Method and device for determining abnormal operation of overhaul and maintenance of power equipment and control equipment

Abstract

The invention provides an abnormal operation determination method and device for power equipment overhaul and maintenance and control equipment, and belongs to the technical field of power equipment overhaul, and the method comprises the steps: collecting image data of an operation behavior of an overhaul and maintenance region, and determining the occurrence moment of the operation behavior according to the image data, and the moment belongs to any preset time period; determining an exception judgment strategy corresponding to the moment from the first configuration file, each preset time period corresponding to one exception judgment strategy; and outputting key behavior data in the image data according to an anomaly judgment strategy corresponding to the moment, and determining whether the key behavior data is abnormal or not. According to the method provided by the invention, the refined time period division is combined with the targeted discrimination strategy, the key behavior data in the image data of the operation behavior is output according to the discrimination strategy, the abnormal operation can be determined, the processing is simple, and the time consumption is low.

Description

Technical Field

[0001] This application relates to the technical field of power equipment maintenance, and particularly to a method, device and control equipment for determining abnormal operations in power equipment maintenance and repair. Background Art

[0002] With the rapid development of China's power industry, the problems in the process of power equipment maintenance and the operations of maintenance personnel have received increasing attention. Ensuring the compliance and safety of the power equipment maintenance process is crucial for preventing potential accidents and ensuring the stable operation of the power system.

[0003] In related technologies, the detection of abnormal operation behaviors in power maintenance mainly includes: performing target recognition on sample data based on the YOLO algorithm, calculating the distance between the identified ROI region box and the camera; processing the ROI regions with a distance less than the threshold and outputting the behavior data of the target human body; evaluating the abnormal behaviors of the behavior data of the target human body. When the abnormal evaluation score is greater than the preset threshold, it is a low-risk warning. When the behavior in the next frame is still evaluated as abnormal, it is a medium-risk warning. When multiple consecutive frames are abnormal, it is determined as a high-risk warning. If the evaluation result is normal, the detection of human abnormal behaviors is completed. Among them, by defining each illegal behavior and assigning corresponding weights, and then using the score accumulation system to evaluate and calculate the behavior data to determine whether the behavior is normal.

[0004] However, when using the above method to evaluate the abnormality of each frame of operation behavior, it is necessary to output all the behavior data, which will increase the complexity of processing and time consumption, and is not conducive to taking preventive measures in a timely manner. Summary of the Invention

[0005] This application provides a method, device and control equipment for determining abnormal operations in power equipment maintenance and repair to solve the deficiencies in related technologies.

[0006] In a first aspect, this application provides a method for determining abnormal operations in power equipment maintenance and repair, including:

[0007] Collecting image data of operation behaviors in the maintenance area and determining the moment when the operation behavior occurs according to the image data, where the moment belongs to any preset time period;

[0008] Determining the abnormal discrimination strategy corresponding to the moment from the first configuration file, where each preset time period corresponds to an abnormal discrimination strategy, and the abnormal discrimination strategy is one of the following abnormal behavior categories: abnormal operation position, abnormal operation sequence, and abnormal operation frequency;

[0009] According to the abnormal discrimination strategy corresponding to the moment, outputting the key behavior data in the image data and determining whether the key behavior data is abnormal.

[0010] In a possible implementation, before determining the anomaly discrimination strategy corresponding to the moment from the first configuration file, where the moment is the start moment of any preset time period, it further includes:

[0011] Obtain multiple historical anomaly behavior categories within each preset time period from the database;

[0012] Use a preset weighted probability calculation model to assign corresponding weights to each historical anomaly behavior category within the same preset time period and cumulatively calculate their respective weighted probability values, and determine the historical anomaly behavior category corresponding to the maximum value among the weighted probability values as the anomaly discrimination strategy for this preset time period, where the weight is inversely proportional to time;

[0013] Combine each anomaly discrimination strategy to form the first configuration file.

[0014] In a possible implementation, the preset weighted probability calculation model refers to

[0015]

[0016] where \((t1, t2)\) is the preset time period, \(K\) is the set of historical anomaly behavior categories within \((t1, t2)\), \(e\) -t represents the weight, represents the probability that the historical anomaly behavior category at time \(t\) is \(k\), and is calculated through the following model:

[0017]

[0018] where \(X\) k is the number of historical anomaly behavior categories \(k\) at time \(t\), and \(X\) K is the total of historical anomaly behavior categories at time \(t\);

[0019] In a possible implementation, the key behavior data includes one of location information, the target device being contacted, and the speed value.

[0020] In a possible implementation, determining whether the key behavior data is abnormal includes:

[0021] Obtain the first discrimination criterion corresponding to the anomaly discrimination strategy corresponding to the moment from the discrimination criterion library, and the first discrimination criterion is one of a preset position range, a preset operation sequence, and a preset frequency range;

[0022] If the key behavior data does not conform to the first discrimination criterion, determine that the operation behavior is abnormal and issue a warning, or, if the key behavior data conforms to the first discrimination criterion, determine that the operation behavior is normal.

[0023] In a possible implementation, when the critical behavior data does not meet the first discrimination criterion, it further includes:

[0024] Retrieving a solution from a preset expert knowledge base.

[0025] In a possible implementation, the database includes cases of human abnormal operations diagnosed manually or automatically; the expert knowledge base includes the emergency level, on-site information, and solutions corresponding to the abnormal behavior categories.

[0026] In a second aspect, the present application provides an abnormal operation determination device for power equipment maintenance, including: a collection module, a determination module, and an execution module, where:

[0027] The collection module is used to collect image data of the operation behavior in the maintenance area and determine the moment when the operation behavior occurs according to the image data, where the moment belongs to any preset time period;

[0028] The determination module is used to determine the abnormal discrimination strategy corresponding to the moment from the first configuration file, where each preset time period corresponds to an abnormal discrimination strategy, and the abnormal discrimination strategy is one of the following abnormal behavior categories: abnormal operation position, abnormal operation sequence, and abnormal operation frequency;

[0029] The execution module is used to output the critical behavior data in the image data according to the abnormal discrimination strategy corresponding to the moment and determine whether the critical behavior data is abnormal.

[0030] In a third aspect, the present application provides a control device, including a memory and a processor. The memory stores program instructions, and the processor is used to call the program instructions in the memory to execute the abnormal operation determination method for power equipment maintenance according to any item in the first aspect.

[0031] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the abnormal operation determination method for power equipment maintenance according to any item in the first aspect.

[0032] The method, device and control device for determining abnormal operation of power equipment overhaul and maintenance provided by the present application first collect image data of operation behavior in the overhaul and maintenance area, and determine the time when the operation behavior occurs based on the image data, and then determine the discrimination strategy corresponding to the moment from the first configuration file, and the discrimination strategy is one of the following abnormal behavior categories: abnormal operation position, abnormal operation sequence and abnormal operation frequency. Thus, according to the discrimination strategy corresponding to the moment, the key behavior data in the image data is output in a targeted manner, and it is determined whether the key behavior data is abnormal, so as to determine whether the operation behavior is correct. The method provided by the present application combines refined time period division with targeted discrimination strategies. It only needs to output the key behavior data in the image data of the operation behavior according to the discrimination strategy to determine the abnormal operation. The processing is simple and time-saving, which is conducive to improving the sensitivity to abnormal operation behavior in the overhaul and maintenance process, thereby improving the overall operation safety and standardization. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0034] Figure 1 A flowchart of a method for determining abnormal operation of power equipment inspection and maintenance provided in an embodiment of the present application;

[0035] Figure 2 A flowchart of another method for determining abnormal operation of power equipment maintenance provided in an embodiment of the present application;

[0036] Figure 3 A schematic diagram of the structure of an abnormal operation determination device for power equipment inspection and maintenance provided in an embodiment of the present application;

[0037] Figure 4 A schematic diagram of the hardware structure of a control device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in the embodiments of this application will be described in more detail below with reference to the accompanying drawings in the preferred embodiments of this application. In the drawings, the same or similar reference numerals denote the same or similar components or components with the same or similar functions throughout. The described embodiments are some, but not all, of the embodiments of this application. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain this application and should not be construed as limiting this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of this application without making creative efforts fall within the scope of protection of this application. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0039] First of all, it should be noted that in the embodiments of this application, the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, article or device comprising such element.

[0040] Secondly, it should be noted that in the embodiments of this application, words such as "exemplary" or "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in this application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner.

[0041] In the related art, a method for detecting abnormal human behaviors includes: performing target recognition on sample data based on the YOLO algorithm, and calculating the distance between the recognized ROI region box and the camera; processing the ROI regions with a distance less than a threshold value and outputting the behavior data of the target human body; performing an abnormal behavior assessment on the behavior data of the target human body. When the abnormal assessment score is greater than a preset threshold, it is a low-risk warning. When the behavior in the next frame is still evaluated as abnormal, it is a medium-risk warning. When multiple consecutive frames are abnormal, it is determined as a high-risk warning. If the evaluation result is normal, the detection of abnormal human behaviors is completed. Among them, by defining each illegal behavior and giving corresponding weights, and then using a score accumulation system to evaluate and calculate the behavior data to determine whether the behavior is normal.

[0042] However, when the above method is used to perform abnormal evaluation on each frame of operation behavior, all behavior data must be fully output on each frame of operation behavior, which makes the processing process complicated and time-consuming, affecting the timely response to abnormal situations and the adoption of preventive measures.

[0043] In addition, if the weight distribution of illegal behaviors is unreasonable, misjudgment may occur, reducing the accuracy of abnormal behavior detection.

[0044] In view of this, the present application provides a method, device and control device for determining abnormal operations in the inspection and maintenance of power equipment. First, by collecting image data of the operation behavior in the inspection and maintenance area, and determining the time when the operation behavior occurs based on the image data, the time belongs to any preset time period. Different abnormality discrimination strategies can correspond to the operation behaviors occurring at different times, and each preset time period is set to correspond to an abnormality discrimination strategy. Then, the abnormality discrimination strategy corresponding to the moment is determined from the first configuration file, wherein the abnormality discrimination strategy is one of the following abnormal behavior categories: abnormal operation position, abnormal operation sequence and abnormal operation frequency. Finally, according to the abnormality discrimination strategy corresponding to the moment, the key behavior data in the image data is output in a targeted manner, and it is determined whether the key behavior data is abnormal, so as to determine whether the operation behavior is correct.

[0045] Furthermore, in order to effectively improve the accuracy of abnormal behavior detection, it is necessary to set a suitable abnormal discrimination strategy for the moment when the operation behavior occurs. By taking the moment when the operation behavior occurs as the starting moment of any preset time period, first, obtain and analyze multiple historical abnormal behavior categories and their occurrence probabilities that have occurred in the time period. Then, considering the influence of time factors on the reference value of abnormal behavior, when discriminating the operation behavior, the historical abnormal behavior category that is consistent with or close to the moment when the operation behavior occurs will be selected as the standard. Therefore, by introducing time weights, the probability of occurrence of each historical abnormal behavior category is weighted, and then the weighted probability is cumulatively calculated. Finally, the historical abnormal behavior category with the largest probability after weighted processing is selected as the abnormal discrimination strategy for judging abnormal operations.

[0046] Through the above method, the processing flow can be simplified and time consumption can be reduced, which is conducive to improving the speed and accuracy of identifying abnormal operating behaviors during the inspection and maintenance process and improving the overall safety and standardization level of the operation.

[0047] Figure 1 A flowchart of a method for determining abnormal operation of power equipment maintenance provided by an embodiment of the present application. Figure 1 , the method may include:

[0048] S101. Collect the image data of the operation behavior in the maintenance area, and determine the moment when the operation behavior occurs according to the image data, where the moment belongs to any preset time period.

[0049] Here, in the scenario of power equipment maintenance, the image data refers to the video stream or static pictures collected by the cameras installed at the work site. Among them, the information contained in the image data can include the position information of the operator, the operation process information, the operation frequency information, the tool usage, the environmental status, and the implementation status of safety measures.

[0050] Specifically, the image data of the operation behavior of the operator can be obtained in real time or regularly through the monitoring cameras or other image acquisition devices installed in the power equipment maintenance area.

[0051] Among them, the image data of the operation behavior is any single-frame image containing operation behavior information in the video stream. The operation behavior information can specifically include: the position information of the operator, the operation sequence, the operation frequency, and the tool usage.

[0052] By timestamping the image sequence of consecutive frame images to the frame level, and combining image processing techniques and computer vision algorithms to identify and locate the moment when each operation behavior occurs.

[0053] Exemplarily, the occurrence moment of the operation behavior belongs to any preset time period. The specific division of the preset time period can be set and adjusted according to the actual operation of the power plant. Exemplarily, three time periods can be set, namely morning (6:00 - 12:00), afternoon (12:00 - 18:00), and evening (18:00 - 6:00). In addition, twenty-four time periods can also be set, that is, each hour in 0:00 - 24:00 is a time period.

[0054] S102. Determine the abnormal discrimination strategy corresponding to the moment from the first configuration file, where each preset time period corresponds to an abnormal discrimination strategy, and the abnormal discrimination strategy is one of the following abnormal behavior categories: abnormal operation position, abnormal operation sequence, and abnormal operation frequency.

[0055] That is to say, after determining the specific moment when an operation behavior occurs, the abnormal discrimination strategy within the preset time period where the moment is located can be retrieved and determined from the first configuration file.

[0056] It should be noted that in specific implementation, the abnormal discrimination strategy corresponding to the preset time period can be comprehensively set based on various factors such as historical data statistical analysis, operation specification requirements, and expert experience. Each preset time period corresponds to an abnormal discrimination strategy, such as abnormal operation position, abnormal operation sequence, or abnormal operation frequency. Thus, it is ensured that the selected abnormal discrimination strategy has good reliability, which is convenient for subsequently judging whether the operation behavior is abnormal based on this abnormal discrimination strategy.

[0057] It can be understood that the reasonable division of the preset time period helps to apply different abnormal discrimination strategies targeted, which is conducive to improving the accuracy and real-time performance of abnormal behavior detection.

[0058] Exemplarily, in the first configuration file, different abnormal discrimination strategies can be configured for three typical time periods: morning (6:00 - 12:00), afternoon (12:00 - 18:00), and evening (18:00 - 6:00). In other examples, corresponding abnormal discrimination strategies can be set for each hour (0:00 - 24:00).

[0059] S103. According to the abnormal discrimination strategy corresponding to the moment, output the key behavior data in the image data and determine whether the key behavior data is abnormal.

[0060] That is to say, according to the abnormal discrimination strategy corresponding to the moment when the operation behavior occurs determined in step S102, it is necessary to output the key behavior data related to the current abnormal discrimination strategy from the image data of the operation behavior. For example, if the abnormal discrimination strategy is abnormal operation position, the position information of the operator in the image needs to be concerned; if the abnormal discrimination strategy is abnormal operation sequence, the execution sequence information of the operation steps in the image is concerned; if the abnormal discrimination strategy is abnormal operation frequency, the number of repetitions or intervals of the operation actions in the image is concerned.

[0061] It can be understood that the image data of the collected operation behavior can output the key behavior data related to the current abnormal discrimination strategy by combining technical means such as computer vision, machine learning, and pattern recognition.

[0062] Furthermore, abnormal operation position indicates that the human operation position is inconsistent with the preset position, abnormal operation sequence means that the human operation sequence does not conform to the preset sequence; abnormal operation frequency means that the human operation frequency is outside the preset frequency range. Based on this, it is possible to determine whether the key behavior data is abnormal, and further determine whether the operation behavior is correct.

[0063] The abnormal operation determination method for the maintenance of power equipment in the embodiments of the present application combines refined time period division with targeted discrimination strategies. Only by obtaining the key behavior data in the image data of the operation behavior according to the discrimination strategy can it be used to determine abnormal operations. The processing is simple and the time consumption is small, which is beneficial to improving the sensitivity to abnormal operation behaviors during the maintenance process, thereby enhancing the overall operation safety and standardization.

[0064] In some embodiments, before determining the abnormal discrimination strategy corresponding to the moment from the first configuration file, the first configuration file can also be reasonably set to further improve the accuracy of abnormal behavior detection. Figure 2 It is a schematic flow chart of an abnormal operation determination method for the maintenance of power equipment provided by the embodiments of the present application. Refer to Figure 2 , the method may include:

[0065] S201. Collect the image data of the operation behavior in the maintenance area, and determine the moment when the operation behavior occurs according to the image data, where the moment is the starting moment of any preset time period.

[0066] Specifically, when specifically dividing the preset time period, make the moment when the operation behavior occurs the starting moment of any preset time period. That is to say, the moments when two adjacent frames of operation behaviors occur are adjacent, and there will be some overlapping moments between the corresponding preset time periods.

[0067] S202. Obtain multiple historical abnormal behavior categories within each preset time period from the database.

[0068] Here, the historical abnormal behavior categories are the same as those in the foregoing embodiments, including abnormal operation positions, abnormal operation sequences, and abnormal operation frequencies.

[0069] Exemplarily, the database includes human abnormal operation cases diagnosed manually or automatically. Thus, various historical abnormal behavior categories that have occurred within each preset time period can be obtained therefrom.

[0070] It can be understood that according to different historical situations, each preset time period may include one or more of abnormal operation positions, abnormal operation sequences, and abnormal operation frequencies, and each type may have one or more.

[0071] S203. Use a preset weighted probability calculation model to assign corresponding weights to each historical abnormal behavior category within the same preset time period and cumulatively calculate their respective weighted probability values, and determine the historical abnormal behavior category corresponding to the maximum value among the weighted probability values as the abnormal discrimination strategy for this preset time period, where the weight is inversely proportional to time.

[0072] This step can be understood as follows: by introducing a weighted probability calculation model, which combines the factors of time correlation and frequency statistics to weight the historical abnormal behavior categories, ensuring that the historical abnormal behavior categories closer to the moment when the collected operation behavior occurs obtain higher weights.

[0073] In this way, by calculating the weighted probability values of operation position abnormality, operation sequence abnormality, and operation frequency abnormality respectively, and selecting the historical abnormal behavior category with the largest weighted probability value as the abnormal discrimination strategy within the preset time period, it can more accurately reflect the possible abnormal situations in the actual environment.

[0074] In a specific example, the preset weighted probability calculation model refers to

[0075]

[0076] where (t1, t2) is the preset time period, K is the set of historical abnormal behavior categories within (t1, t2), e -t represents the weight, represents the probability that the historical abnormal behavior category is k at time t, and is calculated through the following model:

[0077]

[0078] where X k is the number of historical abnormal behavior categories that are k at time t, and X K is the total sum of historical abnormal behavior categories at time t.

[0079] S204. Form a first configuration file by aggregating each abnormal discrimination strategy set.

[0080] That is to say, through the above steps, an optimized abnormal discrimination strategy is obtained for each preset time period. The optimal abnormal discrimination strategies corresponding to each time period are unified and aggregated together to form a complete set of abnormal discrimination strategies including all preset time periods, that is, the first configuration file.

[0081] Thus, for the image data of operation behaviors at different moments collected, the corresponding abnormal discrimination strategy can be determined from the first configuration file to judge whether the operation behavior is correct.

[0082] S205. Determine the abnormal discrimination strategy corresponding to the moment from the first configuration file. Among them, each preset time period corresponds to an abnormal discrimination strategy, and the abnormal discrimination strategy is one of the following abnormal behavior categories: operation position abnormality, operation sequence abnormality, and operation frequency abnormality.

[0083] It should be noted that the execution process of step S205 can refer to the execution process of S102, and will not be elaborated here.

[0084] S206. According to the anomaly discrimination strategy corresponding to the moment, output the key behavior data in the image data, and determine whether the key behavior data is abnormal.

[0085] It should be noted that the execution process of step S206 can refer to the execution process of S103, and will not be elaborated here.

[0086] The method for determining abnormal operations in the maintenance of power equipment according to the embodiments of the present application determines the anomaly discrimination strategy by analyzing historical anomaly behavior categories and applying a reasonable weighting mechanism, which is beneficial to improving the accuracy and reliability of abnormal operation judgment during the maintenance of power equipment.

[0087] In some embodiments, the key behavior data includes one of position information, the target device being contacted, and a frequency value.

[0088] Exemplarily, when the abnormal behavior strategy is abnormal operation position, it is necessary to extract position information from the image data of the operation behavior. Here, target detection and positioning technologies (such as YOLO, Faster R-CNN, etc.) can be combined to identify the key parts of the operator and the equipment, and then calculate the relative position coordinates or distance information of the operator relative to the key parts of the equipment.

[0089] In a specific example, for the image data of the operation behavior collected in real time, there are two situations: the operation behavior at the current moment is abnormal and the operation behavior at the current moment is normal. To ensure that the operators can perform maintenance in a standardized manner, response measures need to be taken for these two situations to improve the safety of the operation process. Therefore, in some embodiments, determining whether the key behavior data is abnormal includes:

[0090] S1. Obtain the first discrimination criterion corresponding to the anomaly discrimination strategy corresponding to the moment from the discrimination standard library, and the first discrimination criterion is one of a preset position range, a preset operation sequence, and a preset frequency range.

[0091] This step can be understood as that different anomaly discrimination strategies correspond to different discrimination criteria. Exemplarily, when the anomaly discrimination strategy corresponding to the moment when the operation behavior occurs is abnormal operation position, the first discrimination criterion obtained from the pre-constructed discrimination standard library is the preset position range.

[0092] The preset position range can be set according to the actual maintenance operation, and the present application does not limit this.

[0093] S2. If the key behavior data does not meet the first discrimination criterion, it is determined that the operation behavior is abnormal and a warning is issued. Or, if the key behavior data meets the first discrimination criterion, it is determined that the operation behavior is normal.

[0094] Exemplarily, if the position of the operator exceeds the preset safe area, it is determined that the key behavior data is abnormal, that is, the operation behavior does not conform to the specification and an error occurs. A warning can be issued through an alarm or an indicator light to remind the staff to suspend the operation.

[0095] If the position of the operator is within the preset reasonable area, it indicates that the operation behavior during the repair and maintenance process is compliant and safe. At this time, the alarm or the indicator light will not issue a warning, thus avoiding unnecessary interference and false alarms.

[0096] As mentioned above, after determining that the key behavior data is abnormal and issuing a warning, the operator can be prompted to suspend the operation, but the potential safety hazards in the repair and maintenance area caused by the abnormal operation need to be dealt with in a timely manner to ensure the safety of the repair and maintenance area. Therefore, in some examples, when the key behavior data does not meet the first discrimination criterion, it further includes:

[0097] Retrieve a solution from the preset expert knowledge base.

[0098] Specifically, the expert knowledge base includes the emergency level, on-site information, and solutions corresponding to the abnormal behavior categories.

[0099] Exemplarily, the emergency level can classify abnormal behaviors into different levels of emergency responses according to the severity of the abnormal behavior and the possible risk level, so as to quickly activate the corresponding emergency plan. The on-site information provides background information such as the on-site environmental conditions and equipment status related to the abnormal behavior, helping the operator better understand the context in which the problem occurs, so as to take more accurate and effective countermeasures.

[0100] The solution can include operation guides, troubleshooting steps, safety protection measures, normal operation process restoration, etc., to guide the on-site personnel to quickly restore the operation specification and effectively solve the safety hazards caused by the abnormal behavior.

[0101] Refer to Figure 3 As shown, the present application provides an abnormal operation determination device 30 for the repair and maintenance of power equipment, including an acquisition module 31, a determination module 32, and an execution module 33, where:

[0102] The acquisition module 31 is used to acquire the image data of the operation behavior in the repair and maintenance area and determine the moment when the operation behavior occurs according to the image data, where the moment belongs to any preset time period.

[0103] The determination module 32 is used to determine the abnormality discrimination strategy corresponding to the moment from the first configuration file, wherein each preset time period corresponds to an abnormality discrimination strategy, and the abnormality discrimination strategy is one of the following abnormal behavior categories: abnormal operation position, abnormal operation sequence and abnormal operation frequency.

[0104] The execution module 33 is used to output the key behavior data in the image data according to the abnormality identification strategy corresponding to the moment, and determine whether the key behavior data is abnormal.

[0105] The abnormal operation determination device 30 for the overhaul and maintenance of electric power equipment in the embodiment of the present application can execute the technical solution of the abnormal operation determination method for the overhaul and maintenance of electric power equipment in the above method embodiment, and its implementation principle and technical effect are similar and will not be repeated here.

[0106] Figure 4 This is a schematic diagram of the structure of the control device provided in the embodiment of the present application. Figure 4 The control device 40 includes: a processor 41 and a memory 42, the memory 42 is used to store computer programs, and the processor 41 is used to execute the computer programs stored in the memory 42 to implement the abnormal operation determination method for the inspection and maintenance of power equipment shown in any of the above method embodiments.

[0107] Specifically, the processor 41 and the memory 42 can communicate; illustratively, the processor 41 and the memory 42 communicate via a communication bus 43.

[0108] Exemplarily, the control device 40 may further include a communication interface, which may include a transmitter and / or a receiver.

[0109] Exemplarily, the processor 41 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application may be directly embodied as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0110] An embodiment of the present application also provides a computer-readable storage medium, in which computer execution instructions are stored. When the computer execution instructions are executed by a processor, the computer executes the above-mentioned abnormal operation determination method for the inspection and maintenance of power equipment.

[0111] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0112] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0113] In addition, in each embodiment of the present application, the functional units can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0114] The above integrated units implemented in the form of software functional units can be stored in a computer-readable storage medium. The above software functional units stored in a storage medium include several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute some steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0115] Those skilled in the art can clearly understand that for the convenience and brevity of description, only the above division of each functional module is used as an example. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the device described above can refer to the corresponding process in the foregoing method embodiments and will not be repeated here.

[0116] So far, the technical solutions of the present application have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easily understood by those skilled in the art that the protection scope of the present application is obviously not limited to these specific embodiments. The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for determining abnormal operations in the overhaul and maintenance of power equipment, characterized in that, Including: Collecting image data of the operation behavior in the maintenance area and determining the moment when the operation behavior occurs according to the image data, where the moment belongs to any preset time period; Determining the abnormal discrimination strategy corresponding to the moment from the first configuration file, where each preset time period corresponds to an abnormal discrimination strategy, and the abnormal discrimination strategy is one of the following abnormal behavior categories: abnormal operation position, abnormal operation sequence, and abnormal operation frequency; According to the abnormal discrimination strategy corresponding to the moment, outputting the key behavior data in the image data and determining whether the key behavior data is abnormal.

2. The method according to claim 1, characterized in that, The moment is the starting moment of any preset time period. Before determining the abnormal discrimination strategy corresponding to the moment from the first configuration file, it further includes: Obtaining multiple historical abnormal behavior categories within each preset time period from the database; Giving corresponding weights to each of the historical abnormal behavior categories within the same preset time period through a preset weighted probability calculation model and cumulatively calculating their respective weighted probability values, and determining the historical abnormal behavior category corresponding to the maximum value among the weighted probability values as the abnormal discrimination strategy for the preset time period, where the weight is inversely proportional to time; Aggregating each of the abnormal discrimination strategies to form the first configuration file.

3. The method according to claim 2, wherein The preset weighted probability calculation model refers to Among them, (t1, t2) is a preset time period, K is a set of historical abnormal behavior categories within (t1, t2), and e -t represents the weight, represents the probability that the historical abnormal behavior category is k at time t, which is calculated through the following model: Among them, X k is the number of historical abnormal behavior categories of k at time t, and X K is the sum of historical abnormal behavior categories at time t.

4. The method according to claim 2, wherein The key behavior data includes one of position information, the target device being contacted, and speed value.

5. The method according to claim 4, characterized in that, Determining whether the key behavior data is abnormal includes: Obtaining a first discrimination criterion corresponding to the abnormal discrimination strategy corresponding to the moment from the discrimination criterion library, where the first discrimination criterion is one of a preset position range, a preset operation sequence, and a preset frequency range; If the key behavior data does not conform to the first discrimination criterion, determining that the operation behavior is abnormal and issuing a warning, or, if the key behavior data conforms to the first discrimination criterion, determining that the operation behavior is normal.

6. The method according to claim 5, characterized in that, When the key behavior data does not conform to the first discrimination criterion, it further includes: Retrieving a solution from a preset expert knowledge base.

7. The method according to claim 6, characterized in that, The database includes human abnormal operation cases diagnosed manually or automatically; The expert knowledge base includes the emergency level, on-site information, and solutions corresponding to the abnormal behavior category.

8. An abnormal operation determination device for the overhaul and maintenance of power equipment, characterized in that Including: A collection module, a determination module, and an execution module, where: The collection module is used to collect image data of the operation behavior in the maintenance area and determine the moment when the operation behavior occurs according to the image data, where the moment belongs to the starting moment of any preset time period; The determination module is used to determine the discrimination strategy corresponding to the moment from the first configuration file, where each preset time period corresponds to a discrimination strategy, and the discrimination strategy is one of the following abnormal behavior categories: abnormal operation position, abnormal operation sequence, and abnormal operation frequency; The execution module is used to determine whether there is an operation abnormality in the collected image data according to the discrimination strategy corresponding to the moment.

9. A control device, characterized in that, It includes a memory and a processor. Program instructions are stored in the memory, and the processor is configured to call the program instructions in the memory to execute the method for determining abnormal operations in the overhaul and maintenance of power equipment according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Computer-executable instructions are stored in the computer-readable storage medium, and when executed by a processor, the computer-executable instructions are used to implement the method for determining abnormal operations in the overhaul and maintenance of power equipment according to any one of claims 1 to 7.

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