A method and system for automatic test scheduling of electric energy meters

Abstract

The present invention discloses a method and system for scheduling an automatic test of an electric energy meter. The scheduling decisions of existing automatic scheduling technologies lack timeliness and accuracy, and it is difficult to achieve efficient automatic test scheduling. The method of the present invention includes: obtaining electric energy meter information, constructing a real-time status model of the test equipment, calculating the priority of each test task according to the real-time status of the test equipment and the urgency and importance of the test task, and ensuring that high-priority test tasks are processed first; combining a multi-level adaptive hybrid optimization strategy, dynamically adjusting the test sequence of the tested samples, selecting the optimal test task and updating the test equipment status by calculating the time and state influencing factors, and performing feedback adjustment; and comprehensively evaluating all completed test tasks and the automatic test scheduling of the test equipment. The present invention improves the automation level and intelligence level of scheduling, and improves the overall efficiency of the electric energy meter test.

Description

Technical Field

[0001] The invention belongs to the technical field of electric energy meters, and in particular to an automatic test scheduling method and system for electric energy meters. Background Art

[0002] With the rapid development of smart grid and smart energy meter technology, the quality and performance testing of energy meters have become particularly important. Energy meters play a key role in the power system, and their accuracy and reliability directly affect the fairness of power measurement and the stability of the power system. Therefore, strict testing and testing of energy meters to ensure that their performance under various working conditions meets the standards is an important link in the production and application of energy meters.

[0003] Traditional meter test scheduling methods usually rely on manual experience and fixed rules. When faced with a large number of meter test tasks, this method is prone to problems such as low scheduling efficiency, insufficient resource utilization, and long test cycles. With the continuous advancement of meter technology, the complexity and diversity of tests are also increasing. More and more factors need to be considered during the test, including the operating status of the equipment, fault conditions, the urgency and importance of the task, etc. The dynamic changes of these factors increase the difficulty and complexity of scheduling, and traditional scheduling methods can no longer meet the needs of efficient and accurate scheduling.

[0004] The development of modern intelligent optimization algorithms and automation technology has provided new solutions for the test scheduling of electric energy meters. By introducing real-time monitoring, data analysis and optimization algorithms, real-time monitoring and dynamic scheduling of equipment status can be achieved, effectively improving the accuracy and efficiency of scheduling. At the same time, the automated scheduling system can reduce manual intervention, reduce operational complexity, and improve work efficiency and test quality.

[0005] Chinese patent application No. 201710901522.2 discloses a hybrid assembly line scheduling method for electric energy meter detection based on a genetic algorithm, including: randomly generating an initialization population with a population size of N; calculating the target value of each individual in the population, as well as the non-dominated level and congestion of each individual; performing crossover and mutation operations on the individuals in the population to obtain a sub-population; combining the population and the sub-population, calculating the target value of each individual in the combined population, as well as the non-dominated level and congestion of each individual; selecting N individuals according to the non-dominated level and congestion of each individual in the combined population to form a new population.

[0006] The scheduling decisions of existing automated scheduling technologies lack timeliness and accuracy, and are prone to equipment idleness or resource conflicts. Based on fixed rules or manual experience, it is difficult to dynamically adjust the test sequence and is unable to effectively respond to changes in the urgency and importance of tasks, resulting in high-priority tasks not being processed in a timely manner, making it difficult to achieve efficient automated test scheduling. Summary of the invention

[0007] 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 automatic test scheduling of electric energy meters, so as to automatically complete the monitoring of equipment status, determination of task priority and optimization of test sequence, improve the automation level and intelligence degree of scheduling, and improve the overall efficiency of electric energy meter testing; through scientific and reasonable scheduling methods and real-time dynamic adjustment, ensure that the test tasks are completed in the shortest time, greatly improving the work efficiency and quality of the test.

[0008] To this end, the present invention adopts the following technical solution.

[0009] In a first aspect, the present invention provides an automatic test scheduling method for an electric energy meter, comprising:

[0010] S1. Obtain the information of the electric energy meter, build the real-time status model of the test equipment, calculate the priority of each test task according to the real-time status of the test equipment and the urgency and importance of the test task, and ensure that high-priority test tasks are processed first;

[0011] S2. According to the calculated test task priority and the real-time status model of the test equipment, combined with the multi-level adaptive hybrid optimization strategy, the test order of the tested samples is dynamically adjusted. By calculating the time and state influencing factors, the optimal test task is selected and the test equipment status is updated, and feedback adjustment is performed to ensure that the test tasks are carried out in the optimal order;

[0012] S3. Comprehensively evaluate all completed test tasks and automated test scheduling of test equipment, construct a comprehensive evaluation objective function to adjust the scheduling strategy of electric energy meters and test equipment, calculate the total impact value of the test tasks, select the current optimal test task combination, and implement the optimal scheduling strategy on a global scale.

[0013] Furthermore, in S1, each test device will have different operating states and test task conditions when conducting a test. The real-time status data of the test device is obtained through the real-time status model of the test device, including the operating time, fault status and current test task of the test device, thereby constructing a dynamic real-time status model.

[0014] Furthermore, the real-time status model is as follows:

[0015]

[0016] in, It is the real-time status of the test equipment, indicating the time t Always test the overall status of the equipment; For the The operating time of each test equipment, For the The total operating time of each test device, and Respectively The phase and frequency parameters of the test equipment operation, is the fault condition of the test equipment, is the fault attenuation coefficient, is the mean time to repair a fault, is the urgency of the current test task, is the average urgency of all test tasks, and are the total number of test equipment and test tasks respectively.

[0017] Furthermore, in S1, each test task needs to schedule multiple test equipment for testing. After establishing the real-time status model of the test equipment, the priority of each test task is determined according to the urgency and importance of the test task, the test tasks are sorted according to the priority, and the destination queue of the test samples is pre-allocated to ensure that high-priority test tasks can be processed first.

[0018] Furthermore, in S1, each test task is evaluated to determine its urgency and importance, and the priority of each test task is calculated by the following formula in combination with the real-time status of the test equipment:

[0019]

[0020] in, It is The priority of each test task, For the The urgency of the test task, For the The importance of the experimental task, For the The resource requirements of each test task, For the The fluctuation frequency of resource requirements of each experimental task, , and are weight coefficients, indicating the influence of different factors on the priority of the test task. To adjust the parameters.

[0021] Furthermore, in S2, in each test task, it is necessary to schedule the tests of multiple electric energy meters. According to the calculated test task priority and the real-time state model of the test equipment, the test order of the tested samples is dynamically adjusted. The scheduling order is optimized by combining a multi-level adaptive hybrid optimization strategy, and the following optimization formula is used:

[0022]

[0023] in, is the overall optimization goal of the test sequence, For the In the experimental task Completion time of each test equipment, For the In the experimental task The priority of each test equipment is is the adjustment factor, is the time window, and Respectively In the experimental task The time and state fluctuation parameters of the test equipment, and Respectively In the experimental task The priority and status adjustment frequency of each test equipment; and are the total number of test equipment and test tasks respectively.

[0024] Furthermore, in order to solve the overall optimization problem of the test sequence, a multi-level adaptive hybrid optimization strategy is adopted. The specific steps are as follows:

[0025] Initialize the real-time status model of the test task set and the test equipment, input the information of all test tasks and the real-time status of the test equipment into the scheduling system, and prepare to start scheduling; calculate the current optimal value of each electric energy meter test in each test task according to the real-time status of the test equipment and the test task priority, and the specific calculation is as follows:

[0026] Calculation time impact factor: For the In the experimental task Test an electric energy meter and calculate the factors affecting its completion time :

[0027]

[0028] Calculate the state impact factor: Calculate the real-time state of the test equipment and its impact on the In the experimental task The impact of the energy meter test :

[0029]

[0030] Calculate the total impact value: Add the time and state impact factors to get In the experimental task Total impact value of each electric energy meter test :

[0031]

[0032] According to the calculated total impact value, select the test task and test equipment with the smallest total impact value, that is:

[0033]

[0034] in, The optimal test task and the test sequence of the electric energy meter in the current test task are selected, and the real-time state model of the test equipment is updated;

[0035] According to the scheduling results and real-time data, feedback adjustments are made: the completed test tasks are removed from the test task set, and the priority and status of the remaining test tasks are updated; according to the actual execution situation, the weight coefficient of the test task priority is adjusted , and , to better reflect the urgency and importance of the test tasks; based on the feedback, adjust the parameters of the scheduling algorithm to optimize the next scheduling decision; repeat the calculation until all test tasks are scheduled.

[0036] Furthermore, in S3, after completing one step of scheduling decision, a comprehensive evaluation is performed on all completed test tasks and automated test scheduling of test equipment, and a comprehensive evaluation objective function is constructed; based on the evaluation results, the priorities and scheduling parameters of the test tasks and tests for the next step of scheduling decision are adjusted, so as to achieve the optimal scheduling strategy in a global scope;

[0037] At each step of scheduling decision, the current optimal solution is selected, and the test sequence is continuously adjusted according to the real-time status of the test equipment; the comprehensive evaluation objective function is:

[0038]

[0039] in, To comprehensively evaluate the objective function, is the estimated completion time of the test task, is the resource consumption of the test task, is the attenuation coefficient, and are adjustment parameters, Feedback parameters for the test task execution, is the adjustment coefficient for the execution deviation of the test task, is the actual completion time, Estimated completion time.

[0040] Furthermore, in S3, the status information of all test tasks and test equipment is initialized, including the priority of the test tasks. , Estimated completion time , Resource consumption , real-time status of test equipment and adjusting parameters and adjustment coefficients;

[0041] According to the initial state, calculate the initial value of each test task: Test task priority influencing factor , Resource consumption influencing factors , state feedback factor ;

[0042] Combining the above factors, calculate the total impact value of each test task: ;

[0043] According to the calculated total impact value, the test task combination with the largest impact value is selected as the current optimal test task combination;

[0044] Execute the optimal test task combination and update the real-time status of the equipment and the test task status. According to the updated equipment status and test task status, recalculate the priority, estimated completion time, resource consumption, adjustment parameters and adjustment coefficients of each test task, and repeat the calculation until all test tasks are completed.

[0045] In a second aspect, the present invention provides an electric energy meter automatic test scheduling system, which is used to implement the above-mentioned electric energy meter automatic test scheduling method, and comprises:

[0046] Test task priority calculation unit: obtains the electric energy meter information, builds the real-time status model of the test equipment, calculates the priority of each test task according to the real-time status of the test equipment and the urgency and importance of the test task, and ensures that high-priority test tasks are processed first;

[0047] Optimal test task selection unit: Based on the calculated test task priority and the real-time status model of the test equipment, combined with a multi-level adaptive hybrid optimization strategy, the test order of the tested samples is dynamically adjusted. By calculating the time and state influencing factors, the optimal test task is selected and the test equipment status is updated. Feedback adjustment is performed to ensure that the test tasks are carried out in the optimal order;

[0048] Optimal scheduling strategy implementation unit: conduct a comprehensive evaluation of all completed test tasks and automated test scheduling of test equipment, construct a comprehensive evaluation objective function to adjust the scheduling strategy of the electric energy meter and test equipment, calculate the total impact value of the test task, select the current optimal test task combination, and implement the optimal scheduling strategy on a global scale.

[0049] The present invention has the following beneficial effects:

[0050] 1. The present invention acquires the operating status, fault status and task information of the equipment in real time, establishes a real-time status model of the equipment, and can accurately reflect the current status of the equipment. On this basis, according to the urgency and importance of the task, the test sequence is dynamically adjusted to ensure that the scheduling decision at each time point is optimal, improve scheduling efficiency, and reduce the waiting time and resource waste of the test; by establishing an optimization objective function and combining a multi-level adaptive hybrid optimization strategy, the test tasks are globally optimized and scheduled, which can maximize the utilization rate of the equipment and the configuration efficiency of the test resources, avoid idleness and conflict of resources, and improve the utilization rate of resources and the overall efficiency of the test.

[0051] 2. The present invention monitors the operating status and fault conditions of the equipment in real time, and makes feedback adjustments based on the actual execution situation. It can promptly discover and handle abnormal situations, ensure that the test is carried out as planned, improve the accuracy and reliability of the test results, and reduce test errors caused by equipment failure or improper scheduling. The present invention adopts a multi-level adaptive hybrid optimization strategy, which can dynamically adjust the scheduling strategy according to the real-time status of the equipment and changes in the task. It has high flexibility and adaptability. Whether it is a new task or equipment failure, the system can respond quickly and make adjustments to ensure that the test task can be completed smoothly.

[0052] 3. The present invention reduces the need for manual intervention and the complexity and workload of manual operations through automated scheduling methods and optimization algorithms. The system can automatically monitor equipment status, determine task priorities, and optimize test sequences, thereby improving the level of automation and intelligence in scheduling and the overall efficiency of electric energy meter testing. Through scientific and reasonable scheduling methods and real-time dynamic adjustments, the present invention ensures that test tasks are completed in the shortest time possible, greatly improving the work efficiency and quality of the test. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of an automatic test scheduling method for an electric energy meter according to the present invention;

[0054] Figure 2 The present invention is a structural diagram of an automatic test and dispatching system for electric energy meters. DETAILED DESCRIPTION

[0055] 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.

[0056] 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.

[0057] Example 1

[0058] See attached Figure 1 , which shows a flow chart of a method for automatic test scheduling of an electric energy meter provided by an embodiment of the present invention, the method comprising the following steps:

[0059] S1. Obtain the information of the electric energy meter, build the real-time status model of the test equipment, calculate the priority of each test task according to the real-time status of the test equipment and the urgency and importance of the test task, and ensure that high-priority tasks are processed first;

[0060] Obtain information such as the operating time, fault status, and current test task of the electric energy meter test and detection equipment (referred to as the test equipment), and summarize and process the acquired information.

[0061] Specifically, sensors are installed on each test equipment to record the operating time, frequency, phase, and time and type of faults of the test equipment. The data is transmitted to the central control system via wireless transmission or wired network; in the central control system, the data is processed and stored to form the historical operating data and real-time status data of the test equipment; the fault status data is determined by analyzing the historical fault records and real-time monitoring data of the test equipment, including the type of fault, occurrence time, and repair time.

[0062] Each test equipment will have different operating states and task conditions when conducting tests. By establishing a real-time state model of the test equipment, we can obtain the real-time state data of the test equipment, including the operating time, fault status, and current test task of the test equipment, and build a dynamic real-time state model. The specific formula is as follows:

[0063]

[0064] in, is the real-time status of the test equipment, indicating the overall status of the test equipment at time t. For the The operating time of each test equipment, For the The total operating time of each test device, and For the The phase and frequency parameters of the test equipment operation, is the fault condition of the test equipment, is the fault attenuation coefficient, is the mean time to repair a fault, is the urgency of the current test task, is the average urgency of all test tasks, and are the total number of test equipment and test tasks respectively.

[0065] Each test task requires scheduling multiple test equipment for testing. After establishing the real-time status model of the test equipment, the priority of the test task is determined according to the urgency and importance of the task, and the destination queue of the test samples is pre-allocated; specifically, each test task is evaluated to determine its urgency and importance, and combined with the real-time status of the test equipment, the priority of each test task is calculated by the following formula:

[0066]

[0067] in, It is The priority of each test task, For the The urgency of the test task, For the The importance of the experimental task, For the The resource requirements of each test task, For the The fluctuation frequency of resource requirements of each experimental task, , and is the weight coefficient, which indicates the influence of different factors on the priority of the test task. To adjust the parameters, sort the test tasks according to priority and pre-allocate the destination queue of the test samples to ensure that high-priority test tasks can be processed first.

[0068] S2. According to the calculated test task priority and the real-time status model of the test equipment, combined with the multi-level adaptive hybrid optimization strategy, the test order of the tested samples is dynamically adjusted. By calculating the time and state influencing factors, the optimal test task is selected and the test equipment status is updated, and feedback adjustment is performed to ensure that the test tasks are carried out in the optimal order;

[0069] In each test task, it is necessary to schedule the tests of multiple electric energy meters. According to the revised task priority and the real-time status model of the test equipment, the test order of the tested samples is dynamically adjusted, and the scheduling order is optimized by combining the multi-level adaptive hybrid optimization strategy. The following optimization formula is used:

[0070]

[0071] is the overall optimization goal of the test sequence, For the In the experimental task Completion time of each test equipment, For the In the experimental task The priority of each test equipment is is the adjustment factor, is the time window, and Respectively In the experimental task The time and state fluctuation parameters of the test equipment, and Respectively In the experimental task The priority and status adjustment frequency of each test equipment; and are the total number of test equipment and test tasks respectively.

[0072] In order to solve the overall optimization problem of the test sequence, a multi-level adaptive hybrid optimization strategy is adopted. The specific steps are as follows:

[0073] Initialize the task set and the real-time status model of the test equipment, input all task information and the real-time status of the test equipment into the scheduling system, and prepare to start scheduling; calculate the current optimal value of each electric energy meter test in each test task according to the real-time status of the test equipment and the task priority. The specific calculation is as follows:

[0074] Calculation time impact factor: For the In the experimental task Test an electric energy meter and calculate the factors affecting its completion time :

[0075]

[0076] Calculate the state impact factor: Calculate the real-time state of the test equipment and its impact on the In the experimental task The impact of the energy meter test :

[0077]

[0078] Calculate the total impact value: Add the time and state impact factors to get In the experimental task Total impact value of each electric energy meter test :

[0079]

[0080] According to the calculated total impact value, select the test task and test equipment with the smallest total impact value, that is:

[0081]

[0082] in, It selects the optimal test task and the test sequence of the electric energy meter in the current test task, and updates the real-time status model of the test equipment.

[0083] According to the scheduling results and real-time data, feedback adjustments are made: the completed test tasks are removed from the task set, and the priority and status of the remaining tasks are updated; according to the actual execution situation, the weight coefficient of the task priority is adjusted , and , to better reflect the urgency and importance of the task; based on the feedback, adjust the parameters of the scheduling algorithm to optimize the next scheduling decision; repeat the calculation until all tasks are scheduled.

[0084] S3. Conduct a comprehensive evaluation of all completed test tasks and equipment automated test scheduling, construct a comprehensive evaluation objective function to adjust the scheduling strategy, calculate the total impact value of the task, select the current optimal test task combination, and implement the optimal scheduling strategy on a global scale.

[0085] After completing one scheduling decision (for example, after completing several test tasks or tests), a comprehensive evaluation is conducted on the automated test scheduling of all completed tasks and test equipment, and a comprehensive evaluation objective function is constructed; based on the evaluation results, the priorities and scheduling parameters of the test tasks and tests for the next scheduling decision are adjusted, so as to achieve the optimal scheduling strategy on a global scale.

[0086] At each step of scheduling decision, the current optimal solution is selected and the test sequence is continuously adjusted according to the real-time status of the test equipment; the comprehensive evaluation objective function is:

[0087]

[0088] in, To comprehensively evaluate the objective function, is the estimated completion time of the test task, is the resource consumption of the test task, is the attenuation coefficient, and To adjust the parameters, Feedback parameters for the test task execution, is the adjustment coefficient for the execution deviation of the test task, is the actual completion time, Estimated completion time.

[0089] Initialize the status information of all tasks and test equipment, including the priority of the test task , Estimated completion time , Resource consumption , real-time status of test equipment And adjust the parameters and adjustment coefficients; according to the initial state, calculate the initial value of each test task: Task priority influencing factor , Resource consumption influencing factors , state feedback factor , combine the above factors to calculate the total impact value of each test task ; According to the calculated total impact value, select the task combination with the largest impact value as the current optimal task combination.

[0090] Execute the optimal task combination and update the real-time status of the test equipment and the task status. According to the updated test equipment status and task status, recalculate the priority, estimated completion time, resource consumption, adjustment parameters and adjustment coefficients of each test task, and repeat the calculation until all test tasks are completed.

[0091] After all tasks are completed, the objective function is comprehensively evaluated. The final result is the maximum total impact value calculated in all iterative processes. The optimization objective function can achieve the optimal scheduling strategy on a global scale, ensuring that the scheduling decision at each time point is based on the current optimal solution, and ultimately achieve the overall optimality through continuous updating and iteration.

[0092] The above-mentioned automatic test scheduling method for electric energy meters is used in the following applications.

[0093] Assume that the electric energy meter test center contains 5 test equipments, each of which is equipped with sensors to record the running time, frequency, phase, and time and type of faults of the test equipments. The test center has 10 test tasks, each of which has different urgency and importance.

[0094] 1. Device initialization

[0095] Number of devices: n=5;

[0096] Number of tasks: m=10;

[0097] Device parameter initialization:

[0098]

[0099] Fault status parameters:

[0100]

[0101] 2. Task initialization

[0102] Task parameter initialization:

[0103]

[0104] The application process is as follows:

[0105] 1. Calculation of real-time status model of test equipment

[0106] Calculate the real-time status of the test equipment at the initial time t=0 :

[0107]

[0108] in,

[0109]

[0110] so:

[0111]

[0112] After calculation, the result of S(0) is:

[0113]

[0114] 2. Task priority calculation

[0115] Calculate the priority of each task :

[0116]

[0117] Calculate the priority of the first task:

[0118]

[0119] because :

[0120]

[0121] Similarly, calculate the priority of other tasks:

[0122]

[0123] 3. Multi-level adaptive hybrid optimization strategy

[0124] Based on the calculated priorities, the optimization formula is applied to select the optimal test tasks and test equipment:

[0125]

[0126] Assume that at time t=0, task 1 and test equipment 1 are scheduled and the influencing factors are calculated:

[0127]

[0128]

[0129]

[0130] Assumptions Hour, , , , , then:

[0131]

[0132]

[0133]

[0134] Through a multi-level adaptive hybrid optimization strategy, the combination of test tasks and test equipment with the smallest total impact value is selected.

[0135] 4. Feedback Adjustment

[0136] Update task priority and test equipment status:

[0137] Assuming that Task 1 is completed, remove Task 1 and recalculate the real-time status of the test equipment and the priorities of the remaining tasks.

[0138] 5. Comprehensive evaluation

[0139] Calculate the comprehensive evaluation objective function G(t):

[0140]

[0141] Assume that the parameters are initialized to , , , , , Hour, Hour:

[0142] Calculate the optimization objective function value after the current completed task:

[0143]

[0144] According to the calculated optimization objective function , select the optimal task combination at the current time t, that is, select the task combination that optimizes the current objective function The task combination with the greatest contribution is taken as the optimal combination. The selected optimal task combination is executed and the tasks are assigned to the corresponding test equipment for testing.

[0145] Update the status of completed tasks and remove completed tasks from the task set; update the real-time status model of the test equipment , including information such as the test equipment's operating time, fault status, and current tasks; update the priorities of remaining tasks and status, and make adjustments based on the execution results.

[0146] Adjust the weight coefficient of task priority according to the actual execution situation , and , adjust the parameters of the scheduling algorithm (such as , , , , etc.) to optimize the next scheduling decision.

[0147] Re-evaluate based on the feedback data of completed tasks and recalculate the optimization objective function to provide a basis for the next round of scheduling; repeat the above steps, from selecting the optimal task combination to feedback and re-evaluation, until all tasks are scheduled and completed.

[0148] Through this iterative process, the scheduling strategy will be gradually optimized to ensure that the task combination selected at each time point is optimal, and ultimately achieve the overall optimal scheduling effect.

[0149] In summary, a method for automatic test scheduling of electric energy meters is completed.

[0150] Example 2

[0151] This embodiment provides an automatic test scheduling system for electric energy meters, such as Figure 2 As shown, it is used to implement the automatic test scheduling method of the electric energy meter described in Example 1, which includes:

[0152] Test task priority calculation unit: obtains the electric energy meter information, builds the real-time status model of the test equipment, calculates the priority of each test task according to the real-time status of the test equipment and the urgency and importance of the test task, and ensures that high-priority test tasks are processed first;

[0153] Optimal test task selection unit: Based on the calculated test task priority and the real-time status model of the test equipment, combined with a multi-level adaptive hybrid optimization strategy, the test order of the tested samples is dynamically adjusted. By calculating the time and state influencing factors, the optimal test task is selected and the test equipment status is updated. Feedback adjustment is performed to ensure that the test tasks are carried out in the optimal order;

[0154] Optimal scheduling strategy implementation unit: conduct a comprehensive evaluation of all completed test tasks and automated test scheduling of test equipment, construct a comprehensive evaluation objective function to adjust the scheduling strategy of the electric energy meter and test equipment, calculate the total impact value of the test task, select the current optimal test task combination, and implement the optimal scheduling strategy on a global scale.

[0155] It should be noted that each unit in the above-mentioned automatic test scheduling system for electric energy meters can be fully or partially implemented by software, hardware and their combination. The above-mentioned units can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of the above-mentioned units. For the specific definition of an automatic test scheduling system for electric energy meters, please refer to the definition of an automatic test scheduling method for electric energy meters above. The two have the same functions and effects, which will not be repeated here.

[0156] 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.

[0157] 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.

[0158] 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 scheduling automatic test of electric energy meters, characterized in that: include: S1. Obtain the information of the electric energy meter, build the real-time status model of the test equipment, calculate the priority of each test task according to the real-time status of the test equipment and the urgency and importance of the test task, and ensure that high-priority test tasks are processed first; S2. According to the calculated test task priority and the real-time status model of the equipment, combined with the multi-level adaptive hybrid optimization strategy, the test order of the tested samples is dynamically adjusted. By calculating the time and state influencing factors, the optimal test task is selected and the test equipment status is updated, and feedback adjustment is performed to ensure that the test tasks are carried out in the optimal order; S3. Comprehensively evaluate all completed test tasks and automated test scheduling of test equipment, construct a comprehensive evaluation objective function to adjust the scheduling strategy of electric energy meters and test equipment, calculate the total impact value of the test tasks, select the current optimal test task combination, and achieve the optimal scheduling strategy in the global scope; In S1, each test device will have different operating states and test task conditions when conducting a test. The real-time state data of the test device is obtained through the real-time state model of the test device, including the operating time, fault state and current test task of the test device, so as to construct a dynamic real-time state model; The real-time status model is as follows: ， in, It is the real-time status of the test equipment, indicating the time t Always test the overall status of the equipment; For the The operating time of each test equipment, For the The total operating time of each test device, and Respectively The phase and frequency parameters of the test equipment operation, is the fault condition of the test equipment, is the fault attenuation coefficient, is the mean time to repair a fault, is the resource consumption of the current test task, is the average resource consumption of all test tasks, and are the total number of test equipment and test tasks respectively; In S1, each test task needs to dispatch multiple test equipments to conduct the test. After establishing the real-time status model of the test equipments, the priority of each test task is determined according to the urgency and importance of the test task, the test tasks are sorted according to the priority, and the destination queue of the test samples is pre-allocated to ensure that the high-priority test tasks can be processed first; In S2, in each test task, it is necessary to schedule the tests of multiple electric energy meters, dynamically adjust the test order of the tested samples according to the calculated test task priority and the real-time state model of the test equipment, and optimize the scheduling order by combining a multi-level adaptive hybrid optimization strategy; The specific steps of the multi-level adaptive hybrid optimization strategy are as follows: Initialize the real-time status model of the test task set and the test equipment, input all the test task information and the real-time status of the test equipment into the dispatching system, and prepare to start dispatching; calculate the current optimal value of each electric energy meter test in each test task according to the real-time status of the test equipment and the test task priority; make feedback adjustments based on the dispatch results and real-time data; adjust the parameters of the dispatching algorithm based on the feedback to optimize the next dispatching decision; repeat the calculation until all test tasks are dispatched; In S3, after completing one step of scheduling decision, a comprehensive evaluation is performed on all completed test tasks and automated test scheduling of test equipment to construct a comprehensive evaluation objective function; based on the evaluation results, the priorities and scheduling parameters of the test tasks and tests for the next step of scheduling decision are adjusted, so as to achieve the optimal scheduling strategy in the global scope; At each step of scheduling decision, the current optimal solution is selected, and the test sequence is continuously adjusted according to the real-time status of the test equipment; the comprehensive evaluation objective function is: ， in, To comprehensively evaluate the objective function, For the The priority of each test task, is the estimated completion time of the test task, is the resource consumption of the test task, is the attenuation coefficient, and are adjustment parameters, Feedback parameters for the test task execution, is the adjustment coefficient for the test task execution deviation, is the actual completion time, is the estimated time of completion; It is the real-time status of the test equipment, indicating the time t Always test the overall condition of your equipment.

2. The automatic test scheduling method for electric energy meters according to claim 1 is characterized in that: In S1, each test task is evaluated to determine its urgency and importance, and the priority of each test task is calculated by the following formula in combination with the real-time status of the test equipment: ， in, It is The priority of each test task, For the The urgency of the test task, For the The importance of the experimental task, For the The resource requirements of each test task, For the The fluctuation frequency of resource requirements of each experimental task, , and are weight coefficients, indicating the influence of different factors on the priority of the test task. To adjust the parameters; It is the real-time status of the test equipment, indicating the time t Always test the overall condition of your equipment.

3. The automatic test scheduling method for electric energy meters according to claim 2 is characterized in that: In S2, the optimization scheduling order uses the following optimization formula: ， in, is the overall optimization goal of the test sequence, For the In the experimental task Completion time of each test equipment, For the In the experimental task The priority of each test equipment is is the adjustment factor, is the time window, and Respectively In the experimental task The time and state fluctuation parameters of the test equipment, and Respectively In the experimental task The priority and status adjustment frequency of each test equipment; and are the total number of test equipment and test tasks respectively.

4. The automatic test scheduling method for electric energy meters according to claim 3 is characterized in that: In S2, the specific calculation of the current optimal value of each electric energy meter test in each test task is as follows: Calculation time impact factor: For the In the experimental task Test an electric energy meter and calculate the factors affecting its completion time : ， Computing status influencing factor: The real-time status of the computing device affects the In the experimental task The impact of the energy meter test : ， Calculate the total impact value: Add the time and state impact factors to get In the experimental task Total impact value of each electric energy meter test : ， According to the calculated total impact value, select the test task and equipment with the smallest total impact value, that is: ， in, The optimal test task and the test sequence of the electric energy meter in the current test task are selected, and the real-time status model of the equipment is updated; The specific content of the feedback adjustment based on the scheduling results and real-time data is: remove the completed test tasks from the test task set, update the priority and status of the remaining test tasks; adjust the weight coefficient of the test task priority according to the actual execution situation , and , to better reflect the urgency and importance of the test task.

5. The automatic test scheduling method for electric energy meters according to claim 1 is characterized in that: In S3, the status information of all test tasks and test equipment is initialized, including the priority of the test tasks. , Estimated completion time , Resource consumption , real-time status of test equipment and adjusting parameters and adjustment coefficients; According to the initial state, calculate the initial value of each test task: Test task priority influencing factor , Resource consumption influencing factors , state feedback factor ; Combining the above factors, calculate the total impact value of each test task: ； According to the calculated total impact value, the test task combination with the largest impact value is selected as the current optimal test task combination; Execute the optimal test task combination and update the real-time status of the equipment and the test task status. According to the updated equipment status and test task status, recalculate the priority, estimated completion time, resource consumption, adjustment parameters and adjustment coefficients of each test task, and repeat the calculation until all test tasks are completed.

6. An automatic test scheduling system for electric energy meters, used to implement the automatic test scheduling method for electric energy meters according to any one of claims 1 to 5, characterized in that: include: Test task priority calculation unit: obtains the electric energy meter information, builds the real-time status model of the test equipment, calculates the priority of each test task according to the real-time status of the test equipment and the urgency and importance of the test task, and ensures that high-priority test tasks are processed first; Optimal test task selection unit: Based on the calculated test task priority and the real-time status model of the test equipment, combined with a multi-level adaptive hybrid optimization strategy, the test order of the tested samples is dynamically adjusted. By calculating the time and state influencing factors, the optimal test task is selected and the test equipment status is updated. Feedback adjustment is performed to ensure that the test tasks are carried out in the optimal order; Optimal scheduling strategy implementation unit: conduct a comprehensive evaluation of all completed test tasks and automated test scheduling of test equipment, construct a comprehensive evaluation objective function to adjust the scheduling strategy of the electric energy meter and test equipment, calculate the total impact value of the test task, select the current optimal test task combination, and implement the optimal scheduling strategy on a global scale.

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