Data query system and method based on big data in OA system

Abstract

The invention discloses a data query system and method based on big data in an OA system, and relates to the technical field of data query, and the method comprises the steps: obtaining employee and project data from a database of the OA system, dividing the employee and project data into a plurality of sub-data sets, and building a connection relation between the employee and project data; when a query request of a client is received, performing data matching according to the keyword, and identifying a data category of the matched data; for employee data, screening out first associated data according to a client identity, and further obtaining project data related to the first associated data as second associated data; for project data, third associated data are screened out according to the identity of the client, and employee data related to the third associated data are obtained to serve as fourth associated data; a priority index is calculated according to the historical query data, and query results are sorted and displayed on the client device; and efficient and accurate query of employee and project data in the OA system is realized.

Description

Technical Field

[0001] The present invention relates to the field of data query technology, and in particular to a data query system and method based on big data in an OA system. Background Art

[0002] With the expansion of enterprise scale and the complexity of business, office automation (OA) system plays an increasingly important role in the daily management and operation of enterprises. OA system not only integrates multiple functional modules such as employee information management, project management, attendance management, etc., but also improves the efficiency of data processing and analysis through big data technology, providing strong support for enterprise decision-making. However, in the face of massive and complex data, how to quickly and accurately query and display the required information has become a key problem that OA system needs to solve.

[0003] Traditional OA systems often rely on manual operations for data query, which is not only inefficient but also prone to errors. At the same time, there is a lack of effective association between employee data and project data in traditional OA systems. In actual applications, when querying employee data or project data, the associated projects of an employee or the associated employees in a project cannot be displayed well, which separates the employee data and project data and makes it impossible to form an effective information chain, making it impossible for users to obtain comprehensive information when querying. In addition, traditional query results are often sorted in the order of storage in the database or in simple alphabetical order. The results cannot be prioritized according to the user's actual needs or historical query behavior, making it impossible for the system to provide more accurate and intelligent services. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In response to the technical problems in the background technology, the present invention proposes a data query system and method based on big data in an OA system. For the successfully matched employee and project data, the system filters the related data according to the client identity, and calculates the priority index to sort and display the query results; thereby solving the technical problems recorded in the background technology.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] A data query method based on big data in an OA system, comprising:

[0009] Step 1: Obtain total employee data and total project data from the database of the OA system, divide the data into several employee sub-data and several project sub-data, and then segment the employee sub-data and project sub-data; establish a connection relationship between employee data and project data;

[0010] Step 2: Get the data query request sent by the client, match the query data according to the keyword, and identify the data category of the matching data after the match is successful; if the data category matched by the query request is employee data, proceed to step 3; if the data category matched by the query request is project data, proceed to step 4;

[0011] Step 3: obtain employee sub-data of the matching employee, and filter out first associated data from the employee sub-data of the matched employee according to the identity of the client; obtain project data from the first associated data, and filter out second associated data from the total project data according to the obtained project data; combine the first associated data with the second associated data and send them to the client;

[0012] Step 4: Obtain project sub-data of the matching project, and filter out third related data from the project sub-data of the matched project according to the identity of the client; obtain employee data from the third related data, and filter out fourth related data from the total employee data according to the obtained employee data; combine the third related data with the fourth related data and send them to the client;

[0013] Step 5: Before the first and second associated data or the third and fourth associated data are displayed on the client device, the priority index Pi of each data in the query result is calculated according to the historical query data. i , sort and display the employee data and project data obtained by the query according to the priority index.

[0014] Specifically, all employee data and project data are obtained from the employee database and project database of the OA system, and the obtained employee data and project data are divided into several employee sub-data and project sub-data according to different employees and different projects; each employee sub-data represents a unique employee; each project sub-data represents a unique project;

[0015] The employee sub-data is divided into first employee sub-data and second employee sub-data. The first employee sub-data is the employee's identity data, and the second employee sub-data is the data of each project in which the employee participates, represented by the project name; the project sub-data is divided into first project sub-data, second project sub-data and third project sub-data. The first project sub-data is the general data of the project; the second project sub-data is the employee data included in the project and the task data corresponding to each employee, and the employee data is represented by the employee's name; the third project sub-data is the allocation data and evaluation data of each member's tasks.

[0016] Furthermore, each client in the OA system is matched with the identity of the corresponding user, and the user identities include ordinary employees, project supervisors, and company supervisors; based on employee sub-data and project sub-data, an employee-project association model is constructed; identity associations are established between ordinary employees in the OA system, including relationships between colleagues in different groups, relationships between colleagues in the same group, and project supervisors of the projects they are responsible for, a correspondence between ordinary employees and project supervisors in each project is established, and a correspondence between company supervisors and all employees is established; an API data access interface is constructed, and data access is controlled based on the user's identity and authority.

[0017] Specifically, the data query request sent by the client is obtained, and the search keyword in the data query request is used to match the data; for employee sub-data, the matching keyword is the employee's name; for project sub-data, the matching keyword is the project name;

[0018] During the keyword matching process, if the search keyword in the data query request is completely consistent with one of the keywords in the employee sub-data or project sub-data, the corresponding employee sub-data or project sub-data will be taken as the result of successful matching; if the search keyword in the data query request is partially consistent with several keywords in the employee sub-data or project sub-data, that is, all the characters in the search keyword are continuously included in several keywords in the employee sub-data or project sub-data, then the keywords of the employee sub-data or project sub-data are sent to the client for further selection or determination by the client; if the search keyword in the data query request is inconsistent with all the keywords in the employee sub-data or project sub-data, information indicating that no corresponding result was found is sent to the client.

[0019] Specifically, when the data category matched by the query request is employee data:

[0020] The identity information of the client is compared with the identity of the employee to be queried. If the client is an ordinary employee and is not a colleague of the queried employee in the same group, the first employee sub-data in the employee sub-data of the queried employee is used as the first associated data; if the client is an ordinary employee and is a colleague of the queried employee in the same group or a project manager, the first employee sub-data in the employee sub-data of the queried employee is combined with the corresponding project name data in the second employee sub-data to obtain the first associated data; if the client is the queried employee himself or a company manager, the first employee sub-data in the employee sub-data of the queried employee is combined with all project name data in the second employee sub-data to obtain the first associated data.

[0021] Furthermore, the project data in the first associated data is obtained. If it does not contain project data, the second associated data is empty; if it contains project data, the second associated data is screened according to the identity of the client, specifically: if the client is an ordinary employee and is a colleague in the same group or a project supervisor of the queried employee, the first project sub-data of the corresponding project for which the client is responsible is combined with the second project sub-data to obtain the second associated data; if the client is the queried employee himself or a company supervisor, the first employee sub-data of all projects for which the queried employee is responsible is combined with all project data in the second employee sub-data to obtain the second associated data.

[0022] Specifically, when the data category matched by the query request is project data:

[0023] The client's identity information is combined with the project to be queried. If the client is an ordinary employee and is not a project member of the queried project, the first project sub-data in the project sub-data of the queried project is used as the third associated data; if the client is an ordinary employee and is a project member of the queried project, the first project sub-data in the project sub-data of the queried project is combined with the second project sub-data to obtain the third associated data; if the client is the project supervisor or company supervisor of the queried project, the first project sub-data, the second project sub-data and the third project sub-data in the project sub-data of the queried project are combined to obtain the third associated data.

[0024] Furthermore, the employee data in the third associated data is obtained. If it does not contain employee data, the fourth associated data is empty; if it contains employee data, the fourth associated data is screened according to the identity of the client, specifically: if the client is an ordinary employee and is a project member or project manager of the queried project, the first employee sub-data of all members in the queried project is combined with the project name data of the current project in the second employee sub-data to obtain the fourth associated data; if the client is a company manager, the first employee sub-data of all members in the queried project is combined with the project name data of all projects in the second employee sub-data to obtain the fourth associated data.

[0025] Furthermore, for the several project data or employee data in the query result, the query records of the several project data or employee data are obtained from the historical query data of the current client, including the number of queries n, the timestamp T of each query i And the time t that the client stays on the query result interface i , according to the timestamp T of each query i And the time t that the client stays on the query result interface i Calculate the priority index Pi of all project data or employee data in the query results i , the expression is:

[0026]

[0027] Among them, T represents the current timestamp, i represents the i-th project data or employee data; all project data or employee data in the query results are sorted according to the priority index Pi i Sort by priority index Pi i The order from largest to smallest is displayed on the client page.

[0028] A data query system based on big data in an OA system, comprising:

[0029] The data acquisition and preprocessing module obtains the total employee data and total project data from the database of the OA system, divides the data into several employee sub-data and several project sub-data, and further divides the employee sub-data and project sub-data; establishes the connection relationship between employee data and project data;

[0030] The keyword matching module matches employee sub-data and project sub-data by searching keywords, where employee name and project name are the key matching basis; if the keyword and the data are completely or partially consistent, it is considered a successful match, otherwise it returns information that no results were found;

[0031] The data query module obtains the query results after the match is successful, and is divided into an employee data query unit and a project data query unit; wherein the employee data query unit obtains the employee sub-data keywords that have been matched successfully from the employee waiting queue, and filters the first associated data according to the identity of the client; filters the second associated data from the project database according to the project data in the first associated data, and combines the first associated data with the second associated data and sends them to the client; the project data query unit obtains the project sub-data keywords that have been matched successfully from the project waiting queue, and filters the third associated data according to the identity of the client; filters the fourth associated data from the employee database according to the employee data in the third associated data, and combines the third associated data with the fourth associated data and sends them to the client;

[0032] The query result display module stores the query results in the search result cache, obtains the query records of employee data and project data in this query result from the historical query data and calculates the priority index respectively, sorts the query results according to the priority index and displays them in the client page in the order of priority.

[0033] (III) Beneficial effects

[0034] The present invention provides a data query system and method based on big data in an OA system, which has the following beneficial effects:

[0035] 1. By finely classifying and processing employee and project data in the OA system, a clear association model is constructed, data standardization and cleaning are achieved, data quality is ensured, and data access is controlled based on user identity and permissions, which not only improves the efficiency of data query and analysis, but also effectively ensures data security and privacy;

[0036] 2. By accurately matching the keywords in the data query request sent by the client, efficient retrieval of employee data and project data is achieved; by setting up a waiting queue to manage the successfully matched keywords, it facilitates subsequent data processing and analysis, and effectively improves the accuracy and efficiency of data query;

[0037] 3. Filter out employee-related data (first related data) or project-related data (third related data) of different levels according to the identity of the client, and further obtain project data related to these employees or employee data related to projects, and filter out project-related data (second related data) or employee-related data (fourth related data); by filtering and combining employees and their related project data or employee data according to the identity and authority of the client, it can be ensured that each client can only access the data that it is authorized to view, thereby effectively protecting the privacy and security of the data; at the same time, it also improves the practicality and query efficiency of the data, so that the client can obtain the required information more quickly;

[0038] 4. By calculating the priority index of each data item, the query results are intelligently sorted; this sorting method takes into account the user's historical query behavior, so that data that the user is more likely to be interested in or more important can be displayed first, thereby improving the user experience and enabling users to find the required information more quickly; at the same time, this personalized data sorting method also reflects the in-depth application of big data processing technology in the OA system and improves the intelligence level of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flowchart of the steps of the data query method based on big data in the OA system provided by the present invention;

[0040] Figure 2 This is a schematic diagram of the structure of a data query system based on big data in the OA system provided by the present invention. DETAILED DESCRIPTION

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

[0042] refer to Figure 1 The present invention provides a data query method based on big data in an OA system, comprising:

[0043] Step 1: Obtain total employee data and total project data from the database of the OA system, divide the data into several employee sub-data and several project sub-data, and then segment the employee sub-data and project sub-data; establish a connection relationship between employee data and project data;

[0044] The step 1 comprises the following steps:

[0045] Step 101: Obtain all employee data and project data from the employee database and project database of the OA system, and divide the obtained employee data and project data into several employee sub-data and project sub-data according to different employees and different projects; each employee sub-data represents a unique employee, including the employee's basic information, work history, and participating projects; each project sub-data represents a unique project, including detailed information of the project, participating employees, and project progress;

[0046] Step 102: The employee sub-data is divided into first employee sub-data and second employee sub-data. The first employee sub-data is the employee's identity data, including basic information, work history and other basic data. The second employee sub-data is the data of each project in which the employee participates, represented by the project name. The project sub-data is divided into first project sub-data, second project sub-data and third project sub-data. The first project sub-data is the general data of the project, that is, the brief information of the project, providing a basic overview of the project. The second project sub-data is the employee data included in the project and the task data corresponding to each employee. The employee data is represented by the employee's name. The project team members can view the task allocation of all project members, but cannot adjust and allocate tasks. The third project sub-data is the allocation data and evaluation data of each member's task. The project supervisor and the company supervisor can view the task allocation of all project members and can adjust and reallocate tasks.

[0047] Step 103: Clean the acquired employee data and project data to remove duplicate, incomplete or erroneous information; standardize the data to ensure consistency of data format to facilitate subsequent analysis and processing;

[0048] Step 104: Match each client in the OA system with the identity of the corresponding user, where the user identities include ordinary employees, project supervisors, and company supervisors. A project involves multiple ordinary employees and a project supervisor. The project supervisor manages all ordinary employees in a project, and the company supervisor manages all employees of the entire company.

[0049] Based on employee sub-data and project sub-data, an association model is constructed to clarify the projects each employee participates in, the role in the project, and the specific circumstances of the project; in addition, identity associations are established between ordinary employees in the OA system, including relationships between colleagues in different groups, relationships between colleagues in the same group, and project supervisors of the projects they are responsible for, a corresponding relationship between ordinary employees and project supervisors in each project, and a corresponding relationship between company supervisors and all employees; an API data access interface is constructed, and data access is controlled based on the user's identity and permissions.

[0050] When using, combine the contents in steps 101 to 104:

[0051] By finely classifying and processing employee and project data in the OA system, a clear association model was constructed, data standardization and cleansing were achieved, and data quality was ensured. At the same time, data access was controlled based on user identity and permissions, which not only improved the efficiency of data query and analysis, but also effectively ensured data security and privacy.

[0052] Step 2: Get the data query request sent by the client, match the query data according to the keyword, and identify the data category of the matching data after the match is successful; if the data category matched by the query request is employee data, proceed to step 3; if the data category matched by the query request is project data, proceed to step 4;

[0053] The step 2 comprises the following steps:

[0054] Step 201: Obtain a data query request sent by a client. The search keyword in the data query request is used to match data. For employee sub-data, the matching keyword is the employee's name. If there are employees with the same name, the specific department of the queried employee needs to be provided for further query. For project sub-data, the matching keyword is the project name.

[0055] Step 202: During the keyword matching process, if the search keyword in the data query request is completely consistent with one of the keywords in the employee sub-data or project sub-data, the corresponding employee sub-data or project sub-data is taken as a successful matching result; if the search keyword in the data query request is partially consistent with several keywords in the employee sub-data or project sub-data, that is, all the characters in the search keyword are continuously included in several keywords in the employee sub-data or project sub-data, the keywords of the employee sub-data or project sub-data are sent to the client for further selection or determination by the client; if the search keyword in the data query request is inconsistent with all the keywords in the employee sub-data or project sub-data, information indicating that no corresponding result is found is sent to the client;

[0056] Step 203: For the successfully matched keywords, determine the data category that matches the query request. If the data category that matches the query request is employee data, proceed to step three and record the corresponding employee sub-data keywords in the employee waiting queue; if the data category that matches the query request is project data, proceed to step four and record the corresponding project sub-data keywords in the project waiting queue.

[0057] When using, combine the contents in steps 201 to 203:

[0058] By accurately matching the keywords in the data query requests sent by the client, efficient retrieval of employee data and project data is achieved; by setting up a waiting queue to manage successfully matched keywords, it facilitates subsequent data processing and analysis, effectively improving the accuracy and efficiency of data queries.

[0059] Step 3: obtain employee sub-data of the matching employee, and filter out first associated data from the employee sub-data of the matched employee according to the identity of the client; obtain project data from the first associated data, and filter out second associated data from the total project data according to the obtained project data; combine the first associated data with the second associated data and send them to the client;

[0060] The step three comprises the following steps:

[0061] Step 301: Take out the employee sub-data keyword that matches successfully from the employee waiting queue, and obtain the employee sub-data that matches it from the employee database of the OA system, including the first employee sub-data and the second employee sub-data;

[0062] Step 302: compare the identity information of the client with the identity of the employee to be queried. If the client is an ordinary employee and is not a colleague of the queried employee in the same group, the first employee sub-data in the employee sub-data of the queried employee is used as the first associated data; if the client is an ordinary employee and is a colleague of the queried employee in the same group or a project manager, the first employee sub-data in the employee sub-data of the queried employee is combined with the corresponding project name data in the second employee sub-data to obtain the first associated data; if the client is the queried employee himself or a company manager, the first employee sub-data in the employee sub-data of the queried employee is combined with all the project name data in the second employee sub-data to obtain the first associated data;

[0063] Step 303, obtaining the project data in the first associated data. If the first associated data does not contain the project data, the second associated data is empty; if the second associated data contains the project data, the second associated data is screened according to the identity of the client. Specifically, if the client is an ordinary employee and is a colleague of the queried employee or a project supervisor, the first project sub-data of the corresponding project in charge is combined with the second project sub-data to obtain the second associated data; if the client is the queried employee himself or a company supervisor, the first employee sub-data of all projects in charge of the queried employee is combined with all project data in the second employee sub-data to obtain the second associated data;

[0064] Step 304: Combine the first associated data and the second associated data and send them to the client.

[0065] When used, combine the contents in steps 301 to 304:

[0066] According to the identity of the client, employee-related data (first related data) of different levels are filtered out, and project data related to these employees are further obtained to filter out project-related data (second related data); by filtering and combining employee and related project data according to the identity and authority of the client, it can be ensured that each client can only access the data that it has the right to view, thereby effectively protecting the privacy and security of the data; at the same time, it also improves the practicality and query efficiency of the data, so that the client can obtain the required information more quickly.

[0067] Step 4: Obtain project sub-data of the matching project, and filter out third related data from the project sub-data of the matched project according to the identity of the client; obtain employee data from the third related data, and filter out fourth related data from the total employee data according to the obtained employee data; combine the third related data with the fourth related data and send them to the client;

[0068] Step 401: Take out the successfully matched project sub-data keyword from the project waiting queue, and obtain the matching project sub-data from the project database of the OA system, including the first project sub-data, the second project sub-data and the third project sub-data;

[0069] Step 402: Combine the identity information of the client with the project to be queried. If the client is an ordinary employee and is not a project member of the queried project, the first project sub-data in the project sub-data of the queried project is used as the third associated data; if the client is an ordinary employee and is a project member of the queried project, combine the first project sub-data with the second project sub-data in the project sub-data of the queried project to obtain the third associated data; if the client is a project supervisor or company supervisor of the queried project, combine the first project sub-data, the second project sub-data and the third project sub-data in the project sub-data of the queried project to obtain the third associated data;

[0070] Step 403, obtaining employee data in the third associated data, if the third associated data does not contain employee data, the fourth associated data is empty; if the third associated data contains employee data, the fourth associated data is screened according to the identity of the client, specifically: if the client is an ordinary employee and a project member or project manager of the queried project, the first employee sub-data of all members in the queried project is combined with the project name data of the current project in the second employee sub-data to obtain the fourth associated data; if the client is a company manager, the first employee sub-data of all members in the queried project is combined with the project name data of all projects in the second employee sub-data to obtain the fourth associated data;

[0071] Step 404: Combine the third associated data with the fourth associated data and send them to the client.

[0072] When using, combine the contents in steps 401 to 404:

[0073] According to the identity of the client, project-related data (third related data) of different levels are filtered out, and the employee data related to these projects is further obtained to filter out the employee-related data (fourth related data); by filtering and combining projects and their associated employee data according to the identity and authority of the client, it can be ensured that each client can only access the data that it has the right to view, thereby effectively protecting the privacy and security of the data; at the same time, it also improves the practicality and query efficiency of the data, so that the client can obtain the required information more quickly.

[0074] Step 5: Before the first and second associated data or the third and fourth associated data are displayed on the client device, the priority index Pi of each data in the query result is calculated according to the historical query data. i , sort and display the employee data and project data obtained by the query according to the priority index respectively;

[0075] The step five comprises the following steps:

[0076] Step 501: store the first and second associated data or the third and fourth associated data obtained by the query in the search result cache of the client; wherein the first associated data and the fourth associated data in the query result include a number of project data, and the second associated data and the third associated data include a number of employee data;

[0077] Step 502: For the project data or employee data in the query result, obtain the query records of the project data or employee data from the historical query data of the current client, including the number of queries n, the timestamp T of each query, and the query result. i And the time t that the client stays on the query result interface i , according to the timestamp T of each query i And the time t that the client stays on the query result interface i Calculate the priority index Pi of all project data or employee data in the query results i , the expression is:

[0078]

[0079] Where T represents the current timestamp, and i represents the i-th project data or employee data;

[0080] Step 503: sort all the project data or employee data in the query result according to the priority index Pi i Sort by priority index Pi i The order from largest to smallest is displayed on the client page.

[0081] When using, combine the contents in steps 501 to 503:

[0082] By calculating the priority index of each data item, the query results are intelligently sorted; this sorting method takes into account the user's historical query behavior, so that the data that the user is more likely to be interested in or more important can be displayed first, thereby improving the user experience and enabling users to find the required information more quickly;

[0083] At the same time, this personalized data sorting method also reflects the in-depth application of big data processing technology in OA systems, improving the intelligence level of the system and user satisfaction.

[0084] refer to Figure 2 The present invention also provides a data query system based on big data in an OA system, comprising:

[0085] The data acquisition and preprocessing module obtains the total employee data and total project data from the database of the OA system, divides the data into several employee sub-data and several project sub-data, and further divides the employee sub-data and project sub-data; establishes the connection relationship between employee data and project data;

[0086] The keyword matching module matches employee sub-data and project sub-data by searching keywords, where employee name and project name are the key matching basis; if the keyword and the data are completely or partially consistent, it is considered a successful match, otherwise it returns information that no results were found;

[0087] The data query module obtains the query results after the match is successful, and is divided into an employee data query unit and a project data query unit; wherein the employee data query unit obtains the employee sub-data keywords that have been matched successfully from the employee waiting queue, and filters the first associated data according to the identity of the client; filters the second associated data from the project database according to the project data in the first associated data, and combines the first associated data with the second associated data and sends them to the client; the project data query unit obtains the project sub-data keywords that have been matched successfully from the project waiting queue, and filters the third associated data according to the identity of the client; filters the fourth associated data from the employee database according to the employee data in the third associated data, and combines the third associated data with the fourth associated data and sends them to the client;

[0088] The query result display module stores the query results in the search result cache, obtains the query records of employee data and project data in this query result from the historical query data and calculates the priority index respectively, sorts the query results according to the priority index and displays them in the client page in the order of priority.

[0089] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer storage medium or transmitted via a computer storage medium.

[0090] Computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. Computer storage media can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid state drives (SSDs)).

[0091] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A data query method based on big data in an OA system, characterized by: include: Step 1: Obtain total employee data and total project data from the database of the OA system, divide the data into several employee sub-data and several project sub-data, and then segment the employee sub-data and project sub-data; establish a connection relationship between employee data and project data; Step 2: Get the data query request sent by the client, match the query data according to the keyword, and identify the data category of the matching data after the match is successful; if the data category matched by the query request is employee data, proceed to step 3; if the data category matched by the query request is project data, proceed to step 4; Step 3: obtain employee sub-data of the matching employee, and filter out first associated data from the employee sub-data of the matched employee according to the identity of the client; obtain project data from the first associated data, and filter out second associated data from the total project data according to the obtained project data; combine the first associated data with the second associated data and send them to the client; Step 4: Obtain project sub-data of the matching project, and filter out third related data from the project sub-data of the matched project according to the identity of the client; obtain employee data from the third related data, and filter out fourth related data from the total employee data according to the obtained employee data; combine the third related data with the fourth related data and send them to the client; Step 5: Before the first and second associated data or the third and fourth associated data are displayed on the client device, the priority index Pi of each data in the query result is calculated according to the historical query data. i , sort and display the employee data and project data obtained by the query according to the priority index.

2. A data query method based on big data in an OA system according to claim 1, characterized in that: Obtain all employee data and project data from the employee database and project database of the OA system, and divide the obtained employee data and project data into several employee sub-data and project sub-data according to different employees and different projects; The employee sub-data is divided into first employee sub-data and second employee sub-data. The first employee sub-data is the employee's identity data, and the second employee sub-data is the data of each project in which the employee participates, represented by the project name; the project sub-data is divided into first project sub-data, second project sub-data and third project sub-data. The first project sub-data is the general data of the project; the second project sub-data is the employee data included in the project and the task data corresponding to each employee, and the employee data is represented by the employee's name; the third project sub-data is the allocation data and evaluation data of each member's tasks.

3. A data query method based on big data in an OA system according to claim 2, characterized in that: Match each client in the OA system with the identity of the corresponding user, including ordinary employees, project supervisors, and company supervisors; build an employee-project association model based on employee sub-data and project sub-data; establish identity associations between ordinary employees in the OA system, including relationships between colleagues in different groups, relationships between colleagues in the same group, and project supervisors of the projects they are responsible for, establish a corresponding relationship between ordinary employees and project supervisors in each project, and establish a corresponding relationship between company supervisors and all employees; build an API data access interface, and control data access based on user identities and permissions.

4. A data query method based on big data in an OA system according to claim 3, characterized in that: Get the data query request sent by the client. The search keyword in the data query request is used to match the data. For employee sub-data, the matching keyword is the employee's name; for project sub-data, the matching keyword is the project name. During the keyword matching process, if the search keyword in the data query request is completely consistent with one of the keywords in the employee sub-data or project sub-data, the corresponding employee sub-data or project sub-data will be taken as the result of successful matching; if the search keyword in the data query request is partially consistent with several keywords in the employee sub-data or project sub-data, that is, all the characters in the search keyword are continuously included in several keywords in the employee sub-data or project sub-data, then the keywords of the employee sub-data or project sub-data are sent to the client for further selection or determination by the client; if the search keyword in the data query request is inconsistent with all the keywords in the employee sub-data or project sub-data, information indicating that no corresponding result was found is sent to the client.

5. A data query method based on big data in an OA system according to claim 4, characterized in that: When the data category matched by the query request is employee data: The identity information of the client is compared with the identity of the employee to be queried. If the client is an ordinary employee and is not a colleague of the queried employee in the same group, the first employee sub-data in the employee sub-data of the queried employee is used as the first associated data; if the client is an ordinary employee and is a colleague of the queried employee in the same group or a project manager, the first employee sub-data in the employee sub-data of the queried employee is combined with the corresponding project name data in the second employee sub-data to obtain the first associated data; if the client is the queried employee himself or a company manager, the first employee sub-data in the employee sub-data of the queried employee is combined with all project name data in the second employee sub-data to obtain the first associated data.

6. A data query method based on big data in an OA system according to claim 5, characterized in that: The project data in the first associated data is obtained. If the project data is not contained therein, the second associated data is empty; if the project data is contained therein, the second associated data is screened according to the identity of the client, specifically: if the client is an ordinary employee and is a colleague in the same group or a project supervisor of the queried employee, the first project sub-data of the corresponding project in charge is combined with the second project sub-data to obtain the second associated data; if the client is the queried employee himself or a company supervisor, the first employee sub-data of all projects in charge of the queried employee is combined with all project data in the second employee sub-data to obtain the second associated data.

7. A data query method based on big data in an OA system according to claim 6, characterized in that: When the data category matched by the query request is project data: The client's identity information is combined with the project to be queried. If the client is an ordinary employee and is not a project member of the queried project, the first project sub-data in the project sub-data of the queried project is used as the third associated data; if the client is an ordinary employee and is a project member of the queried project, the first project sub-data in the project sub-data of the queried project is combined with the second project sub-data to obtain the third associated data; if the client is the project supervisor or company supervisor of the queried project, the first project sub-data, the second project sub-data and the third project sub-data in the project sub-data of the queried project are combined to obtain the third associated data.

8. A data query method based on big data in an OA system according to claim 7, characterized in that: The employee data in the third associated data is obtained. If the third associated data does not contain employee data, the fourth associated data is empty; if the third associated data contains employee data, the fourth associated data is screened according to the identity of the client, specifically: if the client is an ordinary employee and is a project member or project manager of the queried project, the first employee sub-data of all members in the queried project is combined with the project name data of the current project in the second employee sub-data to obtain the fourth associated data; if the client is a company manager, the first employee sub-data of all members in the queried project is combined with the project name data of all projects in the second employee sub-data to obtain the fourth associated data.

9. A data query method based on big data in an OA system according to claim 8, characterized in that: For the several project data or employee data in the query result, the query records of the several project data or employee data are obtained from the historical query data of the current client, including the number of queries n, the timestamp T of each query i And the time t that the client stays on the query result interface i , according to the timestamp T of each query i And the time t that the client stays on the query result interface i Calculate the priority index Pi of all project data or employee data in the query results i , the expression is: Among them, T represents the current timestamp, i represents the i-th project data or employee data; all project data or employee data in the query results are sorted according to the priority index Pi i Sort by priority index Pi i The order from largest to smallest is displayed on the client page.

10. A data query system based on big data in an OA system, characterized in that: include: The data acquisition and preprocessing module obtains the total employee data and total project data from the database of the OA system, divides the data into several employee sub-data and several project sub-data, and further divides the employee sub-data and project sub-data; establishes the connection relationship between employee data and project data; The keyword matching module matches employee sub-data and project sub-data by searching keywords, where employee name and project name are the key matching basis; if the keyword and the data are completely or partially consistent, it is considered a successful match, otherwise it returns information that no results were found; The data query module obtains the query results after the match is successful, and is divided into an employee data query unit and a project data query unit; wherein the employee data query unit obtains the employee sub-data keywords that have been matched successfully from the employee waiting queue, and filters the first associated data according to the identity of the client; filters the second associated data from the project database according to the project data in the first associated data, and combines the first associated data with the second associated data and sends them to the client; the project data query unit obtains the project sub-data keywords that have been matched successfully from the project waiting queue, and filters the third associated data according to the identity of the client; filters the fourth associated data from the employee database according to the employee data in the third associated data, and combines the third associated data with the fourth associated data and sends them to the client; The query result display module stores the query results in the search result cache, obtains the query records of employee data and project data in this query result from the historical query data and calculates the priority index respectively, sorts the query results according to the priority index and displays them in the client page in the order of priority.

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