Intelligent scheduling control method for electric energy meter verification assembly line

Abstract

The invention provides an intelligent scheduling control method for an electric energy meter verification assembly line, and belongs to the technical field of electric energy meters. S1, features extracted from an original data set are expressed as a feature matrix, and a multi-level collaborative algorithm is designed; s2, the global scheduling layer is responsible for distribution of overall resources and macroscopic scheduling decision making, the input is a feature matrix, and the output is a task priority matrix; s3, the local scheduling layer is responsible for distribution of specific tasks and management of execution details, the input is a task priority matrix, and the output is a specific task distribution matrix; and S4, the equipment control layer is responsible for operation and state monitoring of verification equipment, inputting the specific task distribution matrix, outputting the specific task distribution matrix and the equipment control instruction set, and dynamically adjusting the specific task distribution matrix and the equipment control instruction set according to feedback. According to the invention, the Internet of Things and the big data technology are fully utilized, and the intelligent algorithm is combined, so that efficient, intelligent and automatic control of the electric energy meter verification assembly line is realized.

Description

Technical Field

[0001] The invention belongs to the technical field of electric energy meters, and in particular relates to an intelligent dispatching control method for an electric energy meter calibration line. Background Art

[0002] The electric energy meter is an important metering device in the power system. Its accuracy and reliability directly affect the power metering and the calculation of the user's electricity bill. In order to ensure the metering accuracy of the electric energy meter, regular calibration is an indispensable link. The electric energy meter calibration line is an automated equipment specially used for calibrating the electric energy meter. It tests and calibrates the electric energy meter through a series of calibration steps and equipment. However, with the continuous increase in the number of electric energy meters and the increasing complexity of calibration tasks, the traditional calibration line has been unable to meet the needs of modern large-scale calibration.

[0003] The traditional calibration line has the following technical problems: the electricity meter calibration line lacks a systematic and intelligent scheduling mechanism, resulting in unreasonable resource allocation, low calibration efficiency, and difficulty in coping with complex and changing calibration environments, resulting in low equipment utilization and long task waiting time. It is impossible to adjust the scheduling strategy in real time, making it difficult to adapt to dynamically changing calibration needs, uneven task distribution, and serious waste of resources. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, an intelligent dispatching control method for an electric energy meter calibration line is provided.

[0005] The technical solution adopted by the present invention to solve its technical problem is:

[0006] This technical solution proposes an intelligent dispatching control method for an electric energy meter calibration line, comprising the following steps:

[0007] S1: Real-time data collection during the verification of electric energy meters is used to form an original data set. The features extracted from the original data set are expressed as a feature matrix. By introducing a hierarchical structure and a collaborative mechanism, a multi-level collaborative algorithm is designed to achieve task allocation and scheduling. The multi-level collaborative algorithm includes a global scheduling layer, a local scheduling layer, and a device control layer.

[0008] S2: The global scheduling layer is responsible for the allocation of overall resources and macro scheduling decisions. The input of the global scheduling layer is the feature matrix, and the output is the task priority matrix;

[0009] S3: The local scheduling layer is responsible for the allocation of specific tasks and the management of execution details. The input of the local scheduling layer is the task priority matrix, and the output is the specific task allocation matrix;

[0010] S4: The equipment control layer is responsible for the operation and status monitoring of the calibration equipment. The input of the equipment control layer is the specific task allocation matrix, and the output is the equipment control instruction set. The specific task allocation matrix and the equipment control instruction set are dynamically adjusted according to the feedback.

[0011] Preferably, in said S1, the original data set includes the calibration time, calibration result, and device status;

[0012] The calibration time data includes the start time, end time and total time consumed during the calibration process for each electric energy meter;

[0013] The verification result data is the specific result of each electric energy meter verification, including whether it is qualified and the error range;

[0014] The equipment status data is the current status of the calibration equipment, including idle, working, and faulty.

[0015] Preferably, in S1, the feature matrix is ​​in the form of:

[0016] X=[x ij ] (1);

[0017] In the formula, each element x ij represents the jth feature of the i-th task;

[0018] Features include: Task priority: the priority level assigned based on the urgency and importance of the task; Task complexity: the complexity of the task, which affects the calibration time; Equipment adaptability: the degree of matching between the task and the equipment, calculated based on historical data.

[0019] Preferably, in S2, the input of the global scheduling layer is the feature matrix X=[x ij ], the output is the task priority matrix P = [p ij ], where p ij represents the priority of the i-th task on the j-th device. The optimization goal of the global scheduling layer is to maximize the overall efficiency of the system. The optimization function is defined as:

[0020]

[0021] Where J(P) is the optimization function of the global scheduling layer, w ij is the weight of the i-th task on the j-th device, reflecting the degree of adaptation of the task to the device;

[0022] is the priority mean, which is used to measure the uniformity of priority distribution;

[0023] λ is the regularization parameter, which controls the penalty intensity of the priority mean deviation;

[0024] N is the number of tasks, M is the number of devices;

[0025] The maximum optimization function J(P) is realized based on the gradient descent algorithm, which includes the following steps:

[0026] S11: Initialize the task priority matrix P;

[0027] S12: Calculate the gradient of the objective function J(P):

[0028]

[0029] In the formula, is the gradient of the objective function J(P), W is the weight matrix, and sign is the sign function;

[0030] S13: Update priority matrix P:

[0031]

[0032] Where η is the learning rate;

[0033] S14: Repeat S12-S13 until the function converges.

[0034] Preferably, in S3: the local scheduling layer input is the task priority matrix P, and by analyzing the task priority matrix P, a specific task allocation matrix Y=[y ij ], where y ij represents the probability that the i-th task is assigned to the j-th device;

[0035] The optimization goal of the local scheduling layer is to minimize task waiting time and resource conflicts. The optimization function is defined as:

[0036]

[0037] Where L(Y,P) is the optimization function of the local scheduling layer, t ij is the execution time of the i-th task on the j-th device, reflecting the efficiency of task allocation;

[0038] The mean value of task allocation is used to measure the uniformity of task allocation;

[0039] μ is the conflict penalty coefficient, which controls the penalty intensity of task allocation uniformity.

[0040] Preferably, minimizing the optimization function L(Y, P) based on the Lagrange multiplier method comprises the following steps:

[0041] S21: Define the Lagrangian function:

[0042]

[0043] In the formula, is the Lagrange multiplier;

[0044] S22: Compute the gradient of the Lagrangian function

[0045]

[0046] S23: By setting Solve for Y and

[0047]

[0048] S24: Iteratively update Y and Until convergence.

[0049] Preferably, in S4, the device control layer input is a specific task allocation matrix Y, and by analyzing the specific task allocation matrix Y, a device control instruction set C=[c ij ], where c ij It represents the operation instruction of the jth device for the ith task. The optimization function is defined as:

[0050]

[0051] Where G(C,Y) is the optimization function of the device control layer, e ij is the execution efficiency of the jth device on the ith task, reflecting the effectiveness of the control instruction;

[0052] is the control instruction smoothness coefficient, which controls the penalty intensity of the instruction change rate; t is time.

[0053] Preferably, the maximization optimization function G(C,Y) is realized based on a dynamic programming algorithm, comprising the following steps:

[0054] S31: Initialize device control instruction set C;

[0055] S32: Calculate the state transfer function:

[0056] State(t+1)=State(t)+Δt·C(t) (10);

[0057] Where State(t) is the system state at the current time, State(t+1) is the system state at the next time step, Δt is the time step, and C(t) is the control instruction set at the current time;

[0058] S33: Calculate the gradient of the optimization function G(C,Y):

[0059]

[0060] Where E is the execution efficiency matrix. For any element e in the execution efficiency matrix E ij , defined as the ratio of task success rate to execution time, and considering the impact of failure rate, the formula is as follows:

[0061]

[0062] Where, SN ij is the success rate of the jth device for the i-th task, F ij is the failure rate of the jth device for the i-th task;

[0063] S34: Update control instruction set C:

[0064]

[0065] In the formula, γ is the step size parameter;

[0066] S35: Repeat S32-S34 until convergence.

[0067] Preferably, the generated device control instruction set C is transmitted to each verification device through the network, and each verification device receives and analyzes the operation instruction c assigned to it. ij , perform corresponding operations;

[0068] Monitor the device's operating status S in real time through sensors and cameras, and obtain the device's current status information s ij ,Based on the dynamic resource optimization algorithm, through real-time monitoring and feedback mechanism, the resource allocation and scheduling strategy are dynamically adjusted. Based on the feedback adjustment function F(S,Y,C), the state change rate and state deviation are calculated to obtain the feedback adjustment result.

[0069] Preferably, according to the feedback adjustment result, the specific task allocation matrix Y and the device control instruction set C are dynamically adjusted, and the feedback adjustment function is defined:

[0070]

[0071] Where F(S,Y,C) is the feedback adjustment function, α is the state change rate weight, which controls the penalty intensity of the state change rate. is the rate of state change;

[0072] β is the state deviation weight, which controls the penalty intensity of state deviation. It is state deviation;

[0073] s ij Indicates the status information of the j-th device for the i-th task;

[0074] It is the expected state, reflecting the equipment operation status under ideal conditions.

[0075] Compared with the prior art, the present invention has the following advantages:

[0076] 1. This application uses the Internet of Things technology to collect various raw data in the process of electricity meter calibration in real time, and uses these data for intelligent scheduling. It uses big data technology to analyze historical data and real-time data, extracts characteristics such as task priority, task complexity, and equipment adaptability, and makes intelligent decisions to make the scheduling and control process more scientific and accurate; through intelligent scheduling and real-time monitoring, it ensures that each electricity meter is calibrated on appropriate equipment, reducing false detections due to equipment incompatibility and improving the accuracy of calibration results.

[0077] 2. This application uses the global scheduling layer to be responsible for the allocation of overall resources and macro-scheduling decisions, maximizes the overall efficiency of the system, enables each task to quickly find suitable calibration equipment, and significantly improves the calibration efficiency. It manages the allocation and execution details of specific tasks through the local scheduling layer, minimizes task waiting time and resource conflicts, and uses the Lagrange multiplier method to optimize task allocation, ensuring maximum resource utilization and reducing equipment idle time and task waiting time.

[0078] 3. This application ensures efficient and safe operation of the equipment by generating a device control instruction set through the device control layer, optimizes the control instructions using a dynamic programming algorithm, adjusts the operating status and parameters of the equipment in real time, adapts to changing calibration requirements, and makes full use of the Internet of Things and big data technologies, combined with intelligent algorithms, to achieve efficient, intelligent and automated control of the electric energy meter calibration line.

[0079] 4. This application uses sensors and cameras to monitor the operating status of equipment in real time. The dynamic resource optimization algorithm is combined with a feedback adjustment mechanism to timely adjust task allocation and control instructions according to the rate of change and deviation of equipment status to ensure that the system operates in an efficient and stable state. The dynamic resource optimization algorithm can dynamically adjust the system based on real-time monitoring data to prevent equipment failures and resource conflicts and maintain the stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0081] Figure 1 It is the overall flow chart of the present invention. DETAILED DESCRIPTION

[0082] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0083] like Figure 1 As shown, this embodiment proposes an intelligent dispatching control method for an electric energy meter calibration line, comprising the following steps:

[0084] S1: Real-time data collection during the verification of electric energy meters is used to form an original data set. The features extracted from the original data set are expressed as a feature matrix. By introducing a hierarchical structure and a collaborative mechanism, a multi-level collaborative algorithm is designed to achieve task allocation and scheduling. The multi-level collaborative algorithm includes a global scheduling layer, a local scheduling layer, and a device control layer.

[0085] S2: The global scheduling layer is responsible for the allocation of overall resources and macro-scheduling decisions. The input of the global scheduling layer is the feature matrix, and the output is the task priority matrix. The optimization goal of the global scheduling layer is to maximize the overall efficiency of the system.

[0086] S3: The local scheduling layer is responsible for the allocation of specific tasks and the management of execution details. The input of the local scheduling layer is the task priority matrix, and the output is the specific task allocation matrix. The optimization goal of the local scheduling layer is to minimize task waiting time and resource conflicts.

[0087] S4: The equipment control layer is responsible for the operation and status monitoring of the calibration equipment. The input of the equipment control layer is the specific task allocation matrix, and the output is the equipment control instruction set. The specific task allocation matrix and the equipment control instruction set are dynamically adjusted according to the feedback. The optimization goal of the equipment control layer is to ensure the efficient and safe operation of the equipment.

[0088] In S1, various raw data in the process of electric energy meter calibration are collected in real time through the Internet of Things technology to form an original data set, which includes calibration time, calibration results, and equipment status;

[0089] The calibration time data includes the start time, end time and total time consumed during the calibration process for each electric energy meter;

[0090] The verification result data is the specific result of each electric energy meter verification, including whether it is qualified and the error range;

[0091] The equipment status data is the current status of the calibration equipment, including idle, working, and faulty.

[0092] The collected raw data is transmitted wirelessly to the central database for storage and processing. After data collection is completed, data preprocessing is required, which mainly includes steps such as data cleaning, data format conversion and feature extraction to ensure the quality and consistency of the data.

[0093] In S1, the feature matrix is ​​in the form of:

[0094] X=[x ij ] (1);

[0095] In the formula, each element x ij represents the jth feature of the i-th task;

[0096] Features include: Task priority: the priority level assigned based on the urgency and importance of the task; Task complexity: the complexity of the task, which affects the calibration time; Equipment adaptability: the degree of matching between the task and the equipment, calculated based on historical data.

[0097] In S2, the input of the global scheduling layer is the feature matrix X = [x ij ], the output is the task priority matrix P = [p ij ], where p ij represents the priority of the i-th task on the j-th device. The optimization goal of the global scheduling layer is to maximize the overall efficiency of the system. The optimization function is defined as:

[0098]

[0099] Where J(P) is the optimization function of the global scheduling layer, w ij is the weight of the i-th task on the j-th device, reflecting the degree of adaptation of the task to the device;

[0100] is the priority mean, which is used to measure the uniformity of priority distribution;

[0101] λ is the regularization parameter, which controls the penalty intensity of the priority mean deviation;

[0102] N is the number of tasks, M is the number of devices;

[0103] The maximum optimization function J(P) is realized based on the gradient descent algorithm, which includes the following steps:

[0104] S11: Initialize the task priority matrix P;

[0105] S12: Calculate the gradient of the objective function J(P):

[0106]

[0107] In the formula, is the gradient of the objective function J(P), W is the weight matrix, and sign is the sign function;

[0108] S13: Update priority matrix P:

[0109]

[0110] Where η is the learning rate;

[0111] S14: Repeat S12-S13 until the function converges.

[0112] In S3, the local scheduling layer input is the task priority matrix P. By analyzing the task priority matrix P, the specific task allocation matrix Y = [y ij ], where y ij represents the probability that the i-th task is assigned to the j-th device;

[0113] The optimization goal of the local scheduling layer is to minimize task waiting time and resource conflicts. The optimization function is defined as:

[0114]

[0115] Where L(Y,P) is the optimization function of the local scheduling layer, t ij is the execution time of the i-th task on the j-th device, reflecting the efficiency of task allocation;

[0116] The mean value of task allocation is used to measure the uniformity of task allocation;

[0117] μ is the conflict penalty coefficient, which controls the penalty intensity of task allocation uniformity.

[0118] The minimization optimization function L(Y,P) is realized based on the Lagrange multiplier method, which includes the following steps:

[0119] S21: Define the Lagrangian function:

[0120]

[0121] In the formula, is the Lagrange multiplier;

[0122] S22: Compute the gradient of the Lagrangian function

[0123]

[0124] S23: By setting Solve for Y and

[0125]

[0126] S24: Iteratively update Y and Until convergence.

[0127] In S4, the input of the device control layer is the specific task allocation matrix Y. By analyzing the specific task allocation matrix Y, the device control instruction set C = [c ij ], where c ij It represents the operation instruction of the jth device for the ith task. The optimization function is defined as:

[0128]

[0129] Where G(C,Y) is the optimization function of the device control layer, e ij is the execution efficiency of the jth device on the ith task, reflecting the effectiveness of the control instruction;

[0130] is the control instruction smoothness coefficient, which controls the penalty intensity of the instruction change rate; t is time.

[0131] The maximization optimization function G(C,Y) is realized based on the dynamic programming algorithm, including the following steps:

[0132] S31: Initialize device control instruction set C;

[0133] S32: Calculate the state transfer function:

[0134] State(t+1)=State(t)+Δt·C(t) (10);

[0135] Where State(t) is the system state at the current time, State(t+1) is the system state at the next time step, Δt is the time step, and C(t) is the control instruction set at the current time;

[0136] S33: Calculate the gradient of the optimization function G(C,Y):

[0137]

[0138] Where E is the execution efficiency matrix. For any element e in the execution efficiency matrix E ij , defined as the ratio of task success rate to execution time, and considering the impact of failure rate, the formula is as follows:

[0139]

[0140] Where, SN ij is the success rate of the jth device for the i-th task, F ij is the failure rate of the jth device for the i-th task;

[0141] S34: Update control instruction set C:

[0142]

[0143] In the formula, γ is the step size parameter;

[0144] S35: Repeat S32-S34 until convergence.

[0145] The generated device control instruction set C is transmitted to each calibration device through the network, and each calibration device receives and parses the operation instruction c assigned to it. ij , and perform corresponding operations; for example, adjust the parameters of the calibration equipment, start or stop the calibration process, perform specific calibration operations on the electric energy meter, etc. During the execution process, the equipment will continuously monitor its own operating status and feed back the status data S to the central system; the status data includes the current operating status, execution progress, equipment health status, etc.

[0146] Monitor the device's operating status S in real time through sensors and cameras, and obtain the device's current status information s ij ,Based on the dynamic resource optimization algorithm, through real-time monitoring and feedback mechanism,,dynamically adjust the resource allocation and scheduling strategy, and calculate the state change rate and state deviation based on the feedback adjustment function F(S,Y,C),,and obtain the feedback adjustment result;

[0147] According to the feedback adjustment results, the specific task allocation matrix Y and the device control instruction set C are dynamically adjusted to ensure the efficiency and adaptability of the device control instructions in actual operation. The feedback adjustment function is defined as:

[0148]

[0149] Where F(S,Y,C) is the feedback adjustment function, α is the state change rate weight, which controls the penalty intensity of the state change rate. is the rate of state change;

[0150] β is the state deviation weight, which controls the penalty intensity of state deviation. It is state deviation;

[0151] s ij Indicates the status information of the j-th device for the i-th task;

[0152] It is the expected state, reflecting the equipment operation status under ideal conditions.

[0153] Through the Internet of Things technology, various raw data in the process of electricity meter calibration are collected in real time, and these data are used for intelligent scheduling. Through big data technology, historical data and real-time data are analyzed, and characteristics such as task priority, task complexity, and equipment adaptability are extracted to make intelligent decisions, making the scheduling and control process more scientific and accurate; through intelligent scheduling and real-time monitoring, it is ensured that each electricity meter is calibrated on appropriate equipment, reducing false detections due to equipment incompatibility and improving the accuracy of calibration results.

[0154] The global scheduling layer is responsible for the allocation of overall resources and macro-scheduling decisions, maximizing the overall efficiency of the system, enabling each task to quickly find suitable calibration equipment, significantly improving the calibration efficiency. The local scheduling layer manages the allocation and execution details of specific tasks, minimizing task waiting time and resource conflicts, and using the Lagrange multiplier method to optimize task allocation, ensuring maximum resource utilization and reducing equipment idle time and task waiting time.

[0155] The device control layer generates device control instruction sets to ensure efficient and safe operation of the equipment. It uses dynamic programming algorithms to optimize control instructions, adjust the operating status and parameters of the equipment in real time, adapt to changing calibration requirements, and make full use of the Internet of Things and big data technologies, combined with intelligent algorithms, to achieve efficient, intelligent and automated control of the electricity meter calibration line.

[0156] Sensors and cameras are used to monitor the operating status of equipment in real time. The dynamic resource optimization algorithm is combined with a feedback adjustment mechanism to adjust task allocation and control instructions in a timely manner according to the rate of change and deviation of equipment status, ensuring that the system operates in an efficient and stable state. The dynamic resource optimization algorithm can dynamically adjust the system based on real-time monitoring data to prevent equipment failures and resource conflicts, and maintain the stability and reliability of the system.

[0157] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. An intelligent dispatching control method for an electric energy meter calibration line, characterized in that: The following steps are involved: S1: Real-time data collection during the verification of electric energy meters is used to form an original data set. The features extracted from the original data set are expressed as a feature matrix. By introducing a hierarchical structure and a collaborative mechanism, a multi-level collaborative algorithm is designed to achieve task allocation and scheduling. The multi-level collaborative algorithm includes a global scheduling layer, a local scheduling layer, and a device control layer. S2: The global scheduling layer is responsible for the allocation of overall resources and macro scheduling decisions. The input of the global scheduling layer is the feature matrix, and the output is the task priority matrix; S3: The local scheduling layer is responsible for the allocation of specific tasks and the management of execution details. The input of the local scheduling layer is the task priority matrix, and the output is the specific task allocation matrix; S4: The equipment control layer is responsible for the operation and status monitoring of the calibration equipment. The input of the equipment control layer is the specific task allocation matrix, and the output is the equipment control instruction set. The specific task allocation matrix and the equipment control instruction set are dynamically adjusted according to the feedback.

2. According to claim 1, the intelligent dispatching control method for the electric energy meter calibration line is characterized in that: In S1, the original data set includes the calibration time, calibration results, and device status; The calibration time data includes the start time, end time and total time consumed during the calibration process for each electric energy meter; The verification result data is the specific result of each electric energy meter verification, including whether it is qualified and the error range; The equipment status data is the current status of the calibration equipment, including idle, working, and faulty.

3. The intelligent dispatching control method for an electric energy meter calibration line according to claim 1 is characterized in that: In S1, the feature matrix is ​​in the form of: X=[x ij ] (1) In the formula, each element x ij represents the jth feature of the i-th task; Features include: Task priority: the priority level assigned to a task based on its urgency and importance; Task complexity: the complexity of the task, which affects the verification time; Device suitability: The degree of matching between the task and the device, calculated based on historical data.

4. The intelligent dispatching control method for an electric energy meter calibration line according to claim 1 is characterized in that: In S2, the input of the global scheduling layer is the feature matrix X = [x ij ], the output is the task priority matrix P = [p ij ], where p ij represents the priority of the i-th task on the j-th device. The optimization goal of the global scheduling layer is to maximize the overall efficiency of the system. The optimization function is defined as: Where J(P) is the optimization function of the global scheduling layer, w ij is the weight of the i-th task on the j-th device, reflecting the degree of adaptation of the task to the device; is the priority mean, which is used to measure the uniformity of priority distribution; λ is the regularization parameter, which controls the penalty intensity of the priority mean deviation; N is the number of tasks, M is the number of devices; The maximum optimization function J(P) is realized based on the gradient descent algorithm, which includes the following steps: S11: Initialize the task priority matrix P; S12: Calculate the gradient of the objective function J(P): In the formula, is the gradient of the objective function J(P), W is the weight matrix, and sign is the sign function; S13: Update priority matrix P: Where η is the learning rate; S14: Repeat S12-S13 until the function converges.

5. The intelligent dispatching control method for an electric energy meter calibration line according to claim 1 is characterized in that: In S3, the local scheduling layer input is the task priority matrix P, and by analyzing the task priority matrix P, a specific task allocation matrix Y is generated. ij ], where y ij represents the probability that the i-th task is assigned to the j-th device; The optimization goal of the local scheduling layer is to minimize task waiting time and resource conflicts. The optimization function is defined as: Where L(Y, P) is the optimization function of the local scheduling layer, t ij is the execution time of the i-th task on the j-th device, reflecting the efficiency of task allocation; The mean value of task allocation is used to measure the uniformity of task allocation; μ is the conflict penalty coefficient, which controls the penalty intensity of task allocation uniformity.

6. The intelligent dispatching control method for an electric energy meter calibration line according to claim 5 is characterized in that: The minimization optimization function L(Y, P) is realized based on the Lagrange multiplier method, including the following steps: S21: Define the Lagrangian function: In the formula, is the Lagrange multiplier: S22: Compute the gradient of the Lagrangian function S23: By setting Solve for Y and S24: Iteratively update Y and Until convergence.

7. The intelligent dispatching control method for an electric energy meter calibration line according to claim 1 is characterized in that: In S4, the device control layer input is the specific task allocation matrix Y, and by analyzing the specific task allocation matrix Y, a device control instruction set C is generated. ij ], where c ij It represents the operation instruction of the jth device for the ith task. The optimization function is defined as: Where G(C, Y) is the optimization function of the device control layer, e ij is the execution efficiency of the jth device on the ith task, reflecting the effectiveness of the control instruction; is the control instruction smoothness coefficient, which controls the penalty intensity of the instruction change rate; t is time.

8. The intelligent dispatching control method for an electric energy meter calibration line according to claim 7 is characterized in that: The maximization optimization function G(C, Y) is realized based on the dynamic programming algorithm, including the following steps: S31: Initialize device control instruction set C: S32: Calculate the state transfer function: State(t+1)=State(t)+Δt·C(t) (10) Where State(t) is the system state at the current time, State(t+1) is the system state at the next time step, Δt is the time step, and C(t) is the control instruction set at the current time; S33: Calculate the gradient of the optimization function G(C, Y): Where E is the execution efficiency matrix. For any element e in the execution efficiency matrix E ij , defined as the ratio of task success rate to execution time, and considering the impact of failure rate, the formula is as follows: Where, SN ij is the success rate of the jth device for the i-th task, F ij is the failure rate of the jth device for the i-th task; S34: Update control instruction set C: In the formula, γ is the step size parameter; S35: Repeat S32-S34 until convergence.

9. The intelligent dispatching control method for an electric energy meter calibration line according to claim 8 is characterized in that: The generated device control instruction set C is transmitted to each calibration device through the network, and each calibration device receives and parses the operation instruction c assigned to it. ij , perform corresponding operations; Monitor the device's operating status S in real time through sensors and cameras, and obtain the device's current status information s ij ,Based on the dynamic resource optimization algorithm, through real-time monitoring and feedback mechanism, the resource allocation and scheduling strategy are dynamically adjusted. Based on the feedback adjustment function F(S, Y, C), the state change rate and state deviation are calculated to obtain the feedback adjustment result.

10. The intelligent dispatching control method for an electric energy meter calibration line according to claim 9 is characterized in that: According to the feedback adjustment results, the specific task allocation matrix Y and the device control instruction set C are dynamically adjusted, and the feedback adjustment function is defined: Where F(S, Y, C) is the feedback adjustment function, α is the state change rate weight, which controls the penalty intensity of the state change rate. is the rate of state change; β is the state deviation weight, which controls the penalty intensity of state deviation. It is state deviation; s ij Indicates the status information of the j-th device for the i-th task; It is the expected state, reflecting the equipment operation status under ideal conditions.

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