Safety management and control system and method for intelligent lock of transformer substation

Abstract

The invention provides a safety management and control system for an intelligent lock of a transformer substation, and the system comprises an intelligent wearable device which is used for monitoring the physiological data of a wearer in real time; the environment monitoring module is used for monitoring the environment risk of the working area in real time; the video monitoring module is used for monitoring the in-out dynamic state and the behavior standard of the personnel; the intelligent lock linkage management module is used for automatically locking an intelligent lock and giving an alarm according to monitoring results of the intelligent wearable device, the environment monitoring module and the video monitoring module when abnormity is monitored; the risk assessment module is used for establishing a risk assessment model and dynamically adjusting the operation authority of the intelligent lock; and the situation awareness control module is used for changing the operation authority of the intelligent lock according to the situation evaluation result. The invention further discloses a corresponding method. According to the invention, the safety and management efficiency of electric power facilities such as a transformer substation are improved.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a security management and control system and method for a substation smart lock. Background Art

[0002] With the rapid development of the power industry, substations, as the core nodes of the power system, are particularly important for safety and efficiency. Traditional security management methods mainly rely on manual monitoring and physical locks, which have many shortcomings:

[0003] High risk of human factors: Manual monitoring is easily affected by factors such as fatigue and lack of concentration. It is difficult to achieve all-weather monitoring without omissions, which poses a safety hazard.

[0004] Slow response speed: Traditional locks require manual operation. Once an emergency occurs, they cannot respond quickly and control the risk area, which may cause the accident to expand.

[0005] Inefficient management: Physical locks have high management and maintenance costs, complex key management, and it is difficult to track lock usage records.

[0006] Lack of intelligent linkage: Traditional security management methods cannot be effectively integrated with other monitoring systems (such as environmental monitoring and video surveillance), making it difficult to achieve intelligent risk warning and emergency response.

[0007] Therefore, there is an urgent need for an intelligent lock safety management and control system that can perform real-time monitoring, intelligent judgment, and rapid response to overcome the above-mentioned defects and improve the safety management level of substations. Summary of the invention

[0008] The technical problem to be solved by the present invention is to provide a security management and control system and method for substation smart locks, which improves the safety and management efficiency of power facilities such as substations by integrating multiple modules such as smart wearable devices, environmental monitoring, video surveillance, risk assessment and situational awareness control.

[0009] The technical solution adopted by the present invention is that, as a first aspect of the present invention, a security management and control system for a substation smart lock is provided, which at least includes:

[0010] Smart wearable devices are used to monitor the physiological data of workers in real time and compare them with preset warning conditions;

[0011] Environmental monitoring module, equipped with a variety of sensors to monitor environmental risks in the work area in real time;

[0012] The video surveillance module uses a 360-degree panoramic camera to monitor the entry and exit dynamics and behavioral norms of personnel;

[0013] A smart lock linkage management module is used to automatically lock the smart lock and sound an alarm when an abnormality is detected based on the monitoring results of the smart wearable device, the environmental monitoring module and the video monitoring module;

[0014] Risk assessment module, which establishes a risk assessment model and dynamically adjusts the operation permissions of smart locks based on the operator's historical scores, environmental factor scores, and device status scores;

[0015] The situational awareness control module is used to identify and evaluate the current working situation and change the operating permissions of the smart lock according to the situational evaluation results.

[0016] Preferably, the smart wearable device further comprises:

[0017] A physiological data monitoring unit, used to monitor the physiological data of the wearer in real time, the physiological data including: heart rate, fatigue index and body temperature;

[0018] The warning condition comparison unit is used to compare the real-time monitored physiological data with the pre-set warning conditions of heart rate, fatigue index and body temperature.

[0019] Preferably, the environment monitoring module further comprises:

[0020] A sensor monitoring unit for monitoring environmental data using a smoke sensor, a gas detector, a temperature sensor, and a humidity sensor;

[0021] The risk threshold calculation unit is used to calculate the environmental risk index based on the environmental data monitored by the sensor and compare it with the preset safety threshold.

[0022] Preferably, the smart lock linkage management module further includes:

[0023] The first linkage response unit is used to automatically lock the smart lock and send an alarm message when the physiological data monitored by the smart wearable device reaches the warning condition;

[0024] The second linkage response unit is used to automatically lock and send an alarm message when the environmental monitoring module detects that the environmental risk index exceeds the safety threshold;

[0025] The third linkage response unit is used to automatically lock the smart lock and send an alarm message when the video surveillance module recognizes unauthorized entry or irregular behavior.

[0026] Preferably, the risk assessment module further comprises:

[0027] An item score acquisition unit is used to calculate and acquire the operator's historical score, environmental factor score, and equipment status score;

[0028] An overall risk score calculation unit is used to obtain an overall risk score by using a pre-established risk assessment model to calculate based on the operator's historical scores, environmental factor scores, and equipment status scores;

[0029] The dynamic permission adjustment unit is used to dynamically adjust the operation permissions of the smart lock according to the overall risk score.

[0030] Preferably, the situational awareness control module further comprises:

[0031] Work situation identification and assessment unit, which is used to monitor and identify various parameters in the work environment in real time, including current temperature, current humidity, current wind speed and equipment status;

[0032] A safety assessment function building unit is used to build a safety assessment function based on the input working environment parameters. The function comprehensively considers the impact of temperature, humidity, wind speed and equipment status on safety by weighted summation.

[0033] The emergency mode activation unit is used to determine the safety level of the current working environment according to the output result of the safety assessment function, and activate the corresponding emergency mode of the smart lock accordingly;

[0034] The dynamic learning adjustment unit is used to calculate the difference between the actual safety value and the predicted safety value by continuously collecting and analyzing historical data, and dynamically adjust the weight coefficient according to the difference value and the learning rate to optimize the prediction ability and adaptability of the model.

[0035] Accordingly, as another aspect of the present invention, a security management and control method for a substation smart lock is also provided, which comprises the following steps:

[0036] Monitor the physiological data of staff in real time and compare it with the preset warning conditions;

[0037] Use multiple sensors to monitor environmental risks in the work area in real time;

[0038] Use 360-degree panoramic cameras to monitor people's entry and exit dynamics and behavioral norms, and identify unauthorized entry and exit or irregular behavior;

[0039] When at least one of the above monitoring results is abnormal, the smart lock is automatically locked and an alarm is sounded;

[0040] Establish a risk assessment model to dynamically adjust the operation permissions of smart locks based on the operator's historical scores, environmental factor scores, and device status scores;

[0041] Identify and evaluate the current working situation, and change the operation permissions of the smart lock according to the situation evaluation results.

[0042] Preferably, when at least one of the above monitoring results is abnormal, automatically locking the smart lock and sounding an alarm includes at least one of the following steps:

[0043] When the monitored physiological data reaches the warning condition, the smart lock will be automatically locked and an alarm message will be sent;

[0044] Automatically lock and send alarm information when the environmental risk index exceeds the safety threshold;

[0045] When unauthorized access or irregular behavior is detected, the smart lock will be automatically locked and an alarm message will be sent.

[0046] Preferably, the establishment of a risk assessment model to dynamically adjust the operation authority of the smart lock according to the operator's historical score, environmental factor score and device status score further includes:

[0047] Calculate and obtain operator historical scores, environmental factor scores, and equipment status scores;

[0048] Based on the operator's historical scores, environmental factor scores, and equipment status scores, a pre-established risk assessment model is used to calculate and obtain an overall risk score;

[0049] Dynamically adjust the operating permissions of the smart lock based on the overall risk score.

[0050] Preferably, the identifying and evaluating the current working situation and changing the operation authority of the smart lock according to the situation evaluation result further includes:

[0051] Real-time monitoring and identification of multiple parameters in the working environment, including current temperature, current humidity, current wind speed and equipment status;

[0052] Based on the input working environment parameters, a safety assessment function is constructed, which comprehensively considers the impact of temperature, humidity, wind speed and equipment status on safety through weighted summation;

[0053] According to the output result of the security assessment function, the security level of the current working environment is judged, and the corresponding emergency mode of the smart lock is activated accordingly;

[0054] By continuously collecting and analyzing historical data, the difference between the actual safety value and the predicted safety value is calculated, and the weight coefficient is dynamically adjusted according to the difference value and learning rate to optimize the predictive ability and adaptability of the model.

[0055] The implementation of the embodiments of the present invention has the following beneficial effects:

[0056] The present invention provides a security management and control system and method for a substation intelligent lock. Through real-time monitoring and intelligent judgment, the present invention can timely discover and control potential risks, effectively prevent casualties and equipment damage, and significantly improve the security of the substation.

[0057] In the embodiment of the present invention, the management and maintenance costs of the smart lock are much lower than those of traditional physical locks, and it is easy to track usage records, which greatly improves management efficiency.

[0058] The present invention integrates multiple monitoring systems, realizes intelligent risk warning and emergency response, and improves the overall safety management level of the substation.

[0059] Through the situation-aware control module, the present invention can automatically adjust the access control strategy according to different situations, which ensures safety while improving the flexibility and adaptability of the system.

[0060] The implementation of the present invention helps to promote the intelligent transformation of the power industry and provides strong technical support for the fields of intelligent manufacturing, energy management, environmental monitoring, etc.

[0061] To sum up, the security management system, method and storage medium of the substation smart lock provided by the present invention have significant technical advantages and practical application value, can effectively improve the safety and management efficiency of the substation, and contribute to the intelligent development of the power industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the drawings required for use in the embodiments of the present invention or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0063] Figure 1 A schematic diagram of the structure of an embodiment of a security management and control system for a substation smart lock provided by the present invention;

[0064] Figure 2 for Figure 1 Schematic diagram of the structure of the intelligent wearable device;

[0065] Figure 3 for Figure 1 The structural diagram of the medium environment monitoring module;

[0066] Figure 4 for Figure 1 The structural diagram of the intelligent lock linkage management module;

[0067] Figure 5 for Figure 1Schematic diagram of the structure of the risk assessment module;

[0068] Figure 6 for Figure 1 A schematic diagram of the structure of the situational awareness control module;

[0069] Figure 7 A schematic diagram of the main process of an embodiment of a security management and control method for a substation smart lock provided by the present invention. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0071] like Figure 1 FIG. 1 is a schematic diagram showing a structure of an embodiment of a security management and control system for a substation intelligent lock provided by the present invention; and FIG. Figures 2 to 6 As shown, in this embodiment, the security management and control system 1 of the substation smart lock includes at least:

[0072] The smart wearable device 10 is used to monitor the physiological data of the staff in real time and compare it with the preset warning conditions;

[0073] The environmental monitoring module 11 is equipped with a variety of sensors for real-time monitoring of environmental risks in the working area;

[0074] The video monitoring module 12 uses a 360-degree panoramic camera to monitor the entry and exit dynamics and behavioral norms of personnel;

[0075] The smart lock linkage management module 13 is used to automatically lock the smart lock and sound an alarm when an abnormality is detected based on the monitoring results of the smart wearable device, the environment monitoring module and the video monitoring module;

[0076] The risk assessment module 14 establishes a risk assessment model and dynamically adjusts the operation authority of the smart lock according to the operator's historical score, environmental factor score and device status score;

[0077] The situational awareness control module 15 is used to identify and evaluate the current working situation and change the operation authority of the smart lock according to the situational evaluation result.

[0078] like Figure 2 As shown, in a specific example, the smart wearable device 10 further includes:

[0079] The physiological data monitoring unit 100 is used to monitor the physiological data of the wearer in real time, and the physiological data includes: heart rate, fatigue index and body temperature;

[0080] Specifically, smart wearable devices are designed to monitor the physiological status of workers throughout the process to ensure that they are in a safe and healthy state before and after entering a hyperbaric room or other hazardous environment. The monitored physiological data include the following:

[0081] 1. Heart rate HR: Real-time monitoring of heart rate changes. Heart rate data helps to detect abnormalities in time and prevent potential heart disease or health problems caused by excessive work.

[0082] The formula used is as follows:

[0083]

[0084] Where: HR(t) is the heart rate at time \(t\) (beats / minute); N(t) is the number of heartbeats at time \(T\); T is the monitored time interval (minutes).

[0085] 2. Fatigue Index FI: Assess fatigue status by calculating heart rate variability (HRV) and activity level (such as number of steps):

[0086] FI=α*HRV+β*A

[0087] Wherein: FI is fatigue index.

[0088] HRV is heart rate variability (usually expressed as standard deviation).

[0089] A is the activity level (such as number of steps, energy consumption, etc.).

[0090] (α, β) are weight parameters, reflecting the impact of each factor on fatigue.

[0091] In one example, a fatigue index threshold value may be set, such as 80, and an alarm may be issued if the threshold value is exceeded.

[0092] 3. Body temperature: Real-time monitoring of body temperature, defined as:

[0093] T body (t) = T sensor (t)+∈

[0094] in:

[0095] T body (t) is the body temperature at time t.

[0096] T sensor (t) is the body temperature value monitored by the sensor.

[0097] ∈ is the sensor error or adjustment coefficient.

[0098] In one example, if the body temperature T body(t)>37.5℃\), it is judged as potential fever.

[0099] The warning condition comparison unit 101 is used to compare the physiological data monitored in real time with the pre-set warning conditions of heart rate, fatigue index and body temperature.

[0100] Specifically, in the example of the present invention, the warning conditions can be set as follows:

[0101] Fatigue index exceeds the limit: If (FI>80), an alarm is triggered.

[0102] Abnormal heart rate: If (HR<60) or (HR>140), the system considers that there is a risk.

[0103] Abnormal body temperature: If (T_{body}>37.5℃), it is judged as fever.

[0104] In a specific example, data communication can be achieved through Bluetooth or Wi-Fi, and real-time data can be sent to the background system: encryption algorithms are then used to ensure the security of data transmission: managers can view data and alarms in real time through the monitoring platform and make decisions when necessary.

[0105] like Figure 3 As shown, in a specific example, the environment monitoring module 11 further includes:

[0106] The sensor monitoring unit 110 is used to monitor environmental data using a smoke sensor, a gas detector, a temperature sensor and a humidity sensor;

[0107] In a specific example, the following indicators can be monitored:

[0108] Smoke concentration: The concentration of smoke is usually detected using a smoke sensor and is measured in ppm, parts per million.

[0109] Combustible gas concentration C_g: The concentration of combustible gas in the working environment is detected by a gas detector, and the unit is also ppm.

[0110] Temperature T: monitor the temperature change of the environment, the unit can be ℃ or K.

[0111] Humidity H: monitors the humidity of the environment in percentage (%).

[0112] In order to express environmental risk monitoring, a comprehensive risk threshold formula is used:

[0113] R=f(S,C g ,T,H)

[0114] in:

[0115] R is the environmental risk index, a comprehensive indicator that represents the overall safety risk level of the work area.

[0116] S is the smoke concentration, when (S>S threshold ), it means that the smoke concentration exceeds the safe range, where S threshold is the preset safety threshold.

[0117] C g is the concentration of combustible gas. g >C g_hreshols ), it means that the concentration of combustible gas exceeds the safe range, where C g_hreshold is the preset safety threshold.

[0118] T and H are temperature and humidity, which can be used to calculate the risk according to the influence coefficient. Set the appropriate weight parameter ω T and ω H ,but:

[0119] R=ω S *S+ω g *C g +ω T *T+ω H *H

[0120] in:

[0121] ω S is the weight of smoke concentration, reflecting its impact on the total risk. Usually, when it is set to the maximum value, it will lead to a significant increase in risk.

[0122] ω g It is the weight of the combustible gas concentration, indicating its danger to the environment.

[0123] ω T and ω H The degree to which temperature and humidity affect risk can be set.

[0124] The risk threshold calculation unit 111 is used to calculate the environmental risk index according to the environmental data monitored by the sensor, and compare it with the preset safety threshold.

[0125] It is understandable that a safety threshold (R threshold ), if the temperature is too high or the humidity is too high, it will affect the safety of equipment and personnel, and their thresholds and impact weights can be set.

[0126] It can be understood that the environmental monitoring module can effectively identify potential environmental risks and automatically trigger safety response measures to protect staff safety and equipment integrity by real-time monitoring of core environmental parameters (such as smoke and combustible gas concentrations) combined with mathematical models and thresholds.

[0127] In this embodiment, the video monitoring module 13 is mainly used to comprehensively monitor the behavior of personnel to ensure access control and behavioral norms in areas with high security requirements (such as laboratories, warehouses, factories, etc.). This system uses a 360-degree panoramic camera to capture and analyze the entry and exit dynamics of personnel in real time, and perform security management through an intelligent linkage mechanism.

[0128] The device's built-in 360-degree panoramic camera can fully cover a specific area, collect video data in real time, and monitor people's behavior.

[0129] First, set up the video surveillance data model: set the behavior of people in the area to be described by the following model:

[0130] V(t)={I1(t),I1(t),I1(t),......I n (t)}

[0131] in:

[0132] V(t) is the monitoring view at time t.

[0133] I n (t) is the nth frame (image array) captured at time t. Through video analysis algorithms, these frames can be processed to identify and record the movement path of people.

[0134] Monitoring of unauthorized entry and exit: The system detects and identifies unauthorized entry or irregular behavior through intelligent algorithms.

[0135] The behavior recognition function used is: Set the behavior recognition system logic as: Store the list of all authorized personnel. Identify the personnel entering the area in real time. Set the behavior monitoring parameters, such as:

[0136] R(t)=Act ions(I n (t))

[0137] in:

[0138] R(t) is the behavior recognition result at time \(t\).

[0139] Act ions(I n (t)) is the image I n The actions in (t) are recognized, including “enter”, “leave”, “wander”, etc.

[0140] The video surveillance module can identify and handle potential security risks in real time through comprehensive and efficient monitoring methods. The system's intelligent locking and alarm mechanism combined with advanced image recognition technology has greatly improved the effectiveness and response speed of regional security management.

[0141] like Figure 4 As shown, in a specific example, the smart lock linkage management module 13 further includes:

[0142] The first linkage response unit 130 is used to automatically lock the smart lock and send an alarm message when the physiological data monitored by the smart wearable device 10 reaches the warning condition;

[0143] Specifically, when the smart lock linkage management module 13 learns from the background that the physiological data exceeds the warning condition, the system will implement the following response measures:

[0144] Smart lock automatically locks: using the following status check and control system(s):

[0145]

[0146] Send an alert: The system will notify relevant personnel and record the status change through the communication protocol:

[0147] Record data: Use a database to store all physiological data and events for subsequent analysis.

[0148] It is understandable that this smart wearable device can continuously protect the health and safety of workers and improve work efficiency through the full monitoring function and linkage mechanism. Real-time communication with the background system anchors the immediate response to potential risks, which helps to reduce the probability of accidents, thereby providing comprehensive safety protection for enterprises and enhancing employees' health and safety awareness.

[0149] The second linkage response unit 131 is used to automatically lock and send an alarm message when the environmental monitoring module 12 detects that the environmental risk index exceeds the safety threshold;

[0150] Specifically, when the environment monitoring module 12 detects that R>R threshold When

[0151] Among them, R threshold It is a preset safety threshold. When the environmental risk index R exceeds this threshold, it means there is potential danger.

[0152] Linked response actions include: automatically locking the smart locks in the relevant areas, and sending alarms to the security management system to alert staff and managers.

[0153] The third linkage response unit 132 is used to automatically lock the smart lock and send an alarm message when the video monitoring module 13 identifies unauthorized entry or irregular behavior.

[0154] Specifically, when the video monitoring module 13 detects unauthorized entry or irregular behavior, the system will automatically respond and execute a series of security measures.

[0155] When non-compliant behavior is detected, the state of the smart lock is controlled: the state of the smart lock at that time is identified, an alarm is issued, and the ID of the person identified as unauthorized is output.

[0156] like Figure 5 As shown, in a specific example, the risk assessment module 14 further includes:

[0157] The item score acquisition unit 140 is used to calculate and acquire the operator's historical score, environmental factor score and equipment status score;

[0158] The overall risk score calculation unit 141 is used to calculate the overall risk score by using a pre-established risk assessment model according to the acquired operator's historical score, environmental factor score and equipment status score;

[0159] The dynamic permission adjustment unit 142 is used to dynamically adjust the operation permissions of the smart lock according to the overall risk score.

[0160] In the embodiment of the present invention, the following risk assessment model needs to be pre-established in the risk assessment module 14:

[0161] Set the following variables:

[0162] R: overall risk score;

[0163] H: operator’s historical rating;

[0164] E: environmental factor score;

[0165] D: Equipment status score;

[0166] T: current time point.

[0167] Combining these factors, the risk score R can be expressed as:

[0168] R(T)=ω H *H(T)+ω E *E(T)+ω D *D(T)

[0169] Where: (ω H ,ω E ,ω D ) are the weight coefficients of historical score, environmental factors and equipment status (H(T), E(T), D(T)) respectively represent the risk scores of the operator’s operation history, environmental factors and equipment status at time T.

[0170] Then, the sub-item score obtaining unit 140 calculates the following sub-item scores:

[0171] First, the operation history score H(T): based on the violation records and safety training assessment results:

[0172]

[0173] Among them, N v is the number of violations, N t is the total number of operations, Penalty is the penalty weight for violations, and TrainingScore is the corresponding training score.

[0174] Second, environmental factor score E(T): Real-time evaluation based on environmental data obtained from external sensors:

[0175]

[0176] Among them, d i (T) represents the degree of deviation of the i-th monitored environmental factor, that is, the distance from the safety standard.

[0177] Third, equipment status score D(T): estimated based on abnormal warnings and operating time of the equipment:

[0178]

[0179] Where F(T) is the number of equipment failures at time T, F max is the maximum number of failures that the device can tolerate.

[0180] Finally, the dynamic permission adjustment unit 142 is used to perform dynamic permission adjustment:

[0181] Set the safety threshold R th : If [R(T)>R th ], the permission is adjusted to a low level (restricted access); otherwise, the permission remains at a high level (allowed access).

[0182] In an embodiment of the present invention, a monitoring cycle may be set to continuously monitor changes in the risk score, and if the score is higher than a certain warning line for a continuous period of time, an early warning mechanism is triggered.

[0183] The dynamic risk assessment model can flexibly evaluate and monitor the safety risks of operators in a real-time environment, thereby achieving efficient adaptation and high security of smart locks.

[0184] like Figure 6 As shown, in a specific example, the situational awareness control module 15 further includes:

[0185] A working situation identification and evaluation unit 150 is used to monitor and identify various parameters in the working environment in real time, including current temperature (T), current humidity (H), current wind speed (W) and equipment status (S);

[0186] In an example, assume that the parameters monitored by the system include temperature, humidity, wind speed, equipment status, etc., which are defined as follows:

[0187] T = current temperature (unit: °C);

[0188] H = current humidity (unit: %);

[0189] W = current wind speed (unit: m / s);

[0190] S = equipment status (normal / under maintenance / fault);

[0191] E = Environmental safety index, indicating the overall safety situation (unit: between 0 and 1, 1 being completely safe).

[0192] The safety assessment function building unit 151 is used to build a safety assessment function E according to the input working environment parameters, which comprehensively considers the impact of temperature, humidity, wind speed and equipment status on safety by weighted summation;

[0193] In an example, in order to evaluate the current working situation, a safety evaluation function E is constructed:

[0194] E=f(T,H,W,S)=k1*normalize(T)+k2*normalize(H)+k3*

[0195] normalize(W)+k4*status(S)

[0196] in:

[0197] normalize(T) is a function that normalizes the original data T to the interval [0,1] to facilitate comparison between different metrics. The same is true for normalize(H) and normalize(W).

[0198] k1: The importance of temperature to safety assessment; for example, under high temperature conditions, the equipment may face a greater risk of failure.

[0199] k2: Importance of humidity to safety assessment; high humidity environments may cause electrical failures in equipment.

[0200] k3: The importance of wind speed to safety assessment; in extreme weather conditions (such as strong winds), the safety risk of equipment activation increases.

[0201] k4: The importance of equipment status to safety assessment; equipment under maintenance is usually unsafe.

[0202] The emergency mode activation unit 152 is used to determine the security level of the current working environment according to the output result of the security assessment function E, and activate the corresponding emergency mode of the smart lock accordingly;

[0203] In a specific example, based on the above evaluation of the work situation, assume that the system determines that a certain work area is in a high-risk state due to external environment (such as bad weather) (i.e., (E <E threshold ), then activate the emergency mode of the smart lock:

[0204] "No entry for all" if(E<0.5)

[0205] "Only certain people" if(0.5≤E<0.8)

[0206] “Open” if(E>0.8).

[0207] The dynamic learning adjustment unit 153 is used to calculate the difference between the actual safety value and the predicted safety value by continuously collecting and analyzing historical data, and dynamically adjust the weight coefficients (k_1, k_2, k_3, k_4) according to the difference value and the learning rate α to optimize the prediction ability and adaptability of the model.

[0208] In the specific example, in order to further improve the security of the system, a dynamic learning mechanism is introduced. This mechanism continuously collects and analyzes historical data and adjusts the weight coefficients (\(k_1, k_2, k_3, k_4\)) to optimize the prediction ability of the model. Its formula can be expressed as:

[0209]

[0210] in: Represents the updated weight coefficient; represents the previous weight coefficient; α represents the learning rate, which is used to adjust the sensitivity of the model to new data. actual Indicates the true value; E predicted Represents the predicted value.

[0211] It is understandable that in a complex and ever-changing working environment, the implementation of situational awareness control functions can significantly improve the efficiency and pertinence of safety management. The system can not only monitor the working status in real time (such as equipment maintenance, extreme weather, etc.), but also automatically evaluate the impact of these situations on safety and respond accordingly.

[0212] Through the situational awareness control module, not only can security be assessed based on current environmental conditions, but access control policies can also be automatically adjusted through adaptation mechanisms to ensure that only authorized personnel can enter specific areas.

[0213] It can be understood that the present invention is suitable for the safe management of high-reliability power facilities such as substations and distribution stations in the power industry, especially in situations where high safety and high efficiency are required, and is suitable for related fields such as intelligent manufacturing, energy management, and environmental monitoring.

[0214] In the embodiment of the present invention, wearable smart wearable devices are used to monitor the physiological data of workers (such as heart rate, fatigue index, body temperature, etc.) in real time to ensure that they are within the safety threshold. When the physiological data reaches the preset warning condition (such as the fatigue index exceeds the safety threshold), the system automatically locks the related smart lock, prohibiting the worker from entering the high-risk area or operating potentially dangerous equipment to ensure their safety.

[0215] Through environmental monitoring equipment (such as smoke sensors, gas detectors, etc.), the environmental risks in the working area are monitored in real time. When the sensor detects dangerous signals (such as smoke, flammable gas leakage, etc.), the system will automatically identify and lock the smart locks in the relevant areas to prevent any personnel from entering, thereby protecting personal safety and equipment integrity;

[0216] With video surveillance, when unauthorized access or irregular behavior is detected, the system can immediately lock the smart lock, stop normal traffic in the area, and send an alarm to the security manager;

[0217] By establishing a risk assessment model, the operating permissions of the smart lock can be dynamically adjusted according to the real-time risk assessment results to ensure that only personnel who meet the security standards can access the area, thus reducing security risks;

[0218] Use the situational awareness control module to identify and evaluate the current working situation (such as equipment maintenance, extreme weather, etc.) and change the operating permissions of the smart lock.

[0219] like Figure 7 As shown, a schematic diagram of the main process of an embodiment of a security control method for a substation smart lock provided by the present invention is shown. Figures 1 to 6 The system described is implemented, in this embodiment, the method includes the following steps:

[0220] Step S10, real-time monitoring of the physiological data of the staff and comparing it with the preset warning conditions;

[0221] Specifically, in this step, it includes: real-time monitoring of the physiological data of the wearer, the physiological data including: heart rate, fatigue index and body temperature;

[0222] Compare the real-time monitored physiological data with pre-set warning conditions of heart rate, fatigue index and body temperature.

[0223] Step S11, using a variety of sensors to monitor the environmental risks of the working area in real time;

[0224] Specifically, in this step, smoke sensors, gas detectors, temperature sensors and humidity sensors are used to monitor environmental data;

[0225] The risk threshold calculation unit 111 is used to calculate the environmental risk index according to the environmental data monitored by the sensor, and compare it with the preset safety threshold.

[0226] Step S12, using a 360-degree panoramic camera to monitor the entry and exit dynamics and behavior norms of personnel, and identifying unauthorized entry and exit or irregular behavior;

[0227] Step S13, when at least one of the above monitoring results is abnormal, automatically lock the smart lock and sound an alarm;

[0228] In a specific example, the step S13 includes at least one of the following steps:

[0229] When the monitored physiological data reaches the warning condition, the smart lock will be automatically locked and an alarm message will be sent;

[0230] Automatically lock and send alarm information when the environmental risk index exceeds the safety threshold;

[0231] When unauthorized access or irregular behavior is detected, the smart lock will be automatically locked and an alarm message will be sent.

[0232] Step S14, establishing a risk assessment model, and dynamically adjusting the operation authority of the smart lock according to the operator's historical score, environmental factor score, and device status score;

[0233] In a specific example, the step S14 at least includes:

[0234] Calculate and obtain operator historical scores, environmental factor scores, and equipment status scores;

[0235] Based on the operator's historical scores, environmental factor scores, and equipment status scores, a pre-established risk assessment model is used to calculate and obtain an overall risk score;

[0236] Dynamically adjust the operating permissions of the smart lock based on the overall risk score.

[0237] Step S15, identify and evaluate the current working situation, and change the operation authority of the smart lock according to the situation evaluation result.

[0238] In a specific example, step S15 at least includes:

[0239] Real-time monitoring and identification of multiple parameters in the working environment, including current temperature, current humidity, current wind speed and equipment status;

[0240] Based on the input working environment parameters, a safety assessment function is constructed, which comprehensively considers the impact of temperature, humidity, wind speed and equipment status on safety through weighted summation;

[0241] According to the output result of the security assessment function, the security level of the current working environment is judged, and the corresponding emergency mode of the smart lock is activated accordingly;

[0242] By continuously collecting and analyzing historical data, the difference between the actual safety value and the predicted safety value is calculated, and the weight coefficient is dynamically adjusted according to the difference value and learning rate to optimize the predictive ability and adaptability of the model.

[0243] For more details, please refer to and combine the above Figures 1 to 6 The description is not repeated here.

[0244] The implementation of the present invention has the following beneficial effects:

[0245] The present invention provides a security management and control system and method for a substation intelligent lock. Through real-time monitoring and intelligent judgment, the present invention can timely discover and control potential risks, effectively prevent casualties and equipment damage, and significantly improve the security of the substation.

[0246] In the embodiment of the present invention, the management and maintenance costs of the smart lock are much lower than those of traditional physical locks, and it is easy to track usage records, which greatly improves management efficiency.

[0247] The present invention integrates multiple monitoring systems, realizes intelligent risk warning and emergency response, and improves the overall safety management level of the substation.

[0248] Through the situation-aware control module, the present invention can automatically adjust the access control strategy according to different situations, which ensures safety while improving the flexibility and adaptability of the system.

[0249] The implementation of the present invention helps to promote the intelligent transformation of the power industry and provides strong technical support for the fields of intelligent manufacturing, energy management, environmental monitoring, etc.

[0250] To sum up, the security management system, method and storage medium of the substation smart lock provided by the present invention have significant technical advantages and practical application value, can effectively improve the safety and management efficiency of the substation, and contribute to the intelligent development of the power industry.

[0251] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0252] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0253] The above disclosure is only a preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A security control system for intelligent locks in substations, characterized in that: include: Smart wearable devices are used to monitor the wearer’s physiological data in real time and compare it with preset warning conditions; Environmental monitoring module, equipped with a variety of sensors to monitor environmental risks in the work area in real time; The video surveillance module uses a 360-degree panoramic camera to monitor the entry and exit dynamics and behavioral norms of personnel; A smart lock linkage management module is used to automatically lock the smart lock and sound an alarm when an abnormality is detected based on the monitoring results of the smart wearable device, the environmental monitoring module and the video monitoring module; Risk assessment module, which establishes a risk assessment model and dynamically adjusts the operation permissions of smart locks based on the operator's historical scores, environmental factor scores, and device status scores; The situational awareness control module is used to identify and evaluate the current working situation and change the operating permissions of the smart lock according to the situational evaluation results.

2. The system according to claim 1, characterized in that The smart wearable device further comprises: A physiological data monitoring unit, used to monitor the physiological data of the wearer in real time, the physiological data including: heart rate, fatigue index and body temperature; The warning condition comparison unit is used to compare the real-time monitored physiological data with the pre-set warning conditions of heart rate, fatigue index and body temperature.

3. The system according to claim 2, characterized in that The environmental monitoring module further comprises: A sensor monitoring unit for monitoring environmental data using a smoke sensor, a gas detector, a temperature sensor, and a humidity sensor; The risk threshold calculation unit is used to calculate the environmental risk index based on the environmental data monitored by the sensor and compare it with the preset safety threshold.

4. The system according to claim 3, characterized in that The smart lock linkage management module further includes: The first linkage response unit is used to automatically lock the smart lock and send an alarm message when the physiological data monitored by the smart wearable device reaches the warning condition; The second linkage response unit is used to automatically lock and send an alarm message when the environmental monitoring module detects that the environmental risk index exceeds the safety threshold; The third linkage response unit is used to automatically lock the smart lock and send an alarm message when the video surveillance module recognizes unauthorized entry or irregular behavior.

5. The system according to claim 4, characterized in that The risk assessment module further comprises: An item score acquisition unit is used to calculate and acquire the operator's historical score, environmental factor score, and equipment status score; An overall risk score calculation unit is used to obtain an overall risk score by using a pre-established risk assessment model to calculate based on the operator's historical scores, environmental factor scores, and equipment status scores; The dynamic permission adjustment unit is used to dynamically adjust the operation permissions of the smart lock according to the overall risk score.

6. The system according to claim 5, characterized in that The situational awareness control module further comprises: Work situation identification and assessment unit, which is used to monitor and identify various parameters in the work environment in real time, including current temperature, current humidity, current wind speed and equipment status; A safety assessment function building unit is used to build a safety assessment function based on the input working environment parameters. The function comprehensively considers the impact of temperature, humidity, wind speed and equipment status on safety by weighted summation. The emergency mode activation unit is used to determine the safety level of the current working environment according to the output result of the safety assessment function, and activate the corresponding emergency mode of the smart lock accordingly; The dynamic learning adjustment unit is used to calculate the difference between the actual safety value and the predicted safety value by continuously collecting and analyzing historical data, and dynamically adjust the weight coefficient according to the difference value and the learning rate to optimize the prediction ability and adaptability of the model.

7. A security control method for substation smart locks, characterized in that: The following steps are involved: Monitor the physiological data of staff in real time and compare it with the preset warning conditions; Use multiple sensors to monitor environmental risks in the work area in real time; Use 360-degree panoramic cameras to monitor people's entry and exit dynamics and behavioral norms, and identify unauthorized entry and exit or irregular behavior; When at least one of the above monitoring results is abnormal, the smart lock is automatically locked and an alarm is sounded; Establish a risk assessment model to dynamically adjust the operation permissions of smart locks based on the operator's historical scores, environmental factor scores, and device status scores; Identify and evaluate the current working situation, and change the operation permissions of the smart lock according to the situation evaluation results.

8. The method according to claim 7, characterized in that When at least one of the above monitoring results is abnormal, automatically locking the smart lock and sounding an alarm includes at least one of the following steps: When the monitored physiological data reaches the warning condition, the smart lock will be automatically locked and an alarm message will be sent; Automatically lock and send alarm information when the environmental risk index exceeds the safety threshold; When unauthorized access or irregular behavior is detected, the smart lock will be automatically locked and an alarm message will be sent.

9. The method according to claim 8, characterized in that The step of establishing a risk assessment model and dynamically adjusting the operation authority of the smart lock according to the operator's historical score, environmental factor score, and device status score further includes: Calculate and obtain operator historical scores, environmental factor scores, and equipment status scores; Based on the operator's historical scores, environmental factor scores, and equipment status scores, a pre-established risk assessment model is used to calculate and obtain an overall risk score; Dynamically adjust the operating permissions of the smart lock based on the overall risk score.

10. The method according to claim 9, characterized in that The identifying and evaluating the current working situation and changing the operation authority of the smart lock according to the situation evaluation result further includes: Real-time monitoring and identification of multiple parameters in the working environment, including current temperature, current humidity, current wind speed and equipment status; Based on the input working environment parameters, a safety assessment function is constructed, which comprehensively considers the impact of temperature, humidity, wind speed and equipment status on safety through weighted summation; According to the output result of the security assessment function, the security level of the current working environment is judged, and the corresponding emergency mode of the smart lock is activated accordingly; By continuously collecting and analyzing historical data, the difference between the actual safety value and the predicted safety value is calculated, and the weight coefficient is dynamically adjusted according to the difference value and learning rate to optimize the predictive ability and adaptability of the model.

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