Safety supervision method and system for smart parking

Abstract

The invention relates to the technical field of parking safety, in particular to a safety supervision method and system for intelligent parking, and the method comprises the following steps: S1, collecting environment parameters around a charging pile and a charging vehicle in real time; s2, if it is detected that any parameter exceeds the corresponding safety threshold value, a safety alarm is given out immediately; s3, constructing a charging safety evaluation model for predicting potential safety risks in the charging process; s4, when the charging safety risk assessment result exceeds a set risk threshold value, executing safety check; s5, when a safety event of overheating, overflowing or gas leakage occurs, emergency treatment measures are executed; and S6, updating the state transition probability of the charging safety evaluation model. According to the intelligent parking system, intelligent risk assessment and emergency processing are carried out through multi-sensor real-time monitoring in combination with the Markov chain algorithm, accurate risk prediction, real-time response and continuous optimization in the charging process are achieved, and the safety and management efficiency of the intelligent parking system are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of parking safety technology, and in particular to a smart parking safety supervision method and system. Background Art

[0002] With the development of smart cities, smart parking systems have gradually become an important means to solve urban parking problems. Existing smart parking systems mainly focus on functions such as parking space guidance and automatic billing. Although they have improved the convenience of parking lot management, there are still weak links in the supervision of safe charging of electric vehicles. The charging process of electric vehicles involves complex current and voltage control and changes in environmental parameters. If there is a lack of effective real-time monitoring and safety management, safety hazards such as overcurrent, overheating or gas leakage are prone to occur. Especially in large parking lots, there are a large number of charging piles and the equipment operation is complicated. Traditional manual supervision is difficult to detect problems in real time and accurately, and there are potential safety risks.

[0003] The existing smart parking system faces the following major problems in charging safety management: first, there is a lack of intelligent risk assessment mechanism, which cannot timely identify safety hazards in the charging process; second, the traditional safety monitoring system cannot effectively handle complex multi-state changes, such as overheating, overcurrent or power supply fluctuations during charging; in addition, the existing system lacks an automated emergency response mechanism, and when abnormal situations occur, it cannot respond quickly, resulting in further expansion of potential risks. Therefore, the development of a smart parking safety supervision method and system that can monitor in real time, intelligently evaluate and automatically respond has become a necessary means to ensure the safety of electric vehicle charging. Summary of the invention

[0004] Based on the above objectives, the present invention provides a smart parking safety supervision method and system.

[0005] A smart parking safety supervision method comprises the following steps: S1: Deploy multiple types of sensors in the electric vehicle charging area of ​​the parking lot to collect real-time data on environmental parameters around the charging piles and charging vehicles, including temperature, current, voltage, and flammable gas concentration in the air; S2: Compare the collected environmental parameters with the preset safety thresholds. If any parameter is detected to exceed the corresponding safety threshold, a safety alarm is immediately issued to trigger the sound and light alarm device; S3: Based on the historical data of the electric vehicle charging area and the current charging behavior, a charging safety assessment model is constructed using the Markov chain algorithm to analyze the state transition during the vehicle charging process, calculate the transition probability between different states, and then predict the potential safety risks in the charging process to obtain the risk assessment results; S4: When the charging safety risk assessment result exceeds the set risk threshold, the safety inspection step is performed, including checking the working status of the charging pile equipment and the stability of the power supply, and deciding whether to suspend charging or adjust the charging power according to the risk assessment result; S5: During the charging process, the charging status and environmental parameters of each electric vehicle are continuously monitored and recorded. If a safety incident such as overheating, overcurrent or gas leakage occurs, emergency measures will be immediately implemented, including automatically cutting off the power supply, starting the exhaust equipment, and sending an alarm message to the relevant emergency department; S6: Record and analyze each safety event to update the state transition probability of the charging safety assessment model, and optimize the prediction accuracy of the Markov chain in combination with historical data to improve the early warning capability of potential risks in the charging area.

[0006] Optionally, the S1 specifically includes: S11: In the electric vehicle charging area of ​​the parking lot, according to the arrangement structure of charging piles and parking spaces, a temperature sensor is installed above each charging pile, 1.5 to 2 meters from the ground; the current sensor and voltage sensor are integrated on the side of each charging port; the gas sensor is installed in the air circulation around the charging pile, 0.5 meters from the ground, to detect the concentration of leaked combustible gas. All sensors are arranged at regular intervals to cover the entire charging area; S12: The sensor communicates with the data acquisition device through a wired or wireless connection. All sensors collect data once per second. The collected data includes the real-time temperature, voltage, current of the charging device and the changes in gas concentration in the air; S13: Processing the collected environmental data, wherein the processing includes removing noise data and abnormal values.

[0007] Optionally, the S2 specifically includes: S21: Compare the temperature, current, voltage and gas concentration data collected by the sensor with the preset safety thresholds one by one. The safety thresholds are determined according to the working specifications and safety standards of the charging pile equipment, including the maximum working temperature, the maximum allowable current and voltage range, and the upper limit of the gas concentration; S22: Compare each parameter, the formula is: ,in, Indicates the currently collected parameter value. Indicates the preset safety threshold of the corresponding parameter; when When , it means that the parameter exceeds the threshold, and the size and duration of the exceeded value are recorded; S23: Once a parameter is detected to exceed its corresponding safety threshold, an instruction is immediately issued to trigger an audible and visual alarm device through signal control, wherein the alarm device includes a high-frequency sound alarm and a high-brightness LED lamp; S24: When the sound and light alarm is triggered, the parameters exceeding the threshold and their real-time monitoring data are sent to the parking lot management center via wireless signals, along with the information of related equipment, including the charging pile number, location and time.

[0008] Optionally, the S3 specifically includes: S31: Collect historical data of the electric vehicle charging area, including the charging time, charging power, equipment usage frequency, historical charging failure records and environmental parameter change data of the vehicle, and collect current charging behavior based on the sensor in S1, including real-time current, voltage, temperature and gas concentration information; S32: According to the collected historical data and the current charging behavior, each charging process is divided into several states, wherein the states include normal charging, charging overheating, charging overcurrent, gas leakage and voltage fluctuation, and each state and the transition conditions between them are defined; S33: Based on the defined states and transition conditions, a charging safety assessment model is constructed, wherein the assessment model uses a Markov chain algorithm to calculate the transition probability between various states by analyzing the state transition frequency in historical data; S34: Based on the constructed charging safety assessment model, the charging behavior data currently collected in real time is used to predict the change of the charging state, and the next potential state is determined according to the state transition probability. If the predicted state is a high-risk state, the risk assessment result is output.

[0009] Optionally, the S33 specifically includes: S331: Based on the collected historical data and current charging behavior, each charging process is regarded as a discrete Markov chain model, defined as a set of state sets ,in, Represents normal charging status. Indicates overheating condition. Represents the overcurrent state, Represents the gas leakage state, and so on; state set The definition of covers all state changes that occur during the charging process, and is the total number of states; S332: Define the transition conditions between each state and set the transition probability between states , indicating that from the state Transfer to state The probability of is the state transfer matrix, which represents the transition probability of all states; S333: Constructing the initial distribution of state transfers ,in Indicates that it is initially in state The probability of S334: According to the state transfer matrix of the Markov chain and the initial distribution , calculate the system at any time through the recursive formula In state The probability is: ,in, Indicates at time The state distribution when is the initial state distribution, is the state transfer matrix, and is the number of time steps.

[0010] Optionally, the S34 specifically includes: S341: Set the charging behavior data collected in real time as , and combined with the state transfer matrix and the current state distribution , to predict state changes; S342: Distribution according to current status and the transfer matrix , calculated at the next moment The state distribution ; S343: For the predicted state , according to the state transition probability Determine whether the current charging process will transfer to a high-risk state. If a certain state The transition probability Exceeding the preset high risk threshold , then mark the state as a high-risk state; S344: If the predicted state If the risk is high, the risk assessment result is obtained by calculating the state transition probability and the corresponding risk weight. .

[0011] Optionally, the S4 specifically includes: S41: When the charging safety risk assessment results Exceeding the set risk threshold When a security check is triggered immediately; S42: Check the stability of the power supply, monitor the fluctuation of the current and voltage of the power supply line, and detect whether there is abnormal fluctuation. If an abnormal jump of the current or voltage is detected, it is marked as an unstable power supply state; S43: Based on the status inspection results of the charging pile equipment and power supply, compare the current working status of the charging pile with the risk assessment results , if the charging equipment and power supply are both within normal range, but the assessment results show high risk, take preventive measures based on the assessment results; S44: If the risk assessment results If the checked charging equipment status or power supply status shows high risk, charging will be suspended immediately.

[0012] Optionally, the S5 specifically includes: S51: Continuously monitor and record the charging status of each electric vehicle in real time during the charging process. The monitored parameters include current ,Voltage , Charging port temperature and the concentration of gases in the air ; S52: used to monitor whether the charging status parameters are abnormal. Exceeding safety threshold When the current Exceeding the allowed range Time, gas concentration Exceeding safety limit When an abnormal security incident occurs, it is marked as an abnormal security incident and the emergency handling process is triggered; S53: After the emergency handling process is triggered, the step of automatically cutting off the power supply is first performed to disconnect the power connection between the charging pile and the electric vehicle; S54: Start the exhaust equipment in the charging area, use the gas concentration information fed back in real time by the gas sensor installed near the charging pile, control the working intensity of the exhaust system, and quickly reduce the concentration of harmful gases in the air; S55: Sending an alarm message to the relevant emergency department via a wireless signal, the alarm message includes the charging status of the vehicle, the monitored abnormal parameters, the charging pile number, the specific location and the time when the abnormality occurred.

[0013] Optionally, the S6 specifically includes: S61: When each safety event occurs, record all relevant parameters, including the current, voltage, temperature, gas concentration, time of the safety event, and the number of the charging device; S62: Analyze each safety event and determine the main cause of the safety event. Specifically, by comparing the difference between abnormal data and safety threshold, identify the final fault type, including overheating, overcurrent, and gas leakage. S63: Based on the analysis results of the safety event, the event data is input into the charging safety assessment model and the state transition probability matrix is ​​updated. , through the occurrence of each event, dynamically update the relevant probability values ​​in the state transfer matrix; S64: Combine each safety event with historical data and use a weighted average algorithm to smooth the updated transition probability to avoid model instability due to individual abnormal events.

[0014] A smart parking safety supervision system is used to implement the above-mentioned smart parking safety supervision method, including the following modules: Data acquisition module: used to deploy multiple types of sensors in the electric vehicle charging area to collect environmental parameters of the charging pile equipment and vehicles in real time, including current, voltage, temperature and gas concentration; Risk assessment module: connected to the data acquisition module, used to receive the real-time collected charging behavior data, and build a charging safety assessment model based on the Markov chain algorithm. By analyzing the state transition probability and historical data, the safety risk in the charging process is calculated in real time, and the risk assessment results are output; Safety threshold comparison module: used to compare the environmental parameters collected by the data acquisition module with the preset safety thresholds. If any parameter exceeds the corresponding safety threshold, the abnormal data will be transmitted to the risk assessment module for analysis; Emergency handling module: connected to the risk assessment module, used to immediately trigger corresponding emergency handling measures when the risk assessment results show a high risk state, including automatically cutting off the charging power supply, starting the exhaust equipment, and sending alarm information to the relevant emergency departments; State monitoring and feedback module: used to record and analyze safety events during the charging process, and feed back the record of each safety event to the risk assessment module, update the state transition probability and evaluation model parameters, so as to continuously optimize the prediction accuracy of the system.

[0015] Beneficial effects of the present invention: The present invention collects the current, voltage, temperature, gas concentration and other parameters of the charging pile and electric vehicle in real time through multiple types of sensors, and combines the charging safety assessment model constructed by the Markov chain algorithm to achieve accurate prediction of state changes and real-time risk assessment during the charging process. The system can timely discover potential risks such as overheating, overcurrent or gas leakage based on the state transition probability and historical data, and trigger corresponding emergency response mechanisms, such as automatically cutting off the power supply and starting the exhaust equipment, effectively avoiding the possibility of safety accidents during the charging process. The present invention can continuously optimize the prediction accuracy of the safety assessment model by analyzing the data of each safety event and dynamically updating the state transition probability. Combined with the long-term analysis of historical data, it gradually improves the ability to cope with complex and changeable states, ensuring the safety and controllability of each step in the charging process. This automated and intelligent safety supervision method not only reduces the burden of manual supervision, but also significantly improves the intelligence level and reliability of the entire smart parking system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A schematic diagram of a safety supervision method for smart parking according to an embodiment of the present invention; Figure 2 Schematic diagram of a smart parking safety supervision system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0019] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0020] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0021] like Figure 1 As shown, a smart parking safety supervision method includes the following steps: S1: Deploy multiple types of sensors in the electric vehicle charging area of ​​the parking lot to collect real-time data on environmental parameters around the charging piles and charging vehicles, including temperature, current, voltage, and flammable gas concentration in the air; S2: Compare the collected environmental parameters with the preset safety thresholds. If any parameter is detected to exceed the corresponding safety threshold, a safety alarm is immediately issued to trigger the sound and light alarm device; S3: Based on the historical data of the electric vehicle charging area and the current charging behavior, a charging safety assessment model is constructed using the Markov chain algorithm to analyze the state transition during the vehicle charging process, including normal charging, overheating, overcurrent, gas leakage and other states, calculate the transition probability between different states, and then predict the potential safety risks in the charging process to obtain risk assessment results; S4: When the charging safety risk assessment result exceeds the set risk threshold, the safety inspection step is performed, including checking the working status of the charging pile equipment and the stability of the power supply, and deciding whether to suspend charging or adjust the charging power according to the risk assessment result to reduce the safety risk; S5: During the charging process, the charging status and environmental parameters of each electric vehicle are continuously monitored and recorded. If a safety incident such as overheating, overcurrent or gas leakage occurs, emergency measures will be immediately implemented, including automatically cutting off the power supply, starting the exhaust equipment, and sending an alarm message to the relevant emergency department; S6: Record and analyze each safety event to update the state transition probability of the charging safety assessment model, and optimize the prediction accuracy of the Markov chain in combination with historical data to improve the early warning capability of potential risks in the charging area.

[0022] S1 specifically includes: S11: In the electric vehicle charging area of ​​the parking lot, according to the arrangement structure of charging piles and parking spaces, temperature sensors are installed above each charging pile, 1.5 to 2 meters above the ground, to avoid being blocked by vehicles and to ensure that the temperature changes of the charging equipment and vehicle interface can be monitored; current sensors and voltage sensors are integrated on the side of each charging interface to ensure that the fluctuations of current and voltage during charging can be accurately recorded; gas sensors are installed in the air circulation around the charging piles, 0.5 meters above the ground, to detect the concentration of leaked combustible gases. All sensors are arranged at regular intervals to cover the entire charging area; S12: The sensor communicates with the data acquisition device through a wired or wireless connection. All sensors collect data at a frequency of once per second. The collected data includes the real-time temperature, voltage, current of the charging equipment and the changes in the gas concentration in the air, ensuring that the environmental changes during the charging process can be captured in time; the data collected by the sensor is recorded in real time, including the temperature changes at the charging pile and the vehicle charging interface, the instantaneous value of the current, the instantaneous value of the voltage and the concentration of the combustible gas in the air. These data will be recorded in a predetermined format for subsequent analysis and processing; S13: Process the collected environmental data, including removing noise data and outliers; specifically, use a mean filter algorithm to remove short-term high-frequency noise, and the formula is as follows: ,in, is the signal after filtering, is the original signal collected, is the size of the filter window; for the detected outliers, the Z score algorithm is used to eliminate them, the formula is as follows: ,in, is the current data point, is the mean of the data set, is the standard deviation, when When the value exceeds the preset threshold, the data is considered to be an outlier and is eliminated to ensure the accuracy of the data; the above steps reasonably deploy multiple types of sensors in the electric vehicle charging area and ensure the real-time and accuracy of the sensor collected data. This method can achieve comprehensive monitoring of environmental parameters during the charging process, ensure that the safety status of each charging point is mastered in real time, and provide an efficient basic guarantee for subsequent risk assessment.

[0023] S2 specifically includes: S21: Compare the temperature, current, voltage and gas concentration data collected by the sensor with the pre-set safety thresholds one by one. The safety thresholds are determined according to the working specifications and safety standards of the charging pile equipment, including the maximum working temperature, the maximum allowable current and voltage range, and the upper limit of the gas concentration. The thresholds are set as fixed values ​​and stored in the safety parameter database; S22: Compare each parameter, the formula is: ,in, Indicates the currently collected parameter value. Indicates the preset safety threshold of the corresponding parameter; when When , it means that the parameter exceeds the threshold, and the size and duration of the exceeded value are recorded; S23: Once a parameter is detected to exceed its corresponding safety threshold, a command is immediately issued to trigger the sound and light alarm device through signal control. The alarm device includes a high-frequency sound alarm and a high-brightness LED light. The sound and light alarm work synchronously through the electrical signal driving device to remind the on-site personnel to take emergency measures; S24: While triggering the sound and light alarm, the parameters exceeding the threshold and their real-time monitoring data are sent to the parking lot management center via wireless signals, along with information on related equipment, including the charging pile number, location and time, so that management personnel can conduct timely remote or on-site inspections and processing; the above steps compare and monitor the collected environmental parameters one by one in real time. When any parameter exceeds the preset safety threshold, the sound and light alarm device can be immediately triggered and an alarm message can be sent to the management center, thereby realizing an efficient safety early warning function and effectively improving the safety supervision capabilities of the charging area.

[0024] S3 specifically includes: S31: Collect historical data of the electric vehicle charging area, including the vehicle charging time, charging power, equipment usage frequency, historical charging failure records, and environmental parameter change data. At the same time, based on the sensors in S1, collect the current charging behavior, including real-time current, voltage, temperature, and gas concentration information as input data for the evaluation model; S32: Based on the collected historical data and current charging behavior, each charging process is divided into several states, including normal charging, charging overheating, charging overcurrent, gas leakage and voltage fluctuation, and each state and the transition conditions between them are defined to ensure that all state changes in the charging process can be fully described; S33: Based on the defined states and transition conditions, a charging safety assessment model is constructed. The assessment model uses the Markov chain algorithm to analyze the state transition frequency in historical data and calculate the transition probability between states. After the model is constructed, it is used to predict potential state changes and risks during the charging process. S34: Based on the completed charging safety assessment model, the charging behavior data currently collected in real time is used to predict changes in the charging state, and the next potential state is determined based on the state transition probability. If the predicted state is a high-risk state, the risk assessment result is output; the charging safety assessment model constructed by using the Markov chain algorithm can effectively predict state changes during the charging process based on the analysis of historical data, identify potential risks in real time and issue warnings, greatly improving the safety and intelligent management level of the charging area.

[0025] The charging safety assessment model constructed in S33 specifically includes: S331: Based on the collected historical data and current charging behavior, each charging process is regarded as a discrete Markov chain model, defined as a set of state sets ,in, Represents normal charging status. Indicates overheating condition. Represents the overcurrent state, Represents the gas leakage state, and so on; state set The definition of covers all state changes that occur during the charging process, and is the total number of states; S332: Define the transition conditions between each state and set the transition probability between states , indicating that from the state Transfer to state The probability of is the state transfer matrix, which represents the transition probability of all states. The specific expression is: , Each item in the matrix Indicates from the state To status The transition probability, and The number representing the state and meets the following conditions: , which represents the function from each state The sum of the transition probabilities of all possible states of departure is ,in Indicates the total number of states; S333: Constructing the initial distribution of state transfers ,in Indicates that it is initially in state The probability of the initial distribution is determined based on the long-term observation results of historical data; here, Represents the state at the initial moment probability; S334: According to the state transfer matrix of the Markov chain and the initial distribution , calculate the system at any time through the recursive formula In state The probability is: ,in, Indicates at time The state distribution when is the initial state distribution, is the state transfer matrix, and is the number of time steps; through recursive calculation, the possible state at a certain moment in the future can be predicted. If the predicted state is a high-risk state, a risk alert is output; the above steps construct a charging safety assessment model based on Markov chain, which can accurately predict the possibility of state transfer during the charging process. By combining the calculation of the transfer matrix and the initial distribution, the model can calculate the possible future states in real time.

[0026] S34 specifically includes: S341: Set the charging behavior data collected in real time as , data includes the current ,Voltage ,temperature and gas concentration , and use the behavior data as the current moment The input, combined with the state transfer matrix and the current state distribution , to predict state changes; S342: Distribution according to current status and the transfer matrix , calculated at the next moment The state distribution , the recursive formula is: ,in, Indicates at time The probability of being in each state, is the state distribution at the current moment, is the state transfer matrix. Through this recursive formula, the state change in the next step can be predicted; S343: For the predicted state , according to the state transition probability Determine whether the current charging process will transfer to a high-risk state. If a certain state The transition probability Exceeding the preset high risk threshold , then mark the state as a high-risk state; S344: If the predicted state If the risk is high, the risk assessment result is obtained by calculating the state transition probability and the corresponding risk weight. , formula calculation: ,in, For the moment The risk assessment results From the current state Transfer to state The probability of Status The risk weight of is the total number of states; the final risk value is calculated by combining the transition probability of each state and its corresponding risk weight; by constructing a complete charging safety assessment model, the state change is predicted using the real-time collected charging behavior data combined with the state transition probability, which can effectively judge the potential high-risk state in the charging process.

[0027] S4 specifically includes: S41: When the charging safety risk assessment results Exceeding the set risk threshold When a fault occurs, a safety check is triggered immediately. The specific parameters checked include the current, voltage and device temperature of the charging pile, and the parameters are compared with the normal working range of the device to ensure that the device is working within the safe range. S42: Check the stability of the power supply, monitor the fluctuation of the current and voltage of the power supply line, and detect whether there is abnormal fluctuation. If an abnormal jump of the current or voltage is detected, it is marked as an unstable power supply state; S43: Based on the status inspection results of the charging pile equipment and power supply, compare the current working status of the charging pile with the risk assessment results If the charging equipment and power supply are both within the normal range, but the assessment results show a high risk, preventive measures will be taken based on the assessment results to mitigate the potential risk by reducing the charging power. The specific way to adjust the power is to gradually reduce the power until it is within the safe range. S44: If the risk assessment results If the checked charging equipment status or power supply status shows high risk, charging will be suspended immediately to ensure the safety of the equipment and vehicles. After charging is suspended, the management personnel will be notified to conduct on-site inspection and processing, and generate a detailed inspection report; by combining the charging safety risk assessment results and real-time status monitoring of equipment and power supply, reasonable decisions can be made in a timely manner, and measures such as adjusting power or suspending charging can be taken to ensure equipment safety and power stability during the charging process, and effectively avoid equipment damage or safety accidents caused by high-risk situations.

[0028] S5 specifically includes: S51: Continuously monitor and record the charging status of each electric vehicle in real time during the charging process. The monitored parameters include current ,Voltage , Charging port temperature and the concentration of gases in the air ; S52: used to monitor whether the charging status parameters are abnormal. Exceeding safety threshold When the current Exceeding the allowed range Time, gas concentration Exceeding safety limit When an abnormal security incident occurs, it is marked as an abnormal security incident and the emergency handling process is triggered; S53: After the emergency handling process is triggered, the power supply is automatically cut off to disconnect the power connection between the charging pile and the electric vehicle to prevent equipment damage or safety accidents caused by continuous overcurrent or overheating. The power supply is cut off through the relay control circuit to perform an immediate power-off operation; S54: Start the exhaust equipment in the charging area, use the gas concentration information fed back in real time by the gas sensor installed near the charging pile, control the working intensity of the exhaust system, quickly reduce the concentration of harmful gases in the air, and ensure that the air quality in the charging area is restored to a safe level; S55: Send alarm information to relevant emergency departments via wireless signals. The alarm information includes the charging status of the vehicle, the abnormal parameters monitored, the charging pile number, the specific location and the time when the abnormality occurred, to ensure that the emergency department can take further response measures in time. In addition, the alarm information is also sent to the parking lot management personnel for rapid response and processing.

[0029] S6 specifically includes: S61: When each safety event occurs, all relevant parameters are recorded, including the current, voltage, temperature, gas concentration, time of the safety event, and the number of the charging device. All recorded data are stored in the safety event database as the basic data for subsequent analysis. S62: Analyze each safety event and determine the main cause of the safety event. Specifically, by comparing the difference between abnormal data and safety threshold, identify the final fault type, including overheating, overcurrent, and gas leakage. S63: Based on the analysis results of the safety event, the event data is input into the charging safety assessment model and the state transition probability matrix is ​​updated. , specifically by recalculating from the current state Transfer to event state The probability is: ,in, Indicates from the state Transfer to event state The probability of From the state Occurs to state The number of times, Status The total number of occurrences, through the occurrence of each event, dynamically update the relevant probability value in the state transfer matrix; S64: Combine each security event with historical data and use a weighted average algorithm to smooth the updated transition probability to avoid model instability due to individual abnormal events. The weight of historical data and the weight of the new event data According to the preset rule allocation, calculate the updated transition probability for: ,in, The transition probability calculated for historical data, The transition probabilities calculated for new event data, and Represents weights. Through this process, the prediction accuracy of the Markov chain can be continuously optimized. Through detailed recording and analysis of each safety event, the state transition probability of the charging safety assessment model can be dynamically updated, and the Markov chain model can be optimized in combination with historical data. This method effectively improves the accuracy of the model and can more accurately predict potential risks in the future.

[0030] like Figure 2 As shown, a smart parking safety supervision system is used to implement the above-mentioned smart parking safety supervision method, including the following modules: Data acquisition module: used to deploy multiple types of sensors in the electric vehicle charging area to collect environmental parameters of the charging pile equipment and vehicles in real time. Environmental parameters include current, voltage, temperature and gas concentration to ensure real-time monitoring of state changes during the charging process; Risk assessment module: connected to the data acquisition module, used to receive the real-time collected charging behavior data, and build a charging safety assessment model based on the Markov chain algorithm. By analyzing the state transition probability and historical data, the safety risk in the charging process is calculated in real time, and the risk assessment results are output; Safety threshold comparison module: used to compare the environmental parameters collected by the data acquisition module with the preset safety thresholds. If any parameter exceeds the corresponding safety threshold, the abnormal data will be transmitted to the risk assessment module for analysis; Emergency handling module: connected to the risk assessment module, used to immediately trigger corresponding emergency handling measures when the risk assessment results show a high risk state, including automatically cutting off the charging power supply, starting the exhaust equipment, and sending alarm information to the relevant emergency departments; State monitoring and feedback module: used to record and analyze safety events during the charging process, and feed back the record of each safety event to the risk assessment module, update the state transition probability and evaluation model parameters, so as to continuously optimize the prediction accuracy of the system.

[0031] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0032] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A smart parking safety supervision method, characterized in that: The following steps are involved: S1: Deploy multiple types of sensors in the electric vehicle charging area of ​​the parking lot to collect real-time data on environmental parameters around the charging piles and charging vehicles, including temperature, current, voltage, and flammable gas concentration in the air; S2: Compare the collected environmental parameters with the preset safety thresholds. If any parameter is detected to exceed the corresponding safety threshold, a safety alarm is immediately issued to trigger the sound and light alarm device; S3: Based on the historical data of the electric vehicle charging area and the current charging behavior, a charging safety assessment model is constructed using the Markov chain algorithm to analyze the state transition during the vehicle charging process, calculate the transition probability between different states, and then predict the potential safety risks in the charging process to obtain the risk assessment results; S4: When the charging safety risk assessment result exceeds the set risk threshold, the safety inspection step is performed, including checking the working status of the charging pile equipment and the stability of the power supply, and deciding whether to suspend charging or adjust the charging power according to the risk assessment result; S5: During the charging process, the charging status and environmental parameters of each electric vehicle are continuously monitored and recorded. If a safety incident such as overheating, overcurrent or gas leakage occurs, emergency measures will be immediately implemented, including automatically cutting off the power supply, starting the exhaust equipment, and sending an alarm message to the relevant emergency department; S6: Record and analyze each safety event to update the state transition probability of the charging safety assessment model, and optimize the prediction accuracy of the Markov chain in combination with historical data to improve the early warning capability of potential risks in the charging area.

2. A smart parking safety supervision method according to claim 1, characterized in that: The S1 specifically includes: S11: In the electric vehicle charging area of ​​the parking lot, according to the arrangement structure of charging piles and parking spaces, a temperature sensor is installed above each charging pile, 1.5 to 2 meters from the ground; the current sensor and voltage sensor are integrated on the side of each charging port; the gas sensor is installed in the air circulation around the charging pile, 0.5 meters from the ground, to detect the concentration of leaked combustible gas. All sensors are arranged at regular intervals to cover the entire charging area; S12: The sensor communicates with the data acquisition device through a wired or wireless connection. All sensors collect data once per second. The collected data includes the real-time temperature, voltage, current of the charging device and changes in gas concentration in the air; S13: Processing the collected environmental data, wherein the processing includes removing noise data and abnormal values.

3. A smart parking safety supervision method according to claim 1, characterized in that: The S2 specifically includes: S21: Compare the temperature, current, voltage and gas concentration data collected by the sensor with the preset safety thresholds one by one. The safety thresholds are determined according to the working specifications and safety standards of the charging pile equipment, including the maximum working temperature, the maximum allowable current and voltage range, and the upper limit of the gas concentration; S22: Compare each parameter, the formula is: ,in, Indicates the currently collected parameter value. Indicates the preset safety threshold of the corresponding parameter; when When , it means that the parameter exceeds the threshold, and the size and duration of the exceeded value are recorded; S23: Once a parameter is detected to exceed its corresponding safety threshold, an instruction is immediately issued to trigger an audible and visual alarm device through signal control, wherein the alarm device includes a high-frequency sound alarm and a high-brightness LED light; S24: When the sound and light alarm is triggered, the parameters exceeding the threshold and their real-time monitoring data are sent to the parking lot management center via wireless signals, along with the information of related equipment, including the charging pile number, location and time.

4. The method for safety supervision of smart parking according to claim 1, characterized in that: The S3 specifically includes: S31: Collect historical data of the electric vehicle charging area, including the charging time, charging power, equipment usage frequency, historical charging failure records and environmental parameter change data of the vehicle, and collect current charging behavior based on the sensor in S1, including real-time current, voltage, temperature and gas concentration information; S32: According to the collected historical data and the current charging behavior, each charging process is divided into several states, wherein the states include normal charging, charging overheating, charging overcurrent, gas leakage and voltage fluctuation, and each state and the transition conditions between them are defined; S33: Based on the defined states and transition conditions, a charging safety assessment model is constructed, wherein the assessment model uses a Markov chain algorithm to calculate the transition probability between various states by analyzing the state transition frequency in historical data; S34: Based on the constructed charging safety assessment model, the charging behavior data currently collected in real time is used to predict the change of the charging state, and the next potential state is determined according to the state transition probability. If the predicted state is a high-risk state, the risk assessment result is output.

5. A smart parking safety supervision method according to claim 4, characterized in that: The S33 specifically includes: S331: Based on the collected historical data and current charging behavior, each charging process is regarded as a discrete Markov chain model, defined as a set of state sets ,in, Represents normal charging status. Indicates overheating condition. Represents the overcurrent state, Represents the gas leakage state, and so on; state set The definition of covers all state changes that occur during the charging process, and is the total number of states; S332: Define the transition conditions between each state and set the transition probability between states , indicating that from the state Transfer to state The probability of is the state transfer matrix, which represents the transition probability of all states; S333: Constructing the initial distribution of state transfers ,in Indicates that it is initially in state probability; S334: According to the state transfer matrix of the Markov chain and the initial distribution , calculate the system at any time through the recursive formula In state The probability is: ,in, Indicates at time The state distribution when is the initial state distribution, is the state transfer matrix, and is the number of time steps.

6. A smart parking safety supervision method according to claim 5, characterized in that: The S34 specifically includes: S341: Set the charging behavior data collected in real time as , and combined with the state transfer matrix and the current state distribution , to predict state changes; S342: Distribution according to current status and the transfer matrix , calculated at the next moment The state distribution ; S343: For the predicted state , according to the state transition probability Determine whether the current charging process will transfer to a high-risk state. If a certain state The transition probability Exceeding the preset high risk threshold , then mark the state as a high-risk state; S344: If the predicted state If the risk is high, the risk assessment result is obtained by calculating the state transition probability and the corresponding risk weight. .

7. A smart parking safety supervision method according to claim 6, characterized in that: The S4 specifically includes: S41: When the charging safety risk assessment results Exceeding the set risk threshold When a security check is triggered immediately; S42: Check the stability of the power supply, monitor the fluctuation of the current and voltage of the power supply line, and detect whether there is abnormal fluctuation. If an abnormal jump of the current or voltage is detected, it is marked as an unstable power supply state; S43: Based on the status inspection results of the charging pile equipment and power supply, compare the current working status of the charging pile with the risk assessment results , if the charging equipment and power supply are both within normal range, but the assessment results show high risk, take preventive measures based on the assessment results; S44: If the risk assessment results If the checked charging equipment status or power supply status shows high risk, charging will be suspended immediately.

8. The method for safety supervision of smart parking according to claim 1, characterized in that: The S5 specifically includes: S51: Continuously monitor and record the charging status of each electric vehicle in real time during the charging process. The monitored parameters include current ,Voltage , Charging port temperature and the concentration of gases in the air ; S52: used to monitor whether the charging status parameters are abnormal. Exceeding safety threshold When the current Exceeding the allowed range Time, gas concentration Exceeding safety limit When an abnormal security incident occurs, it is marked as an abnormal security incident and the emergency handling process is triggered; S53: After the emergency handling process is triggered, the step of automatically cutting off the power supply is first performed to disconnect the power connection between the charging pile and the electric vehicle; S54: Start the exhaust equipment in the charging area, use the gas concentration information fed back in real time by the gas sensor installed near the charging pile, control the working intensity of the exhaust system, and quickly reduce the concentration of harmful gases in the air; S55: Sending an alarm message to the relevant emergency department via a wireless signal, the alarm message includes the charging status of the vehicle, the monitored abnormal parameters, the charging pile number, the specific location and the time when the abnormality occurred.

9. The method for safety supervision of smart parking according to claim 1, characterized in that: The S6 specifically includes: S61: When each safety event occurs, record all relevant parameters, including the current, voltage, temperature, gas concentration, time of the safety event, and the number of the charging device; S62: Analyze each safety event and determine the main cause of the safety event. Specifically, by comparing the difference between abnormal data and safety threshold, identify the final fault type, including overheating, overcurrent, and gas leakage. S63: Based on the analysis results of the safety event, the event data is input into the charging safety assessment model and the state transition probability matrix is ​​updated. , through the occurrence of each event, dynamically update the relevant probability values ​​in the state transfer matrix; S64: Combine each safety event with historical data and use a weighted average algorithm to smooth the updated transition probability to avoid model instability due to individual abnormal events.

10. A smart parking safety supervision system, used to implement a smart parking safety supervision method as claimed in any one of claims 1 to 9, characterized in that: Includes the following modules: Data acquisition module: used to deploy multiple types of sensors in the electric vehicle charging area to collect environmental parameters of the charging pile equipment and vehicles in real time, including current, voltage, temperature and gas concentration; Risk assessment module: connected to the data acquisition module, used to receive the real-time collected charging behavior data, and build a charging safety assessment model based on the Markov chain algorithm. By analyzing the state transition probability and historical data, the safety risk in the charging process is calculated in real time, and the risk assessment results are output; Safety threshold comparison module: used to compare the environmental parameters collected by the data acquisition module with the preset safety thresholds. If any parameter exceeds the corresponding safety threshold, the abnormal data will be transmitted to the risk assessment module for analysis; Emergency handling module: connected to the risk assessment module, used to immediately trigger corresponding emergency handling measures when the risk assessment results show a high risk state, including automatically cutting off the charging power supply, starting the exhaust equipment, and sending alarm information to the relevant emergency departments; State monitoring and feedback module: used to record and analyze safety events during the charging process, and feed back the record of each safety event to the risk assessment module, update the state transition probability and evaluation model parameters, so as to continuously optimize the prediction accuracy of the system.

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