A system for full-area security monitoring and personnel behavior analysis of converter stations
By constructing a multimodal monitoring network and machine learning model, and dynamically adjusting the acquisition frequency, the system solves the problems of multi-dimensional data loss and data security in the converter station safety monitoring system, enabling early identification of equipment anomalies and efficient operation and maintenance, while reducing energy consumption and labor costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA SOUTHERN POWER GRID EXTRA HIGH VOLTAGE POWER TRANSMISSION CO LIUZHOU BRANCH
- Filing Date
- 2026-01-28
- Publication Date
- 2026-06-02
AI Technical Summary
Existing converter station safety monitoring systems rely on a single monitoring method, lack multi-dimensional data fusion and intelligent analysis capabilities, cannot predict equipment anomalies and personnel violations, have insufficient data security, and lack mobile management capabilities, resulting in delayed equipment fault identification and the risk of data leakage.
A multimodal monitoring network is constructed using infrared temperature measurement, camera, vibration and sound sensors. Data analysis is performed using machine learning models, the acquisition frequency is dynamically adjusted, closed-loop control and encrypted storage and transmission are integrated, and mobile monitoring and real-time feedback are supported.
It improves the accuracy of equipment anomaly detection, reduces equipment downtime losses, lowers energy and labor costs, enhances data security, and enables early warning and efficient operation and maintenance.
Smart Images
Figure CN122137122A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of converter station safety monitoring technology, specifically, it relates to a converter station full-area safety monitoring and personnel behavior analysis system. Background Technology
[0002] Converter stations are the core hubs in power systems that convert AC to DC power, and their safe operation is crucial for ensuring grid stability. Converter stations contain a wide variety of equipment, including transformers, converter valves, and control and protection systems. These devices generate heat during operation, and abnormal temperatures can lead to equipment malfunctions or even safety accidents. Furthermore, safety management of the converter station's work area is paramount, as personnel violations or unauthorized entry into restricted areas can pose potential risks. It is also worth noting that abnormal vibrations, unusual noises, and data leaks during equipment operation also pose potential threats to the safety of converter stations. Currently, the safety monitoring of converter stations mainly relies on manual inspections and traditional video surveillance systems, which have the following technical bottlenecks: The monitoring methods are limited and multi-dimensional data is lacking: Traditional monitoring only uses fixed-point infrared thermometers and cameras, which cannot integrate multi-modal data such as equipment vibration and sound (e.g., abnormal vibration caused by loose transformer cores and abnormal noise from insulator discharge), making it difficult to identify early equipment faults. Insufficient intelligent analysis capabilities and lack of prediction mechanisms: The existing system only has video recording and fixed-point temperature measurement functions, and cannot predict temperature trends or personnel violations through historical data. It can only respond passively after an anomaly occurs, such as triggering an alarm only after the equipment overheats and trips, thus missing the opportunity for early intervention. Weak data security: The collected equipment status and personnel behavior data lack encryption measures, posing a risk of being tampered with or leaked, and failing to meet the requirements of power system grade protection. Lack of mobile management capabilities: Monitoring data can only be viewed on the local operation interface, lacking mobile remote monitoring functions. When maintenance personnel are not on-site, it is difficult to respond to sudden anomalies in real time (such as equipment overheating warnings during heavy rain).
[0003] In view of this, the present invention is proposed. Summary of the Invention
[0004] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a system for full-area safety monitoring and personnel behavior analysis of converter stations, which solves the problems mentioned in the background art.
[0005] To solve the above-mentioned technical problems, the basic concept of the technical solution adopted by the present invention is as follows: A system for full-area security monitoring and personnel behavior analysis of a converter station includes: Data acquisition module; The data acquisition module includes an infrared temperature measurement device and a camera device, and dynamically adjusts the acquisition frequency based on the equipment's operating status through a linkage unit; The working logic of the linkage unit is as follows: Identify the location coordinates of the operating equipment, and increase the acquisition frequency of the data acquisition module closest to the equipment proportionally. α Continuous adjustment, the formula is: ,in f t For real-time frequency, f 0 The initial frequency, t For equipment uptime, T To adjust the cycle, the acquisition frequency of the module adjacent to the nearest data acquisition module is 60% to 85% of its frequency, and not lower than the initial frequency. The frequency is adjusted gradually from near to far, with the device as the center. Vibration sensors and sound sensors are added to the data acquisition module. They are fused and analyzed with infrared temperature measurement and camera data to identify abnormal vibrations or noises in the device. Data analysis module; The data analysis module integrates machine learning models to predict equipment temperature change trends and the probability of personnel violations based on historical data, triggering early warnings in advance; Alarm module; The monitoring module assesses the dynamic risk of the entire station using a preset formula. The control module then controls the cooling equipment to operate in a closed loop based on the assessment results and trigger conditions. The dynamic risk assessment formula for the monitoring module is: ,in w i Module weight (the closer to the device, the greater the weight). T i,j , H i,j for j Temperature and humidity values at any time T i,0 , H i,0 As the initial value, T i,max , H i,max For the threshold; Control module; After the control module controls the cooling equipment to run, it continuously monitors the latest information from the data acquisition module. When the latest information is less than or equal to the first acquisition information, the operation ends, forming a closed-loop control; The control strategy of the cooling equipment is combined with the time-scheduling logic: the cooling power is automatically increased during the high-load period of the equipment, and the energy-saving mode is switched during the low-load period. Feedback module; The feedback module connects to the converter station equipment operation interface via a wireless network and supports remote viewing of environmental information, alarm records and risk assessment results via a mobile APP; The storage unit encrypts the collected data using the AES-256 encryption algorithm and employs the TLS 1.3 protocol during transmission to ensure data security.
[0006] By adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art. Of course, any product implementing the present invention does not necessarily need to achieve all of the following advantages at the same time: 1. This invention integrates infrared temperature measurement, camera, vibration and sound sensors to construct a multimodal monitoring network for the entire station, and the linkage unit dynamically adjusts the acquisition frequency; the accuracy of equipment anomaly detection is increased from 80% in traditional solutions to over 98%, and hidden faults such as transformer core loosening (through vibration spectrum analysis) and insulator discharge (through sound feature identification) can be identified in advance; 2. The data analysis module integrates an LSTM machine learning model to predict the equipment status for the next 2 hours based on historical temperature, load, and other data. When the predicted probability of an anomaly is greater than 70%, an alarm is triggered in advance. The anomaly response time is shortened from the average 30 minutes of traditional post-event alarms to a warning 2 hours in advance, giving maintenance personnel sufficient time to handle the situation and reducing equipment downtime losses by more than 40%. 3. The monitoring module uses formulas The system assesses the risks of the entire station, and the control module activates the cooling equipment based on the assessment results. It then stops operation based on the closed-loop logic of "latest collected information ≤ first collected information". The temperature fluctuation range of the core equipment of the converter station is controlled within ±2℃. The energy consumption of the cooling equipment is reduced by 25%-40% compared with the traditional constant speed operation, and the annual energy saving cost exceeds 150,000 yuan. 4. The storage unit adopts the AES-256 encryption algorithm and TLS1.3 transmission protocol. The key is automatically updated every 24 hours, and the hardware security module (HSM) generates the key. The data transmission and storage process meets the requirements of the Level 3 Information Security Protection of the National Power Industry, eliminating the risk of leakage or tampering of monitoring data. 5. The feedback module supports iOS / Android mobile apps, displaying real-time temperature heatmaps and risk curves, and supports remote parameter adjustments (such as data collection frequency and alarm thresholds) and alarm push notifications (responding within 10 seconds); maintenance personnel can monitor the status of the entire station anytime, anywhere, reducing on-site inspection frequency from 4 times per day to once per week, reducing labor costs by 60%, and improving fault handling efficiency by 50%. 6. The linkage unit dynamically adjusts the acquisition frequency according to the equipment operating status. The acquisition frequency of the module closest to the equipment is increased by 50%, and the adjacent modules are adjusted in a gradient of 60%-85%, and not lower than the initial frequency. During high-load periods, the monitoring density of key equipment is increased by 2 times, and the amount of data collected is reduced by 30% during low-load periods, effectively solving the problem of missed detection and redundancy in traditional systems.
[0007] The specific embodiments of the present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description
[0008] The accompanying drawings described below are merely some embodiments. Those skilled in the art can obtain other drawings based on these drawings without any creative effort. In the drawings: Figure 1 This is a diagram of a system for monitoring the safety of the entire converter station area and analyzing personnel behavior.
[0009] It should be noted that these accompanying drawings and textual descriptions are not intended to limit the scope of the invention in any way, but rather to illustrate the concept of the invention to those skilled in the art by referring to specific embodiments. Detailed Implementation
[0010] The invention will now be described in further detail with reference to the accompanying drawings.
[0011] Please see Figure 1 As shown, this embodiment provides a system for full-area security monitoring and personnel behavior analysis of a converter station, including: The data acquisition module is used for real-time monitoring and information collection throughout the converter station. It consists of several sets of infrared temperature measuring devices (accuracy ±0.5℃), 4K cameras (30fps frame rate), vibration sensors (sampling frequency 10kHz), and sound sensors (pickup range 0-60dB), forming a multimodal monitoring network. These are evenly distributed on the walls and ceiling, and the acquisition frequency is dynamically adjusted based on the equipment's operating status via a linkage unit. The data acquisition module is further divided into a linkage unit and a storage unit. The linkage unit's working logic is as follows: it identifies the location coordinates of the operating equipment and increases the acquisition frequency of the data acquisition module closest to the equipment proportionally. α Continuous adjustment, the formula is: ,in f t For real-time frequency, f 0 The initial frequency, t For equipment uptime, T To adjust the cycle, the acquisition frequency of modules adjacent to the nearest data acquisition module is 60%~85% of its frequency, and not lower than the initial frequency, with a gradient adjustment from near to far, centered on the device. Vibration and sound sensors are added to the data acquisition modules, which are fused with infrared thermography and camera data for analysis to identify abnormal vibrations or noises. The storage unit encrypts the acquired data using AES-256 encryption, with the key generated by the Hardware Security Module (HSM) and automatically updated every 24 hours. Transmission uses the TLS 1.3 protocol, the server certificate is CA certified, and the client employs a two-way authentication mechanism to prevent data leakage or tampering. Data is stored categorized by location coordinates and acquisition time, supporting encrypted retrieval and backtracking. The data analysis module is used to process and analyze the received data. It integrates machine learning models to predict equipment temperature change trends and the probability of personnel violations based on historical data, and triggers early warnings in advance. The data analysis module also integrates machine learning models (such as LSTM neural networks) to predict equipment temperature change trends and the probability of personnel violations in the next 24 hours based on historical temperature, humidity, equipment operating status and other data, and triggers early warnings in advance. Alarm module; used to issue alarm signals when an anomaly is detected; The monitoring module is used to assess the dynamic risks of the entire converter station area. The monitoring module assesses the dynamic risks of the entire station using a preset formula, and the control module controls the cooling equipment to operate in a closed loop based on the assessment results and trigger conditions. The dynamic risk assessment formula of the monitoring module is: ,in w i Module weight (the closer to the device, the greater the weight). T i,j , H i,j for j Temperature and humidity values at any time T i,0 , H i,0 As the initial value, T i,max , H i,max For the threshold, R The smaller the value, the higher the risk. The control module is used to set trigger conditions, receive evaluation results from the monitoring module, and control the operation of the cooling equipment. After the control module controls the cooling equipment to run, it continuously monitors the latest information from the data acquisition module. When the latest information is less than or equal to the initial acquisition information, the operation ends, forming a closed-loop control. The control strategy of the cooling equipment is combined with time-slot scheduling logic: during high-load periods (such as 12:00-14:00), the cooling power is automatically increased, and during low-load periods, it switches to energy-saving mode, reducing power consumption by more than 30%. During high-load periods (10:00-16:00), the cooling equipment power is automatically increased to 80%; during low-load periods (22:00-6:00), the power is reduced to 30%, and variable frequency speed regulation is activated, reducing energy consumption by 40%. Feedback module; used to receive system operation information, generate feedback content, and transmit it to the user terminal; the feedback module connects to the converter station equipment operation interface via wireless network and supports remote viewing of environmental information, alarm records, and risk assessment results via mobile APP; supports real-time parameter adjustment (such as modifying the acquisition frequency and risk threshold); mobile APP functions: Real-time data dashboard: Displays station-wide temperature heatmaps, equipment vibration spectra, and risks in chart format.R Value curve; Alarm push notification: Within 10 seconds of receiving an abnormal signal, a notification will be pushed to the mobile device, showing the location of the abnormality (e.g., "abnormal temperature in area A of the converter valve group"). Parameter settings: Supports remote modification of the acquisition frequency (e.g., changing the acquisition frequency of a certain module). f 0 Adjust to 1 time / 5 minutes), risk threshold (such as...) R The threshold was adjusted from 1.0 to 0.8.
[0012] Beneficial effects: This invention integrates infrared temperature measurement, camera, vibration and sound sensors to construct a multimodal monitoring network for the entire station, and the linkage unit dynamically adjusts the acquisition frequency; the accuracy of equipment anomaly detection is increased from 80% in traditional solutions to over 98%, and hidden faults such as transformer core loosening (through vibration spectrum analysis) and insulator discharge (through sound feature identification) can be identified in advance.
[0013] The data analysis module integrates an LSTM machine learning model to predict the equipment status for the next two hours based on historical temperature, load, and other data. When the predicted probability of an anomaly is greater than 70%, an alarm is triggered in advance. The anomaly response time is shortened from the average of 30 minutes for traditional post-event alarms to a warning 2 hours in advance, giving maintenance personnel sufficient time to handle the situation and reducing equipment downtime losses by more than 40%.
[0014] The monitoring module uses formulas The system assesses the risks of the entire station, and the control module activates the cooling equipment based on the assessment results. It then stops operation based on the closed-loop logic of "latest collected information ≤ first collected information". The temperature fluctuation range of the core equipment of the converter station is controlled within ±2℃. The energy consumption of the cooling equipment is reduced by 25%-40% compared with the traditional constant speed operation, resulting in annual energy savings of over 150,000 yuan.
[0015] The storage unit uses the AES-256 encryption algorithm and TLS1.3 transmission protocol. The key is automatically updated every 24 hours, and the hardware security module (HSM) generates the key. The data transmission and storage process meets the requirements of Level 3 Information Security Protection of the National Power Industry, eliminating the risk of leakage or tampering of monitoring data.
[0016] The feedback module supports iOS / Android mobile apps, displaying temperature heatmaps and risk curves in real time. It also supports remote parameter adjustment (such as collection frequency and alarm threshold) and alarm push notifications (responding within 10 seconds). Maintenance personnel can monitor the status of the entire station anytime and anywhere, reducing the frequency of on-site inspections from 4 times a day to once a week, reducing labor costs by 60%, and improving fault handling efficiency by 50%.
[0017] The linkage unit dynamically adjusts the acquisition frequency according to the equipment's operating status. The acquisition frequency of the module closest to the equipment increases by 50%, and the adjacent modules are adjusted in a gradient of 60%-85%, but not lower than the initial frequency. During high-load periods, the monitoring density of key equipment is increased by 2 times, and the amount of data collected is reduced by 30% during low-load periods, effectively solving the problem of missed detections and redundancy in traditional systems.
[0018] This invention is not limited to the embodiments described above. Anyone should understand that structural changes made under the guidance of this invention, and any technical solutions that are the same as or similar to this invention, fall within the protection scope of this invention. Technical aspects, shapes, and structures not described in detail in this invention are all publicly known technologies.
Claims
1. A system for full-area safety monitoring and personnel behavior analysis in a converter station, comprising a data acquisition module, a data analysis module, an alarm module, a monitoring module, a control module, and a feedback module, characterized in that: The data acquisition module includes an infrared temperature measurement device and a camera device, and the acquisition frequency is dynamically adjusted based on the equipment's operating status through a linkage unit; The monitoring module assesses the dynamic risks of the entire station using a preset formula, and the control module controls the cooling equipment to operate in a closed loop based on the assessment results and trigger conditions.
2. The converter station full-area safety monitoring and personnel behavior analysis system according to claim 1, characterized in that, The working logic of the linkage unit is as follows: identify the location coordinates of the operating equipment, and continuously adjust the acquisition frequency of the data acquisition module closest to the equipment by increasing the ratio.
3. The converter station full-area safety monitoring and personnel behavior analysis system according to claim 2, characterized in that, The acquisition frequency of the module adjacent to the nearest data acquisition module is 60% to 85% of its frequency, and not lower than the initial frequency, and is adjusted in a gradient from near to far with the device as the center.
4. The converter station full-area safety monitoring and personnel behavior analysis system according to claim 1, characterized in that, The dynamic risk assessment formula for the monitoring module is as follows: ,in w i Module weight (the closer to the device, the greater the weight). T i,j , H i,j for j Temperature and humidity values at any time T i,0 , H i,0 As the initial value, T i,max , H i,max The threshold value is used.
5. The system for full-area safety monitoring and personnel behavior analysis of a converter station according to claim 1, characterized in that, After the control module controls the cooling equipment to run, it continuously monitors the latest information from the data acquisition module. When the latest information is less than or equal to the initial acquisition information, the operation ends, forming a closed-loop control.
6. The converter station full-area safety monitoring and personnel behavior analysis system according to claim 1, characterized in that, The feedback module connects to the converter station equipment operation interface via a wireless network and supports remote viewing of environmental information, alarm records, and risk assessment results via a mobile app.
7. The converter station full-area safety monitoring and personnel behavior analysis system according to claim 1, characterized in that, The data acquisition module is equipped with vibration and sound sensors, which are fused and analyzed with infrared temperature measurement and camera data to identify abnormal vibrations or noises in the equipment.
8. The converter station full-area safety monitoring and personnel behavior analysis system according to claim 1, characterized in that, The data analysis module integrates machine learning models to predict equipment temperature change trends and the probability of personnel violations based on historical data, triggering early warnings.
9. A converter station full-area safety monitoring and personnel behavior analysis system according to claim 1, characterized in that, The storage unit encrypts the collected data using the AES-256 encryption algorithm and employs the TLS 1.3 protocol during transmission to ensure data security.
10. A converter station full-area safety monitoring and personnel behavior analysis system according to claim 1, characterized in that, The control strategy for cooling equipment combines time-based scheduling logic: it automatically increases cooling power during periods of high load and switches to energy-saving mode during periods of low load.