CN115630143A - Recommendation method and device for fault handling scheme, terminal equipment and storage medium - Google Patents
Recommendation method and device for fault handling scheme, terminal equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a recommendation method, a recommendation device, terminal equipment and a storage medium of a fault handling scheme, wherein the method comprises the following steps: acquiring a fault description text, and processing the fault description text into a numerical characteristic vector based on a preset processing model; matching the numerical characteristic vector with all fault processing schemes in a preset scheme library, and calculating semantic similarity between the numerical characteristic vector and each fault processing scheme, wherein all fault processing schemes are converted into vectors by using the processing model in advance; and determining a target fault processing scheme based on the semantic similarity, and performing fault processing based on the target fault processing scheme. The invention can realize vectorization of the fault description text, determine the fault processing scheme based on the similarity between the vectors and realize efficient recommendation of the optimal scheme.
Description
Technical Field
The present invention relates to the field of fault handling technologies, and in particular, to a method and an apparatus for recommending a fault handling scheme, a terminal device, and a storage medium.
Background
The fault disposal of the numerical control machine tool in the machining process mostly depends on the experience of operators, and the subjectivity is strong. When the numerical control machine tool fails, related solutions are basically manually searched in a file system, so that the efficiency is low, and the failure is not timely treated.
Thus, there is a need for improvements and enhancements in the art.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a method, an apparatus, a terminal device and a storage medium for recommending a fault handling scheme, aiming at solving the problems in the prior art that the efficiency of determining a fault handling scheme is low and the fault handling is not timely.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
in a first aspect, the present invention provides a method for recommending a fault handling scheme, wherein the method includes:
acquiring a fault description text, and processing the fault description text into a numerical characteristic vector based on a preset processing model;
matching the numerical characteristic vector with all fault processing schemes in a preset scheme library, and calculating semantic similarity between the numerical characteristic vector and each fault processing scheme, wherein all fault processing schemes are converted into vectors by using the processing model in advance;
and determining a target fault processing scheme based on the semantic similarity, and performing fault processing based on the target fault processing scheme.
In one implementation, the obtaining a fault description text and processing the fault description text into a numerical feature vector based on a preset processing model includes:
carrying out life cycle monitoring on the operation process of the numerical control machine tool, and storing monitoring data at different time intervals;
performing anomaly analysis on the monitoring data, determining fault data in the monitoring data, and acquiring a fault description text corresponding to the fault data;
and calling a preset processing model, and inputting the fault description text into the processing model to obtain the numerical characteristic vector, wherein the processing model is a doc2vec model.
In an implementation manner, the obtaining a fault description text corresponding to the fault data includes:
performing semantic recognition on the fault data to obtain a semantic recognition result;
and performing text conversion on the semantic recognition result to obtain the fault description text.
In one implementation, the matching the numerical feature vector with all fault handling schemes in a preset scheme library and calculating semantic similarity between the numerical feature vector and each fault handling scheme includes:
acquiring fault vector data corresponding to each fault processing scheme, and sequentially matching the numerical characteristic vector with each fault vector data;
determining semantic similarity between the numerical feature vector and each fault vector data.
In one implementation, the determining semantic similarity between the numerical feature vector and each fault vector data includes:
respectively calculating Euclidean distances between the numerical characteristic vectors and each fault vector data;
and determining the semantic similarity based on the Euclidean distance.
In one implementation, the determining a target failure handling scheme based on the semantic similarity includes:
and acquiring the fault vector data corresponding to the shortest Euclidean distance, and taking the fault processing scheme corresponding to the fault vector data with the shortest Euclidean distance as the target fault processing scheme.
In one implementation, the scheme library is constructed in a manner including:
the method comprises the steps of carding typical fault types in the machining process of the numerical control machine tool in advance and fault names corresponding to each typical fault type, and recording fault processing schemes corresponding to each typical fault type in a text format;
saving a text by using a relational database, wherein fields are the typical fault type and the fault name, and values are the fault processing scheme;
and inputting the fault processing scheme into a preset processing model to obtain the fault vector data.
In a second aspect, an embodiment of the present invention further provides a device for recommending a fault handling scheme, where the device includes:
the text processing module is used for acquiring a fault description text and processing the fault description text into a numerical characteristic vector based on a preset processing model;
the vector matching module is used for matching the numerical characteristic vector with all fault processing schemes in a preset scheme library and calculating semantic similarity between the numerical characteristic vector and each fault processing scheme, wherein all the fault processing schemes are converted into vectors by using the processing model in advance;
and the scheme determining module is used for determining a target fault processing scheme based on the semantic similarity and carrying out fault processing based on the target fault processing scheme.
In a third aspect, an embodiment of the present invention further provides a terminal device, where the terminal device includes a memory, a processor, and a recommendation program of a fault handling scheme, which is stored in the memory and is executable on the processor, and when the processor executes the recommendation program of the fault handling scheme, the step of implementing the method for recommending a fault handling scheme according to any one of the foregoing schemes is implemented.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a recommendation program of a fault handling scheme, and when the recommendation program of the fault handling scheme is executed by a processor, the steps of the recommendation method of the fault handling scheme according to any one of the above schemes are implemented.
Has the advantages that: compared with the prior art, the invention provides a recommendation method of a fault processing scheme. And then, matching the numerical characteristic vector with all fault processing schemes in a preset scheme library, and calculating semantic similarity between the numerical characteristic vector and each fault processing scheme, wherein all fault processing schemes are converted into vectors by using the processing model in advance. And finally, determining a target fault processing scheme based on the semantic similarity, and performing fault processing based on the target fault processing scheme. The invention can realize vectorization of the fault description text, quickly determine the fault processing scheme based on the semantic similarity between the vectors, realize high-efficiency recommendation of the optimal scheme and ensure the timely processing of the fault.
Drawings
Fig. 1 is a flowchart illustrating a specific implementation of a method for recommending a fault handling scheme according to an embodiment of the present invention.
Fig. 2 is a schematic block diagram of a recommendation apparatus for a fault handling scheme according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and effects of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment provides a recommendation method for a fault handling scheme, which can implement vectorization of a fault description text based on the recommendation method, quickly determine the fault handling scheme based on semantic similarity between vectors, implement efficient recommendation of an optimal scheme, and ensure timely handling of faults. Specifically, the present embodiment first obtains a fault description text, and processes the fault description text into a numerical feature vector based on a preset processing model. And then, matching the numerical characteristic vector with all fault processing schemes in a preset scheme library, and calculating semantic similarity between the numerical characteristic vector and each fault processing scheme, wherein all fault processing schemes are converted into vectors by using the processing model in advance. And finally, determining a target fault processing scheme based on the semantic similarity, and performing fault processing based on the target fault processing scheme.
Exemplary method
The recommendation method of the fault handling scheme of the embodiment can be applied to terminal equipment, and the terminal equipment can be intelligent product terminals such as computers, intelligent televisions and mobile phones. In this embodiment, as shown in fig. 1, the method for recommending a failure handling scheme specifically includes the following steps:
and S100, acquiring a fault description text, and processing the fault description text into a numerical characteristic vector based on a preset processing model.
In specific application, the terminal device of the embodiment monitors the operation process of the full life cycle of the numerical control machine tool, so that monitoring data can be obtained, and the monitoring data can reflect all processing records of the numerical control machine tool in the operation process, including the use of a cutter, the switching of processing steps and even the adjustment of a processing surface. In order to ensure that the monitoring data is convenient to be called, in this embodiment, after the monitoring data is obtained, the monitoring data is stored in a specific storage area in a time period, for example, a plurality of storage areas are stored in a storage space atmosphere in advance, each storage area corresponds to the storage of the monitoring data in a time period, when the terminal device needs to analyze the monitoring data in a certain time period, the monitoring data in the corresponding time period can be directly found, and then the monitoring data is analyzed. In addition, after the monitoring data is acquired, it is necessary to determine whether the numerical control machine tool is in failure or not from the monitoring data.
In one implementation, the monitoring data includes various data, specifically including noise data, equipment temperature data, abnormal operation data and the like generated when the numerical control machine tool itself operates, and also includes data such as wear of a tool used on the numerical control machine tool. Therefore, the embodiment can classify and sort the monitoring data, which is mainly divided into three categories, wherein the first category is operation monitoring data, environment monitoring data and equipment monitoring data, and the equipment monitoring data includes monitoring data of the numerical control machine tool equipment and the cutter. Then, the embodiment performs special analysis on the three types of monitoring data, and performs exception analysis on the three types of monitoring data, so as to find out fault data in the three types of monitoring data. Specifically, in this embodiment, when performing an anomaly analysis on operation monitoring data, this embodiment may compare the operation monitoring data with an operation instruction in a user operation manual, find out operation non-specification data in the operation monitoring data, and further use the operation non-specification data as fault data. For the environmental monitoring data, the noise decibel and the equipment temperature in the environmental monitoring data are respectively matched with the preset standard noise range and the preset standard equipment temperature range, the abnormal noise data and the abnormal temperature data in the environmental monitoring data are determined, and the abnormal noise data and the abnormal temperature data are used as fault data. For the equipment monitoring data, the embodiment takes the data of abnormal interruption of the numerical control machine tool and the tool abrasion amount exceeding the abrasion threshold value in the equipment monitoring data as the fault data.
After the fault data is obtained, the embodiment can perform semantic recognition on the fault data to obtain a semantic recognition result. And then, performing text conversion on the semantic recognition result to obtain the fault description text. The embodiment converts the fault data into the fault description text, which is beneficial to analyzing the fault data in the subsequent steps, and the fault description text of the embodiment also more intuitively embodies the fault of the numerical control machine. Then, in this embodiment, a preset processing model is called, and the fault description text is input into the processing model to obtain the numerical feature vector. The preprocessing model of this embodiment is a doc2vec model, and the doc2vec model can convert the fault description text into a numerical feature vector.
Step S200, matching the numerical characteristic vector with all fault processing schemes in a preset scheme library, and calculating semantic similarity between the numerical characteristic vector and each fault processing scheme, wherein all fault processing schemes are converted into vectors by using the processing model in advance.
After the numerical characteristic vector is obtained, the present embodiment obtains a preset scheme library, and obtains all fault handling schemes. In this embodiment, all fault handling schemes in the scheme library are vector-transformed using the handling model in advance. Specifically, in the embodiment, when the scheme library is constructed, typical fault types and fault names corresponding to each typical fault type in the machining process of the numerical control machine tool are carded in advance, and a fault processing scheme corresponding to each typical fault type is recorded in a text format. Then, a relational database is used for storing texts, wherein fields are the typical fault type and the fault name, and values are the fault processing scheme. The relational database in the embodiment is a database established on the basis of a relational model, and data in the database is processed by means of mathematical concepts and methods such as set algebra and the like. Such as MySQL, postpresql. And finally, inputting the fault processing scheme into a preset processing model to obtain the fault vector data. The preprocessing model of this embodiment is a doc2vec model, and the model initializes the text of the fault handling scheme by using a pre-trained semantic vector, that is, each word corresponds to one initialization vector, and initializes the entire fault handling scheme to 1 document vector. Next, the model adopts a sliding window strategy, the window width is set to 3, and vectors of 3 words are sequentially extracted by using a sliding window from the first word of the text of the failure handling scheme. Then, 2 vectors at two sides and a document vector (document vector) are spliced into a matrix and used as input, the matrix is input into a multilayer neural network, and the vector is output and compared with a predicted intermediate word vector. And finally, performing back propagation by using the mean square error of the output vector and the intermediate word vector, and updating the neural network parameters and the document vector to obtain fault vector data corresponding to the fault processing scheme. Based on the above manner, the present embodiment can obtain the fault vector data in each fault handling scenario in the scenario library.
In this embodiment, after the numerical feature vector is obtained, the fault vector data corresponding to each fault processing scheme is obtained, and the numerical feature vector is sequentially matched with each fault vector data. Since each fault processing scheme is converted into corresponding fault vector data, the present embodiment may calculate the euclidean distance between the numerical feature vector and each fault vector data, where the euclidean distance may reflect the semantic similarity between the numerical feature vector and the fault vector data, and therefore, the present embodiment may determine the semantic similarity between the numerical feature vector and each fault vector data by calculating the euclidean distance. If the Euclidean distance between the numerical value feature vector and certain fault vector data is shorter, the semantic similarity between the numerical value feature vector and the fault vector data is larger, and if the Euclidean distance between the numerical value feature vector and certain fault vector data is longer, the semantic similarity between the numerical value feature vector and the fault vector data is smaller.
And S300, determining a target fault processing scheme based on the semantic similarity, and performing fault processing based on the target fault processing scheme.
Specifically, in this embodiment, after the euclidean distance between the numerical feature vector and each fault vector data is obtained, all the euclidean distances may be compared to find out the fault vector data with the shortest euclidean distance, where the semantic similarity between the fault vector data with the shortest euclidean distance and the numerical feature vector is the largest, and therefore, the fault processing scheme corresponding to the fault vector data with the shortest euclidean distance may be used as the target fault processing scheme in this embodiment. After the target fault processing scheme is obtained, the present embodiment may perform fault processing based on the target fault processing scheme to solve the fault of the numerical control machine.
In summary, in the present embodiment, a fault description text is first obtained, and the fault description text is processed into a numerical feature vector based on a preset processing model. And then, matching the numerical characteristic vector with all fault processing schemes in a preset scheme library, and calculating semantic similarity between the numerical characteristic vector and each fault processing scheme, wherein all fault processing schemes are converted into vectors by using the processing model in advance. And finally, determining a target fault processing scheme based on the semantic similarity, and performing fault processing based on the target fault processing scheme. The embodiment can realize vectorization of the fault description text, quickly determine the fault processing scheme based on the semantic similarity between the vectors, realize efficient recommendation of the optimal scheme and ensure timely processing of the fault.
Exemplary devices
Based on the foregoing embodiment, the present invention further provides a device for recommending a fault handling scheme, as shown in fig. 2, the device includes: a text processing module 10, a vector matching module 20, and a scheme determination module 30. Specifically, the text processing module 10 is configured to obtain a fault description text, and process the fault description text into a numerical feature vector based on a preset processing model. The vector matching module 20 is configured to match the numerical feature vector with all fault handling schemes in a preset scheme library, and calculate semantic similarity between the numerical feature vector and each fault handling scheme, where all fault handling schemes are converted into vectors using the processing model in advance. The scheme determining module 30 is configured to determine a target fault handling scheme based on the semantic similarity, and perform fault handling based on the target fault handling scheme.
In one implementation, the text processing module 10 includes:
the monitoring data acquisition unit is used for carrying out full-life cycle monitoring on the operation process of the numerical control machine tool and storing monitoring data at different time intervals;
the fault data analysis unit is used for carrying out exception analysis on the monitoring data, determining fault data in the monitoring data and acquiring a fault description text corresponding to the fault data;
and the fault data conversion unit is used for calling a preset processing model and inputting the fault description text into the processing model to obtain the numerical characteristic vector, wherein the processing model is a doc2vec model.
In one implementation, the failure data conversion unit includes:
the semantic recognition subunit is used for performing semantic recognition on the fault data to obtain a semantic recognition result;
and the text conversion subunit is used for performing text conversion on the semantic recognition result to obtain the fault description text.
In one implementation, the vector matching module 20 includes:
the data matching unit is used for acquiring fault vector data corresponding to each fault processing scheme and sequentially matching the numerical characteristic vector with each fault vector data;
and the similarity determining unit is used for determining the semantic similarity between the numerical characteristic vector and each fault vector data.
In one implementation, the similarity determination unit includes:
the Euclidean distance calculating subunit is used for calculating the Euclidean distance between the numerical characteristic vector and each fault vector data respectively;
and the similarity determining subunit is used for determining the semantic similarity based on the Euclidean distance.
In one implementation, the scheme determination module 30 includes:
and the Euclidean distance comparison unit is used for acquiring the fault vector data corresponding to the shortest Euclidean distance, and taking the fault processing scheme corresponding to the fault vector data with the shortest Euclidean distance as the target fault processing scheme.
In one implementation, the apparatus further includes a solution library construction module, the solution library construction module including:
the data carding unit is used for carding typical fault types in the machining process of the numerical control machine tool and fault names corresponding to each typical fault type in advance, and recording fault processing schemes corresponding to each typical fault type in a text format;
the data storage unit is used for storing a text by using a relational database, the fields are the typical fault type and the fault name, and the value is the fault processing scheme;
and the data processing unit is used for inputting the fault processing scheme into a preset processing model to obtain the fault vector data.
The execution principle of each functional module in the recommendation apparatus of the fault handling scheme of this embodiment is the same as the execution process of each step in the foregoing method embodiment, and details are not described here.
Based on the above embodiment, the present invention further provides a terminal device, and a schematic block diagram of the terminal device may be as shown in fig. 3. The terminal device may include one or more processors 100 (only one shown in fig. 3), a memory 101, and a computer program 102, e.g., a recommended program of a fault handling scheme, stored in the memory 101 and executable on the one or more processors 100. The steps in the method embodiments of APP theme scene control may be implemented by one or more processors 100 executing a computer program 102. Alternatively, the one or more processors 100, when executing the computer program 102, may implement the functions of the modules/units of the proposed apparatus embodiment of the fault handling scheme, which is not limited herein.
In one embodiment, processor 100 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In one embodiment, the storage 101 may be an internal storage unit of the electronic device, such as a hard disk or a memory of the electronic device. The memory 101 may also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Memory Card (SMC), a Secure Digital (SD) card, a flash memory card (flash card), and the like provided on the electronic device. Further, the memory 101 may also include both an internal storage unit and an external storage device of the electronic device. The memory 101 is used to store computer programs and other programs and data required by the terminal device. The memory 101 may also be used to temporarily store data that has been output or is to be output.
It will be understood by those skilled in the art that the block diagram shown in fig. 3 is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation to the terminal device to which the solution of the present invention is applied, and a specific terminal device may include more or less components than those shown in the figure, or may combine some components, or have different arrangements of components.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware instructions of a computer program, and the computer program may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, storage, operational databases, or other media used in embodiments provided herein may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct Rambus Dynamic RAM (DRDRAM), and Rambus Dynamic RAM (RDRAM).
In summary, the present invention discloses a method, an apparatus, a terminal device and a storage medium for recommending a fault handling scheme, wherein the method comprises: acquiring a fault description text, and processing the fault description text into a numerical characteristic vector based on a preset processing model; matching the numerical characteristic vector with all fault processing schemes in a preset scheme library, and calculating semantic similarity between the numerical characteristic vector and each fault processing scheme, wherein all fault processing schemes are converted into vectors by using the processing model in advance; and determining a target fault processing scheme based on the semantic similarity, and performing fault processing based on the target fault processing scheme. The invention can realize vectorization of the fault description text, determine the fault processing scheme based on the similarity between vectors and realize efficient recommendation of the optimal scheme.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for recommending a fault handling scheme, the method comprising:
acquiring a fault description text, and processing the fault description text into a numerical characteristic vector based on a preset processing model;
matching the numerical characteristic vector with all fault processing schemes in a preset scheme library, and calculating semantic similarity between the numerical characteristic vector and each fault processing scheme, wherein all fault processing schemes are converted into vectors by using the processing model in advance;
and determining a target fault processing scheme based on the semantic similarity, and performing fault processing based on the target fault processing scheme.
2. The method for recommending a fault handling scheme according to claim 1, wherein the obtaining a fault description text and processing the fault description text into a numerical feature vector based on a preset processing model comprises:
carrying out whole life cycle monitoring on the operation process of the numerical control machine tool, and storing monitoring data at different time intervals;
performing anomaly analysis on the monitoring data, determining fault data in the monitoring data, and acquiring a fault description text corresponding to the fault data;
and calling a preset processing model, and inputting the fault description text into the processing model to obtain the numerical characteristic vector, wherein the processing model is a doc2vec model.
3. The method for recommending a failure handling scheme according to claim 2, wherein said obtaining a failure description text corresponding to the failure data includes:
performing semantic recognition on the fault data to obtain a semantic recognition result;
and performing text conversion on the semantic recognition result to obtain the fault description text.
4. The method for recommending a fault handling scheme according to claim 1, wherein said matching the numerical feature vector with all fault handling schemes in a preset scheme library and calculating semantic similarity between the numerical feature vector and each fault handling scheme comprises:
acquiring fault vector data corresponding to each fault processing scheme, and sequentially matching the numerical characteristic vector with each fault vector data;
determining semantic similarity between the numerical feature vector and each fault vector data.
5. The method of recommending a fault handling scheme according to claim 4, wherein said determining semantic similarity between said numerical feature vector and each fault vector data comprises:
respectively calculating Euclidean distances between the numerical characteristic vectors and each fault vector data;
and determining the semantic similarity based on the Euclidean distance.
6. The method for recommending a failure handling scheme according to claim 5, wherein said determining a target failure handling scheme based on said semantic similarity comprises:
and acquiring the fault vector data corresponding to the shortest Euclidean distance, and taking the fault processing scheme corresponding to the fault vector data with the shortest Euclidean distance as the target fault processing scheme.
7. The method for recommending a failure handling scenario according to claim 4, wherein the scenario library is constructed in a manner that includes:
the method comprises the steps of carding typical fault types in the machining process of the numerical control machine tool in advance and fault names corresponding to each typical fault type, and recording fault processing schemes corresponding to each typical fault type in a text format;
saving a text by using a relational database, wherein fields are the typical fault type and the fault name, and values are the fault processing scheme;
and inputting the fault processing scheme into a preset processing model to obtain the fault vector data.
8. An apparatus for recommending a failure handling scheme, the apparatus comprising:
the text processing module is used for acquiring a fault description text and processing the fault description text into a numerical characteristic vector based on a preset processing model;
the vector matching module is used for matching the numerical characteristic vector with all fault processing schemes in a preset scheme library and calculating semantic similarity between the numerical characteristic vector and each fault processing scheme, wherein all the fault processing schemes are converted into vectors by using the processing model in advance;
and the scheme determining module is used for determining a target fault processing scheme based on the semantic similarity and carrying out fault processing based on the target fault processing scheme.
9. A terminal device, characterized in that the terminal device comprises a memory, a processor and a recommendation program of a fault handling scheme stored in the memory and operable on the processor, and the processor implements the steps of the recommendation method of a fault handling scheme according to any one of claims 1 to 7 when executing the recommendation program of a fault handling scheme.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a recommendation program of a failure handling scheme, which when executed by a processor, implements the steps of the failure handling scheme recommendation method according to any one of claims 1 to 7.
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| CN112711947A (en) * | 2021-01-09 | 2021-04-27 | 国网湖北省电力有限公司电力科学研究院 | Text vectorization-based handling reference method in fault power failure repair work |
| CN114564947A (en) * | 2022-03-04 | 2022-05-31 | 上海富欣智能交通控制有限公司 | Rail transit signal fault operation and maintenance method and device and electronic equipment |
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