content as the network conditions allow, continuously improving the rendering quality.

[0060] Service management module, which is used to transmit the rendered map information to the client for entrepreneurs to view.

[0061] Some of the data in the above formulas are calculated by taking their numerical values, and are not substituted into parameter units for calculation. At the same time, the contents not described in detail in this specification belong to the existing technologies known to those skilled in the art.

[0062] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An innovation and entrepreneurship map information service system, characterized in that: include: The regional block optimization processing module receives the map data transmitted by the map information acquisition module, divides the map data into blocks according to the rules, generates optimized data through data preprocessing, and then transmits it to the server request analysis module; The server request analysis module is used to receive the request signal and classify it into a single quantity signal and a multi-type quantity signal, and for the single quantity signal, compare the requested data volume with a preset value to classify it into excess data and under-volume data; Analyze the excess data, divide the excess data equally to obtain the equally distributed tasks, assign and sum the memory bandwidth and memory latency of the server to obtain the server performance value, determine the corresponding weight of the server, and perform weighted polling allocation on the equally distributed tasks according to the weight to generate request matching information; For the analysis of low-volume data, the servers to be analyzed are screened based on the processing speed, and the servers to be analyzed with the largest idle computing resources are selected as the standard to generate request matching information, and the request matching information is transmitted to the comprehensive processing and analysis module; For multiple types of quantity signals, the server computing resources are matched from large to small with the request time corresponding to the request signal from first to last, and request matching information is generated and transmitted to the comprehensive processing and analysis module at the same time; The comprehensive processing and analysis module is used to analyze the device network and device performance, and assign values ​​based on the network speed and CPU processing speed. The two are summed up to get the effect score, and the devices are classified into high-performance devices and low-performance devices according to the scoring criteria. High-performance devices use real-time cloud rendering to obtain map information, and low-performance devices use progressive rendering to obtain map information, which is finally transmitted to the service management module.

2. The innovation and entrepreneurship map information service system according to claim 1, characterized in that: It also includes a map information acquisition module and a service management module; The map information acquisition module is used to acquire the map data and the entrepreneur's operation data, and transmit the two to the regional block optimization processing module, and the entrepreneur's operation data is specifically represented as the map area corresponding to the click; The service management module is used to transmit the rendered map information to the client for entrepreneurs to view.

3. The innovation and entrepreneurship map information service system according to claim 1 is characterized in that: The specific method of generating optimized data by the regional block optimization processing module through data preprocessing is: The acquired map data is divided into blocks according to certain rules to obtain block division information. At the same time, a cache mechanism is set up on the user side and the server side. At the same time, data preprocessing is performed on the obtained block division information to obtain optimized data.

4. The innovation and entrepreneurship map information service system according to claim 1, characterized in that: The specific method of classifying the excess data and the under-volume data by the server request analysis module is as follows: Obtaining a request data volume corresponding to a single request signal, and comparing the request data volume with a request preset value, if the request data volume is greater than the request preset value, classifying the corresponding request data volume as excess data, otherwise classifying it as under-volume data; Get all servers and label them as i, where i=1, 2, ..., j, where j represents the number of servers. Get the processing speed of the servers as Vi, and sort the servers from large to small according to the processing speed Vi.

5. The innovation and entrepreneurship map information service system according to claim 1, characterized in that: The specific method in which the server request analysis module generates request matching information by analyzing low-volume data is as follows: The server that meets the processing requirements is recorded as the server to be analyzed, and the satisfied processing here means that the corresponding processing speed Vi can meet the processing requirements of the current request data volume, and the idle computing resources corresponding to the server to be analyzed are obtained. At the same time, the server to be analyzed with the largest idle computing resources is selected as the standard to generate request matching information.

6. The innovation and entrepreneurship map information service system according to claim 1, characterized in that: The specific method of the server request analysis module to generate request matching information by analyzing the excess data is as follows: Divide the excess data into a tasks, and calculate the performance value of each server i. First, obtain the memory bandwidth and memory latency of server i, assign corresponding values ​​to obtain the memory bandwidth assignment and memory latency assignment, add the two together to obtain the memory assignment, then obtain the processing latency of server i and assign a value to obtain the processing latency assignment. The memory assignment and processing latency assignment are added together to obtain the performance of server i. Calculate the sum of the values ​​of memory assignment and processing delay assignment to obtain the performance value corresponding to server i, and determine the weight based on the calculated performance value. Then obtain the weight of server i, and perform weighted polling allocation on the obtained equal-share tasks based on the lowest weight, and generate request matching information at the same time.

7. The innovation and entrepreneurship map information service system according to claim 1, characterized in that: The specific method of the server request analysis module generating request matching information for multiple types of quantity signals is as follows: All request signals are obtained and labeled as a, where a=1, 2, ..., b, where b represents the number of request signals. At the same time, the computing resources corresponding to server i are obtained, and server i is sorted from large to small according to the computing resources. At the same time, the request time corresponding to the request signal a is matched with the sorted server i in sequence, and request matching information is generated. The generated request matching information is then transmitted to the comprehensive processing and analysis module.

8. The innovation and entrepreneurship map information service system according to claim 1, characterized in that: The specific method in which the comprehensive processing and analysis module analyzes the device network and device performance to obtain the effect score is as follows: The network status corresponding to the device is obtained, and the network status is represented by the corresponding periodic network speed. Specifically, the unit network transmission speed corresponding to the device in the time period t is obtained, and the average network transmission speed in the time period t is calculated, and the calculated average is used as the network value of the device; Get the CPU processing speed of the device, and assign corresponding values ​​based on the CPU processing speed to get the corresponding performance value; The obtained network value and performance value are summed to obtain the effect score corresponding to the device, and the effect score is processed at the same time.

9. The innovation and entrepreneurship map information service system according to claim 8, characterized in that: The specific method in which the comprehensive processing and analysis module processes the effect score is as follows: Match the effect score with the scoring criteria, and classify the devices into high-performance devices and low-performance devices, and analyze the two separately; For high-performance devices obtained through classification, the obtained request matching information is rendered in real time on the cloud, and the processed map information is transmitted to the service management module; For low-performance devices obtained through classification, progressive rendering is performed on the obtained request matching information, and the processed map information is transmitted to the service management module.

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