Intelligent inspection method and equipment for power supply and distribution facilities

Abstract

The invention discloses a power supply and distribution facility intelligent inspection method and equipment, and belongs to the technical field of power supply and distribution facility detection. Configuration data and historical inspection data of a power supply and distribution facility are analyzed, the operation condition of the power supply and distribution facility is predicted regularly, and an inspection mode is adjusted according to a prediction result; in this way, targeted inspection can be carried out on different power supply and distribution facilities during inspection, so that the inspection efficiency is improved, the inspection pertinence is higher, the faulty power supply and distribution facilities can be inspected more timely, and the current operation data and environmental data of the facilities are collected; whether the surrounding environment affects the safe operation of the power supply and distribution facility and whether the power supply and distribution facility has defects are judged, the two are combined for analysis to obtain an inspection result, more data are acquired, analysis is more rigorous, and the accuracy of detecting the operation state of the power supply and distribution facility during inspection is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply and distribution facility detection, and in particular to a method for troubleshooting cable hidden dangers based on environmental pre-sensing technology. Background Art

[0002] Traditional manual inspection methods have been gradually eliminated due to their time-consuming and labor-intensive nature and poor real-time performance. In the era of artificial intelligence development, intelligent robots are gradually entering human life. Using "intelligent inspection robots" to replace manual inspections has been widely used in various fields. With the introduction of intelligent inspection robots, labor costs can be saved, manual risks can be reduced, and work quality and efficiency can be improved; the application of intelligent rail inspection robot systems can effectively reduce equipment maintenance costs, improve the automation and intelligence level of equipment inspection and equipment management, and provide new technical detection methods and reliable safety guarantees for intelligent inspections and unmanned stations. However, the existing intelligent inspection robots are mostly inspected according to preset inspection procedures or manually controlled during the inspection process. It is difficult to automatically adjust the inspection mode according to the specific conditions of the power supply and distribution facilities. The inspection is not flexible enough, and the inspection is limited to the detection of dial information, appearance information and ambient temperature on the power supply and distribution facilities. There are few variables considered and it is difficult to accurately reflect the operating status of the power supply and distribution facilities.

[0003] Chinese patent, publication number: CN110533771A, publication date: December 3, 2019, discloses a smart inspection method for a substation, which establishes a three-dimensional model of the substation and displays the substation in three-dimensional video fusion; sets the inspection path of the inspection robot in the three-dimensional model; the substation inspection robot inspects according to the set inspection path and uploads the inspection data to the centralized control main station; the centralized control main station stores the inspection data and analyzes and processes the inspection data. Although the control of the intelligent inspection of the substation is realized, it can only inspect through the set inspection path, and cannot automatically adjust the inspection mode according to the actual situation. In addition, the inspection data is relatively single, and the accuracy of the inspection is not high. Summary of the invention

[0004] The present invention aims at the problem that the existing intelligent inspection equipment can only perform inspections according to preset inspection modes and cannot flexibly adjust the inspection modes according to actual conditions, thus causing low inspection efficiency. The present invention provides an intelligent inspection method for power supply and distribution facilities. By analyzing the configuration data and historical inspection data of the power supply and distribution facilities, the operating conditions of the power supply and distribution facilities are regularly predicted, and the inspection mode is adjusted according to the prediction results, so that targeted inspections can be performed on different power supply and distribution facilities during inspections, thereby improving the inspection efficiency. The current operating data and environmental data of the facilities are collected to determine whether the surrounding environment will affect the safe operation of the power supply and distribution facilities and whether the power supply and distribution facilities have defects. The two are combined for analysis to obtain inspection results, more data are collected, and the analysis is more rigorous, thereby improving the accuracy of detecting the operating status of the power supply and distribution facilities during inspections.

[0005] In a first aspect, a technical solution provided in an embodiment of the present invention is: a method for intelligent inspection of power supply and distribution facilities, comprising the following steps: S1. Obtain configuration data of power supply and distribution facilities, encode all power supply and distribution facilities based on the configuration data, and obtain historical operation data of power supply and distribution facilities corresponding to each code; S2. Regularly predict the operating status of power supply and distribution facilities based on historical operating data to obtain predicted operating status, and build an inspection model based on the predicted operating status; S3, inspect the power supply and distribution facilities based on the inspection mode, and collect the current operation data and environmental data of the facilities in real time; S4. Obtain the facility operation status based on the current operation data of the facility, and obtain the facility operation trend based on the environmental data combined with the facility operation status; S5. If the facility operation status is abnormal, a facility failure alarm is issued; if the facility operation trend is abnormal, a facility abnormality warning is issued, and a solution is generated based on the abnormal operation status and abnormal operation trend.

[0006] In this scheme, by analyzing historical inspection data, the operating behavior of power supply and distribution facilities is predicted in real time, and it is determined which power supply and distribution facilities have a higher probability of failure, so as to automatically generate the best inspection route and increase the inspection frequency of these power supply and distribution facilities, thereby improving the pertinence and efficiency of inspections; by encoding all power supply and distribution facilities based on configuration data, the coding information is associated with the corresponding power supply and distribution facility-related configuration data, which facilitates data exchange and improves the speed of data processing; by collecting the current operating data and environmental data of the facilities, based on the current operating data and environmental data of the facilities, the current operating data of the facilities is used as the main analysis factor, and the environmental data is used as the secondary influencing factor. Taking into account the influence relationship between the two, the operating status and operating trend of the power supply and distribution facilities are judged, avoiding rough judgment of the operating status of the power supply and distribution facilities based on some surface data, thereby improving the accuracy of detection during inspections.

[0007] Preferably, in S1, obtaining configuration data of power supply and distribution facilities, and encoding all power supply and distribution facilities based on the configuration data, comprises the following steps: Acquire configuration data of the power supply and distribution facility, wherein the configuration data at least includes an installation location of the power supply and distribution facility, a type of the power supply and distribution facility, and an operation mode; The power supply and distribution facilities are coded based on the installation order of their installation locations, and the power supply and distribution facility types and operating modes are associated with the codes.

[0008] In this scheme, in order to integrate the relevant data of the power supply and distribution facilities, the power supply and distribution facilities are encoded in the installation order based on the installation location of the power supply and distribution facilities, and the type and operation mode of the power supply and distribution facilities are associated with the code. This facilitates the subsequent analysis of the relevant data of the power supply and distribution facilities, which can be directly obtained based on the coding information, thereby improving the speed of data interaction, thereby improving the processing speed of the detected data during the inspection, and also facilitating the subsequent construction of the inspection mode based on the relevant data of the power supply and distribution facilities, thereby improving the inspection efficiency.

[0009] Preferably, in S2, regularly predicting the operating status of the power supply and distribution facilities based on historical operating data to obtain the predicted operating status includes the following steps: Based on machine learning technology, the mapping relationship of facility operation status is learned through training data sets to establish a power supply and distribution facility operation prediction model; The historical operation data is used as the input of the power supply and distribution facility operation prediction model, the predicted operation status of the power supply and distribution facility is output, and the codes of the power supply and distribution facilities corresponding to the predicted operation status are associated, and the predicted operation status at least includes the probability of failure and the time period of failure.

[0010] In this scheme, in order to build a patrol mode that can be flexibly adjusted for power supply and distribution facilities, the historical patrol data of power supply and distribution facilities, including but not limited to equipment status, fault type, maintenance records, operating environment parameters (such as temperature, humidity, voltage, etc.) and observation reports of patrol personnel, are deeply analyzed by using statistical analysis methods to explore the laws and characteristics of equipment operating status, identify potential risk factors and failure modes, and establish a prediction model to predict the operation of power supply and distribution facilities by using machine learning technologies such as support vector machines, neural networks, random forests, etc. It can be obtained that the probability of failure of each power supply and distribution facility is the highest in which time period. Subsequently, according to the prediction results, the facilities with high failure probability and the time period with high possibility of failure can be inspected, thereby improving the inspection efficiency and effectively improving the speed of identifying faulty facilities.

[0011] Preferably, in S2, constructing an inspection mode based on the predicted operating status includes the following steps: Based on the predicted probability of failure in the operating conditions, the codes of the corresponding power supply and distribution facilities are arranged in descending order of the probability of failure to obtain a list of hidden danger facilities; based on the list of hidden danger facilities, the inspection frequency of the power supply and distribution facilities with the corresponding codes is generated; Generate inspection time of power supply and distribution facilities with corresponding codes based on the predicted fault time period in the operation status; An inspection path is generated based on the installation location of the power supply and distribution facilities in the configuration data, and an inspection mode is generated based on the inspection path, inspection frequency and inspection time.

[0012] In this scheme, in order to construct the inspection mode, at least the inspection time, inspection route and inspection frequency need to be determined. Based on the historical number of failures of each power supply and distribution facility in the historical inspection data, the failure probability of each power supply and distribution facility is predicted, and the facilities are arranged from large to small according to the size of the failure probability. The power supply and distribution facility with the highest failure probability has the highest inspection frequency, and the inspection frequency is reduced in sequence according to the arrangement order. In certain time periods, the failure probability of power supply and distribution facilities will also increase, such as in summer and peak electricity consumption periods. During these time periods, the increase in electricity consumption will significantly increase the probability of facility failure. Therefore, if it is in these time periods, it is necessary to focus on inspections in these time periods, such as conducting inspections at a higher inspection frequency during these time periods. The inspection route is planned with the goal of minimizing the distance traveled by the inspection equipment during one inspection, so as to maximize the inspection quality and ensure the normal operation of the power supply and distribution facilities without reducing the inspection quality.

[0013] Preferably, in S3, the power supply and distribution facilities are detected based on the inspection mode, and the current operation data and environmental data of the facilities are collected in real time, including the following steps: Based on image recognition technology, image recognition is performed on power supply and distribution facilities and surrounding areas to obtain meter recognition data, behavior data, partial discharge detection data, and facility appearance data, and the above data are integrated to obtain the current operation data of the facilities; Based on the sensing facilities, the power supply and distribution facilities and the surrounding areas are identified to obtain the ambient temperature data and gas data, and the above data are integrated to obtain the environmental data.

[0014] In this solution, by analyzing the environmental data around the power supply and distribution facilities, obtaining the environmental temperature data and gas data, it is determined whether the surrounding environment will affect the safe operation of the power supply and distribution facilities; then the power supply and distribution facilities and the surrounding areas are imaged, and partial discharge sensors can be used, and then video patrols and partial discharge detection are used in combination. Ultrasonic and location waves are used to collect the partial discharge amplitude and periodic spectrum waveform of the facilities to determine whether the facilities are defective. Then, combined with meter recognition data, behavior data and facility appearance data, a variety of monitoring methods are used to coordinate and comprehensively analyze the relevant data of the operation of various power supply and distribution facilities, and the power supply and distribution facilities are monitored and inspected, thereby improving the detection accuracy and problem discovery speed of problems during inspections.

[0015] Preferably, in S4, obtaining the facility operation status based on the current operation data of the facility, and obtaining the facility operation trend based on the environmental data combined with the facility operation status, comprises the following steps: Based on the behavior data and facility appearance data in the current operation data, determine whether the facility operation area is normal. If normal, determine whether the facility is operating normally based on the meter identification data and partial discharge detection data in the current operation data. Obtain the facility operation status based on the judgment result. If the facility is not operating normally, determine that the facility operation status is abnormal. The ambient temperature data and gas data in the environmental data are analyzed to obtain the facility operation trend. If the ambient temperature exceeds the preset threshold or abnormal gas is generated, it is judged that the power supply and distribution facilities have an abnormal operation trend.

[0016] In this solution, in order to improve the accuracy of status detection of each power supply and distribution facility during inspection, the first level of judgment on the power supply and distribution facilities is performed through behavioral data and facility appearance data, because this data is the most obvious indicator of whether the power supply and distribution facilities are in a normal operating state, which can be intuitively displayed from these data. If it is detected that there are no other people or objects in the area that may affect the operation of the facilities and there are no other interferences on the appearance of the facilities, it can be judged that the facilities can operate normally in this area, and then the meter identification data and partial discharge detection data are used to judge whether the indicator lights and dials on the power supply and distribution facilities are normal and whether the internal discharge phenomenon is normal. Often, not only external observations but also combined with internal phenomena can intuitively show whether the power supply and distribution facilities are working normally. The operating status of the facility is determined through double judgment, which reduces the probability of misjudgment. If the operation is normal, the ambient temperature data and gas data are used to judge whether the temperature of the facility is too high or is in a state of rapid heating, as well as the surrounding gases such as CH4, H2S, SF6, etc. when the facility is running. When the content of any of these gases is too high, it will affect the operation of the facility and will most likely cause facility failure. Therefore, when these phenomena are detected, early warnings can be given in time to avoid facility failures and ensure electricity safety.

[0017] Preferably, the partial discharge detection data at least includes the partial discharge amplitude and periodic spectrum waveform of the power supply and distribution facilities, and the partial discharge amplitude and periodic spectrum waveform of the power supply and distribution facilities are collected based on ultrasonic waves and ground waves.

[0018] In this solution, partial discharge detection devices can be used to diagnose the partial discharge amplitude and periodic spectrum waveform of power supply and distribution facilities, and monitor and evaluate the partial discharge phenomenon inside the power supply and distribution facilities. Partial discharge refers to the discharge phenomenon caused by the insulation material inside the electrical equipment at the weak insulation position due to uneven electric field distribution. This discharge phenomenon will generate a unique electromagnetic signal. Through signal analysis and processing, the discharge location, discharge intensity and other information can be determined, thereby judging the working status of the electrical equipment and whether there are defects.

[0019] In a second aspect, a technical solution also provided in an embodiment of the present invention is: an intelligent inspection device for power supply and distribution facilities, including an intelligent inspection robot and a control module; The intelligent inspection robot is arranged on a running track and includes an operation data detection module, an environmental data detection module, and a lifting module; The operation data detection module is used to detect meter identification data, behavior data, partial discharge detection data and facility appearance data; the environmental data detection module is used to detect environmental temperature data and gas data; The lifting module is used to change the detection height of the operation data detection module and the environmental data detection module; The control module obtains the configuration data and historical operation data of the power supply and distribution facilities, adjusts the inspection mode based on the configuration data and the historical operation data, the intelligent inspection robot performs inspections based on the inspection mode and collects detection data according to the operation data detection module and the environmental data detection module and sends it to the control module, the control module analyzes the detection data, and outputs the facility operation status and facility operation trend based on the analysis results.

[0020] In this solution, the operation data of the facility is detected by an operation data detection module, wherein the operation data detection module at least includes a high-definition camera and a partial discharge collector, wherein the high-definition camera collects meter identification data, behavior data and facility appearance data, and uses image recognition technology to determine whether the dials and indicator lights on the power supply and distribution facilities are normal, and whether there are people or objects in the area around the power supply and distribution facilities that may affect the normal operation of the power supply and distribution facilities. The partial discharge collector is used to diagnose the discharge amplitude and periodic spectrum waveform, and observe whether there are defects in the electrical equipment inside the power supply and distribution facilities; the environmental data detection module at least includes sensors such as temperature and humidity, gas, smoke, and infrared thermal imaging equipment, and the above-mentioned equipment is used to identify some common factors in the environment, and by detecting the external and internal conditions of the power supply and distribution facilities, combined with external factors such as the environment, the equipment itself and factors outside the equipment are comprehensively considered to obtain inspection results, which greatly improves the accuracy of the inspection.

[0021] Preferably, the meter identification data at least includes dial data, switch opening and closing, and indicator light status on the power supply and distribution facilities; The behavior data at least includes the gathering behavior of people, the intrusion behavior of animals and the crossing of boundaries behavior of objects; The facility appearance data at least includes electrical appearance, equipment contamination, oil and liquid leakage, hanging floating objects and construction remains.

[0022] In this scheme, when there are people, animals or some objects around the power supply and distribution facilities, these factors may affect the normal operation of the power supply and distribution facilities, such as accidentally pressing certain buttons, which will cause unpredictable effects, so these factors need to be detected; when the facilities are in operation, as the operating time goes by, the facilities may age. Although there will be no failure for a while, when the electrical appearance of the facilities is damaged, oil and liquid leakage, floating objects hanging in the air, equipment damage, etc., are all hidden factors that may cause facility failures. Therefore, detecting these parameters helps to detect facility abnormalities in time and reduce losses; and the most intuitive display of whether the facilities are abnormal requires the identification of the dial data, opening and closing switches and indicator light status on the power supply and distribution facilities. When the power supply and distribution facilities are in operation, if they are operating normally, the dial data, opening and closing switches and indicator light status are all in the preset normal state. If one of the dials, opening and closing switches or indicator lights is abnormal, it means that the facility is also abnormal. Therefore, as the most intuitive reflection of the operating status of the power supply and distribution facilities, these data need to be detected.

[0023] Preferably, the control module analyzes the facility operation status and facility operation trend, and controls the inspection robot to issue a facility failure alarm if the facility operation status is abnormal; and controls the inspection robot to issue a facility abnormality warning if the facility operation trend is abnormal.

[0024] In this solution, the inspection robot can be equipped with communication equipment, voice equipment, alarm equipment, etc. The main purpose is to send an alarm signal to the staff in time when an abnormality is found in the power supply and distribution facilities, so that the staff can deal with it in time, thereby reducing the economic losses caused by the failure.

[0025] Beneficial effects of the present invention: (1) The present invention analyzes the configuration data and historical inspection data of power supply and distribution facilities, regularly predicts the operating conditions of power supply and distribution facilities, and adjusts the inspection mode according to the prediction results, so that targeted inspections can be carried out on different power supply and distribution facilities during inspections, thereby improving the inspection efficiency; (2) The present invention encodes all power supply and distribution facilities based on configuration data, and associates the encoding information with the corresponding configuration data related to the power supply and distribution facilities, thereby facilitating data exchange and improving the speed of data processing; (3) The present invention analyzes the environmental data around the power supply and distribution facilities, obtains the environmental temperature data and the gas data, and determines whether the surrounding environment will affect the safe operation of the power supply and distribution facilities. It then performs image recognition on the power supply and distribution facilities and the surrounding areas, and uses a combination of video inspection and partial discharge detection to collect the partial discharge amplitude and periodic spectrum waveform of the facilities by using ultrasonic and location waves to determine whether the facilities are defective. It then combines the meter recognition data, behavior data and facility appearance data, and uses a combination of multiple monitoring means to comprehensively analyze the relevant data of the operation of multiple power supply and distribution facilities, and monitors and inspects the power supply and distribution facilities, thereby improving the detection accuracy and problem discovery speed of problems during inspections.

[0026] The above invention content is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0028] Figure 1 This is a flow chart of an intelligent inspection method for power supply and distribution facilities of the present invention; Figure 2 A schematic block diagram of an intelligent inspection device for power supply and distribution facilities according to the present invention; Figure 3 A front view of an embodiment of the intelligent inspection robot of the present invention; Figure 4 A side view of an embodiment of the intelligent inspection robot of the present invention; Figure 5 An implementation method of the running track of the present invention; In the figure: 1- obstacle avoidance ultrasonic module, 2- robot body, 3- status indicator light, 4- lifting mechanism, 5- control module, 6- high-definition camera, 7- infrared thermal imaging device DETAILED DESCRIPTION

[0029] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0030] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0031] Example: Figure 1 As shown, in order to solve the problem that the existing intelligent inspection equipment can only perform inspections according to a preset inspection mode and cannot flexibly adjust the inspection mode according to actual conditions, resulting in low inspection efficiency, this embodiment provides an intelligent inspection method for power supply and distribution facilities, including the following steps: S1: Obtain configuration data of power supply and distribution facilities, encode all power supply and distribution facilities based on the configuration data, and obtain historical operation data of the power supply and distribution facilities corresponding to each code.

[0032] In this embodiment, obtaining configuration data of power supply and distribution facilities and encoding all power supply and distribution facilities based on the configuration data includes the following steps: Acquire configuration data of the power supply and distribution facility, wherein the configuration data at least includes an installation location of the power supply and distribution facility, a type of the power supply and distribution facility, and an operation mode; The power supply and distribution facilities are coded based on the installation order of their installation locations, and the power supply and distribution facility types and operating modes are associated with the codes.

[0033] In order to integrate the relevant data of the power supply and distribution facilities, the present embodiment encodes the power supply and distribution facilities in an installation order based on the installation location of the power supply and distribution facilities, and associates the type and operation mode of the power supply and distribution facilities with the code, so that the subsequent analysis of the relevant data of the power supply and distribution facilities can be directly obtained based on the coding information, thereby improving the speed of data interaction, thereby improving the processing speed of the detected data during the inspection, and also facilitating the subsequent construction of the inspection mode according to the relevant data of the power supply and distribution facilities, thereby improving the inspection efficiency.

[0034] S2: Regularly predict the operating status of power supply and distribution facilities based on historical operating data to obtain the predicted operating status, and build an inspection model based on the predicted operating status.

[0035] In this embodiment, the operation status of the power supply and distribution facilities is regularly predicted based on the historical operation data to obtain the predicted operation status, including the following steps: Based on machine learning technology, the mapping relationship of facility operation status is learned through training data sets to establish a power supply and distribution facility operation prediction model; The historical operation data is used as the input of the power supply and distribution facility operation prediction model, the predicted operation status of the power supply and distribution facility is output, and the codes of the power supply and distribution facilities corresponding to the predicted operation status are associated, and the predicted operation status at least includes the probability of failure and the time period of failure.

[0036] In order to construct an inspection mode that can be flexibly adjusted for power supply and distribution facilities, this embodiment uses statistical analysis methods to conduct in-depth analysis of historical inspection data of power supply and distribution facilities, including but not limited to equipment status, fault type, maintenance records, operating environment parameters (such as temperature, humidity, voltage, etc.) and observation reports of inspection personnel, to explore the laws and characteristics of equipment operating status, identify potential risk factors and failure modes, and establish a prediction model by using machine learning technologies such as support vector machines, neural networks, random forests, etc. to predict the operation of power supply and distribution facilities. It can be obtained that the probability of failure of each power supply and distribution facility is the highest in which time period, and subsequently, according to the prediction results, the facilities with high failure probability and the time period with high possibility of failure can be inspected, thereby improving the inspection efficiency and effectively improving the speed of identifying faulty facilities.

[0037] In this embodiment, the inspection mode is constructed based on the predicted operating status, including the following steps: Based on the predicted probability of failure in the operating conditions, the codes of the corresponding power supply and distribution facilities are arranged in descending order of the probability of failure to obtain a list of hidden danger facilities; based on the list of hidden danger facilities, the inspection frequency of the power supply and distribution facilities with the corresponding codes is generated; Generate inspection time of power supply and distribution facilities with corresponding codes based on the predicted fault time period in the operation status; An inspection path is generated based on the installation location of the power supply and distribution facilities in the configuration data, and an inspection mode is generated based on the inspection path, inspection frequency and inspection time.

[0038] In order to construct the inspection mode, the present embodiment at least needs to determine the inspection time, inspection path and inspection frequency, and the inspection mode can be rotated according to the demand. The inspection mode can be preset to make the inspection equipment adopt a fixed inspection route, time and frequency for inspection, and can also be adjusted automatically. Based on the historical failure times of each power supply and distribution facility in the historical inspection data, the failure probability of each power supply and distribution facility is predicted, and the power supply and distribution facility is arranged from large to small according to the size of the failure probability. The power supply and distribution facility with the highest failure probability has the highest inspection frequency, and the inspection frequency is reduced in sequence according to the arrangement order; and in certain time periods, the failure probability of the power supply and distribution facility will also increase, such as summer and peak power consumption periods. In these time periods, the increase in power consumption will significantly increase the probability of facility failure. Therefore, if it is in these time periods, it is necessary to focus on inspection in these time periods, such as inspecting at a higher inspection frequency in these time periods; and the inspection path is planned with the goal of minimizing the distance traveled by the inspection equipment during one inspection, so as to ensure the inspection quality to the greatest extent without reducing the inspection quality, and to ensure the normal operation of the power supply and distribution facilities.

[0039] S3: Inspect power supply and distribution facilities based on the inspection mode, and collect the current operation data and environmental data of the facilities in real time.

[0040] In this embodiment, the power supply and distribution facilities are detected based on the inspection mode, and the current operation data and environmental data of the facilities are collected in real time, including the following steps: Based on image recognition technology, image recognition is performed on power supply and distribution facilities and surrounding areas to obtain meter recognition data, behavior data, partial discharge detection data, and facility appearance data, and the above data are integrated to obtain the current operation data of the facilities; Based on the sensing facilities, the power supply and distribution facilities and the surrounding areas are identified to obtain the ambient temperature data and gas data, and the above data are integrated to obtain the environmental data.

[0041] This embodiment analyzes the environmental data around the power supply and distribution facilities to obtain environmental temperature data and gas data to determine whether the surrounding environment will affect the safe operation of the power supply and distribution facilities. It then performs image recognition on the power supply and distribution facilities and the surrounding areas, and can use partial discharge sensors, video patrols and partial discharge detection in a coordinated manner to determine whether the facilities are defective by collecting the partial discharge amplitude and periodic spectrum waveform of the facilities using ultrasonic and location waves. It then combines meter recognition data, behavior data and facility appearance data, and uses a variety of monitoring methods to comprehensively analyze the relevant data of the operation of various power supply and distribution facilities, monitor and inspect the power supply and distribution facilities, and improve the detection accuracy and problem discovery speed of problems during inspections.

[0042] S4: Obtain the facility operation status based on the current operation data of the facility, and obtain the facility operation trend based on the environmental data combined with the facility operation status.

[0043] In this embodiment, obtaining the facility operation status based on the current operation data of the facility, and obtaining the facility operation trend based on the environmental data combined with the facility operation status include the following steps: Based on the behavior data and facility appearance data in the current operation data, determine whether the facility operation area is normal. If normal, determine whether the facility is operating normally based on the meter identification data and partial discharge detection data in the current operation data. Obtain the facility operation status based on the judgment result. If the facility is not operating normally, determine that the facility operation status is abnormal. The ambient temperature data and gas data in the environmental data are analyzed to obtain the facility operation trend. If the ambient temperature exceeds the preset threshold or abnormal gas is generated, it is judged that the power supply and distribution facilities have an abnormal operation trend.

[0044] In order to improve the accuracy of status detection of each power supply and distribution facility during inspection, this embodiment performs the first-level judgment on the power supply and distribution facility through behavior data and facility appearance data, because this data is the most obvious indicator of whether the power supply and distribution facility is in a normal operating state, which can be intuitively displayed from these data. If it is detected that there are no other people or objects in the area that may affect the operation of the facility and there are no other interference objects on the appearance of the facility, it can be judged that the facility can operate normally in this area. Then, the indicator lights and dials on the power supply and distribution facility are normal and the internal discharge phenomenon is normal, which can be intuitively displayed not only from external observations but also in combination with internal phenomena. The operating state of the facility is determined by double judgment, which reduces the probability of misjudgment. If the operation is normal, it is judged whether the temperature of the facility is too high or in a state of rapid heating according to the ambient temperature data and gas data, as well as the surrounding gases such as CH4, H2S, SF6, etc. when the content of a certain gas is too high, it will affect the operation of the facility and will most likely cause facility failure. Therefore, when these phenomena are detected, early warnings can be given in time to avoid facility failure and ensure electricity safety.

[0045] S5: If the facility operation status is abnormal, a facility failure alarm is issued; if the facility operation trend is abnormal, a facility abnormality warning is issued, and a solution is generated based on the abnormal operation status and abnormal operation trend.

[0046] This embodiment analyzes historical inspection data to predict the operating behavior of power supply and distribution facilities in real time, determines which power supply and distribution facilities have a higher probability of failure, and automatically generates the best inspection route, thereby increasing the inspection frequency of these power supply and distribution facilities, thereby improving the targetedness and efficiency of inspections; encodes all power supply and distribution facilities based on configuration data, and associates the encoding information with the corresponding power supply and distribution facility-related configuration data to facilitate data exchange and improve the speed of data processing; collects the current operating data and environmental data of the facilities, based on the current operating data and environmental data of the facilities, takes the current operating data of the facilities as the main analysis factor, and takes the environmental data as the secondary influencing factor, and takes into account the influence relationship between the two, to judge the operating status and operating trend of the power supply and distribution facilities, thereby avoiding a rough judgment of the operating status of the power supply and distribution facilities based on some surface data, thereby improving the accuracy of detection during inspections.

[0047] Example 2: Figure 2 As shown, this embodiment also provides an intelligent inspection device for power supply and distribution facilities, including an intelligent inspection robot and a control module; The intelligent inspection robot is arranged on a running track and includes an operation data detection module, an environmental data detection module, and a lifting module; The operation data detection module is used to detect meter identification data, behavior data, partial discharge detection data and facility appearance data; the environmental data detection module is used to detect environmental temperature data and gas data; The lifting module is used to change the detection height of the operation data detection module and the environmental data detection module; The control module obtains the configuration data and historical operation data of the power supply and distribution facilities, adjusts the inspection mode based on the configuration data and the historical operation data, the intelligent inspection robot performs inspections based on the inspection mode and collects detection data according to the operation data detection module and the environmental data detection module and sends it to the control module, the control module analyzes the detection data, and outputs the facility operation status and facility operation trend based on the analysis results.

[0048] This embodiment detects facility operation data through an operation data detection module, wherein the operation data detection module at least includes a high-definition camera and a partial discharge collector, wherein the high-definition camera collects meter identification data, behavior data and facility appearance data, and uses image recognition technology to determine whether the dial and indicator light on the power supply and distribution facility are normal, and whether there are personnel or objects in the area around the power supply and distribution facility that may affect the normal operation of the power supply and distribution facility. The partial discharge collector is used to diagnose the discharge amplitude and periodic spectrum waveform, and observe whether there are defects in the electrical equipment inside the power supply and distribution facility; the environmental data detection module at least includes sensors such as temperature and humidity, gas, and smoke, as well as infrared temperature measuring equipment. The above equipment is used to identify some common factors in the environment, and the external and internal conditions of the power supply and distribution facility are detected, and then combined with external factors such as the environment, the equipment itself and the factors outside the equipment are comprehensively considered to obtain the inspection results, which greatly improves the accuracy of the inspection.

[0049] In this embodiment, the intelligent inspection robot can be used as follows Figure 3 and Figure 4 The structure shown in the figure, the intelligent inspection robot includes an obstacle avoidance ultrasonic module, a robot body, a status indicator light, a lifting mechanism, a control module, a high-definition camera, and an infrared thermal imaging device; The robot body is used to be connected to the running track to ensure that the robot body can slide freely on the running track. The status indicator light is installed on the robot body. When it is detected that the power supply and distribution facilities are in an abnormal operating state, a red light is turned on. If it is detected that the power supply and distribution facilities have an abnormal operating trend, a yellow light is turned on. If the power supply and distribution facilities are in a normal operating state, a green light is turned on.

[0050] The obstacle avoidance ultrasonic module is installed on the side of the robot body and is used to keep a distance from other intelligent inspection robots on the same track to prevent collision between the two and ensure inspection safety.

[0051] A lifting mechanism is connected to the bottom of the robot body. The lifting mechanism adopts a folding frame structure. When the folding frame is unfolded, it drives the high-definition camera connected to the bottom of the lifting mechanism to move downward, and can detect the operating data and environmental data below the power supply and distribution facilities. When the folding frame is retracted, it drives the high-definition camera connected to the bottom of the lifting mechanism to move upward, and can detect the operating data and environmental data above the power supply and distribution facilities, thereby ensuring that the operating data and environmental data of the power supply and distribution facilities can be fully detected, reducing the detection blind spots; the lifting mechanism can also be lifted and lowered with two symmetrical fixed lifting platforms; the lifting mechanism is set with a limit. When the machine moves downward, the robot will automatically stop descending when it hits a person or an object, so as to avoid the robot from being damaged or the human body from being injured. The height of the retracted state is proportional to the stroke. By adding a position detection device, the current lifting position can be obtained in real time, thereby realizing the function of position control.

[0052] The control module can be connected to the monitoring platform to transmit the detected data to the monitoring platform. The monitoring platform can also issue control instructions to the control module, and the control module controls the operation of the intelligent inspection robot.

[0053] The high-definition camera is equipped with infrared thermal imaging equipment, which can detect the temperature of power supply and distribution facilities. After the intelligent inspection robot collects the corresponding data, it can transmit the network digital signal or analog signal to the control platform of the monitoring center through the data transmission channel (wired, wireless, carrier, etc.); the core decoder supports direct control of TCP / IP network or RS485 serial communication mode to realize the online control function of the monitoring platform.

[0054] The control module can automatically store, count and intelligently analyze and process massive amounts of data, such as inspection point data query and statistics, alarm information query and defect anomaly analysis, environmental record data and infrared temperature measurement data analysis. All reports and statements support query, export and printing. Exported data supports EXECL format and text format, with complete exported information, accurate and complete records, convenient for storage, reporting, decision-making and archiving. When communication is interrupted or the content of the received message is abnormal, the system equipment may not work properly after losing the network, and the background has a network communication disconnection alarm. Multiple photos of the inspection results of each device are uniformly collected in the directory of each device, and the system logic is clear for later troubleshooting.

[0055] Figure 5 It is an implementation method of the running track. The intelligent inspection robot moves on the running track. In order to locate the intelligent inspection robot in real time, a photoelectric encoder and a RIFD electronic tag can be used together. RIFD tags are set at key positions to accurately locate the set working position and curve.

[0056] When the intelligent inspection robot approaches the task point, it slows down and stops in advance, adjusts the high-definition camera, and collects information on the power supply and distribution facilities at the task point.

[0057] When the intelligent inspection robot approaches a turning point, it automatically decelerates and passes through. According to the set permissible speed of the curve, the robot is decelerated to the permissible speed to pass through.

[0058] The intelligent inspection robot is positioned during movement by combining the servo motor encoder and positioning tag positioning, ensuring that the intelligent inspection robot control module and monitoring platform can understand the inspection robot's position information in real time.

[0059] In this embodiment, the meter identification data at least includes dial data, switch opening and closing, and indicator light status on the power supply and distribution facility; The behavior data at least includes the gathering behavior of people, the intrusion behavior of animals and the crossing of boundaries behavior of objects; The facility appearance data at least includes electrical appearance, equipment contamination, oil and liquid leakage, hanging floating objects and construction remains.

[0060] In this embodiment, when there are people, animals or some objects around the power supply and distribution facilities, these factors may affect the normal operation of the power supply and distribution facilities, such as accidentally pressing certain buttons, which will cause unpredictable effects, so these factors need to be detected; when the facilities are in operation, as the operating time goes by, the facilities may age. Although there will be no failure for a while, when the electrical appearance of the facilities is damaged, oil and liquid leakage, floating objects hanging in the air, equipment damage, etc., are all hidden factors that may cause facility failures. Therefore, detecting these parameters helps to timely discover facility abnormalities and reduce losses; and the most intuitive display of whether the facilities are abnormal requires the identification of the dial data, opening and closing switches and indicator light status on the power supply and distribution facilities. When the power supply and distribution facilities are in operation, if they are operating normally, the dial data, opening and closing switches and indicator light status are all in the preset normal state. If one of the dials, opening and closing switches or indicator lights is abnormal, it means that the facility is also abnormal. Therefore, as the most intuitive reflection of the operating status of the power supply and distribution facilities, these data need to be detected.

[0061] In this embodiment, the control module analyzes the facility operation status and facility operation trend. If the facility operation status is abnormal, the inspection robot is controlled to issue a facility failure alarm; if the facility operation trend is abnormal, the inspection robot is controlled to issue a facility abnormality warning.

[0062] The inspection robot of this embodiment can be equipped with communication equipment, voice equipment, alarm equipment, etc. The main purpose is to send an alarm signal to the staff in time when an abnormality is found in the power supply and distribution facilities, so that the staff can deal with it in time, thereby reducing the economic losses caused by the failure.

[0063] It can be seen from the above embodiments that at least the following substantial effects are achieved: (1) The present invention analyzes the configuration data and historical inspection data of power supply and distribution facilities, regularly predicts the operating conditions of power supply and distribution facilities, and adjusts the inspection mode according to the prediction results, so that targeted inspections can be carried out on different power supply and distribution facilities during inspections, thereby improving the inspection efficiency; (2) The present invention encodes all power supply and distribution facilities based on configuration data, and associates the encoding information with the corresponding configuration data related to the power supply and distribution facilities, thereby facilitating data exchange and improving the speed of data processing; (3) The present invention analyzes the environmental data around the power supply and distribution facilities, obtains the environmental temperature data and the gas data, and determines whether the surrounding environment will affect the safe operation of the power supply and distribution facilities. It then performs image recognition on the power supply and distribution facilities and the surrounding areas, and uses a combination of video inspection and partial discharge detection to collect the partial discharge amplitude and periodic spectrum waveform of the facilities by using ultrasonic and location waves to determine whether the facilities are defective. It then combines the meter recognition data, behavior data and facility appearance data, and uses a combination of multiple monitoring means to comprehensively analyze the relevant data of the operation of multiple power supply and distribution facilities, and monitors and inspects the power supply and distribution facilities, thereby improving the detection accuracy and problem discovery speed of problems during inspections.

[0064] The specific implementation described above is a preferred implementation of a method and device for intelligent inspection of power supply and distribution facilities of the present invention, and is not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A method for intelligent inspection of power supply and distribution facilities, characterized in that: The following steps are involved: S1. Obtain configuration data of power supply and distribution facilities, encode all power supply and distribution facilities based on the configuration data, and obtain historical operation data of power supply and distribution facilities corresponding to each code; S2. Regularly predict the operating status of power supply and distribution facilities based on historical operating data to obtain predicted operating status, and build an inspection model based on the predicted operating status; S3, inspect the power supply and distribution facilities based on the inspection mode, and collect the current operation data and environmental data of the facilities in real time; S4. Obtain the facility operation status based on the current operation data of the facility, and obtain the facility operation trend based on the environmental data combined with the facility operation status; S5. If the facility operation status is abnormal, a facility failure alarm is issued; if the facility operation trend is abnormal, a facility abnormality warning is issued, and a solution is generated based on the abnormal operation status and abnormal operation trend.

2. The intelligent inspection method for power supply and distribution facilities according to claim 1, characterized in that: In S1, configuration data of power supply and distribution facilities are obtained, and all power supply and distribution facilities are encoded based on the configuration data, including the following steps: Acquire configuration data of the power supply and distribution facility, wherein the configuration data at least includes an installation location of the power supply and distribution facility, a type of the power supply and distribution facility, and an operation mode; The power supply and distribution facilities are coded based on the installation order of their installation locations, and the power supply and distribution facility types and operating modes are associated with the codes.

3. The intelligent inspection method for power supply and distribution facilities according to claim 1, characterized in that: In S2, the operation status of the power supply and distribution facilities is regularly predicted based on the historical operation data to obtain the predicted operation status, including the following steps: Based on machine learning technology, the mapping relationship of facility operation status is learned through training data sets to establish a power supply and distribution facility operation prediction model; The historical operation data is used as the input of the power supply and distribution facility operation prediction model, the predicted operation status of the power supply and distribution facility is output, and the codes of the power supply and distribution facilities corresponding to the predicted operation status are associated, and the predicted operation status at least includes the probability of failure and the time period of failure.

4. The intelligent inspection method for power supply and distribution facilities according to claim 1, characterized in that: In S2, the inspection mode is constructed based on the predicted operating status, including the following steps: Based on the predicted probability of failure in the operating conditions, the codes of the corresponding power supply and distribution facilities are arranged in descending order of the probability of failure to obtain a list of hidden danger facilities; based on the list of hidden danger facilities, the inspection frequency of the power supply and distribution facilities with the corresponding codes is generated; Generate inspection time of power supply and distribution facilities with corresponding codes based on the predicted fault time period in the operation status; An inspection path is generated based on the installation location of the power supply and distribution facilities in the configuration data, and an inspection mode is generated based on the inspection path, inspection frequency and inspection time.

5. The intelligent inspection method for power supply and distribution facilities according to claim 1, characterized in that: In S3, the power supply and distribution facilities are inspected based on the inspection mode, and the current operation data and environmental data of the facilities are collected in real time, including the following steps: Based on image recognition technology, image recognition is performed on power supply and distribution facilities and surrounding areas to obtain meter recognition data, behavior data, partial discharge detection data, and facility appearance data, and the above data are integrated to obtain the current operation data of the facilities; Based on the sensing facilities, the power supply and distribution facilities and the surrounding areas are identified to obtain the ambient temperature data and gas data, and the above data are integrated to obtain the environmental data.

6. The intelligent inspection method for power supply and distribution facilities according to claim 1, characterized in that: In S4, the operation status of the facility is obtained based on the current operation data of the facility, and the operation trend of the facility is obtained based on the environmental data combined with the operation status of the facility, including the following steps: Based on the behavior data and facility appearance data in the current operation data, determine whether the facility operation area is normal. If normal, determine whether the facility is operating normally based on the meter identification data and partial discharge detection data in the current operation data. Obtain the facility operation status based on the judgment result. If the facility is not operating normally, determine that the facility operation status is abnormal. The ambient temperature data and gas data in the environmental data are analyzed to obtain the facility operation trend. If the ambient temperature exceeds the preset threshold or abnormal gas is generated, it is judged that the power supply and distribution facilities have an abnormal operation trend.

7. A method for intelligent inspection of power supply and distribution facilities according to claim 5 or 6, characterized in that: The partial discharge detection data at least includes the partial discharge amplitude and periodic spectrum waveform of the power supply and distribution facilities, and the partial discharge amplitude and periodic spectrum waveform of the power supply and distribution facilities are collected based on ultrasonic waves and ground waves.

8. An intelligent inspection device for power supply and distribution facilities, applicable to an intelligent inspection method for power supply and distribution facilities according to any one of claims 1 to 7, characterized in that: Including intelligent inspection robot and control module; The intelligent inspection robot is arranged on a running track and includes an operation data detection module, an environmental data detection module, and a lifting module; The operation data detection module is used to detect meter identification data, behavior data, partial discharge detection data and facility appearance data; the environmental data detection module is used to detect environmental temperature data and gas data; The lifting module is used to change the detection height of the operation data detection module and the environmental data detection module; The control module obtains the configuration data and historical operation data of the power supply and distribution facilities, adjusts the inspection mode based on the configuration data and the historical operation data, the intelligent inspection robot performs inspections based on the inspection mode and collects detection data according to the operation data detection module and the environmental data detection module and sends it to the control module, the control module analyzes the detection data, and outputs the facility operation status and facility operation trend based on the analysis results.

9. The intelligent inspection device for power supply and distribution facilities according to claim 8, characterized in that: The meter identification data at least includes the dial data, switch opening and closing and indicator light status on the power supply and distribution facilities; The behavior data at least includes the gathering behavior of people, the intrusion behavior of animals and the crossing of boundaries behavior of objects; The facility appearance data at least includes electrical appearance, equipment contamination, oil and liquid leakage, hanging floating objects and construction remains.

10. The intelligent inspection device for power supply and distribution facilities according to claim 8, characterized in that: The control module analyzes the facility operation status and facility operation trend, and controls the inspection robot to issue a facility failure alarm if the facility operation status is abnormal, and controls the inspection robot to issue a facility abnormality warning if the facility operation trend is abnormal.

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