CN117574185A

CN117574185A - Processing method, device and equipment of metering equipment

Info

Publication number: CN117574185A
Application number: CN202410078913.9A
Authority: CN
Inventors: 李问溪; 陈亚珠; 杨林鹏; 姚强; 张惠泽; 杨公富
Original assignee: Beijing Zhixiang Technology Co Ltd
Current assignee: Beijing Zhixiang Technology Co Ltd
Priority date: 2024-01-19
Filing date: 2024-01-19
Publication date: 2024-02-20
Anticipated expiration: 2044-01-19

Abstract

The application provides a processing method, a device and equipment of metering equipment, and relates to the technical field of electronic equipment, comprising the following steps: determining a plurality of abnormal metering devices in the plurality of metering devices, wherein the abnormal metering devices comprise first metering devices in abnormal states and second metering devices with abnormal index values larger than a preset index threshold; based on the distance between every two abnormal metering devices in the abnormal metering devices, carrying out region clustering processing on the abnormal metering devices to obtain a plurality of region clusters; determining processing resources allocated to each of the plurality of regional clusters based on the number and types of abnormal metering devices each of the plurality of regional clusters includes; and determining the target processing sequence corresponding to the plurality of target abnormal metering devices based on the installation addresses of the plurality of target abnormal metering devices included in the regional cluster. The scheme of this application can improve the efficiency that metering equipment changed the processing.

Description

Processing method, device and equipment of metering equipment

Technical Field

The present disclosure relates to the field of electronic devices, and in particular, to a processing method, an apparatus, and a device for a metering device.

Background

The metering device is a device for metering relevant indexes of households in a district, and may include, for example, an ammeter, a transformer, a concentrator, a collector, and the like.

Various faults of the metering equipment can occur in a period of use, and the state of the metering equipment in a district can be remotely monitored through the misalignment on-line monitoring system. When the metering equipment is in fault, the fault is monitored through the misalignment on-line monitoring system, and a work order is distributed to instruct a technician to go to the installation address of the metering equipment to replace the metering equipment so as to ensure the normal operation of the metering equipment.

However, the above-mentioned treatment method of the measuring apparatus has low efficiency of the replacement treatment.

Disclosure of Invention

The application provides a processing method, a processing device and processing equipment of metering equipment, which are used for solving the technical problem that the processing mode of the existing metering equipment is low in replacement processing efficiency.

In a first aspect, the present application provides a method for processing a metering device, including:

determining a plurality of abnormal metering devices in the plurality of metering devices, wherein the abnormal metering devices comprise first metering devices in abnormal states and second metering devices with abnormal index values larger than a preset index threshold;

Based on the distances between every two abnormal metering devices in the plurality of abnormal metering devices, carrying out region clustering processing on the plurality of abnormal metering devices to obtain a plurality of region clusters;

determining processing resources allocated to each of the plurality of regional clusters based on the number and types of abnormal metering devices each of the plurality of regional clusters includes;

for each region cluster, determining a target processing sequence corresponding to a plurality of target abnormal metering devices based on respective installation addresses of the target abnormal metering devices included in the region cluster, wherein the target processing sequence is used for indicating processing resources based on the region cluster to process the plurality of target abnormal metering devices.

In a possible implementation manner, the performing area clustering processing on the plurality of abnormal metering devices based on the distances between every two abnormal metering devices in the plurality of abnormal metering devices to obtain a plurality of area clusters includes:

determining the abnormality weight of each of the plurality of abnormality metering devices, wherein the abnormality weight of the first type metering device is greater than or equal to the abnormality weight of the second type metering device;

determining reachable distance matrixes corresponding to the plurality of abnormal metering devices based on the distance between the two abnormal metering devices and the respective abnormal weights of the two abnormal metering devices;

And carrying out region clustering processing on the plurality of abnormal metering devices based on the reachable distance matrix, the preset neighborhood radius and the preset quantity threshold value to obtain a plurality of region clusters.

In one possible implementation manner, the determining, based on the installation addresses of each of the plurality of target abnormal metering devices included in the regional cluster, a target processing order corresponding to the plurality of target abnormal metering devices includes:

randomly generating a first generation population corresponding to the plurality of target abnormal metering devices, wherein the first generation population comprises a plurality of first generation population individuals, and the plurality of first generation population individuals are used for indicating a plurality of processing sequences corresponding to the plurality of target abnormal metering devices;

determining the fitness corresponding to each of the first generation population individuals based on the processing resources of the regional clusters and the installation addresses of each of the target abnormal metering devices;

performing an iterative operation, the iterative operation comprising: generating a next generation population based on the fitness corresponding to each of a plurality of current generation population individuals included in the current generation population, wherein the next generation population includes a plurality of next generation population individuals; for a first iteration operation, the current generation population is the first generation population;

Determining the next generation population as a new current generation population in the case that the iteration termination condition is not satisfied, and repeatedly executing the iteration operation;

and determining target population individuals in the next generation population, wherein the target population individuals are used for indicating the target processing sequence under the condition that the iteration termination condition is met.

In a possible implementation manner, the generating the next generation population based on the fitness corresponding to each of the plurality of current generation population individuals included in the current generation population includes:

processing the current generation population individuals based on the fitness corresponding to the current generation population individuals respectively to generate candidate populations;

generating the next generation population based on the fitness of each population individual included in the candidate population and the fitness of each of the plurality of current generation population individuals, wherein the plurality of next generation population individuals included in the next generation population are a preset number of population individuals with the minimum fitness in the candidate population and the current generation population.

In a possible implementation manner, the processing the plurality of current generation population individuals based on the fitness corresponding to each of the plurality of current generation population individuals to generate a candidate population includes:

Determining first type population individuals, second type population individuals and third type population individuals from the plurality of current generation population individuals, wherein the fitness of the first type population individuals, the second type population individuals and the third type population individuals is increased in sequence;

inheriting the first type population individuals to obtain first target type population individuals;

crossing the individuals of the second type population to obtain individuals of a second target type population;

performing mutation on the third type population individuals to obtain third target type population individuals;

wherein the candidate population comprises the first target type population individual, the second target type population individual and the third target type population individual.

In one possible embodiment, the method further comprises:

determining, for each of the regional clusters, a time cost corresponding to the regional cluster based on a target processing order corresponding to a plurality of target abnormal metering devices included in the regional cluster and respective installation addresses of the plurality of target abnormal metering devices;

determining a first region cluster corresponding to the maximum time cost and a second region cluster corresponding to the minimum time cost from the plurality of region clusters based on the time costs corresponding to the plurality of region clusters;

Determining a first updated processing resource of the first regional cluster and a second updated processing resource of the second regional cluster based on the maximum time cost and the minimum time cost, wherein the first updated processing resource is the sum of the processing resources of the first regional cluster and a target processing resource, and the second updated processing resource is the difference between the processing resources of the second regional cluster and the target processing resource; the target processing resources are a subset of the processing resources of the second regional cluster;

updating the processing sequence corresponding to the abnormal metering equipment included in the first region cluster based on the first updating processing resource;

and updating the processing sequence corresponding to the abnormal metering equipment included in the second region cluster based on the second updating processing resource.

In a possible implementation manner, the determining, based on the time costs corresponding to each of the plurality of region clusters, a first region cluster corresponding to a maximum time cost and a second region cluster corresponding to a minimum time cost from the plurality of region clusters includes:

determining a time cost standard deviation among the plurality of regional clusters based on the time cost corresponding to each of the plurality of regional clusters;

And determining the first region cluster and the second region cluster from the plurality of region clusters based on the time cost corresponding to each of the plurality of region clusters under the condition that the time cost standard deviation is larger than a preset standard deviation threshold.

In a second aspect, the present application provides a processing apparatus for a metering device, comprising:

the identification module is used for determining a plurality of abnormal metering devices from the plurality of metering devices, wherein the abnormal metering devices comprise first metering devices in abnormal states and second metering devices with abnormal index values larger than a preset index threshold;

the clustering module is used for carrying out region clustering processing on the plurality of abnormal metering devices based on the distance between every two abnormal metering devices in the plurality of abnormal metering devices to obtain a plurality of region clusters;

the distribution module is used for determining processing resources distributed to each of the plurality of regional clusters based on the number and the type of the abnormal metering devices included in each of the plurality of regional clusters;

the processing module is used for determining target processing sequences corresponding to the target abnormal metering devices based on the installation addresses of the target abnormal metering devices included in the regional clusters, wherein the target processing sequences are used for indicating processing resources based on the regional clusters to process the target abnormal metering devices.

In a possible implementation manner, the clustering module is specifically configured to:

In a possible implementation manner, the processing module is specifically configured to:

In one possible embodiment, the processing module is further configured to:

In a third aspect, the present application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a method of processing a metering device according to any one of the first aspects when the program is executed.

In a fourth aspect, the present application provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of processing a metrology device according to any of the first aspects.

According to the processing method, the processing device and the processing equipment for the metering equipment, firstly, a plurality of abnormal metering equipment is determined in the plurality of metering equipment, the abnormal metering equipment comprises first metering equipment in an abnormal state and second metering equipment with an abnormal index value larger than a preset index threshold, and then, based on the distances between every two abnormal metering equipment in the plurality of abnormal metering equipment, the plurality of abnormal metering equipment is subjected to regional clustering processing to obtain a plurality of regional clusters. After the plurality of regional clusters are obtained, processing resources allocated to the plurality of regional clusters are determined based on the number and types of the abnormal metering devices included in the plurality of regional clusters, target processing sequences corresponding to the plurality of target abnormal metering devices can be determined for each regional cluster based on the installation addresses of the plurality of target abnormal metering devices included in the regional clusters, and the corresponding plurality of target abnormal metering devices are processed based on the target processing sequences and the processing resources of the regional clusters. According to the scheme, the abnormal metering equipment close in distance is distributed to the same area cluster through area clustering processing, batch processing is facilitated, then the target processing sequence corresponding to each area cluster is determined, and processing resources are distributed and processed by taking the area cluster as a unit.

Drawings

For a clearer description of the present application or of the prior art, the drawings that are used in the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description below are some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application;

FIG. 2 is a flowchart of a method for processing a metering device according to an embodiment of the present disclosure;

FIG. 3 is a flowchart of a region clustering process provided in an embodiment of the present application;

FIG. 4 is a flowchart of determining a target processing order according to an embodiment of the present disclosure;

FIG. 5 is a flowchart of updating processing resources and processing sequences provided by an embodiment of the present application;

FIG. 6 is a second flowchart of a processing method of the metering device according to the embodiment of the present application;

fig. 7 is a schematic structural diagram of a processing device of a metering device according to an embodiment of the present application;

fig. 8 is a schematic entity structure diagram of an electronic device according to an embodiment of the present application.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the present application will be clearly and completely described below with reference to the drawings in the present application, and it is apparent that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.

An application scenario applicable to the embodiment of the present application will be described first with reference to fig. 1.

Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application, as shown in fig. 1, where a plurality of cells exist in a jurisdiction, and each cell has an unequal number of households, and each household is installed with a corresponding metering device, where the metering devices may include, for example, an ammeter, a transformer, a concentrator, a collector, and so on. For example, in fig. 1, the system includes a cell a and a cell B, where the cell a includes a resident 1, a resident 2, and a resident 3, and the cell B includes a resident 4, a resident 5, and a resident 6, where each resident is equipped with a corresponding metering device.

The metering state of metering equipment installed by each resident in the district can be monitored remotely through the misalignment on-line monitoring system, so that a work order is timely dispatched under the condition of abnormal metering state to arrange technicians to perform site replacement.

In some cases, the number of metering devices in a district is large, and thus the number of metering devices in an abnormal state is also often large, which requires a large amount of technicians to go from the power supply company to the individual households for replacement.

However, this approach is inefficient. In practice, some metering devices of households with close addresses may be abnormal, and the above processing method needs to arrange technicians to go back and forth between the power supply company and each household for many times, so that not only is the efficiency of replacing the metering devices low, but also a great amount of time and cost are consumed.

In some cases, there may be some metering states of the metering devices being normal, but in practice the metering devices have some problems, in which case replacement of the metering devices for the households cannot be performed in time, and the running risk of the metering devices is high.

Based on the technical problems, the embodiment of the application provides a processing method of metering equipment, so as to improve the efficiency of replacement processing of the metering equipment. Embodiments of the present application will be described below with reference to the accompanying drawings.

The execution body of each embodiment of the present application may be, for example, a server, a processor, a microprocessor, or the like, or may be a device that integrates a server, a processor, a microprocessor, for example, a terminal device, a client, or the like, and any device that has a computing processing capability may be used as the execution body of each embodiment of the present application. For example, in fig. 1, the server 10 may be the execution subject. In the following embodiments, for convenience of description, the execution subject is taken as a server to be described.

Fig. 2 is a flowchart one of a processing method of a metering device according to an embodiment of the present application, as shown in fig. 2, where the method includes:

s21, determining a plurality of abnormal metering devices in the metering devices, wherein the abnormal metering devices comprise a first metering device in an abnormal state and a second metering device with an abnormal index value larger than a preset index threshold.

The plurality of metering devices are metering devices monitored by the misalignment on-line monitoring system, and related parameters of the plurality of metering devices can be monitored by the misalignment on-line monitoring system, so that a plurality of abnormal metering devices are determined in the plurality of metering devices based on the related parameters.

In the embodiment of the application, the abnormal metering equipment comprises two types, wherein one type is the first type metering equipment in an abnormal state, and the other type is the second type metering equipment with the abnormal index being larger than a preset index threshold value.

The first type of metering equipment is metering equipment which is monitored by a misalignment on-line monitoring system and is in an abnormal state. The misalignment online monitoring system may count relevant parameters of the metrology devices, determining the metrology devices that have exhibited significant anomaly characteristics as a first type of metrology device. Obvious metering devices may include, for example, metering errors greater than or equal to a preset error threshold, or other possible anomalies, occurring for M consecutive days.

The second type of metrology device is a metrology device that is not monitored for an abnormal condition by the misalignment on-line monitoring system, but is highly likely to be abnormal in a short time in the future. The misalignment online monitoring system can count related parameters of metering devices, and determine the metering state as normal, but the corresponding device with the abnormal index value larger than the preset index threshold value as second-class metering device.

The abnormality index value may include a plurality of values, and may include, for example, a metering error, a failure rate of the same batch metering equipment, and the like. If the metering error of the metering device is smaller than the preset error threshold value, but the metering error approaches the preset error threshold value for M consecutive days, the metering device can be determined to have a high probability of being abnormal, and the metering device can be determined to be the second type of metering device. If the failure rate of the other metering devices in the same batch as the metering device is larger, because the time of failure of the metering device in the same batch is usually smaller, when the failure rate of the other metering devices in the same batch as the metering device is larger than or equal to a preset failure rate threshold, the metering device can be determined to have a larger probability of being abnormal, and the metering device is determined to be the second type metering device.

The first type of metering device is one that must be replaced as soon as possible, and the second type of metering device is one that requires replacement, but is less urgent than the first type of metering device.

S22, based on the distance between every two abnormal metering devices in the abnormal metering devices, performing region clustering processing on the abnormal metering devices to obtain a plurality of region clusters.

After the plurality of abnormal metering devices are determined, the installation addresses of the plurality of abnormal metering devices can be obtained, and then the distance between every two abnormal metering devices is determined based on the installation addresses of the plurality of abnormal metering devices.

Then, based on the distance between every two abnormal metering devices, region clustering processing is performed on the plurality of abnormal metering devices. The process of regional clustering processing is a process of classifying a plurality of abnormal metering devices to obtain a plurality of regional clusters, wherein each abnormal metering device only belongs to one regional cluster, and each regional cluster comprises at least one abnormal metering device. The plurality of abnormal metering devices are clustered in regions based on the distance between every two abnormal metering devices, and the abnormal metering devices with the relatively close distance can be distributed into one region cluster. For any regional cluster, the distance between any two abnormal metering devices in the regional cluster is smaller than or equal to a preset distance threshold.

S23, determining processing resources allocated to each of the plurality of regional clusters based on the number and types of abnormal metering devices included in each of the plurality of regional clusters.

In an embodiment of the present application, the processing resource includes a vehicle and a corresponding technician, the vehicle is used to carry a mounting address of the technician to the abnormal measuring equipment, and the technician replaces the abnormal measuring equipment.

After determining the plurality of regional clusters, determining total installation time required by each of the plurality of regional clusters based on the number and types of abnormal metering devices included by each of the plurality of regional clusters. For any regional cluster, the number and types of abnormal metering devices included in the regional cluster are determined first, and the installation time periods required by different types of abnormal metering devices are different. For example, in the case of an electric meter, the types of electric meter include a single-phase meter and a three-phase meter, and the time required for installing the three-phase meter is longer than that for installing the single-phase meter. After the type of each abnormal metering device in the regional cluster is determined, the required installation time length of each abnormal metering device can be obtained, and then the required installation time lengths of the abnormal metering devices in the regional cluster are added to obtain the total installation time length required by the regional cluster.

Then, processing resources are allocated to each of the plurality of regional clusters based on the total installation time period required for each regional cluster. For example, a normalized duty cycle for each regional cluster may be calculated based on the total installation time required for each regional cluster, and corresponding processing resources may be allocated for each regional cluster based on the normalized duty cycle. For example, a total of 3 area clusters are included, and the ratio of the installation time lengths required by the 3 area clusters is 1:2:2, then 1/5 of the total processing resources, 2/5 of the total processing resources, and 2/5 of the total processing resources can be allocated to the 3 area clusters.

S24, determining target processing sequences corresponding to the target abnormal metering devices based on the installation addresses of the target abnormal metering devices included in the region clusters for the region clusters, wherein the target processing sequences are used for indicating processing resources based on the region clusters to process the target abnormal metering devices.

For any regional cluster, after the installation addresses of the target abnormal metering devices included in the regional cluster are obtained, the target processing sequence corresponding to the target abnormal metering devices can be determined, and the target processing sequence is the sequence for replacing and installing the target abnormal metering devices. Because the distances among the plurality of target abnormal metering devices in the same regional cluster are smaller than the preset distance threshold, the installation addresses of the plurality of target abnormal metering devices can be traversed in sequence and replaced in sequence based on the processing resources of the regional cluster.

According to the processing method of the metering equipment, firstly, a plurality of abnormal metering equipment is determined in the plurality of metering equipment, the abnormal metering equipment comprises first metering equipment in an abnormal state and second metering equipment with an abnormal index value larger than a preset index threshold, and then, based on the distance between every two abnormal metering equipment in the plurality of abnormal metering equipment, region clustering processing is conducted on the plurality of abnormal metering equipment to obtain a plurality of region clusters. After the plurality of regional clusters are obtained, processing resources allocated to the plurality of regional clusters are determined based on the number and types of the abnormal metering devices included in the plurality of regional clusters, target processing sequences corresponding to the plurality of target abnormal metering devices can be determined for each regional cluster based on the installation addresses of the plurality of target abnormal metering devices included in the regional clusters, and the corresponding plurality of target abnormal metering devices are processed based on the target processing sequences and the processing resources of the regional clusters. According to the scheme, the abnormal metering equipment close to the area is distributed to the same area cluster through area clustering processing, batch processing is facilitated, then the target processing sequence corresponding to each area cluster is determined, and processing resources are distributed and processed by taking the area cluster as a unit.

On the basis of any embodiment, the scheme of the embodiment of the application is further described below with reference to the accompanying drawings.

The procedure of the region clustering process is first described with reference to fig. 3.

Fig. 3 is a flowchart of a region clustering process provided in an embodiment of the present application, as shown in fig. 3, including:

s31, determining the abnormality weight of each of the plurality of abnormal metering devices, wherein the abnormality weight of the first type metering device is greater than or equal to the abnormality weight of the second type metering device.

The server sets respective anomaly weights for the plurality of anomaly metering devices in advance, wherein the anomaly weights are used for indicating the anomaly degrees of the corresponding anomaly metering devices. Alternatively, the server may set the same anomaly weight for all the first type of metering devices and set the same anomaly weight for all the second type of metering devices. Optionally, the server may set a corresponding anomaly weight for each anomaly metering device separately, that is, the anomaly weights of the anomaly metering devices in the first class of metering devices may be different, and the anomaly weights of the anomaly metering devices in the second class of metering devices may also be different.

However, the anomaly weight of the first type of metering device needs to be greater than or equal to the anomaly weight of the second type of metering device, because the first type of metering device is a metering device that is already in an abnormal state, the second type of metering device is a metering device that is about to be in an abnormal state, and the degree of anomaly of the second type of metering device is lower than the degree of anomaly of the first type of metering device.

S32, determining an reachable distance matrix corresponding to the plurality of abnormal metering devices based on the distance between every two abnormal metering devices and the abnormal weight of each abnormal metering device.

The reachable distance matrix comprises the weighted distances of the distances between every two abnormal metering devices after being weighted by the abnormal weights of the two abnormal metering devices. If the number of the abnormal metering devices is N, N is a positive integer, the dimension of the reachable distance matrix is N rows and N columns, the element of any ith row and jth column represents the weighted distance between the ith abnormal metering device and the jth abnormal metering device, and the weighted distance between the ith abnormal metering device and the jth abnormal metering device is the distance between the ith abnormal metering device and the jth abnormal metering device multiplied by the abnormal weight of the ith abnormal metering device and the abnormal weight of the jth abnormal metering device.

S33, carrying out region clustering processing on the plurality of abnormal metering devices based on the reachable distance matrix, the preset neighborhood radius and the preset quantity threshold value to obtain a plurality of region clusters.

The preset neighborhood radius indicates the size of the regional cluster, and the distance between two abnormal metering devices in any regional cluster cannot be more than twice the preset neighborhood radius. The preset number threshold is used for indicating the minimum number of abnormal metering devices included in the regional cluster, namely, the number of abnormal metering devices included in any regional cluster is greater than or equal to the preset number threshold.

After the preset neighborhood radius and the preset quantity threshold are set, the reachable distance matrix is combined to perform region clustering processing on the plurality of abnormal metering devices, so that a plurality of region clusters are obtained. The weighted distance between every two abnormal metering devices can be obtained through the reachable distance matrix, and under the condition that the limiting conditions corresponding to the preset neighborhood radius and the preset quantity threshold are met, the abnormal metering devices with the relatively close weighted distance are required to be distributed to the same area cluster. The reachable distance matrix not only reflects the distance between every two abnormal metering devices, but also comprises the abnormal weight of every two abnormal metering devices, so that the subsequent region clustering processing not only considers the distance factor, but also considers the abnormal degree of the abnormal metering devices, so that the region clustering result is more accurate, and the subsequent processing is facilitated.

The region clustering method can be, for example, a weighted K-means clustering method, a weighted hierarchical clustering method and the like. The weighted K-means is a clustering method based on distance, and the key step is to calculate the distance between the abnormal metering equipment and the center of each regional cluster and allocate each abnormal metering equipment to the nearest regional cluster according to the distances. The weighted hierarchical clustering is a clustering method based on hierarchical relation, and the closest clusters are combined by calculating the distance between the abnormal metering devices until all the abnormal metering devices are combined into one cluster.

It should be noted that the weighted K-means clustering method and the weighted hierarchical clustering method are only one example, and do not constitute a specific limitation on the region clustering process.

After obtaining the plurality of area clusters, for each area cluster, determining the target processing sequence corresponding to the plurality of target abnormal metering devices based on the respective installation addresses of the plurality of target abnormal metering devices included in the area cluster. Taking arbitrary region clustering as an example, a process of determining target processing sequences corresponding to a plurality of target abnormality metering devices in the region clustering is described below with reference to fig. 4.

Fig. 4 is a flowchart of determining a target processing sequence according to an embodiment of the present application, as shown in fig. 4, including:

s41, randomly generating a first generation population corresponding to the plurality of target abnormal metering devices, wherein the first generation population comprises a plurality of first generation population individuals, and the plurality of first generation population individuals are used for indicating a plurality of processing sequences corresponding to the plurality of target abnormal metering devices.

For any regional cluster, based on the processing resources allocated by the regional cluster, first generation population is randomly generated, the first generation population comprises a plurality of first generation population individuals, each first generation population individual represents a processing sequence corresponding to a plurality of randomly generated target abnormal metering devices, and a plurality of first generation population individuals correspond to a plurality of randomly generated processing sequences.

It should be noted that, in the embodiment of the present application, the processing sequence corresponding to the plurality of target abnormal measurement devices is not a sequence of sequentially traversing the installation addresses of the plurality of target abnormal measurement devices from the power supply company, because the processing resource of the regional cluster may include, for example, a plurality of vehicles and a plurality of technicians, each vehicle may traverse a corresponding part of the target abnormal measurement devices, and the plurality of vehicle technicians may traverse the plurality of target abnormal measurement devices of the regional cluster together, so that for any vehicle, it is not necessary to traverse all the target abnormal measurement devices. Therefore, the processing sequences corresponding to the plurality of target abnormal measuring devices represent the sequences in which each vehicle-mounted technician traverses the corresponding target abnormal measuring device in the processing resource.

S42, determining the fitness corresponding to each of the first generation population individuals based on the processing resources of the regional clusters and the installation addresses of each of the target abnormal metering devices.

The corresponding adaptability of the system is mainly determined by three parts aiming at any first generation population individuals, the first part is based on the processing sequence indicated by the first generation population individuals and the distance between corresponding target abnormal metering devices, the driving distance of the vehicle to the corresponding target abnormal metering devices is determined sequentially, the second part is the time in transit required by a technician on the vehicle and the installation time required by updating each abnormal metering device at each installation address, the installation time is determined by the type of the abnormal metering devices, and the third part is the conversion time aiming at the number of days of advanced scrapping of the second type of metering devices in the target abnormal metering devices.

For any first generation population individuals, as the first generation population individuals indicate a plurality of processing sequences corresponding to a plurality of target abnormal metering devices, and the processing resources distributed by the regional clusters are certain, the driving distance of the vehicle to the corresponding target abnormal metering devices, the time in transit required by technicians on the vehicle, the installation time required by updating each abnormal metering device at each installation address, and the conversion time of the number of days of advanced scrapping of the second type metering devices are determined, so that the corresponding adaptability can be obtained. The fitness is calculated as follows:

（1）

wherein F represents the adaptability of the first generation population individuals, S represents the sequential driving of the vehicle to the corresponding destinationThe travel distance of the abnormal measuring device is marked, a represents a first preset parameter, T ₁ Representing the sum of the time in transit required by the technician on the vehicle and the installation time required to update each of the anomaly metering devices at each of the installation addresses, b representing a second preset parameter, T ₂ And c represents a third preset parameter.

The lower the fitness of the population individuals is, the better the corresponding distribution scheme is.

S43, performing iterative operation, wherein the iterative operation comprises the following steps: generating a next generation population based on the fitness of each of a plurality of current generation population individuals included in the current generation population, wherein the next generation population includes a plurality of next generation population individuals; for the first iteration operation, the current generation population is the first generation population.

The iterative operation is a process of generating a next generation population based on a current generation population, which is initially a first generation population.

The current generation population comprises a plurality of current generation population individuals, each current generation population individual represents a processing sequence corresponding to the generated plurality of target abnormal metering devices, each current generation population individual has a corresponding fitness, the calculation mode of the fitness corresponding to the current generation population individual is similar to that of the fitness corresponding to the first generation population individual, and the details can be seen from the related description of S42, and the details are not repeated here.

After the fitness corresponding to each of the plurality of current generation population individuals is obtained, the plurality of current generation population individuals are processed based on the fitness corresponding to each of the plurality of current generation population individuals, and candidate populations are generated.

For example, the cross-over or mutation process may be performed on the plurality of current generation population individuals, so as to generate a candidate population, where the candidate population includes population individuals obtained by the cross-over or mutation process on the plurality of current generation population individuals. The method is characterized in that the method can randomly select the processing mode of the current generation population individuals from the three processing modes of inheritance, intersection and variation aiming at any current generation population individuals.

In one possible implementation manner, in order to retain the dominant population, first, from a plurality of current generation population individuals, a first type population individual, a second type population individual and a third type population individual may be determined, where the fitness corresponding to the first type population individual, the second type population individual and the third type population individual increases sequentially.

For example, the plurality of current generation population individuals may be sorted according to the fitness corresponding to each of the plurality of current generation population individuals, the sorted plurality of current generation population individuals may be sorted according to the sequence from low to high of the fitness, the current generation population individual category with the lowest fitness is used as the first type population individual, the current generation population individual category with the next highest fitness is used as the second type population individual, and the current generation population individual category with the highest fitness is used as the third type population individual.

Then inheriting the first type population individuals to obtain first target type population individuals; crossing individuals of the second type population to obtain individuals of a second target type population; performing mutation on individuals of the third type population to obtain individuals of a third target type population; the candidate population comprises a first target type population individual, a second target type population individual and a third target type population individual.

Because the first type population individuals are population individuals with lower fitness and belong to relatively more advantageous population individuals, the first type population individuals are subjected to inheritance treatment, inheritance is a fine adjustment process of the first type population individuals, grouping arrangement of vehicles and technicians on the vehicles in treatment resources is reserved, and only the installation sequence of a plurality of target abnormal metering devices is adjusted, so that the first target type population individuals are obtained.

Because the third type population individuals are population individuals with higher adaptability and belong to relatively less advantageous population individuals, mutation treatment is adopted for the third type population individuals, the mutation is a process of randomly adjusting on the basis of the first type population individuals, the grouping arrangement of vehicles and technicians on the vehicles in the treatment resources can be randomly adjusted, and the installation sequence of a plurality of target abnormal metering devices can be adjusted, so that the third target type population individuals are obtained.

Because the fitness of the individuals of the second type population is higher than that of the individuals of the first type population and lower than that of the individuals of the third type population, the individuals of the second type population are subjected to cross treatment, and the degree of the cross treatment is between inheritance and variation, so that the individuals of the dominant population have more hybridization opportunities and have the opportunity to generate new dominant individuals.

By classifying the individuals of the current generation population and adopting different processing modes aiming at the individuals of different types of population, the individuals of the dominant population can be kept as much as possible, and new individuals of the dominant population can be generated opportunistically, so that the new individuals of the dominant population can be generated step by step through continuous iterative operation.

After the candidate population is obtained, a next generation population is generated based on the fitness of the population individuals included in the candidate population and the fitness of the population individuals of the current generation, wherein the population individuals of the next generation are the preset number of population individuals with the minimum fitness in the candidate population and the current generation population.

The preset number is the number of population individuals included in each generation of population, and the number of population individuals included in each generation of population is the same.

Taking the preset number as 20 as an example, the current generation population comprises 20 current generation population individuals, and after the 20 current generation population individuals are subjected to inheritance, intersection, mutation and the like, the obtained candidate population comprises 20 candidate population individuals. Then, the fitness of each of the 20 candidate populations is calculated. The 20 current population individuals and the 20 candidate population individuals form a set, and the 20 population individuals with the minimum corresponding adaptability are selected from the set as the next-generation population individuals, so that the next-generation population is generated.

And S44, determining the next generation population as a new current generation population, and repeatedly executing the iteration operation in the case that the iteration termination condition is not met.

In this embodiment of the present application, the iteration termination conditions may include two conditions, where the first condition is that the iteration number reaches a preset iteration number threshold, and the second condition is that no more optimal population individuals occur in successive R generations, and R is a positive integer.

More optimal population of individuals refers to a population of individuals with less fitness. For example, if there are no individuals in the ith generation of population that have a smaller fitness than the plurality of ith-1 generation of population individuals in the ith-1 generation of population, then no more optimal population individuals are present in the ith generation; if no population individuals with smaller fitness than a plurality of ith generation population individuals in the ith generation population exist in the ith generation population, no more optimal population individuals exist in the ith generation of the (i+1). If no more optimal population of individuals appears for consecutive R generations, it is determined that the second condition is satisfied.

If the first condition and/or the second condition is/are not met, it is determined that the iteration termination condition is not met, and therefore the generated next generation population is determined to be a new current generation population, and the iteration operation is repeatedly performed.

S45, under the condition that the iteration termination condition is met, determining target population individuals in the next generation population, wherein the target population individuals are used for indicating a target processing sequence.

If the first condition and the second condition are met, it is determined that an iteration termination condition is met, thereby terminating the iteration, and then target population individuals are determined in the generated next generation population. The target population individuals are population individuals with the lowest adaptability among a plurality of next generation population individuals included in the next generation population.

In the above embodiment, an implementation scheme of determining the target processing order corresponding to the plurality of target abnormality measuring devices in each region cluster is described in connection with fig. 4. In one possible implementation, the target processing order may be taken as a processing order of a plurality of target exception metering devices in the corresponding region cluster, thereby indicating that the corresponding plurality of target exception metering devices are processed by the processing resource based on the region cluster.

In one possible implementation manner, the time cost corresponding to each regional cluster can be determined based on the processing resources and the processing sequence of each regional cluster, and the processing resources are adjusted based on the time cost, so that the time duration difference required by each regional cluster to complete the processing process of the abnormal metering equipment is smaller. This process is described below in conjunction with fig. 5.

Fig. 5 is a flowchart of updating processing resources and processing sequences provided in an embodiment of the present application, as shown in fig. 5, including:

S51, determining time cost corresponding to the region cluster based on target processing sequences corresponding to the target abnormal metering devices and installation addresses of the target abnormal metering devices, wherein the target processing sequences are included in the region cluster, aiming at the region cluster.

For each region cluster, in the above embodiment, the target processing sequences corresponding to the multiple target abnormal metering devices in the region cluster are finally determined through iterative operation, so that the time cost corresponding to the region cluster is determined by combining the target processing sequences and the respective installation addresses of the multiple target abnormal metering devices.

In the embodiment of the present application, the time cost corresponding to the regional cluster refers to the time required for completing the processing of each target abnormal metering device in the regional cluster. Since the installation addresses of the respective plurality of target abnormality measuring devices in the regional cluster are determined, the target processing order is determined, and thus the time required to travel through the vehicle to the installation addresses of the respective plurality of target abnormality measuring devices is also determined. Since the type of each target abnormal meter is determined, the time required to replace each target abnormal meter is also determined. And combining the two times, so that the time cost corresponding to the regional cluster can be determined.

S52, determining a first region cluster corresponding to the maximum time cost and a second region cluster corresponding to the minimum time cost from the region clusters based on the time costs corresponding to the region clusters.

The greater the time cost, the longer the time required for the plurality of abnormal measuring equipment included in the regional cluster to complete processing; the smaller the time cost, the shorter the time required for the plurality of abnormal meter devices included in the regional cluster to complete processing.

In one possible implementation manner, it may be determined first whether the time difference required for processing by the plurality of abnormal metering devices included in each regional cluster is large, if the time difference is not large, updating of the processing resources and the processing sequence may not be performed, and if the time difference is large, updating of the processing resources and the processing sequence may be performed.

Specifically, first, a time cost standard deviation among a plurality of regional clusters is determined based on the time costs corresponding to the plurality of regional clusters. And under the condition that the time cost standard deviation is larger than a preset standard deviation threshold value, determining a first region cluster and a second region cluster from the region clusters based on the time cost corresponding to each of the region clusters.

S53, determining a first updating processing resource of the first area cluster and a second updating processing resource of the second area cluster based on the maximum time cost and the minimum time cost.

In order to balance processing resources, the processing resources are distributed more reasonably, and the time required for processing the plurality of abnormal metering devices included in each regional cluster is relatively close, so that a first regional cluster corresponding to the maximum time cost and a second regional cluster corresponding to the minimum time cost can be determined from the plurality of regional clusters, and the processing resources distributed by the first regional cluster and the second regional cluster are updated.

The first updating processing resource is the sum of the processing resources of the first regional cluster and the target processing resource, and the second updating processing resource is the difference between the processing resources of the second regional cluster and the target processing resource; the target processing resources are a subset of the processing resources of the second regional cluster.

That is, a part of the processing resources (i.e., target processing resources) in the processing resources of the second regional cluster is allocated to the first regional cluster, so that the processing resources of the first regional cluster are increased. The processing resources of the first region cluster are increased, and the time required for processing completion of the plurality of abnormal measuring devices included in the first region cluster can be reduced to some extent.

S54, based on the first updating processing resource, updating the processing sequence corresponding to the abnormal metering equipment included in the first area cluster.

For the first area cluster, after determining the first update processing resource, the processing sequence corresponding to the abnormal measurement device included in the first area cluster may be redetermined, and the implementation manner of determining the processing sequence may be referred to the related description of the embodiment of fig. 4, which is not repeated herein.

S55, updating the processing sequence corresponding to the abnormal metering equipment included in the second region cluster based on the second updating processing resource.

For the second area cluster, after determining the second update processing resource, the processing sequence corresponding to the abnormal measurement device included in the second area cluster may be redetermined, and the implementation manner of determining the processing sequence may be referred to the related description of the embodiment of fig. 4, which is not repeated herein.

A summary of the solution according to the embodiments of the present application is presented below in conjunction with fig. 6.

Fig. 6 is a second flowchart of a processing method of the metering device according to the embodiment of the present application, as shown in fig. 6, including:

s601, determining a first type metering device and a second type metering device.

The first type metering equipment is metering equipment in an abnormal state, and a metering equipment list to be replaced can be generated aiming at the first type metering equipment, wherein the metering equipment list to be replaced comprises the identification, equipment parameter information, abnormal state judgment basis, installation address and other information of the first type metering equipment.

The second type of metering equipment is metering equipment with an abnormality index value larger than a preset index threshold value, and an alarm metering equipment list can be generated aiming at the second type of metering equipment, wherein the alarm metering equipment list comprises the identification, equipment parameter information, alarm judging basis, installation address and other information of the second type of metering equipment.

S602, performing region clustering processing on the first type metering equipment and the second type metering equipment to obtain a plurality of region clusters.

And combining the metering equipment list to be replaced and the warning metering equipment list to generate a comprehensive demand list, wherein the comprehensive demand list comprises all abnormal metering equipment, the first type of metering equipment is given higher abnormal weight, and the second type of metering equipment is given lower abnormal weight.

And then carrying out region clustering, wherein the abnormal metering equipment included in different region clusters is different.

S603, acquiring total processing resources.

The total processing resources include the number of vehicles available, technicians, and the like.

S604, processing resources are allocated to each regional cluster.

Processing resources may be allocated based on the needs of each regional cluster, the needs being proportional to the allocated processing resources. For example, a total installation time period of the abnormal meter device included in each regional cluster may be calculated, and the processing resources may be allocated based on the total installation time period.

S605, randomly generating a first generation population.

A plurality of first generation population individuals are randomly generated, and the fitness of different processing sequences is different corresponding to a plurality of processing sequences.

S606, performing iterative operation to generate a next generation population of the current generation population.

The next generation population is generated by inheritance, intersection, mutation and the like, so that the next generation population is continuously and iteratively updated.

S607, judging whether the iteration termination condition is satisfied, if yes, executing S608, otherwise executing S606.

The iteration termination condition comprises that the iteration times reach a preset iteration times threshold value, and no more optimal population individuals appear in the successive R generations. And when the iteration termination condition is not reached, repeating the iteration process, and when the iteration termination condition is reached, terminating the iteration process.

S608, it is determined whether or not the resource allocation needs to be adjusted, if so, S604 is executed, and if not, S609 is executed.

Whether or not the resource configuration needs to be adjusted can be determined by calculating the time-cost standard deviation between the plurality of regional clusters. If the time cost standard deviation among the plurality of area clusters is larger than the preset standard deviation threshold, partial processing resources of the second area cluster corresponding to the minimum time cost are distributed to the first area cluster corresponding to the maximum time cost, so that the processing resource distribution is more balanced. And then correspondingly updating the processing sequence corresponding to the abnormal metering equipment included in the first area cluster and the processing sequence corresponding to the abnormal metering equipment included in the second area cluster.

S609, generating a final processing scheme.

The final processing scheme comprises processing resources and processing sequences allocated by each regional cluster.

In summary, according to the scheme of the embodiment of the application, the abnormal metering equipment close to the area cluster is distributed to the same area cluster through the area cluster processing, batch processing is facilitated, then the target processing sequence corresponding to each area cluster is determined, and the distribution and the processing of the processing resources are carried out by taking the area cluster as a unit.

The processing device of the metering device provided by the application is described below, and the processing device of the metering device described below and the processing method of the metering device described above can be referred to correspondingly.

Fig. 7 is a schematic structural diagram of a processing device of a metering device according to an embodiment of the present application, as shown in fig. 7, where the device includes:

an identifying module 71, configured to determine a plurality of abnormal metering devices among the plurality of metering devices, where the abnormal metering devices include a first type metering device in an abnormal state, and a second type metering device having an abnormal index value greater than a preset index threshold;

the clustering module 72 is configured to perform area clustering on the plurality of abnormal metering devices based on distances between every two abnormal metering devices in the plurality of abnormal metering devices, so as to obtain a plurality of area clusters;

An allocation module 73, configured to determine processing resources allocated to each of the plurality of regional clusters based on the number and types of abnormal metrology devices included in each of the plurality of regional clusters;

the processing module 74 is configured to determine, for each area cluster, a target processing order corresponding to a plurality of target abnormal measurement devices based on respective installation addresses of the target abnormal measurement devices included in the area cluster, where the target processing order is used to instruct processing resources based on the area cluster to process the plurality of target abnormal measurement devices.

In one possible implementation, the clustering module 72 is specifically configured to:

In one possible implementation, the processing module 74 is specifically configured to:

In one possible implementation, the processing module 74 is further configured to:

Fig. 8 illustrates a physical structure diagram of an electronic device, as shown in fig. 8, which may include: processor 810, communication interface (Communications Interface) 820, memory 830, and communication bus 840, wherein processor 810, communication interface 820, memory 830 accomplish communication with each other through communication bus 840. The processor 810 may invoke logic instructions in the memory 830 to perform a method of processing of a metering device, the method comprising: determining a plurality of abnormal metering devices in the plurality of metering devices, wherein the abnormal metering devices comprise first metering devices in abnormal states and second metering devices with abnormal index values larger than a preset index threshold; based on the distances between every two abnormal metering devices in the plurality of abnormal metering devices, carrying out region clustering processing on the plurality of abnormal metering devices to obtain a plurality of region clusters; determining processing resources allocated to each of the plurality of regional clusters based on the number and types of abnormal metering devices each of the plurality of regional clusters includes; for each region cluster, determining a target processing sequence corresponding to a plurality of target abnormal metering devices based on respective installation addresses of the target abnormal metering devices included in the region cluster, wherein the target processing sequence is used for indicating processing resources based on the region cluster to process the plurality of target abnormal metering devices.

Further, the logic instructions in the memory 830 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

In another aspect, the present application also provides a computer program product comprising a computer program storable on a non-transitory computer readable storage medium, the computer program when executed by a processor being capable of performing a method of processing a metrology device provided by the methods described above, the method comprising: determining a plurality of abnormal metering devices in the plurality of metering devices, wherein the abnormal metering devices comprise first metering devices in abnormal states and second metering devices with abnormal index values larger than a preset index threshold; based on the distances between every two abnormal metering devices in the plurality of abnormal metering devices, carrying out region clustering processing on the plurality of abnormal metering devices to obtain a plurality of region clusters; determining processing resources allocated to each of the plurality of regional clusters based on the number and types of abnormal metering devices each of the plurality of regional clusters includes; for each region cluster, determining a target processing sequence corresponding to a plurality of target abnormal metering devices based on respective installation addresses of the target abnormal metering devices included in the region cluster, wherein the target processing sequence is used for indicating processing resources based on the region cluster to process the plurality of target abnormal metering devices.

In yet another aspect, the present application also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform a method of processing a metrology device provided by the methods described above, the method comprising: determining a plurality of abnormal metering devices in the plurality of metering devices, wherein the abnormal metering devices comprise first metering devices in abnormal states and second metering devices with abnormal index values larger than a preset index threshold; based on the distances between every two abnormal metering devices in the plurality of abnormal metering devices, carrying out region clustering processing on the plurality of abnormal metering devices to obtain a plurality of region clusters; determining processing resources allocated to each of the plurality of regional clusters based on the number and types of abnormal metering devices each of the plurality of regional clusters includes; for each region cluster, determining a target processing sequence corresponding to a plurality of target abnormal metering devices based on respective installation addresses of the target abnormal metering devices included in the region cluster, wherein the target processing sequence is used for indicating processing resources based on the region cluster to process the plurality of target abnormal metering devices.

The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.

From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims

1. A method of processing a metering device, comprising:

2. The method of claim 1, wherein the performing area clustering on the plurality of abnormal metering devices based on the distances between every two abnormal metering devices to obtain a plurality of area clusters includes:

3. The method according to claim 1 or 2, wherein the determining, based on the installation addresses of each of the plurality of target abnormal metering devices included in the regional cluster, a target processing order corresponding to the plurality of target abnormal metering devices includes:

4. The method of claim 3, wherein generating the next generation population based on respective fitness of a plurality of current generation population individuals included in the current generation population comprises:

5. The method of claim 4, wherein the processing the plurality of current generation population individuals based on their respective fitness to generate a candidate population comprises:

6. The method according to claim 1 or 2, characterized in that the method further comprises:

7. The method of claim 6, wherein determining a first region cluster corresponding to a maximum time cost and a second region cluster corresponding to a minimum time cost from the plurality of region clusters based on the respective time costs of the plurality of region clusters comprises:

8. A processing apparatus for a metering device, comprising:

9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of processing a metering device according to any one of claims 1 to 7 when the program is executed by the processor.

10. A non-transitory computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements a method of processing a metering device according to any one of claims 1 to 7.