Power supply enterprise dispatching method and system based on personnel portrait, medium and equipment

Abstract

The embodiment of the application provides a power supply enterprise dispatching method and system based on personnel portraits, a medium and equipment, the method comprises the following steps: obtaining personnel information of a plurality of personnel to be dispatched and task information of a task to be allocated; constructing a personnel portrait of each of the personnel to be dispatched according to each of the personnel information and a trained personnel portrait model, wherein the portrait model comprises three dimensions of personnel information layer, time layer and business layer; matching the personnel portrait of each of the personnel to be dispatched with the task information, and selecting a personnel to be dispatched with the highest priority from the plurality of personnel to be dispatched based on an intelligent dispatching strategy of an optimization method; and allocating the task to be allocated to the personnel to be dispatched with the highest priority. Through the embodiment of the application, the power supply enterprise can allocate tasks to more suitable personnel.

Description

Technical Field

[0001] This application relates to the field of work order dispatch technology, and in particular to power supply companies' work dispatch methods, systems, media, and equipment based on personnel profiling. Background Technology

[0002] Power supply stations are the basic operational units for the company's strategic implementation. The State Grid Corporation of China has explicitly required strengthening the service support capabilities of township power supply stations and promoting the digital transformation of their management and services. The execution of various on-site work orders is the main task of the distribution area staff, and the scientific and rational nature of task assignment is a prerequisite for efficient task execution.

[0003] Currently, power supply companies mainly rely on managers to assign tasks to personnel, which can easily lead to unreasonable assignments. They are unable to conduct comprehensive comparative analysis and evaluation of personnel based on their abilities, skill levels, competency requirements, and work performance, thus failing to match tasks with the most suitable personnel.

[0004] Regarding the aforementioned technologies, current power supply companies' work assignment methods are unable to assign tasks to more suitable personnel, and cannot intelligently assign personnel based on the tasks. Summary of the Invention

[0005] In order to assign tasks to more suitable personnel and achieve intelligent personnel dispatch, this application provides a method, system, medium and equipment for power supply companies to dispatch tasks based on personnel profiles.

[0006] The first aspect of this application provides a method for dispatching power supply companies based on personnel profiles, employing the following technical solution: Obtain personnel information for multiple employees awaiting assignment and task information for tasks to be assigned; Based on the personnel information and the trained personnel profile model, a personnel profile of each of the personnel to be dispatched is constructed. The personnel profile model includes three dimensions: personnel information layer, time layer, and business layer. The personnel profiles of each of the personnel to be assigned are matched with the task information, and the highest priority personnel to be assigned is selected from the multiple personnel to be assigned based on the intelligent assignment strategy of the optimization method. The tasks to be assigned are assigned to the highest priority personnel awaiting dispatch.

[0007] By adopting the above technical solution, a personnel profile of each person to be assigned is constructed based on information from multiple personnel to be assigned and a trained personnel profile model. The personnel profile can comprehensively and clearly reflect the basic situation of the personnel, providing a good foundation for subsequent personnel evaluation, job promotion or personnel assignment. Then, based on the personnel profile and task information, a matching method based on the intelligent assignment strategy selects the highest priority personnel from multiple personnel to be assigned, which can assign tasks to more suitable personnel and maximize the efficiency and quality of task processing.

[0008] Preferably, before obtaining the personnel information of multiple personnel to be dispatched and the task information of the tasks to be assigned, the method further includes: obtaining the sample personnel information of at least one sample personnel and the sample task information of at least one sample task and creating an initial personnel profile model; training the initial personnel profile model based on the sample personnel information of the at least one sample personnel and the sample task information of the at least one sample task to obtain a trained personnel profile model.

[0009] By adopting the above technical solution, the initially created personnel profile model is trained before the personnel profile model is built, making the trained personnel profile model more accurate and enabling better construction of subsequent personnel profiles, thus ensuring the accuracy of personnel profile construction.

[0010] Preferably, the step of training the personnel profile model based on the sample personnel information of at least one sample personnel and the sample task information of at least one sample task to obtain the trained personnel profile model includes: obtaining the feature labels contained in each of the personnel information, the feature variables corresponding to each feature label, and the feature variable values ​​corresponding to each feature variable; determining the feature variables whose feature variable values ​​are greater than a first preset value under the same feature label, and obtaining a first screening result; Calculate the correlation coefficient of every two feature variables under the same feature label in the first screening result, retain any one of the two feature variables whose correlation coefficient is less than or equal to a second preset value and any one of the two feature variables whose correlation coefficient is greater than the second preset value, to obtain the second screening result; sort the feature variables in the second screening result in descending order of feature variable value, select the first few feature variables, and input the first few feature variables into the trained personnel profile model to construct the personnel profile of each of the personnel to be dispatched.

[0011] By adopting the above technical solution, the feature labels contained in the personnel information, the feature variables corresponding to each feature label, and the feature variable values ​​corresponding to each feature variable are obtained. The top few feature variable values ​​are then selected, that is, the feature variables with strong correlation and representativeness are selected as the input feature variables of the personnel profile model. This reduces the amount of computation and makes the final constructed personnel profile more accurate.

[0012] Preferably, after constructing the personnel profiles of each of the personnel to be dispatched based on the personnel information and the trained personnel profile model, the method further includes: outputting relevant suggestions for personnel growth based on the personnel profiles and pre-set human resource talent evaluation theories.

[0013] By adopting the above technical solutions, and through personnel profiling, combined with human resource talent evaluation theories, relevant suggestions for personnel growth can be output so that the company can make reasonable adjustments or training to its personnel in the subsequent process.

[0014] Preferably, after constructing the personnel profiles of each of the personnel to be dispatched based on the personnel information and the trained personnel profile model, the method further includes: displaying the personnel profiles of each of the personnel to be dispatched through the three dimensions of personnel information layer, time layer and business layer.

[0015] By adopting the above technical solution, personnel are analyzed and compared horizontally and vertically through three dimensions: personnel information layer, time layer, and business layer. Personnel profiles of each person to be assigned are displayed through the three dimensions of personnel information layer, time layer, and business layer, so that personnel profiles can be displayed in a three-dimensional spatial model through visualization.

[0016] Preferably, the step of matching each personnel profile with the task information and selecting the highest priority personnel from the plurality of personnel to be dispatched based on the intelligent dispatching strategy of the optimization method includes: obtaining the task indicator scores of each personnel to be dispatched in multiple dimensions of tasks according to the personnel profile of each personnel to be dispatched; obtaining the proportion of each personnel to be dispatched whose task indicator scores are above the benchmark value according to the task indicator scores of each personnel to be dispatched; and selecting the personnel to be dispatched with the highest proportion of their task indicator scores above the benchmark value as the highest priority personnel to be dispatched.

[0017] By adopting the above technical solution, the scores of each task indicator for each person to be assigned are obtained based on the personnel profile of the personnel to be assigned. Furthermore, the personnel with the highest percentage of task indicator scores above the benchmark value are designated as the highest priority personnel to be assigned, which can effectively realize personnel assignment under multi-dimensional tasks.

[0018] Preferably, the step of matching each personnel profile with the task information and selecting the highest-priority personnel from the plurality of personnel to be assigned based on the intelligent dispatching strategy of the optimization method includes: obtaining the ability performance, skill level, ability demand, and vacancy score of each personnel to be assigned in a one-dimensional task based on the personnel profile of each personnel to be assigned; obtaining the weights corresponding to the ability performance, skill level, ability demand, and vacancy of each personnel to be assigned in the one-dimensional task based on the machine learning weight coefficient method; multiplying the ability performance, skill level, ability demand, and vacancy score of each personnel to be assigned by the corresponding weight and adding them together to obtain the final score value of each personnel to be assigned; and selecting the personnel with the highest final score value as the highest-priority personnel to be assigned.

[0019] By adopting the above technical solution, the ability performance, skill level, ability demand, and vacancy score of each person to be assigned work in a one-dimensional task are obtained based on the personnel profile of each person to be assigned work. Then, the weights corresponding to the ability performance, skill level, ability demand, and vacancy score of each person to be assigned work in a one-dimensional task are obtained by using the machine learning weight coefficient method. The final score value of each person to be assigned work can be obtained by multiplying the index score of each person to be assigned work by the corresponding weight and adding them together. The person with the highest final score value is selected as the highest priority person to be assigned work, which can effectively realize personnel assignment in a single-dimensional task.

[0020] The second aspect of this application provides a system for dispatching power supply companies based on personnel profiles.

[0021] A power supply company dispatching system based on personnel profiles includes: The personnel information acquisition module is used to acquire personnel information of multiple personnel to be assigned and task information of tasks to be assigned. The personnel profile construction module is used to construct personnel profiles for each of the personnel to be dispatched based on the personnel information and the trained personnel profile model. The profile model includes three dimensions: personnel information layer, time layer, and business layer. The highest priority personnel selection module is used to match the personnel profiles of each personnel to be assigned with the task information, and select the highest priority personnel from the multiple personnel to be assigned based on the intelligent assignment strategy of the optimization method. The personnel dispatch module is used to assign the tasks to be assigned to the personnel with the highest priority.

[0022] By adopting the above technical solution, a personnel profile of each person to be assigned is constructed based on information from multiple personnel to be assigned and a trained personnel profile model. The personnel profile can comprehensively and clearly reflect the basic situation of the personnel, providing a good foundation for subsequent personnel evaluation, job promotion or personnel assignment. Then, based on the personnel profile and task information, a matching method based on the intelligent assignment strategy selects the highest priority personnel from multiple personnel to be assigned, which can assign tasks to more suitable personnel and maximize the efficiency and quality of task processing.

[0023] A third aspect of this application provides a computer storage medium, employing the following technical solution: A computer storage medium storing a plurality of instructions adapted for loading by a processor and executing the method steps described above.

[0024] In a fourth aspect, this application provides an electronic device that adopts the following technical solution: An electronic device includes a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to execute the above-described method steps.

[0025] In summary, this application includes at least one of the following beneficial technical effects: 1. This application can match personnel profiles and task information, and select the highest priority personnel from multiple personnel to be assigned based on the intelligent dispatching strategy of the optimization method, so that the task can be assigned to more suitable personnel and the efficiency and quality of task processing can be guaranteed to the greatest extent. 2. This application can determine the performance of personnel's corresponding characteristic variables by measuring the magnitude of the characteristic variable values ​​corresponding to the characteristic variables under the same label, and can output relevant suggestions for personnel growth based on human resource talent evaluation theory. The company can then make reasonable adjustments or training to personnel in the subsequent process. 3. This application can effectively realize personnel dispatching under single-dimensional tasks as well as personnel dispatching under multi-dimensional tasks. 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 flowchart illustrating the power supply company's dispatching method based on personnel profiles, according to an embodiment of this application. Figure 2 This is a flowchart illustrating another embodiment of the power supply company dispatching method based on personnel profiles, as described in this application. Figure 3 This is a schematic diagram illustrating the percentage of personnel scores under multiple tasks in this application embodiment; Figure 4 This is a schematic diagram illustrating the final scores of individuals under a single-dimensional task in an embodiment of this application. Figure 5 This is a schematic diagram of the power supply company dispatching system based on personnel profiles, according to an embodiment of this application. Figure 6 This is another structural diagram of the power supply company dispatching system based on personnel profiles, according to an embodiment of this application. Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0028] Figure labeling: 1. Personnel information acquisition module; 2. Personnel profile construction module; 3. Module for selecting the highest priority personnel to be assigned; 4. Assignment module; 5. Personnel profile training completion module; 6. Personnel profile training completion module; 7. Personnel profile display module; 8. Growth suggestion output module; 9. Personnel assignment module under multi-dimensional tasks; 10. Personnel assignment module under single-dimensional tasks; 1000. Electronic device; 1001. Processor; 1002. Communication bus; 1003. User interface; 1004. Network interface; 1005. Memory. Detailed Implementation

[0029] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0030] In the description of the embodiments of this application, words such as "illustrative," "for example," or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "illustrative," "for example," or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Rather, the use of words such as "illustrative," "for example," or "for example" is intended to present the relevant concepts in a specific manner.

[0031] The present application will now be described in detail with reference to specific embodiments.

[0032] This application discloses a method for dispatching power supply companies based on personnel profiles. In one embodiment, such as... Figure 1As shown, the power supply company's dispatching method based on personnel profiles includes: Step 101: Obtain personnel information for multiple workers awaiting assignment and task information for tasks to be assigned.

[0033] Specifically, the personnel and task information in this embodiment comes from the human resources database and operation and maintenance database of the power supply company to which the personnel belong. Personnel information for multiple personnel awaiting assignment and task information for tasks to be assigned are obtained from the human resources database and operation and maintenance database of the power supply company to which the personnel belong. Personnel information may include basic personnel information, work item information, and performance level information. Basic personnel information may include: name, age, gender, major, education, and length of service; work item information may include: work content, monthly working hours, and single working hours; performance level information may include: technical title, skill level, skill certificate, and time elapsed since obtaining the skill level. Task information to be assigned may include: task category, task role, task time, and task location. Task categories may include: power station maintenance, equipment inspection, and fault repair; task roles may include: supervisor, operator, and person in charge.

[0034] Step 102: Construct personnel profiles for each person to be assigned based on their information and the trained personnel profile model. The profile model includes three dimensions: personnel information layer, time layer, and business layer.

[0035] Specifically, personnel information is input into the trained personnel profile model, which can then output corresponding personnel profiles. The personnel profile model includes three dimensions: personnel information layer, time layer, and business layer, enabling comparative analysis of personnel from both horizontal and vertical perspectives. The personnel information layer, business layer, and time layer form a three-dimensional spatial model, which can also be projected into a user profile layer. The personnel profiles of each person awaiting assignment are displayed through the three dimensions of personnel information layer, time layer, and business layer.

[0036] Step 103: Match the personnel profiles of each person to be assigned with the task information, and select the highest priority person from among the multiple persons to be assigned based on the intelligent assignment strategy of the optimization method.

[0037] Specifically, the personnel profiles of each person awaiting assignment are matched with task information. The business layer of the personnel profiles yields the scores for each person's task metrics. This embodiment enables personnel assignment under both multi-dimensional and single-dimensional tasks. Multi-dimensional task assignment refers to assigning the person with the highest percentage of their metric scores above a benchmark across multiple tasks as the highest priority among all assigned personnel. Single-dimensional task assignment involves calculating the ability performance, skill level, skill requirement, and vacancy score for each person in a single-dimensional task based on their personnel profile. These scores are then multiplied by the weights calculated using machine learning weighting methods to obtain the final score for each person. The person with the highest final score is then assigned as the highest priority.

[0038] Step 104: Assign the tasks to be assigned to the highest priority personnel.

[0039] Specifically, based on the highest-priority personnel identified under either multi-dimensional or single-dimensional tasks, the tasks are assigned to these personnel. The highest-priority personnel are those whose scores in the multi-dimensional metrics represent the highest percentage above the benchmark value, or those with the highest final score among all personnel in the single-dimensional metrics. It should be noted that a single task assignment involves multiple rounds of allocation; once a task is assigned to the highest-priority personnel, that person will not participate in subsequent rounds of priority allocation. This embodiment effectively assigns tasks to personnel most suitable for them, maximizing both efficiency and quality in task processing.

[0040] like Figure 2 As shown, Figure 2 This is a flowchart illustrating another embodiment of the power supply company dispatching method based on personnel profiling proposed in this application.

[0041] Step 201: Obtain the sample personnel information of at least one sample person and the sample task information of at least one sample task, and create an initial personnel profile model. Train the initial personnel profile model to obtain a trained personnel profile model.

[0042] Specifically, at least one sample person's information and at least one sample task's information are obtained from the power supply company's database, and an initial personnel profile model is created. The initial personnel profile model is a neural network model based on existing technology. The feature labels contained in the personnel information, the feature variables corresponding to each feature label, and the feature variable values ​​corresponding to each feature variable are obtained based on the personnel information. To reduce computational load and select highly relevant and representative feature variables, the feature variables corresponding to the feature labels are screened. The screening process includes: identifying feature variables with values ​​greater than a first preset value under the same feature label to obtain the first screening result; calculating the correlation coefficient between every two feature variables under the same feature label in the first screening result, wherein the method for calculating the correlation coefficient between every two feature variables under the same feature label is the existing correlation coefficient calculation method; retaining two feature variables with correlation coefficients less than or equal to a second preset value and retaining any one of the two feature variables with correlation coefficients greater than the second preset value to obtain the second screening result; sorting the feature variables in the second screening result in descending order of feature variable values ​​and selecting the top few feature variables; inputting the top few feature variables into the initial personnel profile model, and training the initial personnel profile model using a preset standard personnel profile as the standard, so that the initial personnel profile model converges to obtain the trained personnel profile model, wherein the preset standard personnel profile can be obtained by manually evaluating multiple standard feature variable values.

[0043] For example, in this embodiment, the first preset value is 0.2. From the original 41 feature variables, feature variables with values ​​greater than 0.2 under the same feature label are identified, and 19 feature variables are selected to obtain the first selection result. The correlation coefficient of each pair of feature variables under the same feature label in the first selection result is calculated. In this embodiment, the second preset value is 0.7, that is, any one of the two feature variables with a correlation coefficient less than or equal to 0.7 and any one of the two feature variables with a correlation coefficient greater than 0.7 is retained. For example, if the label of skill level includes the first feature variable being senior technician and the second feature variable being senior engineer, and the correlation coefficient of the first feature variable and the second feature variable is both greater than 0.7, then any one of the feature variables is retained, and 14 feature variables are selected to obtain the second selection result. The feature variables in the second selection result are sorted in descending order of feature variable value, and the first 8 feature variables are selected. The first 8 feature variables are input into the initial personnel profile model for training, and finally the trained personnel profile model is obtained.

[0044] Step 202: Obtain personnel information for multiple workers awaiting assignment and task information for tasks to be assigned.

[0045] Specifically, to obtain personnel information for multiple workers awaiting assignment and task information for tasks to be assigned, please refer to step 101, which will not be elaborated here.

[0046] Step 203: Construct personnel profiles for each person to be assigned based on their information and the trained personnel profile model. The profile model includes three dimensions: personnel information layer, time layer, and business layer.

[0047] Specifically, personnel information is input into the trained personnel profile model, which outputs corresponding personnel profiles. The personnel profile model includes three dimensions: personnel information layer, time layer, and business layer. It can realize horizontal and vertical comparative analysis of personnel. The personnel information layer, business layer, and time layer form a three-dimensional spatial model, which can also be projected into a user profile layer. The personnel profiles of each person to be assigned work are displayed through the three dimensions of personnel information layer, time layer, and business layer. The personnel information layer includes basic personnel information, the business layer includes the personnel's task indicator scores in each task, and the time layer includes the personnel's single work duration, monthly work duration, and total work time.

[0048] Step 204: Based on personnel profiles and pre-defined human resource talent evaluation theories, output relevant suggestions for personnel development.

[0049] Specifically, based on personnel information, we obtain the feature labels contained within the personnel information, the feature variables corresponding to each feature label, and the value of each feature variable. For example, the education level in the basic information of personnel is a feature label, and "undergraduate" and "graduate" are two feature variables corresponding to the same feature label. We compare and analyze the value of the feature variables corresponding to the same feature label. For example, in the feature label of education level, the first feature variable is "graduate" and the second feature variable is "undergraduate." The value of the first feature variable corresponding to "graduate" is 0.26, while the value of the second feature variable corresponding to "undergraduate" is 0.19. This indicates that the second feature variable performs better than the first feature variable. Therefore, we can output that personnel with an undergraduate degree perform better than those with a graduate degree, and suggest that more personnel with undergraduate degrees should be trained.

[0050] Step 205: Based on the personnel profile of each person awaiting assignment, obtain the corresponding task indicator scores for each person in multiple dimensions of tasks; based on the task indicator scores of each person awaiting assignment, obtain the percentage of each person's task indicator score that is above the benchmark value; select the person awaiting assignment with the highest percentage of their task indicator score that is above the benchmark value as the highest priority person awaiting assignment.

[0051] Specifically, the personnel profiles of each pending worker are matched with task information. Based on the personnel profiles of each pending worker, the business layer can derive the task indicator scores for each worker across multiple task dimensions. Then, based on these task indicator scores, the percentage of each pending worker's task indicator score that is above a benchmark value is calculated. The benchmark value is the standard score for the task indicator and can be adjusted according to actual circumstances; in this embodiment, the benchmark value is set to 1. Finally, the pending worker with the highest percentage of their task indicator score above the benchmark value is designated as the highest priority pending worker.

[0052] For example, such as Figure 3 As shown, through Figure 3 It can be seen that among the six task dimensions, A has five items above the benchmark value, B has four items above the benchmark value, C has two items above the benchmark value, and D and E have four and five task indicators below the benchmark value respectively. Therefore, it can be concluded that A has the highest proportion of items above the benchmark value, and A is the highest priority personnel to be assigned tasks.

[0053] Step 206: Based on the personnel profiles of each candidate, the business layer obtains the scores of each candidate's ability performance, skill level, ability requirement, and vacancy in a single-dimensional task. These scores are then multiplied by the weights corresponding to each candidate's ability performance, skill level, ability requirement, and vacancy in a single-dimensional task, obtained using a machine learning weighting coefficient method, and summed to obtain the final score for each candidate. The candidate with the highest final score is designated as the highest priority candidate for assignment.

[0054] Specifically, the personnel profiles of each candidate are matched with task information. One dimension of the task can refer to a specific task among multiple tasks. Based on the personnel profiles of each candidate, their ability performance, skill level, ability requirement, and vacancy score in one dimension of the task are calculated. Then, the weights corresponding to each candidate's ability performance, skill level, ability requirement, and vacancy in one dimension of the task are calculated using the machine learning weight coefficient method. The machine learning weight coefficient method is an existing technology and will not be elaborated here. Using the machine learning weight coefficient method, the calculated weights can be changed according to changes in the environment and adjustments, achieving a timely effect. Finally, the ability performance, skill level, ability requirement, and vacancy score of each candidate in one dimension of the task are multiplied by the corresponding weights and added together to obtain the final score value of each candidate. The candidate with the highest final score value is selected as the highest priority candidate for assignment.

[0055] For example, such as Figure 4As shown in the embodiment of this application, the machine learning weight coefficient method is used to obtain the following weights: ability performance accounts for 45%, skill level accounts for 35%, ability demand accounts for 15%, and vacancy rate accounts for 5%. The scores for each person's ability performance, skill level, ability demand, and vacancy rate in a single dimension are multiplied by their corresponding weights to obtain the final score for each person awaiting assignment. Then, the person with the highest final score is selected as the highest priority person awaiting assignment. Figure 4 As shown, Person A ultimately scored the highest, and was therefore designated as the highest priority person to be assigned work.

[0056] Step 207: Assign the tasks to be assigned to the highest priority personnel.

[0057] Specifically, based on the highest-priority personnel identified under multi-dimensional or single-dimensional tasks, the tasks to be assigned are distributed to the corresponding highest-priority personnel. It should be noted that after a task is assigned to the highest-priority personnel, this personnel will not participate in the next priority allocation. This embodiment can effectively distribute work tasks to personnel who are more suitable for the task, and can maximize the efficiency and quality of task processing.

[0058] The implementation principle of the power supply enterprise dispatching method based on personnel profiles in this application embodiment is as follows: Obtain sample personnel information of at least one sample person and sample task information of at least one sample task, and create an initial personnel profile model; train the initial personnel profile model to obtain a trained personnel profile model; obtain personnel information of multiple personnel to be dispatched and task information of tasks to be assigned; construct personnel profiles for each personnel to be dispatched based on the personnel information and the trained personnel profile model, the profile model including three dimensions: personnel information layer, time layer, and business layer; match the personnel profiles of each personnel to be dispatched with the task information, and select the highest priority personnel from multiple personnel to be dispatched based on an intelligent dispatching strategy using an optimization method; finally, assign the tasks to be dispatched to the highest priority personnel to be dispatched.

[0059] This application also discloses a system for dispatching power supply companies based on personnel profiles.

[0060] Reference Figure 5 This illustration shows a schematic diagram of a power supply company dispatching system based on personnel profiles, provided in an exemplary embodiment of this application. The power supply company dispatching system based on personnel profiles includes a personnel information acquisition module 1, a personnel profile construction module 2, a selection module 3 for the highest priority personnel to be dispatched, and a personnel dispatching module 4.

[0061] Personnel information acquisition module 1 is used to acquire personnel information of multiple personnel to be assigned and task information of tasks to be assigned. Personnel profile construction module 2 is used to construct personnel profiles for each of the personnel to be dispatched based on the personnel information and the trained personnel profile model. The profile model includes three dimensions: personnel information layer, time layer, and business layer. The highest priority personnel selection module 3 is used to match the personnel profiles of each personnel to be assigned with the task information, and select the highest priority personnel to be assigned from the multiple personnel to be assigned based on the intelligent assignment strategy of the optimization method. The personnel dispatch module 4 is used to assign the tasks to be assigned to the personnel with the highest priority.

[0062] Optional, such as Figure 6 The diagram illustrates another structural schematic of a power supply company dispatching system based on personnel profiles, provided in an exemplary embodiment of this application. This system further includes: a personnel profile training module 5, a personnel profile training completion module 6, a personnel profile display module 7, a growth suggestion output module 8, a personnel dispatching module for multi-dimensional tasks 9, and a personnel dispatching module for single-dimensional tasks 10.

[0063] Personnel profiling training module 5 is used to acquire sample personnel information of at least one sample personnel and sample task information of at least one sample task; and to create an initial personnel profiling model; and to train the initial personnel profiling model based on the sample personnel information of the at least one sample personnel and the sample task information of the at least one sample task. Personnel profiling training completion module 6 is used to acquire feature labels contained in each of the personnel information, feature variables corresponding to each feature label, and feature variable values ​​corresponding to each feature variable; determine feature variables whose feature variable values ​​are greater than a first preset value under the same feature label to obtain a first screening result; calculate the correlation coefficient of every two feature variables under the same feature label in the first screening result, retain any one of the two feature variables whose correlation coefficient is less than or equal to a second preset value and the two feature variables whose correlation coefficient is greater than the second preset value to obtain a second screening result; sort the feature variables in the second screening result in descending order of feature variable values, select the first few feature variables; input the first few feature variables into the initial personnel profiling model to obtain the personnel profiling model after training; Personnel profile display module 7 is used to display the personnel profiles of each of the personnel to be dispatched through the three dimensions of personnel information layer, time layer and business layer; The growth suggestion output module 8 is used to output relevant suggestions for personnel growth based on the personnel profile and the preset human resource talent evaluation theory. The multi-dimensional task assignment module 9 is used to obtain the task indicator scores of each person to be assigned in the multi-dimensional task based on the personnel profile of each person to be assigned; to obtain the proportion of each person to be assigned whose task indicator score is above the benchmark value based on the task indicator scores of each person to be assigned; and to select the person to be assigned with the highest proportion of task indicator scores above the benchmark value as the highest priority person to be assigned. The single-dimensional task personnel dispatch module 10 is used to obtain the ability performance, skill level, ability demand, and vacancy score of each person to be dispatched in a single-dimensional task based on the personnel profile of each person to be dispatched; to obtain the weights corresponding to the ability performance, skill level, ability demand, and vacancy of each person to be dispatched in the single-dimensional task according to the machine learning weight coefficient method; to multiply the ability performance, skill level, ability demand, and vacancy score of each person to be dispatched by the corresponding weight and add them together; and to select the person with the highest final score as the highest priority person to be dispatched.

[0064] It should be noted that the system provided in the above embodiments 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 system and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0065] 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. Figures 1-4 The method for dispatching power supply companies based on personnel profiles described in the illustrated embodiment can be further explained in the following steps: Figures 1-4 The specific details of the illustrated embodiments will not be elaborated here.

[0066] Please see Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 7 As shown, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0067] The communication bus 1002 is used to realize the connection and communication between these components.

[0068] The user interface 1003 may include a display screen and a camera. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0069] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0070] The processor 1001 may include one or more processing cores. The processor 1001 connects to various parts within the server 1000 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 the memory 1005, and by calling data stored in the memory 1005. Optionally, the processor 1001 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 1001 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 be implemented as a separate chip without being integrated into the processor 1001.

[0071] The memory 1005 may include random access memory (RAM) or read-only memory. Optionally, the memory 1005 may include a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 1005 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 1005 may also be at least one storage device located remotely from the aforementioned processor 1001. Figure 7 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a power supply company dispatching method based on personnel profiles.

[0072] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical 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 apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0073] exist Figure 7 In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 1001 can be used to call the application program stored in the memory 1005 for the power supply company dispatching method based on personnel profile. When executed by one or more processors, the electronic device performs one or more of the methods described in the above embodiments.

[0074] An electronic device readable storage medium stores instructions that, when executed by one or more processors, cause the electronic device to perform one or more of the methods described in the above embodiments.

[0075] Those skilled in the art will clearly understand that the technical solutions of this application can be implemented using software and / or hardware. In this specification, "unit" and "module" refer to software and / or hardware capable of independently performing or cooperating with other components to perform specific functions. Hardware may include, for example, a Field-Programmable Gate Array (FPGA), an Integrated Circuit (IC), etc.

[0076] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0077] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0078] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0079] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0080] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0081] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0082] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0083] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and the disclosure of practical truths. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure.

Claims

1. A power supply company dispatching method based on personnel profiling, characterized in that, The method includes: Obtain sample personnel information for at least one sample person and sample task information for at least one sample task, and create an initial personnel profile model; Obtain the feature tags contained in the information of each person, the feature variables corresponding to each feature tag, and the feature variable values ​​corresponding to each feature variable; Identify feature variables whose feature variable values ​​are greater than a first preset value under the same feature label to obtain the first screening result; Calculate the correlation coefficient of every two feature variables under the same feature label in the first screening result, retain any one of the two feature variables whose correlation coefficient is less than or equal to a second preset value and any one of the two feature variables whose correlation coefficient is greater than the second preset value, and obtain the second screening result; The feature variables in the second screening results are sorted in descending order of their values, and the first few feature variables are selected. The first few feature variables are input into the initial personnel profile model to obtain the personnel profile model after training. Obtain personnel information for multiple employees awaiting assignment and task information for tasks to be assigned; Based on the personnel information and the trained personnel profile model, a personnel profile of each of the personnel to be dispatched is constructed. The personnel profile model includes three dimensions: personnel information layer, time layer, and business layer. The personnel profiles of each of the personnel to be assigned are matched with the task information, and the highest priority personnel to be assigned is selected from the multiple personnel to be assigned based on the intelligent assignment strategy of the optimization method. The tasks to be assigned are assigned to the highest priority personnel awaiting work. The step of matching each personnel profile with the task information and selecting the highest-priority personnel from the plurality of personnel to be dispatched based on an intelligent dispatching strategy using an optimization method includes: Based on the personnel profiles of each of the aforementioned personnel awaiting assignment, the scores of each of the aforementioned personnel awaiting assignment in various task indicators across multiple dimensions are obtained. Based on the scores of each task indicator of the personnel to be assigned, the proportion of each personnel's task indicator score that is above the benchmark value is obtained. The employee with the highest percentage of task indicator scores above the benchmark value among all employees awaiting assignment is designated as the highest priority employee.

2. The power supply company dispatching method based on personnel profiling according to claim 1, characterized in that, After constructing the personnel profiles of each of the personnel to be dispatched based on the personnel information and the trained personnel profile model, the method further includes: Based on the aforementioned personnel profiles and pre-defined human resource talent evaluation theories, relevant suggestions for personnel development are generated.

3. The power supply company dispatching method based on personnel profiling according to claim 1, characterized in that, After constructing the personnel profiles of each of the personnel to be dispatched based on the personnel information and the trained personnel profile model, the method further includes: The personnel profiles of each person awaiting assignment are displayed through the three dimensions of personnel information layer, time layer, and business layer.

4. The power supply company dispatching method based on personnel profiling according to claim 1, characterized in that, The step of matching the personnel profiles with the task information and selecting the highest-priority personnel from the plurality of personnel to be dispatched based on an intelligent dispatching strategy using an optimization method includes: Based on the personnel profiles of each person awaiting assignment, scores are obtained for each person's ability performance, skill level, ability requirement, and vacancy rate in a single-dimensional task. The weights corresponding to the ability performance, skill level, ability demand, and vacancy rate of each worker to be assigned in the one-dimensional task are obtained by the machine learning weight coefficient method. The scores of each candidate's ability performance, skill level, ability demand, and vacancy rate are multiplied by the corresponding weights and then added together to obtain the final score of each candidate. The person with the highest final score among all the personnel awaiting assignment will be designated as the highest priority personnel awaiting assignment.

5. A power supply enterprise dispatching system based on personnel profiling as described in any one of claims 1-4, characterized in that, The system includes: Personnel information acquisition module (1) is used to acquire personnel information of multiple personnel to be assigned and task information of tasks to be assigned; The personnel profile construction module (2) is used to construct the personnel profile of each of the personnel to be dispatched based on the personnel information and the trained personnel profile model. The profile model includes three dimensions: personnel information layer, time layer and business layer. The highest priority personnel selection module (3) is used to match the personnel profiles of each of the personnel to be assigned with the task information, and select the highest priority personnel from the multiple personnel to be assigned based on the intelligent assignment strategy of the optimization method. The personnel dispatch module (4) is used to assign the task to be assigned to the highest priority personnel.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the steps of the method as described in any one of claims 1-4.

7. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-4.

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