Visual monitoring system and method for electric power facilities

Abstract

The invention discloses an electric power facility visual monitoring system and method, and relates to the technical field of electric power facility operation and maintenance, the system comprises the following components: a multi-source heterogeneous data acquisition module, a multi-source heterogeneous data processing module, a real-time risk assessment and maintenance strategy adjustment module and a visual display module; according to the method, the electric parameters, the environment data, the image video data, the audio data and other multi-source heterogeneous data are integrated, the intelligent fusion algorithm and the association analysis model are utilized, the potential association relationship among different types of data is deeply mined, and therefore the operation condition of the electric power facility can be reflected more comprehensively and accurately, and the operation efficiency of the electric power facility is improved. According to the method, the accuracy and integrity of monitoring data are improved, the system can timely find and pre-judge potential risks, powerful decision support is provided for operation and maintenance personnel, and safety risks and maintenance cost increase caused by incomplete or inaccurate monitoring are effectively avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of power facility operation and maintenance, and in particular to a power facility visual monitoring system and method thereof. Background Art

[0002] With the continuous expansion of the scale and increase in complexity of power facilities, the requirements for operation and maintenance monitoring technology are becoming higher and higher. Modern power facility monitoring technology has gradually developed from single data monitoring to comprehensive monitoring and analysis of multi-source heterogeneous data, aiming to achieve real-time monitoring of the operating status of power facilities and prediction of potential risks through comprehensive and accurate data collection and analysis.

[0003] However, there are still many limitations in the existing technical fields related to power facility monitoring and maintenance. On the one hand, when processing monitoring data, traditional monitoring systems often only focus on simple aggregation or independent analysis of a single type of data, and fail to fully explore and utilize the potential correlation between different types of data. This single data analysis method leads to insufficient comprehensiveness and accuracy of the actual operating conditions of power facilities, especially in the ability to predict potential risks. On the other hand, the visual interface layout of common monitoring systems is usually fixed, lacking flexibility and adaptability, and cannot be flexibly adjusted according to the diverse needs of users and the dynamic changes of monitoring scenarios, thereby affecting the convenience and timeliness of users in obtaining key information. In addition, traditional power facility maintenance plans are mostly formulated according to fixed time intervals or equipment operation time. This method does not fully consider the real-time operating status and potential risks of the equipment, which can easily lead to untimely or unnecessary maintenance operations, making the maintenance work lack of pertinence and efficiency, which is not conducive to the stable operation and efficient operation and maintenance of power facilities.

[0004] In view of the above problems, it is necessary to optimize the existing visual monitoring system and method of power facilities, through innovative data processing, analysis and display methods, combined with real-time risk assessment to dynamically adjust the maintenance strategy, in order to overcome the many limitations of the existing technology. Therefore, it is of great significance to develop a visual monitoring system and method of power facilities that can comprehensively realize the above characteristics. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and to provide a visual monitoring system and method for power facilities. It can comprehensively collect various types of data of power facilities through a multi-source heterogeneous data acquisition module, and use a multi-source heterogeneous data processing module to perform deep fusion and correlation analysis of the data, so as to comprehensively and accurately reflect the operating status of the power facilities. At the same time, through the real-time risk assessment and maintenance strategy adjustment module, combined with advanced risk assessment models, the real-time risks of equipment under different working conditions are quantitatively assessed, and the maintenance strategy is dynamically adjusted according to the risk value. In addition, the present invention also uses a visual display module to display the operating status, potential risks and data details of power facilities in an intuitive and flexible manner, thereby improving the work efficiency and accuracy of operation and maintenance personnel.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: On the one hand, a visual monitoring system for electric power facilities includes the following components: a multi-source heterogeneous data acquisition module, a multi-source heterogeneous data processing module, a real-time risk assessment and maintenance strategy adjustment module and a visual display module; The multi-source heterogeneous data acquisition module is equipped with various types of sensors for collecting multi-source heterogeneous data from different levels of power facilities. Each sensor transmits the collected data to the multi-source heterogeneous data processing module in real time according to the acquisition frequency and rules; The multi-source heterogeneous data processing module includes a data receiving unit, a data fusion unit, a correlation analysis unit, an operation status evaluation unit and an information transmission unit, wherein the data receiving unit receives various types of multi-source heterogeneous data transmitted from the multi-source heterogeneous data acquisition module, and performs preliminary format verification and sorting operations on the received data; the data fusion unit adopts an intelligent fusion algorithm to perform deep fusion processing on the received electrical parameters, environmental data, image video data and audio data; the correlation analysis unit uses a pre-built and trained intelligent correlation model to perform correlation analysis based on the data generated after the fusion processing, and further mines the correlation relationship between the various features in the fused data to further explore the power facilities in operation. The operation status assessment unit assesses the operation status of the power facilities based on the analysis results and fused data produced by the correlation analysis unit. Specifically, by comparing the relevant data with the preset standard normal operation status indicators one by one, it is determined whether the power facilities are in normal operation. If the result of the determination is that there is an abnormality, the specific type and degree of the abnormality are clarified to provide data support and basis for subsequent fault diagnosis and maintenance suggestion generation operations. The information transmission unit transmits the relevant data information generated after the above processing, analysis and evaluation of a series of operations to the real-time risk assessment and maintenance strategy adjustment module and the visualization display module; The real-time risk assessment and maintenance strategy adjustment module includes a real-time risk assessment unit and a maintenance strategy adjustment unit, wherein the real-time risk assessment unit continuously collects the fused data transmitted by the multi-source heterogeneous data processing module, and integrates and processes the equipment parameters and basic operating condition data of the power facilities, and uses the risk assessment model to carry out quantitative assessment operations on the real-time risks of the equipment under different working conditions, and determines the risk value of the equipment at the current moment by deeply analyzing the relationship between the integrated various data and the parameters of the risk assessment model. The maintenance strategy adjustment unit sets a matching risk threshold based on the specific type of the power facility, the degree of importance, and historical maintenance data factors. When the risk value exceeds the preset risk threshold, the maintenance task mechanism is automatically triggered, and the maintenance priority, scope, and specific measures are adjusted in a targeted manner based on the difference in risk levels; The visualization display module includes an adaptive layout optimization unit, an operation status display unit, a potential risk display unit and a data detail display unit, wherein the adaptive layout optimization unit monitors the user's operating habits, the priority of the current monitoring task and the real-time changes of the monitoring data in real time, and uses the adaptive visualization layout algorithm to automatically optimize the layout of the visualization interface based on the monitoring information. The operation status display unit uses visualization means to display the current operation status of the power facilities evaluated by the multi-source heterogeneous data processing module. When it is predicted that there is a potential risk, the potential risk display unit marks the parts that may have risks on the equipment schematic diagram, highlights them with different colors or flashing effects, and provides text descriptions. The data detail display unit provides the function of viewing various types of original collected data and fused data in detail in the form of tables, images and audio playback.

[0007] Furthermore, the multi-source heterogeneous data acquisition module is equipped with various types of sensors, specifically including electrical parameter sensors, environmental sensors, image acquisition devices and audio acquisition devices, wherein the electrical parameter sensors include voltage sensors and current sensors, and the environmental sensors include temperature sensors, humidity sensors and wind speed sensors.

[0008] Furthermore, the data fusion unit of the multi-source heterogeneous data processing module adopts an intelligent fusion algorithm to perform deep fusion processing on the received electrical parameters, environmental data, image and video data, and audio data. The algorithm formula of the intelligent fusion algorithm is: ,in: represents the comprehensive feature value of the fused data, represents the weight coefficient, where , are the fusion weight coefficients corresponding to electrical parameter data, environmental data, image data and audio data, respectively. Their values ​​are determined according to the relative importance of each type of data to the evaluation of the operating status of power facilities. Represents different types of raw data vectors, specifically: for electrical parameter data, Contains the collected voltage value and current value , for environmental data , containing the collected ambient temperature value , Ambient humidity value , wind speed value Data, for image data, , that is, the image feature vector collected and quantized by the image acquisition device. For audio data, , the audio spectrum feature vector collected and converted by the audio acquisition device.

[0009] Furthermore, the association analysis unit of the multi-source heterogeneous data processing module uses a pre-built and trained intelligent association model to perform association analysis based on the data generated after fusion processing. The association mining algorithm formula is: ,in, Represents the potential risk correlation index, which is used to measure the degree of correlation between the current data feature combination and the potential risk. is the number of key features extracted from the fused data for analyzing potential risks. It is The weight coefficient of the key features, It is The cosine similarity between the key feature vector and the potential risk vector is calculated to measure the similarity between them, thereby reflecting the correlation between the key feature and the potential risk.

[0010] Furthermore, the operation status evaluation unit in the multi-source heterogeneous data processing module evaluates the operation status of the power facility based on the analysis results and the fused data produced by the correlation analysis unit. The comprehensive operation deviation evaluation algorithm formula is ,in, It represents an operating status evaluation indicator, which is used to measure the overall deviation between the current operating status of power facilities and the standard normal operating status. is the number of different types of data involved in the evaluation, It is The running deviation value of this data.

[0011] Furthermore, in the operation status evaluation unit in the multi-source heterogeneous data processing module, It is The operating deviation value of this data is: , assuming its rated voltage is ,but , for the current value , assuming its rated current is ,but , in the environmental data, for the ambient temperature value , assuming that the suitable temperature range is ,but Similarly, the deviation between the ambient humidity and wind speed data and the corresponding standard values ​​can be calculated. In the image data, by comparing the current image feature vector Compared with the image feature vector under standard normal operation The difference is used to calculate the deviation value, and the calculation result is set to ,but As the running deviation value of the image data, it is calculated. For audio data, by comparing the current audio spectrum feature vector Compared with the audio spectrum feature vector under standard normal operation The difference is used to calculate the deviation value, and the calculation result is set to ,but It is used as a running deviation value of the audio data in the calculation.

[0012] Furthermore, the real-time risk assessment and maintenance strategy adjustment module uses a risk assessment model to carry out quantitative assessment operations on the real-time risks of equipment under different working conditions. The risk quantification algorithm formula is: ,in, Indicates the current real-time risk probability of the device. It is the number of risk influencing factors considered, including electrical parameters, environmental factors, equipment operation time and equipment load, which have an impact on the equipment operation status and risk. It is The weight coefficient of each risk influencing factor is determined according to the relative importance of each factor in causing equipment risk. is a vector containing all relevant data, i.e. ,in , , and They are the electrical parameter data vector, environmental data vector, image data vector, and audio data vector mentioned above, as well as the device operating time and device load data. It is for The risk quantification function of each risk influencing factor is used to calculate the contribution of the factor to the equipment risk based on all relevant data, and to set the risk threshold based on the type, importance and historical maintenance data of the power facility. ,when When a risk occurs, the maintenance task mechanism is triggered. The maintenance priority is adjusted according to the difference between the risk value and the threshold and the severity of the risk type. The maintenance scope is adjusted according to the equipment risk area situation shown in the risk assessment. Specific maintenance measures are determined based on the risk assessment results and the actual situation of the equipment.

[0013] Furthermore, the visualization display module uses an adaptive visualization layout algorithm to automatically optimize the layout of the visualization interface based on the monitoring information. The algorithm formula of the adaptive visualization layout algorithm is: ,in, Indicates the layout position indicator of the data element on the visualization interface, which is used to determine the relative position of a data element on the visualization interface. is the data importance weight, which is determined according to the importance of the data to the current monitoring task. It is a data importance score, which quantitatively scores the importance of data based on the data type and the current risk assessment results. is the user preference weight, which is determined according to the user's operating habits and the priority of the current monitoring task. It is a user preference score, which quantitatively scores user preferences based on the user's operation behavior on the visual interface.

[0014] In another aspect, a method for visual monitoring of electric power facilities is provided, the method comprising the following specific steps: Data collection: Through the sensors in the multi-source heterogeneous data collection module, the electrical parameters, environment, image video and audio data of the power facilities are collected synchronously according to the preset collection requirements; Data fusion: After preliminary verification of various types of data received, the intelligent fusion algorithm is used for deep fusion, to establish the correlation mapping between data and generate the fused data representation; Correlation analysis: Use pre-trained intelligent correlation models to conduct in-depth correlation analysis on the fused data, identify the corresponding relationship between data features and the operating status and potential risks of power facilities, and explore potential patterns and risk factors; Operation status assessment: Based on the correlation analysis results and fusion data, conduct a comprehensive assessment of the operation status of power facilities to determine whether they are operating normally and identify the type and degree of abnormality; Real-time risk assessment and maintenance strategy adjustment: Continuously collect data, use risk assessment models to quantitatively assess equipment risks, set thresholds based on risk values, and automatically trigger and adjust maintenance tasks when thresholds are exceeded; Visual display: Automatically optimize the layout of the visual interface based on user operating habits, monitoring task priorities and data changes, display facility operation status and potential risk information, and provide detailed data viewing function.

[0015] Compared with the prior art, the electric power facility visualization monitoring system and method thereof have the following beneficial effects: 1. The present invention integrates multi-source heterogeneous data such as electrical parameters, environmental data, image and video data, and audio data, and uses intelligent fusion algorithms and association analysis models to deeply explore the potential correlation between different types of data, so as to more comprehensively and accurately reflect the operating conditions of power facilities. It not only improves the accuracy and completeness of monitoring data, but also enables the system to timely discover and predict potential risks, providing powerful decision-making support for operation and maintenance personnel, and effectively avoiding safety risks and increased maintenance costs caused by incomplete or inaccurate monitoring.

[0016] 2. The present invention uses an adaptive visualization layout algorithm and a real-time risk assessment mechanism to dynamically adjust the layout of the visualization interface according to the user's operating habits, the priority of the monitoring task, and the real-time changes of the monitoring data, to ensure that key information can be highlighted, thereby improving the convenience and timeliness of users in obtaining key information. At the same time, through the real-time risk assessment and maintenance strategy adjustment module, the system can automatically trigger and adjust maintenance tasks based on the current real-time risk value of the equipment, thereby achieving the flexibility and pertinence of the maintenance strategy, which not only improves the efficiency and quality of maintenance work, but also reduces the waste of resources and cost increase caused by improper or excessive maintenance.

[0017] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 It is a structural schematic diagram of a visual monitoring system for electric power facilities; Figure 2 The figure is a flow chart of a visual monitoring method for power facilities. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Embodiment 1

[0021] This embodiment describes in detail a specific application of a power facility visual monitoring system and method thereof in substation facility monitoring and maintenance. Through the coordinated operation of various modules of the present invention, comprehensive and accurate monitoring and maintenance of substation facilities are achieved.

[0022] In the substation, various types of sensors are reasonably arranged to collect various types of multi-source heterogeneous data. Voltage sensors are installed at various outgoing terminals and important bus connection points to collect the voltage value of the line. The current sensor is installed on the current transmission path and cooperates with the voltage sensor to collect the current value. These electrical parameter data provide basic information for understanding the power transmission status of the substation power grid. At the same time, temperature sensors are widely distributed in indoor transformer rooms, switch cabinet rooms and around outdoor key equipment to collect ambient temperature values. , humidity sensors are installed in the same area to collect ambient humidity values , wind speed sensors are placed at appropriate locations around the substation to collect wind speed values ,Environmental data helps to analyze the impact of environmental factors on equipment operation.,In addition, the camera is installed in a position where it can clearly capture the visible area and overall appearance of key equipment such as transformers, switchgear, and busbars.,The collected image data can be quantified into feature vectors , whose elements can reflect different features of the image. The microphone is placed near equipment that may produce abnormal sounds, such as transformers and distribution cabinets. The collected audio data is converted into a spectrum feature vector , whose elements can represent different spectral characteristics of audio. Each sensor transmits the collected data to the multi-source heterogeneous data processing module according to the preset collection frequency and data format rules.

[0023] In the multi-source heterogeneous data processing module, the data receiving unit is equipped with a data receiving interface that is compatible with the commonly used communication protocols of substations (such as Modbus, IEC61850, etc.), and receives various types of multi-source heterogeneous data transmitted from the multi-source heterogeneous data acquisition module. When receiving, the data will be format checked to check its integrity and legality, and the data that fails the check will be marked and recorded, and retransmission will be requested at the same time. The data that passes the check will be classified and stored according to the data type, collection time, collection equipment and other dimensions to prepare for subsequent processing. Data fusion unit: In order to deeply fuse multi-source heterogeneous data, a collaborative fusion algorithm based on multi-source data is adopted. First, the fusion weight coefficient is determined according to the relative importance of each type of data to the evaluation of the operating status of substation facilities. For example, when evaluating the operating condition of a transformer, electrical parameter data (vector ) and environmental data (vector ) is usually more critical, and its corresponding and The value may be relatively large, and then the corresponding feature normalization function is performed for different types of data vectors , such as the electrical parameter data vector The normalization function can be used to map the voltage value, current value, etc. to the [0, 1] interval, and the environmental data vector Perform appropriate standardization on the image data vector Use feature scaling algorithm to adjust the element range and apply it to the audio data vector Using spectrum amplitude normalization processing, etc., finally, according to the formula Calculate the comprehensive eigenvalue of the fused data , this value can fully reflect the overall operation status of the substation facilities and provide a unified data basis for subsequent analysis. The correlation analysis unit extracts key features for analyzing potential risks from the fused data. The number of key features is set to These key features include voltage fluctuation amplitude in electrical parameters, humidity change rate in environmental data, device appearance color change extracted from image data, sound frequency change extracted from audio data, etc. The weight coefficient is determined according to the importance of each key feature in predicting different risks (such as transformer overheating, insulation aging, equipment failure, etc.). ( For example, the voltage fluctuation amplitude may be important in judging the leakage risk, and its corresponding The value is relatively large, and then the potential risk is quantified and encoded into a vector form, and then the cosine similarity between the key feature vector and the potential risk vector is calculated. , according to the formula Calculate potential risk correlation index , whose value range is -1 to 1, can be used to judge the possible risk of substation facilities. The closer it is to 1, the greater the possibility of potential risk. The operation status assessment unit first determines the standard value of each data type according to the standard normal operation status of the substation equipment. For example, if the rated voltage of the transformer is , rated current is The suitable range of ambient temperature is Etc., and then calculate the running deviation value of each data type separately , for electrical parameter data, voltage value The running deviation value , current value The running deviation value , for environmental data, the ambient temperature value The running deviation value Similarly, the deviations of ambient humidity, wind speed, etc. can be calculated. For image data, by comparing the current image feature vector Compared with the image feature vector under standard normal operation The difference calculation deviation value is set to For audio data, similarly, by comparing the current audio spectrum feature vector Compared with the audio spectrum feature vector under standard normal operation , the difference calculation deviation value is set to Finally, according to the formula Calculating health evaluation metrics , operation and maintenance personnel can intuitively judge whether the operating status of substation facilities is close to the standard state or there is a large deviation, so as to decide whether further inspection or maintenance measures are needed.

[0024] In the real-time risk assessment and maintenance strategy adjustment module, the real-time risk assessment unit continuously collects the fused data transmitted by the multi-source heterogeneous data processing module and the monitoring data changes fed back by the visualization display module, and integrates the basic data such as the equipment parameters of the substation equipment (such as transformer model, rated power, rated voltage, etc.), operating conditions (such as current load, operating time, operating mode, etc.), etc., to build a complete data set, and determines the weight coefficient of each risk influencing factor based on the various risk influencing factors considered (such as abnormal electrical parameters, changes in environmental factors, excessive equipment operation time, etc.) , construct a vector containing all relevant data ,in , , , The above mentioned electrical parameter data vector, environment data vector, image data vector, audio data vector, etc. may also include other related data. Risk quantification function for each risk influencing factor , according to the formula Calculate the current real-time risk probability of the device This probability value can reflect the current risk level of the substation equipment. The value range is 0 to 1. The closer to 1, the higher the risk. The maintenance strategy adjustment unit sets the risk threshold based on factors such as the importance of the substation (such as the main transformer is more important than general auxiliary equipment) and historical maintenance data (such as the frequency of transformer failures, maintenance cycles, maintenance effects, etc.). , when the risk value calculated by the real-time risk assessment unit When the threshold is exceeded, the maintenance task mechanism is triggered, and the maintenance priority is adjusted according to the difference between the risk value and the threshold and the severity of the risk type. If the risk value far exceeds the threshold and the risk type is serious, the maintenance task priority is set to high. If the risk value is close to the threshold and the risk type is relatively light, the priority is set to low. The maintenance scope is adjusted according to the equipment risk area situation shown by the risk assessment. If it is determined that the risk of overheating of a certain winding of the transformer is high, the maintenance scope will be focused on the local area for targeted inspection and maintenance. If the risk involves the entire equipment, such as the risk caused by aging of the entire equipment, the maintenance scope will be expanded to the entire equipment. Specific maintenance measures are determined based on the risk assessment results and the actual situation of the equipment. For example, if it is determined that the risk is caused by abnormal electrical parameters, measures such as adjusting the voltage regulator may be taken. If it is determined that the risk is caused by environmental factors, measures such as improving the equipment operating environment may be taken. If it is determined that the risk is caused by aging of equipment components, measures such as replacing aging components may be taken.

[0025] In the visualization module, the adaptive layout optimization unit determines the layout algorithm formula based on data weight and user preference by monitoring the operation behavior of the operation and maintenance personnel on the visualization interface (such as frequently checking transformer-related data, focusing on certain equipment images, etc.), receiving task priority information from other modules (such as focusing on faulty equipment-related data during troubleshooting), and receiving the latest data transmitted from the multi-source heterogeneous data processing module in real time. The various parameters in the data, among which the data importance weight is determined according to the importance of the data to the current monitoring task For example, during troubleshooting, data directly related to the faulty equipment (such as the electrical parameters and images of the transformer) are of high importance. The value is relatively large, and the importance of the data is quantitatively scored based on factors such as data type and current risk assessment results. If a piece of data is judged to be associated with high risk, then May be close to 1; the user preference weight is determined based on factors such as the user's operating habits and the priority of the current monitoring task. If the user frequently checks a certain type of data during daily inspections, then this type of data will be The value is relatively large, and the user's preference is quantitatively scored based on the user's operation behavior on the visual interface (such as the number of clicks, browsing time, etc.) If users frequently click to view certain data, It may be higher. According to this formula, the layout position index of data elements on the visual interface is determined. , realize automatic optimization of the layout of the visual interface, so that key information is easier for operation and maintenance personnel to pay attention to. The operation status display unit displays the current operation status of the substation facilities evaluated by the multi-source heterogeneous data processing module in a variety of visual forms. For example, a line graph is used to display the voltage and current change trends of each line in the substation over time, so that the operation and maintenance personnel can intuitively understand the dynamic situation of power transmission. The distribution of ambient temperature and humidity in different areas (such as transformer rooms, switch cabinet rooms, etc.) is displayed with a bar graph to grasp the distribution trend of environmental factors. The operation status of key parts of key equipment such as transformers and switch cabinets is marked with the help of equipment schematics, so that the operation and maintenance personnel can fully understand the operation of various parts of the equipment. When the potential risk correlation index calculated by the correlation analysis unit meets the preset conditions (such as greater than a certain threshold), the potential risk display unit marks the parts that may have risks on the equipment schematic, and takes It is highlighted in a specific visual way (such as red mark, flashing effect, etc.), and accompanied by detailed text description, explaining the type of potential risk (such as transformer overheating risk, insulation aging risk, etc.), possible causes (such as high ambient temperature and abnormal changes in electrical parameters, etc.) and estimated scope of impact (such as impact on local equipment operation, impact on the power supply of the entire substation, etc.), so that operation and maintenance personnel can quickly perceive and pay attention to potential risks. The data details display unit provides the function of viewing various types of original collected data and fused data in detail in the form of tables, images, audio playback, etc. Operation and maintenance personnel can view the values ​​of electrical parameters such as voltage and current collected at different times through tables, view the appearance photos of equipment such as transformers and switch cabinets through images, and listen to the sound of the equipment during operation through audio playback, so that they can have an in-depth understanding of the specific data when necessary and conduct more detailed analysis and troubleshooting. Embodiment 2

[0026] This embodiment describes in detail a specific application of a power facility visualization monitoring system and method in the operation management of transmission lines. Through the present invention, all-round monitoring, accurate risk assessment and efficient maintenance management of transmission line facilities are achieved, which effectively guarantees the safe and stable operation of transmission lines.

[0027] Multi-source heterogeneous data acquisition module Along the transmission line, voltage sensors and current sensors are installed at key line nodes (such as the connection point between the transmission tower and the transmission line, line branch points, etc.) to accurately collect the voltage and current values ​​on the line, and grasp the electrical parameter status of the transmission line in real time, so as to timely discover the hidden dangers of electrical faults. Temperature sensors and humidity sensors are installed around the transmission tower base and some specific areas where the line passes (such as valleys, rivers, etc. that may be greatly affected by environmental factors) to monitor changes in ambient temperature and humidity, because changes in ambient temperature and humidity may affect the insulation performance of the transmission line and the corrosion of metal parts. Cameras are installed on the transmission tower and the transmission Near the key parts of the power lines (such as insulator strings, line connections, etc.), the appearance of these parts and the appearance of the entire line can be clearly captured. By continuously collecting image and video data, operation and maintenance personnel can remotely check whether there are any parts damage, deformation, insulator rupture, etc. in the transmission lines. Microphones are placed in areas near the transmission lines where abnormal sounds may be generated (such as areas where wind blows the transmission lines and makes sounds, areas where line discharge may produce sounds, etc.) to collect audio data during the operation of the equipment. Once the transmission lines have abnormal conditions such as discharge, loose components and friction, the microphones can capture the corresponding abnormal sound signals and provide audio clues for fault diagnosis.

[0028] In the multi-source heterogeneous data processing module, the data receiving unit successfully receives the multi-source heterogeneous data collected and transmitted by various sensors through the data receiving interface compatible with the commonly used communication protocol for transmission line monitoring. After format verification and integrity check, the data is classified and stored according to the type, collection time, collection equipment and other dimensions to prepare for subsequent processing. The data fusion unit determines the fusion weight coefficient based on the relative importance of each type of data to the evaluation of the operating status of the transmission line facilities. For example, when evaluating the insulation performance of the transmission line, electrical parameter data (voltage, current) and environmental data (temperature, humidity) are more critical to determine whether it is operating normally. Therefore, the weight coefficients corresponding to these two types of data are relatively large during fusion. After mapping the characteristic values ​​of each type of data to the appropriate scale range through the feature normalization function, the comprehensive characteristic value of the fused data is calculated according to the fusion algorithm formula. This value can comprehensively reflect the overall operating status of the transmission line facilities. The correlation analysis unit extracts voltage fluctuation amplitude, temperature and humidity from the fused data. The key features such as rate of change, change in equipment appearance color (extracted from image data), and change in sound frequency (extracted from audio data) are selected. The weight coefficients are determined according to the importance of these key features in predicting different risks (such as transmission line short circuit, insulation aging, component damage, etc.). Combined with the vector form after the potential risk is quantified and encoded, the potential risk correlation index is obtained by calculating the cosine similarity and other methods, so as to predict the possible risks of transmission line facilities in advance. The operation status assessment unit calculates the operation deviation values ​​of various types of data (electrical parameters, environmental data, equipment appearance and sound status determined by image video and audio data, etc.) according to the standard normal operating status parameters of the transmission line equipment (such as rated voltage, rated current, suitable temperature range, etc.), and then calculates the operation status assessment index according to the comprehensive operation deviation assessment algorithm formula. Based on this, the operation and maintenance personnel can intuitively judge whether the operation status of the transmission line facilities is close to the standard status or there is a large deviation, so as to decide whether to take maintenance measures.

[0029] In the real-time risk assessment and maintenance strategy adjustment module, the real-time risk assessment unit collects the fused data transmitted by the multi-source heterogeneous data processing module and the monitoring data changes fed back by the visualization display module, and integrates the basic data such as the equipment parameters of the transmission line equipment (such as transmission line model, rated power, rated voltage, etc.), operating conditions (such as current load, operating time, operating mode, etc.), etc., to build a complete data set. Then, based on the various risk influencing factors considered (such as abnormal electrical parameters, changes in environmental factors, excessive equipment operation time, etc.), the weight coefficient of each risk influencing factor is determined, and the current real-time risk probability of the equipment is calculated according to the risk quantification algorithm formula through the relevant risk quantification function. For example, when the transmission line is operating at a high load and the ambient temperature is high, the real-time risk of the transmission line short circuit risk can be accurately assessed by combining data such as electrical parameters, ambient temperature and equipment operation time. Risk probability: The maintenance strategy adjustment unit sets a suitable risk threshold based on factors such as the importance of the transmission line (e.g., the importance of key transmission lines is higher than that of general transmission lines) and historical maintenance data (e.g., the frequency of previous transmission line failures, maintenance cycles, maintenance effects, etc.). When the risk value calculated by the real-time risk assessment unit exceeds the threshold, the maintenance task mechanism is triggered. For example, if the short-circuit risk value of the transmission line exceeds the set threshold, the maintenance priority will be adjusted according to the difference in risk levels, and the maintenance task of the transmission line may be set to a high priority. Based on the equipment risk area situation displayed by the risk assessment, if it is judged that the short-circuit risk of a certain section of the transmission line is high, the maintenance scope will be focused on the local area for targeted inspection and maintenance. Based on the risk assessment results and the actual situation of the equipment, if it is judged that the risk is caused by abnormal electrical parameters, specific maintenance measures such as adjusting the voltage regulator may be taken.

[0030] In the visualization display module, the adaptive layout optimization unit monitors the operation behavior of the operation and maintenance personnel on the visualization interface (such as frequently checking the electrical parameters of the transmission line, focusing on the image of the transmission line, etc.), receives task priority information from other modules (such as focusing on the data related to the faulty equipment during troubleshooting), and receives the latest data transmitted from the multi-source heterogeneous data processing module in real time, determines the various parameters in the layout algorithm formula based on data weights and user preferences, and thus realizes automatic optimization of the visualization interface layout. For example, when troubleshooting, the electrical parameters, images, audio and other data related to the faulty transmission line are highlighted in the core position of the visualization interface, which is convenient for the operation and maintenance personnel to quickly focus on key information. The operation status display unit displays the voltage and current change trends of the transmission line over time through a line graph, and displays the distribution of ambient temperature and humidity in different areas (such as around the transmission tower base, the valley through which the line passes, etc.) through a bar graph. The operation status of each key part of the transmission line is marked with the help of the equipment schematic diagram, so that the operation and maintenance personnel can intuitively understand the operation status of the transmission line facilities, which has the potential to Risk display unit: When the potential risk correlation index calculated by the correlation analysis unit meets the preset conditions (such as greater than a certain threshold), the parts that may have risks are marked on the equipment schematic diagram, highlighted in a specific visual way (such as red marking, flashing effect, etc.), and accompanied by detailed text descriptions, explaining the type of potential risks (such as transmission line short circuit risk, insulation aging risk, etc.), possible causes (such as high ambient temperature and abnormal changes in electrical parameters, etc.) and the estimated scope of impact (such as the impact on the operation of local equipment, the impact on the power supply of the entire transmission line, etc.), so that operation and maintenance personnel can quickly perceive and pay attention to potential risks. The data details display unit provides the function of viewing various types of original collected data and fused data in detail, and displays data details in the form of tables, images, audio playback, etc. For example, operation and maintenance personnel can view the voltage and current values ​​at different time points through tables, view the appearance photos of the transmission line through images, and listen to the sound of the equipment during operation through audio playback, so that they can have an in-depth understanding of the specific data when necessary and conduct more detailed analysis and troubleshooting.

[0031] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A visual monitoring system for electric power facilities, characterized in that: The system includes the following components: multi-source heterogeneous data acquisition module, multi-source heterogeneous data processing module, real-time risk assessment and maintenance strategy adjustment module and visualization display module; The multi-source heterogeneous data acquisition module is equipped with various types of sensors for collecting multi-source heterogeneous data from different levels of power facilities. Each sensor transmits the collected data to the multi-source heterogeneous data processing module in real time according to the acquisition frequency and rules; The multi-source heterogeneous data processing module includes a data receiving unit, a data fusion unit, a correlation analysis unit, an operation status evaluation unit and an information transmission unit, wherein the data receiving unit receives various types of multi-source heterogeneous data transmitted from the multi-source heterogeneous data acquisition module, and performs preliminary format verification and sorting operations on the received data; the data fusion unit adopts an intelligent fusion algorithm to perform deep fusion processing on the received electrical parameters, environmental data, image video data and audio data; the correlation analysis unit uses a pre-built and trained intelligent correlation model to perform correlation analysis based on the data generated after the fusion processing, and further mines the correlation relationship between the various features in the fused data to further explore the power facilities in operation. The operation status assessment unit assesses the operation status of the power facilities based on the analysis results and fused data produced by the correlation analysis unit. Specifically, by comparing the relevant data with the preset standard normal operation status indicators one by one, it is determined whether the power facilities are in normal operation. If the result of the determination is that there is an abnormality, the specific type and degree of the abnormality are clarified to provide data support and basis for subsequent fault diagnosis and maintenance suggestion generation operations. The information transmission unit transmits the relevant data information generated after the above processing, analysis and evaluation of a series of operations to the real-time risk assessment and maintenance strategy adjustment module and the visualization display module; The real-time risk assessment and maintenance strategy adjustment module includes a real-time risk assessment unit and a maintenance strategy adjustment unit, wherein the real-time risk assessment unit continuously collects the fused data transmitted by the multi-source heterogeneous data processing module, and integrates and processes the equipment parameters and basic operating condition data of the power facilities, and uses the risk assessment model to carry out quantitative assessment operations for the real-time risks of the equipment under different working conditions, and determines the risk value of the equipment at the current moment by deeply analyzing the relationship between the integrated various data and the risk assessment model parameters. The maintenance strategy adjustment unit sets a matching risk threshold based on the specific type of the power facility, the degree of importance, and historical maintenance data factors. When the risk value exceeds the preset risk threshold, the maintenance task mechanism is automatically triggered, and the maintenance priority, scope, and specific measures are adjusted in a targeted manner based on the difference in risk levels; The visualization display module includes an adaptive layout optimization unit, an operation status display unit, a potential risk display unit and a data detail display unit, wherein the adaptive layout optimization unit monitors the user's operating habits, the priority of the current monitoring task and the real-time changes of the monitoring data in real time, and uses the adaptive visualization layout algorithm to automatically optimize the layout of the visualization interface based on the monitoring information. The operation status display unit uses visualization means to display the current operation status of the power facilities evaluated by the multi-source heterogeneous data processing module. When it is predicted that there is a potential risk, the potential risk display unit marks the parts that may have risks on the equipment schematic diagram, highlights them with different colors or flashing effects, and provides text descriptions. The data detail display unit provides the function of viewing various types of original collected data and fused data in detail in the form of tables, images and audio playback.

2. A visual monitoring system for electric power facilities according to claim 1, characterized in that: The multi-source heterogeneous data acquisition module is equipped with various types of sensors, including electrical parameter sensors, environmental sensors, image acquisition devices and audio acquisition devices, wherein the electrical parameter sensors include voltage sensors and current sensors, and the environmental sensors include temperature sensors, humidity sensors and wind speed sensors.

3. A visual monitoring system for electric power facilities according to claim 1, characterized in that: The data fusion unit of the multi-source heterogeneous data processing module adopts an intelligent fusion algorithm to perform deep fusion processing on the received electrical parameters, environmental data, image and video data, and audio data. The algorithm formula of the intelligent fusion algorithm is: ,in: represents the comprehensive feature value of the fused data, represents the weight coefficient, where , are the fusion weight coefficients corresponding to electrical parameter data, environmental data, image data and audio data, respectively. Their values ​​are determined according to the relative importance of each type of data to the evaluation of the operating status of power facilities. Represents different types of raw data vectors, specifically: for electrical parameter data, Contains the collected voltage value and current value , for environmental data , containing the collected ambient temperature value , Ambient humidity value , wind speed value Data, for image data, , that is, the image feature vector collected and quantized by the image acquisition device. For audio data, , the audio spectrum feature vector collected and converted by the audio acquisition device.

4. A visual monitoring system for electric power facilities according to claim 1, characterized in that: The association analysis unit of the multi-source heterogeneous data processing module uses a pre-built and trained intelligent association model to perform association analysis based on the data generated after fusion processing. The association mining algorithm formula is: ,in, Represents the potential risk correlation index, which is used to measure the degree of correlation between the current data feature combination and the potential risk. is the number of key features extracted from the fused data for analyzing potential risks. It is The weight coefficient of the key features, It is The cosine similarity between the key feature vector and the potential risk vector is calculated to measure the similarity between them, thereby reflecting the correlation between the key feature and the potential risk.

5. The visual monitoring system for electric power facilities according to claim 1, characterized in that: The operation status evaluation unit in the multi-source heterogeneous data processing module evaluates the operation status of the power facilities based on the analysis results and the fused data produced by the correlation analysis unit. The comprehensive operation deviation evaluation algorithm formula is ,in, It represents an operating status evaluation indicator, which is used to measure the overall deviation between the current operating status of power facilities and the standard normal operating status. is the number of different types of data involved in the evaluation, It is The running deviation value of this data.

6. A visual monitoring system for electric power facilities according to claim 5, characterized in that: In the operation status evaluation unit in the multi-source heterogeneous data processing module, It is The operating deviation value of this data is: , assuming its rated voltage is ,but , for the current value , assuming its rated current is ,but , in the environmental data, for the ambient temperature value , assuming that the suitable temperature range is ,but Similarly, the deviation between the ambient humidity and wind speed data and the corresponding standard values ​​can be calculated. In the image data, by comparing the current image feature vector Compared with the image feature vector under standard normal operation The difference is used to calculate the deviation value, and the calculation result is ,but As the running deviation value of the image data, it is calculated. For audio data, by comparing the current audio spectrum feature vector Compared with the audio spectrum feature vector under standard normal operation The difference is used to calculate the deviation value, and the calculation result is ,but It is used as a running deviation value of the audio data in the calculation.

7. A visual monitoring system for electric power facilities according to claim 1, characterized in that: The real-time risk assessment and maintenance strategy adjustment module uses a risk assessment model to conduct quantitative assessment operations on the real-time risks of equipment under different working conditions. The risk quantification algorithm formula is: ,in, Indicates the current real-time risk probability of the device. It is the number of risk influencing factors considered, including electrical parameters, environmental factors, equipment operation time and equipment load, which have an impact on the equipment operation status and risk. It is The weight coefficient of each risk influencing factor is determined according to the relative importance of each factor in causing equipment risk. is a vector containing all relevant data, i.e. ,in , , and They are the electrical parameter data vector, environmental data vector, image data vector, and audio data vector mentioned above, as well as the device operating time and device load data. It is for The risk quantification function of each risk influencing factor is used to calculate the contribution of the factor to the equipment risk based on all relevant data, and to set the risk threshold based on the type, importance and historical maintenance data of the power facility. ,when When a risk occurs, the maintenance task mechanism is triggered. The maintenance priority is adjusted according to the difference between the risk value and the threshold and the severity of the risk type. The maintenance scope is adjusted according to the equipment risk area situation shown in the risk assessment. Specific maintenance measures are determined based on the risk assessment results and the actual situation of the equipment.

8. A visual monitoring system for electric power facilities according to claim 1, characterized in that: The visualization display module automatically optimizes the layout of the visualization interface using an adaptive visualization layout algorithm based on the monitoring information. The algorithm formula of the adaptive visualization layout algorithm is: ,in, Indicates the layout position indicator of the data element on the visualization interface, which is used to determine the relative position of a data element on the visualization interface. is the data importance weight, which is determined according to the importance of the data to the current monitoring task. It is a data importance score, which quantitatively scores the importance of data based on the data type and the current risk assessment results. is the user preference weight, which is determined according to the user's operating habits and the priority of the current monitoring task. It is a user preference score, which quantitatively scores user preferences based on the user's operation behavior on the visual interface.

9. A method for visual monitoring of electric power facilities, applicable to a visual monitoring system for electric power facilities as claimed in any one of claims 1 to 8, characterized in that: The method comprises the following specific steps: Data collection: Through the sensors in the multi-source heterogeneous data collection module, the electrical parameters, environment, image video and audio data of the power facilities are collected synchronously according to the preset collection requirements; Data fusion: After preliminary verification of various types of data received, the intelligent fusion algorithm is used for deep fusion, to establish the correlation mapping between data and generate the fused data representation; Correlation analysis: Use pre-trained intelligent correlation models to conduct in-depth correlation analysis on the fused data, identify the corresponding relationship between data features and the operating status and potential risks of power facilities, and explore potential patterns and risk factors; Operation status assessment: Based on the correlation analysis results and fusion data, conduct a comprehensive assessment of the operation status of power facilities to determine whether they are operating normally and identify the type and degree of abnormality; Real-time risk assessment and maintenance strategy adjustment: Continuously collect data, use risk assessment models to quantitatively assess equipment risks, set thresholds based on risk values, and automatically trigger and adjust maintenance tasks when thresholds are exceeded; Visual display: Automatically optimize the layout of the visual interface based on user operating habits, monitoring task priorities and data changes, display facility operation status and potential risk information, and provide detailed data viewing function.

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