CN109615086B - Method and system for generating operation and maintenance auxiliary label - Google Patents
Method and system for generating operation and maintenance auxiliary label Download PDFInfo
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- CN109615086B CN109615086B CN201811183792.5A CN201811183792A CN109615086B CN 109615086 B CN109615086 B CN 109615086B CN 201811183792 A CN201811183792 A CN 201811183792A CN 109615086 B CN109615086 B CN 109615086B
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Abstract
The invention relates to a power supply equipment operation and maintenance technology, in particular to a method and a system for generating an operation and maintenance auxiliary label, which comprises the following steps: A) importing equipment ledger and historical information; B) collecting and returning equipment information and label setting when operation and maintenance personnel maintain on site; C) establishing a neural network model and training; D) after subsequent similar equipment is maintained on site, operation and maintenance personnel upload equipment information and labels, update the equipment information and substitute the updated equipment information into the neural network model in the step C to obtain model labels, and count the accuracy of the model in a period; E) and if the model accuracy reaches a set threshold value in the statistical period, generating labels for all similar devices. The invention has the beneficial effects that: by generating and accumulating the operation and maintenance labels, operation and maintenance-oriented information guidance and experience reference can be provided for subsequent same equipment and similar equipment, the operation and maintenance efficiency is improved in a targeted and auxiliary mode, and the problem of heavy operation and maintenance of the power grid at present is solved.
Description
Technical Field
The invention relates to the power supply equipment operation and maintenance technology, in particular to a method and a system for generating an operation and maintenance auxiliary label.
Background
The intelligent construction of the power grid makes great progress, and the automatic control equipment occupies the main part of the power supply equipment. However, the automation equipment has the characteristics of strong specialization and easy failure, which leads to the increase of the operation and maintenance tasks of the power grid. At present, the average daily fault work orders generated by the provincial power grid exceed 7000 work orders, and the corresponding number of operation and maintenance personnel and skill training are not followed with rapid development, so that the operation and maintenance personnel are heavy in task, and the operation and maintenance efficiency is reduced due to the fact that the operation and maintenance personnel are not familiar with new equipment and lack of experience. The contradiction between the urgent operation and maintenance requirements and the relatively low operation and maintenance efficiency is increasingly prominent. In the current work order processing, due to the lack of acquisition and analysis of field data of equipment, part of work orders which do not need to be dispatched are dispatched, and operation and maintenance resources are wasted. The same problem is repeatedly checked and solved due to lack of accumulation and propagation of the solution experience of part of technical problems, which wastes time and labor. How to rapidly improve the operation and maintenance efficiency of the power grid becomes the first key task of the operation and maintenance construction of the power grid.
Chinese patent CN206639241U, published 2017, 11, 4, a passive ultrahigh frequency RFID special tag for operation and maintenance of electrical equipment, comprising an upper shell and a lower shell, wherein an ultrahigh frequency RFID chip and an antenna are arranged in the upper shell, and a baffle is arranged between the upper shell and the lower shell; the upper shell is sleeved on the opening of the lower shell, the upper shell and the lower shell form a closed shell, and the ultrahigh frequency RFID chip is an ultrahigh frequency RFID chip for power equipment with an equipment identification function and a data storage function; the outer side end of the upper shell is provided with a sinking platform, the utility model provides a passive ultrahigh frequency RFID special label for operation and maintenance of electrical equipment, which has a novel structure; the ultrahigh frequency RFID special label for the operation and maintenance of the power equipment is specially designed, so that the functions of the ultrahigh frequency RFID special label for the operation and maintenance of the power equipment can be met, clear code marking and data storage management of the operation and maintenance of the power equipment are realized, and the management application efficiency for the operation and maintenance of the power equipment is improved; in addition the utility model discloses support multiple mounting means to include that gum pastes mode, screw riveting mode, metal ribbon tie up mode etc.. The special label body is required to be manufactured for each power supply device, the cost is high, effective collection and accumulation are lacked for data, and the requirement for improving the operation and maintenance efficiency is difficult to meet.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the technical problem of inexperienced data accumulation of the current operation and maintenance activities is lacking. The operation and maintenance auxiliary label generation method and system for the equipment are provided, wherein the operation and maintenance label is oriented to the equipment, and the operation and maintenance efficiency can be effectively improved in an auxiliary mode.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a method for generating an operation and maintenance auxiliary label comprises the following steps: A) establishing a data server, and importing account information and historical information of regional power grid power supply equipment; B) when the operation and maintenance personnel maintain on site, acquiring equipment information and setting a label, and transmitting the information and the label back to the data server; C) establishing a neural network model, and training the neural network model by using the first N labels and the equipment information as sample data after the first N labels are associated with the equipment information; D) after subsequent similar equipment is maintained on site, uploading equipment information and a label by operation and maintenance personnel, updating the equipment information, substituting the updated equipment information into the neural network model in the step C to obtain a model label, if the model label is the same as the label uploaded by the operation and maintenance personnel, adding 1 to the correct times of the model, otherwise, adding 1 to the wrong times of the model, and taking the label uploaded by the operation and maintenance personnel as a target result to adjust the neural network model; E) and if the model accuracy reaches a set threshold value in the statistical period, substituting the information of all similar devices into the neural network model, and taking the output result of the neural network model as the label of the corresponding device.
Preferably, when the accuracy of the neural network model corresponding to a certain label is still lower than the set threshold after training the previous (N + M) sample data, the following steps are performed: C1) taking H sample data from the previous (N + M) sample data, distinguishing a data volume field and a state quantity field in the sample data, converting the data volume field into the state quantity field by segmentation processing according to a numerical value interval, splitting all the state quantity fields into N Boolean value fields, wherein N is the number of values which can be taken by the state quantity field; C2) adding, subtracting, dividing and multiplying results of the data volume fields of the sample data into new data fields; C3) calculating the similarity between each field of H sample data after being processed, and taking the field with the similarity higher than a set threshold value as a reference field; c4) And D, acquiring the reference fields of the previous (N + M) sample data, associating the reference fields with the labels, then retraining the neural network model as new sample data, and then continuing to execute from the step D.
Preferably, the tags reflect operation and maintenance oriented equipment feature information, and include presence tags and process tags, the presence tags represent field data features, and the process tags represent features related to historical operating states of equipment.
Preferably, the status label is directly judged and generated by operation and maintenance personnel according to the equipment field data and the standing book data.
Preferably, when the neural network model is trained, the current label is associated with the latest machine account information of the equipment and then used as sample data to train the neural network model.
Preferably, the characteristic represented by the process tag is a characteristic formed by the historical state of the equipment and the field state together, and the process tag is generated by operation and maintenance personnel according to the field state data of the equipment and the historical information of the equipment.
Preferably, when the neural network model is trained, the process label is associated with the equipment standing book information and the historical maintenance data to serve as sample data, and the neural network model is trained.
An operation and maintenance auxiliary tag generation system is suitable for the operation and maintenance auxiliary tag generation method, and includes a handheld terminal, a memory, a processor, and a communication device, where the communication device and the memory are connected with the processor, the handheld terminal is connected with the processor through the communication device, the handheld terminal accepts data input by an operation and maintenance person and transmits the data to the processor, and the processor executes the following steps: A) establishing a data server, and importing account information and historical information of regional power grid power supply equipment; B) when operation and maintenance personnel maintain on site, the handheld terminal receives the equipment information and the label setting, and transmits the information and the label back to the data server; C) establishing a neural network model, and training the neural network model by using the first N labels and the equipment information as sample data after the first N labels are associated with the equipment information; D) after subsequent similar equipment is maintained on site, operation and maintenance personnel upload equipment information and labels through a handheld terminal, update the equipment information and substitute the updated equipment information into the neural network model in the step C to obtain a model label, if the model label is the same as the label uploaded by the operation and maintenance personnel, the correct times of the model is increased by 1, otherwise, the wrong times of the model is increased by 1, and the label uploaded by the operation and maintenance personnel is used as a target result to adjust the neural network model; E) and if the model accuracy reaches a set threshold value in the statistical period, substituting the information of all similar devices into the neural network model, and taking the output result of the neural network model as the label of the corresponding device.
The substantial effects of the invention are as follows: by generating and accumulating the operation and maintenance labels, operation and maintenance-oriented information guidance and experience reference can be provided for subsequent same equipment and similar equipment, the operation and maintenance efficiency is improved in a targeted and auxiliary mode, and the problem of heavy operation and maintenance of the power grid at present is solved.
Drawings
Fig. 1 is a diagram of an operation and maintenance auxiliary label generation system.
Fig. 2 is a flow chart of a method for generating an operation and maintenance auxiliary tag.
FIG. 3 is a block diagram of a method flow for generating new sample data.
Fig. 4 is an example of label-assisted operation and maintenance.
The mobile terminal comprises a base station, a mobile terminal and a communication device, wherein the base station comprises 100, a memory, 200, a processor, 300, the communication device, 400 and the handheld terminal.
Detailed Description
The following provides a more detailed description of the present invention, with reference to the accompanying drawings.
As shown in fig. 1, in order to generate a system structure diagram of an operation and maintenance auxiliary tag, a communication device 300 and a memory 100 are connected to a processor 200, a handheld terminal 400 is connected to the processor 200 through the communication device 300, the handheld terminal 400 receives data input by an operation and maintenance worker and transmits the data to the processor 200, and the operation and maintenance worker receives device information and tag setting by the handheld terminal 400 and transmits the information and tag back to a data server during field maintenance.
As shown in fig. 2, a flow chart of a method for generating an operation and maintenance auxiliary tag includes the following steps: A) establishing a data server, and importing account information and historical information of regional power grid power supply equipment; B) when the operation and maintenance personnel maintain on site, acquiring equipment information and setting a label, and transmitting the information and the label back to the data server; C) establishing a neural network model, and training the neural network model by using the first N labels and the equipment information as sample data after the first N labels are associated with the equipment information; D) after subsequent similar equipment is maintained on site, uploading equipment information and a label by operation and maintenance personnel, updating the equipment information, substituting the updated equipment information into the neural network model in the step C to obtain a model label, if the model label is the same as the label uploaded by the operation and maintenance personnel, adding 1 to the correct times of the model, otherwise, adding 1 to the wrong times of the model, and taking the label uploaded by the operation and maintenance personnel as a target result to adjust the neural network model; E) and if the model accuracy reaches a set threshold value in the statistical period, substituting the information of all similar equipment into the neural network model, and taking the output result of the neural network model as the label of the corresponding equipment.
As shown in fig. 3, to generate a new sample data, when the accuracy of the neural network model corresponding to a certain label is still lower than the set threshold after training the previous (N + M) sample data, the following steps are performed: C1) taking H sample data from the previous (N + M) sample data, distinguishing a data volume field and a state quantity field in the sample data, carrying out normalization processing on the data volume field, and splitting the state quantity field into N Boolean value fields, wherein N is the number of values which can be taken by the state quantity field; C2) adding, subtracting, dividing and multiplying results of the data volume fields of the sample data into new data fields; C3) calculating the similarity between each field of H sample data after being processed, and taking the field with the similarity higher than a set threshold value as a reference field; c4) And D, acquiring the reference fields of the previous (N + M) sample data, associating the reference fields with the labels, then retraining the neural network model as new sample data, and then continuing to execute from the step D. The data size field normalization processing method is that the maximum value of the data size field in H sample data is taken, and the ratio of the value of the data size field to the maximum value is used as the value of the field after normalization in each sample data. Manually dividing intervals such as high, middle and low intervals according to the data value distribution of the data volume field, and then replacing the data values by the interval names to convert the data volume field into the state volume field. The state quantity field split is exemplified as follows: if the working state field of a certain device comprises an opening state, a closing state and a fault maintenance state, the field is divided into three fields of opening, closing and fault maintenance, the three fields are Boolean values, after certain field operation and maintenance, the device state is opening, the value of the opening state field is true, and the value of the closing and fault maintenance state field is false. The partial data values have no meaning, but the intervals of the partial data values have meaning, if a certain transformer power supply user is 300 households, and another transformer power supply user is 200 households, the two transformers are both provided with a label of large fault influence range, and operation and maintenance personnel are guided to preferably repair the transformer. If the numerical values 300 and 200 are introduced into the neural network model, the neural network model is difficult to adjust to appropriate coefficients, the results are similar after two numerical operations, and after the data value interval is divided, the numerical values 200 and 300 are divided into the same numerical value interval, so that the difficulty of adjusting parameters of the neural network can be reduced, and the results are more accurate. After the data are processed, the device data are all Boolean values, fields which are not convenient to express by numerical values can be quickly and conveniently converted into data forms which can be processed by a neural network model, and the training efficiency is improved.
The label reflects equipment characteristic information facing operation and maintenance, the label comprises a current label and a process label, the current label represents field data characteristics, and the process label represents characteristics related to historical working states of equipment. The current label is directly judged and generated by operation and maintenance personnel through equipment field data and standing book data. And when the neural network model is trained, associating the current label with the latest machine account information of the equipment to serve as sample data, and training the neural network model.
The characteristic of the process label is a characteristic formed by the historical state of the equipment and the field state together, and the process label is generated by operation and maintenance personnel according to the field state data of the equipment and the historical information of the equipment. And when the neural network model is trained, the process label is associated with the equipment standing book information and the historical maintenance data to serve as sample data, and the neural network model is trained.
As shown in fig. 4, for an example of explaining how the label assists operation and maintenance, the work order 1 is a user power failure fault actively reported by the smart meter, but after the user is checked by the operation and maintenance personnel, the "arrearage" label is associated with the user, so that the power failure fault uploaded by other smart devices of the user will display the "arrearage" label, and the work order processing flow personnel processes the work order according to no dispatch of the work order, so that the operation and maintenance resources are saved. The work order 2 is a power failure fault of the whole platform area, however, operation and maintenance personnel or service personnel associate a label of 'service expansion construction' with the platform area in advance, the label is displayed on the power failure fault work order of the platform area reported by all intelligent devices in the platform area, the work order processing flow personnel hang up to process according to the work order, and the situation is checked and processed after the service expansion construction is finished. The work order 3 is a user equipment fault, and the operation and maintenance personnel associate the user with a label with a high preferred processing level when dispatching the on-site operation and maintenance last time, so that the fault work order can be dispatched preferentially for processing, the pertinence and the overall operation and maintenance efficiency are improved, and the loss is reduced.
The above-described embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the spirit of the invention as set forth in the claims.
Claims (9)
1. A generation method of an operation and maintenance auxiliary label is characterized in that,
the method comprises the following steps:
A) establishing a data server, and importing account information and historical information of regional power grid power supply equipment;
B) when the operation and maintenance personnel maintain on site, acquiring equipment information and setting a label, and transmitting the information and the label back to the data server;
C) establishing a neural network model, and training the neural network model by using the first N labels and the equipment information as sample data after the first N labels are associated with the equipment information;
D) after subsequent similar equipment is maintained on site, uploading equipment information and a label by operation and maintenance personnel, updating the equipment information, substituting the updated equipment information into the neural network model in the step C to obtain a model label, if the model label is the same as the label uploaded by the operation and maintenance personnel, adding 1 to the correct times of the model, otherwise, adding 1 to the wrong times of the model, and taking the label uploaded by the operation and maintenance personnel as a target result to adjust the neural network model;
E) if the model accuracy reaches a set threshold value in the statistical period, substituting the information of all similar devices into the neural network model, and taking the output result of the neural network model as the label of the corresponding device;
when the accuracy of a neural network model corresponding to a certain label is lower than a set threshold value in a statistical period after N + M sample data are trained, executing the following steps:
C1) taking H sample data from the N + M sample data, distinguishing a data volume field and a state quantity field in the sample data, converting the data volume field into the state quantity field by sectional processing according to a numerical value interval, splitting all the state quantity fields into N Boolean value fields, wherein N is the number of values which can be taken by the state quantity fields;
C2) adding, subtracting, dividing and multiplying results of the data volume fields of the sample data into new data fields;
C3) calculating the similarity between each field of H sample data after being processed, and taking the field with the similarity higher than a set threshold value as a reference field;
C4) and D, acquiring reference fields of the N + M sample data, associating the reference fields with the tags, using the reference fields as new sample data to retrain the neural network model, and returning to the step D to continue executing.
2. The method for generating an operation and maintenance auxiliary label according to claim 1,
the label reflects operation and maintenance-oriented equipment characteristic information, the label comprises a current label and a process label, the current label represents field data characteristics, and the process label represents characteristics related to historical working states of equipment.
3. The method for generating an operation and maintenance auxiliary label according to claim 2,
the current label is directly judged and generated by operation and maintenance personnel through equipment field data and standing book data.
4. The method for generating an operation and maintenance auxiliary label according to claim 3,
and when the neural network model is trained, associating the current label with the latest machine account information of the equipment and then using the current label as sample data to train the neural network model.
5. The method for generating an operation and maintenance auxiliary label according to claim 2,
the process label is characterized by being a feature formed by the historical state of the equipment and the field state together, and the process label is generated by operation and maintenance personnel according to the field state data of the equipment and the historical information of the equipment.
6. The method for generating an operation and maintenance auxiliary label according to claim 3,
the process label is characterized by being a feature formed by the historical state of the equipment and the field state together, and the process label is generated by operation and maintenance personnel according to the field state data of the equipment and the historical information of the equipment.
7. The method for generating an operation and maintenance auxiliary label according to claim 2,
and when the neural network model is trained, associating the process label with the equipment standing book information and the historical maintenance data to serve as sample data, and training the neural network model.
8. The method for generating an operation and maintenance auxiliary label according to claim 3,
and when the neural network model is trained, associating the process label with the equipment standing book information and the historical maintenance data to serve as sample data, and training the neural network model.
9. An operation and maintenance auxiliary label generation system, which is applied to the operation and maintenance auxiliary label generation method according to any one of claims 1-8,
the system comprises a handheld terminal, a memory, a processor and a communication device, wherein the communication device and the memory are connected with the processor, the handheld terminal is connected with the processor through the communication device, the handheld terminal receives data input by operation and maintenance personnel and transmits the data to the processor, and the processor executes the following steps:
A) establishing a data server, and importing account information and historical information of regional power grid power supply equipment;
B) when operation and maintenance personnel maintain on site, the handheld terminal receives the equipment information and the label setting, and transmits the information and the label back to the data server;
C) establishing a neural network model, and training the neural network model by using the first N labels and the equipment information as sample data after the first N labels are associated with the equipment information;
D) after subsequent similar equipment is maintained on site, operation and maintenance personnel upload equipment information and labels through a handheld terminal, update the equipment information and substitute the updated equipment information into the neural network model in the step C to obtain a model label, if the model label is the same as the label uploaded by the operation and maintenance personnel, the correct times of the model is increased by 1, otherwise, the wrong times of the model is increased by 1, and the label uploaded by the operation and maintenance personnel is used as a target result to adjust the neural network model;
E) if the model accuracy reaches a set threshold value in the statistical period, substituting the information of all similar devices into the neural network model, and taking the output result of the neural network model as the label of the corresponding device;
when the accuracy of a neural network model corresponding to a certain label is lower than a set threshold value in a statistical period after N + M sample data are trained, executing the following steps:
C1) taking H sample data from the N + M sample data, distinguishing a data volume field and a state quantity field in the sample data, converting the data volume field into the state quantity field by sectional processing according to a numerical value interval, splitting all the state quantity fields into N Boolean value fields, wherein N is the number of values which can be taken by the state quantity fields;
C2) adding, subtracting, dividing and multiplying results of the data volume fields of the sample data into new data fields;
C3) calculating the similarity between each field of H sample data after being processed, and taking the field with the similarity higher than a set threshold value as a reference field;
C4) and D, acquiring reference fields of the N + M sample data, associating the reference fields with the tags, using the reference fields as new sample data to retrain the neural network model, and returning to the step D to continue executing.
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