Logistics control integrated electric energy meter verification service management method and system

Abstract

The invention discloses an integrated logistics control electric energy meter verification service management method and system, and relates to the technical field of integrated innovation, and the method comprises the steps: receiving an electric energy meter verification task, analyzing the verification task demand, generating a logistics scheduling scheme according to the position of an electric energy meter, sending a logistics scheduling instruction, and triggering a dynamic verification task distribution mechanism after the electric energy meter arrives. The method comprises the following steps: collecting electric energy meter parameters, carrying out centralized preprocessing, carrying out multi-stage quality detection on processed verification data, updating a verification state in real time, implementing an abnormity early warning mechanism by utilizing the verification data and a preset threshold value, and adjusting a verification task distribution strategy according to an early warning result when an abnormal condition is detected. According to the invention, through integration of logistics control and automatic verification technologies, efficient management of the electric energy meter verification business is realized, the automation degree of the verification process, the accuracy of data processing and the rationality of resource utilization are improved, and the management level and efficiency of the whole verification business are improved.

Description

Technical Field

[0001] The present invention relates to the field of integrated innovation technology, and in particular to a method and system for managing electric energy meter verification business with integrated logistics control. Background Art

[0002] With the rapid development of the power industry, the accuracy and reliability of electric energy meters, as key equipment in the power system, are crucial to power supply and billing. Therefore, the calibration business management of electric energy meters has become an indispensable technical field. Traditional electric energy meter calibration business management mainly relies on manual operation and simple information recording, which greatly limits the efficiency and quality of calibration. In recent years, although some calibration centers have begun to adopt information management systems, there are still many deficiencies. For example, logistics scheduling in existing technologies often lacks real-time linkage with calibration tasks, resulting in unreasonable resource allocation in the calibration process and extended calibration cycles. In addition, the processing and analysis of calibration data mostly rely on manual judgment, which is not only inefficient, but also prone to errors due to subjective factors.

[0003] In response to these problems, existing technologies have attempted to introduce some automated and intelligent elements, such as using electronic tags to record electricity meter information and using basic filtering algorithms for data preprocessing. However, these methods still have obvious deficiencies in terms of integrated logistics control, dynamic task allocation, multi-level quality inspection, and abnormal warning mechanisms. In particular, in the handling of abnormal situations, existing technologies lack effective early warning and dynamic adjustment strategies, making it difficult to achieve efficient management of calibration tasks and optimal allocation of resources. Therefore, in the field of integrated logistics control and automated calibration technology, there is a need for a system and method that can update the calibration status in real time, intelligently analyze calibration data, and dynamically adjust the calibration task allocation strategy based on the warning results, so as to improve the automation level and overall efficiency of the calibration business. Summary of the invention

[0004] In view of the above-mentioned existing problems, the present invention provides a method and system for managing electric energy meter calibration business with integrated logistics control, so as to solve the problems in the prior art of insufficient integrated logistics control, inability to dynamically allocate tasks, inaccurate quality inspection and imperfect abnormal warning mechanism.

[0005] In order to solve the above technical problems, a method for managing the electric energy meter verification business with integrated logistics control is proposed, including:

[0006] Receive the calibration task of the electric energy meter, analyze the calibration task requirements, generate the logistics scheduling plan according to the location of the electric energy meter, and send the logistics scheduling instructions; trigger the dynamic calibration task allocation mechanism after the electric energy meter arrives, collect the parameters of the electric energy meter, perform centralized preprocessing, and conduct multi-level quality inspection on the processed calibration data; update the calibration status in real time, and implement the abnormal warning mechanism using the calibration data and preset thresholds. When an abnormal situation is detected, adjust the calibration task allocation strategy according to the warning result.

[0007] As a preferred solution of the method for integrated logistics control electric energy meter calibration business management described in the present invention, wherein: the analysis of calibration task requirements includes receiving electric energy meter calibration tasks, and a dedicated person is responsible for reviewing the completeness and accuracy of the calibration tasks, marking the tasks after determining that they are correct, conducting a demand analysis on the calibration tasks, determining the required calibration equipment types and estimating the number of equipment required during the calibration process; the electric energy meter calibration tasks include the model, specifications, serial number, manufacturing date, calibration type, calibration standards and expected completion time of the electric energy meter.

[0008] As a preferred solution of the method for managing the calibration business of electric energy meters with integrated logistics control described in the present invention, the sending of logistics scheduling instructions includes obtaining the current location information of the electric energy meter through positioning technology, generating the optimal logistics scheduling plan in combination with the resource status of the calibration center, and automatically generating logistics scheduling instructions, sending the generated instructions to the transportation platform, and transporting the electric energy meters; the resource status of the calibration center includes the working status of the calibration equipment, the scheduling of the calibration personnel and the available space in the calibration workshop.

[0009] As a preferred solution of the method for integrated logistics control of electricity meter calibration business management described in the present invention, the dynamic calibration task allocation mechanism includes: when the electricity meter arrives at the calibration center according to the scheduling plan, the RFID tag of the electricity meter is scanned, the arrival time is automatically recorded, and the dynamic calibration task allocation mechanism is triggered. According to the priority of the calibration task, the calibration type and urgency of the electricity meter, the calibration task is dynamically allocated to the most suitable calibration equipment and calibration personnel to calibrate the electricity meter.

[0010] The collecting of electric energy meter parameters includes collecting the voltage, current, power factor, frequency and electric energy of the electric energy meter once per second.

[0011] As a preferred solution of the method for integrated logistics control electric energy meter calibration business management described in the present invention, the multi-level quality inspection includes centralized preprocessing of the collected electric energy meter parameters, and pre-inspection, main inspection and re-inspection of the preprocessed data to generate a calibration report.

[0012] The centralized preprocessing includes cleaning, denoising, normalization and outlier processing, and uses a sliding window filtering algorithm to change data quality; the preliminary inspection includes the inspection personnel confirming whether the display, reading and recording of the electric energy meter are normal, checking whether the appearance of the electric energy meter is damaged, worn and contaminated, confirming whether the connecting cables of the electric energy meter are firm and the interfaces are intact, and classifying the data using a density-based clustering algorithm; the main inspection includes using a convolutional neural network to perform feature extraction and pattern recognition on the data that has passed the preliminary inspection; the re-inspection includes using an integrated learning algorithm to review the main inspection results.

[0013] The density-based clustering algorithm formula is:

[0014]

[0015] Among them, D is the density distance from the detection point to the cluster center, Z is the feature vector of the current detection point, and Z k is the eigenvector of the cluster center, C is the covariance matrix, and T is the transposed sign.

[0016] The ensemble learning algorithm formula is:

[0017]

[0018] Among them, B(x0 is the final integrated prediction output, N is the total number of decision trees, and w i is the importance weight of the i-th decision tree, g i( x) is the predicted output of the i-th decision tree for input x, and i is the variable index.

[0019] As a preferred solution of the method for managing the calibration business of electric energy meters with integrated logistics control described in the present invention, the abnormal warning mechanism includes collecting the calibration data of the electric energy meters in real time, analyzing the calibration data, calculating the abnormal index, setting the warning threshold, and adjusting the calibration task allocation strategy according to the calculation results.

[0020] The calculation formula of the abnormal index is:

[0021]

[0022] Among them, F is the abnormality index, P is the current detection data, μ is the average value of the historical verification data, and τ is the standard deviation of the historical verification data.

[0023] As a preferred solution of the method for managing the electric energy meter calibration business of integrated logistics control described in the present invention, the adjustment of the calibration task allocation strategy includes adjusting the calibration task allocation strategy according to the early warning results, and adjusting the parameters in the mathematical model according to the exception handling results.

[0024] When F≤Fth When F>F th When the current detection data is abnormal, it is proved that the early warning mechanism is triggered. According to the early warning result, the verification task allocation strategy is adjusted, and the abnormal electric energy meter is assigned to the inspector for processing first. According to the abnormal processing result, the particle swarm optimization algorithm is used to adjust the parameters in the mathematical model.

[0025] The particle swarm optimization algorithm formula is:

[0026] v(t+1)=w·v(t)+c1·r1·(pb-z(t))+c2·r2·(gb-z(t))

[0027] Among them, v(t+1) is the particle velocity at time t+1, w is the inertia weight, v(t) is the particle velocity at time t, c1 and c2 are learning factors, r1 and r2 are random numbers, pb is the individual optimal solution, z(t) is the solution at time t, and gb is the global optimal solution.

[0028] Another object of the present invention is to provide a system for managing the calibration business of electric energy meters with integrated logistics control. The present invention realizes efficient and accurate management of the calibration business through technological innovation. The system of the present invention realizes efficient management of the calibration business of electric energy meters by integrating logistics control and automated calibration technology, improves the degree of automation of the calibration process, the accuracy of data processing and the rationality of resource utilization, thereby improving the management level and efficiency of the entire calibration business.

[0029] As a preferred solution of the system for integrated logistics control of electric energy meter calibration business management described in the present invention, it is characterized by including a logistics scheduling and task receiving module, a dynamic calibration task allocation module, a multi-level quality detection and data preprocessing module, and an abnormal warning and parameter adjustment module.

[0030] The logistics scheduling and task receiving module is used to receive the electricity meter calibration task, analyze the task requirements, and generate a logistics scheduling plan based on the location of the electricity meter. It also obtains the location information of the electricity meter through positioning technology, generates the optimal logistics scheduling plan based on the resource status of the calibration center, and sends the logistics scheduling instructions to the transportation platform to realize the transportation of the electricity meter.

[0031] The dynamic verification task allocation module is used to automatically record the arrival time by scanning the RFID tag of the electric energy meter, trigger the dynamic verification task allocation mechanism, and dynamically match the verification task with the verification equipment and verification personnel according to the priority of the verification task, the verification type and urgency of the electric energy meter.

[0032] The multi-level quality inspection and data preprocessing module is used to perform centralized preprocessing, and to perform preliminary inspection, main inspection and re-inspection on the preprocessed data to generate a verification report.

[0033] The abnormal warning and parameter adjustment module is used to collect the verification data of the electric energy meter in real time, analyze the data and calculate the abnormal index, set the warning threshold, and when an abnormal situation is detected, adjust the verification task allocation strategy according to the warning result.

[0034] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the method described in the management of electric energy meter calibration business with integrated logistics control are implemented.

[0035] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the method described in the management of electric energy meter calibration business for integrated logistics control are implemented.

[0036] Beneficial effects of the present invention: The present invention realizes efficient and accurate management of electric energy meter calibration business through the innovative application of integrated logistics control and automated calibration technology; realizes rapid response and efficient scheduling of calibration tasks by receiving calibration tasks, analyzing requirements, generating logistics scheduling plans, and sending instructions, thereby improving the efficiency and resource utilization of the calibration process; realizes real-time monitoring and quality control of calibration data by dynamic calibration task allocation, centralized data preprocessing, and multi-level quality inspection, thereby ensuring the accuracy and reliability of calibration results; realizes rapid response and effective handling of abnormal situations by real-time updating of calibration status, implementation of abnormal early warning mechanism, and adjustment of task allocation strategy, thereby reducing the risks and losses caused by abnormal situations; realizes accurate configuration and rational utilization of calibration resources by analyzing calibration task requirements, estimating the number of equipment, sending logistics scheduling instructions, and generating the optimal logistics scheduling plan, thereby improving the overall efficiency of calibration business. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0038] Figure 1 An overall flow chart of a method for managing electric energy meter calibration services with integrated logistics control provided in one embodiment of the present invention.

[0039] Figure 2A system solution flow chart of an electric energy meter calibration business management system with integrated logistics control provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0041] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0042] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments, either individually or selectively.

[0043] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0044] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0045] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0046] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides a method for managing electric energy meter verification services integrated with logistics control, comprising:

[0047] S1: Receive the electricity meter calibration task, analyze the calibration task requirements, generate a logistics scheduling plan based on the location of the electricity meter, and send a logistics scheduling instruction.

[0048] The analysis of calibration task requirements includes receiving the calibration task of the electric energy meter, and having a dedicated person to review the completeness and accuracy of the calibration task, marking the task after determining that it is correct, conducting a demand analysis on the calibration task, determining the type of calibration equipment required, and estimating the number of equipment required during the calibration process.

[0049] The electric energy meter verification task includes the model, specification, serial number, manufacturing date, verification type, verification standard and expected completion time of the electric energy meter.

[0050] It should be noted that the sending of logistics scheduling instructions includes obtaining the current location information of the electric energy meter through positioning technology, generating the optimal logistics scheduling plan based on the resource status of the calibration center, and automatically generating logistics scheduling instructions, sending the generated instructions to the transportation platform, and transporting the electric energy meter.

[0051] The resource status of the calibration center includes the working status of the calibration equipment, the scheduling of the calibration personnel and the available space in the calibration workshop.

[0052] S2: After the arrival of the electric energy meter, the dynamic verification task allocation mechanism is triggered, and the electric energy meter parameters are collected and centrally pre-processed, and the processed verification data is subjected to multi-level quality inspection.

[0053] Furthermore, the dynamic calibration task allocation mechanism includes: when the electric energy meter arrives at the calibration center according to the scheduling plan, the RFID tag of the electric energy meter is scanned, the arrival time is automatically recorded, and the dynamic calibration task allocation mechanism is triggered. According to the priority of the calibration task, the calibration type and urgency of the electric energy meter, the calibration task is dynamically allocated to the most suitable calibration equipment and calibration personnel to calibrate the electric energy meter.

[0054] The collecting of electric energy meter parameters includes collecting the voltage, current, power factor, frequency and electric energy of the electric energy meter once per second.

[0055] Furthermore, the multi-level quality detection includes centrally preprocessing the collected electric energy meter parameters, and performing preliminary inspection, main inspection and re-inspection on the preprocessed data to generate a calibration report.

[0056] The centralized preprocessing includes cleaning, denoising, normalization and outlier processing, and uses a sliding window filtering algorithm to change the data quality; the preliminary inspection includes the inspection personnel confirming whether the display, reading and recording of the electric energy meter are normal, checking whether the appearance of the electric energy meter is damaged, worn and contaminated, confirming whether the connecting cables of the electric energy meter are firm and whether the interfaces are intact, and using a density-based clustering algorithm to classify the data and preliminarily screen out potential abnormal data; the main inspection includes using a convolutional neural network to perform feature extraction and pattern recognition on the data that has passed the preliminary inspection to further confirm the accuracy of the data; the re-inspection includes using an integrated learning algorithm to review the main inspection results to improve the reliability of the detection.

[0057] It should be noted that the sliding window filtering algorithm formula is:

[0058] Y i =αX i +(1-α)Y i-1

[0059] Among them, Y i is the filtering output at the i-th time point, α is the time attenuation factor, X i is the observed value at the i-th time point, i is the variable index, Y i-1 is the filtered output at the i-1th time point.

[0060] The density-based clustering algorithm formula is:

[0061]

[0062] Among them, D is the density distance from the detection point to the cluster center, Z is the feature vector of the current detection point, and Z k is the eigenvector of the cluster center, C is the covariance matrix, and T is the transposed sign.

[0063] The convolutional neural network formula is:

[0064] A=σ(W T tanh(VH))

[0065] Among them, A is the activation output of the neural network, σ is the activation function, W and V are weight matrices, H is the input feature vector, tanh is the hyperbolic tangent activation function, and T is the transposed sign.

[0066] The ensemble learning algorithm formula is:

[0067]

[0068] Among them, B(x) is the final integrated prediction output, N is the total number of decision trees, and w i is the importance weight of the i-th decision tree, g i(x) is the predicted output of the i-th decision tree for input x, and i is the variable index.

[0069] S3: Update the calibration status in real time, and use the calibration data and preset thresholds to implement an abnormal warning mechanism. When an abnormal situation is detected, adjust the calibration task allocation strategy according to the warning results.

[0070] Furthermore, the abnormal warning mechanism includes collecting the calibration data of the electric energy meter in real time, analyzing the calibration data, calculating the abnormal index, setting the warning threshold, and adjusting the calibration task allocation strategy according to the calculation results.

[0071] The calculation formula of the abnormal index is:

[0072]

[0073] Among them, F is the abnormality index, P is the current detection data, μ is the average value of the historical verification data, and τ is the standard deviation of the historical verification data.

[0074] The formula for setting the warning threshold is:

[0075] F th =μ+k*τ

[0076] Among them, F th is the warning threshold, μ is the average value of historical verification data, τ is the standard deviation of historical verification data, and k is the adjustment coefficient.

[0077] Furthermore, the adjusting of the verification task allocation strategy includes adjusting the verification task allocation strategy according to the early warning result, and adjusting the parameters in the mathematical model according to the abnormality processing result.

[0078] When F≤F th When F>F th When the current detection data is abnormal, it is proved that the early warning mechanism is triggered. According to the early warning result, the verification task allocation strategy is adjusted, and the abnormal electric energy meter is assigned to the inspector for processing first. According to the abnormal processing result, the particle swarm optimization algorithm is used to adjust the parameters in the mathematical model.

[0079] The particle swarm optimization algorithm formula is:

[0080] v(t+1)=w·v(t)+c1·r1·(pb-z(t))+c2·r2·(gb-z(t))

[0081] Among them, v(t+1) is the particle velocity at time t+1, w is the inertia weight, v(t) is the particle velocity at time t, c1 and c2 are learning factors, r1 and r2 are random numbers, pb is the individual optimal solution, z(t) is the solution at time t, and gb is the global optimal solution.

[0082] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0083] Example 2, reference Figure 2 , which is the second embodiment of the present invention, provides a system for managing the calibration business of electric energy meters with integrated logistics control, including a logistics scheduling and task receiving module 100, a dynamic calibration task allocation module 200, a multi-level quality inspection and data preprocessing module 300, and an abnormal warning and parameter adjustment module 400.

[0084] The logistics scheduling and task receiving module 100 is used to receive the electricity meter calibration task, analyze the task requirements, and generate a logistics scheduling plan based on the location of the electricity meter. It also obtains the location information of the electricity meter through positioning technology, generates the optimal logistics scheduling plan based on the resource status of the calibration center, and sends logistics scheduling instructions to the transportation platform to realize the transportation of the electricity meter.

[0085] The dynamic calibration task allocation module 200 is used to automatically record the arrival time by scanning the RFID tag of the electric energy meter, trigger the dynamic calibration task allocation mechanism, and dynamically match the calibration task with the calibration equipment and calibration personnel according to the priority of the calibration task, the calibration type and urgency of the electric energy meter.

[0086] The multi-level quality inspection and data preprocessing module 300 is used to perform centralized preprocessing, and perform preliminary inspection, main inspection and re-inspection on the preprocessed data to generate a verification report.

[0087] The abnormal warning and parameter adjustment module 400 is used to collect the verification data of the electric energy meter in real time, analyze the data and calculate the abnormal index, set the warning threshold, and when an abnormal situation is detected, adjust the verification task allocation strategy according to the warning result.

[0088] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0089] Embodiment 3, the third embodiment of the present invention, is different from the first two embodiments in that:

[0090] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0091] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0092] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0093] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. A method for managing electric energy meter verification business with integrated logistics control, characterized in that: include, Receive the energy meter calibration task, analyze the calibration task requirements, generate a logistics scheduling plan based on the energy meter location, and send a logistics scheduling instruction; After the arrival of the electric energy meter, the dynamic verification task allocation mechanism is triggered, and the parameters of the electric energy meter are collected and centrally pre-processed, and the processed verification data is subjected to multi-level quality inspection; The calibration status is updated in real time, and the abnormal warning mechanism is implemented using the calibration data and preset thresholds. When an abnormal situation is detected, the calibration task allocation strategy is adjusted according to the warning results.

2. The method for managing electric energy meter verification business with integrated logistics control as claimed in claim 1, characterized in that: The analysis of the verification task requirements includes receiving the electric energy meter verification task, and having a dedicated person review the completeness and accuracy of the verification task, marking the task after determining that it is correct, conducting a demand analysis on the verification task, determining the type of verification equipment required, and estimating the number of equipment required during the verification process; The electric energy meter verification task includes the model, specification, serial number, manufacturing date, verification type, verification standard and expected completion time of the electric energy meter.

3. The method for managing electric energy meter verification services integrated with logistics control as claimed in claim 2, characterized in that: The sending of the logistics dispatch instruction includes obtaining the current location information of the electric energy meter through positioning technology, generating the optimal logistics dispatch plan in combination with the resource status of the calibration center, and automatically generating the logistics dispatch instruction, sending the generated instruction to the transportation platform, and transporting the electric energy meter; The resource status of the calibration center includes the working status of the calibration equipment, the scheduling of the calibration personnel and the available space in the calibration workshop.

4. The method for managing electric energy meter verification business integrated with logistics control as claimed in claim 3, characterized in that: The dynamic verification task allocation mechanism includes: when the electric energy meter arrives at the verification center according to the scheduling plan, the RFID tag of the electric energy meter is scanned, the arrival time is automatically recorded, and the dynamic verification task allocation mechanism is triggered. According to the priority of the verification task, the verification type and urgency of the electric energy meter, the verification task is dynamically allocated to the most suitable verification equipment and verification personnel to conduct the verification of the electric energy meter; The collecting of electric energy meter parameters includes collecting the voltage, current, power factor, frequency and electric energy of the electric energy meter once per second.

5. The method for managing electric energy meter verification business integrated with logistics control as claimed in claim 4, characterized in that: The multi-level quality inspection includes centrally preprocessing the collected electric energy meter parameters, and performing pre-inspection, main inspection and re-inspection on the pre-processed data to generate a verification report; The centralized preprocessing includes cleaning, denoising, normalization and outlier processing, and uses a sliding window filtering algorithm to change the data quality; the pre-inspection includes the verification personnel confirming whether the display, reading and recording of the electric energy meter are normal, checking whether the appearance of the electric energy meter is damaged, worn and contaminated, confirming whether the connection cables of the electric energy meter are firm and the interfaces are intact, and classifying the data using a density-based clustering algorithm; the main inspection includes using a convolutional neural network to perform feature extraction and pattern recognition on the data that has passed the pre-inspection; the re-inspection includes using an integrated learning algorithm to review the main inspection results; The density-based clustering algorithm formula is: Among them, D is the density distance from the detection point to the cluster center, Z is the feature vector of the current detection point, and Z k is the eigenvector of the cluster center, C is the covariance matrix, and T is the transposed sign; The ensemble learning algorithm formula is: Among them, B(x0 is the final integrated prediction output, N is the total number of decision trees, and w i is the importance weight of the i-th decision tree, g i (x0 is the predicted output of the i-th decision tree for input x, and i is the variable index.

6. The method for managing electric energy meter verification services integrated with logistics control as claimed in claim 5, characterized in that: The abnormal warning mechanism includes collecting the verification data of the electric energy meter in real time, analyzing the verification data, calculating the abnormal index, setting the warning threshold, and adjusting the verification task allocation strategy according to the calculation results; The calculation formula of the abnormal index is: Among them, F is the abnormality index, P is the current detection data, μ is the average value of historical verification data, and τ is the standard deviation of historical verification data.

7. The method for managing electric energy meter verification services integrated with logistics control as claimed in claim 6, characterized in that: The adjusting of the verification task allocation strategy includes adjusting the verification task allocation strategy according to the early warning result, and adjusting the parameters in the mathematical model according to the abnormality processing result; When F≤F th When F>F th When the current detection data is abnormal, it is proved that the early warning mechanism is triggered. According to the early warning result, the verification task allocation strategy is adjusted to give priority to assigning the abnormal electric energy meter to the inspector for processing. According to the abnormal processing result, the particle swarm optimization algorithm is used to adjust the parameters in the mathematical model. The particle swarm optimization algorithm formula is: v(t+1)=w·v(t)+c1·r1·(pb-z(t))+c2·r2·(gb-z(t)) Among them, v(t+1) is the particle velocity at time t+1, w is the inertia weight, v(t) is the particle velocity at time t, c1 and c2 are learning factors, r1 and r2 are random numbers, pb is the individual optimal solution, z(t) is the solution at time t, and gb is the global optimal solution.

8. A system using the method for managing electric energy meter verification business with integrated logistics control as claimed in any one of claims 1 to 7, characterized in that: It includes logistics scheduling and task receiving module, dynamic inspection task allocation module, multi-level quality inspection and data preprocessing module, and abnormal warning and parameter adjustment module; The logistics scheduling and task receiving module is used to receive the electric energy meter calibration task, analyze the task requirements, and generate a logistics scheduling plan according to the location of the electric energy meter, and obtain the location information of the electric energy meter through positioning technology, generate the optimal logistics scheduling plan in combination with the resource status of the calibration center, and send the logistics scheduling instructions to the transportation platform to realize the transportation of the electric energy meter; The dynamic verification task allocation module is used to automatically record the arrival time by scanning the RFID tag of the electric energy meter, trigger the dynamic verification task allocation mechanism, and dynamically match the verification task with the verification equipment and the verification personnel according to the priority of the verification task, the verification type and the urgency of the electric energy meter; The multi-level quality inspection and data preprocessing module is used to perform centralized preprocessing, and perform preliminary inspection, main inspection and re-inspection on the preprocessed data to generate a verification report; The abnormal warning and parameter adjustment module is used to collect the verification data of the electric energy meter in real time, analyze the data and calculate the abnormal index, set the warning threshold, and when an abnormal situation is detected, adjust the verification task allocation strategy according to the warning result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for managing electric energy meter calibration business for integrated logistics control according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for managing electric energy meter calibration business for integrated logistics control according to any one of claims 1 to 7 are implemented.

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