Data processing method and system based on edge cloud and storage medium

Abstract

The invention provides a data processing method and system based on edge cloud and a storage medium, and belongs to the technical field of data processing.The method specifically comprises the steps that standby servers in servers are determined on the basis of the distance between the servers and different common access sites, the standby servers are freely combined to obtain a server group, and the server group is stored in the server group; determining a matched server group in the server groups according to access processing delays and residual storage spaces of standby servers in different server groups on different dates, and determining the distance data between the different standby servers in the matched server group and a common access place and the access data of the matched access user according to the distance data between the different standby servers in the matched server group and the common access place and the access data of the matched access user. According to the method and the device, the data processing sequence of the standby servers corresponding to the different common access sites is determined, and when the access processing delay of the standby server does not meet the requirement, the next standby server in the data processing sequence is automatically switched, so that the data processing efficiency is improved.

Description

Technical Field

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

[0002] In order to use the edge cloud to process user operation data and improve the efficiency of data processing, the invention patent application CN202410246539.9 "A data sharing method and system based on edge computing" determines the matching server of the user device based on the operation location of the user device and the location of the commonly used computing server, thereby avoiding the waste of storage space caused by storing the user's operation data in different servers. However, it is not difficult to find the following technical problems through analysis: In the data processing process based on edge cloud, users often have multiple frequently visited locations. Therefore, if the matching server cannot be constructed based on the distance data to different frequently visited locations and the occupancy data of the communication link, the timeliness of data processing cannot be guaranteed.

[0003] In response to the above technical problems, the present application specifically provides a data processing method, system and storage medium based on edge cloud. Summary of the invention

[0004] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: According to one aspect of the present invention, a data processing method based on edge cloud is provided.

[0005] A data processing method based on edge cloud, specifically comprising: S1 determines the frequently visited locations of the visiting user based on the historical visit data of the visiting user at different visited locations; S2, 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, when it is determined that there is no matching server for the visiting user, proceed to the next step; S3 determines the backup servers in the server based on the distances to different frequently visited locations, and freely combines the backup servers to obtain a server group, and determines 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; S4 determines the data processing order of the backup servers corresponding to different commonly visited locations based on the distance data between different backup servers in the matching server group and the commonly visited locations and the access data of the matching visiting users, and automatically switches to the next backup server in the data processing order when the access processing delay of the backup server does not meet the requirements.

[0006] The beneficial effects of the present invention are: Based on the distance data between the server in the edge cloud and the frequently visited locations of the visiting users and the historical occupancy data of the communication links, it is determined whether there is a matching server for the visiting users. This not only takes into account the difference in data processing delay caused by the distance data between the server and different frequently visited access points, but also takes into account the difference in idle conditions of the communication links with the frequently visited access points caused by the historical occupancy data of the server's communication links. This achieves the screening of matching servers that meet the requirements for processing access data from multiple frequently visited access points, and also lays the foundation for determining differentiated allocation and processing strategies for servers used to process access data of visiting users.

[0007] The data processing order of the backup servers corresponding to different commonly visited locations is determined based on the distance data between different backup servers and commonly visited locations and the access data of the matching visiting users. This enables the data processing order of the backup servers to be determined based on the busyness of the backup servers in processing data of the visiting users and the distances from the commonly visited locations. This not only improves the efficiency of data processing, but also improves the reliability of data processing.

[0008] A further technical solution is that the historical access data includes the number of visits of the visiting user to the visiting location, the visiting times corresponding to different visiting times, and the amount of access data.

[0009] A further technical solution is that the method for determining the frequently visited locations of the visiting user is: 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.

[0010] A further technical solution is that the access processing date in the date is a date with a number of accesses greater than a preset number of accesses.

[0011] A further technical solution is that when the number of the access processing dates is greater than the preset number of access processing dates, the place is determined to be a frequently visited place.

[0012] A further technical solution is that the method for determining the data processing order of the backup servers corresponding to the commonly accessed locations is: 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.

[0013] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned edge cloud-based data processing method when running the computer program.

[0014] In a third aspect, the present invention provides a computer storage medium having a computer program stored thereon, which, when executed in a computer, enables the computer to execute the above-mentioned edge cloud-based data processing method.

[0015] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0016] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0018] Figure 1 is a flow chart of a data processing method based on edge cloud; Figure 2 is a flow chart of a method for determining a frequently visited location of a user; Figure 3 is a flow chart of a method for accessing a determination of a user's matching server; Figure 4 is a flow chart of a method for determining a matching server group in a server group; Figure 5 The present invention is a flow chart of a method for determining a data processing sequence of backup servers corresponding to commonly accessed locations. DETAILED DESCRIPTION

[0019] 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 drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0020] In the edge cloud-based data processing process, users often have multiple frequently visited locations. Therefore, it is necessary to build matching servers based on the distance data to different frequently visited locations and the occupancy data of the communication links to improve the reliability of data processing.

[0021] Frequently visited places of visiting users: Determine the visit processing dates in the dates according to the number of visits in different dates, and when the number of visit processing dates is greater than the preset number of visit processing dates, determine the place as a frequently visited place.

[0022] Matching server: The maximum value of the distance to different frequently visited locations is used to determine the maximum distance, and the average value of the historical occupancy rate of the communication link to different frequently visited locations at different times is used as the average occupancy rate. The matching coefficient of the server is determined according to the ratio of the maximum distance to the preset distance and the average value of the occupancy rate. When the matching coefficient is greater than the preset matching coefficient threshold, the server is determined to be the matching server for the visiting user.

[0023] Matching server groups: determine the historical access times of different commonly used access points based on the historical access data of visiting users at different commonly used access points, determine the access demand coefficients of different commonly used access points based on the ratio of the historical access times of different commonly used access points to the set access times, determine the total access demand coefficient using the sum of the access demand coefficients of different commonly used access points, determine the demand quantity of backup servers using the preset demand quantity corresponding to the total access demand, and based on the demand quantity of backup servers, freely combine the backup servers to obtain a server group.

[0024] Data processing order of backup servers: determine the distance priority coefficient of the backup server according to the distance to the commonly visited locations, use the visiting users used for data processing on the backup server as matching visiting users, determine the access demand coefficients of different matching visiting users according to the average daily visit times of different matching visiting users, determine the usage busy coefficient of the backup server according to the sum of the access demand coefficients of different matching visiting users in the backup server, determine the processing priority coefficients of different backup 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 backup servers for commonly visited locations. Embodiment 1 To solve the above problem, according to one aspect of the present invention, Figure 1 As shown, a data processing method based on edge cloud is provided, which specifically includes: S1 determines the frequently visited locations of the visiting user based on the historical visit data of the visiting user at different visited locations; Furthermore, the historical access data includes the number of visits of the visiting user at the visited location, the visit times corresponding to different visit times, and the amount of access data.

[0025] Specifically, Figure 2 As shown, the method for determining the frequently visited locations of the visiting user is: 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.

[0026] Furthermore, the access processing date in the date is a date with a number of accesses greater than a preset number of accesses.

[0027] It can be understood that when the number of the access processing dates is greater than the preset number of access processing dates, the place is determined to be a frequently visited place.

[0028] Optionally, the method for determining the frequently visited locations of the visiting user is: Determine the number of visits to the visited location on different dates using the visit data of the visited location; Determine the average number of visits to the visit location on different dates according to the number of visits on different dates, and use it as the average number of visits; It is determined whether the place is a frequently visited place based on the average number of visits.

[0029] Optionally, the method for determining the frequently visited locations of the visiting user is: S11 determines the number of visits to the visited location on different dates based on the visit data of the visited location; Optionally, the above step S11 includes the following contents: S111: Determine the number of visits to the visit location on different dates based on the visit data of the visit location; when the total number of visits to the visit point on different dates is less than a preset number threshold, determine that the visit location is not a frequently visited location; when the total number of visits to the visit point on different dates is greater than a preset number threshold, determine that the visit location is a frequently visited location; when the total number of visits to the visit point on different dates is not less than the preset number threshold and not greater than the preset number threshold, proceed to step S112; S112: determining the average number of visits to the visited place on different dates according to the number of visits on different dates, and taking it as the average number of visits; when the average number of visits is greater than the average value threshold, determining that the visited place is a frequently visited point; when the average number of visits is not greater than the average value threshold, proceeding to step S113; S113 determines the visit processing date in the date according to the number of visits in different dates. When the number of the visit processing dates is within the preset date number range, the place is determined to be a frequently visited place. When the number of the visit processing dates is not within the preset date number range, the process goes to step S12.

[0030] S12 determines the access frequency coefficients of different dates according to the number of visits of the visiting users at the visited locations on different dates and the amount of access data corresponding to the different number of visits, and determines the frequent visit dates among the dates using the access frequency coefficients of different dates; Optionally, the above step S12 includes the following contents: S121 determines the access frequency coefficients of different dates according to the number of visits of the visiting users at the visiting location on different dates and the amount of access data corresponding to the different number of visits. If there is no date with an access frequency coefficient greater than a preset frequency coefficient threshold, the process proceeds to step S122. If there is a date with an access frequency coefficient greater than the preset frequency coefficient threshold, the process proceeds to step S123. S122: determining whether the sum of the access frequency coefficients on different dates is greater than a preset coefficient threshold value, if so, determining that the location is not a frequently visited location, if not, determining that the location is a frequently visited location; S123 takes the dates whose visit frequency coefficient is greater than the preset frequency coefficient threshold as the frequently visited dates. When the number of the frequently visited dates is greater than the preset frequency number threshold, it is determined that the place is a frequently visited place. When the number of the frequently visited dates is not greater than the preset frequency number threshold, it goes to step S13.

[0031] S13 obtains the proportion of frequently visited dates among the dates, and determines a comprehensive frequency coefficient based on the average values ​​of the frequency coefficients of different dates, and uses the comprehensive frequency coefficient to determine whether the place is a frequently visited place.

[0032] Specifically, the method for determining the access frequency coefficient is: Determine the frequent coefficient weight values ​​corresponding to different access times according to the access data volumes corresponding to different access times; The access frequency coefficient of the date is determined according to the sum of the frequency coefficient weight values ​​corresponding to different access times.

[0033] Optionally, the comprehensive frequency coefficient is determined according to the product of the proportion of the number of frequently visited dates among the dates and the average value of the visit frequency coefficients of different dates.

[0034] S2, 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, when it is determined that there is no matching server for the visiting user, proceed to the next step; Furthermore, 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.

[0035] Specifically, Figure 3 As shown, 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.

[0036] Furthermore, the matching coefficient of the server is determined according to the ratio of the maximum distance to the preset distance and the average value of the occupancy rate average value.

[0037] It should be noted that when the matching coefficient is greater than a preset matching coefficient threshold, the server is determined to be a matching server for the accessing user.

[0038] Optionally, when a matching server exists, the matching server with the largest matching coefficient is used to process the access data of the access user.

[0039] In another embodiment, the method for determining the matching server of the accessing user is: S21: 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 preset distance coefficients between the server and the different frequently visited locations by using the distances between the server and the different frequently visited locations; S22 determines the proportion of idle time between the communication link and different commonly visited locations based on the communication link between the server and different commonly visited locations, and uses the proportion of idle time as the proportion of idle time; S23 determines the location matching coefficient between the server and different commonly used service locations based on the idle time ratio and the preset distance coefficient, determines the matching coefficient of the server according to the location matching coefficient with different commonly used service locations, and uses the matching coefficient to determine whether the server is a matching server for the visiting user.

[0040] Furthermore, the matching coefficient of the server is determined by using an average value of location matching coefficients with different common service locations.

[0041] S3 determines the backup servers in the server based on the distances to different frequently visited locations, and freely combines the backup servers to obtain a server group, and determines 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; Furthermore, the backup server among the servers is a server whose distances to all commonly visited locations are within a preset distance interval.

[0042] Specifically, the standby servers are freely combined to obtain a server group, which specifically includes: Determine the number of historical visits at different frequently used access points based on the historical access data of the access user at different frequently used access points, and determine the required number of the backup servers based on the total number of historical visits at different frequently used access points; Based on the required number of the backup servers, the backup servers are freely combined to obtain a server group.

[0043] Optionally, the required number of backup servers is determined according to a preset required number corresponding to the total number of historical accesses.

[0044] In another embodiment, the standby servers are freely combined to obtain a server group, specifically including: Determine the number of historical visits to different frequently used access points based on the historical access data of the visiting user at different frequently used access points; Determine the access demand coefficients of different frequently used access points according to the ratio of the historical access times of different frequently used access points to the set access times, and determine the total access demand coefficient by the sum of the access demand coefficients of different frequently used access points; The demand quantity of the backup servers is determined by using the preset demand quantity corresponding to the total access demand, and based on the demand quantity of the backup servers, the backup servers are freely combined to obtain a server group.

[0045] Specifically, Figure 4 As shown, the method for determining the matching server group in the server group is: Determine the proportion of standby servers whose remaining storage space is less than the preset storage space based on the remaining storage space of the standby servers in the server group, and determine the matching deviation coefficient of the storage space based on the proportion of standby servers whose remaining storage space is less than the preset storage space; According to the access processing delays of the backup servers in the server group on different dates, determining the proportion of the number of dates on which the access processing delays of different backup servers are greater than the preset processing delay, determining the access delay coefficient of the backup server based on the proportion of the number of dates, and determining the matching deviation coefficient of the access delay according to the sum of the access delay coefficients of different backup servers; A comprehensive matching deviation coefficient is determined by using an average value of the matching deviation coefficient of the storage space and the matching deviation coefficient of the access delay, and a matching server group in the server group is determined by using the comprehensive matching deviation coefficient.

[0046] Furthermore, the matching server group is a server group with the smallest comprehensive matching deviation coefficient.

[0047] S4 determines the data processing order of the backup servers corresponding to different commonly visited locations based on the distance data between different backup servers in the matching server group and the commonly visited locations and the access data of the matching visiting users, and automatically switches to the next backup server in the data processing order when the access processing delay of the backup server does not meet the requirements.

[0048] It should be noted that if Figure 5As shown, the method for determining the data processing order of the backup servers corresponding to the commonly accessed locations is: 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.

[0049] Furthermore, the distance priority coefficient of the standby server is determined according to the product of the distance to the commonly visited location and a preset proportional factor.

[0050] It can be understood that the access demand coefficient of the matched access user is determined according to a preset access demand coefficient corresponding to the average daily access times of the matched access user.

[0051] It should be noted that the data processing order of the backup servers of the frequently visited locations is determined by using the processing priority coefficients, specifically including: The data processing order of the backup servers of the frequently visited locations is determined according to the processing priority coefficients from large to small. Embodiment 2 In a second aspect, the present invention provides a computer system, comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-mentioned edge cloud-based data processing method when running the computer program.

[0052] Optionally, the above step S21 includes the following contents: S211 determines 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. When the distances between the server and the different frequently visited locations are not within a preset distance interval, it is determined that the server is not a matching server. When any of the distances between the server and the different frequently visited locations is not within the preset distance interval, the process proceeds to step S212. S212: taking the frequently visited places within the preset distance interval as the short-distance visit points. When the number of the short-distance visit places is less than the preset number of visit points, the process proceeds to step S213. When the number of the short-distance visit places is not less than the preset number of visit points, the process proceeds to step S214. S213: When the average value of the distances between the server and different frequently visited locations is greater than a preset distance threshold, it is determined that the server does not belong to a matching server. When the average value of the distances between the server and different frequently visited locations is not greater than the preset distance threshold, the process proceeds to step S214.

[0053] Optionally, the above step S22 includes the following contents: S221 determines the idle time ratio of the communication link with different common access points based on the communication links between the server and different common access points, and uses it as the idle time ratio. When the idle time ratios with different common access points are all less than a preset ratio threshold, it is determined that the server does not belong to the matching server. When there is a common access point whose idle time ratio is not less than the preset ratio threshold, it goes to step S222. S222: When the proportion of the number of common access points whose idle time proportion is not less than the preset proportion threshold is less than the preset access point proportion, it is determined that the server does not belong to the matching server; when the proportion of the number of common access points whose idle time proportion is not less than the preset proportion threshold is not less than the preset access point proportion, the process proceeds to step S23.

[0054] Optionally, the above step S23 includes the following contents: S231: determining the location matching coefficients between the server and different common service locations based on the idle time ratio 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, determining that the server is not a matching server; when there is a common service location whose location matching coefficient is not less than the preset matching coefficient threshold, proceeding to step S232; 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 a matching server for the accessing user. 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 in a computer, the computer is caused to execute the above-mentioned edge cloud-based data processing method.

[0055] Optionally, a method for determining a matching server group in the server group is: Determine the proportion of standby servers whose remaining storage space is less than the preset storage space based on the remaining storage space of the standby servers in the server group; when the proportion of standby servers whose remaining storage space is less than the preset storage space is less than the proportion of standby servers, determine that the server group does not belong to the matching server group; When the remaining storage space is less than the proportion of standby servers with preset storage space is not less than the proportion of standby servers: According to the access processing delays of the backup servers in the server group on different dates, determine the proportion of the number of dates on which the access processing delays of different backup servers are greater than the preset processing delay, determine the access delay coefficient of the backup server based on the proportion of the number of dates, determine the matching deviation coefficient of the access delay according to the sum of the access delay coefficients of different backup servers, and when the matching deviation coefficient of the access delay does not meet the requirements, determine that the server group does not belong to the matching server group; When the matching deviation coefficient of the access delay meets the requirement: The server matching deviation coefficient of the standby server is determined based on the remaining storage space and the access delay coefficient of the standby server in the server group. When there is a standby server whose server matching deviation coefficient does not meet the requirement: When the number of standby servers whose server matching deviation coefficients do not meet the requirements does not meet the requirements, it is determined that the server group does not belong to the matching server group; When there is no standby server whose server matching deviation coefficient does not meet the requirement or the number of standby servers whose server matching deviation coefficient does not meet the requirement meets the requirement: An average value of the server matching deviation coefficients of the backup servers in the server group is used to determine a comprehensive matching deviation coefficient, and a matching server group in the server group is determined using the comprehensive matching deviation coefficient.

[0056] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0057] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0058] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in 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; According to the distance data between different backup servers in the matching server group and the commonly visited locations and the access data of the matched visiting users, the data processing order of the backup servers corresponding to the different commonly visited locations is determined, and when the access processing delay of the backup server does not meet the requirements, it is automatically switched to the next backup server in the data processing order.

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. The edge cloud-based data processing method according to claim 1, characterized in that: 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.

9. 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 1-8 when running the computer program.

10. 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 enabled to execute the edge cloud-based data processing method described in any one of claims 1 to 8.

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