An intelligent platform data processing system based on big data

Abstract

The application relates to the technical field of data processing, in particular to an intelligent platform data processing system based on big data, which comprises an enterprise data acquisition module, an employee data analysis module, an access information analysis module, a server state analysis module, a priority analysis module and a server access management module; the enterprise data acquisition module is used for collecting employee information, platform access information and server information of an enterprise server of users in an enterprise platform; the employee data analysis module is used for analyzing employee data parameters; the access information analysis module is used for dividing employee groups according to the employee information, analyzing employee access characteristics and judging the group types of the employee groups; the server state analysis module is used for judging the server state of the enterprise server; the priority analysis module is used for analyzing access priority parameters; and the server access management module is used for managing the priority of user access request processing of the enterprise server according to the access priority. The application realizes accurate processing of the priority of user access request of the enterprise server.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to an intelligent platform data processing system based on big data. Background Technology

[0002] In today's era of data explosion, the stability and efficiency of enterprise platform databases are of paramount importance. As business operations continue to expand, the volume of access to database systems increases, especially under high load. Ensuring that the database can respond quickly to each request while guaranteeing data correctness and consistency has become a pressing issue.

[0003] Chinese Patent Publication No. CN114547038A discloses a data processing method and apparatus for a priority database, comprising: obtaining stored data and its corresponding storage priority according to a user's data storage instruction, wherein the stored data and its corresponding storage priority are associated through a primary key; storing the stored data in the priority database; determining a target data block according to the data storage quantity of each data block in the priority database and the storage priority; inserting the primary key corresponding to the storage priority into the target data block; and storing the target data block in the priority database. This invention achieves the analysis of database processing data priority based on database storage conditions, but it does not achieve a comprehensive analysis of access processing priority based on user characteristics and server response request data. This results in low efficiency in analyzing platform server access data and inaccurate priority analysis of platform server processing of user access. Summary of the Invention

[0004] The purpose of this invention is to provide a data processing system for an intelligent platform based on big data, so as to solve at least one of the problems existing in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A big data-based intelligent platform data processing system includes:

[0007] The enterprise data acquisition module is used to collect employee information, platform access information, and server information of users on the enterprise platform.

[0008] The employee data analysis module is used to analyze employee data types based on employee information and to analyze employee data parameters based on employee data types.

[0009] The access information analysis module is used to divide employees into groups based on employee information, analyze employee access characteristics based on employee information and platform access information, analyze group characteristic parameters based on employee access characteristics and employee groups, and determine the group type of employee groups based on group characteristic parameters.

[0010] The server status analysis module is used to analyze server utilization based on server information and determine the server status of the enterprise's servers based on the server utilization.

[0011] The priority analysis module is used to analyze access priority parameters based on employee data parameters, employee access characteristics, employee group type, and enterprise server status.

[0012] The server access management module is used to manage the priority of how enterprise servers process user access requests based on access priorities.

[0013] Furthermore, the employee data analysis module is equipped with an employee data verification unit, which is used to determine the employee data type based on whether the data corresponding to the employee ID in the employee information is a missing value and whether the data corresponding to the employee ID in the employee information is a unique value. The employee data types include: missing data, duplicate data, and complete data.

[0014] Furthermore, the employee data analysis module also includes a data integrity analysis unit, which counts the number of employee information with missing data as the missing quantity, the number of employee information with duplicate data as the duplicate quantity, and the number of employee information with complete data as the complete quantity. The module analyzes the employee data parameters based on the missing quantity N1, the duplicate quantity N2, and the complete quantity N3 to obtain the employee data parameter Q.

[0015] Furthermore, the access information analysis module includes an employee grouping unit, which is used to group employee information with the same responsibilities into the same employee group;

[0016] The access information analysis module also includes an access feature analysis unit, which is used to count the number of times each employee accesses the website with the purpose of uploading as the employee upload count, the number of times each employee accesses the website with the purpose of modifying as the employee modification count, the number of times each employee accesses the website with the purpose of downloading as the employee download count, and the number of times each employee accesses the website with the purpose of searching as the employee search count. The module analyzes the employee access features based on the employee upload count B1(i), employee modification count B2(i), employee download count B3(i), and employee search count B4(i) to obtain the employee access feature A(i).

[0017] Furthermore, the access information analysis module is also equipped with a group feature analysis unit, which is used to analyze the group feature parameters based on the employee access feature A(i) and the employee group to obtain the group feature parameters R(j).

[0018] The access information analysis module also includes a group type judgment unit, which is used to compare the group feature parameter R(j) with the feature comparison threshold r, and judge the group type of the employee group based on the comparison result. The group types of the employee group include: Class I and Class II.

[0019] Furthermore, the server status analysis module uses the ratio of server throughput Z1(t) to maximum throughput Z2 as the server occupancy rate P(t), and compares the server occupancy rate P(t) with the occupancy rate threshold p. Based on the comparison result, the server status of the enterprise server is analyzed to determine the server status of the enterprise server. The server status of the enterprise server includes: low load and high load.

[0020] Furthermore, the priority analysis module is equipped with a priority analysis unit, which is used to analyze the access priority parameter based on the employee data parameter Q and the employee access feature A(i) when the server status of the enterprise server is high load, so as to obtain the access priority parameter F(i).

[0021] Furthermore, the priority analysis module also includes a group type analysis unit, which is used to adjust the analysis process of the access priority parameter according to the group type of the employee group. When the group type of the employee group is one type, the analysis process of the access priority parameter is adjusted, and the adjusted access priority parameter is F1(i).

[0022] Furthermore, the priority analysis module also includes a lag analysis unit, which is used to take the time interval between the employee's access time when the enterprise server does not respond to the user's access request as the lag time when the employee's access purpose is to modify and the system time of the enterprise server. The lag time V(i) is compared with the lag threshold v. If the lag time does not meet the threshold, the process of adjusting the access priority parameter is optimized. The optimized access priority parameter is F2(i).

[0023] Furthermore, the server access management module manages the priority of the enterprise server's processing of user access based on access priority. The server access management module arranges the access priority parameters from largest to smallest and processes user access requests to the enterprise server in the order of arrangement.

[0024] The beneficial effects of this invention are as follows: By collecting employee information, platform access information, and server information of the enterprise platform through the enterprise data acquisition module, and through comprehensive analysis of the collected data by other modules, access priority parameters are analyzed. This enables comprehensive analysis of user characteristics and enterprise server access data on access priority, thereby prioritizing the processing of high-priority user access requests when the enterprise server is under high load, thus improving the system's efficiency in analyzing server access data and the accuracy of the server's priority analysis of user access. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of the data processing system of the big data-based intelligent platform in this embodiment.

[0027] Figure 2 This is a schematic diagram of the employee data analysis module in this embodiment.

[0028] Figure 3 This is a schematic diagram of the access information analysis module in this embodiment.

[0029] Figure 4 This is a schematic diagram of the priority analysis module in this embodiment. Detailed Implementation

[0030] To more clearly illustrate the present invention, the following description, in conjunction with preferred embodiments and accompanying drawings, further explains the invention. Similar components in the drawings are indicated by the same reference numerals. Those skilled in the art should understand that the specific description below is illustrative rather than restrictive and should not be construed as limiting the scope of protection of the present invention.

[0031] It should be noted that although the terms first, second, third, etc., may be used in the embodiments of this application for description, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of this application, first can also be referred to as second, and similarly, second can also be referred to as first.

[0032] Please see Figure 1 As shown, this is the intelligent platform data processing system based on big data in this embodiment, including:

[0033] The enterprise data acquisition module is used to collect employee information, platform access information, and server information of users on the enterprise platform. The employee information includes employee ID, employee responsibilities, and employee length of service, etc. In this embodiment, the employee information includes, but is not limited to, employee ID, employee responsibilities, and employee length of service. Those skilled in the art can freely set these parameters. The employee information is various data about employees stored on the enterprise server. The platform access information includes the employee's access purpose and access time. The employee's access purpose includes uploading, modifying, downloading, and retrieving. The server information includes server operation information and stored data content. The server operation information includes server throughput and maximum throughput. The employee information, platform access information, and server information of users on the enterprise platform are collected by importing data from the backend of the enterprise platform data management system.

[0034] Specifically, this embodiment is applied to the enterprise platform cloud. When the enterprise platform server is running under high load, the priority of employee access is analyzed and processed based on user data characteristics and enterprise platform database information.

[0035] Please continue reading. Figure 1 As shown, the big data-based intelligent platform data processing system also includes:

[0036] The employee data analysis module is used to analyze employee data types based on employee information and to analyze employee data parameters based on employee data types. The employee data analysis module is connected to the enterprise data acquisition module.

[0037] Please see Figure 2 As shown, the employee data analysis module includes:

[0038] The employee data verification unit is used to analyze the employee data types based on employee information, classifying the employee information into different data types according to the completeness of the data.

[0039] Specifically, in this embodiment, the employee data verification unit determines whether each piece of data corresponding to the employee ID in the employee information is missing. If there are missing values ​​in each piece of data corresponding to the employee ID in the employee information, the employee data verification unit determines that the data type of the employee information being analyzed is missing data; if there are no missing values ​​in each piece of data corresponding to the employee ID in the employee information, the employee data verification unit determines that the data type of the employee information being analyzed is complete data.

[0040] Specifically, in this embodiment, the employee data verification unit determines whether each piece of data corresponding to the employee ID in the employee information is a unique value. If there is duplicate data in each piece of data corresponding to the employee ID in the employee information, the employee data verification unit determines that the employee data type of the currently analyzed employee information is duplicate data; if there is no duplicate data in each piece of data corresponding to the employee ID in the employee information, the employee data verification unit determines that the employee data type of the currently analyzed employee information is complete data.

[0041] Specifically, in this embodiment, the employee data verification unit judges the missing and unique values ​​in the employee information to determine the employee data type, thereby classifying and processing the employee information, increasing the diversity of system analysis, ensuring that the employee information meets the requirements of user-friendly employee feature analysis, and thus improving the system's efficiency in analyzing server access data and the accuracy of the server's priority analysis of user access.

[0042] Specifically, in this embodiment, the missing value judgment process is to determine whether there is blank data in each item of data in the collected employee information. If the content corresponding to the data in the employee information is blank, then the current data is determined to be a missing value. The unique value judgment process is to analyze whether the data in the employee information are one-to-one. If the content of a certain data corresponds to multiple values, then the data is determined to be duplicate data. If the data in the employee information corresponds to only one value, then the data is determined to be a unique value.

[0043] Please continue reading. Figure 2 As shown, the employee data analysis module also includes:

[0044] The data integrity analysis unit is used to analyze employee data parameters based on employee data types. The employee data parameters represent the data type characteristics of employee information stored in the enterprise platform. The data integrity analysis unit is connected to the employee data verification unit.

[0045] Specifically, in this embodiment, the data integrity analysis unit counts the number of employee information with missing data as the missing quantity, the number of employee information with duplicate data as the duplicate quantity, and the number of employee information with complete data as the complete quantity. Based on the missing, duplicate, and complete quantities, the employee data parameters are analyzed. The employee data parameter is set as Q, and Q = (N3 - N1 / 2 - N2 / 2) / N3, where N1 represents the missing quantity, N2 represents the duplicate quantity, and N3 represents the complete quantity. Through the analysis of employee data types by the data integrity analysis unit, the missing, duplicate, and complete quantities are counted, achieving statistical analysis of the quantity of employee data of various data types in the platform. This allows for the analysis of employee data parameters, enabling comprehensive characteristic analysis of employee data of various data types in the platform, thereby improving the system's efficiency in analyzing server access data and increasing the accuracy of the server's priority analysis of user access.

[0046] Please continue reading. Figure 1 As shown, the big data-based intelligent platform data processing system also includes:

[0047] The access information analysis module is used to divide employees into groups based on employee information, analyze employee access characteristics based on employee information and platform access information, analyze group characteristic parameters based on employee access characteristics and employee groups, and determine the group type of employee groups based on group characteristic parameters. The access information analysis module is connected to the employee data analysis module.

[0048] Please see Figure 3 As shown, the access information analysis module includes:

[0049] The employee grouping unit is used to divide employees into groups based on their responsibilities. The employee grouping unit groups employees with the same responsibilities into the same employee group, thereby dividing employees into groups based on different responsibilities and increasing the number of samples for system analysis.

[0050] Please continue reading. Figure 3 As shown, the access information analysis module further includes:

[0051] The access feature analysis unit is used to analyze employee access features based on employee information and platform access information. The employee access features represent the characteristic relationships between different access targets in the employee access data every day. The access feature analysis unit is connected to the employee group segmentation unit.

[0052] Specifically, in this embodiment, the access feature analysis unit counts the number of times each employee accesses the site with the purpose of uploading as the employee upload count, the number of times each employee accesses the site with the purpose of modifying as the employee modification count, the number of times each employee accesses the site with the purpose of downloading as the employee download count, and the number of times each employee accesses the site with the purpose of searching as the employee search count. The employee access features are analyzed based on these data, and the employee access feature is set as A(i). Where i represents the employee ID, b k Indicates the preset target parameter, B k (i) represents the employee's purpose number parameter, k represents the employee's access purpose number, when k=1, b1 represents the preset upload parameter, 0.3≤b1≤0.5, B1(i) represents the number of times the employee uploads, when k=2, b2 represents the preset modification parameter, 0.6≤b2≤0.8, B2(i) represents the number of times the employee modifies, when k=3, b3 represents the preset download parameter, 1.1≤b3≤1.3, B3(i) represents the number of times the employee downloads, when k=4, b4 represents the preset retrieval parameter, 0.9≤b4≤1, B4(i) represents the number of times the employee retrieves; through the statistics of employee information and platform access information by the access feature analysis unit, in order to The system statistically analyzes the number of times each employee visits a service each day, thereby identifying employee access characteristics. These characteristics represent the quantitative relationships between different access purposes each day, improving the system's efficiency in analyzing server access data and enhancing the accuracy of the server's priority analysis of user access. It is understood that this embodiment does not impose specific limitations on the values ​​of the preset target parameters; those skilled in the art can freely set them, as long as they satisfy the analysis of employee access characteristics. The optimal values ​​for the preset target parameters are: b1=0.4, b2=0.7, b3=1.2, b4=0.9.

[0053] Please continue reading. Figure 3 As shown, the access information analysis module further includes:

[0054] The group feature analysis unit is used to analyze group feature parameters based on employee access characteristics and employee groups. The group feature parameters represent the differences in employee access characteristics among employees in the employee group. The group feature analysis unit is connected to the access feature analysis unit.

[0055] Specifically, in this embodiment, the group feature analysis unit analyzes the group feature parameters based on employee access characteristics and employee groups, setting the group feature parameter as R(j), and setting... Where U(j) represents the set of employee IDs in the employee group, j represents the employee group number, and NU(j) represents the number of employee IDs in the employee group; through the analysis of employee access characteristics and employee groups by the group feature analysis unit, group feature parameters are analyzed to achieve comprehensive analysis of employee information in each employee group, determine the differences in access characteristics between employees in the group, thereby improving the system's efficiency in analyzing server access data and improving the accuracy of the server's priority analysis of user access.

[0056] Please continue reading. Figure 3 As shown, the access information analysis module further includes:

[0057] The group type determination unit is used to determine the group type of the employee group based on the group characteristic parameters, and classifies the employee group according to the differences in the access characteristics of each employee in the group. The group type determination unit is connected to the group characteristic analysis unit.

[0058] Specifically, in this embodiment, the group type judgment unit compares the group feature parameters with the feature comparison threshold and determines the group type of the employee group based on the comparison result. If R(j) < r, the group type judgment unit determines that the employee group is of type one; if R(j) ≥ r, the group type judgment unit determines that the employee group is of type two. Here, r represents the feature comparison threshold, 0.1 ≤ r ≤ 0.15. Through the analysis of the group feature parameters by the group type judgment unit, the employee group is divided into two categories according to the differences in the characteristics of each employee in the group. Type one indicates that the characteristics of employees in the employee group are relatively small, and type two indicates that the characteristics of employees in the employee group are relatively large. This improves the efficiency of the system in analyzing server access data and improves the accuracy of the server in prioritizing user access. It is understood that this embodiment does not specifically limit the value of the feature comparison threshold. Those skilled in the art can set it freely, as long as it satisfies the judgment of the group type of the employee group. The optimal value of the feature comparison threshold is: r = 0.1.

[0059] Please continue reading. Figure 1 As shown, the big data-based intelligent platform data processing system also includes:

[0060] The server status analysis module is used to analyze server utilization based on server information and determine the server status of the enterprise server based on the server utilization, thereby realizing the analysis of server operating load. The server status analysis module is connected to the enterprise data acquisition module.

[0061] Specifically, in this embodiment, the server status analysis module analyzes the server occupancy rate based on server operation information. The server occupancy rate is set to P(t), and P(t) = Z1(t) / Z2. The server occupancy rate is compared with a occupancy rate threshold. Based on the comparison result, the server status of the enterprise server is analyzed. If P(t) < p, the server status analysis module determines that the enterprise server is in a low-load state; if P(t) ≥ p, the server status analysis module determines that the enterprise server is in a high-load state. Here, Z1(t) represents server throughput, t represents a time period, Z2 represents maximum throughput, and p represents... The occupancy threshold is set to 0.8 ≤ p ≤ 0.9. The server status analysis module analyzes server throughput and maximum throughput to determine server occupancy, enabling analysis of resource usage on the server and thus determining its status. Enterprise servers are categorized into two classes based on load, thereby improving the system's efficiency in analyzing server access data and increasing the accuracy of priority analysis for handling user access. It is understood that this embodiment does not impose specific limitations on the occupancy threshold value; those skilled in the art can freely set it, as long as it satisfies the analysis of the enterprise server's status. The optimal occupancy threshold value is p = 0.85.

[0062] Please continue reading. Figure 1 As shown, the big data-based intelligent platform data processing system also includes:

[0063] The priority analysis module is used to analyze access priority parameters based on employee data parameters, employee access characteristics, employee group type, and enterprise server status. The priority analysis module is connected to the access information analysis module and the server status analysis module.

[0064] Please see Figure 4 As shown, the priority analysis module includes:

[0065] The priority analysis unit is used to analyze access priority parameters based on employee data parameters, employee access characteristics, and the server status of the enterprise server. The access priority parameters represent the priority of a user when accessing the enterprise server.

[0066] Specifically, in this embodiment, when the enterprise server is under high load, the priority analysis unit analyzes the access priority parameter based on employee data parameters and employee access characteristics, setting the access priority parameter as F(i), and defining F(i) = Q × A(i). Through the analysis of the enterprise server's server status, employee data parameters, and employee access characteristics by the priority analysis unit, the access priority parameter is determined. This achieves a comprehensive analysis of access priority based on user access characteristics and the characteristics of employee data in the enterprise platform, thereby improving the system's efficiency in analyzing server access data and increasing the accuracy of the server's priority analysis for handling user access.

[0067] Please continue reading. Figure 4 As shown, the priority analysis module further includes:

[0068] The group type analysis unit is used to adjust the analysis process of access priority parameters according to the group type of the employee group, so that the adjusted access priority parameters are related to the characteristics of the employee group. The group type analysis unit is connected to the priority analysis unit.

[0069] Specifically, in this embodiment, the group type analysis unit adjusts the analysis process of the access priority parameter according to the group type of the employee group. If the group type of the employee group is one type, the analysis process of the access priority parameter is adjusted, and the adjusted access priority parameter is F1(i), which is set as F1(i) = F(i) / e. -R(j) If the employee group is of type II, the analysis process of the access priority parameter is not adjusted. The analysis of the employee group type by the group type analysis unit is used to adjust the analysis process of the access priority parameter. When the characteristic differences in the employee group are small, the access priority parameter is increased, thereby improving the system's efficiency in analyzing server access data and improving the accuracy of the server's priority analysis of user access.

[0070] Please continue reading. Figure 4 As shown, the priority analysis module further includes:

[0071] The lag analysis unit is used to analyze the modification lag time based on platform access information and enterprise server information, and optimize the adjustment process of access priority parameters based on the modification lag time, so that the optimized access priority is related to the lag time of the server processing user access. The lag analysis unit is connected to the group type analysis unit.

[0072] Specifically, in this embodiment, when the enterprise server does not respond to a user's access request, the lag analysis unit takes the time interval between the employee's access time (when the employee's access purpose is modification) and the enterprise server's system time as the lag duration, compares the lag duration with the lag threshold, and optimizes the adjustment process of the access priority parameter based on the comparison result. If V(i)≤v, the lag analysis unit determines that the lag duration meets the threshold and does not optimize the adjustment process of the access priority parameter. If V(i)>v, the lag analysis unit determines that the lag duration does not meet the threshold and optimizes the adjustment process of the access priority parameter. The optimized access priority parameter is F2(i), and F2(i) = F1(i) × lgV(i); where V(i) represents the lag duration, v represents the lag threshold, and 50≤v≤70. The lag analysis unit analyzes whether the enterprise server responds to user access requests to determine the lag duration. This lag duration represents the time the enterprise server has not responded to a user request. By optimizing the adjustment process of access priority parameters when the lag duration is long, the system's efficiency in analyzing server access data and the accuracy of the server's priority analysis of user access processing are improved. It is understood that this embodiment does not specifically limit the value of the lag threshold; those skilled in the art can freely set it, as long as it optimizes the access priority adjustment process. The optimal value for the lag threshold is v=60. The units for both the lag duration and the lag threshold are seconds.

[0073] Please continue reading. Figure 1 As shown, the big data-based intelligent platform data processing system also includes:

[0074] The server access management module is used to manage the priority of how enterprise servers process user access requests based on access priorities.

[0075] Specifically, in this embodiment, the server access management module manages the priority of the enterprise server's processing of user access based on the access priority. The server access management module arranges the access priority parameters from largest to smallest and processes user access requests to the enterprise server in the order of arrangement.

[0076] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. For those skilled in the art, other variations or modifications can be made based on the above description. It is impossible to exhaustively list all the implementation methods here. All obvious variations or modifications derived from the technical solutions of the present invention are still within the protection scope of the present invention.

Claims

1. A data processing system for an intelligent platform based on big data, characterized in that, include: The enterprise data acquisition module is used to collect employee information, platform access information, and server information of users on the enterprise platform. The employee data analysis module is used to analyze employee data types based on employee information and to analyze employee data parameters based on employee data types. The access information analysis module is used to divide employees into groups based on employee information, analyze employee access characteristics based on employee information and platform access information, analyze group characteristic parameters based on employee access characteristics and employee groups, and determine the group type of employee groups based on group characteristic parameters. The server status analysis module is used to analyze server utilization based on server information and determine the server status of the enterprise's servers based on the server utilization. The priority analysis module is used to analyze access priority parameters based on employee data parameters, employee access characteristics, employee group type, and enterprise server status. The server access management module is used to manage the priority of the enterprise server in processing user access requests based on access priority. The access information analysis module is equipped with an employee grouping unit, which is used to group employee information with the same responsibilities into the same employee group; The access information analysis module also includes an access feature analysis unit, which is used to count the number of times each employee accesses the website with the purpose of uploading (as the employee upload count), the number of times each employee accesses the website with the purpose of modifying (as the employee modification count), the number of times each employee accesses the website with the purpose of downloading (as the employee download count), and the number of times each employee accesses the website with the purpose of searching (as the employee search count). Based on the employee upload count B1(i), employee modification count B2(i), employee download count B3(i), and employee search count B4(i), the employee access features are analyzed to obtain employee access feature A(i). Where i represents the employee ID, b k B represents the preset target parameter. k (i) represents the number of times an employee visits a destination, and k represents the number of the destination visited by the employee. The employee data analysis module also includes a data integrity analysis unit, which counts the number of employee information with missing data as the missing number, the number of employee information with duplicate data as the duplicate number, and the number of employee information with complete data as the complete number. The module analyzes the employee data parameters based on the missing number N1, the duplicate number N2, and the complete number N3 to obtain the employee data parameter Q, where Q = (N3 - N1 / 2 - N2 / 2) / N3. The access information analysis module also includes a group characteristic analysis unit, which is used to analyze the group characteristic parameters based on the employee access characteristics A(i) and the employee group to obtain the group characteristic parameters R(j). , where U(j) represents the set of employee IDs in the employee group, j represents the employee group number, and NU(j) represents the number of employee IDs in the employee group; The access information analysis module also includes a group type judgment unit, which is used to compare the group feature parameter R(j) with the feature comparison threshold r, and judge the group type of the employee group based on the comparison result. The group types of the employee group include: Category I and Category II. The priority analysis module is equipped with a priority analysis unit, which is used to analyze the access priority parameter based on the employee data parameter Q and the employee access feature A(i) when the server status of the enterprise server is high load, so as to obtain the access priority parameter F(i), F(i)=Q×A(i); The priority analysis module also includes a group type analysis unit, which adjusts the analysis process of the access priority parameters according to the group type of the employee group. When the employee group is of the same group type, the analysis process of the access priority parameters is adjusted, and the adjusted access priority parameter is F1(i), where F1(i) = F(i) / e -R(j) ; The priority analysis module also includes a lag analysis unit, which is used to take the time interval between the employee's access time when the enterprise server does not respond to the user's access request and the system time of the enterprise server as the lag time when the employee's access purpose is modification. The lag time V(i) is compared with the lag threshold v. If the lag time does not meet the threshold, the process of adjusting the access priority parameter is optimized. The optimized access priority parameter is F2(i), F2(i) = F1(i) × lgV(i).

2. The intelligent platform data processing system based on big data according to claim 1, characterized in that, The employee data analysis module is equipped with an employee data verification unit, which is used to determine the employee data type based on whether the data corresponding to the employee ID in the employee information is missing or unique. The employee data types include: missing data, duplicate data, and complete data.

3. The intelligent platform data processing system based on big data according to claim 1, characterized in that, The server status analysis module uses the ratio of server throughput Z1(t) to maximum throughput Z2 as the server occupancy rate P(t), and compares the server occupancy rate P(t) with the occupancy rate threshold p. Based on the comparison result, the server status of the enterprise server is analyzed to determine the server status of the enterprise server. The server status of the enterprise server includes: low load and high load.

4. The intelligent platform data processing system based on big data according to claim 1, characterized in that, The server access management module manages the priority of the enterprise server in processing user access according to the access priority. The server access management module arranges the access priority parameters from largest to smallest and processes user access requests to the enterprise server in the order of arrangement.

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