Power supply and distribution environment safety monitoring system, method, device and medium

Abstract

This invention provides a power supply and distribution environment safety monitoring system, method, equipment, and medium, belonging to the field of power supply safety. The system includes: a wireless temperature measurement system for real-time monitoring of the temperature of key components of power supply and distribution equipment; a power distribution monitoring system for real-time monitoring of various state parameters of the power supply and distribution environment through sensors; a perfluorohexanone fire extinguishing system for releasing perfluorohexanone extinguishing agent for fire extinguishing; and an analysis and control unit with a built-in multi-source data fusion analysis algorithm model for receiving and fusing temperature data and state parameter data; performing real-time analysis of the fused data based on the model to identify potential safety hazards in the power supply and distribution environment; and generating and executing control strategies based on the safety hazards. By introducing an intelligent analysis and control unit integrating a multi-source data fusion analysis algorithm model, a fundamental shift from traditional discrete, passive response monitoring to proactive, intelligent, and collaborative protection is achieved.

Description

Technical Field

[0001] This invention relates to the field of power supply safety technology, specifically to a power supply and distribution environment safety monitoring system, method, equipment, and medium. Background Technology

[0002] Electricity is the lifeblood of industrial production; without it, industrial production cannot proceed. Electricity can be generated and converted into various forms for use, and its transportation is convenient and fast. Control methods are relatively simple, and automation is easily achieved in production applications. The importance of electricity to industrial production is mainly reflected in increased output, improved product quality, higher production efficiency, lower production costs, and reduced production intensity—all of which, to a certain extent, demonstrate the automation of factory production. Therefore, the most crucial aspect of daily factory production is the design of a safe and efficient power distribution system.

[0003] The task of a power supply and distribution safety system is to ensure a safe, reliable, economical, and high-quality power supply. This system ensures the stable operation of the entire power system and prevents various accidents caused by power supply and distribution system failures. Economic and industrial development is closely related to social construction; the healthy development of industries directly promotes positive economic development. Currently, more energy is being devoted to factory development, and the importance of industrial production is gradually being recognized. During factory operation, the power supply and distribution safety system is a crucial influencing factor, directly affecting the safety of industrial production activities. However, currently, electric shock, fire, explosion, and other accidents are relatively common in the power supply and distribution systems of most factories. When accidents occur, they directly affect factory production activities and pose a threat to relevant personnel. Therefore, it is necessary to carry out maintenance work on the power supply and distribution system to ensure the stability and safety of the factory's power supply and distribution system operation.

[0004] Existing studies on the physical properties of perfluorohexane (PFH) fire extinguishing agents indicate that PFH does not exhibit significant chemical reactions with commonly used rubber sealing materials. Verification studies have shown that commercially available rubber materials such as nitrile rubber, EPDM rubber, silicone rubber, and fluorosilicone rubber do not show significant changes at room temperature. However, high-temperature environmental experiments in the development of fire extinguishing devices revealed changes in the physical properties of rubber seals under high temperatures. Some seals lost their resilience, while others showed weakened resilience, leading to sealing failure of the fire extinguishing device. Studies on the service life of fire extinguishing devices have revealed that after prolonged storage, PFH can be corrosive to certain untreated metal storage tanks. The physicochemical parameters of PFH show that its boiling point is 49.2℃, and its vapor pressure in a closed container at 25℃ is 0.0404 MPa. Due to its high volatility, the sealing components of fire extinguishing devices must be designed for airtight sealing. Summary of the Invention

[0005] The purpose of this invention is to provide a power supply and distribution environment safety monitoring system, method, device and medium. By introducing an intelligent analysis and control unit that integrates a multi-source data fusion analysis algorithm model, a fundamental transformation from the traditional discrete and passive response monitoring mode to an active and intelligent collaborative protection mode is achieved.

[0006] To achieve the above objectives, embodiments of the present invention provide a power supply and distribution environment safety monitoring system, comprising: Wireless temperature measurement system is used to monitor the temperature of key parts of power supply and distribution equipment in real time; A power distribution monitoring system is used to monitor various state parameters of the power supply and distribution environment in real time through sensors, including odor data collected by an AI olfactory sensor. Perfluorohexanone fire extinguishing systems are used to extinguish fires by releasing perfluorohexanone extinguishing agent; and... The analysis and control unit is communicatively connected to the wireless temperature measurement system, the power distribution monitoring system, and the perfluorohexanone fire extinguishing system, respectively. The analysis and control unit has a built-in multi-source data fusion analysis algorithm model, which is used to receive and fuse temperature data from the wireless temperature measurement system and status parameter data from the power distribution monitoring system. Based on the multi-source data fusion analysis algorithm model, the fused data is analyzed in real time to identify potential safety hazards in the power supply and distribution environment. Based on the aforementioned safety hazards, a control strategy is generated and executed, wherein the control strategy includes: triggering early warning information, sending an instruction to the power distribution monitoring system to start auxiliary heat dissipation equipment, or sending an activation instruction to the perfluorohexanone fire extinguishing system.

[0007] Optionally, the wireless temperature measurement system includes: Multiple temperature sensors, including at least active strap temperature sensors, passive strap temperature sensors, active magnetic temperature sensors, passive miniature temperature sensors, passive multi-loop temperature sensors, active multi-loop temperature sensors, and visual temperature, humidity, and dew point combined sensors. The wireless communication module is used to upload data collected by multiple temperature sensors.

[0008] Optionally, the power distribution monitoring system includes: Edge computing nodes are used to perform local preprocessing on the data collected by the AI ​​olfactory sensor; wherein, the power distribution monitoring system has built an odor spectrum database, and the multi-source data fusion analysis algorithm model calls the odor spectrum database to identify and classify odor anomalies.

[0009] Optionally, the perfluorohexanone fire extinguishing system includes: Storage containers for storing perfluorohexanone fire extinguishing agent; Fire extinguishing equipment, including nozzles and piping; The control module is used to receive the start command from the analysis and control unit and control the fire extinguishing device to start.

[0010] Optionally, the multi-source data fusion analysis algorithm model is a deep learning-based time-series-feature fusion network model, comprising a time-series feature extraction layer, a spatial feature fusion layer, and a hazard identification decision layer connected sequentially; wherein, The temporal feature extraction layer employs a long short-term memory network branch and a one-dimensional convolutional neural network branch, which are used to extract long-term trend features of temperature data and current data, and local abrupt change features of odor feature data and humidity data, respectively. The spatial feature fusion layer receives the long-term trend features and local mutation features, and uses an attention mechanism to weightedly fuse the multi-source features to generate a multi-dimensional fusion feature vector that characterizes the overall operating status of the device. The hazard identification decision layer is a fully connected neural network, which is used to map the multi-dimensional fused feature vector into specific hazard type identifiers and risk levels.

[0011] Optionally, the training process of the multi-source data fusion analysis algorithm model is as follows: Historical operation datasets are collected, which include temperature time series from the wireless temperature measurement system, odor feature time series from the power distribution monitoring system, ambient humidity time series, and equipment current time series. All sequences are timestamped based on a unified clock source. Each data sample is associated with a hazard label marked manually or by a confirmed fault event. The label includes at least the hazard type and risk level. In the cloud server, the time-series-feature fusion network model is iteratively trained based on the historical running dataset; wherein... In each iteration, the hazard identification result is calculated through forward propagation, the error between the hazard identification result and the real label is calculated using the cross-entropy loss function, and the weight parameters of the temporal-feature fusion network model are adjusted through backpropagation and optimizer to minimize the error, thereby obtaining the trained temporal-feature fusion network model. The trained temporal-feature fusion network model is deployed on the edge computing device within the analysis and control unit; Real-time feedback data from edge computing devices during operation is collected, and incremental learning and fine-tuning of the time-series-feature fusion network model are performed based on the real-time feedback data to achieve continuous optimization and adaptive optimization of the time-series-feature fusion network model.

[0012] Optionally, the analysis and control unit is specifically used for: In the temporal feature extraction layer, the long short-term memory network branch processes temperature data and current sequences to extract temporal dependency features that characterize their long-term trends and periodic patterns; the one-dimensional convolutional neural network branch processes odor feature sequences and humidity sequences to extract their local mutation features. The spatial feature fusion layer receives the temporal dependency features and local mutation features from the two branches; it calculates the importance weight of each feature channel to the current running state evaluation through an attention mechanism, and performs weighted fusion to generate a multi-dimensional fusion feature vector; The multidimensional fusion feature vector is input into the hazard identification decision layer, so that it maps the multidimensional fusion feature vector into a specific hazard type identifier and corresponding risk level based on the hazard pattern learned from historical fault data. Based on the hazard type identifier and risk level output by the hazard identification decision layer, hierarchical decision-making and linkage control are executed, including generating and pushing alarm information when the risk level is warning level; generating instructions to activate auxiliary heat dissipation when the risk level is action level and the identification feature indicates rapid temperature rise or specific decomposition products; and generating instructions to activate the perfluorohexanone fire extinguishing system when the risk level is emergency level and the feature indicates the precursor of open flame. The state parameter data includes odor feature sequences, ambient humidity sequences, and current sequences of critical circuits collected by AI olfactory sensors.

[0013] Secondly, the present invention also provides a method for monitoring the safety of power supply and distribution environment, applied to a power supply and distribution environment safety monitoring system, the method comprising: Real-time monitoring of the temperature of key components of power supply and distribution equipment; The power supply and distribution environment is monitored in real time by sensors, including odor data collected by an AI olfactory sensor. The perfluorohexanone fire extinguishing system uses perfluorohexanone extinguishing agent to extinguish the fire. The system receives and integrates temperature data from the wireless temperature measurement system and status parameter data from the power distribution monitoring system. It then uses a pre-built multi-source data fusion analysis algorithm model to perform real-time analysis on the fused data in order to identify potential safety hazards in the power supply and distribution environment. Based on the aforementioned safety hazards, a control strategy is generated and executed, wherein the control strategy includes: triggering early warning information, sending an instruction to the power distribution monitoring system to start auxiliary heat dissipation equipment, or sending an activation instruction to the perfluorohexanone fire extinguishing system.

[0014] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the power supply and distribution environment safety monitoring method described above.

[0015] Fourthly, the present invention also provides a storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of the power supply and distribution environment safety monitoring method described above.

[0016] Through the aforementioned technical solution, by introducing an intelligent analysis and control unit integrating multi-source data fusion analysis algorithms, a fundamental shift has been achieved from the traditional discrete, passive response monitoring mode to proactive, intelligent, and collaborative protection. It can perform real-time fusion and in-depth analysis of multi-dimensional heterogeneous data from wireless temperature measurement, power distribution monitoring (including AI-powered olfactory sensing), etc., thereby accurately identifying early complex hazards that cannot be detected by a single sensor. Based on the analysis results, it intelligently executes hierarchical and differentiated control strategies, from early warning and coordinated heat dissipation to automatic fire suppression. This improves the accuracy of early warning and the automation level of emergency response in power supply and distribution environment safety monitoring, effectively preventing serious accidents such as fires, and ensuring the continuity of power supply and the safety of equipment assets.

[0017] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the structure of a power supply and distribution environment safety monitoring system provided in an embodiment of the present invention; Figure 2 This is a flowchart of a power supply and distribution environment safety monitoring method provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0019] Various embodiments of this disclosure will be described more fully in the following detailed description. This disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of this disclosure to the specific embodiments disclosed herein, but rather this disclosure should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of this disclosure.

[0020] In the following, the terms “comprising” or “may include”, which may be used in various embodiments of this disclosure, indicate the presence of the disclosed functions or operations and do not limit the addition of one or more functions or operations. Furthermore, as used in various embodiments of this disclosure, the terms “comprising,” “having,” and their cognates are intended only to indicate a specific feature, number, step, operation, or combination of the foregoing and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, or combinations of the foregoing, or the possibility of adding one or more features, numbers, steps, operations, or combinations of the foregoing.

[0021] In various embodiments of this disclosure, the expression "or" or "at least one of A and / or B" includes any combination or all combinations of the words listed simultaneously. For example, the expression "A or B" or "at least one of A and / or B" may include A, may include B, or may include both A and B.

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] See Figure 1 The diagram shown is a structural schematic of a power supply and distribution environment safety monitoring system in a specific embodiment, including: Wireless temperature measurement system is used to monitor the temperature of key parts of power supply and distribution equipment in real time; A power distribution monitoring system is used to monitor various state parameters of the power supply and distribution environment in real time through sensors, including odor data collected by an AI olfactory sensor. Perfluorohexanone fire extinguishing systems are used to extinguish fires by releasing perfluorohexanone extinguishing agent; and... The analysis and control unit is communicatively connected to the wireless temperature measurement system, the power distribution monitoring system, and the perfluorohexanone fire extinguishing system, respectively. The analysis and control unit has a built-in multi-source data fusion analysis algorithm model, which is used to receive and fuse temperature data from the wireless temperature measurement system and status parameter data from the power distribution monitoring system. Based on the multi-source data fusion analysis algorithm model, the fused data is analyzed in real time to identify potential safety hazards in the power supply and distribution environment. Based on the aforementioned safety hazards, a control strategy is generated and executed, wherein the control strategy includes: triggering early warning information, sending an instruction to the power distribution monitoring system to start auxiliary heat dissipation equipment, or sending an activation instruction to the perfluorohexanone fire extinguishing system.

[0024] In one specific embodiment, the wireless temperature measurement system includes: multiple temperature sensors, including at least an active strap temperature sensor, a passive strap temperature sensor, an active magnetic temperature sensor, a passive miniature temperature sensor, a passive multi-loop temperature sensor, an active multi-loop temperature sensor, and a visual temperature, humidity, and dew point combined sensor; and a wireless communication module for uploading data collected by the multiple temperature sensors.

[0025] For example, during the operation of a power system, high-voltage electrical equipment such as switchgear and transformers may experience abnormal heating due to factors such as current overload, component aging, and loose connections. The temperature of these heated components is difficult to detect effectively using traditional monitoring methods, while wireless temperature measurement technology can accurately sense these temperature changes. From the perspective of the enterprise itself, the necessity of a wireless temperature measurement system is mainly reflected in the following points: (1) Monitor the temperature of key parts in real time and detect abnormalities in a timely manner.

[0026] (2) The online monitoring method can provide early warning during the formation of an accident, thus avoiding equipment damage and power outages.

[0027] (3) Avoid close-range manual operation, reduce personnel safety hazards, and improve safety.

[0028] (4) Reduce the frequency of manual inspections, lower maintenance costs, and extend equipment life.

[0029] (5) The overall reliability of the power system has been improved through real-time monitoring and early warning of key power nodes.

[0030] (6) The wireless temperature measurement system can adapt to harsh conditions such as high temperature, high humidity and strong electromagnetic interference, and provide stable data.

[0031] (7) With powerful background management software, the system can monitor and record the relationship between the current, ambient temperature and the temperature at the monitoring point.

[0032] When the temperature exceeds the system's specified range, the system will issue an alarm, providing maintenance personnel with accurate fault location information. The application of wireless temperature measurement technology in power systems not only ensures the safe operation of equipment but also lays a solid foundation for the intelligent and information-based development of the power industry. With further technological improvements and deepening applications, wireless temperature measurement technology will continue to play an indispensable role in the power industry.

[0033] For example, the temperature monitoring system can be deployed in various ways according to user needs. For small substations, an intranet deployment can be used, where the temperature acquisition host directly uploads data to the local computer via an RS485 communication line. On-duty personnel can monitor the substation's temperature changes in real time from a standalone client installed on the local computer. For large and medium-sized substations with large data volumes and high user access requirements, a local / private / public cloud server deployment can be used. All collected data is uploaded to the local / cloud server via a smart gateway before being distributed. Users can monitor various sensor data and operational status within the substation anytime, anywhere via a B / S client or mobile app.

[0034] In one specific embodiment, the power distribution monitoring system includes: an edge computing node for local preprocessing of data collected by the AI ​​olfactory sensor; wherein, the power distribution monitoring system constructs an odor spectrum database, and the multi-source data fusion analysis algorithm model calls the odor spectrum database to identify and classify odor anomalies.

[0035] The intelligent temperature monitoring system can also be expanded in terms of functionality. It can monitor the sensing data and working status of various sensors such as temperature, current, humidity, smoke detector, dehumidifier, partial discharge, water immersion, noise, and sulfur hexafluoride through communication methods such as 433M, 2.4G, LoRa, and RS485. The data is aggregated to the server via RS485, Ethernet, 4G or 5G to complete services such as data acquisition, protocol conversion, data transmission, alarm output, and linkage control.

[0036] For example, the odor spectrum database is shown in Table 1: Table 1 Odor Spectrum Database

[0037] In one specific implementation, the power distribution monitoring system utilizes local AI-powered olfactory sensors for real-time monitoring, remote management, and a cloud platform to ensure users receive the most timely and accurate data analysis results. By constructing an intelligent and interconnected architecture, the system not only supports localized, immediate response but also achieves centralized data management and advanced analysis, providing users with a comprehensive environmental monitoring solution.

[0038] Cloud computing: Collects data from different monitoring hosts, uses big data models to dynamically perceive the overall situation, uncovers potential faults and hidden dangers, and provides in-depth insights that go beyond the computing power limitations of a single host. The cloud platform supports access to system information from various client devices, ensuring that all stakeholders can obtain the latest monitoring data and analysis reports.

[0039] Management / Control Node: Serving as a bridge connecting the field monitoring host and the cloud platform, the main control room workstation allows operators to remotely view and control the system, while also assisting in data uploading tasks.

[0040] Edge computing nodes are responsible for receiving data from sensors, performing preliminary data processing and analysis, and immediately triggering an alarm mechanism when an anomaly is detected to ensure rapid response. The host computer has the ability to directly connect to the cloud platform and can also forward data through workstations in the control room, ensuring data security and transmission efficiency.

[0041] In one specific embodiment, the perfluorohexanone fire extinguishing system includes: Storage containers for storing perfluorohexanone fire extinguishing agent; Fire extinguishing equipment, including nozzles and piping; The control module is used to receive the start command from the analysis and control unit and control the fire extinguishing device to start.

[0042] For example, the perfluorohexanone (PFH) fire extinguishing system is an advanced gaseous fire extinguishing system widely used in various high-risk fire locations with strict environmental and equipment requirements. This article will introduce the system composition, working principle, operation procedure, unique advantages, features, precautions, and application scenarios of the cabinet-type PPH fire extinguishing system. The cabinet-type PPH fire extinguishing device is a prefabricated, total flooding-based, independent, and portable fire extinguishing device. It is flexible and convenient to install, aesthetically pleasing, has low pressure loss in the extinguishing agent delivery pipeline, and high extinguishing efficiency. It is a fire extinguishing device that pre-assembles components such as extinguishing agent cylinders, nozzles, and signal feedback components. The storage pressure at 20℃ is 2.5MPa, and the maximum working pressure is 3.2MPa. It can be connected to a fire control center and automatically extinguished by a fire alarm and extinguishing controller. The cabinet-type PPH fire extinguishing device does not require a dedicated equipment room; the entire fire extinguishing system is located within the protected area. When a fire occurs, driven by the extinguishing controller, the device directly sprays the extinguishing agent into the protected area, which is convenient and quick. For the protection of larger spaces, several fire extinguishing systems can be used in combination. This device can also be used when a dedicated cylinder room cannot be set up in the building, or when a cylinder room exists but the delivery distance is too far to meet the requirements of the engineering design, or when it is inconvenient to install a system pipeline network in the protected area.

[0043] 2. System Components: Storage Container: Steel cylinders or tanks used to store perfluorohexanone (PFH) extinguishing agent. Extinguishing Device: Includes extinguishing heads, nozzles, piping, and other equipment used to release PPH extinguishing agent into the fire area. Control System: Includes control panel, pressure switches, alarm devices, etc., used to monitor and control the operation of the PPH extinguishing system. Detectors: Such as smoke detectors and temperature detectors, used to detect fires and trigger system activation.

[0044] Perfluorohexanone fire extinguishing systems employ the principle of chemical fire extinguishing, which involves altering the fire environment through the release of extinguishing agents to achieve the fire extinguishing effect. Perfluorohexanone is a colorless, odorless, and non-toxic gas that rapidly evaporates into an inert gas after release. It operates on the following principles: (1) Inhibiting chain reactions: Perfluorohexanone can inhibit the chain reaction of a fire, block the spread of flames, and reduce the intensity of the fire. (2) Reducing oxygen concentration: After release, perfluorohexanone evaporates into an inert gas, reducing the oxygen concentration at the fire scene and preventing further combustion. (3) Rapidly extinguishing the fire source: Perfluorohexanone has a rapid fire extinguishing speed, which can quickly extinguish the fire source and reduce the losses caused by the fire.

[0045] Operating Procedures: The operating procedures of a perfluorohexanone fire extinguishing system typically include the following steps: (1) Fire Detection: The system detects a fire through a detector and triggers an alarm signal. (2) Alarm and Warning: The system issues an audible and visual alarm signal to alert personnel of the fire and warn of the activation of the fire extinguishing system. (3) Activation of Fire Extinguishing Device: Based on the warning signal, the control system will activate the perfluorohexanone fire extinguishing device and release perfluorohexanone extinguishing agent into the fire extinguishing area. (4) Fire Extinguishing Process: The perfluorohexanone extinguishing agent rapidly evaporates, forming an inert gas that reduces the oxygen concentration at the fire scene, inhibits flame propagation, and extinguishes the fire source. (5) Personnel Evacuation: The system activates safety passages and emergency lighting to guide personnel to safely evacuate the fire area. (6) System Reset and Maintenance: After the fire is brought under control, the system is reset and maintained, including checking the status of the fire extinguishing device, detector, and alarm device to ensure the normal operation of the system.

[0046] In one specific embodiment, the multi-source data fusion analysis algorithm model is a deep learning-based time-series-feature fusion network model, comprising a time-series feature extraction layer, a spatial feature fusion layer, and a hazard identification decision layer connected in sequence. The time-series feature extraction layer employs a long short-term memory network branch and a one-dimensional convolutional neural network branch to extract long-term trend features of temperature and current data, and local mutation features of odor and humidity data, respectively. The spatial feature fusion layer receives the long-term trend features and local mutation features, and performs weighted fusion of the multi-source features through an attention mechanism to generate a multi-dimensional fusion feature vector representing the overall operating status of the equipment. The hazard identification decision layer is a fully connected neural network used to map the multi-dimensional fusion feature vector to specific hazard type identifiers and risk levels.

[0047] In one specific embodiment, the training process of the multi-source data fusion analysis algorithm model includes the following steps: S1: Collect historical operation datasets, wherein the historical operation datasets include temperature time series from the wireless temperature measurement system, odor feature time series from the power distribution monitoring system, ambient humidity time series, and equipment current time series, and all sequences are timestamped based on a unified clock source; each data sample is associated with a hazard label marked by manual or confirmed fault events, and the label includes at least the hazard type and risk level.

[0048] S2: On the cloud server, the time-series-feature fusion network model is iteratively trained based on the historical running dataset.

[0049] In each iteration, the hazard identification result is calculated through forward propagation, the error between the hazard identification result and the real label is calculated using the cross-entropy loss function, and the weight parameters of the temporal-feature fusion network model are adjusted through backpropagation and optimizer to minimize the error, thereby obtaining a trained temporal-feature fusion network model.

[0050] For example, the forward propagation process is as follows: The linear transformation (fully connected layer) outputs a logits vector z:

[0051] In the formula, and The weight matrix and bias vector for the last layer. This is the activation output of the previous layer.

[0052] The predicted probability for each hazard category is calculated using the softmax function:

[0053] Where C represents the total number of hazard categories, This represents the predicted probability that a sample belongs to the i-th class.

[0054] The cross-entropy loss function is as follows:

[0055] The gradient of the loss function with respect to any parameter θ of the model is calculated using the chain rule:

[0056] in, The gradient vector of all parameters is denoted as:

[0057] Parameter update: Let t be the current iteration step, For the current parameters, gradient The Adam optimizer update process is as follows: Calculate the first moment estimate (momentum) using the following formula:

[0058] The second moment estimate (adaptive learning rate) is calculated using the following formula: The squaring operation is an element-wise operation.

[0059] Deviation correction:

[0060] Parameter update:

[0061] Where α is the learning rate. The attenuation rate, is the numerical stability constant.

[0062] The optimal parameters are obtained by iteratively optimizing and minimizing the loss function. : .

[0063] S3: Deploy the trained temporal-feature fusion network model on the edge computing device within the analysis and control unit.

[0064] S4: Collect real-time feedback data from edge computing devices during operation, and perform incremental learning and fine-tuning of the time-series-feature fusion network model based on the real-time feedback data to achieve continuous optimization and adaptive optimization of the time-series-feature fusion network model.

[0065] In one specific embodiment, the analysis control unit is specifically used to perform the following steps: SA: In the time-series feature extraction layer, the long short-term memory network branch processes the temperature data and current sequence to extract time-series dependent features that characterize their long-term trends and periodic patterns; the one-dimensional convolutional neural network branch processes the odor feature sequence and humidity sequence to extract their local mutation features.

[0066] SB: The spatial feature fusion layer receives the temporal dependency features and local mutation features from the two branches; it calculates the importance weight of each feature channel to the current running state evaluation through an attention mechanism, and performs weighted fusion to generate a multi-dimensional fusion feature vector.

[0067] SC: The multi-dimensional fusion feature vector is input into the hazard identification decision layer, so that it maps the multi-dimensional fusion feature vector into a specific hazard type identifier and corresponding risk level based on the hazard pattern learned from historical fault data.

[0068] SD: Based on the hazard type identifier and risk level output by the hazard identification decision layer, perform hierarchical decision-making and linkage control, including generating and pushing alarm information when the risk level is warning level; generating instructions to activate auxiliary heat dissipation when the risk level is action level and the identification feature indicates rapid temperature rise or specific decomposition products; and generating instructions to activate the perfluorohexanone fire extinguishing system when the risk level is emergency level and the feature indicates the precursor of open flame.

[0069] SE: The state parameter data includes odor feature sequences, ambient humidity sequences, and current sequences of critical circuits collected by AI olfactory sensors.

[0070] See Figure 2 The diagram shows a flowchart of a power supply and distribution environment safety monitoring method in a specific embodiment, including the following execution steps: Step 200: Monitor the temperature of key components of power supply and distribution equipment in real time.

[0071] Specifically, the following types of temperature sensors can be selected: active strap temperature sensor, passive strap temperature sensor, active magnetic temperature sensor, passive miniature temperature sensor, passive multi-loop temperature sensor, active multi-loop temperature sensor, visual temperature, humidity and dew point combined sensor (wireless), and visual temperature, humidity and dew point combined sensor (wired).

[0072] This active band-type temperature sensor is battery-powered, allowing for flexible application in environments where power is unavailable. A power supply voltage self-test provides real-time monitoring of the supply voltage, effectively reflecting the operating status. The exterior is made of flame-retardant materials, ensuring safe use. It supports sleep and wake-up modes to minimize power consumption and extend product lifespan. The sensor actively alarms when the temperature changes by more than 2°C. Utilizing sensitive temperature-sensitive materials, it offers high temperature measurement accuracy.

[0073] The passive band-type temperature sensor draws power from current sensing, with a minimum starting current of less than 5A, and even as low as 3A; it can detect the power supply voltage in real time and effectively provide feedback on the working status; the sensor will actively alarm if the temperature change exceeds 2℃.

[0074] The active magnetic temperature sensor is battery powered, making it flexible for environments where power is unavailable; it monitors the power supply voltage in real time, effectively providing feedback on its operating status; it uses flame-retardant materials, achieving an IP68 protection rating; it supports sensor sleep and wake-up modes to minimize power consumption and extend product lifespan; the sensor actively alarms when the temperature changes by more than 2°C; it uses sensitive temperature-sensitive materials for high temperature measurement accuracy; and the probe can be selected from M4 ring probes (default), cylindrical probes, or magnetic probes to meet various scenario requirements.

[0075] This passive miniature temperature sensor draws power from current sensing, with a minimum starting current of 3A. With a custom steel strip, the minimum starting current can reach 1.7A. It can detect the power supply voltage in real time, effectively providing feedback on the operating status. The sensor actively alarms when the temperature change exceeds 2℃. The product's size is smaller than similar products on the market, allowing it to be installed on contacts with currents exceeding 2000A without being affected by the contact joists. The housing is made of flame-retardant PC plastic and has an IP50 rating (can be potted to reach IP66).

[0076] The passive multi-loop temperature sensor draws power from current sensing, with a minimum starting current of 5A; the power supply voltage self-test can detect the power supply voltage in real time and effectively provide feedback on the working status; the sensor will actively alarm when the temperature change exceeds 2℃; it collects temperature data from 3 points and uploads it uniformly; the sensor uses sensitive temperature materials, resulting in high temperature measurement accuracy.

[0077] This active multi-loop temperature sensor is battery-powered, allowing for flexible application in environments where power is unavailable. Its power supply voltage self-test provides real-time monitoring and effective feedback on operating status. The exterior is made of flame-retardant materials, achieving an IP68 protection rating. It supports sensor sleep and wake-up modes to minimize power consumption and extend product lifespan. The sensor actively alarms when the temperature change exceeds 2°C. Utilizing sensitive temperature-sensitive materials, it offers high temperature measurement accuracy. Probe options include an M4 ring probe (default), a cylindrical probe, and a magnetic probe to meet various scenario requirements. Temperature data is collected from three points and uploaded uniformly.

[0078] The wireless temperature, humidity, and dew point sensor features a local 2.8-inch LCD screen for real-time data preview; real-time temperature, humidity, and dew point detection: the sensor samples temperature, humidity, and dew point signals every 2 minutes; real-time power supply voltage detection provides effective feedback on operating status; powered by a high-performance lithium-ion battery, with a customizable external power supply unit to adapt to various environments where power is unavailable.

[0079] The wired temperature, humidity, and dew point sensor features a local display on a 2.8-inch LCD screen for real-time data preview; it samples temperature, humidity, and dew point signals every 2 minutes; and it has a power supply voltage self-test function to monitor the power supply voltage in real time and provide effective feedback on the operating status.

[0080] Step 201: Monitor various state parameters of the power supply and distribution environment in real time using sensors, including odor data collected by an AI olfactory sensor.

[0081] Step 202: Use the perfluorohexanone fire extinguishing system to release perfluorohexanone extinguishing agent to extinguish the fire.

[0082] Step 203: Receive and fuse temperature data from the wireless temperature measurement system and status parameter data from the power distribution monitoring system, and use a pre-built multi-source data fusion analysis algorithm model to perform real-time analysis on the fused data in order to identify potential safety hazards in the power supply and distribution environment.

[0083] Step 204: Based on the aforementioned security risks, generate and execute control strategies.

[0084] The control strategies include: triggering early warning information, sending instructions to the power distribution monitoring system to start auxiliary heat dissipation equipment, or sending start instructions to the perfluorohexanone fire extinguishing system.

[0085] For example, to achieve wireless communication functionality, this system uses the ESP-01S wireless communication module based on the ESP8266 chip for information transmission. The ESP-01S uses the common 2.4 GHz frequency band and supports three operating modes: STA / AP / STA+AP. This WIFI module requires a stable 3.3V power supply and has a standby power consumption as low as 1.0mW. The ESP-01S module can connect to a microcontroller via serial communication, with a maximum serial port speed of 4Mbps. The module has AT firmware burned into it, allowing the microcontroller to send AT commands via the serial port to perform various operations on the ESP-01S. When the ESP-01S is connected to the microcontroller, the TX pin is connected to the microcontroller's P3.0 pin, and the RX pin is connected to the microcontroller's P3.1 pin to achieve serial communication. The ESP-01S is powered by an independent 3.3V power supply, and the GND pin, in addition to being connected to the independent power ground, also needs to share a common ground with the microcontroller.

[0086] The audible and visual alarm system uses a buzzer and an LED, with the buzzer connected to a PNP transistor. One end of the buzzer and LED is connected to the power supply, and the other end is connected to pins P1.7 and P1.6 of the microcontroller, respectively. When the audible and visual alarm is activated, pins P1.6 and P1.7 of the microcontroller are set to a low level. The system employs a three-keyboard design to implement system settings for adjusting the alarm values ​​for temperature and smoke. An LCD1602 liquid crystal display shows the temperature and smoke values ​​in real time. The LCD1602 uses parallel communication, with its parallel port pin connected to the P0 port of the microcontroller.

[0087] The heat dissipation function is achieved through a cooling fan, LED indicator, relay, and PNP transistor. The LED is connected in parallel with the relay, with one end of the parallel circuit connected to the power supply and the other end connected to the PNP transistor, which is connected to the P2.1 pin of the microcontroller. The microcontroller controls the conduction or disconnection of the parallel circuit through the transistor, which in turn controls the relay to turn the cooling fan on or off.

[0088] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0089] Figure 3 This is a schematic diagram of the hardware structure of an electronic device that implements various embodiments of the present invention.

[0090] The power supply and distribution environment safety monitoring method provided in this application embodiment can be applied to electronic devices. Those skilled in the art will understand that the electronic device structure involved in the embodiments of this invention does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, the electronic device includes, but is not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0091] Electronic devices may include processors, external memory interfaces, internal memory, universal serial bus (USB) interfaces, charging management modules, power management modules, batteries, wireless communication modules, audio modules, speakers, microphones, sensor modules, buttons, cameras, displays, and SIM card interfaces, etc.

[0092] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device. In other embodiments of this application, the electronic device may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0093] A processor may include one or more processing units, such as: a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0094] The processor can serve as the nerve center and command center of an electronic device. The controller can generate operation control signals based on the instruction opcode and timing signals to control the fetching and execution of instructions.

[0095] The processor may also include memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from this memory. This avoids repeated accesses, reduces processor latency, and thus improves system efficiency.

[0096] An external storage interface (ESI) can be used to connect external memory cards, such as microSD cards, to expand the storage capacity of electronic devices. The external memory card communicates with the processor through the ESI to perform data storage functions, such as saving music and video files on the external memory card.

[0097] Internal memory can be used to store computer executable program code, which includes instructions. The processor executes various functional applications and data processing of electronic devices by running the instructions stored in internal memory. Internal memory can include a program storage area and a data storage area. Internal memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0098] Wireless communication functionality in electronic devices can be achieved through antennas, wireless communication modules, modem processors, and baseband processors.

[0099] Wireless communication modules can provide solutions for wireless communication applications in electronic devices, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies.

[0100] Electronic devices can implement audio functions through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0101] Electronic devices can achieve shooting functions through ISPs, cameras, video codecs, GPUs, displays, and application processors.

[0102] Electronic devices can achieve display functions through GPUs, displays, and application processors.

[0103] A GPU is a microprocessor for image processing, connected to the display screen and application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering. A processor may include one or more GPUs, which execute program instructions to generate or modify display information.

[0104] A display screen is used to display images, videos, etc. A display screen includes a display panel.

[0105] The storage medium provided in this application stores a program product capable of implementing a method for safe monitoring of the power supply and distribution environment.

[0106] In some possible implementations, the subject matter of this disclosure, namely, the method and system for monitoring the safety of power supply and distribution environment, can be implemented as a program product comprising program code. When the program product is run on a terminal device, the program code is used to cause the terminal device to perform the steps described in the "Exemplary Methods" section above according to various exemplary embodiments of this disclosure.

[0107] The storage medium disclosed herein may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0108] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A power supply and distribution environment safety monitoring system, characterized in that, include: Wireless temperature measurement system for real-time monitoring of the temperature of critical parts of power supply and distribution equipment; The power distribution monitoring system is used to monitor various state parameters of the power supply and distribution environment in real time through sensors, including odor data collected by an AI olfactory sensor. Perfluorohexanone fire extinguishing systems are used to extinguish fires by releasing perfluorohexanone extinguishing agent; and... The analysis and control unit is communicatively connected to the wireless temperature measurement system, the power distribution monitoring system, and the perfluorohexanone fire extinguishing system, respectively. The analysis and control unit has a built-in multi-source data fusion analysis algorithm model, which is used to receive and fuse temperature data from the wireless temperature measurement system and status parameter data from the power distribution monitoring system. Based on the multi-source data fusion analysis algorithm model, the fused data is analyzed in real time to identify potential safety hazards in the power supply and distribution environment. Based on the aforementioned safety hazards, a control strategy is generated and executed, wherein the control strategy includes: triggering early warning information, sending an instruction to the power distribution monitoring system to start auxiliary heat dissipation equipment, or sending an activation instruction to the perfluorohexanone fire extinguishing system.

2. The power supply and distribution environment safety monitoring system according to claim 1, characterized in that, The wireless temperature measurement system includes: Multiple temperature sensors, including at least active strap temperature sensors, passive strap temperature sensors, active magnetic temperature sensors, passive miniature temperature sensors, passive multi-loop temperature sensors, active multi-loop temperature sensors, and visual temperature, humidity, and dew point combined sensors. The wireless communication module is used to upload data collected by multiple temperature sensors.

3. The power supply and distribution environment safety monitoring system according to claim 1, characterized in that, The power distribution monitoring system includes: Edge computing nodes are used to perform local preprocessing on the data collected by the AI ​​olfactory sensor; wherein, the power distribution monitoring system has built an odor spectrum database, and the multi-source data fusion analysis algorithm model calls the odor spectrum database to identify and classify odor anomalies.

4. The power supply and distribution environment safety monitoring system according to claim 1, characterized in that, The perfluorohexanone fire extinguishing system includes: Storage containers for storing perfluorohexanone fire extinguishing agent; Fire extinguishing equipment, including nozzles and piping; The control module is used to receive the start command from the analysis and control unit and control the fire extinguishing device to start.

5. The power supply and distribution environment safety monitoring system according to claim 1, characterized in that, The multi-source data fusion analysis algorithm model is a deep learning-based time-series-feature fusion network model, comprising a time-series feature extraction layer, a spatial feature fusion layer, and a hazard identification decision layer connected sequentially; wherein, The temporal feature extraction layer employs a long short-term memory network branch and a one-dimensional convolutional neural network branch, which are used to extract long-term trend features of temperature data and current data, and local abrupt change features of odor feature data and humidity data, respectively. The spatial feature fusion layer receives the long-term trend features and local mutation features, and uses an attention mechanism to weightedly fuse the multi-source features to generate a multi-dimensional fusion feature vector that characterizes the overall operating status of the device. The hazard identification decision layer is a fully connected neural network, which is used to map the multi-dimensional fused feature vector into specific hazard type identifiers and risk levels.

6. The power supply and distribution environment safety monitoring system according to claim 5, characterized in that, The training process of the multi-source data fusion analysis algorithm model is as follows: Historical operation datasets are collected, which include temperature time series from the wireless temperature measurement system, odor feature time series from the power distribution monitoring system, ambient humidity time series, and equipment current time series. All sequences are timestamped based on a unified clock source. Each data sample is associated with a hazard label marked by manual annotation or confirmed fault events. The label includes at least the hazard type and risk level. In the cloud server, the time-series-feature fusion network model is iteratively trained based on the historical running dataset; wherein... In each iteration, the hazard identification result is calculated through forward propagation, the error between the hazard identification result and the real label is calculated using the cross-entropy loss function, and the weight parameters of the temporal-feature fusion network model are adjusted through backpropagation and optimizer to minimize the error, thereby obtaining the trained temporal-feature fusion network model. The trained temporal-feature fusion network model is deployed on the edge computing device within the analysis and control unit; Real-time feedback data from edge computing devices during operation is collected, and incremental learning and fine-tuning of the time-series-feature fusion network model are performed based on the real-time feedback data to achieve continuous optimization and adaptive optimization of the time-series-feature fusion network model.

7. The power supply and distribution environment safety monitoring system according to claim 6, characterized in that, The analysis and control unit is specifically used for: In the temporal feature extraction layer, the long short-term memory network branch processes temperature data and current sequences to extract temporal dependency features that characterize their long-term trends and periodic patterns; the one-dimensional convolutional neural network branch processes odor feature sequences and humidity sequences to extract their local mutation features. The spatial feature fusion layer receives the temporal dependency features and local mutation features from the two branches; The importance weight of each feature channel to the current running state evaluation is calculated through an attention mechanism, and then weighted and fused to generate a multi-dimensional fused feature vector. The multidimensional fusion feature vector is input into the hazard identification decision layer, so that it maps the multidimensional fusion feature vector into a specific hazard type identifier and corresponding risk level based on the hazard pattern learned from historical fault data. Based on the hazard type identifier and risk level output by the hazard identification decision layer, hierarchical decision-making and linkage control are executed, including generating and pushing alarm information when the risk level is warning level; generating instructions to activate auxiliary heat dissipation when the risk level is action level and the identification feature indicates rapid temperature rise or specific decomposition products; and generating instructions to activate the perfluorohexanone fire extinguishing system when the risk level is emergency level and the feature indicates the precursor of open flame. The state parameter data includes odor feature sequences, ambient humidity sequences, and current sequences of critical circuits collected by AI olfactory sensors.

8. A method for monitoring the safety of power supply and distribution environment, applied to the power supply and distribution environment safety monitoring system according to any one of claims 1-7, characterized in that, The power supply and distribution environment safety monitoring method includes: Real-time monitoring of the temperature of key components of power supply and distribution equipment; The power supply and distribution environment is monitored in real time by sensors, including odor data collected by an AI olfactory sensor. The perfluorohexanone fire extinguishing system uses perfluorohexanone extinguishing agent to extinguish the fire. The system receives and integrates temperature data from the wireless temperature measurement system and status parameter data from the power distribution monitoring system. It then uses a pre-built multi-source data fusion analysis algorithm model to perform real-time analysis on the fused data in order to identify potential safety hazards in the power supply and distribution environment. Based on the aforementioned safety hazards, a control strategy is generated and executed, wherein the control strategy includes: triggering early warning information, sending an instruction to the power distribution monitoring system to start auxiliary heat dissipation equipment, or sending an activation instruction to the perfluorohexanone fire extinguishing system.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the power supply and distribution environment safety monitoring method as described in claim 8.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the power supply and distribution environment safety monitoring method as described in claim 8.

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