Electric power security Internet of Things system

Abstract

The invention belongs to the technical field of electric power security, and provides an electric power security Internet of Things system, which comprises an environment monitoring module, an Internet of Things module and a cloud server, the intrusion monitoring module monitors a power equipment site in real time and snapshots and tracks intruders; the state monitoring module monitors the operation state of the power equipment in real time; the state evaluation module quantitatively evaluates the health condition of the equipment through the health index of the equipment; the fault prediction module predicts operation parameters of the power equipment and sends out a fault signal according to a state threshold value; the smoke and fire identification module identifies the smoke and fire phenomenon of the power equipment and sends out a fire alarm signal according to a smoke concentration threshold; the feedback module sends a signal to a terminal corresponding to a manager; by collecting and analyzing data from multiple dimensions, integration and intelligent processing of information are realized, the safety management level of power equipment can be effectively improved, quantitative evaluation of equipment health is realized, the probability of fault occurrence is reduced, and timely fire early warning information is provided.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power security, and in particular to an electric power security Internet of Things system. Background Art

[0002] With the rapid development of the power industry, the safety and reliability of power equipment are receiving increasing attention. The operation of power systems relies on the proper functioning of numerous devices, which may face various risks during long-term operation, such as equipment aging, environmental interference, and sabotage. Therefore, traditional power safety monitoring methods often fail to fully reflect the actual operating status of equipment.

[0003] However, traditional monitoring systems often monitor the operating environment of power equipment in a relatively simple and scattered manner. Workers are unable to detect changes in environmental factors in a timely manner, making it difficult to take effective protective measures before a fault occurs.

[0004] Furthermore, traditional equipment condition monitoring technologies mostly focus on monitoring a single operating parameter of the equipment, lacking a comprehensive, quantitative assessment of the equipment's overall health. For example, in the past, only basic electrical parameters such as voltage and current were considered. If potential problems such as partial discharge or insulation aging occur within the transformer, these parameters may not change significantly in the initial stages, and existing monitoring methods are unable to promptly detect deteriorating trends in the equipment's health. Furthermore, due to the lack of a scientific equipment health index system, managers struggle to intuitively and clearly understand the overall health of the equipment. When formulating equipment maintenance plans, they often rely on experience and lack accurate data support. This results in either excessive equipment maintenance, resulting in a waste of resources, or insufficient maintenance, leaving the equipment in a sub-healthy state for extended periods, increasing the risk of failure.

[0005] At the same time, some power facilities rely solely on simple access control systems, failing to monitor illegal intrusions around the site in real time. When criminals bypass access control and enter through other locations, such as walls, there's no timely alert. Some systems equipped with cameras lack intelligent tracking and analysis capabilities and can only store recorded footage. Staff must manually review the footage afterward to detect intrusions. This results in a lack of timely capture and tracking of intruders, preventing effective clues and evidence for subsequent security measures. This puts power equipment at serious risk of theft and damage, resulting in significant economic losses for power companies.

[0006] In most cases, fires can only be detected when they reach a serious stage, producing heavy smoke or flames. For example, in some cable trenches, when a cable overload or short circuit begins to slowly burn, initially producing only a small amount of smoke and heat, existing monitoring equipment is unable to promptly identify these early signs of fire and sound an alarm in its infancy. By the time the fire has significantly intensified and the monitoring equipment sounds an alarm, it has often already caused significant damage to power equipment, greatly complicating firefighting and rescue efforts, severely impacting the normal operation of the power system, and potentially even threatening the lives of on-site workers.

[0007] To this end, technicians in this field have proposed an electric power security Internet of Things system, which aims to achieve information integration and intelligent processing by collecting and analyzing data from multiple dimensions. It can effectively improve the safety management level of power equipment, realize quantitative assessment of equipment health, reduce the probability of failure, and provide timely and effective support for fire warning. Summary of the Invention

[0008] In order to solve the above technical problems, the present invention provides an electric power security Internet of Things system to solve the problems raised in the background technology.

[0009] An electric power security Internet of Things system, comprising:

[0010] Environmental monitoring module, used to monitor the operating environment parameters of power equipment in real time and obtain environmental monitoring data;

[0011] The intrusion monitoring module is used to monitor the power equipment site in real time using cameras and electronic fences. When the electronic fence is triggered, it sends an intrusion signal and captures and tracks the intruder through the camera to obtain tracking data.

[0012] The status monitoring module is used to monitor the operating status of power equipment in real time and obtain operating status data;

[0013] A status assessment module is used to quantitatively assess the health status of the equipment through the equipment health index based on the operating status data to obtain a status assessment result;

[0014] a fault prediction module, configured to predict operating parameters of the power equipment based on the state assessment result, obtain a fault prediction result, and issue a fault signal based on the fault prediction result when a preset state threshold is exceeded;

[0015] a fire and smoke recognition module, configured to recognize fire and smoke phenomena in power equipment based on the environmental monitoring data, obtain fire and smoke recognition results, and issue a fire alarm signal when the smoke concentration exceeds a preset threshold value based on the fire and smoke recognition results;

[0016] The feedback module is used to receive the intrusion signal, fault signal and fire alarm signal, and send the corresponding signal to the terminal corresponding to the management personnel; the intrusion signal includes tracking data.

[0017] Preferably, the intrusion monitoring module is used to obtain an intrusion signal when the electronic fence is triggered, including:

[0018] The electronic fence is equipped with n sensors, and the physical quantity collected by the i-th sensor is x i , then the triggering condition of the electronic fence is expressed as:

[0019]

[0020] Among them, the normal threshold range of the i-th sensor is [a i ,b i ], when the trigger signal is 1, it means that the electronic fence is triggered and sends an intrusion signal.

[0021] Preferably, the intrusion monitoring module is further used to:

[0022] The image collected by the camera at time t is I t At the time t0 when the electronic fence is triggered, the feature vector of the intruder detected by the target detection algorithm is F0;

[0023] At the subsequent time t>t0, calculate the feature vector F of each target in the current image t j Similarity to F0:

[0024]

[0025] in, is the eigenvector F t j Similarity with F0, ||·|| represents the modulus of the vector;

[0026] Select the target with the greatest similarity as the tracking target:

[0027]

[0028] when When the similarity is greater than the preset threshold value θ, it is considered that the intruder is successfully tracked and the tracking data is obtained.

[0029] Preferably, the status assessment module is further used to:

[0030] The operating status data obtained by the status monitoring module includes m indicators. The measurement value of the ath indicator at time e is x ea ,a=1,2,....,m;

[0031] Map the running status data to the interval [0, 1] and normalize the measured values:

[0032]

[0033] in, is the minimum value of the ath indicator, is the maximum value of the ath indicator;

[0034] Different operating status indicators have different impacts on the health of the equipment. Each indicator is assigned a weight ω a , and satisfies

[0035] Calculate the device health index to quantitatively assess the health of the device:

[0036]

[0037] Among them, H e The equipment health index is divided into different levels to obtain the status assessment result.

[0038] Preferably, the fault prediction module is further used to:

[0039] According to the state evaluation results H1, H2, ..., H n And the corresponding operating parameters P1, P2, ..., P n , establish the relationship between the status assessment results and the operating parameters:

[0040] P=β0+β1H+ε

[0041] Where P is the operating parameter, H is the state assessment result, β0 and β1 are the parameters to be estimated, and ε is the error term;

[0042] Estimate the parameters β0 and β1 using the least squares method:

[0043]

[0044] in,

[0045] For the new state assessment results, the predicted operating parameters are:

[0046]

[0047] Among them, H new For the new status assessment results, is the predicted operating parameter, and the fault prediction result is obtained;

[0048] The fault prediction module is further configured to:

[0049] When the predicted operating parameters When a fault signal is issued, the P th The preset status threshold.

[0050] Preferably, the fireworks recognition module is further used to:

[0051] Extracting fireworks indicators based on the environmental monitoring data, including smoke concentration S, temperature T, and light intensity L, to obtain an environmental monitoring data vector X = (S, T, L);

[0052] Set a corresponding threshold for each fire indicator, and obtain the fire identification result F through threshold judgment:

[0053]

[0054] Among them, S th is the smoke concentration threshold, T th is the temperature threshold, L th is the light intensity threshold. When F=1, it means that fireworks are recognized, and when F=0, it means that fireworks are not recognized.

[0055] The fireworks recognition module is also used for:

[0056] Based on the smoke and fire recognition results, the smoke concentration S is compared with the preset fire alarm threshold and a fire alarm signal Z is issued:

[0057]

[0058] Among them, S al is the preset fire alarm threshold. When Z=1, it indicates that a fire alarm signal is issued; when Z=0, it indicates that no fire alarm signal is issued.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] 1. The present invention can more comprehensively and accurately assess the safety status of power equipment by integrating and analyzing data from multiple dimensions including environmental monitoring, equipment status monitoring, intrusion monitoring, and fire and smoke monitoring. It can also quantitatively assess the health status of equipment through the equipment health index, making the health status of equipment more intuitive and clear, and facilitating decision-making and management by managers.

[0061] 2. The present invention predicts faults based on the status assessment results and issues early warning signals, enabling measures to be taken in advance to prevent the occurrence of faults, thereby helping to improve the safety and stability of the power system.

[0062] 3. Based on the smoke and fire identification results, the present invention also compares the smoke concentration with the preset fire alarm threshold, which can promptly detect the smoke and fire around the power equipment and issue an alarm at the early stage of the fire, thereby buying more time for firefighters to extinguish the fire and reducing the harm of the fire to power equipment and personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a block diagram of the power security Internet of Things system of the present invention. DETAILED DESCRIPTION

[0064] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0065] As attached Figure 1 As shown:

[0066] Embodiment: The present invention provides a power security Internet of Things system, comprising:

[0067] Environmental monitoring module, used to monitor the operating environment parameters of power equipment in real time and obtain environmental monitoring data;

[0068] Through real-time monitoring of environmental parameters, the potential impact of environmental factors on power equipment can be discovered in a timely manner. For example, high temperature and high humidity may cause equipment failure, and abnormal smoke concentration may indicate fire hazards, so that measures can be taken in advance to ensure the safe operation of power equipment.

[0069] The intrusion monitoring module is used to monitor the power equipment site in real time based on cameras and electronic fences. When the electronic fence is triggered, an intrusion signal is issued and the intruder is captured and tracked through the camera to obtain tracking data;

[0070] The electronic fence is equipped with n sensors, and the physical quantity collected by the i-th sensor is x i , then the triggering condition of the electronic fence is expressed as:

[0071]

[0072] Among them, the normal threshold range of the i-th sensor is [a i ,b i ], when the trigger signal is 1, it means that the electronic fence is triggered and an intrusion signal is issued.

[0073] The image captured by the camera at time t is I t ,At the time t0 when the electronic fence is triggered, the feature vector of the intruder detected by the target detection algorithm is F0;

[0074] At the subsequent time t>t0, calculate the feature vector F of each target in the current imaget j Similarity to F0:

[0075]

[0076] in, is the eigenvector F t j Similarity with F0, ||·|| represents the modulus of the vector;

[0077] Select the target with the greatest similarity as the tracking target:

[0078]

[0079] when When the similarity is greater than the preset threshold value θ, it is considered that the intruder is successfully tracked and the tracking data is obtained.

[0080] Use cameras and electronic fences to monitor power equipment sites in real time. When the electronic fence is triggered, an intrusion signal is issued, and the intruder is captured and tracked through the camera to obtain tracking data. It can effectively prevent illegal intrusions, promptly detect and record intrusions, and provide evidence for subsequent security processing, which helps to ensure the safety of power equipment sites and prevent equipment from being stolen or damaged.

[0081] The status monitoring module is used to monitor the operating status of power equipment in real time and obtain operating status data; real-time monitoring of the operating status of power equipment, including equipment parameters such as voltage, current, power, etc., and obtaining operating status data helps to timely understand the operating status of power equipment, discover abnormal conditions during equipment operation, provide a basis for equipment maintenance and fault diagnosis, and help improve equipment reliability and service life.

[0082] The status assessment module is used to quantitatively assess the health status of the equipment through the equipment health index based on the operating status data and obtain the status assessment results;

[0083] The operating status data obtained by the status monitoring module contains m indicators. The measurement value of the ath indicator at time e is x ea ,a=1,2,....,m;

[0084] Map the running status data to the [0,1] interval and normalize the measured values:

[0085]

[0086] in, is the minimum value of the ath indicator, is the maximum value of the ath indicator;

[0087] Different operating status indicators have different impacts on the health of the equipment. Each indicator is assigned a weight ω a , and satisfies

[0088] Calculate the device health index to quantitatively assess the health of the device:

[0089]

[0090] Among them, H e The equipment health index is divided into different levels to obtain the status assessment results; the classification levels are as follows:

[0091] When H e When ∈[0.8,1], the health status of the device is “good”;

[0092] When H e ∈[0.6,0.8), the health status of the device is “general”;

[0093] When H e ∈[0.4,0.6), the health status of the device is “poor”;

[0094] When H e ∈[0,0.4), the health status of the equipment is “dangerous”.

[0095] Based on the operating status data, the equipment health status is quantitatively assessed through the equipment health index to obtain the status assessment results. This can scientifically and accurately assess the health status of power equipment, enable managers to have a clear understanding of the overall condition of the equipment, facilitate the formulation of reasonable maintenance plans, and prevent equipment failures in advance.

[0096] A fault prediction module is used to predict the operating parameters of the power equipment based on the status assessment results, obtain a fault prediction result, and issue a fault signal when the fault prediction result exceeds a preset status threshold;

[0097] According to the status evaluation results H1,H2,...,H n And the corresponding operating parameters P1, P2, ..., P n , establish the relationship between the status assessment results and the operating parameters:

[0098] P=β0+β1H+ε

[0099] Where P is the operating parameter, H is the state assessment result, β0 and β1 are the parameters to be estimated, and ε is the error term;

[0100] Estimate the parameters β0 and β1 using the least squares method:

[0101]

[0102] in,

[0103] For the new state assessment results, the predicted operating parameters are:

[0104]

[0105] Among them, H new For the new status assessment results, is the predicted operating parameter, and the fault prediction result is obtained;

[0106] When the predicted operating parameters When the fault signal is issued, P th The preset status threshold.

[0107] Based on the status assessment results, the operating parameters of the power equipment are predicted to obtain fault prediction results, and a fault signal is issued when the preset status threshold is exceeded. This realizes the early prediction of power equipment failures, enables maintenance personnel to prepare maintenance resources in advance, take maintenance measures in time, reduce the impact of equipment failures on the operation of the power system, and improve the stability and reliability of the power system.

[0108] The smoke and fire recognition module is used to identify the smoke and fire phenomena of power equipment based on environmental monitoring data, obtain the smoke and fire recognition results, and issue a fire alarm signal when the smoke concentration exceeds a preset threshold based on the smoke and fire recognition results;

[0109] According to the environmental monitoring data, feature extraction of fireworks indicators, including smoke concentration S, temperature T and light intensity L, is performed. The environmental monitoring data vector is obtained as X = (S, T, L);

[0110] Set a corresponding threshold for each fire indicator, and obtain the fire identification result F through threshold judgment:

[0111]

[0112] Among them, S th is the smoke concentration threshold, T th is the temperature threshold, L th is the light intensity threshold. When F=1, it means that fireworks are recognized, and when F=0, it means that fireworks are not recognized.

[0113] Based on the smoke and fire recognition results, the smoke concentration S is compared with the preset fire alarm threshold and a fire alarm signal Z is issued:

[0114]

[0115] Among them, S alis the preset fire alarm threshold. When Z=1, it indicates that a fire alarm signal is issued; when Z=0, it indicates that no fire alarm signal is issued.

[0116] Fire and smoke phenomena in power equipment are identified based on environmental monitoring data, and fire and smoke recognition results are obtained. A fire alarm signal is issued when the preset fire alarm threshold is exceeded. This allows for timely detection of fire and smoke conditions around power equipment, and an alarm is issued at the early stages of a fire, buying more time for firefighters to extinguish the fire and reducing the harm of fire to power equipment and personnel.

[0117] The feedback module receives intrusion signals, fault signals, and fire alarm signals and sends them to the management personnel's terminals. The intrusion signals include tracking data. This ensures that management personnel receive various security information in a timely manner, allowing them to respond quickly and take appropriate measures, thus improving the emergency response capabilities of the power security system.

[0118] As can be seen above, by comprehensively monitoring the environment, equipment status, intrusion, and fire, the operating environment and safety status of power equipment can be comprehensively and real-time monitored, promptly identifying potential safety hazards. Furthermore, the equipment health index is used to assess equipment health and predict failures, achieving a shift from passive maintenance to proactive prevention. This improves equipment reliability and power system stability, while reducing maintenance costs. The early warning module promptly transmits various safety signals to management personnel, enabling them to make quick decisions and take appropriate measures. This effectively enhances the system's emergency response capabilities and reduces losses caused by safety incidents.

[0119] It is important to note that the construction and arrangement of the present application shown in a plurality of different exemplary embodiments are merely illustrative. Although only a few embodiments are described in detail in this disclosure, it will be readily understood by those who consult this disclosure that many modifications are possible without departing substantially from the novel teachings and advantages of the subject matter described in this application. Other replacements, modifications, changes, and omissions may be made in the design, operating conditions, and arrangement of the exemplary embodiments without departing from the scope of the present invention. Therefore, the present invention is not limited to specific embodiments, but extends to a variety of modifications still falling within the scope of the appended claims.

[0120] Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment (i.e., those features that are not relevant to the best mode presently contemplated for carrying out the invention or those that are not relevant to implementing the invention) may not be described.

[0121] It will be appreciated that in the development of any actual embodiment, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but will, for those of ordinary skill having the benefit of this disclosure, be a routine undertaking of design, fabrication, and production without undue experimentation.

[0122] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An electric power security Internet of Things system, characterized in that: include: Environmental monitoring module, used to monitor the operating environment parameters of power equipment in real time and obtain environmental monitoring data; The intrusion monitoring module is used to monitor the power equipment site in real time using cameras and electronic fences. When the electronic fence is triggered, it sends an intrusion signal and captures and tracks the intruder through the camera to obtain tracking data. The status monitoring module is used to monitor the operating status of power equipment in real time and obtain operating status data; A status assessment module is used to quantitatively assess the health status of the equipment through the equipment health index based on the operating status data to obtain a status assessment result; a fault prediction module, configured to predict operating parameters of the power equipment based on the state assessment result, obtain a fault prediction result, and issue a fault signal based on the fault prediction result when a preset state threshold is exceeded; a fire and smoke recognition module, configured to recognize fire and smoke phenomena in power equipment based on the environmental monitoring data, obtain fire and smoke recognition results, and issue a fire alarm signal when the smoke concentration exceeds a preset threshold value based on the fire and smoke recognition results; The feedback module is used to receive the intrusion signal, fault signal and fire alarm signal, and send the corresponding signal to the terminal corresponding to the management personnel; the intrusion signal includes tracking data.

2. The power security Internet of Things system according to claim 1, characterized in that: The intrusion monitoring module is used to obtain an intrusion signal when the electronic fence is triggered, including: The electronic fence is equipped with n sensors, and the physical quantity collected by the i-th sensor is x i , then the triggering condition of the electronic fence is expressed as: Among them, the normal threshold range of the i-th sensor is [a i ,b i ], when the trigger signal is 1, it means that the electronic fence is triggered and sends an intrusion signal.

3. The power security Internet of Things system according to claim 1, characterized in that: The intrusion monitoring module is also used to: The image collected by the camera at time t is I t At the time t0 when the electronic fence is triggered, the feature vector of the intruder detected by the target detection algorithm is F0; At the subsequent time t>t0, calculate the feature vector F of each target in the current image t j Similarity to F0: in, is the eigenvector F t j Similarity with F0, ||·|| represents the modulus of the vector; Select the target with the greatest similarity as the tracking target: when When the similarity is greater than the preset threshold value θ, it is considered that the intruder is successfully tracked and the tracking data is obtained.

4. The power security Internet of Things system according to claim 1, characterized in that: The status assessment module is further configured to: The operating status data obtained by the status monitoring module includes m indicators. The measurement value of the ath indicator at time e is x ea ,a=1,2,....,m; Map the running status data to the interval [0, 1] and normalize the measured values: in, is the minimum value of the ath indicator, is the maximum value of the ath indicator; Different operating status indicators have different impacts on the health of the equipment. Each indicator is assigned a weight ω a , and satisfies Calculate the device health index to quantitatively assess the health of the device: Among them, H e The equipment health index is divided into different levels to obtain the status assessment result.

5. The power security Internet of Things system according to claim 1, characterized in that: The fault prediction module is further configured to: According to the state evaluation results H1, H2, ..., H n And the corresponding operating parameters P1, P2, ..., P n , establish the relationship between the status assessment results and the operating parameters: P=β0+β1H+ε Where P is the operating parameter, H is the state assessment result, β0 and β1 are the parameters to be estimated, and ε is the error term; Estimate the parameters β0 and β1 using the least squares method: in, For the new state assessment results, the predicted operating parameters are: Among them, H new For the new status assessment results, is the predicted operating parameter, and the fault prediction result is obtained; The fault prediction module is further configured to send a fault signal, including: When the predicted operating parameters When a fault signal is issued, the P th The preset status threshold.

6. The power security Internet of Things system according to claim 1, characterized in that: The fireworks recognition module is also used for: Extracting fireworks indicators based on the environmental monitoring data, including smoke concentration S, temperature T, and light intensity L, to obtain an environmental monitoring data vector X = (S, T, L); Set a corresponding threshold for each fire indicator, and obtain the fire identification result F through threshold judgment: Among them, S th is the smoke concentration threshold, T th is the temperature threshold, L th is the light intensity threshold. When F=1, it means that fireworks are recognized, and when F=0, it means that fireworks are not recognized. The smoke and fire recognition module is also used to issue a fire alarm signal, including: Based on the smoke and fire recognition results, the smoke concentration S is compared with the preset fire alarm threshold and a fire alarm signal Z is issued: Among them, S al is the preset fire alarm threshold. When Z=1, it indicates that a fire alarm signal is issued; when Z=0, it indicates that no fire alarm signal is issued.

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