Methods, devices, storage media, and computer equipment for matching reviewers

Abstract

This application discloses a method, apparatus, storage medium, and computer equipment for matching reviewers, relating to the field of office automation. This application calculates the capability scores of all reviewers corresponding to the current unit, then calculates the required capability score of the current unit, and finally calculates the matching rate between each reviewer and the current unit. The reviewer with the highest matching rate is selected as the optimal reviewer for the current unit. This method of selecting the optimal reviewer based on the matching between the capability scores of the reviewers in a unit and the required capability score of the current unit reduces the workload of selecting the optimal reviewer for each unit, greatly improving review effectiveness and efficiency.

Description

Technical Field

[0001] This application relates to the field of office automation, and more particularly to a method, apparatus, storage medium, and computer equipment for matching reviewers. Background Technology

[0002] Power generation companies are required to conduct regular safety, health, and environmental (HHE) system assessments. These assessments include self-assessment, two-party reviews, and external third-party reviews, covering 13 units and 58 elements, involving multiple areas of expertise. The assessment determines whether the company's system meets standards and is assigned a rating. Currently, the system assessment workflow involves: before the assessment begins, the user selects professional assessors with HHE management experience or auditors with prior system assessment experience to form a system assessment team. The assessment tasks are then assigned to each assessor according to the system assessment elements and workload. Therefore, the current method of manually selecting assessors is inefficient and lacks accuracy. Similar problems exist in other system assessments. Summary of the Invention

[0003] This application provides a method, apparatus, storage medium, and computer equipment for matching reviewers, which can solve the problems of low accuracy and low efficiency caused by manual selection of reviewers in the prior art. The technical solution is as follows:

[0004] In a first aspect, embodiments of this application provide a method for matching reviewers, the method comprising:

[0005] Select the current unit from the unit list of the review management system;

[0006] The system retrieves all reviewers in the expert database who are proficient in a particular unit, including the current unit, and generates a list of review experts based on the query results.

[0007] Calculate the competency score of each reviewer in the list of review experts using the following formula:

[0008] Where n1 and n2 represent the integrals associated with the current unit, n1≠n2, n represents the number of times the reviewer reviews the current unit, and q represents the weight value, which is related to the most recent time the reviewer participated in the review of the current unit;

[0009] The required capacity integral of the current unit is calculated according to the following formula:

[0010] Where t represents the number of elements contained in the current unit, a represents the element score, m represents the difficulty level, n represents the workload, and g represents the correlation coefficient with other units;

[0011] Based on the competency scores of each reviewer and the required competency scores, the matching rate between each reviewer and the current unit is calculated;

[0012] The reviewer with the highest matching rate will be selected as the optimal reviewer.

[0013] Secondly, embodiments of this application provide a reviewer matching device, the device comprising:

[0014] Select Unit: Used to select the current unit from the unit list of the review management system;

[0015] The query unit is used to query all reviewers in the expert database who are proficient in a particular unit, including the current unit, and to generate a list of review experts based on the query results.

[0016] The calculation unit is used to calculate the competency score of each reviewer in the list of review experts according to the following formula:

[0017] Where n1 and n2 represent the integrals associated with the current unit, n1≠n2, n represents the number of times the reviewer reviews the current unit, and q represents the weight value, which is related to the most recent time the reviewer participated in the review of the current unit;

[0018] The calculation unit is also used to calculate the demand capacity integral of the current unit according to the following formula:

[0019] Where t represents the number of elements contained in the current unit, a represents the element score, m represents the difficulty level, n represents the workload, and g represents the correlation coefficient with other units;

[0020] The calculation unit is also used to calculate the matching rate between each reviewer and the current unit based on the ability score of each reviewer and the required ability score;

[0021] The matching unit is used to select the reviewer with the highest matching rate as the optimal reviewer.

[0022] Thirdly, embodiments of this application provide a computer storage medium storing a plurality of instructions adapted for loading by a processor and executing the above-described method steps.

[0023] Fourthly, embodiments of this application provide a computer device, which may include: a processor and a memory; wherein the memory stores a computer program, the computer program being adapted to be loaded by the processor and to execute the above-described method steps.

[0024] The beneficial effects of the technical solutions provided in some embodiments of this application include at least the following:

[0025] By calculating the competence scores of all reviewers in the current unit, then calculating the required competence scores of the current unit, and finally calculating the matching rate between each reviewer and the current unit, the reviewer with the highest matching rate is selected as the optimal reviewer for the current unit. This application selects the optimal reviewer based on the matching method between the competence scores of the unit's reviewers and the required competence scores of the current unit. This reduces the workload of selecting the optimal reviewer for each unit and greatly improves the review effect and efficiency. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a schematic diagram of the system architecture provided in the embodiments of this application;

[0028] Figure 2 This is a flowchart illustrating the reviewer matching method provided in the embodiments of this application;

[0029] Figure 3 This is a schematic diagram of the structure of a matching device for reviewers provided in this application;

[0030] Figure 4 This is a schematic diagram of the structure of a computer device provided in this application. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0032] It should be noted that the reviewer matching method provided in this application is generally executed by computer equipment, and correspondingly, the reviewer matching device is generally set in the computer equipment.

[0033] Figure 1 An exemplary system architecture is shown for a reviewer matching method or a reviewer matching device that can be applied to this application.

[0034] like Figure 1As shown, the system architecture may include a computer device 101 and a server 102. The computer device 101 and the server 102 can communicate via a network, which serves as the medium for providing communication links between the various units. The network may include various types of wired or wireless communication links, such as wired communication links including fiber optic cables, twisted-pair cables, or coaxial cables, and wireless communication links including Bluetooth communication links, Wi-Fi communication links, or microwave communication links, etc.

[0035] The server 102 stores attribute information for each unit, including but not limited to: the number of elements, the score of each element, the difficulty level of each element, the workload of each element, and the correlation coefficient between each element and other units. Additionally, the server 102 stores attribute information for each reviewer, including but not limited to: units they are proficient in, the number of reviews they have conducted in each unit, the review time for each unit, and the corresponding points for each unit. The computer device 102 reads the attribute information stored in the server 102 to calculate the most suitable reviewer for the current unit.

[0036] It should be noted that computer device 101 and server 102 can be either hardware or software. When computer device 101 and server 102 are hardware, they can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When computer device 101 and server 102 are software, they can be implemented as multiple software programs or software modules (e.g., used to provide distributed services), or as a single software program or software module; no specific limitations are made here.

[0037] The computer device described in this application can be equipped with various communication client applications, such as video recording applications, video playback applications, voice interaction applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0038] Computer devices can be either hardware or software. When a computer device is hardware, it can be any computer device with a display screen, including but not limited to smartphones, tablets, laptops, and desktop computers. When a computer device is software, it can be installed on the computer devices listed above. It can be implemented as multiple software programs or software modules (e.g., used to provide distributed services) or as a single software program or software module; no specific limitation is made here.

[0039] When a computer device is used as hardware, it can also be equipped with a display device and a camera. The display device can be any device capable of displaying information, and the camera is used to capture video streams. Examples of display devices include cathode ray tube displays (CR), light-emitting diode displays (LED), e-ink screens, liquid crystal displays (LCD), and plasma display panels (PDP). Users can utilize the display device on their computer to view displayed text, images, videos, and other information.

[0040] It should be understood that Figure 1 The number of computer devices, networks, and servers shown is for illustrative purposes only. The number of computer devices, networks, and servers can be any number, depending on the implementation requirements.

[0041] The following will be combined with the appendix Figure 2 This application provides a detailed description of the reviewer matching method provided in its embodiments. The reviewer matching device in these embodiments may be... Figure 1 The computer equipment shown is illustrated. Furthermore, this application uses an environmental, health, and safety management system as an example to illustrate the invention.

[0042] Please see Figure 2 This is a flowchart illustrating a method for matching reviewers, as provided in this application embodiment. Figure 1 As shown, the method described in this application embodiment may include the following steps:

[0043] S201. Select the current unit from the unit list of the health, safety and environmental management system.

[0044] The Occupational Health and Safety Management System (System) refers to an integrated assessment and management system for Occupational Health and Safety (OHSAS 18001) and Environment (ISO 14001). This system is associated with a unit list, which contains multiple units, each containing multiple elements. Units represent major assessment items, and elements represent specific assessment content within those items. For example, the unit list might contain 13 units: Safety, Health and Environment Concepts and Policies, Organizational Assurance Management, Hazard Identification and Risk Assessment, Workplace Management, Production Equipment Management, Production Management, Occupational Health System, Environmental Protection, Behavior Management, Incident Management, Emergency Management and Response, and Monitoring, Corrective and Preventive Measures. This application iterates through the unit list, selecting a current unit each time, until the optimal assessor is matched for all units.

[0045] S202. Search the expert database for all reviewers specializing in a particular unit, including the current unit, and generate a list of review experts based on the search results.

[0046] This application pre-stores an expert database, which includes multiple reviewers. Each reviewer's associated attributes include: identity identifier, area of ​​expertise, number of areas of expertise, and number of reviews conducted. A reviewer can have one or more areas of expertise. A query in the expert database will retrieve all reviewers whose areas of expertise include "Behavior Management." For example, if the current area's field is "Behavior Management," a query in the expert database will retrieve all reviewers whose areas of expertise include "Behavior Management." Based on the query results, a list of review experts will be generated, which can contain one or more reviewers.

[0047] In one or more possible embodiments, it also includes:

[0048] If no reviewer with expertise in a given unit is found in the expert database, the reviewer with the most reviews in the expert database will be selected as the optimal reviewer.

[0049] For example, if the current unit's field is "behavior management", and the expert database includes reviewer 1, reviewer 2, and reviewer 3, and no reviewer with expertise in the current unit is found in the expert database, then the number of reviews for each reviewer is obtained: reviewer 1 has reviewed 100 times, reviewer 2 has reviewed 80 times, and reviewer 3 has reviewed 70 times. Therefore, reviewer 1 is selected as the optimal reviewer for the current unit.

[0050] S203. Calculate the competency score of each reviewer in the list of review experts.

[0051] This involves calculating the competency score of each reviewer in the expert reviewer list. Assuming the expert reviewer list contains w reviewers, where w is an integer greater than or equal to 1, the competency scores of each of the w reviewers are calculated to obtain w competency scores. These competency scores can be expressed on a percentage or ten-point scale; this application does not impose any restrictions. This application calculates the reviewers' competency scores using the following formula:

[0052]

[0053] Wherein, n1 represents the integral associated with the current unit. This application pre-stores the mapping relationship between units, integral n1, and integral n2. The integrals n1 and n2 associated with different units may not be equal. n represents the number of times the reviewer reviews the current unit. Each time the reviewer participates in the review of the current unit, the integral n2 is added. q represents the weight value, which is related to the most recent time when the reviewer participated in the review of the current unit. The closer the most recent time is to the current time, the larger the value of q. The farther the most recent time is from the current time, the smaller the value of q.

[0054] Further optionally, when the time interval between the most recent time of the reviewer's participation in the current unit and the current time is less than or equal to 3 years, q = 1.0; when the time interval is greater than 3 years and less than or equal to 5 years, q = 0.8; when the time interval is greater than 5 years, q = 0.5.

[0055] S204. Calculate the demand capacity integral of the current unit.

[0056] The required capability scores for different units may differ. Both the required capability scores and the reviewer's capability scores use the same counting method, such as a percentage or a ten-point scale. This application calculates the required capability score for the current unit using the following formula:

[0057]

[0058] Where t represents the number of elements contained in the current unit, a represents the element score, m represents the difficulty level, n represents the workload, and g represents the correlation coefficient with other units. This application pre-stores attribute information for each element contained in each unit, including: element ID, element score, difficulty level, workload, and correlation coefficient between the element and other units. The demand capability score for the current unit is calculated based on the pre-stored attribute information. The values ​​of difficulty level, workload, and correlation coefficient between the element and other units can range from 0 to 1, and the values ​​of difficulty level, workload, and correlation coefficient are positively correlated with the numerical values.

[0059] S205. Calculate the matching rate between each reviewer and the current unit based on each reviewer's competency score and required competency score.

[0060] The matching rate represents the degree of matching between the reviewer and the current unit. Optionally, this application uses the deviation between the reviewer's competence score and the current unit's required competence score to represent the matching rate. The smaller the deviation, the better the match, and vice versa.

[0061] Optionally, this application calculates the matching rate between the reviewer and the current unit according to the following formula:

[0062] Where P represents the reviewer's matching rate, F represents the reviewer's competence score, and T represents the current unit's required competence score.

[0063] S206. Select the reviewer with the highest matching rate as the optimal reviewer.

[0064] Among them, multiple matching rates calculated in S205 are obtained, and the reviewer with the highest matching rate is selected as the optimal reviewer. The highest matching rate indicates the highest degree of matching.

[0065] In one or more possible embodiments, it also includes:

[0066] If there are multiple maximum values ​​in the calculated matching rates, then the reviewer who is most proficient in the most units among the reviewers corresponding to the multiple maximum values ​​will be the optimal reviewer.

[0067] For example, if the matching rate of reviewer 1 and reviewer 2 calculated in S206 is 95% and is the maximum value, then the number of reviewer 1's areas of expertise is 2 and the number of reviewer 2's areas of expertise is 5, and reviewer 2 is selected as the optimal reviewer.

[0068] In one or more possible embodiments, it also includes:

[0069] Generate the review task information of the optimal reviewer and push the review task information to the mobile terminal of the optimal reviewer; wherein, the review task information includes the following parameters: task ID, unit identifier, reviewer identifier, review date, and review elements.

[0070] The task ID represents a unique identifier for the review task performed by the reviewer; the unit identifier represents a unique identifier for the unit that the reviewer will be reviewing; and the reviewer identifier represents a unique identifier for the reviewer performing the review task. The review date is the date the review task is performed, and the review elements are the various elements contained in the unit that will be reviewed. This application can store the communication accounts of each reviewer, such as email addresses, mobile phone numbers, or instant messaging accounts, and send the review task information to the corresponding mobile terminal through the pre-bound communication accounts so that the reviewers can be informed of the details of the review task.

[0071] Furthermore, this application also includes:

[0072] When the number of days between the current date and the review date equals a threshold, a review task reminder is sent to the mobile terminal of the optimal reviewer.

[0073] The threshold can be determined according to actual needs. For example, if the threshold is 1 day, when the interval between the current date and the review date of the review task is equal to 1 day, a reminder will be sent to the corresponding mobile terminal according to the pre-bound communication account so that the reviewer can perform the review task on the specified date and avoid erroneous review tasks.

[0074] This application's embodiments calculate the capability scores of all reviewers corresponding to the current unit, then calculate the required capability scores of the current unit, and finally calculate the matching rate between each reviewer and the current unit. The reviewer with the highest matching rate is selected as the optimal reviewer for the current unit. This application selects the optimal reviewer based on the matching method between the capability scores of the unit's reviewers and the required capability scores of the current unit, which reduces the workload of selecting the optimal reviewer for each unit and greatly improves the review effect and review efficiency.

[0075] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0076] Please see Figure 3 This illustration shows a schematic diagram of a reviewer matching device provided in an exemplary embodiment of this application, hereinafter referred to as device 3. Device 3 can be implemented as all or part of a computer device through software, hardware, or a combination of both. Device 3 includes: a selection unit 301, a query unit 302, a calculation unit 303, and a matching unit 304.

[0077] Select Unit 301 to select the current unit from the unit list of the health, safety and environmental management system;

[0078] The query unit 302 is used to query all reviewers in the expert database who are proficient in the current unit, and to generate a list of review experts based on the query results.

[0079] Calculation unit 303 is used to calculate the competency score of each reviewer in the list of review experts according to the following formula:

[0080] Where n1 and n2 represent the integrals associated with the current unit, n1≠n2, n represents the number of times the reviewer reviews the current unit, and q represents the weight value, which is related to the most recent time the reviewer participated in the review of the current unit;

[0081] The calculation unit 303 is also used to calculate the demand capacity integral of the current unit according to the following formula:

[0082] Where t represents the number of elements contained in the current unit, a represents the element score, m represents the difficulty level, n represents the workload, and g represents the correlation coefficient with other units;

[0083] The calculation unit 303 is also used to calculate the matching rate between each reviewer and the current unit based on the ability score of each reviewer and the required ability score;

[0084] Matching unit 304 is used to select the reviewer with the highest matching rate as the optimal reviewer.

[0085] In one or more possible embodiments, calculating the matching rate between each reviewer and the current unit based on the competence scores of each reviewer and the required competence scores includes:

[0086] The matching rate between each review unit and the current unit is calculated using the following formula:

[0087] Where P represents the reviewer's matching rate, F represents the reviewer's competence score, and T represents the current unit's required competence score.

[0088] In one or more possible embodiments, the matching unit 304 is further configured to:

[0089] If there are multiple maximum values ​​in the calculated matching rates, then the reviewer who is most proficient in the most units among the reviewers corresponding to the multiple maximum values ​​will be the optimal reviewer.

[0090] In one or more possible embodiments, the matching unit 304 is further configured to:

[0091] If no reviewer with expertise in a given unit is found in the expert database, the reviewer with the most reviews in the expert database will be selected as the optimal reviewer.

[0092] In one or more possible embodiments, it also includes:

[0093] The push unit is used to generate the review task information of the best reviewer and push the review task information to the mobile terminal of the best reviewer; wherein, the review task information includes the following parameters: task ID, unit identifier, reviewer identifier, review date, and review elements.

[0094] In one or more possible embodiments, the push unit is further configured to:

[0095] When the number of days between the current date and the review date equals a threshold, a review task reminder is sent to the mobile terminal of the optimal reviewer.

[0096] In one or more possible embodiments, q = 1.0 when the time interval between the most recent time of reviewer participation in the current unit and the current time is less than or equal to 3 years; q = 0.8 when the time interval is greater than 3 years and less than or equal to 5 years; and q = 0.5 when the time interval is greater than 5 years.

[0097] It should be noted that the device 3 provided in the above embodiments, when executing the reviewer matching method, is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the reviewer matching device and the reviewer matching method embodiments provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiments, which will not be repeated here.

[0098] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0099] This application also provides a computer storage medium that can store multiple instructions, which are adapted to be loaded and executed by a processor as described above. Figure 2 The method steps of the illustrated embodiment can be found in the following documentation for detailed execution. Figure 2 The specific details of the illustrated embodiments will not be elaborated here.

[0100] This application also provides a computer program product that stores at least one instruction, which is loaded and executed by the processor to implement the reviewer matching method as described in the above embodiments.

[0101] Please see Figure 4 This document provides a schematic diagram of the structure of a computer device according to an embodiment of this application. Figure 4 As shown, the computer device 400 may include: at least one processor 401, at least one network interface 404, user interface 403, memory 405, and at least one communication bus 402.

[0102] The communication bus 402 is used to enable communication between these components.

[0103] The user interface 403 may include a display screen and a camera. Optionally, the user interface 403 may also include a standard wired interface and a wireless interface.

[0104] The network interface 404 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0105] The processor 401 may include one or more processing cores. The processor 401 connects to various parts of the computer device 400 using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 405, and by calling data stored in memory 405. Optionally, the processor 401 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 401 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 401 and may be implemented as a separate chip.

[0106] The memory 405 may include random access memory (RAM) or read-only memory. Optionally, the memory 405 may include a non-transitory computer-readable storage medium. The memory 405 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 405 may also be at least one storage device located remotely from the aforementioned processor 401. Figure 4 As shown, the memory 405, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and application programs.

[0107] exist Figure 4In the computer device 400 shown, the user interface 403 is mainly used to provide an input interface for the user and to obtain the user's input data; while the processor 401 can be used to call the application program stored in the memory 405 and specifically execute, such as Figure 2 The method shown can be referred to for details. Figure 2 As shown, it will not be elaborated further here.

[0108] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory, or random access memory, etc.

[0109] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.

Claims

1. A method for matching reviewers, characterized in that, include: Select the current unit from the unit list of the review management system; The system retrieves all reviewers in the expert database who are proficient in a particular unit, including the current unit, and generates a list of review experts based on the query results. Calculate the competency score of each reviewer in the list of review experts using the following formula: Where n1 and n2 represent the integrals associated with the current unit, n1≠n2, n represents the number of times the reviewer reviews the current unit, and q represents the weight value, which is related to the most recent time the reviewer participated in the review of the current unit; when the time interval between the most recent time the reviewer participated in the review of the current unit and the current time is less than or equal to 3 years, q=1.0; when the time interval is greater than 3 years and less than or equal to 5 years, q=0.8; when the time interval is greater than 5 years, q=0.

5. The demand capacity integral of the current unit is calculated according to the following formula: Where t represents the number of elements contained in the current unit, a represents the element score, m represents the difficulty level, n represents the workload, and g represents the correlation coefficient with other units; Based on the competency scores of each reviewer and the required competency scores, the matching rate between each reviewer and the current unit is calculated; the matching rate between each review unit and the current unit is calculated according to the following formula: Where P represents the reviewer's matching rate, F represents the reviewer's competence score, and T represents the current unit's required competence score. The reviewer with the highest matching rate will be selected as the optimal reviewer. If there are multiple maximum values ​​in the calculated matching rates, then the reviewer who is most proficient in the most units among the reviewers corresponding to the multiple maximum values ​​will be the optimal reviewer.

2. The method according to claim 1, characterized in that, Also includes: If no reviewer with expertise in a given unit is found in the expert database, the reviewer with the most reviews in the expert database will be selected as the optimal reviewer.

3. The method according to claim 2, characterized in that, Also includes: Generate the review task information of the optimal reviewer and push the review task information to the mobile terminal of the optimal reviewer; wherein, the review task information includes the following parameters: task ID, unit identifier, reviewer identifier, review date, and review elements.

4. The method according to claim 3, characterized in that, Also includes: When the number of days between the current date and the review date equals a threshold, a review task reminder is sent to the mobile terminal of the optimal reviewer.

5. A reviewer matching device, characterized in that, include: Select Unit: Used to select the current unit from the unit list of the review management system; The query unit is used to query all reviewers in the expert database who are proficient in a particular unit, including the current unit, and to generate a list of review experts based on the query results. The calculation unit is used to calculate the competency score of each reviewer in the list of review experts according to the following formula: Where n1 and n2 represent the integrals associated with the current unit, n1≠n2, n represents the number of times the reviewer reviews the current unit, and q represents the weight value, which is related to the most recent time the reviewer participated in the review of the current unit; when the time interval between the most recent time the reviewer participated in the review of the current unit and the current time is less than or equal to 3 years, q=1.0; when the time interval is greater than 3 years and less than or equal to 5 years, q=0.8; when the time interval is greater than 5 years, q=0.

5. The calculation unit is also used to calculate the demand capacity integral of the current unit according to the following formula: Where t represents the number of elements contained in the current unit, a represents the element score, m represents the difficulty level, n represents the workload, and g represents the correlation coefficient with other units; The calculation unit is further configured to calculate the matching rate between each reviewer and the current unit based on the competency scores of each reviewer and the required competency scores; the matching rate between each review unit and the current unit is calculated according to the following formula: Where P represents the reviewer's matching rate, F represents the reviewer's competence score, and T represents the current unit's required competence score. The matching unit is used to select the reviewer with the highest matching rate as the optimal reviewer; if there are multiple maximum values ​​among the calculated matching rates, the reviewer with the most expertise in any of the multiple maximum values ​​will be selected as the optimal reviewer.

6. A computer storage medium, characterized in that, The computer storage medium stores a plurality of instructions, which are adapted to be loaded by a processor and executed as method steps as claimed in any one of claims 1 to 4.

7. A computer device, characterized in that, include: A processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and executed the method steps as claimed in any one of claims 1 to 4.

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