Edge Cloud-Based Data Processing Method, System, and Storage Medium

Abstract

The present invention provides a data processing method, system and storage medium based on edge cloud, belonging to the technical field of data processing. Specifically, it includes: determining standby servers in a server based on the distances from different common access locations, freely combining the standby servers to obtain server groups, determining a matching server group in the server groups based on the access processing delays and remaining storage spaces of the standby servers in different server groups on different dates, determining the data processing order of the standby servers corresponding to different common access locations according to the distance data between different standby servers in the matching server group and the common access locations and the access data of the matching access users, and automatically switching to the next standby server in the data processing order when the access processing delay of the standby server does not meet the requirements, thereby improving the efficiency of data processing.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and particularly relates to a data processing method, system and storage medium based on edge cloud. Background Art

[0002] In order to utilize edge cloud for data processing of user operations and improve the efficiency of data processing, in the patent application for invention CN202410246539.9 "A Data Sharing Method and System Based on Edge Computing", the matching server of the user device is determined based on the operation location of the user device and the location of the common computing server, so as to avoid the waste of storage space caused by storing the operation data of the user in different servers. However, through analysis, it is not difficult to find the following technical problems:

[0003] In the process of data processing based on edge cloud, users often have multiple common access locations. Therefore, if the matching server cannot be constructed according to the distance data from different common access locations and the historical occupancy data of the communication link, the timeliness of data processing cannot be guaranteed.

[0004] In view of the above technical problems, specifically, the present application provides a data processing method, system and storage medium based on edge cloud. Summary of the Invention

[0005] To achieve the object of the present invention, the present invention adopts the following technical solutions:

[0006] According to one aspect of the present invention, a data processing method based on edge cloud is provided.

[0007] A data processing method based on edge cloud specifically includes:

[0008] S1 Access the historical access data of the access user at different access locations to determine the common access locations of the access user;

[0009] S2 Based on the distance data between the servers in the edge cloud and the common access locations of the access user and the historical occupancy data of the communication link, when it is determined that there is no matching server for the access user, proceed to the next step;

[0010] S3 Determine the standby servers in the servers based on the distances from different common access locations, and freely combine the standby servers to obtain server groups. Determine the matching server group in the server groups based on the access processing delay and remaining storage space of the standby servers in different server groups on different dates;

[0011] S4 determines the data processing order of the standby servers corresponding to different common access locations according to the distance data between different standby servers in the matching server group and the common access locations, and the access data of the matched access users, and automatically switches to the next standby server in the data processing order when the access processing delay of the standby server does not meet the requirements.

[0012] The beneficial effects of the present invention are as follows:

[0013] Based on the distance data between the servers in the edge cloud and the common access locations of the access users and the historical occupancy data of the communication links, it is determined whether there is a matching server for the access users, thus not only considering the differences in data processing delays caused by the distance data between the servers and different common access points, but also considering the differences in the idle conditions of the communication links with the common access points caused by the historical occupancy data of the communication links of the servers, realizing the screening of matching servers that meet the requirements for the processing of access data from multiple common access points, and also laying a foundation for determining the allocation processing strategy of the servers for processing the access data of access users differently.

[0014] Determine the data processing order of the standby servers corresponding to different common access locations according to the distance data between different standby servers and the common access locations, and the access data of the matched access users, realizing the determination of the data processing order of the standby servers from the busy degree of data processing of the access users processed by the standby servers and the distance from the common access locations, not only improving the efficiency of data processing, but also improving the reliability of data processing.

[0015] A further technical solution is that the historical access data includes the number of accesses of the access user at the access location, the access times corresponding to different numbers of accesses, and the amount of access data.

[0016] A further technical solution is that the method for determining the common access location of the access user is as follows:

[0017] Determine the number of accesses of the access location on different dates based on the access data of the access location;

[0018] Determine the access processing date in the date according to the number of accesses on different dates;

[0019] Based on the number of access processing dates, determine whether the location is a common access location.

[0020] A further technical solution is that the access processing date in the date is the date when the number of accesses is greater than the preset number of accesses.

[0021] A further technical solution is that when the number of the access processing dates is greater than a preset number of access processing dates, the location is determined as a frequently accessed location.

[0022] A further technical solution is that the method for determining the data processing order of the standby servers corresponding to the frequently accessed location is as follows:

[0023] Determine the distance between the standby server and the frequently accessed location based on the distance data between the standby server and the frequently accessed location, and use the distance from the frequently accessed location to determine the distance priority coefficient of the standby server;

[0024] Take the access users using the standby server for data processing as matching access users, determine the access demand coefficients of different matching access users according to the average daily access times of different matching access users, and determine the usage busy coefficient of the standby server according to the sum of the access demand coefficients of different matching access users in the standby server;

[0025] Determine the processing priority coefficients of different standby servers based on the ratio of the distance priority coefficient to the usage busy coefficient, and use the processing priority coefficients to determine the data processing order of the standby servers of the frequently accessed location.

[0026] In a second aspect, the present invention provides a computer system, including: a memory and a processor connected by communication, and a computer program stored on the memory and capable of running on the processor, wherein when the processor runs the computer program, it executes the above-mentioned data processing method based on edge cloud.

[0027] In a third aspect, the present invention provides a computer storage medium, on which a computer program is stored, and when the computer program is executed in a computer, the computer is made to execute the above-mentioned data processing method based on edge cloud.

[0028] Other features and advantages will be described in the following description, and the objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the description and the drawings.

[0029] To make the above objectives, features and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given in conjunction with the accompanying drawings for detailed description as follows. Description of the Drawings

[0030] By referring to the accompanying drawings and describing its exemplary embodiments in detail, the above and other features and advantages of the present invention will become more obvious.

[0031] Figure 1 It is a flowchart of a data processing method based on edge cloud;

[0032] Figure 2 It is a flowchart of a method for determining the common access locations of accessing users;

[0033] Figure 3 It is a flowchart of a method for determining the matching server of an accessing user;

[0034] Figure 4 It is a flowchart of a method for determining the matching server group in a server group;

[0035] Figure 5 It is a flowchart of a method for determining the data processing sequence of the standby server corresponding to the common access location. Detailed implementation mode

[0036] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this specification.

[0037] In the data processing process based on the edge cloud, users often have multiple common access locations. Therefore, it is necessary to construct a matching server according to the distance data from different common access locations and the occupancy data of the communication link to improve the reliability of data processing.

[0038] Common access locations of accessing users: Determine the access processing date in the date according to the number of access times in different dates. When the number of access processing dates is greater than the preset number of access processing dates, the location is determined as a common access location.

[0039] Matching server: Determine the maximum distance value by using the maximum value of the distances from different common access locations, use the average value of the historical occupancy rates of the communication links from different common access locations at different times as the average occupancy rate, and determine the matching coefficient of the server according to the ratio of the maximum distance value to the preset distance and the average value of the average occupancy rates. When the matching coefficient is greater than the preset matching coefficient threshold, the server is determined as the matching server of the accessing user.

[0040] Matching server group: Based on the historical access data of the access user at different common access points, determine the historical access times at different common access points, determine the access demand coefficients of different common access points according to the ratio of the historical access times at different common access points to the set value of the access times, use the sum of the access demand coefficients of different common access points to determine the total access demand coefficient, use the preset demand quantity corresponding to the total access demand to determine the demand quantity of the standby servers, and based on the demand quantity of the standby servers, freely combine the standby servers to obtain the server group.

[0041] Data processing order of the standby servers: Determine the distance priority coefficient of the standby servers using the distance from the common access location, use the access users of the standby servers for data processing as the matching access users, determine the access demand coefficients of different matching access users according to the average daily access times of different matching access users, determine the usage busy coefficient of the standby servers according to the sum of the access demand coefficients of different matching access users in the standby servers, determine the processing priority coefficient of different standby servers based on the ratio of the distance priority coefficient to the usage busy coefficient, and use the processing priority coefficient to determine the data processing order of the standby servers at the common access location.

[0042] Embodiment 1 To solve the above problems, according to one aspect of the present invention, as Figure 1 shown, provide a data processing method based on edge cloud, specifically including:

[0043] S1 Based on the historical access data of the access user at different access locations, determine the common access locations of the access user;

[0044] Furthermore, the historical access data includes the access times of the access user at the access location, the access times corresponding to different access times, and the access data volume.

[0045] Specifically, as Figure 2 shown, the method for determining the common access locations of the access user is:

[0046] Based on the access data of the access location, determine the access times of the access location on different dates;

[0047] Determine the access processing dates in the dates according to the access times on different dates;

[0048] Based on the quantity of the access processing dates, determine whether the location is a common access location.

[0049] Furthermore, the access processing dates in the dates are the dates when the access times are greater than the preset access times.

[0050] It is understandable that when the number of the access processing dates is greater than the preset number of access processing dates, it is determined that the location is a frequently visited location.

[0051] Optionally, the method for determining the frequently visited location of the access user is as follows:

[0052] Based on the access data of the access location, determine the number of accesses of the access location on different dates;

[0053] According to the number of accesses on different dates, determine the average value of the number of accesses of the access location on different dates, and use it as the average number of accesses;

[0054] Based on the average number of accesses, determine whether the location is a frequently visited location.

[0055] Optionally, the method for determining the frequently visited location of the access user is as follows:

[0056] S11 Based on the access data of the access location, determine the number of accesses of the access location on different dates;

[0057] Optionally, the above step S11 includes the following content:

[0058] S111 Based on the access data of the access location, determine the number of accesses of the access location on different dates. When the total number of accesses of the access point on different dates is less than the preset number threshold, it is determined that the access location does not belong to the frequently visited location. When the total number of accesses of the access point on different dates is greater than the preset access number threshold, it is determined that the access location belongs to the frequently visited point. When the total number of accesses of the access point on different dates is not less than the preset number threshold and not greater than the preset access number threshold, go to step S112;

[0059] S112 According to the number of accesses on different dates, determine the average value of the number of accesses of the access location on different dates, and use it as the average number of accesses. When the average number of accesses is greater than the average value threshold, it is determined that the access location belongs to the frequently visited point. When the average number of accesses is not greater than the average value threshold, go to step S113;

[0060] S113 According to the number of accesses on different dates, determine the access processing dates in the dates. When the number of the access processing dates is within the preset date number range, it is determined that the location is a frequently visited location. When the number of the access processing dates is not within the preset date number range, go to step S12.

[0061] S12 determines the access frequency coefficients for different dates based on the number of accesses by the accessing user at the access location on different dates and the amount of access data corresponding to different numbers of accesses, and determines the frequently accessed dates in the dates using the access frequency coefficients for different dates;

[0062] Optionally, the above step S12 includes the following content:

[0063] S121 determines the access frequency coefficients for different dates based on the number of accesses by the accessing user at the access location on different dates and the amount of access data corresponding to different numbers of accesses. When there is no date with an access frequency coefficient greater than the preset frequency coefficient threshold, it proceeds to step S122. When there is a date with an access frequency coefficient greater than the preset frequency coefficient threshold, it proceeds to step S123;

[0064] S122 determines whether the sum of the access frequency coefficients for different dates is greater than the preset coefficient threshold. If so, it determines that the location does not belong to a frequently accessed location. If not, it determines that the location belongs to a frequently accessed location;

[0065] S123 takes the dates with access frequency coefficients greater than the preset frequency coefficient threshold as frequently accessed dates. When the number of frequently accessed dates is greater than the preset frequent number threshold, it determines that the location belongs to a frequently accessed location. When the number of frequently accessed dates is not greater than the preset frequent number threshold, it proceeds to step S13.

[0066] S13 obtains the proportion of the number of frequently accessed dates in the dates, and determines the comprehensive frequency coefficient in combination with the average value of the access frequency coefficients for different dates, and determines whether the location is a frequently accessed location using the comprehensive frequency coefficient.

[0067] Specifically, the method for determining the access frequency coefficient is as follows:

[0068] Determines the frequent coefficient weight values corresponding to different numbers of accesses based on the amount of access data corresponding to different numbers of accesses;

[0069] Determines the access frequency coefficient for the date based on the sum of the frequent coefficient weight values corresponding to different numbers of accesses.

[0070] Optionally, the comprehensive frequency coefficient is determined based on the product of the proportion of the number of frequently accessed dates in the dates and the average value of the access frequency coefficients for different dates.

[0071] S2, based on the distance data between the server in the edge cloud and the frequently accessed location of the accessing user and the historical occupancy data of the communication link, when it is determined that there is no matching server for the accessing user, it proceeds to the next step;

[0072] Further, the historical occupancy data of the communication link includes the historical occupancy rate and historical occupancy volume of the communication link at different times.

[0073] Specifically, as Figure 3 shown, the method for determining the matching server of the accessing user is:

[0074] Determine the distances between the server and different common access locations according to the distance data between the server and the common access locations of the accessing user, and use the maximum value of the distances to different common access locations to determine the maximum distance value;

[0075] Based on the communication links between the server and different common access locations, determine the average value of the historical occupancy rates of the communication link at different times, and use it as the average occupancy rate;

[0076] Determine the matching coefficient of the server based on the maximum distance value and the average occupancy rate, and use the matching coefficient to determine whether the server is the matching server of the accessing user.

[0077] Further, the matching coefficient of the server is determined according to the ratio of the maximum distance value to the preset distance and the average value of the average occupancy rates.

[0078] It should be noted that when the matching coefficient is greater than the preset matching coefficient threshold, it is determined that the server is the matching server of the accessing user.

[0079] Optionally, when there is a matching server, the matching server with the largest matching coefficient is used to process the access data of the accessing user.

[0080] In another embodiment, the method for determining the matching server of the accessing user is:

[0081] S21 Determine the distances between the server and different common access locations according to the distance data between the server and the common access locations of the accessing user, and use the distances to different common access locations to determine the preset distance coefficients to different common access locations;

[0082] S22 Based on the communication links between the server and different common access locations, determine the proportion of idle times of the communication link to different common access points, and use it as the proportion of idle times;

[0083] S23 Determine the location matching coefficients of the server to different common service locations based on the proportion of idle times and the preset distance coefficients, determine the matching coefficient of the server according to the location matching coefficients to different common service locations, and use the matching coefficient to determine whether the server is the matching server of the accessing user.

[0084] Further, the matching coefficient of the server is determined by using the average value of the location matching coefficients of different common service locations.

[0085] S3 determines the standby servers in the server based on the distances from different common access locations, freely combines the standby servers to obtain server groups, and determines the matching server group in the server groups based on the access processing delays and remaining storage spaces of the standby servers in different server groups on different dates;

[0086] Further, the standby servers in the server are servers whose distances from all common access locations are within a preset distance range.

[0087] Specifically, freely combining the standby servers to obtain server groups specifically includes:

[0088] Based on the historical access data of the access user at different common access points, determine the historical access times at different common access points, and use the total historical access times at different common access locations to determine the required quantity of the standby servers;

[0089] Based on the required quantity of the standby servers, freely combine the standby servers to obtain server groups.

[0090] Optionally, the required quantity of the standby servers is determined according to the preset required quantity corresponding to the total historical access times.

[0091] In another embodiment, freely combining the standby servers to obtain server groups specifically includes:

[0092] Based on the historical access data of the access user at different common access points, determine the historical access times at different common access points;

[0093] Determine the access demand coefficients of different common access points according to the ratios of the historical access times at different common access points to the access times setting values, and use the sum of the access demand coefficients of different common access points to determine the total access demand coefficient;

[0094] Determine the required quantity of the standby servers by using the preset required quantity corresponding to the total access demand, and based on the required quantity of the standby servers, freely combine the standby servers to obtain server groups.

[0095] Specifically, as Figure 4 shown, the method for determining the matching server group in the server group is:

[0096] Determine the proportion of standby servers with remaining storage space less than the preset storage space among the standby servers in the server group, and use the proportion of standby servers with remaining storage space less than the preset storage space to determine the matching deviation coefficient of the storage space;

[0097] According to the access processing delays of the standby servers in the server group on different dates, determine the proportion of the number of dates when the access processing delays of different standby servers are greater than the preset processing delay, and based on the proportion of the number of dates, determine the access delay coefficient of the standby servers. According to the sum of the access delay coefficients of different standby servers, determine the matching deviation coefficient of the access delay;

[0098] Use the average value of the matching deviation coefficient of the storage space and the matching deviation coefficient of the access delay to determine the comprehensive matching deviation coefficient, and use the comprehensive matching deviation coefficient to determine the matching server group in the server group.

[0099] Furthermore, the matching server group is the server group with the smallest comprehensive matching deviation coefficient.

[0100] S4 According to the distance data between different standby servers in the matching server group and the common access locations, and the access data of the matching access users, determine the data processing order of the standby servers corresponding to different common access locations, and when the access processing delay of the standby server does not meet the requirements, automatically switch to the next standby server in the data processing order.

[0101] It should be noted that as Figure 5 shown, the method for determining the data processing order of the standby servers corresponding to the common access locations is:

[0102] Determine the distance between the standby server and the common access location based on the distance data between the standby server and the common access location, and use the distance to the common access location to determine the distance priority coefficient of the standby server;

[0103] Use the access users for whom the standby server is used for data processing as matching access users, and determine the access demand coefficients of different matching access users based on the average daily access times of different matching access users. According to the sum of the access demand coefficients of different matching access users in the standby server, determine the usage busy coefficient of the standby server;

[0104] Based on the ratio of the distance priority coefficient to the usage busy coefficient, determine the processing priority coefficients of different standby servers, and use the processing priority coefficients to determine the data processing order of the standby servers corresponding to the common access locations.

[0105] Further, the distance priority coefficient of the standby server is determined according to the product of the distance from the common access location and a preset proportionality factor.

[0106] It can be understood that the access demand coefficient for matching the access user is determined according to the preset access demand coefficient corresponding to the average daily access times of the matching access user.

[0107] It should be noted that using the processing priority coefficient to determine the data processing order of the standby server at the common access location specifically includes:

[0108] Determine the data processing order of the standby server at the common access location in descending order of the processing priority coefficient.

[0109] Embodiment 2 In a second aspect, the present invention provides a computer system, including: a memory and a processor connected by communication, and a computer program stored on the memory and capable of running on the processor. When the processor runs the computer program, it executes the above-mentioned data processing method based on edge cloud.

[0110] Optionally, the above step S21 includes the following content:

[0111] S211 Determine the distances between the server and different common access locations according to the distance data between the server and the common access location of the access user. When the distances between the server and different common access locations are not within the preset distance range, it is determined that the server does not belong to the matching server. When any one of the distances between the server and different common access locations is not within the preset distance range, go to step S212;

[0112] S212 Take the common access locations with distances within the preset distance range as the close access points. When the number of the close access points is less than the preset number of access points, go to step S213. When the number of the close access points is not less than the preset number of access points, go to step S214;

[0113] S213 When the average value of the distances between the server and different common access locations is greater than the preset distance threshold, it is determined that the server does not belong to the matching server. When the average value of the distances between the server and different common access locations is not greater than the preset distance threshold, go to step S214.

[0114] Optionally, the above step S22 includes the following content:

[0115] S221 determines the proportion of idle moments of the communication link with different common access points based on the communication links between the server and different common access locations, and takes it as the proportion of idle moments. When the proportion of idle moments with different common access points is less than the preset proportion threshold, it is determined that the server does not belong to the matching server. When there are common access points with the proportion of idle moments not less than the preset proportion threshold, it proceeds to step S222;

[0116] S222 When the proportion of the number of common access points with the proportion of idle moments not less than the preset proportion threshold is less than the preset proportion of the number of access points, it is determined that the server does not belong to the matching server. When the proportion of the number of common access points with the proportion of idle moments not less than the preset proportion threshold is not less than the preset proportion of the number of access points, it proceeds to step S23.

[0117] Optionally, the following content is included in the above step S23:

[0118] S231 determines the location matching coefficient between the server and different common service locations based on the proportion of idle moments and the preset distance coefficient. When the location matching coefficients between the server and different common service locations are all less than the preset matching coefficient threshold, it is determined that the server does not belong to the matching server. When there are common service locations with the location matching coefficient not less than the preset matching coefficient threshold, it proceeds to step S232;

[0119] S232 determines the matching coefficient of the server according to the location matching coefficients with different common service locations, and uses the matching coefficient to determine whether the server is the matching server of the access user.

[0120] Embodiment 3 In a third aspect, the present invention provides a computer storage medium, on which a computer program is stored. When the computer program is executed on a computer, the computer is made to execute the above-mentioned data processing method based on edge cloud.

[0121] Optionally, the method for determining the matching server group in the server group is as follows:

[0122] Based on the remaining storage space of the standby servers in the server group, determines the proportion of standby servers with the remaining storage space less than the preset storage space. When the proportion of standby servers with the remaining storage space less than the preset storage space is less than the proportion of standby servers, it is determined that the server group does not belong to the matching server group;

[0123] When the proportion of standby servers with the remaining storage space less than the preset storage space is not less than the proportion of standby servers:

[0124] Determine the proportion of the number of dates when the access processing delay of different standby servers in the server group is greater than the preset processing delay based on the access processing delays of the standby servers in the server group on different dates. Determine the access delay coefficient of the standby server based on the proportion of the number of dates. Determine the matching deviation coefficient of the access delay based on the sum of the access delay coefficients of different standby servers. When the matching deviation coefficient of the access delay does not meet the requirements, it is determined that the server group does not belong to the matching server group;

[0125] When the matching deviation coefficient of the access delay meets the requirements:

[0126] Determine the server matching deviation coefficient of the standby server in the server group based on the remaining storage space and the access delay coefficient of the standby server. When there is a standby server whose server matching deviation coefficient does not meet the requirements:

[0127] When the number of standby servers whose server matching deviation coefficient does not meet the requirements does not meet the requirements, it is determined that the server group does not belong to the matching server group;

[0128] When there is no standby server whose server matching deviation coefficient does not meet the requirements or the number of standby servers whose server matching deviation coefficient does not meet the requirements meets the requirements:

[0129] Determine the comprehensive matching deviation coefficient using the average value of the server matching deviation coefficients of the standby servers in the server group, and determine the matching server group in the server group using the comprehensive matching deviation coefficient.

[0130] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key points of each embodiment are the differences from other embodiments. In particular, for the embodiments of the device, equipment, and non-volatile computer storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0131] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the specific order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0132] The above description is only for one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, various changes and modifications can be made to one or more embodiments of this specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included within the scope of the claims of this specification.

Claims

1. A data processing method based on edge cloud, characterized in that: Specifically include: Determine the frequently visited locations of the visiting user based on the historical visit data of the visiting user at different visited locations; When it is determined that there is no matching server for the visiting user based on the distance data between the server in the edge cloud and the frequently visited places of the visiting user and the historical occupancy data of the communication link, proceed to the next step; Determine the backup servers in the server based on the distances to different frequently visited locations, freely combine the backup servers to obtain a server group, and determine the matching server group in the server group based on the access processing delays and remaining storage spaces of the backup servers in different server groups on different dates; Determine the data processing order of the backup servers corresponding to the different frequently visited locations according to the distance data between the different backup servers in the matching server group and the frequently visited locations and the access data of the matched visiting users, and automatically switch to the next backup server in the data processing order when the access processing delay of the backup server does not meet the requirements; The method for determining the data processing order of the standby servers corresponding to the commonly accessed locations is as follows: Determining the distance between the standby server and the frequently visited location using the distance data between the standby server and the frequently visited location, and determining the distance priority coefficient of the standby server using the distance to the frequently visited location; The access users used by the standby server for data processing are regarded as matching access users, the access demand coefficients of different matching access users are determined according to the average daily access times of different matching access users, and the usage busy coefficient of the standby server is determined according to the sum of the access demand coefficients of different matching access users in the standby server; The processing priority coefficients of different backup servers are determined based on the ratio of the distance priority coefficient to the usage busy coefficient, and the data processing order of the backup servers of the frequently visited locations is determined using the processing priority coefficients.

2. The edge cloud-based data processing method according to claim 1, characterized in that: The historical access data includes the number of visits of the visiting user at the visiting location, the visiting times corresponding to different visiting times, and the amount of access data.

3. The edge cloud-based data processing method according to claim 1, characterized in that: The method for determining the frequently visited locations of the visiting user is as follows: Determine the number of visits to the visited location on different dates using the visit data of the visited location; Determining a visit processing date in said dates based on the number of visits in different dates; Whether the place is a frequently visited place is determined based on the number of the visit processing dates.

4. The edge cloud-based data processing method according to claim 3, characterized in that: The access processing date in the date is a date with a number of accesses greater than a preset number of accesses.

5. The edge cloud-based data processing method according to claim 3, characterized in that: When the number of the visit processing dates is greater than the preset number of visit processing dates, the place is determined to be a frequently visited place.

6. The edge cloud-based data processing method according to claim 1, characterized in that: The historical occupancy data of the communication link includes the historical occupancy rate and the historical occupancy amount of the communication link at different times.

7. The edge cloud-based data processing method according to claim 1, characterized in that: The method for determining the matching server of the access user is: Determining the distances between the server and different frequently visited locations according to the distance data between the server and the frequently visited locations of the visiting user, and determining the maximum distance using the maximum value of the distances to the different frequently visited locations; Based on the communication links between the server and different frequently visited locations, determine the average value of the historical occupancy rate of the communication links at different times, and use it as the average occupancy rate; A matching coefficient of the server is determined based on the maximum distance and the average occupancy rate, and the matching coefficient is used to determine whether the server is a matching server for the accessing user.

8. A computer system comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes an edge cloud-based data processing method as described in any one of claims 1 to 7 when running the computer program.

9. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed in a computer, the computer is caused to execute the edge cloud-based data processing method described in any one of claims 1 to 7.

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