A transformer-based power plant employee task scheduling method, device and medium

Abstract

This invention relates to a Transformer-based method, equipment, and medium for scheduling employee tasks in thermal power plants, belonging to the field of task scheduling technology. The method includes the following steps: acquiring employee text sequences and task text sequences; using the employee text sequences and task text sequences as input to a Transformer model, outputting employee skill profiles and task requirement profiles; calculating the similarity between the employee skill profiles and task requirement profiles, and obtaining employee-task matching degree based on the task title requirements, task qualification requirements, employee title level, employee qualification set, and the similarity; constructing an objective function to maximize the employee-task matching degree, and formulating constraints based on the task headcount requirements; and solving the objective function to obtain the employee task scheduling decision result. This invention achieves optimal matching between employees and tasks by calculating the similarity between employee skills and task requirements, combined with hard requirements such as employee title level and qualification set.

Description

Technical Field

[0001] This invention relates to a method, equipment, and medium for scheduling tasks for employees in thermal power plants based on Transformer, belonging to the field of task scheduling technology. Background Technology

[0002] With the rapid development of the power industry and the profound adjustment of the energy structure, thermal power plants, as an important pillar of my country's power supply, face increasingly stringent safety standards, continuously increasing unit capacity, and increasingly complex operating conditions. The production and operation of thermal power plants involve multiple professional fields such as boilers, steam turbines, generators, electrical systems, and thermal control. Various maintenance, repair, operation, and emergency response tasks place stringent demands on the professional skills, qualification levels, and practical experience of personnel.

[0003] Traditional task scheduling for thermal power plant employees relies heavily on manual experience and judgment. Schedulers need to comprehensively consider multiple dimensions of information, including the task's technical difficulty, risk level, time constraints, and employee qualifications, professional certifications, and historical performance. However, with the expansion of power plant personnel and the diversification of task types, manual scheduling has gradually revealed problems such as low efficiency, strong subjectivity, and difficulty in global optimization. Especially in complex scenarios such as emergency repairs and major equipment maintenance, quickly and accurately matching the optimal task execution team from a vast pool of candidates has become a key bottleneck in improving the operational efficiency and safety of thermal power plants. Therefore, there is an urgent need for a task scheduling technology for thermal power plants that integrates deep semantic understanding and mathematical optimization methods. This technology should fully explore the deep semantic relationships between employee profiles and task requirements while taking into account both rigid qualification constraints and task resource allocation requirements, achieving intelligent and precise personnel-task matching. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention proposes a method, equipment, and medium for scheduling tasks for employees in thermal power plants based on Transformer.

[0005] The technical solution of the present invention is as follows: On the one hand, this invention provides a Transformer-based method for scheduling tasks for employees in thermal power plants, comprising the following steps: Obtain employee file text data for each employee in the thermal power plant and task requirement text data for each task in the thermal power plant. Based on the employee file text data and task requirement text data, obtain the employee text sequence and the task text sequence; The Transformer model takes employee text sequences and task text sequences as input and outputs employee skill profiles and task requirement profiles. Obtain the task title requirements, task qualification requirements, and task number requirements from the task requirement text data, as well as the employee title level and employee qualification set from the employee text sequence; Calculate the similarity between the employee skill profile and the task requirement profile, and obtain the employee task matching degree based on the task title requirements, task qualification requirements, employee title level, employee qualification set and similarity. Construct an objective function that maximizes the matching degree between employees and tasks, and formulate constraints based on the required number of employees for the tasks. Solving the objective function yields the employee task scheduling decision results.

[0006] Preferably, the employee text sequence is used as input to the Transformer model to output an employee skill profile. The specific steps are as follows: Initialize the word embedding matrix, and obtain the word embedding position encoding matrix based on the employee text sequence and the word embedding matrix, expressed by the formula: ; In the formula, Indicates the first Word embedding position encoding matrix for each employee The Middle The word embedding position encoding vector of the line. Represents the word embedding matrix, Indicates the first Employee text sequence of 1 employee The Middle One index value, Represents the position encoding matrix The Middle The position encoding vector of the row; Construct a query matrix, a key matrix, and a value matrix based on the word embedding position encoding matrix; The attention matrix is ​​obtained based on the query matrix, key matrix, and value matrix, expressed by the formula: ; In the formula, Indicates the first The employee in the Attention matrix of attention heads, express function, The dimension representing the attention head. Key matrix transpose, Indicates the first The employee in the A query matrix with attention heads Indicates the first The employee in the The key matrix of each attention head. Indicates the first The employee in the A matrix of values ​​for each attention head; The attention matrices of each attention head are concatenated to obtain the multi-head attention matrix, which can be expressed by the formula: ; In the formula, Indicates the first A multi-headed attention matrix for each employee Indicates the number of attention heads. This indicates a splicing operation. Indicates the output projection matrix; The multi-head attention matrix is ​​concatenated with the word embedding position encoding matrix and then subjected to layer normalization to obtain the multi-head attention normalization matrix, expressed by the formula: ; In the formula, Indicates the first Multi-head attention normalization matrix for individual employees Presentation layer normalization processing; Using the multi-head attention normalization matrix as input to the feedforward neural network, the output is an employee skill encoding matrix, expressed by the formula: ; In the formula, Indicates the first Employee skill coding matrix for each employee This represents a feedforward neural network; The employee skill profile is obtained based on the employee skill coding matrix, expressed by the formula: ; In the formula, Indicates the first Employee skills profile for each employee Indicates the first Employee text sequence of 1 employee The number of index values, i.e., the number of word segments. Indicates the first Employee Skills Coding Matrix The Middle The employee skill coding vector of the row.

[0007] Preferably, the task requirement profile is expressed by the formula: ; In the formula, Indicates the first Task requirement profile for each task. Indicates the first A sequence of task texts for each task The number of index values, Indicates the first Task requirement encoding matrix for each task No. The task requirement encoding vector for the line.

[0008] Preferably, the similarity between the employee skill profile and the task requirement profile is calculated, expressed by the formula: ; In the formula, Indicates the first The employee skills profile of the first employee and the first The similarity of the task requirement profiles of the tasks. Indicates the first Task requirement profile for each task. Indicates the first Employee skills profile for each employee.

[0009] Preferably, the employee-task matching degree is obtained based on the task title requirements, task qualification requirements, employee title level, employee qualification set, and similarity, expressed by the formula: ; In the formula, Indicates the first The employee and the first The employee task matching degree for each task. This represents a constant; in this embodiment, it is taken as... , Indicates the first Employee job title level Indicates the first The job title requirements for each task. Indicates the first The qualification requirements for each task Indicates the first A collection of employee qualifications for each employee.

[0010] Preferably, an objective function is constructed to maximize the employee task matching degree, and constraints are formulated based on the task number requirement. The specific steps are as follows: Construct an objective function that maximizes employee task matching, expressed as the formula: ; In the formula, Represents the maximum value function. Indicates the number of employees. Indicates the number of tasks. Indicates the first Is the task assigned to the first...? Decision variables for individual employees; Based on the aforementioned task personnel requirements, constraints are established, expressed as a formula: ; In the formula, Indicates the first The number of people required for each task.

[0011] Preferably, the employee text sequence and task text sequence are obtained based on the employee file text data and task requirement text data. The specific steps are as follows: The employee file text data and task requirement text data are processed by word segmentation; The word segmentation results are statistically analyzed and deduplicated. A vocabulary is constructed based on the deduplicated word segmentation results, and an index is set for each word segmentation in the vocabulary. Based on the vocabulary, the word segments in the employee file text data are replaced with the corresponding index values ​​of the word segments in the vocabulary to obtain the employee text sequence; Based on the vocabulary, the word segments in the task requirement text data are replaced with their corresponding index values ​​in the vocabulary to obtain the task text sequence.

[0012] In another aspect, the present invention also provides an electronic device having a computer program stored thereon, which, when executed by a processor, implements the Transformer-based task scheduling method for thermal power plant employees as described in any embodiment of the present invention.

[0013] In another aspect, the present invention also provides a computer-readable storage medium for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the Transformer-based task scheduling method for thermal power plant employees as described in any embodiment of the present invention.

[0014] The present invention has the following beneficial effects: 1. This invention utilizes the Transformer model to quickly generate employee skill profiles and task requirement profiles. Compared to the traditional manual scheduling method that requires checking employee skills and task requirements one by one, this invention can process massive amounts of data simultaneously and complete deep semantic matching, significantly reducing the time and labor intensity of manual analysis.

[0015] 2. This invention achieves optimal matching between employees and tasks by calculating the similarity between employee skills and task requirements, combined with hard requirements such as employee professional title level and qualification set. This matching method considers not only the employee's professional skills but also the specific requirements of the task, ensuring that each task is assigned to the most suitable employee. By maximizing the employee-task matching degree, it avoids the waste and idleness of human resources, enabling a more rational allocation of human resources in thermal power plants. Attached Figure Description Figure 1 This is a flowchart illustrating the implementation of the method in an embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0018] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0019] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0020] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0021] Example 1: See Figure 1 This embodiment provides a Transformer-based method for scheduling tasks for employees in thermal power plants, including the following steps: S1. Obtain employee file text data for each employee in the thermal power plant and task requirement text data for each task in the thermal power plant.

[0022] In one embodiment, the employee file text data is: "Wang Ming, male, 35 years old, senior engineer, holds a registered electrical engineer certificate and a high-voltage electrician certificate. He has been engaged in the maintenance of electrical systems in thermal power plants for 10 years and is familiar with relay protection and automation control. He has participated in 3 unit overhauls and has fault diagnosis capabilities." The task requirement text is: "One senior engineer is needed, holding a registered electrical engineer certificate, familiar with relay protection and automation control, and with more than 10 years of experience in electrical maintenance of thermal power plants. Number of positions required: 1. Job duties: Responsible for the comprehensive overhaul of the electrical system of Unit 3." S2. Perform word segmentation on the employee file text data and task requirement text data, and construct a vocabulary based on the word segmentation results, specifically: The word segmentation results are statistically analyzed and deduplicated. A vocabulary is constructed based on the deduplicated word segmentation results, and an index is set for each word in the vocabulary.

[0023] In one embodiment, the word segmentation result of the employee file text data is: [“Wang Ming”, “male”, “35 years old”, “senior engineer”, “hold”, “registered electrical engineer certificate”, “and”, “high voltage electrician certificate”, “engaged in”, “thermal power plant”, “electrical system”, “maintenance”, “10 years”, “familiar with”, “relay protection”, “automation control”, “have participated in”, “3 times”, “unit overhaul”, “possess”, “fault diagnosis capability”].

[0024] Since the vocabulary is indexed according to the order of word segmentation results, the employee text sequence is [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21].

[0025] S3. Based on the vocabulary, employee file text data, and task requirement text data, obtain the employee text sequence and the task text sequence, specifically as follows: Based on the vocabulary, the word segments in the employee file text data are replaced with the corresponding index values ​​of the word segments in the vocabulary to obtain the employee text sequence; Based on the vocabulary, the word segments in the task requirement text data are replaced with their corresponding index values ​​in the vocabulary to obtain the task text sequence.

[0026] S4. Using the employee text sequence as input to the Transformer model, the output is an employee skill profile. The specific steps are as follows: S41. Initialize the word embedding matrix. Based on the employee text sequence and the word embedding matrix, obtain the word embedding position encoding matrix, expressed by the formula: ; In the formula, Indicates the first Word embedding position encoding matrix for each employee The Middle The word embedding position encoding vector of the line. The word embedding matrix is ​​randomly initialized based on the size of the vocabulary and updated through backpropagation (i.e., the number of words in the vocabulary corresponds to the number of rows in the word embedding matrix). Indicates the first Employee text sequence of 1 employee The Middle One index value, Represents the position encoding matrix The Middle The row position encoding vector, in this embodiment, uses sine coding. The word embedding matrix represents the first... The word embedding vector of the row; S42. Construct a query matrix, a key matrix, and a value matrix based on the word embedding position encoding matrix; The query matrix is ​​expressed by the formula: ; In the formula, Indicates the first The employee in the A query matrix with attention heads Indicates the first The query projection matrix of each attention head; The key matrix is ​​expressed by the formula: ; In the formula, Indicates the first The employee in the The key matrix of each attention head. Indicates the first The key projection matrix of each attention head; The value matrix is ​​expressed by the formula: ; In the formula, Indicates the first The employee in the The value matrix of each attention head, Indicates the first The value projection matrix of each attention head; S43. Obtain the attention matrix based on the query matrix, key matrix, and value matrix, expressed by the formula: ; In the formula, Indicates the first The employee in the Attention matrix of attention heads, express function, The dimension representing the attention head. Key matrix Transpose of; S44. Concatenate the attention matrices of each attention head to obtain the multi-head attention matrix, expressed by the formula: ; In the formula, Indicates the first A multi-headed attention matrix for each employee Indicates the number of attention heads. This indicates a splicing operation. Indicates the output projection matrix; S45. Concatenate the multi-head attention matrix and the word embedding position encoding matrix, and then perform layer normalization to obtain the multi-head attention normalization matrix, expressed by the formula: ; In the formula, Indicates the first Multi-head attention normalization matrix for individual employees Presentation layer normalization processing; S46. Using the multi-head attention normalization matrix as input to the feedforward neural network, the output is the employee skill encoding matrix, expressed by the formula: ; In the formula, Indicates the first Employee skill coding matrix for each employee This represents a feedforward neural network; The feedforward neural network can be expressed by the following formula: ; In the formula, This represents the weight matrix of the first layer of the feedforward neural network. This represents the weight matrix of the second layer of the feedforward neural network. This represents the bias vector of the first layer of the feedforward neural network. This represents the bias vector of the second layer of the feedforward neural network; S47. Obtain employee skill profiles based on the employee skill coding matrix, expressed as a formula: ; In the formula, Indicates the first Employee skills profile for each employee Indicates the first Employee text sequence of 1 employee The number of index values, i.e., the number of word segments. Indicates the first Employee Skills Coding Matrix The Middle The employee skill coding vector of the row.

[0027] S5. Take the task text sequence as input to the Transformer model and output a task requirement profile, which is expressed by the formula: ; In the formula, Indicates the first Task requirement profile for each task. Indicates the first A sequence of task texts for each task The number of index values, Indicates the first Task requirement encoding matrix for each task No. The task requirement encoding vector for the row is derived from the Transformer model used in step S5, which shares the model parameters of the Transformer model used in step S4, i.e., the task requirement encoding matrix. The method for obtaining the employee skill coding matrix can be found in step S4. The process of obtaining it.

[0028] S6. Obtain the task title requirements, task qualification requirements, and task number requirements from the task requirement text data.

[0029] S7. Calculate the similarity between the employee skill profile and the task requirement profile, expressed by the formula: ; In the formula, Indicates the first The employee skills profile of the first employee and the first The similarity of the task requirement profiles for each task.

[0030] S8. Obtain the employee title level and employee qualification set from the employee text sequence.

[0031] S9. Based on the task title requirements, task qualification requirements, employee title level, employee qualification set, and similarity, obtain the employee task matching degree, expressed by the formula: ; In the formula, Indicates the first The employee and the first The employee task matching degree for each task. This represents a constant; in this embodiment, it is taken as... , Indicates the first Employee job title level Indicates the first The job title requirements for each task. Indicates the first The qualification requirements for each task Indicates the first A collection of employee qualifications for each employee.

[0032] S10. Construct an objective function that maximizes the matching degree between employees and tasks, and formulate constraints based on the required number of employees for the tasks. The specific steps are as follows: S101. Construct an objective function that maximizes the task matching degree of employees, expressed by the formula: ; In the formula, Represents the maximum value function. Indicates the number of employees. Indicates the number of tasks. Indicates the first Is the task assigned to the first...? The decision variables for each employee are 1 for allocation and 0 for no allocation. S102. Based on the required number of personnel for the task, establish constraints, expressed as a formula: ; In the formula, Indicates the first The number of people required for each task.

[0033] S11. Solve the objective function to obtain the employee task scheduling decision result.

[0034] Example 2: This embodiment provides an electronic device that stores a computer program. When the computer program is executed by a processor, it implements the Transformer-based task scheduling method for thermal power plant employees as described in any embodiment of the present invention.

[0035] Example 3: This embodiment provides a computer-readable storage medium for storing one or more programs, which, when executed by one or more processors, enable the one or more processors to implement the Transformer-based task scheduling method for thermal power plant employees as described in any embodiment of the present invention.

[0036] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0037] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0038] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0039] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium 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 storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0040] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A Transformer-based method for scheduling employee tasks in thermal power plants, characterized in that, Includes the following steps: Obtain employee file text data for each employee in the thermal power plant and task requirement text data for each task in the thermal power plant. Based on the employee file text data and task requirement text data, obtain the employee text sequence and the task text sequence; The Transformer model takes employee text sequences and task text sequences as input and outputs employee skill profiles and task requirement profiles. Obtain the task title requirements, task qualification requirements, and task number requirements from the task requirement text data, as well as the employee title level and employee qualification set from the employee text sequence; Calculate the similarity between the employee skill profile and the task requirement profile, and obtain the employee task matching degree based on the task title requirements, task qualification requirements, employee title level, employee qualification set and similarity. Construct an objective function that maximizes the matching degree between employees and tasks, and formulate constraints based on the required number of employees for the tasks. Solving the objective function yields the employee task scheduling decision results.

2. The method for scheduling employee tasks in thermal power plants based on Transformer according to claim 1, characterized in that, The Transformer model takes employee text sequences as input and outputs employee skill profiles. The specific steps are as follows: Initialize the word embedding matrix, and obtain the word embedding position encoding matrix based on the employee text sequence and the word embedding matrix, expressed by the formula: ； In the formula, Indicates the first Word embedding position encoding matrix for each employee The Middle The word embedding position encoding vector of the line. Represents the word embedding matrix, Indicates the first Employee text sequence of 1 employee The Middle One index value, Represents the position encoding matrix The Middle The position encoding vector of the row; Construct a query matrix, a key matrix, and a value matrix based on the word embedding position encoding matrix; The attention matrix is ​​obtained based on the query matrix, key matrix, and value matrix, expressed by the formula: ； In the formula, Indicates the first The employee in the Attention matrix of attention heads, express function, The dimension representing the attention head, Key matrix transpose, Indicates the first The employee in the A query matrix with attention heads Indicates the first The employee in the The key matrix of each attention head. Indicates the first The employee in the A matrix of values ​​for each attention head; The attention matrices of each attention head are concatenated to obtain the multi-head attention matrix, which can be expressed by the formula: ； In the formula, Indicates the first A multi-headed attention matrix for each employee Indicates the number of attention heads. This indicates a splicing operation. Indicates the output projection matrix; The multi-head attention matrix is ​​concatenated with the word embedding position encoding matrix and then subjected to layer normalization to obtain the multi-head attention normalization matrix, expressed by the formula: ； In the formula, Indicates the first Multi-head attention normalization matrix for individual employees Presentation layer normalization processing; Using the multi-head attention normalization matrix as input to the feedforward neural network, the output is an employee skill encoding matrix, expressed by the formula: ； In the formula, Indicates the first Employee skill coding matrix for each employee This represents a feedforward neural network; The employee skill profile is obtained based on the employee skill coding matrix, expressed by the formula: ； In the formula, Indicates the first Employee skills profile for each employee Indicates the first Employee text sequence of 1 employee The number of index values, i.e., the number of word segments. Indicates the first Employee skill coding matrix for each employee The Middle The employee skill coding vector of the row.

3. The method for scheduling employee tasks in thermal power plants based on Transformer according to claim 1, characterized in that, The task requirement profile is expressed as a formula: ； In the formula, Indicates the first Task requirement profile for each task. Indicates the first A sequence of task texts for each task The number of index values, Indicates the first Task requirement encoding matrix for each task No. The task requirement encoding vector for the line.

4. The method for scheduling employee tasks in thermal power plants based on Transformer according to claim 1, characterized in that, The similarity between employee skill profiles and task requirement profiles is calculated using the following formula: ； In the formula, Indicates the first The employee skills profile of the first employee and the first The similarity of the task requirement profiles of the tasks. Indicates the first Task requirement profile for each task. Indicates the first Employee skills profile for each employee.

5. The Transformer-based task scheduling method for thermal power plant employees according to claim 4, characterized in that, The employee-task matching degree is obtained based on the task title requirements, task qualification requirements, employee title level, employee qualification set, and similarity, expressed by the formula: ； In the formula, Indicates the first The employee and the first The employee task matching degree for each task. This represents a constant; in this embodiment, it is taken as... , Indicates the first Employee job title level Indicates the first The job title requirements for each task. Indicates the first The qualification requirements for each task Indicates the first A collection of employee qualifications for each employee.

6. The Transformer-based task scheduling method for thermal power plant employees according to claim 5, characterized in that, Construct an objective function that maximizes the matching degree between employees and tasks, and formulate constraints based on the required number of employees for the tasks. The specific steps are as follows: Construct an objective function that maximizes employee task matching, expressed as the formula: ； In the formula, Represents the maximum value function. Indicates the number of employees. Indicates the number of tasks. Indicates the first Is the task assigned to the first...? Decision variables for individual employees; Based on the aforementioned task personnel requirements, constraints are established, expressed as a formula: ； In the formula, Indicates the first The number of people required for each task.

7. The method for scheduling employee tasks in thermal power plants based on Transformer according to claim 1, characterized in that, Based on the employee file text data and task requirement text data, obtain the employee text sequence and task text sequence. The specific steps are as follows: The employee file text data and task requirement text data are processed by word segmentation; The word segmentation results are statistically analyzed and deduplicated. A vocabulary is constructed based on the deduplicated word segmentation results, and an index is set for each word segmentation in the vocabulary. Based on the vocabulary, the word segments in the employee file text data are replaced with the corresponding index values ​​of the word segments in the vocabulary to obtain the employee text sequence; Based on the vocabulary, the word segments in the task requirement text data are replaced with the corresponding index values ​​in the vocabulary to obtain the task text sequence.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the Transformer-based task scheduling method for thermal power plant employees as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the Transformer-based task scheduling method for thermal power plant employees as described in any one of claims 1 to 7.

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