A method and system based on fine management of power supply system personnel

Abstract

A method and system based on the fine management of power supply system personnel, the system includes: a client, a front-end application layer module, a business service layer module and a platform support layer module. The method and system can promote the deep integration of power supply station data: the system urgently needs to further carry out deep integration of data, improve the timeliness, accuracy and granularity of data, comprehensively establish a power supply station production data model, further promote terminal business integration, and promote comprehensive "burden reduction" at the grassroots level.

Description

Technical Field

[0001] The present invention relates to the technical field of management systems, and in particular to a method and system based on fine management of power supply system personnel. Background Art

[0002] The healthy and sustainable development of an enterprise requires the continuous absorption of outstanding talents, so efficient human resource management is indispensable. Human resource management is divided into six major modules: human resource planning, recruitment and placement, training and development, performance management, salary and benefits management, and employee relationship management. Among them, recruitment and placement, training and development, performance management, and salary and benefits management are particularly important, especially the management of employee privacy information needs to be protected. For enterprises, how to effectively protect and organize the complex personal information of employees is one of the important tasks in human resource management. The existing human resource management system applies several modules in a scattered manner, and the data between the modules are not interconnected, and need to be repeatedly input, which not only reduces work efficiency, but also fails to protect the security of employees' personal information data. In addition, some confidential business content of the enterprise is not fully protected.

[0003] In recent years, my country's power supply has achieved rapid growth. All enterprises are actively exploring and establishing regional centralized control centers to realize the power production and operation management mode of "remote centralized control, unmanned duty, integrated operation and maintenance, and regional maintenance" to further improve management efficiency and refinement, improve operational efficiency, reduce production costs, and increase economic benefits. At present, power companies are gradually developing towards regionalized management and control and intensive resources, establishing regional centralized control centers and group dispatching centers, and building a large-scale planning, construction, operation, and maintenance system. The traditional power production and operation performance management methods need to be improved urgently. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method and system based on the fine management of power supply system personnel, which can promote the deep integration of power supply station data: the system urgently needs to further carry out deep integration of data, improve the timeliness, accuracy and granularity of data, comprehensively establish a power supply station production data model, further promote terminal business integration, and promote comprehensive "burden reduction" at the grassroots level.

[0005] The present invention provides a system based on fine management of power supply system personnel, comprising: a client, a front-end application layer module, a business service layer module and a platform support layer module;

[0006] Among them, the client connects to the intranet through a secure access gateway;

[0007] The front-end application layer module adopts the micro-application method, including the work order center module, performance management module, electronic data module, precision training module, early warning processing module, indicator and benchmarking module, auxiliary management module, three major portrait modules, and mobile application front-end module;

[0008] The platform support layer module is divided into independent microservices according to the business, which includes the platform support layer module, including identity authentication components, message components, log components, cache components, unstructured components, monitoring components, and external integration interfaces.

[0009] Among them, the work order center module is used for work order management process processing.

[0010] Among them, the work order management process is specifically handled as follows: initiate the work order distribution process and send it to the station area processing personnel; the station area processing personnel perform relevant processing on the work order, sign for and return the work order; after the work order is completed, the completed work order is scored.

[0011] Among them, the warning management module is used to realize warning generation and warning process processing.

[0012] Among them, the warning generation and warning process processing settings are specifically to set the power supply station's indicators, materials, work order warning thresholds, work order warning thresholds, indicator warning thresholds and supervision generation conditions; according to the thresholds and rules, corresponding warning information is generated daily and pushed to different objects, and the warning information is controlled for uniqueness; at the same time, according to the supervision generation conditions, the supervision work order is pushed to different objects.

[0013] The performance management module is used to evaluate the performance of the power supply system personnel according to the task completion status of the personnel based on a preset evaluation algorithm.

[0014] The present invention also provides a method for fine management of power supply system personnel implemented by using a system based on fine management of power supply system personnel, comprising the following steps:

[0015] The client connects to the intranet through a secure access gateway;

[0016] Use micro-applications to achieve front-end application management;

[0017] Use microservices to implement application management of multiple components.

[0018] Furthermore, it also includes the work order management process processing steps: initiating the work order distribution process and sending it to the area processing personnel; the area processing personnel perform relevant processing on the work order, sign for and return the work order; after the work order is completed, the completed work order is scored.

[0019] Furthermore, it also includes the steps of warning generation and warning process processing: setting the power supply station's indicators, materials, work order warning thresholds, work order warning thresholds, indicator warning thresholds and supervision generation conditions; based on the thresholds and rules, generating corresponding warning information every day, and pushing it to different objects, controlling the uniqueness of the warning information; at the same time, based on the supervision generation conditions, pushing supervision work orders to different objects.

[0020] Furthermore, it also includes a performance management step: evaluating the performance of the power supply system personnel according to the task completion status of the personnel based on a preset evaluation algorithm.

[0021] The method and system based on fine management of power supply system personnel of the present invention can achieve:

[0022] 1) Promote the deep integration of power supply station data: The system urgently needs to further carry out the deep integration of data, improve the timeliness, accuracy and granularity of data, increase the frequency of data integration to real-time, and refine the terminal data from power supply stations to grids, substations and even substation managers.

[0023] 2) Establish a comprehensive production data model for the power supply station: Establish a production data model for the power supply station, and use a "picture" to grasp the overall situation of the power supply station, substation, and substation manager to support the daily production of the power supply station.

[0024] 3) Further promote the integration of terminal businesses: Taking the reduction of basic burdens as the starting point, realize the terminal integration of marketing systems, PMS, and other system businesses, and centrally and uniformly handle the business management and business assessment of various professions at the power supply station level, which will become a good means of "reducing burdens" at the grassroots level.

[0025] 4) Promote comprehensive “burden reduction” at the grassroots level: reduce the burden on grassroots personnel by optimizing existing functions, deepening data applications, and building personalized functions.

[0026] 5) Avoid distributed database transactions, reduce unnecessary aggregation services, and implement a complete cohesive business function with one microservice. Split out high-load points from existing business services; analyze concurrent loads, long connection loads, high computing loads, database loads, file operation loads, etc. based on the load points. You can use a centralized architecture + high-load microservice approach; or split out high-load microservices from business-based microservices. Avoid over-splitting business application systems into a large number of microservices to reduce system deployment complexity, upgrade complexity, and operation and maintenance complexity. Microapplications are deployed on the company's enterprise management cloud or public service cloud, mainly using the "application transformation to the cloud" approach, that is, deploying applications to the cloud platform through application upgrades and microapplication transformations. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a work order management flow chart;

[0028] Figure 2 Flowchart of warning generation and warning processing. DETAILED DESCRIPTION

[0029] The specific implementation of the present invention is described in detail below. It is necessary to point out that the following implementation is only used for further explanation of the present invention and cannot be understood as limiting the scope of protection of the present invention. Some non-essential improvements and adjustments made to the present invention by persons skilled in the art based on the above-mentioned content of the present invention still fall within the scope of protection of the present invention.

[0030] The present invention provides a method and system based on fine management of power supply system personnel, which adopts the principle of high cohesion and low coupling, and divides the system into a basic platform and multiple business application modes according to business independence. The basic platform provides unified microservice publishing, front-end interactive integration, basic development framework and development components. Business module development is divided into several modules according to the relative independence of each demand, and each module is further divided into several micro-applications and micro-services according to the principle of front-end and back-end separation, and the front-end and back-end functions are connected through unified interface rules.

[0031] The system design will divide functional modules based on business areas. Each module is relatively independent and supports independent design, independent development, and independent deployment. The relationship between modules will be achieved through application interfaces.

[0032] Based on the SG-UAP 3.0 development platform, the micro-application and micro-service framework is used to separate the front-end presentation layer from the back-end service layer. The service-oriented architecture packages the functions of each business module into multiple micro-services, and the systems are associated through the interfaces defined by the micro-services.

[0033] The backend of the "all-round" township power supply station intelligent application system (deepening research and development) adopts mature J2EE specifications and uses SG-UAP as the development and operation platform. At the same time, the client is developed and constructed in C++. In order to ensure the future expansion of the system, the "all-round" township power supply station intelligent application system (deepening research and development) will pay more attention to loose coupling in system design, pay more attention to the standardization of interfaces, and pay more attention to the use of open technologies to realize the underlying functions. The "all-round" township power supply station intelligent application system (deepening research and development) is aimed at the vast number of grassroots front-line employees, and in terms of deployment mode, it adopts a two-level deployment mode.

[0034] Next, we will introduce the method and system based on the fine management of power supply system personnel. The specific functional layered design consists of four parts: client, front-end application layer module, business service layer module, and platform support layer module. The terminal is connected to the intranet through a secure access gateway. The front-end application layer is developed in a micro-application manner and is divided into work order center, performance management, electronic data, precision training, early warning processing, indicators and benchmarking, auxiliary management, three major portraits, and mobile application front-end, totaling 9 functional modules.

[0035] The business service layer is divided into independent microservices according to business, including early warning management, indicators and benchmarking management, auxiliary management, three major portraits, system management, log management, and cache loading, a total of 7 microservice modules.

[0036] The platform support layer provides six basic service components such as identity authentication components and message components, as well as external integration interface applications.

[0037] Specifically, the system based on the fine management of power supply system personnel includes: client, front-end application layer module, business service layer module and platform support layer module;

[0038] Among them, the client connects to the intranet through a secure access gateway;

[0039] The front-end application layer module adopts the micro-application method, including the work order center module, performance management module, electronic data module, precision training module, early warning processing module, indicator and benchmarking module, auxiliary management module, three major portrait modules, and mobile application front-end module;

[0040] The platform support layer module is divided into independent microservices according to the business, which includes the platform support layer module, including identity authentication components, message components, log components, cache components, unstructured components, monitoring components, and external integration interfaces.

[0041] The corresponding method based on fine management of power supply system personnel includes the following steps:

[0042] The client connects to the intranet through a secure access gateway;

[0043] Use micro-applications to achieve front-end application management;

[0044] Use microservices to implement application management of multiple components.

[0045] For the above-mentioned specific processing process, the work order center module is used to implement the work order management process, specifically: initiate the work order distribution process and send it to the station area processing personnel; the station area processing personnel perform relevant processing on the work order, sign for and return the work order; after the work order is completed, the completed work order is scored.

[0046] The early warning management module is used to realize early warning generation and early warning process processing, specifically to set the indicators, materials, work order early warning thresholds, work order early warning thresholds, indicator early warning thresholds and supervision generation conditions of the power supply station; according to the thresholds and rules, the corresponding early warning information is generated daily and pushed to different objects, and the early warning information is controlled uniquely; at the same time, according to the supervision generation conditions, the supervision work order is pushed to different objects.

[0047] The performance management module is used to evaluate the performance of power supply system personnel based on the preset evaluation algorithm according to the task completion of the personnel.

[0048] The corresponding method based on fine management of power supply system personnel also includes the following steps:

[0049] The work order management process processing steps: initiate the work order distribution process and send it to the area processing personnel; the area processing personnel perform relevant processing on the work order, sign for and return the work order; after the work order is completed, the completed work order is scored.

[0050] Warning generation and warning process processing steps: Set the power supply station's indicators, materials, work order warning thresholds, work order warning thresholds, indicator warning thresholds and supervision generation conditions; based on the thresholds and rules, generate corresponding warning information every day and push it to different objects to control the uniqueness of the warning information; at the same time, according to the supervision generation conditions, push the supervision work order to different objects.

[0051] Performance management steps: Evaluate the performance of power supply system personnel based on their task completion status based on a preset evaluation algorithm.

[0052] Among them, the work order management process of the work order center is as follows Figure 1 As shown:

[0053] First, initiate the work order dispatch process and send it to the area processing personnel.

[0054] Secondly, the area processing personnel will perform relevant processing on the work order, and the work order can be signed and returned.

[0055] Finally, after the work order is completed, a score is given for the completed work order.

[0056] Early warning management module, realizes early warning generation and early warning processing process such as Figure 2 As shown:

[0057] Set the indicators, materials, work order warning thresholds, work order warning thresholds, indicator warning thresholds and supervision generation conditions of the power supply station.

[0058] According to the threshold and rules, the corresponding warning information is generated daily and pushed to different objects, and the warning information is uniquely controlled. At the same time, according to the supervision generation conditions, the supervision work order is pushed to different objects.

[0059] During the processing, a lot of text data needs to be processed. In order to identify it as a readable mode faster and improve efficiency, the following methods can be used:

[0060] The text processing module realizes the extraction of task text. Based on the text preprocessing model, the task text is processed with specific keywords. Specifically, the specific keywords are processed with dimension reduction to refine the text feature information and obtain the specific keyword feature set, as shown in the following formula;

[0061]

[0062] Wherein, e is a natural constant, N is the number of specific keywords for each subtask, Π is a multiplication, argmax is a function for finding a set, represents the stroke width ratio of the specific keyword, γ is the geometric size ratio of the specific keyword, is the stroke width frequency ratio of the specific keyword, is the geometric size to frequency ratio of the specific keyword, is the frequency weight ratio of the specific keyword, The specific keyword feature set is formed after the specific keywords in the specific task text of each subtask are processed by dimensionality reduction;

[0063] Among them, the stroke width ratio π is the circumference of a circle, r is the font radius, dp is the stroke differential, and the geometric size ratio log is a logarithmic function, P is the stroke area of ​​the keyword, and dγ is the geometric differential value of the stroke;

[0064] The obtained specific keyword feature set is mapped and derived according to the following formula to establish a specific keyword index matrix for each subtask;

[0065]

[0066] Wherein, M is the total number of elements in the specific keyword feature set, i and j are the number of rows and columns of the specific keyword index matrix, is the derivative function of the feature set corresponding to the specific keyword of each subtask, ai is the row value of the specific keyword word frequency sorted in descending order, bj is the column value of the specific keyword word frequency sorted in descending order, and F(ai,bj) is the specific keyword index matrix of the specific keyword feature set of each subtask after mapping and derivation processing;

[0067] The specific keyword index matrix is ​​matched with the task keywords established by the system based on the fine management of power supply system personnel for the executors to perform the corresponding tasks, and the following result is used to determine whether each task should be assigned to the executor's operation;

[0068]

[0069] Wherein, ln is the natural logarithm with constant e as base, s is the serial number of the task division table, g k 、h l They are respectively the ratio of keyword stroke width and geometric size ratio. When the calculated value of D(s) is close to 0, it means that the specific keyword index matrix is ​​matched with the task keywords established by the executor to perform the corresponding task, and is automatically assigned to the executor to perform the corresponding operation.

[0070] Embodiment 1:

[0071] The following is a specific analysis of the method of fine management of power supply system personnel and its system examples, and specific analysis and testing of parameter design and performance of database servers, application servers, etc.:

[0072] First, the CPU processing power, memory size, storage I / O, and network I / O test formulas of the database server are as follows:

[0073] 1. Estimation of CPU processing power

[0074] Step 1: Total TPM (transactions per minute) of the business system

[0075] Total system TPM = TASK × S × F / (C × T)

[0076] TASK: The total volume of business during the daily peak hours. Calculation formula: = (total time during the peak hours (seconds) / single business processing time (seconds)) * number of concurrent terminals;

[0077] S: The ratio of the complexity of the business to the standard TPC-C benchmark environment transaction. The core business of transaction processing systems is implemented in the database. The complexity value range of each transaction in the TPC-C standard test is: 5-15.

[0078] F: The system reserves for business volume development in the next three years. This transaction design reserves 20%-50% of the processing capacity, that is, F = 120%-150%.

[0079] T: The duration of the daily business peak period, in minutes.

[0080] C: CPU utilization. According to actual experience with information systems, a server's CPU utilization rate above 80% will cause a system bottleneck, and the utilization rate is at its best when it is 75%. Therefore, when calculating server performance indicators, the CPU utilization rate is set to 75%, that is, C = 75%.

[0081] Step 2: Single Server TPM in a Cluster Environment

[0082] Single server TPM = system total TPM / (1+(N-1)×80%)

[0083] N: is the number of cluster nodes.

[0084] 2. Memory capacity estimation

[0085] The memory size of transaction database servers is calculated based on five factors: the memory size occupied by the operating system, the memory size occupied by the database management process, the memory size occupied by the data cache, the memory size occupied by the client connection process, and memory utilization.

[0086] Normally, the memory occupied by the operating system and database management process is relatively small, not exceeding 512M. Each client connection process occupies about 2M-3M of memory. The memory occupied by the data cache is the most important component of the database server memory and also the largest proportion. The data cache is mainly used to cache data.

[0087] According to the characteristics of business operations, the business system has a huge number of concurrent requests, and daily operations will involve a huge amount of application query and summary operations. Excessive memory utilization (over 80%) will affect server performance.

[0088] The ratio of server memory capacity GB to CPU core number is configured at 8:1.

[0089] Secondly, the application server CPU / memory capacity is estimated as follows:

[0090] (1) CPU processing power estimation

[0091] The server performance and configuration should be determined based on the system's data processing volume and concurrent processing requirements during business peak periods. Application servers use SPECjbb2005 as the measurement standard, and the formula is as follows:

[0092] SPECjbb2005=A×B / (1-CD)

[0093] Parameter explanation:

[0094] A: The maximum number of business transactions that need to be processed simultaneously per second.

[0095] B: The SPECjbb2005 peak value consumed by each business transaction. According to experience, each business transaction generally consumes 200 bops. It can also be calculated based on the number of Java statements in the business transaction, B = number of Java statements in the business transaction / 5.

[0096] C: The system's redundant processing capabilities.

[0097] D: The percentage of system resources occupied by non-Java applications.

[0098] For example, if the peak transaction volume of a system is 1,000 transactions per second, 30% of the system's redundant processing capacity is reserved, and 20% of the system resources are occupied by non-Java applications, the server's SPECjbb2005 performance value is calculated as follows: 1000*200 / (1-30%-20%)=400,000.

[0099] Note: When server manufacturers release servers, they also release performance indicators based on the SPECjbb2005 test.

[0100] (2) Calculation of application server memory requirements

[0101] The memory size estimation of the application server mainly includes: the memory size occupied by the operating system, the memory size occupied by the application server management process, the memory size occupied by the Java virtual machine (hereinafter referred to as JVM) and the memory utilization rate.

[0102] Normally, the memory occupied by the operating system and application server management process is relatively small, not exceeding 512M.

[0103] When processing large amounts of Java data, a larger JVM memory needs to be allocated. In order to achieve better performance, the JVM of the application server usually occupies a separate memory.

[0104] Application server: Each server needs to deploy 11 applications, each application is allocated 2 GB of memory, and it is recommended to configure 32 GB of memory for the application server.

[0105] Support server: deploy 4 nodes, 1 GB each for the registration center and configuration center, 6 GB each for Redis and Kafka, and 16 GB memory is recommended.

[0106] Microservice server: Each server needs to deploy 6 microservices, as well as data extraction services and cache loading services, for a total of 8 nodes. Each node is allocated 3GB of memory. It is recommended to configure 32GB of memory for the microservice server.

[0107] Finally, the hardware environment design is introduced as follows:

[0108] 1. The calculation formula for the application server hardware node is as follows:

[0109] Common concurrent calculation formula:

[0110] N=[(n*0.8*S*P) / (T*0.2)]*R;

[0111] n is the number of application users; S is the number of transactions per user (QPS);

[0112] P is the time required to access the server for each transaction, in seconds;

[0113] T is the time of using the service, in seconds; R is the adjustment factor, the default value is 1;

[0114] Number of nodes = N / 100. The maximum concurrency supported by a single Tomcat node is recommended not to exceed 100.

[0115] Tomcat100 concurrency requires server memory 8G and CPU 4C 3.0GHZ or above.

[0116] a) Large

[0117] Assume that the network has more than 80,000 users (N); the number of transactions for each user is 3-5 with a median of 4 (S); each transaction takes 60-180 seconds to access the server with a median of 90 seconds (P); the daily time for transaction is 28,800 seconds (T); and R is adjusted to 0.6.

[0118] N=[<(80000*0.8*4*90) / (28800*0.2)]*0.6=2400

[0119] N / 100=24

[0120] Therefore, on a server with 16C CPU and 32G memory, 4 Tomcats can be installed on each server, and 6 or more servers are required.

[0121] b) Medium

[0122] Assume that the network has 30,000 to 80,000 users (N); the number of transactions per user is 3-5 with a median of 4 (S); each transaction takes 60-180 seconds to access the server with a median of 90 seconds (P); the daily service usage time is 28,800 seconds (T); and R is adjusted to 0.6.

[0123] N=[<(60000*0.8*4*90) / (28800*0.2)]*0.6=1800

[0124] N / 100=18

[0125] Therefore, on a server with 16C CPU and 32G memory, 4 Tomcats can be installed on each server, and 4 or more servers are required.

[0126] c) Small

[0127] Assume a network of 30,000 users (N); the number of transactions per user is 3-5 with a median of 4 (S); each transaction takes 60-180 seconds to access the server with a median of 90 seconds (P); the daily time spent on the transaction is 28,800 seconds (T); and R is adjusted to 0.6.

[0128] N=[<(30000*0.8*4*90) / (28800*0.2)]*0.6=900

[0129] N / 100=9;

[0130] Therefore, on a server with 16C CPU and 32G memory, 4 Tomcats can be installed on each server, and 2 or more servers are required.

[0131] 3. Task text recognition

[0132] During the processing, a lot of text data needs to be processed. In order to identify it as a readable mode faster and improve efficiency, the following methods can be used:

[0133] Extract the task text, and process the task text with specific keywords based on the text preprocessing model. Specifically, perform dimensionality reduction processing on the specific keywords to refine the text feature information, and obtain the specific keyword feature set, as shown in the following formula;

[0134]

[0135] Wherein, e is a natural constant, N is the number of specific keywords for each subtask, Π is a multiplication, argmax is a function for finding a set, represents the stroke width ratio of the specific keyword, γ is the geometric size ratio of the specific keyword, is the stroke width frequency ratio of the specific keyword, is the geometric size to frequency ratio of the specific keyword, is the frequency weight ratio of the specific keyword, The specific keyword feature set is formed after the specific keywords in the specific task text of each subtask are processed by dimensionality reduction;

[0136] Among them, the stroke width ratio π is the circumference of a circle, r is the font radius, dp is the stroke differential, and the geometric size ratio log is a logarithmic function, P is the stroke area of ​​the keyword, and dγ is the geometric differential value of the stroke;

[0137] The obtained specific keyword feature set is mapped and derived according to the following formula to establish a specific keyword index matrix for each subtask;

[0138]

[0139] Wherein, M is the total number of elements in the specific keyword feature set, i and j are the number of rows and columns of the specific keyword index matrix, is the derivative function of the feature set corresponding to the specific keyword of each subtask, ai is the row value of the specific keyword word frequency sorted in descending order, bj is the column value of the specific keyword word frequency sorted in descending order, and F(ai,bj) is the specific keyword index matrix of the specific keyword feature set of each subtask after mapping and derivation processing;

[0140] The specific keyword index matrix is ​​matched with the task keywords established by the system based on the fine management of power supply system personnel for the executors to perform the corresponding tasks, and the following result is used to determine whether each task should be assigned to the executor's operation;

[0141]

[0142] Wherein, ln is the natural logarithm with constant e as base, s is the serial number of the task division table, g k 、h l They are respectively the ratio of keyword stroke width and geometric size ratio. When the calculated value of D(s) is close to 0, it means that the specific keyword index matrix is ​​matched with the task keywords established by the executor to perform the corresponding task, and is automatically assigned to the executor to perform the corresponding operation.

[0143] Although exemplary embodiments of the present invention have been described for the purpose of illustration, it will be understood by those skilled in the art that various modifications, additions and substitutions may be made in form and detail without departing from the scope and spirit of the invention disclosed in the appended claims, and all such changes shall fall within the scope of protection of the appended claims of the present invention, and the various steps in the various departments and methods of the products claimed by the present invention may be combined together in any combination. Therefore, the description of the embodiments disclosed in the present invention is not intended to limit the scope of the present invention, but is used to describe the present invention. Accordingly, the scope of the present invention is not limited by the above embodiments, but is defined by the claims or their equivalents.

Claims

1. A system based on fine management of power supply system personnel, characterized in that: include: Client, front-end application layer module, business service layer module, platform support layer module, text processing module; Among them, the client connects to the intranet through a secure access gateway; The front-end application layer module adopts the micro-application method, including the work order center module, performance management module, electronic data module, precision training module, early warning processing module, indicator and benchmarking module, auxiliary management module, three major portrait modules, and mobile application front-end module; The platform support layer module is divided into independent microservices according to business, which includes the platform support layer module, including identity authentication components, message components, log components, cache components, unstructured components, monitoring components, and external integration interfaces; The text processing module realizes the extraction of task text. Based on the text preprocessing model, the task text is processed with specific keywords. Specifically, the specific keywords are processed with dimension reduction to refine the text feature information and obtain the specific keyword feature set, as shown in the following formula; Among them, e is a natural constant, N is the number of specific keywords for each subtask, Π is a multiplication, and argmax is a function for finding the set. represents the stroke width ratio of the specific keyword, γ is the geometric size ratio of the specific keyword, is the stroke width frequency ratio of the specific keyword, is the geometric size to frequency ratio of the specific keyword, is the frequency weight ratio of the specific keyword, The specific keyword feature set is formed after the specific keywords in the specific task text of each subtask are processed by dimensionality reduction; Among them, the stroke width ratio π is the circumference of a circle, r is the font radius, dp is the differential value of the stroke, and the geometric size ratio log is a logarithmic function, P is the stroke area of ​​the keyword, and dγ is the geometric differential value of the stroke; The obtained specific keyword feature set is mapped and derived according to the following formula to establish a specific keyword index matrix for each subtask; Wherein, M is the total number of elements in the specific keyword feature set, i and j are the number of rows and columns of the specific keyword index matrix, is the derivative function of the feature set corresponding to the specific keyword of each subtask, ai is the row value of the specific keyword word frequency sorted in descending order, bj is the column value of the specific keyword word frequency sorted in descending order, and F(ai,bj) is the specific keyword index matrix of the specific keyword feature set of each subtask after mapping and derivation processing; The specific keyword index matrix is ​​matched with the task keywords established by the system based on the fine management of power supply system personnel for the executors to perform the corresponding tasks, and the following result is used to determine whether each task should be assigned to the executor's operation; Wherein, ln is the natural logarithm with constant e as base, s is the serial number of the task division table, g k 、h l They are respectively the ratio of keyword stroke width and geometric size ratio. When the calculated value of D(s) is close to 0, it means that the specific keyword index matrix is ​​matched with the task keywords established by the executor to perform the corresponding task, and is automatically assigned to the executor to perform the corresponding operation.

2. The system according to claim 1, characterized in that: The work order center module is used for work order management process processing.

3. The system according to claim 2, characterized in that: The work order management process is specifically as follows: initiate the work order dispatch process and send it to the area processing personnel; the area processing personnel perform relevant processing on the work order, sign for and return the work order; after the work order is completed, score the completed work order.

4. The system according to claim 1 or 3, characterized in that: The warning management module is used to realize warning generation and warning process processing.

5. The system according to claim 4, characterized in that: The warning generation and warning process processing are specifically as follows: setting the indicators, materials, and work order warning thresholds of the power supply station, the work order warning thresholds, the indicator warning thresholds, and the supervision generation conditions; according to the thresholds and rules, the corresponding warning information is generated daily, and pushed to different objects, and the warning information is uniquely controlled; at the same time, according to the supervision generation conditions, the supervision work orders are pushed to different objects.

6. The system according to claim 5, characterized in that: The performance management module is used to evaluate the performance of the power supply system personnel according to the task completion status of the personnel based on a preset evaluation algorithm.

7. A method for fine management of power supply system personnel implemented by using the system for fine management of power supply system personnel according to any one of claims 1 to 6, characterized in that: The steps include: The client connects to the intranet through a secure access gateway; Use micro-applications to achieve front-end application management; Use microservices to implement application management of multiple components.

8. The method according to claim 7, characterized in that: It also includes the work order management process processing steps, specifically: initiating the work order distribution process and sending it to the area processing personnel; the area processing personnel perform relevant processing on the work order, sign for and return the work order; after the work order is completed, the completed work order is scored.

9. The method according to claim 8, characterized in that: It also includes the steps of warning generation and warning process processing, specifically: setting the warning thresholds for indicators, materials, and work orders of the power supply station, the work order warning thresholds, the indicator warning thresholds, and the supervision generation conditions; based on the thresholds and rules, the corresponding warning information is generated daily and pushed to different objects to control the uniqueness of the warning information; at the same time, according to the supervision generation conditions, the supervision work orders are pushed to different objects.

10. The method according to claim 9, characterized in that: It also includes a performance management step, specifically: evaluating the performance of the power supply system personnel based on the task completion status of the personnel based on a preset evaluation algorithm.

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