Electric energy meter full-performance test task allocation management method and system

Abstract

The invention discloses an electric energy meter full-performance test task allocation management method and system. The method provided by the invention comprises the following steps: constructing a resource state matrix and a resource dependency matrix by monitoring and acquiring the state characteristics of test resources and the dependency relationship among the test resources in real time, and calculating the initial utilization rate of the test resources by using a resource utilization rate calculation algorithm; calculating the priority of the task by using a priority evaluation algorithm; calculating a matching priority between the task and the test resource by using a bidirectional matching priority calculation algorithm, performing a preliminary decision of task allocation, and constructing a task allocation matrix; and calculating by using a task allocation balance optimization algorithm to obtain a target function, further optimizing task allocation by using a gradient descent method, and calculating to obtain updated task allocation matrix elements. According to the method, the current state and the mutual dependency relationship of each test resource can be accurately mastered, and resource waste and test task failure caused by incomplete or wrong information during task allocation are avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of task allocation management, and in particular to a method and system for managing the task allocation of a full performance test of an electric energy meter. Background Art

[0002] With the development of smart grid, the types and functions of electric energy meters are becoming increasingly diverse. The complexity and multifunctionality of electric energy meters make the content and process of full performance tests extremely complicated. Traditional manual management methods can no longer meet the modern power system's demand for accurate and automated testing and management of electric energy meters. In order to achieve full performance testing of electric energy meters through automated task allocation management, the electric energy meter full performance test task allocation management system came into being.

[0003] The task allocation and management system for the full performance test of electric energy meters has important theoretical and practical significance for improving the quality and efficiency of electric energy meter testing, reducing the cost and risk of electric energy meter testing, and improving the traceability and transparency of electric energy meter testing. With the continuous development of the power industry and the widespread promotion of smart grids, the task allocation and management system for the full performance test of electric energy meters will play an increasingly important role and promote the electric energy meter industry towards an intelligent and efficient future.

[0004] However, the above technologies have at least the following technical problems: the perception and initialization process of resource status relies on manual monitoring and manual recording, and lacks a systematic and real-time monitoring mechanism; the lack of scientific and reasonable basis for task allocation will lead to blind and arbitrary resource utilization; the priority of tasks is set according to fixed rules or experience, and lacks consideration of multi-dimensional factors, which will lead to unreasonable task priority evaluation; the matching of tasks and resources lacks dynamic consideration of resource adaptability and actual availability, resulting in inappropriate resources being allocated to inappropriate tasks, causing task failure or resource waste; the lack of an optimization mechanism for task allocation can easily lead to unreasonable task allocation. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the defects of the above-mentioned prior art and provide a method and system for managing the allocation of full-performance test tasks for electric energy meters. The method and system realize comprehensive perception and initialization of test resources by constructing a resource status matrix and a resource dependency matrix. Through precise resource status description, the method ensures that the current status and interdependence of each test resource can be accurately grasped, thereby avoiding resource waste and test task failure caused by incomplete or erroneous information during task allocation. Through the task allocation balance optimization algorithm, the allocation matrix between tasks and resources is calculated and optimized, aiming to achieve balanced use of resources, prevent resource overload or idleness, and ensure fair allocation of tasks.

[0006] To this end, the present invention adopts the following technical solution.

[0007] In a first aspect, the present invention provides a method for managing the task allocation of a full performance test of an electric energy meter, which comprises the steps of: S1, by real-time monitoring and obtaining the state characteristics of the test resources and the dependency relationship between the test resources, a resource state matrix and a resource dependency matrix are constructed, and based on the resource state matrix and the resource dependency matrix, the initial utilization rate of the test resources is calculated using a resource utilization rate calculation algorithm; S2, based on the initial utilization of experimental resources, the priority of the task is calculated using the priority evaluation algorithm; S3, based on the initial utilization rate of the experimental resources and the priority of the tasks, the matching priority between the tasks and the experimental resources is calculated using a two-way matching priority calculation algorithm, and a preliminary decision on task allocation is made according to the matching priority between the tasks and the experimental resources, and a task allocation matrix is ​​constructed; S4, based on the matching priority of tasks and resources and the task allocation matrix, uses the task allocation balance optimization algorithm to calculate the objective function. Based on the objective function, the task allocation is further optimized by the gradient descent method to calculate the updated task allocation matrix elements.

[0008] Furthermore, S1 specifically includes: it is necessary to fully perceive and initialize the resource status in the test environment, and obtain the status characteristics of the test resources and the dependencies between the test resources through real-time monitoring; to systematically describe the status of the test resources, a resource status matrix is ​​constructed, wherein the rows of the resource status matrix represent the test resources, the columns represent the status characteristics of the test resources, and each element Indicates The first resource The value of each state feature ranges from 0 to 1; At the same time, considering the mutual influence between experimental resources, in order to improve the rationality of experimental task allocation, a resource dependency matrix is ​​constructed to describe the dependency relationship between experimental resources; each element in the resource dependency matrix Indicates Experimental resources and The degree of dependency between experimental resources, ranging from 0 to 1.

[0009] Furthermore, in S1, the resource utilization calculation algorithm is based on the resource state matrix and the resource dependency matrix. According to the state characteristics in the resource state matrix, an initial weight value is set for each test resource to measure the current usage status of the test resource, and the dependency between the test resources is identified in combination with the resource dependency matrix. Through exponential function transformation, the impact of the dependency between the test resources on the initial utilization is attenuated, and an adjustment factor is introduced to control the intensity of the impact of the test resource dependency on the initial utilization. The initial utilization is normalized to make it comparable under different resources and different state numbers, ensuring that the value of the initial utilization is within a reasonable range.

[0010] Furthermore, in S2, the priority evaluation algorithm uses a logarithmic function to calculate the impact of the task urgency, avoids excessive interference from extremely urgent tasks, and at the same time ensures a certain priority for emergency task processing, thereby enhancing the robustness of the electric energy meter full performance test task allocation management system; uses the reciprocal form of the square root to calculate the impact of the time required for the task, and the reciprocal square root is used to enhance the priority of short-term tasks; by evaluating the matching degree between the resources required for the task and the currently available resources, to ensure that the resource requirements can be met, and introduces a resource requirement impact weight factor to balance the impact of the task resource requirements on the priority.

[0011] Furthermore, in S2, the calculation formula of the task priority is:

[0012] in, Indicates The priority of a task, that is, its priority among all pending tasks; Represents the weight coefficient, which is used to balance the impact of task urgency on priority; Indicates The urgency of the task; Indicates that the logarithmic function is used to process the The urgency of each task to smooth out the impact of extreme values; Represents the time impact weight factor, which is used to balance the impact of task time on priority; Indicates Time required for each task; Indicates The reciprocal square root of the time required for each task is used to prioritize tasks with shorter time. Represents the weight factor of resource demand impact, which is used to balance the impact of task resource demand on priority; Indicates The resource requirements of the task, that is, The amount of specific resources required for each task; Represents the fitness influencing factor, indicating all the factors suitable for the The remaining availability of resources for each task; Indicates Task and The adaptability of each experimental resource is determined to determine the rationality of the adaptation task for each resource. , set according to the specific implementation scenario.

[0013] Furthermore, in S3, based on the initial utilization rate of the test resources, the compatibility between the task and the test resources, and the priority of the task, a bidirectional matching priority calculation algorithm is used to calculate the matching priority between the task and the test resources; The two-way matching priority calculation algorithm introduces the priority of the task to evaluate the urgency and importance of the task. The compatibility between the task and the experimental resources is used to determine whether the experimental resources are suitable for executing the task, ensuring that when allocating tasks, task failure or resource waste caused by incompatible resource allocation is avoided. The initial utilization rate of the experimental resources is used to calculate the remaining availability of resources, and the actual availability of resources is dynamically considered to improve utilization efficiency. The introduction of an adjustment factor that controls the balance between task priority and compatibility helps to flexibly adjust the task allocation strategy, further consider the sensitivity of the task to the average utilization rate of all adapted resources, and adjust the responsiveness of the matching priority to the dynamic demand for resources by enhancing the adjustment parameters of the sensitivity of the task to the dynamic demand for resources.

[0014] Furthermore, in S4, the task allocation balance optimization algorithm aims to optimize the task allocation process in the full performance test of the electric energy meter, and maximizes the efficiency and rationality of task allocation by combining the matching priority of tasks and resources and the balance of resource utilization, thereby ensuring that the test tasks can be executed under reasonable resource conditions; the balance of resource utilization takes into account the balanced use of each test resource, aiming to prevent the test resources from being overused or idle, and measures the actual use of each resource by comparing it with the ideal balanced use, thereby ensuring fair distribution of tasks and avoiding waste and overload of test resources.

[0015] Furthermore, in S4, the calculation formula of the objective function is:

[0016] in, represents the objective function, which aims to maximize both the matching priority between tasks and resources and the balance of resource utilization; Indicates the number of tasks; Indicates Tasks and Matching priority between experimental resources; It represents the elements of resource allocation matrix, reflecting the preliminary decision of task allocation; Indicates The experimental resource utilization balance factor is used to measure the The balanced use of experimental resources can prevent overuse or idleness of experimental resources and ensure fair distribution of tasks. The calculation formula is:

[0017] in, Indicates The number of tasks assigned to each experimental resource; Indicates the average number of tasks per resource under ideal conditions.

[0018] Furthermore, in S4, the gradient descent method uses a dynamic learning rate and a momentum term to improve the convergence speed and the ability to find the global optimal solution. The momentum term is based on the update direction of the previous iteration. Part of the momentum is retained during the gradient descent process to prevent falling into a local optimal solution. The dynamic learning rate is adjusted based on the number of iterations. As the number of iterations increases, the learning rate gradually decreases, making it easier to quickly find an optimal solution.

[0019] In a second aspect, the present invention provides an electric energy meter full performance test task allocation management system, which includes a resource status monitoring and initialization module, an initial utilization calculation module, a task priority evaluation module, a two-way matching priority calculation module, a task allocation matrix construction module and a task allocation optimization module; The resource status monitoring and initialization module: constructs a resource status matrix and a resource dependency matrix by real-time monitoring and acquiring the status characteristics of the test resources and the dependency relationships between the test resources, and outputs the resource status matrix and the resource dependency matrix to the initial utilization calculation module; The initial utilization calculation module: based on the resource state matrix and resource dependency matrix of the resource state monitoring and initialization module, uses the resource utilization calculation algorithm to calculate the initial utilization of the test resources, and outputs the initial utilization of the test resources to the task priority evaluation module and the two-way matching priority calculation module; The task priority evaluation module: based on the initial utilization rate of the test resources of the initial utilization rate calculation module, uses the priority evaluation algorithm to calculate the priority of the task, and outputs the priority of the task to the two-way matching priority calculation module; The two-way matching priority calculation module: based on the initial utilization rate of the test resources of the initial utilization rate calculation module and the priority of the task of the task priority evaluation module, uses a two-way matching priority calculation algorithm to calculate the matching priority between the task and the test resources; The task allocation matrix construction module: makes a preliminary decision on task allocation based on the matching priority between the tasks and the experimental resources of the two-way matching priority calculation module, constructs a task allocation matrix, outputs the task allocation matrix to the task allocation optimization module, and receives the updated task allocation matrix elements from the task allocation optimization module to update the task allocation; The task allocation optimization module: based on the matching priority of tasks and resources of the two-way matching priority calculation module and the task allocation matrix of the task allocation matrix construction module, uses the task allocation balance optimization algorithm to calculate the objective function, based on the objective function, further optimizes the task allocation through the gradient descent method, calculates the updated task allocation matrix elements, and outputs the updated task allocation matrix elements to the task allocation matrix construction module.

[0020] The present invention has the following beneficial effects: 1. By real-time monitoring and acquiring the status characteristics of test resources and the dependencies between resources, the resource status matrix and resource dependency matrix are constructed to achieve comprehensive perception and initialization of test resources. Through precise resource status description, it is ensured that the current status and interdependencies of each test resource can be accurately grasped, thereby avoiding resource waste and test task failure caused by incomplete or erroneous information during task allocation.

[0021] 2. Based on the resource status matrix and resource dependency matrix, the resource utilization calculation algorithm is used to calculate the initial utilization of the experimental resources. The impact of the dependency relationship between resources on the utilization is scientifically quantified and normalized, so that the utilization of different resources and different state quantities can be compared, which significantly improves the scientificity and rationality of resource allocation.

[0022] 3. Based on the initial utilization of experimental resources, the compatibility between tasks and experimental resources, and the task priority, the matching priority between tasks and resources is evaluated through a two-way matching priority calculation algorithm, which dynamically considers the actual availability of resources.

[0023] 4. Through the task allocation balance optimization algorithm, the allocation matrix between tasks and resources is calculated and optimized, aiming to achieve balanced use of resources, prevent resource overload or idleness, and ensure fair allocation of tasks; through the gradient descent method, the convergence speed of task allocation decisions and the ability to find the global optimal solution are further improved, thereby maximizing resource utilization efficiency and task allocation completion. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a structural diagram of an electric energy meter full performance test task allocation management system according to the present invention; Figure 2The present invention is a flowchart of a method for allocating and managing tasks for a full performance test of an electric energy meter. DETAILED DESCRIPTION

[0025] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0027] The specific scheme of an electric energy meter full performance test task allocation and management system provided by the present invention is described in detail below with reference to the accompanying drawings.

[0028] See attached Figure 1 , which shows a structural diagram of an electric energy meter full performance test task allocation management system provided by an embodiment of the present invention, the system includes the following parts: a resource status monitoring and initialization module, an initial utilization calculation module, a task priority evaluation module, a two-way matching priority calculation module, a task allocation matrix construction module and a task allocation optimization module.

[0029] The resource status monitoring and initialization module: constructs a resource status matrix and a resource dependency matrix by real-time monitoring and acquiring the status characteristics of the test resources and the dependencies between the test resources, and outputs the resource status matrix and the resource dependency matrix to the initial utilization calculation module.

[0030] The initial utilization calculation module: based on the resource status matrix and resource dependency matrix of the resource status monitoring and initialization module, uses the resource utilization calculation algorithm to calculate the initial utilization of the test resources, and outputs the initial utilization of the test resources to the task priority evaluation module and the two-way matching priority calculation module.

[0031] The task priority evaluation module: based on the initial utilization of the test resources of the initial utilization calculation module, uses the priority evaluation algorithm to calculate the priority of the task, and outputs the priority of the task to the two-way matching priority calculation module.

[0032] The bidirectional matching priority calculation module: based on the initial utilization of the test resources of the initial utilization calculation module and the priority of the task of the task priority evaluation module, uses a bidirectional matching priority calculation algorithm to calculate the matching priority between the task and the test resources.

[0033] The task allocation matrix construction module described herein makes a preliminary decision on task allocation based on the matching priority between the tasks and experimental resources of the two-way matching priority calculation module, constructs a task allocation matrix, outputs the task allocation matrix to the task allocation optimization module, and receives the updated task allocation matrix elements from the task allocation optimization module to update the task allocation.

[0034] The task allocation optimization module: based on the matching priority of tasks and resources of the two-way matching priority calculation module and the task allocation matrix of the task allocation matrix construction module, uses the task allocation balance optimization algorithm to calculate the objective function, based on the objective function, further optimizes the task allocation through the improved gradient descent method, calculates the updated task allocation matrix elements, and outputs the updated task allocation matrix elements to the task allocation matrix construction module.

[0035] See attached Figure 2 , which shows a flow chart of a method for allocating and managing tasks for a full performance test of an electric energy meter provided by an embodiment of the present invention, the method comprising the following steps: S1. By real-time monitoring and obtaining the status characteristics of the test resources and the dependencies between the test resources, a resource status matrix and a resource dependency matrix are constructed. Based on the resource status matrix and the resource dependency matrix, the initial utilization of the test resources is calculated using a resource utilization calculation algorithm.

[0036] In the task allocation and management system for full-performance testing of electric energy meters, it is necessary to comprehensively perceive and initialize the resource status in the test environment, and obtain the status characteristics of the test resources and the dependencies between the test resources in real time; the test resources include test benches and operators; the status characteristics include occupancy status and expected release time.

[0037] In order to systematically describe the status of experimental resources, a resource status matrix is ​​constructed The rows of the resource status matrix represent the experimental resources, the columns represent the state characteristics of the experimental resources, and each element Indicates The first resource The value of each state feature ranges from 0 to 1; At the same time, considering the mutual influence between experimental resources, in order to improve the rationality of experimental task allocation, a resource dependency matrix is ​​constructed. , to describe the dependencies between experimental resources; each element in the resource dependency matrix Indicates Experimental resources and The degree of dependency between experimental resources, ranging from 0 to 1.

[0038] In order to understand the usage status and load of experimental resources in real time and ensure that the decision-making basis for task allocation is scientific and reasonable, before allocating experimental tasks, the initial utilization rate of experimental resources is calculated based on the resource status matrix and resource dependency matrix using the resource utilization calculation algorithm, which can effectively avoid blindness and arbitrariness in task allocation and improve resource utilization efficiency.

[0039] The resource utilization calculation algorithm is based on the resource state matrix and the resource dependency matrix. According to the state characteristics in the resource state matrix, an initial weight value is set for each test resource to measure the current usage status of the test resource. The dependency between the test resources is identified in combination with the resource dependency matrix. Through exponential function transformation, the influence of the dependency relationship between the test resources on the initial utilization is attenuated. An adjustment factor is introduced to control the influence of the dependency relationship between the test resources on the initial utilization. The initial utilization is normalized to make it comparable under different resources and different state numbers, ensuring that the value of the initial utilization is within a reasonable range.

[0040] The calculation formula for the initial utilization rate of experimental resources is:

[0041] in, Indicates The initial utilization of experimental resources; Indicates The first resource The value of each state feature ranges from 0 to 1; Indicates Experimental resources and The degree of dependency between experimental resources ranges from 0 to 1; represents an exponential function used to convert the cumulative impact of experimental resource dependence into a decay factor, thereby affecting the initial utilization of experimental resources; represents the adjustment factor, which is used to control the intensity of the impact of experimental resource dependencies on initial utilization; Indicates the total number of experimental resources; Indicates the total number of resource status characteristics.

[0042] S2. Based on the initial utilization of experimental resources, the priority of the task is calculated using the priority evaluation algorithm.

[0043] In the allocation and management of the full performance test tasks of the electric energy meter, the execution efficiency of the tasks directly affects the cycle and cost of the entire test. Reasonable task priority sorting is the key to ensuring that the test tasks are completed on time. In order to improve the task execution efficiency, the priority of the task is calculated based on the initial utilization of the test resources using the priority evaluation algorithm.

[0044] The priority evaluation algorithm adopts a logarithmic function to calculate the impact of the urgency of the task, avoids excessive interference from extremely urgent tasks, and at the same time ensures a certain priority for handling urgent tasks, thereby enhancing the robustness of the electric energy meter full performance test task allocation and management system; uses the reciprocal form of the square root to calculate the impact of the time required for the task, and the reciprocal square root is used to enhance the priority of short-term tasks, which can quickly release resources and avoid resource waste caused by long-term tasks monopolizing resources, thereby improving the resource turnover efficiency of the electric energy meter full performance test task allocation and management system; by evaluating the matching degree between the resources required for the task and the currently available resources, it is ensured that the resource demand can be met, and a resource demand impact weight factor is introduced to balance the impact of the task resource demand on the priority, thereby improving the rationality and effectiveness of task allocation.

[0045] The calculation formula for the task priority is:

[0046] in, Indicates The priority of a task, that is, its priority among all pending tasks; Represents the weight coefficient, which is used to balance the impact of task urgency on priority; Indicates The urgency of the task; Indicates that the logarithmic function is used to process the The urgency of each task to smooth out the impact of extreme values; Represents the time impact weight factor, which is used to balance the impact of task time on priority; Indicates Time required for each task; Indicates The reciprocal square root of the time required for each task is used to prioritize tasks with shorter time. Represents the weight factor of resource demand impact, which is used to balance the impact of task resource demand on priority; Indicates The resource requirements of the task, that is, The amount of specific resources required for each task; Represents the fitness influencing factor, indicating all the factors suitable for the The remaining availability of resources for each task; Indicates Task and The adaptability of each experimental resource is determined to determine the rationality of the adaptation task for each resource. , which can be set according to the specific implementation scenario and is not limited here.

[0047] By using the priority evaluation algorithm to calculate the priority of the task, the relationship between the urgency of the task, the required time and resource requirements can be effectively balanced, thereby optimizing the task allocation management of the full performance test of the electric energy meter.

[0048] S3. Based on the initial utilization rate of experimental resources, the compatibility between tasks and experimental resources, and the priority of tasks, a two-way matching priority calculation algorithm is used to calculate the matching priority between tasks and experimental resources. A preliminary decision on task allocation is made based on the matching priority between tasks and experimental resources, and a task allocation matrix is ​​constructed.

[0049] The two-way matching priority calculation algorithm introduces the priority of the task to evaluate the urgency and importance of the task. The compatibility between the task and the experimental resources is used to determine whether the experimental resources are suitable for executing the task, ensuring that when allocating tasks, task failure or resource waste caused by incompatible resource allocation is avoided. The initial utilization rate of the experimental resources is used to calculate the remaining availability of resources, and the actual availability of resources is dynamically considered to improve utilization efficiency. The introduction of an adjustment factor that controls the balance between task priority and compatibility helps to flexibly adjust the task allocation strategy, further consider the sensitivity of the task to the average utilization rate of all adapted resources, and adjust the responsiveness of the matching priority to the dynamic demand for resources by enhancing the adjustment parameters of the sensitivity of the task to the dynamic demand for resources.

[0050] The calculation formula for the matching priority between tasks and experimental resources is:

[0051] in, Indicates Tasks and Matching priority between experimental resources; Indicates The priority of a task, that is, its priority among all pending tasks; Indicates Task and The suitability of the experimental resources; Indicates The initial utilization of experimental resources; represents the adjustment factor that controls the balance between task priority and fitness, ; The adjustment parameter represents the sensitivity of the enhanced task to the dynamic demand for resources, which is used to adjust the responsiveness of the matching priority to the dynamic demand for resources. ; Expressing the The sum of the utilization rates of all experimental resources adapted to a task is used to dynamically adjust the matching priority.

[0052] Make preliminary decisions on task allocation based on the matching priorities between tasks and experimental resources, ensure the rationality of task allocation, and build a task allocation matrix : .

[0053] S4. Based on the matching priority of tasks and resources and the task allocation matrix, the objective function is calculated using the task allocation balance optimization algorithm. Based on the objective function, the task allocation is further optimized through the improved gradient descent method to calculate the updated task allocation matrix elements.

[0054] The task allocation balance optimization algorithm is designed to optimize the task allocation process in the full performance test of the electric energy meter, and maximize the efficiency and rationality of task allocation by combining the matching priority of tasks and resources and the balance of resource utilization, so as to ensure that the test tasks can be executed under reasonable resource conditions; The balance of resource utilization takes into account the balanced use of each experimental resource, aims to prevent the experimental resources from being overused or idle, and is measured by comparing the actual use of each resource with the ideal balanced use, thereby ensuring fair distribution of tasks and avoiding waste and overload of experimental resources.

[0055] The calculation formula of the objective function is:

[0056] in, represents the objective function, which aims to maximize both the matching priority between tasks and resources and the balance of resource utilization; Indicates the number of tasks; Indicates Tasks and Matching priority between experimental resources; It represents the elements of resource allocation matrix, reflecting the preliminary decision of task allocation; Indicates The experimental resource utilization balance factor is used to measure the The balanced use of experimental resources can prevent overuse or idleness of experimental resources and ensure fair distribution of tasks. The calculation formula is:

[0057] in, Indicates The number of tasks assigned to each experimental resource; Indicates the average number of tasks per resource under ideal conditions.

[0058] The improved gradient descent method uses a dynamic learning rate and a momentum term to improve the convergence speed and the ability to find the global optimal solution. The momentum term is based on the update direction of the previous iteration. Part of the momentum is retained during the gradient descent process to prevent falling into a local optimal solution. The dynamic learning rate is adjusted based on the number of iterations. As the number of iterations increases, the learning rate gradually decreases, making it easier to quickly find an optimal solution.

[0059] The calculation formula of the updated task allocation matrix elements is:

[0060] in, and Respectively expressed in The first iteration and the The first iteration Assign the task to A trial resource, i.e., an element of the updated task allocation matrix; Indicated in The momentum at the iteration helps accelerate convergence. The specific calculation method is a technical means well known to those skilled in the art and will not be described in detail here; Represents the partial derivative of the objective function with respect to the resource allocation matrix elements, reflecting the Assign the task to The impact of each experimental resource on the objective function guides the updating direction and magnitude of the task allocation matrix; Indicated in The learning rate at the iteration is used to control the step size of the gradient update. The calculation formula is:

[0061] in, Represents the initial learning rate, which can be set according to the specific implementation scenario and is not limited here; Represents the adjustment factor that adjusts the decay rate of the learning rate.

[0062] The termination condition for optimizing task allocation is that the change in the objective function is less than the set threshold :

[0063] in, and Respectively expressed in The first iteration and the The objective function at the iteration; Indicates setting a threshold value, which can be set according to the specific implementation scenario and is not limited here.

[0064] The above-mentioned task allocation management method for full performance test of electric energy meter is used as follows.

[0065] This application has the following experimental resources: Test bench position: 2 (test bench 1, test bench 2) Operators: 2 (Operator 1, Operator 2) In order to systematically describe the status of experimental resources, a resource status matrix is ​​constructed as follows:

[0066] in, Indicates the occupancy status of test bench position 1 is ; The estimated release time is ; Indicates that the occupancy status of test bench position 2 is ; The estimated release time is ; Indicates that the occupancy status of operator 1 is ; The estimated release time is ; Indicates that the occupancy status of operator 2 is ; Indicates that the occupancy status of operator 2 is ; At the same time, considering the mutual influence between experimental resources, in order to improve the rationality of experimental task allocation, a resource dependency matrix is ​​constructed. as follows:

[0067] The rows and columns of the resource dependency matrix represent different experimental resources (test bench 1, test bench 2, operator 1, operator 2), respectively. The value on the diagonal is 1, indicating that each resource is completely dependent on itself. The value of the non-diagonal elements reflects the interdependence between resources. The larger the value, the stronger the dependence. The calculation formula for the initial utilization rate of experimental resources is:

[0068] Set adjustment factor , the initial utilization rate of test bench position 1 is:

[0069]

[0070] Similarly,

[0071]

[0072]

[0073] Based on the initial utilization of experimental resources, the priority of the task is calculated using the priority evaluation algorithm; The calculation formula for the task priority is:

[0074] Assume the following tasks: Task 1: Urgency , time required hours, resources required: 1 test bench, 1 operator; Task 2: Urgency , time required hours, resources required: 2 test benches, 2 operators; Task 3: Urgency , time required hours, resources required: test bench 1, operator 2; Assume that the resource adaptation matrix is ​​as follows:

[0075] Task priority weight coefficient:

[0076] First, calculate the impact of task 1's task urgency and time:

[0077]

[0078] The urgency and time components of a task's priority:

[0079] Next, calculate the impact of resource fitness and available resources:

[0080] The amount of resources required for Task 1 (Test bench 1 and operator 1).

[0081] Final calculation of task priority for task 1:

[0082]

[0083] Similarly,

[0084]

[0085] Based on the initial utilization rate of the experimental resources, the compatibility between the tasks and the experimental resources, and the priority of the tasks, the matching priority between the tasks and the experimental resources is calculated using a two-way matching priority calculation algorithm. The calculation formula for the matching priority between tasks and experimental resources is:

[0086] Set the adjustment factor to balance the task priority and fitness , a tuning parameter that increases the sensitivity of tasks to dynamic resource demands ; For each task, calculate the weighted sum of the initial utilization of the adaptation resources , where the adaptation resource for Task 1 is Test Bench 1 ( )、Operator 1( ), the sum of the initial utilization of the adaptation resources:

[0087] Matching priority between Task 1 and Testbed:

[0088]

[0089]

[0090] Similarly, the matching priorities between different tasks and experimental resources are obtained; The matching priority is expressed in a matrix as follows:

[0091] Make preliminary decisions on task allocation based on the matching priorities between tasks and experimental resources, and construct a task allocation matrix; The task allocation matrix is ​​as follows:

[0092] Based on the matching priority of tasks and resources and the task allocation matrix, the objective function is calculated using the task allocation balance optimization algorithm; Before calculating the objective function, in order to measure the balanced use of experimental resources, prevent excessive use or idleness of experimental resources, and ensure fair distribution of tasks, the experimental resource utilization balance factor is calculated. The calculation formula is:

[0093] The number of assigned tasks for test bench 1 is:

[0094] Ideally, the number of tasks assigned to each resource is ;

[0095] Similarly, ; ; ; The calculation formula of the objective function is:

[0096] Based on the objective function, the task allocation is further optimized through the improved gradient descent method, and the updated task allocation matrix elements are calculated; Set the initial learning rate , attenuation factor ; At the first iteration, the momentum term is initialized to 0: ; Update the learning rate to control the step size of the gradient update:

[0097] The calculation formula of the updated task allocation matrix elements is:

[0098]

[0099]

[0100]

[0101] Similarly, other task allocation matrix elements will also be updated; The termination condition for optimizing task allocation is that the change in the objective function is less than the set threshold : .

[0102] The present invention has the following beneficial effects: 1. By real-time monitoring and obtaining the state characteristics of test resources and the dependencies between resources, the resource state matrix and resource dependency matrix are constructed to achieve comprehensive perception and initialization of test resources. Through precise resource state description, the current state and interdependencies of each test resource can be accurately grasped, thereby avoiding resource waste and test task failure caused by incomplete or erroneous information during task allocation; 2. Based on the resource state matrix and resource dependency matrix, the resource utilization calculation algorithm is used to calculate the initial utilization of the experimental resources. Through the exponential function transformation and the introduction of the adjustment factor, the influence of the dependency relationship between resources on the utilization is scientifically quantified and normalized, so that the utilization of different resources and different state quantities can be compared, which significantly improves the scientificity and rationality of resource allocation; 3. In task allocation management, reasonable task priority evaluation is the key to ensuring efficient test execution. The priority evaluation algorithm can balance the urgency, time and resource requirements of the task to obtain the priority of each task. In particular, the application of logarithmic functions and inverse square root forms can avoid excessive interference from extreme tasks and improve the priority processing efficiency of short-time tasks at the same time, significantly improving the robustness and resource turnover efficiency when handling multi-task scenarios; 4. Based on the initial utilization rate of experimental resources, the adaptability of tasks and experimental resources, and the task priority, the matching priority between tasks and resources is evaluated through a two-way matching priority calculation algorithm, which dynamically considers the actual availability of resources and flexibly adjusts the task allocation strategy by introducing adjustment factors, effectively preventing the misuse or waste of resources, thereby ensuring the rationality of task allocation; 5. Through the task allocation balance optimization algorithm, the allocation matrix between tasks and resources is calculated and optimized, aiming to achieve balanced use of resources, prevent resource overload or idleness, and ensure fair allocation of tasks. Through the improved gradient descent method, the convergence speed of task allocation decisions and the ability to find the global optimal solution are further improved, thereby maximizing resource utilization efficiency and task allocation completion.

[0103] The sequence of the embodiments of the present invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0104] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0105] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for allocating and managing tasks for full performance testing of electric energy meters, characterized in that: Includes steps: S1, by real-time monitoring and obtaining the state characteristics of the test resources and the dependency relationship between the test resources, a resource state matrix and a resource dependency matrix are constructed, and based on the resource state matrix and the resource dependency matrix, the initial utilization rate of the test resources is calculated using a resource utilization rate calculation algorithm; S2, based on the initial utilization of experimental resources, the priority of the task is calculated using the priority evaluation algorithm; S3, based on the initial utilization rate of the experimental resources and the priority of the tasks, the matching priority between the tasks and the experimental resources is calculated using a two-way matching priority calculation algorithm, and a preliminary decision on task allocation is made according to the matching priority between the tasks and the experimental resources, and a task allocation matrix is ​​constructed; S4, based on the matching priority of tasks and resources and the task allocation matrix, uses the task allocation balance optimization algorithm to calculate the objective function. Based on the objective function, the task allocation is further optimized by the gradient descent method to calculate the updated task allocation matrix elements.

2. The method for allocating and managing tasks for the full performance test of electric energy meters according to claim 1, characterized in that: The S1 specifically includes: In the task allocation and management system for full performance test of electric energy meter, it is necessary to fully perceive and initialize the resource status in the test environment, and obtain the status characteristics of test resources and the dependencies between test resources through real-time monitoring; in order to systematically describe the status of test resources, a resource status matrix is ​​constructed, in which the rows of the resource status matrix represent the test resources, the columns represent the status characteristics of the test resources, and each element Indicates The first resource The value of each state feature ranges from 0 to 1; At the same time, considering the mutual influence between experimental resources, in order to improve the rationality of experimental task allocation, a resource dependency matrix is ​​constructed to describe the dependency relationship between experimental resources; each element in the resource dependency matrix Indicates Experimental resources and The degree of dependency between experimental resources, ranging from 0 to 1.

3. The method for allocating and managing tasks for the full performance test of electric energy meters according to claim 2 is characterized in that: In S1, the resource utilization calculation algorithm is based on the resource state matrix and the resource dependency matrix. According to the state characteristics in the resource state matrix, an initial weight value is set for each test resource to measure the current usage status of the test resource. The dependency between the test resources is identified in combination with the resource dependency matrix. Through exponential function transformation, the impact of the dependency relationship between the test resources on the initial utilization is attenuated. An adjustment factor is introduced to control the intensity of the impact of the test resource dependency relationship on the initial utilization. The initial utilization is normalized to make it comparable under different resources and different state numbers, ensuring that the value of the initial utilization is within a reasonable range.

4. The method for allocating and managing tasks for full performance test of electric energy meter according to claim 1, characterized in that: In S2, the priority evaluation algorithm uses a logarithmic function to calculate the impact of the task urgency, avoids excessive interference from extremely urgent tasks, and at the same time ensures a certain priority for emergency task processing, thereby enhancing the robustness of the electric energy meter full performance test task allocation management system; uses the reciprocal form of the square root to calculate the impact of the time required for the task, and the reciprocal square root is used to enhance the priority of short-term tasks; by evaluating the matching degree between the resources required for the task and the currently available resources, it is ensured that the resource requirements can be met, and a resource requirement impact weight factor is introduced to balance the impact of the task resource requirements on the priority.

5. The method for allocating and managing tasks for full performance test of electric energy meter according to claim 4, characterized in that: In S2, the calculation formula of the task priority is: ， in, Indicates The priority of a task, that is, its priority among all pending tasks; Represents the weight coefficient, which is used to balance the impact of task urgency on priority; Indicates The urgency of the task; Indicates that the logarithmic function is used to process the The urgency of each task to smooth out the impact of extreme values; Represents the time impact weight factor, which is used to balance the impact of task time on priority; Indicates Time required for each task; Indicates The reciprocal square root of the time required for each task is used to prioritize tasks with shorter time. Represents the weight factor of resource demand impact, which is used to balance the impact of task resource demand on priority; Indicates The resource requirements of the task, that is, The amount of specific resources required for each task; Represents the fitness influencing factor, indicating all the factors suitable for the The remaining availability of resources for each task; Indicates Task and The adaptability of each experimental resource is determined to determine the rationality of the adaptation task for each resource. , set according to the specific implementation scenario.

6. The method for allocating and managing tasks for full performance test of electric energy meter according to claim 1, characterized in that: In S3, based on the initial utilization rate of the test resources, the compatibility between the task and the test resources, and the priority of the task, a bidirectional matching priority calculation algorithm is used to calculate the matching priority between the task and the test resources; The two-way matching priority calculation algorithm introduces the priority of the task to evaluate the urgency and importance of the task. The compatibility between the task and the experimental resources is used to determine whether the experimental resources are suitable for executing the task, ensuring that when allocating tasks, task failure or resource waste caused by incompatible resource allocation is avoided. The initial utilization rate of the experimental resources is used to calculate the remaining availability of resources, and the actual availability of resources is dynamically considered to improve utilization efficiency. The introduction of an adjustment factor that controls the balance between task priority and compatibility helps to flexibly adjust the task allocation strategy, further consider the sensitivity of the task to the average utilization rate of all adapted resources, and adjust the responsiveness of the matching priority to the dynamic demand for resources by enhancing the adjustment parameters of the sensitivity of the task to the dynamic demand for resources.

7. The method for allocating and managing tasks for full performance test of electric energy meters according to claim 1, characterized in that: In S4, the task allocation balance optimization algorithm aims to optimize the task allocation process in the full performance test of the electric energy meter, and maximizes the efficiency and rationality of task allocation by combining the matching priority of tasks and resources and the balance of resource utilization, thereby ensuring that the test tasks can be executed under reasonable resource conditions; the balance of resource utilization takes into account the balanced use of each test resource, aiming to prevent the test resources from being overused or idle, and measures the actual use of each resource by comparing it with the ideal balanced use, thereby ensuring fair distribution of tasks and avoiding waste and overload of test resources.

8. The method for allocating and managing tasks for full performance testing of electric energy meters according to claim 1, characterized in that: In S4, the calculation formula of the objective function is: ， in, represents the objective function, which aims to maximize both the matching priority between tasks and resources and the balance of resource utilization; Indicates the number of tasks; Indicates Tasks and Matching priority between experimental resources; It represents the elements of resource allocation matrix, reflecting the preliminary decision of task allocation; Indicates The experimental resource utilization balance factor is used to measure the The balanced use of experimental resources can prevent overuse or idleness of experimental resources and ensure fair distribution of tasks. The calculation formula is: ， in, Indicates The number of tasks assigned to each experimental resource; Indicates the average number of tasks per resource under ideal conditions.

9. The method for allocating and managing tasks for full performance test of electric energy meters according to claim 1, characterized in that: In S4, the gradient descent method uses a dynamic learning rate and a momentum term to improve the convergence speed and the ability to find the global optimal solution. The momentum term is based on the update direction of the previous iteration. Part of the momentum is retained during the gradient descent process to prevent falling into a local optimal solution. The dynamic learning rate is adjusted based on the number of iterations. As the number of iterations increases, the learning rate gradually decreases, making it easier to quickly find an optimal solution.

10. A task allocation and management system for full performance test of electric energy meters, characterized in that: It includes resource status monitoring and initialization module, initial utilization calculation module, task priority evaluation module, two-way matching priority calculation module, task allocation matrix construction module and task allocation optimization module; The resource status monitoring and initialization module: constructs a resource status matrix and a resource dependency matrix by real-time monitoring and acquiring the status characteristics of the test resources and the dependency relationships between the test resources, and outputs the resource status matrix and the resource dependency matrix to the initial utilization calculation module; The initial utilization calculation module: based on the resource state matrix and resource dependency matrix of the resource state monitoring and initialization module, uses the resource utilization calculation algorithm to calculate the initial utilization of the test resources, and outputs the initial utilization of the test resources to the task priority evaluation module and the two-way matching priority calculation module; The task priority evaluation module: based on the initial utilization rate of the test resources of the initial utilization rate calculation module, uses the priority evaluation algorithm to calculate the priority of the task, and outputs the priority of the task to the two-way matching priority calculation module; The two-way matching priority calculation module: based on the initial utilization rate of the test resources of the initial utilization rate calculation module and the priority of the task of the task priority evaluation module, uses a two-way matching priority calculation algorithm to calculate the matching priority between the task and the test resources; The task allocation matrix construction module: makes a preliminary decision on task allocation based on the matching priority between the tasks and the experimental resources of the two-way matching priority calculation module, constructs a task allocation matrix, outputs the task allocation matrix to the task allocation optimization module, and receives the updated task allocation matrix elements from the task allocation optimization module to update the task allocation; The task allocation optimization module: based on the matching priority of tasks and resources of the two-way matching priority calculation module and the task allocation matrix of the task allocation matrix construction module, uses the task allocation balance optimization algorithm to calculate the objective function, based on the objective function, further optimizes the task allocation through the gradient descent method, calculates the updated task allocation matrix elements, and outputs the updated task allocation matrix elements to the task allocation matrix construction module.

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