Fault pre-diagnosis method and system for electric power communication engineering equipment

Abstract

The invention relates to the technical field of equipment maintenance, in particular to a fault pre-diagnosis method and system for electric power communication engineering equipment, and the method comprises the steps: obtaining current monitoring information and historical monitoring information, judging whether equipment fault abnormity exists in the current monitoring information or not, and if not, executing the step 1; and if not, arranging the historical monitoring information according to a time sequence to obtain equipment operation data. And deducing the equipment operation data to obtain operation derivative data. And performing data arrangement on the operation derivative data to obtain an operation condition proportion and equipment operation data, determining whether the operation condition proportion exceeds a preset condition proportion, if so, determining an abnormal time node according to the operation derivative data, and matching the equipment operation data with a preset fault diagnosis standard to obtain fault diagnosis information. And controlling to display the abnormal time node, the equipment operation data and the fault diagnosis information. According to the invention, the maintenance efficiency and reliability of the electric power communication engineering equipment are improved.

Description

Technical Field

[0001] This application relates to the technical field of equipment maintenance, and in particular, to a fault pre-diagnosis method and system for power communication engineering equipment. Background Art

[0002] Power communication engineering equipment is the core infrastructure to ensure the safe, stable, and efficient operation of the power grid, covering communication transmission equipment (such as optical fiber communication terminals, microwave relay stations), switching equipment (such as program-controlled switches, softswitch systems), terminal equipment (such as distribution automation terminals, intelligent meter communication modules), and supporting power supplies, antennas and other auxiliary facilities. With the rapid development of smart grids, energy Internet, and new power systems, the scale of power communication networks continues to expand, the equipment complexity increases significantly, and its operating status directly affects the dispatching control, data acquisition, and remote monitoring capabilities of power systems.

[0003] Currently, the monitoring and diagnosis of power communication engineering equipment mainly rely on traditional regular inspections and post-fault maintenance modes. Although regular inspections can discover potential problems of equipment to a certain extent, due to the fixed inspection cycle, they cannot capture the dynamic changes of equipment in real time, and it is difficult to detect some sudden fault hazards in time. Moreover, the inspection process is often limited by factors such as personnel experience and the accuracy of detection tools, and it is easy to miss inspections and misjudge inspections. The post-fault maintenance mode is to carry out maintenance after obvious faults occur in the equipment. This passive maintenance method not only prolongs the equipment downtime and affects the normal operation of power communication projects, but also causes a chain reaction and more serious consequences. Thus, it reduces the maintenance efficiency and reliability of power communication engineering equipment. Summary of the Invention

[0004] To solve at least one of the above technical problems, this application provides a fault pre-diagnosis method and system for power communication engineering equipment.

[0005] In a first aspect, this application provides a fault pre-diagnosis method for power communication engineering equipment, adopting the following technical solutions: Obtain the current monitoring information of power communication engineering equipment in the current time period and the historical monitoring information in the historical cycle time period; Judge whether there is equipment fault abnormality in the current monitoring information. If not, arrange the historical monitoring information in a time series to obtain equipment operation data; Deduce the equipment operation data to obtain operation derivative data in the future time period; Sort out the operation derivative data to obtain the operation situation ratio and the equipment operation data corresponding to the operation situation ratio; Determine whether the proportion of the operation situation exceeds a preset situation proportion. If it exceeds, determine the abnormal time node corresponding to the proportion of the operation situation according to the operation derivative data, and match the equipment operation data with a preset fault diagnosis standard to obtain fault diagnosis information; Control to display the abnormal time node, equipment operation data, and the fault diagnosis information.

[0006] By adopting the above technical solution, the monitoring information of the power communication engineering equipment in the current and historical cycle time periods is obtained, providing a comprehensive data basis for subsequent analysis. Determine whether there is equipment fault abnormality in the current monitoring information. If not, arrange the historical monitoring information in time series to obtain equipment operation data, order the scattered data, and facilitate the mining of data rules. Deduce the future operation derivative data from the equipment operation data, predict the future situation using historical data, and create conditions for discovering potential problems in advance. Organize the operation derivative data to obtain the proportion of the operation situation and the corresponding equipment operation data, clarify the proportion of different operation situations in the whole and the specific data performance. Determine whether the proportion of the operation situation exceeds the preset situation proportion to timely detect abnormal situations. If it exceeds, determine the abnormal time node according to the operation derivative data, and match the equipment operation data with the preset fault diagnosis standard to obtain the fault diagnosis information, accurately locate the problem and give the diagnosis result, providing a clear direction for subsequent processing. Control to display the abnormal time node, equipment operation data, and the fault diagnosis information, and intuitively present the key information obtained from the analysis and processing. The abnormal time node enables the staff to quickly know the time period when the problem occurs, the equipment operation data provides detailed background information, and the fault diagnosis information clarifies the location of the problem. This intuitive display method enables the staff to quickly understand the equipment status, take targeted measures in a timely manner, effectively ensure the stable operation of the power communication engineering equipment, and thus improve the equipment maintenance efficiency and reliability.

[0007] In a possible implementation manner, the deducing the operation derivative data in the future time period from the equipment operation data includes: Perform data decomposition on the equipment operation data to obtain the operation performance data of each type of parameter in the equipment operation data and the operation time series length corresponding to the operation performance data; Perform time series arrangement on the operation performance data according to the operation time series length to obtain the operation derivative set corresponding to each type of parameter; Perform basic distribution exploration and analysis on the operation derivative set to obtain the development law of the operation parameters of each type of parameter at different time nodes; Extend the data sequence in the operation derivative set based on the development law of the operation parameters to obtain the operation data range at different time nodes in the future time period; Arrange the operation data range in time sequence to obtain a set of future operation parameters corresponding to each type of parameter; Summarize the set of future operation parameters to obtain operation derivative data within a future time period.

[0008] In a possible implementation manner, the data arrangement of the operation derivative data to obtain the operation situation ratio and the device operation data corresponding to the operation situation ratio includes: Arrange the operation derivative data in a matrix according to the time series to obtain a data set matrix; Extract the time operation data corresponding to each time node in the data set matrix; Perform data extraction and combination on the operation data ranges corresponding to different types of parameters in the time operation data to obtain multiple operation data groups; Evaluate the abnormal level of each operation data group respectively to obtain an abnormal level score corresponding to each operation data group; Classify multiple operation data groups based on the abnormal level scores to obtain abnormal data categories; Calculate the ratio of the number of operation data groups corresponding to the abnormal data category to the total number of the multiple operation data groups to obtain the operation situation ratio corresponding to each abnormal data category; Correspondingly bind the operation data group with the operation situation ratio to obtain the device operation data corresponding to the operation situation ratio.

[0009] In a possible implementation manner, the performing data extraction and combination on the operation data ranges corresponding to different types of parameters in the time operation data to obtain multiple operation data groups includes: Determine the number of digits in the operation data range, and perform combination one by one according to the number of digits until each digit in the number of digits is combined with each digit in the operation data range corresponding to the remaining types of this parameter, and then terminate the data digit combination operation to obtain multiple operation data groups. The number of digits includes the first operation data, the last operation data, and the middle operation data between the first operation data and the last operation data in the operation data range.

[0010] In a possible implementation manner, the evaluating the abnormal level of each operation data group respectively to obtain an abnormal level score corresponding to each operation data group includes: Collect the device application information of power communication engineering equipment; Match the device application information with the device application information in the preset abnormal evaluation model library to obtain a target abnormal model; Input each of the described job data groups into the target anomaly model for evaluation to obtain an anomaly level score corresponding to each job data group.

[0011] In a possible implementation, classifying the multiple job data groups based on the anomaly level score to obtain anomaly data categories includes: Determine a preset fault risk standard based on the device application information; Divide the anomaly level score according to the preset fault risk standard to obtain multiple risk score intervals; According to the correspondence between the anomaly level score and the multiple job data groups, correspond and bind the multiple job data groups with the multiple risk score intervals to obtain a set of job data groups corresponding to each risk score interval; Classify the set of job data groups according to the risk score interval to obtain anomaly data categories.

[0012] In a second aspect, the present application provides a fault pre-diagnosis system for power communication engineering equipment, adopting the following technical solution: A fault pre-diagnosis system for power communication engineering equipment includes: An information acquisition module, configured to acquire current monitoring information of power communication engineering equipment in the current time period and historical monitoring information in the historical cycle time period; An anomaly judgment module, configured to judge whether there is a device fault anomaly in the current monitoring information. If not, arrange the historical monitoring information in a time series to obtain device job data; A data deduction module, configured to deduce the device job data to obtain job derivative data in a future time period; A data sorting module, configured to sort the job derivative data to obtain a job situation ratio and device job data corresponding to the job situation ratio; A diagnosis confirmation module, configured to determine whether the job situation ratio exceeds a preset situation ratio. If so, determine an anomaly time node corresponding to the job situation ratio according to the job derivative data, and match the device job data with a preset fault diagnosis standard to obtain fault diagnosis information; A control display module, configured to control the display of the anomaly time node, device job data, and the fault diagnosis information.

[0013] In a possible implementation, when the data deduction module deduces the device job data to obtain job derivative data in a future time period, it is specifically configured to: Decompose the device operation data to obtain the operation performance data of each type of parameter in the device operation data and the operation time series length corresponding to the operation performance data; Perform time series arrangement on the operation performance data according to the operation time series length to obtain the operation derivative set corresponding to each type of parameter; Conduct basic distribution exploration analysis on the operation derivative set to obtain the development law of operation parameters of each type of parameter at different time nodes; Based on the development law of operation parameters, extend the data sequence in the operation derivative set to obtain the operation data range at different time nodes in the future time period; Perform time series arrangement on the operation data range to obtain the future operation parameter set corresponding to each type of parameter; Summarize the future operation parameter sets to obtain the operation derivative data in the future time period.

[0014] In another possible implementation manner, when the data arrangement module arranges the operation derivative data to obtain the operation situation ratio and the device operation data corresponding to the operation situation ratio, it specifically is used for: Arrange the operation derivative data in a matrix according to the time series to obtain a data set matrix; Extract the time operation data corresponding to each time node in the data set matrix; Extract and combine the operation data ranges corresponding to different types of parameters in the time operation data to obtain multiple operation data groups; Evaluate the abnormal level of each operation data group respectively to obtain the abnormal level score corresponding to each operation data group; Classify multiple operation data groups based on the abnormal level score to obtain abnormal data categories; Calculate the ratio of the number of operation data groups corresponding to the abnormal data category to the total number of the multiple operation data groups to obtain the operation situation ratio corresponding to each abnormal data category; Correspondingly bind the operation data group with the operation situation ratio to obtain the device operation data corresponding to the operation situation ratio.

[0015] In another possible implementation manner, when the data arrangement module extracts and combines the operation data ranges corresponding to different types of parameters in the time operation data to obtain multiple operation data groups, it specifically is used for: Determine the number of digits of the operation data within the operation data range, and perform combinations one by one according to the number of digits until each digit in the number of digits is combined with each digit in the operation data range corresponding to the remaining types of this parameter, then terminate the operation of combining the number of digits to obtain multiple operation data groups. The number of digits includes the first operation data, the last operation data, and the middle operation data between the first operation data and the last operation data in the operation data range.

[0016] In another possible implementation, when the data sorting module evaluates the exception level of each operation data group respectively to obtain the exception level score corresponding to each operation data group, it specifically is used for: Collect the device application information of the power communication engineering equipment; Match the device application information with the device application information in the preset exception evaluation model library to obtain the target exception model; Input each of the operation data groups into the target exception model for evaluation to obtain the exception level score corresponding to each operation data group.

[0017] In another possible implementation, when the data sorting module classifies multiple operation data groups based on the exception level score to obtain the exception data category, it specifically is used for: Determine the preset fault risk standard based on the device application information; Divide the interval of the exception level score according to the preset fault risk standard to obtain multiple risk score intervals; According to the corresponding relationship between the exception level score and the multiple operation data groups, bind the multiple operation data groups to the multiple risk score intervals correspondingly to obtain the set of operation data groups corresponding to each risk score interval; Classify the set of operation data groups according to the risk score interval to obtain the exception data category.

[0018] In a third aspect, the present application provides an electronic device, adopting the following technical solution: At least one processor; A memory; At least one application program, where at least one application program is stored in the memory and is configured to be executed by at least one processor. The at least one application program is configured to: execute a fault pre-diagnosis method for power communication engineering equipment as described in any item of the first aspect.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium, adopting the following technical solution: A computer-readable storage medium stores a computer program thereon. When the computer program is executed on a computer, the computer is made to execute a fault pre-diagnosis method for power communication engineering equipment according to any one of the first aspect.

[0020] In summary, the present application includes at least one of the following beneficial technical effects: By adopting the above technical solution, the monitoring information of the power communication engineering equipment in the current and historical cycle time periods is obtained, providing a comprehensive data basis for subsequent analysis. It is judged whether there is equipment fault abnormality in the current monitoring information. If not, the historical monitoring information is arranged in time series to obtain the equipment operation data, orderly arranging the scattered data and facilitating the excavation of data rules. By deducing the equipment operation data, future operation derivative data is obtained, predicting future situations using historical data and creating conditions for early discovery of potential problems. The operation derivative data is sorted out to obtain the operation situation ratio and the corresponding equipment operation data, clarifying the proportion of different operation situations in the whole and the specific data performance. It is determined whether the operation situation ratio exceeds the preset situation ratio, and abnormal situations can be discovered in time. If it exceeds, the abnormal time node is determined according to the operation derivative data, and the equipment operation data is matched with the preset fault diagnosis standard to obtain the fault diagnosis information, accurately positioning the problem and giving the diagnosis result, providing a clear direction for subsequent processing. The abnormal time node, the equipment operation data and the fault diagnosis information are controlled and displayed, visually presenting the key information obtained from the analysis and processing. The abnormal time node enables the staff to quickly know the time period when the problem occurs, the equipment operation data provides detailed background information, and the fault diagnosis information clarifies the location of the problem. This intuitive display method enables the staff to quickly understand the equipment status, take targeted measures in time, effectively ensure the stable operation of the power communication engineering equipment, and thus improve the equipment maintenance efficiency and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic flow chart of a fault pre-diagnosis method for power communication engineering equipment provided by an embodiment of the present application.

[0022] Figure 2 It is a schematic structural diagram of a fault pre-diagnosis system for power communication engineering equipment provided by an embodiment of the present application.

[0023] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] The following further elaborates on the present application in conjunction with the attached Figures 1-3 for a more detailed description.

[0025] This specific embodiment is only an interpretation of the present application and does not limit the present application. After reading this specification, those skilled in the art can make modifications to this embodiment without creative contributions as needed, but as long as it is within the scope of the present application, it is protected by the patent law.

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without making creative efforts belong to the scope of protection of the present application.

[0027] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, unless otherwise specified.

[0028] The embodiments of the present application will be further described in detail below with reference to the accompanying drawings of the specification.

[0029] The embodiments of the present application provide a method for fault pre-diagnosis of power communication engineering equipment, which is executed by an electronic device. Among them, the electronic device can be an independent physical electronic device, an electronic device cluster or a distributed system composed of multiple physical electronic devices, or a cloud electronic device providing cloud computing services. The embodiments of the present application do not limit this here, as Figure 1 shown, the method includes: Step S10: Obtain the current monitoring information of the power communication engineering equipment in the current time period and the historical monitoring information in the historical cycle time period.

[0030] Specifically, the power communication engineering equipment refers to various hardware devices used in the power communication system, such as transformers, communication base stations, optical cables, etc., which are responsible for the transmission and processing of power and communication signals. The current monitoring information in the current time period refers to the real-time data collected for the above-mentioned equipment at a specific time point or time period, including but not limited to temperature, humidity, voltage, current, communication signal strength, etc. The historical monitoring information in the historical cycle time period refers to the monitoring data of the above-mentioned equipment collected in the past period of time according to a certain cycle (such as daily, weekly, monthly), which is used to analyze the long-term operation status and trend of the equipment.

[0031] In the embodiments of the present application, sensors and data acquisition modules are installed on the device, and these modules can monitor the operating status of the device in real time and convert it into digital signals; secondly, a data acquisition system is configured, and the acquisition period and parameters are set to ensure that the system can automatically read data from the sensors according to the settings; finally, the acquired data is transmitted to the data center or monitoring platform through the network for subsequent analysis.

[0032] Step S11: Determine whether there is any device failure anomaly in the current monitoring information. If not, arrange the historical monitoring information in a time series to obtain device operation data.

[0033] Specifically, a device failure anomaly refers to an abnormal situation detected in the current monitoring information that does not conform to the normal operating state of the device and indicates that the device has a failure, such as excessive current, abnormal voltage fluctuation, too high temperature, communication signal interruption, etc. A time series refers to the way of arranging historical monitoring information in chronological order, which is usually used to show the operating status of the device over time. Device operation data refers to a set of data that can reflect the operation of the device over a period of time after being sorted and analyzed, and these data are usually used for device performance evaluation, fault prediction, and operation and maintenance decision-making. For example: if the current monitoring information shows that the temperature of a power communication engineering device rises abnormally and exceeds the preset safety threshold, it may be determined as a device failure anomaly; if the current monitoring information is normal, the temperature monitoring data at the same time point every day in the past week can be arranged in a time series to obtain the temperature operation data of the device in a week.

[0034] Step S12: Deduce the device operation data to obtain operation derivative data for a future time period.

[0035] Specifically, disassemble the device operation data to obtain the operation performance data of each type of parameter in the device operation data and the operation time series length corresponding to the operation performance data. Arrange the operation performance data in time series according to the operation time series length to obtain the operation derivative set corresponding to each type of parameter. Conduct a basic distribution exploration analysis on the operation derivative set to obtain the development law of the operation parameters of each type of parameter at different time nodes. Extend the data sequence in the operation derivative set based on the development law of the operation parameters to obtain the operation data range at different time nodes in the future time period. Arrange the operation data range in time series to obtain the future operation parameter set corresponding to each type of parameter, and summarize the future operation parameter sets to obtain the operation derivative data for the future time period.

[0036] Step S13: Organize the operation derivative data to obtain the operation situation ratio and the device operation data corresponding to the operation situation ratio.

[0037] Specifically, arrange the job-derived data in a matrix according to the time series to obtain a data set matrix, and extract the time job data corresponding to each time node in the data set matrix. Extract and combine the job data ranges corresponding to different types of parameters in the time job data to obtain multiple job data groups, and evaluate the anomaly levels of each job data group respectively to obtain the anomaly level scores corresponding to each job data group. Classify the multiple job data groups based on the anomaly level scores to obtain anomaly data categories. Calculate the ratio of the number of job arrays corresponding to the anomaly data categories to the total number of the multiple job data groups to obtain the job situation ratio corresponding to each anomaly data category, and bind the job data groups and the job situation ratio correspondingly to obtain the device job data corresponding to the job situation ratio.

[0038] Specifically, then determine the number of digits in the job data range, and perform combinations one by one according to the number of digits until each digit in the number of digits is combined with each digit in the job data ranges corresponding to the other types of this parameter, and then terminate the operation of combining the number of digits to obtain multiple job data groups. The number of digits includes the first job data, the last job data, and the middle job data between the first job data and the last job data in the job data range.

[0039] For example, there are three different types of job data ranges for the current device, and each job data range contains multiple different job data. Mark the serial numbers of the positions of each job data to form the first job data, the second job data, the third job data..... the Nth job data. Combine the first job data in the first type of job data range with the first job data in the second type of job data range and the first job data in the third type of job data range respectively to obtain the first job data group. Then, combine the first job data in the first type of job data range with the first job data in the second type of job data range and the second job data in the third type of job data range to obtain the second job data group. And so on, until the combination of the number of digits in one type of job data range and the number of digits in the job data ranges of the other types is traversed, and then stop this operation, so as to obtain multiple job data groups.

[0040] Specifically, collect the device application information of the power communication engineering device, match the device application information with the device application information in the preset anomaly evaluation model library to obtain the target anomaly model. Input each job data group into the target anomaly model for evaluation to obtain the anomaly level score corresponding to each job data group.

[0041] Specifically, a preset fault risk standard is determined based on the device application information, and the abnormal level score is divided into intervals according to the preset fault risk standard to obtain multiple risk score intervals. According to the correspondence between the abnormal level score and the multiple groups of operation data, the multiple groups of operation data are correspondingly bound to the multiple risk score intervals to obtain a set of operation data groups corresponding to each risk score interval. The set of operation data groups is classified according to the risk score interval to obtain abnormal data categories.

[0042] Step S14: Determine whether the operation situation ratio exceeds a preset situation ratio. If it exceeds, determine an abnormal time node corresponding to the operation situation ratio according to the operation-derived data, and match the device operation data with a preset fault diagnosis standard to obtain fault diagnosis information.

[0043] Specifically, the preset situation ratio refers to a threshold set in advance for judging whether the operation situation ratio is within the normal range. If it exceeds this ratio, it is considered that the operation state is abnormal. The operation-derived data refers to the data generated during the operation process and directly related to the operation situation. These data can reflect the changes and trends of the operation state and are used for further analysis of the abnormal reasons. The preset fault diagnosis standard refers to the standard formulated according to the device characteristics and historical experience for judging whether the device has faults and the types of faults.

[0044] In the embodiment of the present application, since power communication engineering equipment may have abnormal conditions that meet the normal standards such as downtime during daily work, such abnormal types will be determined as level-1 abnormalities, that is, abnormalities that can be ignored. At the same time, power communication engineering equipment may also have other abnormal types other than the above-mentioned abnormalities. The levels of other abnormal types may not be level-1 abnormalities. Therefore, the operation situation ratio in the present application is the ratio of other abnormalities except level-1 abnormalities to all abnormalities.

[0045] Step S15: Control to display the abnormal time node, the device operation data, and the fault diagnosis information.

[0046] An embodiment of the present application provides a fault pre-diagnosis method for power communication engineering equipment. By acquiring the monitoring information of the power communication engineering equipment in the current and historical cycle time periods, a comprehensive data basis is provided for subsequent analysis. It is determined whether there is an abnormal equipment fault in the current monitoring information. If not, the historical monitoring information is arranged in a time series to obtain equipment operation data, which orders the scattered data and facilitates the mining of data patterns. By deducing the equipment operation data, future operation derivative data is obtained, and historical data is used to predict future situations, creating conditions for early detection of potential problems. The operation derivative data is sorted out to obtain the operation situation ratio and the corresponding equipment operation data, clarifying the proportion of different operation situations in the whole and the specific data performance. It is determined whether the operation situation ratio exceeds the preset situation ratio, enabling the timely discovery of abnormal situations. If it exceeds, the abnormal time node is determined according to the operation derivative data, and the equipment operation data is matched with the preset fault diagnosis standard to obtain the fault diagnosis information, accurately locating the problem and giving the diagnosis result, providing a clear direction for subsequent processing. The abnormal time node, equipment operation data, and fault diagnosis information are controlled and displayed, visually presenting the key information obtained from the analysis and processing. The abnormal time node allows the staff to quickly know the time period when the problem occurs, the equipment operation data provides detailed background information, and the fault diagnosis information clarifies the location of the problem. This intuitive display method enables the staff to quickly understand the equipment status, take targeted measures in a timely manner, effectively ensure the stable operation of the power communication engineering equipment, and thus improve the equipment maintenance efficiency and reliability.

[0047] The following introduces a fault pre-diagnosis system for power communication engineering equipment provided by an embodiment of the present application. The fault pre-diagnosis system for power communication engineering equipment described below can be mutually referred to with the fault pre-diagnosis method for power communication engineering equipment described above. Please refer to Figure 2 , Figure 2 FIG. 7 is a schematic structural diagram of a fault pre-diagnosis system 20 for power communication engineering equipment provided by an embodiment of the present application, including: An information acquisition module 21, configured to acquire the current monitoring information of the power communication engineering equipment in the current time period and the historical monitoring information in the historical cycle time period; An abnormality determination module 22, configured to determine whether there is an abnormal equipment fault in the current monitoring information. If not, arrange the historical monitoring information in a time series to obtain equipment operation data; A data deduction module 23, configured to deduce the equipment operation data to obtain operation derivative data in a future time period; A data sorting module 24, configured to sort out the operation derivative data to obtain an operation situation ratio and equipment operation data corresponding to the operation situation ratio; The diagnosis confirmation module 25 is configured to determine whether the proportion of the operation situation exceeds a preset situation proportion. If it exceeds, an abnormal time node corresponding to the proportion of the operation situation is determined according to the operation derivative data, and the equipment operation data is matched with a preset fault diagnosis standard to obtain fault diagnosis information; The control and display module 26 is configured to control and display the abnormal time node, the equipment operation data, and the fault diagnosis information.

[0048] In a possible implementation manner of the embodiment of the present application, when the data deduction module 23 deduces the equipment operation data to obtain operation derivative data in a future time period, it is specifically configured to: Perform data decomposition on the equipment operation data to obtain operation performance data of each type of parameter in the equipment operation data and the operation time sequence length corresponding to the operation performance data; Perform time sequence arrangement on the operation performance data according to the operation time sequence length to obtain an operation derivative set corresponding to each type of parameter; Perform basic distribution exploration and analysis on the operation derivative set to obtain the development law of operation parameters of each type of parameter at different time nodes; Based on the development law of the operation parameters, extend the data sequence in the operation derivative set to obtain the operation data range at different time nodes in the future time period; Perform time sequence arrangement on the operation data range to obtain a future operation parameter set corresponding to each type of parameter; Summarize the future operation parameter sets to obtain operation derivative data in a future time period.

[0049] In another possible implementation manner of the embodiment of the present application, when the data sorting module 24 sorts the operation derivative data to obtain the proportion of the operation situation and the equipment operation data corresponding to the proportion of the operation situation, it is specifically configured to: Arrange the operation derivative data in a matrix according to the time sequence to obtain a data set matrix; Extract the time operation data corresponding to each time node in the data set matrix; Extract and combine the operation data ranges corresponding to different types of parameters in the time operation data to obtain a plurality of operation data groups; Evaluate the abnormal level of each operation data group respectively to obtain an abnormal level score corresponding to each operation data group; Classify the plurality of operation data groups based on the abnormal level score to obtain abnormal data categories; Calculate the ratio of the number of job data groups corresponding to each abnormal data category to the total number of the multiple job data groups, and obtain the job situation ratio corresponding to each abnormal data category; Correspondingly bind the job data groups with the job situation ratio to obtain device job data corresponding to the job situation ratio.

[0050] In another possible implementation manner of the embodiment of the present application, when the data sorting module 24 performs data extraction and combination on the job data ranges corresponding to different type parameters in the time job data to obtain multiple job data groups, it specifically is used for: Determine the number of digits of the data in the job data range, and perform combination one by one according to the number of digits until each digit in the number of digits is combined with each digit in the job data range corresponding to the same type parameter of the remaining types, and then terminate the data digit combination operation to obtain multiple job data groups. The number of digits includes the first job data, the last job data, and the middle job data between the first job data and the last job data in the job data range.

[0051] In another possible implementation manner of the embodiment of the present application, when the data sorting module 24 respectively evaluates the abnormal levels of each job data group to obtain the abnormal level scores corresponding to each job data group, it specifically is used for: Collect the device application information of the power communication engineering equipment; Match the device application information with the device application information in the preset abnormal evaluation model library to obtain a target abnormal model; Input each of the job data groups into the target abnormal model for evaluation to obtain the abnormal level scores corresponding to each job data group.

[0052] In another possible implementation manner of the embodiment of the present application, when the data sorting module 24 classifies multiple job data groups based on the abnormal level scores to obtain abnormal data categories, it specifically is used for: Determine the preset fault risk standard based on the device application information; Divide the abnormal level scores according to the preset fault risk standard to obtain multiple risk score intervals; According to the corresponding relationship between the abnormal level scores and the multiple job data groups, correspondingly bind the multiple job data groups with the multiple risk score intervals to obtain a set of job data groups corresponding to each risk score interval; Classify the set of job data groups according to the risk score intervals to obtain abnormal data categories.

[0053] The embodiment of the present application provides an electronic device, such asFigure 3 As shown Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present application Figure 3 The electronic device 300 shown includes: a processor 301 and a memory 303. Among them, the processor 301 and the memory 303 are connected, such as connected through a bus 302. Optionally, the electronic device 300 may further include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation to the embodiments of the present application.

[0054] The processor 301 may be a CPU (Central Processing Unit, central processor), a general-purpose processor, a DSP (Digital Signal Processor, data signal processor), an ASIC (Application Specific Integrated Circuit, application-specific integrated circuit), an FPGA (Field Programmable Gate Array, field programmable gate array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logic blocks, modules and circuits described in connection with the disclosed content of the embodiments of the present application. The processor 301 may also be a combination that implements a computing function, such as a combination including one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0055] The bus 302 may include a path for transmitting information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus or an EISA (Extended Industry Standard Architecture, extended industry standard architecture) bus, etc. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 3 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0056] The memory 303 can be a ROM (ReadOnlyMemory), or other types of static storage devices that can store static information and instructions, a RAM (RandomAccessMemory), or other types of dynamic storage devices that can store information and instructions. It can also be an EEPROM (ElectricallyErasableProgrammableReadOnlyMemory), a CD-ROM (CompactDiscReadOnlyMemory), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0057] The memory 303 is used to store the application program code for executing the solution of the embodiment of the present application, and is controlled by the processor 301 to execute. The processor 301 is used to execute the application program code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0058] Among them, the electronic device includes but is not limited to: mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), PMPs (Portable Multimedia Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0059] Next, a computer-readable storage medium provided by the embodiments of the present application will be introduced. The computer-readable storage medium described below can be correspondingly referred to the method described above.

[0060] The embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the above method for fault pre-diagnosis of power communication engineering equipment.

[0061] Since the embodiments of the computer-readable storage medium part correspond to the embodiments of the method part, please refer to the description of the embodiments of the method part for the embodiments of the computer-readable storage medium part.

[0062] It should be understood that although the steps in the flowchart of the accompanying drawings are shown sequentially as indicated by the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless otherwise clearly stated in this document, there is no strict order restriction for the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0063] The above are only some embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. A fault pre-diagnosis method for electric power communication engineering equipment, characterized in that: include: Obtain current monitoring information of power communication engineering equipment in the current time period and historical monitoring information in the historical period time period; Determine whether there is an equipment failure abnormality in the current monitoring information. If not, arrange the historical monitoring information in time series to obtain equipment operation data; Deducing the equipment operation data to obtain operation-derived data in a future time period; Arrange the operation-derived data to obtain an operation situation ratio and equipment operation data corresponding to the operation situation ratio; Determine whether the operation situation ratio exceeds a preset situation ratio, and if so, determine an abnormal time node corresponding to the operation situation ratio according to the operation-derived data, and match the equipment operation data with a preset fault diagnosis standard to obtain fault diagnosis information; Control and display the abnormal time node, equipment operation data and the fault diagnosis information.

2. A fault pre-diagnosis method for electric power communication engineering equipment according to claim 1, characterized in that: The deducing of the equipment operation data to obtain operation-derived data in a future time period includes: Decomposing the equipment operation data to obtain operation performance data of each type of parameter in the equipment operation data and the operation time sequence length corresponding to the operation performance data; Arrange the job performance data in time sequence according to the job time sequence length to obtain a job derivative set corresponding to each type of parameter; Perform basic distribution exploration and analysis on the derived set of operations to obtain the development rules of operation parameters of each type of parameter at different time nodes; Based on the operation parameter development law, the data sequence in the operation derivative set is extended to obtain the operation data range at different time nodes in the future time period; The operation data range is sorted in time series to obtain a future operation parameter set corresponding to each type of parameter; The future operation parameter set is aggregated to obtain operation-derived data in a future time period.

3. A fault pre-diagnosis method for electric power communication engineering equipment according to claim 1, characterized in that: The data collating of the operation-derived data to obtain the operation situation ratio and the equipment operation data corresponding to the operation situation ratio includes: Arranging the operation-derived data in a matrix according to a time series to obtain a data set matrix; Extracting the time operation data corresponding to each time node in the data set matrix; Extracting and combining the operation data ranges corresponding to the different types of parameters in the time operation data to obtain a plurality of operation data groups; Performing an abnormality level evaluation on each operation data group respectively to obtain an abnormality level score corresponding to each operation data group; Classifying the plurality of operation data groups based on the abnormality level scores to obtain abnormal data categories; Calculate the ratio of the number of operation data groups corresponding to the abnormal data category to the total number of the multiple operation data groups to obtain the proportion of operation conditions corresponding to each abnormal data category; The operation data group is correspondingly bound to the operation situation ratio to obtain equipment operation data corresponding to the operation situation ratio.

4. A fault pre-diagnosis method for electric power communication engineering equipment according to claim 3, characterized in that: The operation data ranges corresponding to the different types of parameters in the time operation data are extracted and combined to obtain multiple operation data groups, including: The number of data bits in the operation data range is determined, and the data bits are combined one by one according to the number of data bits until each number of data bits is combined with each number of data bits in the operation data range corresponding to the other types of the parameter, and then the data bit combination operation is terminated to obtain multiple operation data groups, wherein the number of data bits includes the first operation data, the last operation data, and the middle operation data between the first operation data and the last operation data in the operation data range.

5. A fault pre-diagnosis method for electric power communication engineering equipment according to claim 3, characterized in that: The step of evaluating each operation data group for anomaly level to obtain an anomaly level score corresponding to each operation data group includes: Collect equipment application information of power communication engineering equipment; Matching the device application information with the device application information in a preset anomaly assessment model library to obtain a target anomaly model; Each of the operation data groups is input into the target anomaly model for evaluation to obtain an anomaly level score corresponding to each operation data group.

6. A fault pre-diagnosis method for electric power communication engineering equipment according to claim 5, characterized in that: The method of classifying the plurality of operation data groups based on the abnormality level scores to obtain abnormal data categories includes: Determining a preset fault risk standard based on the device application information; Dividing the abnormality level score into intervals according to the preset fault risk standard to obtain multiple risk score intervals; According to the correspondence between the abnormality level score and the multiple operation data groups, the multiple operation data groups are correspondingly bound to the multiple risk score intervals to obtain a set of operation data groups corresponding to each risk score interval; The operation data set is divided into risk categories according to the risk score interval to obtain abnormal data categories.

7. A fault pre-diagnosis system for electric power communication engineering equipment, characterized in that: include: An information acquisition module is used to acquire current monitoring information of power communication engineering equipment in the current time period and historical monitoring information in the historical period time period; An abnormality judgment module is used to judge whether there is an equipment failure abnormality in the current monitoring information. If not, the historical monitoring information is arranged in time series to obtain equipment operation data; A data deduction module, used to deduce the equipment operation data to obtain operation derivative data in a future time period; A data sorting module is used to sort the operation-derived data to obtain the operation situation ratio and the equipment operation data corresponding to the operation situation ratio; A diagnosis confirmation module is used to determine whether the operation situation ratio exceeds a preset situation ratio. If it exceeds, an abnormal time node corresponding to the operation situation ratio is determined according to the operation derivative data, and the equipment operation data is matched with a preset fault diagnosis standard to obtain fault diagnosis information; A control display module is used to control the display of the abnormal time node, equipment operation data and the fault diagnosis information.

8. A fault pre-diagnosis system for electric power communication engineering equipment according to claim 7, characterized in that: When the data deduction module deduces the equipment operation data to obtain the operation derivative data in the future time period, it is specifically used to: Decomposing the equipment operation data to obtain operation performance data of each type of parameter in the equipment operation data and the operation time sequence length corresponding to the operation performance data; Arrange the job performance data in time sequence according to the job time sequence length to obtain a job derivative set corresponding to each type of parameter; Perform basic distribution exploration and analysis on the derived set of operations to obtain the development rules of operation parameters of each type of parameter at different time nodes; Based on the operation parameter development law, the data sequence in the operation derivative set is extended to obtain the operation data range at different time nodes in the future time period; The operation data range is sorted in time series to obtain a future operation parameter set corresponding to each type of parameter; The future operation parameter set is aggregated to obtain operation-derived data in a future time period.

9. An electronic device, characterized in that: The electronic device includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute a fault pre-diagnosis method for power communication engineering equipment according to any one of claims 1-6.

10. A computer-readable storage medium, characterized in that: include: A computer program is stored which can be loaded by a processor and execute a fault pre-diagnosis method for electric power communication engineering equipment as described in any one of claims 1 to 6.

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