Motor home security system and method based on artificial intelligence technology

Abstract

The invention discloses a motor home security system based on an artificial intelligence technology, the system comprises a security monitoring module, an energy consumption monitoring module, a data processor, an alarm response module and a calling management module, motor home security monitoring is carried out from local to whole by constructing a motor home security management model, an alarm equipment management scheme is generated, and the calling management module is used for calling the motor home security management scheme. According to the method, multiple security assurance of touring car security is realized, and a security abnormal component is specifically positioned to perform alarm processing, so that the efficiency and accuracy of alarm work are improved, the risk response efficiency is further improved, and the safety and intelligent level of touring car application are improved; security energy consumption demand prediction, monitoring equipment calling scheme generation and optimization refreshing of the limo security management model are performed through the AI energy consumption prediction sub-model, so that the energy consumption of the monitoring equipment is reduced, the sustainable stability of limo energy is ensured, and limo security management with high efficiency and low energy consumption and adaptive training optimization of artificial intelligence are realized.

Description

Technical Field

[0001] The present invention relates to the field of RV security technology, and in particular to an RV security system and method based on artificial intelligence technology. Background Art

[0002] Traditional security systems usually rely on monitoring methods such as manual inspections and closed-circuit television. Although they can serve as warnings to a certain extent, their limitations are also very obvious. Therefore, RV security systems based on artificial intelligence (AI) technology have shown great potential in protecting RVs and their internal property. However, the existing RV security system has the problem of insufficient comprehensiveness and accuracy in security anomaly monitoring and processing, resulting in inefficient risk response and insufficient security protection. In addition, the infrared cameras and sensors that require continuous monitoring may consume a lot of electricity, thus affecting the overall RV power consumption system, reducing battery life and increasing electricity energy costs, resulting in low efficiency of RV security management. In view of the above technical defects, a solution is now proposed. Summary of the invention

[0003] The purpose of the present invention is to solve the problems of insufficient comprehensiveness and accuracy of security anomaly monitoring and processing in the prior art, as well as the resulting defects of low risk response efficiency, insufficient safety protection, and low efficiency of RV security management. The present invention constructs an RV security management model to conduct RV security anomaly monitoring and in-depth analysis of RV safety, conducts RV security monitoring from local to overall, realizes multiple security guarantees for RV security, and locates security anomaly components in a targeted manner for alarm processing, thereby improving the efficiency and accuracy of alarm work, and then through overall safety monitoring, improves risk response efficiency, and improves the safety and intelligence level of RV applications, and then optimizes and refreshes the RV security management model through the AI ​​energy consumption prediction sub-model, thereby reducing the energy consumption of monitoring equipment, ensuring the sustainable stability of RV energy, and realizing efficient and low-energy RV security management and adaptive training optimization of artificial intelligence.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions: A RV security system based on artificial intelligence technology, comprising a security monitoring module, an energy consumption monitoring module, a data processor, an alarm response module and a call management module, wherein the security monitoring module, the energy consumption monitoring module, the data processor, the alarm response module and the call management module are communicatively connected; The security monitoring module is used to monitor the security of the RV: monitor the abnormality of the RV safety through monitoring equipment, and collect and obtain security monitoring data; the security monitoring data includes vehicle safety parameters, anti-theft safety parameters, fire safety parameters and environmental safety parameters; The energy consumption monitoring module is used to monitor the energy of the monitoring equipment: the energy of the monitoring equipment is monitored through the power meter, and the energy data of the equipment is collected; The data processor is used to construct an RV security management model for data processing, and manage and call the monitoring equipment of the security monitoring module through the RV security management model: the RV security management model includes a local analysis sub-model, an overall analysis sub-model and an AI energy consumption prediction sub-model; wherein, the security monitoring data is analyzed through the local analysis sub-model to evaluate the abnormality of vehicle safety, the abnormality of anti-theft safety, the abnormality of fire safety and the abnormality of environmental safety, and integrate them into a safety abnormality parameter vector to generate an alarm device management plan; and then the safety abnormality parameter vector is deeply analyzed through the overall analysis sub-model to evaluate the comprehensive safety level of the RV; and the equipment energy data is analyzed through the AI ​​energy consumption prediction sub-model to evaluate the security energy consumption level of the RV, and the equipment energy consumption prediction analysis is performed to generate a monitoring equipment call plan; The alarm response module is used to receive the alarm device management plan and make corresponding alarm response to the security abnormality parameter vector; The call management module is used to receive the monitoring device call plan to manage the monitoring device of the safety monitoring module, thereby adjusting the RV safety management model to achieve optimization and refresh of the model.

[0005] Furthermore, the RV security management model includes a local analysis sub-model, an overall analysis sub-model and an AI energy consumption prediction sub-model. The specific process is as follows: A1, analyze the safety monitoring data through the local analysis sub-model: By analyzing vehicle safety parameters, the vehicle safety risk coefficient is obtained and the degree of vehicle safety abnormality is evaluated; By analyzing anti-theft security parameters, the anti-theft security risk coefficient is obtained and the degree of anti-theft security abnormality is evaluated; By analyzing fire safety parameters, the fire safety risk coefficient is obtained and the degree of fire safety abnormality is evaluated; By analyzing environmental safety parameters, we can obtain environmental safety risk factors and assess the degree of environmental safety anomalies; The vehicle safety risk factor, the anti-theft safety risk factor, the fire safety risk factor and the environmental safety risk factor are integrated and marked as a safety anomaly parameter vector; Set the evaluation interval of each component of the safety anomaly parameter vector, determine the risk level of the safety anomaly component by comparing the intervals, and generate an alarm device management plan; A2, conducts in-depth analysis of safety monitoring data through the overall analysis sub-model: By analyzing the safety anomaly parameter vector, the vehicle safety risk factor, the anti-theft safety risk factor, the fire safety risk factor and the environmental safety risk factor are combined to obtain the RV comprehensive safety index and evaluate the comprehensive safety level of the RV. A3, RV security energy consumption monitoring through AI energy consumption prediction management model: By analyzing and processing equipment energy data, we can obtain the RV security energy consumption index and evaluate the RV security energy consumption level; The RV comprehensive safety index and RV security energy consumption index are used to conduct equipment energy consumption forecast analysis to obtain the security energy consumption demand forecast index, thereby generating a monitoring equipment calling plan.

[0006] Furthermore, the specific process of analyzing vehicle safety parameters is as follows: Vehicle safety parameters include tire pressure a1, engine oil level a2, and brake sound frequency a3; Construct a parameter analysis model, input parameter index a, set the standard interval [Da, Ga] of parameter index a, obtain and output the evaluation coefficient Xa of parameter index a by combining parameter index a and its standard interval [Da, Ga]; Set the standard ranges of tire pressure a1, engine oil volume a2, and brake sound frequency a3, and substitute them into the parameter analysis model to obtain the tire quality assessment coefficient Xa1, engine status assessment coefficient Xa2, and brake system assessment coefficient Xa3 in turn, and then comprehensively obtain the vehicle safety risk coefficient Risk1; Set the evaluation interval of the vehicle safety risk factor Risk1, evaluate the degree of vehicle safety abnormality through interval comparison, and generate a corresponding vehicle safety alarm signal.

[0007] Furthermore, the specific process of analyzing the anti-theft security parameters is as follows: The anti-theft safety parameters include the door and window vibration amplitude b1, the door and window vibration frequency b2 and the number of people outside the vehicle b3; The anti-theft safety risk factor Risk2 is obtained by combining the door and window vibration amplitude b1, the door and window vibration frequency b2 and the number of people outside the vehicle b3; An assessment interval for the anti-theft safety risk factor Risk2 is set, the degree of anti-theft safety abnormality is assessed through interval comparison, and a corresponding anti-theft safety alarm signal is generated.

[0008] Furthermore, the specific process of analyzing fire safety parameters is as follows: Fire safety parameters include smoke particle concentration c1 and combustion gas concentration assessment coefficient c2; There are n0 types of combustion gases to be detected by the gas detector, and the concentration of any combustion gas q is marked as Cq. By combining the concentrations Cq of the n0 types of combustion gases q, the combustion gas concentration evaluation coefficient c2 is obtained; By combining the smoke particle concentration c1 and the combustion gas concentration assessment coefficient c2, the fire safety risk factor Risk3 is obtained; Set the evaluation interval of the fire safety risk factor Risk3, evaluate the degree of fire safety abnormality through interval comparison, and generate a corresponding fire safety alarm signal.

[0009] Furthermore, the specific process of analyzing environmental safety parameters is as follows: Environmental safety parameters include ambient temperature d1, ambient humidity d2 and harmful gas concentration assessment coefficient d3; The ambient temperature d1 and ambient humidity d2 are collected by the temperature and humidity sensor, and the standard ranges of the ambient temperature d1 and ambient humidity d2 are set respectively; Substitute the ambient temperature d1 and its standard range into the parameter analysis model to obtain the ambient temperature evaluation coefficient Xd1; Substitute the ambient humidity d2 and its standard range into the parameter analysis model to obtain the ambient humidity assessment coefficient Xd2; The environmental safety risk factor Risk4 is obtained by combining the environmental temperature assessment coefficient Xd1, the environmental humidity assessment coefficient Xd2 and the harmful gas concentration assessment coefficient d3; Set the assessment interval of the environmental safety risk factor Risk4, evaluate the degree of environmental safety abnormality through interval comparison, and generate corresponding environmental safety alarm signals.

[0010] Furthermore, the specific generation process of the alarm device management solution is as follows: The vehicle safety risk factor Risk1, the anti-theft safety risk factor Risk2, the fire safety risk factor Risk3 and the environmental safety risk factor Risk4 are integrated and marked as the safety anomaly parameter vector Rsafe: ; Set the evaluation interval of each component of the safety anomaly parameter vector, and determine the risk level of the safety anomaly component by comparing the intervals; Mark any safety anomaly component of the safety anomaly parameter vector Rsafe as , set the safety anomaly component There are Ng evaluation intervals, and the safety anomaly component Any evaluation interval is marked as Qr, when the safety abnormal component When it is in the evaluation interval Qr, the safety abnormal component is determined The risk level is R level, and the output safety abnormal component Rg-level safety alarm signal; The risk levels of the four components of the safety anomaly parameter vector are determined, and the safety alarm signals of the corresponding levels are output. The R1-level vehicle safety alarm signal, the R2-level anti-theft safety alarm signal, the R3-level fire safety alarm signal and the R3-level environmental safety alarm signal are integrated and marked as an alarm signal group, thereby generating an alarm equipment management plan.

[0011] Furthermore, the specific process of in-depth analysis of safety monitoring data is as follows: Set a data calculation cycle to perform regular calculations on the safety anomaly parameter vector Rsafe, combine the vehicle safety risk factor Risk1, the anti-theft safety risk factor Risk2, the fire safety risk factor Risk3 and the environmental safety risk factor Risk4 of the safety anomaly parameter vector Rsafe to obtain the RV comprehensive safety index Com; Set the evaluation range of the RV comprehensive safety index Com, and evaluate the comprehensive safety level of the RV through interval comparison.

[0012] Furthermore, the specific process of analyzing equipment energy data is as follows: The equipment energy data includes the power consumption of M0 monitoring devices. The power consumption of any monitoring device p is marked as Wp. The RV security energy consumption index Eneg is obtained by combining the power consumption of M0 monitoring devices. Extract the RV comprehensive safety index Com and RV security energy consumption index Eneg corresponding to N2 data measurement cycles, so as to conduct equipment energy consumption prediction analysis: By calculating the standard deviation of the RV comprehensive safety index Com corresponding to N2 data measurement cycles, the floating coefficient of the RV comprehensive safety index Com is obtained. ; By calculating the difference between the RV security energy consumption index Eneg corresponding to two adjacent data calculation cycles, the change range of the RV security energy consumption index Eneg within a data calculation cycle is obtained, thereby obtaining the average increase Kg of the RV security energy consumption index Eneg; Through the RV comprehensive safety index Com and its floating coefficient , and the RV security energy consumption index Eneg and its average increase Kg to obtain the security energy consumption demand forecast index Ec; The control scheme is set for the M0 monitoring devices of the security monitoring module through the security energy consumption demand prediction index Ec, and it is integrated into a monitoring device calling scheme, thereby generating and outputting the monitoring device calling scheme.

[0013] A RV security method based on artificial intelligence technology, the method applies the above-mentioned RV security system based on artificial intelligence technology, and comprises the following steps: Step 1: Build the initial RV security management model: The RV security management model includes a local analysis sub-model, an overall analysis sub-model, and an AI energy consumption prediction sub-model; Step 2: Monitor the abnormality of RV security through the local analysis sub-model: Collect and analyze security monitoring data to construct security anomaly parameter vectors, evaluate security anomaly components, and generate alarm device management plans; Step 3: Conduct an in-depth safety analysis of the RV through the overall analysis sub-model: Evaluate the comprehensive safety level of the RV through the safety abnormality parameter vector; Step 4: Monitor RV security energy consumption through the AI ​​energy consumption prediction sub-model: Collect and analyze equipment energy data, evaluate the security energy consumption of the RV, and conduct equipment energy consumption forecast analysis to generate monitoring equipment call plans; Step 5: Response to the abnormal safety component of the RV: Respond to and enable corresponding alarm devices through the alarm device management solution, so as to deal with security anomalies in a targeted manner; Step 6: Optimize and refresh the RV security management model: The RV safety management model is adjusted through the monitoring device calling plan, and the corresponding monitoring devices of the security monitoring module are called and managed, so as to achieve model optimization and refresh, and realize RV security management with high efficiency and low energy consumption.

[0014] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: The present invention constructs an RV security management model to perform RV security anomaly monitoring and RV safety in-depth analysis, performs RV security monitoring from local to overall, generates an alarm device management plan, realizes multiple security guarantees for RV security, and locates the security anomaly components in a targeted manner for alarm processing, thereby improving the efficiency and accuracy of the alarm work, and then deeply analyzes and evaluates the comprehensive safety level of the RV, thereby performing overall safety monitoring, improving risk response efficiency, and enhancing the safety and intelligence level of RV applications; The present invention combines the comprehensive safety level of the RV with the security energy consumption level through an AI energy consumption prediction sub-model, predicts the security energy consumption demand, generates a monitoring equipment calling plan, and optimizes and refreshes the RV security management model, thereby reducing the energy consumption of the monitoring equipment, ensuring the sustainable stability of the RV energy, and realizing efficient and low-energy RV security management and adaptive training optimization of artificial intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A connection diagram of the system modules of the present invention is shown; Figure 2 A schematic diagram showing the steps of the solution process of the present invention is shown. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Embodiment 1: like Figure 1-Figure 2 As shown, a RV security system based on artificial intelligence technology includes a security monitoring module, an energy consumption monitoring module, a data processor, an alarm response module and a call management module, wherein the security monitoring module, the energy consumption monitoring module, the data processor, the alarm response module and the call management module are communicatively connected; The security monitoring module is used to monitor the security of the RV: monitor the abnormality of the RV safety through monitoring equipment, and collect and obtain security monitoring data; the security monitoring data includes vehicle safety parameters, anti-theft safety parameters, fire safety parameters and environmental safety parameters; The vehicle safety parameters include tire pressure a1, engine oil volume a2 and brake sound frequency a3; the vehicle safety parameters are collected by accessing the existing vehicle fault self-diagnosis system of the RV. The vehicle fault self-diagnosis system automatically checks and monitors the working status of various parts such as the transmission system and control system in the vehicle by setting a microprocessor control unit; The anti-theft safety parameters include the vibration amplitude of doors and windows b1, the vibration frequency of doors and windows b2, and the number of people outside the car b3; vibration sensors are set on the doors and windows of the RV to collect the vibration status of the doors and windows; infrared cameras are set to monitor the environment outside the RV to record the video and obtain the number of people in the image. Specifically, the existing infrared camera is used to capture the thermal radiation of the target object. Since the human body emits infrared rays of a specific wavelength, usually around 10μm, the infrared camera uses a filter to enhance the concentration of these infrared rays, and then receives these radiations through pyroelectric elements to generate infrared images of the human body, and the number of people outside the car in the image is accumulated; Fire safety parameters include smoke particle concentration c1 and combustion gas concentration assessment coefficient c2; smoke detectors are set to collect smoke particle concentration c1, and gas detectors are used to detect and collect flammable gases in fires and waste gases generated after combustion. Among them, flammable gases include methane CH4, hydrogen H2, etc., and waste gases generated after combustion include carbon dioxide C02, carbon monoxide CO, etc.; Environmental safety parameters include ambient temperature d1, ambient humidity d2 and harmful gas concentration assessment coefficient d3; harmful gases include formaldehyde CH2O, hydrogen sulfide H2S and other gases that affect health. The analysis process of harmful gas concentration assessment coefficient d3 refers to the combustion gas concentration assessment coefficient c2; ambient temperature d1 and ambient humidity d2 are collected through temperature and humidity sensors; The energy consumption monitoring module is used to monitor the energy of the monitoring equipment: the energy of the monitoring equipment is monitored through the power meter, and the energy data of the equipment is collected; The data processor is used to construct a RV security management model for data processing: the RV security management model includes a local analysis sub-model, an overall analysis sub-model and an AI energy consumption prediction sub-model; wherein, the security monitoring data is analyzed through the local analysis sub-model to evaluate the vehicle safety anomaly, the anti-theft safety anomaly, the fire safety anomaly and the environmental safety anomaly, and they are integrated into a safety anomaly parameter vector to generate an alarm device management plan; then, the safety anomaly parameter vector is deeply analyzed through the overall analysis sub-model to evaluate the comprehensive safety level of the RV; then, the equipment energy data is analyzed through the AI ​​energy consumption prediction sub-model to evaluate the security energy consumption level of the RV, and the equipment energy consumption prediction analysis is performed to generate a monitoring device call plan; The alarm response module is used to receive the alarm device management plan and make corresponding alarm response to the security abnormality parameter vector; The call management module is used to receive the monitoring device call plan to manage the monitoring device of the safety monitoring module, thereby adjusting the RV safety management model to achieve optimization and refresh of the model.

[0018] A RV security method based on artificial intelligence technology, the method applies the above-mentioned RV security system based on artificial intelligence technology, and the working steps are as follows: S1, build the initial RV security management model: The RV security management model includes a local analysis sub-model, an overall analysis sub-model, and an AI energy consumption prediction sub-model. The specific process is as follows: A1, analyze the safety monitoring data through the local analysis sub-model: By analyzing vehicle safety parameters, the vehicle safety risk coefficient is obtained and the degree of vehicle safety abnormality is evaluated; By analyzing anti-theft security parameters, the anti-theft security risk coefficient is obtained and the degree of anti-theft security abnormality is evaluated; By analyzing fire safety parameters, the fire safety risk coefficient is obtained and the degree of fire safety abnormality is evaluated; By analyzing environmental safety parameters, we can obtain environmental safety risk factors and assess the degree of environmental safety anomalies; The vehicle safety risk factor, the anti-theft safety risk factor, the fire safety risk factor and the environmental safety risk factor are integrated and marked as a safety anomaly parameter vector; Set the evaluation interval of each component of the safety anomaly parameter vector, determine the risk level of the safety anomaly component by comparing the intervals, and generate an alarm device management plan; A2, conducts in-depth analysis of safety monitoring data through the overall analysis sub-model: By analyzing the safety anomaly parameter vector, the vehicle safety risk factor, the anti-theft safety risk factor, the fire safety risk factor and the environmental safety risk factor are combined to obtain the RV comprehensive safety index and evaluate the comprehensive safety level of the RV. A3, RV security energy consumption monitoring through AI energy consumption prediction management model: By analyzing and processing equipment energy data, we can obtain the RV security energy consumption index and evaluate the RV security energy consumption level; The comprehensive safety index of the RV and the security energy consumption index of the RV are used to predict the equipment energy consumption, and the security energy consumption demand prediction index is obtained, so as to generate a monitoring equipment calling plan; S2, RV security anomaly monitoring through local analysis sub-model: Collect and analyze security monitoring data to construct security anomaly parameter vectors, evaluate security anomaly components, generate alarm equipment management solutions, achieve multiple security guarantees, and locate security anomaly components for alarm processing, thereby improving the efficiency and accuracy of alarm work; S2-1, the specific process of analyzing vehicle safety parameters is as follows: Construct a parameter analysis model, input parameter index a, set the standard interval [Da, Ga] of parameter index a, and obtain and output the evaluation coefficient Xa of parameter index a by combining parameter index a and its standard interval [Da, Ga]: , where, when parameter index a is in the standard interval [Da, Ga], the evaluation coefficient Xa of parameter index a is less than or equal to 0, and the evaluation parameter index a is normal; conversely, when parameter index a is not in the standard interval [Da, Ga], the evaluation coefficient Xa of parameter index a is greater than 0, and the evaluation parameter index a has an abnormal risk, and the higher the evaluation coefficient Xa, the higher the abnormal risk degree of the evaluation parameter index a; Set the standard interval [Da1, Ga1] of tire pressure a1 and substitute it into the parameter analysis model, and obtain the tire quality assessment coefficient Xa1 by combining the tire pressure a1 and its standard interval [Da1, Ga1]; Set the standard interval [Da2, Ga2] of the engine oil quantity a2, and substitute it into the parameter analysis model, and obtain the engine state evaluation coefficient Xa2 by combining the engine oil quantity a2 and its standard interval [Da2, Ga2]; Set the standard interval [Da3, Ga3] of the brake sound frequency a3 and substitute it into the parameter analysis model, and obtain the brake system evaluation coefficient Xa3 by combining the brake sound frequency a3 and its standard interval [Da3, Ga3]; By combining the tire quality assessment coefficient Xa1, the engine status assessment coefficient Xa2 and the brake system assessment coefficient Xa3, the vehicle safety risk coefficient Risk1 is obtained: ; Among them, α1, α2 and α3 are weight coefficients of tire quality assessment coefficient Xa1, engine state assessment coefficient Xa2 and brake system assessment coefficient Xa3 respectively. The weight coefficients are preset and obtained after calculation through a large amount of experimental data. When the tire quality assessment coefficient Xa1, the engine state assessment coefficient Xa2 and the brake system assessment coefficient Xa3 are all greater than 0, then α1, α2 and α3 are preset to be greater than 1; when the tire quality assessment coefficient Xa1 is less than or equal to 0, then α1 is preset to be equal to 0; when the engine state assessment coefficient Xa2 is less than or equal to 0, then α2 is preset to be equal to 0; when the brake system assessment coefficient Xa3 is less than or equal to 0, then α3 is preset to be equal to 0; therefore, when the tire quality assessment coefficient Xa1, the engine state assessment coefficient Xa2 and the brake system assessment coefficient Xa3 are all greater than 0 and the higher the value, the higher the vehicle safety risk coefficient Risk1, and the higher the assessed vehicle safety risk level; Set the evaluation interval of the vehicle safety risk factor Risk1, evaluate the degree of vehicle safety abnormality through interval comparison, and generate a corresponding vehicle safety alarm signal; S2-2, the specific process of analyzing anti-theft security parameters is: By combining the door and window vibration amplitude b1, door and window vibration frequency b2 and the number of people outside the car b3, the anti-theft safety risk factor Risk2 is obtained: ; Among them, β1, β2 and β3 are weight coefficients of the door and window vibration amplitude b1, the door and window vibration frequency b2 and the number of people outside the vehicle b3, respectively, and β1, β2 and β3 are all preset to be greater than 0; when the door and window vibration amplitude b1, the door and window vibration frequency b2 and the number of people outside the vehicle b3 are higher, the anti-theft safety risk coefficient Risk2 is higher, indicating a higher degree of anti-theft safety risk; Set the assessment interval of the anti-theft safety risk factor Risk2, evaluate the degree of anti-theft safety abnormality through interval comparison, and generate a corresponding anti-theft safety alarm signal; S2-3, the specific process of analyzing fire safety parameters is: There are n0 types of combustion gases to be detected by the gas detector. The concentration of any combustion gas q is marked as Cq. By combining the concentrations Cq of n0 types of combustion gases q, the combustion gas concentration evaluation coefficient c2 is obtained: ,in, Refers to the maximum threshold of the concentration Cq of the combustion gas q. When the concentration Cq of the combustion gas q exceeds the maximum threshold When the fire is detected, it is determined that a fire is about to occur or is in a fire state; Refers to the concentration weight coefficient of the combustion gas q. When the concentration Cq of the combustion gas q exceeds the maximum threshold When Greater than 0; on the contrary, when the concentration Cq of the combustion gas q does not exceed the maximum threshold When is equal to 0; By weighted summing the concentrations Cq of n0 kinds of combustion gases q, the combustion gas concentration assessment coefficient c2 is obtained to comprehensively assess the combustion gas concentration. When the concentration Cq of n0 kinds of combustion gases q exceeds the maximum threshold The larger the range, the higher the combustion gas concentration assessment coefficient c2, and the more serious the comprehensive assessment of the combustion gas concentration situation; By combining the smoke particle concentration c1 and the combustion gas concentration assessment coefficient c2, the fire safety risk factor Risk3 is obtained: ,in, Refers to the maximum threshold of smoke particle concentration c1. When the smoke particle concentration c1 exceeds the maximum threshold When the smoke particle concentration c1 exceeds the maximum threshold, it is determined that there may be a fire state; μ1 and μ2 are the weight coefficients of the smoke particle concentration c1 and the combustion gas concentration evaluation coefficient c2, and μ1 and μ2 are both greater than 0; when the smoke particle concentration c1 exceeds the maximum threshold The higher the amplitude and the higher the combustion gas concentration assessment coefficient c2, the higher the fire risk; μ0 is the correction constant. and When both are less than or equal to 0, the preset value of the correction constant μ0 is 1, and the fire safety risk factor Risk3 is 0; when and When any term of is greater than 0, the correction constant μ0 is to ensure A preset constant value that is always greater than 1, at which point the fire safety risk factor Risk3 is greater than 0, and the higher the fire safety risk factor Risk3 is, the higher the degree of fire safety abnormality is assessed; Set the assessment interval of the fire safety risk factor Risk3, evaluate the degree of fire safety abnormality through interval comparison, and generate a corresponding fire safety alarm signal; S2-4, the specific process of analyzing environmental safety parameters is: The standard ranges of ambient temperature d1 and ambient humidity d2 are respectively set; the ambient temperature d1 and its standard range are substituted into the parameter analysis model to obtain the ambient temperature evaluation coefficient Xd1; the ambient humidity d2 and its standard range are substituted into the parameter analysis model to obtain the ambient humidity evaluation coefficient Xd2; By combining the environmental temperature assessment coefficient Xd1, the environmental humidity assessment coefficient Xd2 and the harmful gas concentration assessment coefficient d3, the environmental safety risk coefficient Risk4 is obtained: ; Among them, ω1, ω2 and ω3 are weight coefficients of the environmental temperature assessment coefficient Xd1, the environmental humidity assessment coefficient Xd2 and the harmful gas concentration assessment coefficient d3 respectively. When the environmental temperature assessment coefficient Xd1, the environmental humidity assessment coefficient Xd2 and the harmful gas concentration assessment coefficient d3 are all greater than 0, then the preset ω1, ω2 and ω3 are all greater than 1; when the environmental temperature assessment coefficient Xd1 is less than or equal to 0, then the preset ω1 is equal to 0; when the environmental humidity assessment coefficient Xd2 is less than or equal to 0, then the preset ω2 is equal to 0; when the harmful gas concentration assessment coefficient d3 is equal to 0, then the preset ω3 is equal to 0; therefore, when the environmental temperature assessment coefficient Xd1, the environmental humidity assessment coefficient Xd2 and the harmful gas concentration assessment coefficient d3 are all greater than 0 and the higher the value, the higher the environmental safety risk coefficient Risk4 is, and the higher the degree of environmental safety abnormality is assessed; Set the assessment interval of the environmental safety risk factor Risk4, evaluate the degree of environmental safety abnormality through interval comparison, and generate corresponding environmental safety alarm signals; S2-5, the specific generation process of the alarm device management plan is as follows: The vehicle safety risk factor Risk1, the anti-theft safety risk factor Risk2, the fire safety risk factor Risk3 and the environmental safety risk factor Risk4 are integrated and marked as the safety anomaly parameter vector Rsafe: ; Set the evaluation interval of each component of the safety anomaly parameter vector, and determine the risk level of the safety anomaly component by comparing the intervals; Mark any safety anomaly component of the safety anomaly parameter vector Rsafe as , set the safety anomaly component There are Ng evaluation intervals, and the safety anomaly component Any evaluation interval is marked as Qr, when the safety abnormal component When it is in the evaluation interval Qr, the safety abnormal component is determined The risk level is R level, and the output safety abnormal component Rg-level safety alarm signal; The four components of the safety anomaly parameter vector are judged for risk level, and the safety alarm signals of corresponding levels are output, and the R1-level vehicle safety alarm signal, the R2-level anti-theft safety alarm signal, the R3-level fire safety alarm signal and the R3-level environmental safety alarm signal are integrated and marked as an alarm signal group; By presetting the current safety abnormality component according to the actual application situation The alarm prompt scheme when the risk level is Rg level is obtained, and the alarm prompt schemes corresponding to the four components of the safety abnormality parameter vector are integrated, and then marked as the alarm device management scheme; S3, conduct in-depth analysis of RV safety through overall analysis sub-model: Through the safety abnormality parameter vector, the comprehensive safety level of the RV is evaluated, and the safety monitoring data is deeply analyzed to conduct overall safety monitoring and improve the efficiency of risk response. The specific process is as follows: Set the data calculation cycle to perform regular calculation on the safety anomaly parameter vector Rsafe, combine the vehicle safety risk factor Risk1, anti-theft safety risk factor Risk2, fire safety risk factor Risk3 and environmental safety risk factor Risk4 of the safety anomaly parameter vector Rsafe to obtain the RV comprehensive safety index Com: ; Among them, σ1, σ2, σ3 and σ4 are the fluctuation coefficients of the vehicle safety risk coefficient Risk1, the anti-theft safety risk coefficient Risk2, the fire safety risk coefficient Risk3 and the environmental safety risk coefficient Risk4 respectively. The fluctuation coefficient refers to the variance value of the safety risk coefficient corresponding to N1 data measurement cycles. For example, the vehicle safety risk coefficient Risk1 corresponding to N1 data measurement cycles is extracted and the variance value is calculated to obtain the fluctuation coefficient σ1; when the fluctuation coefficient is higher, it means that the corresponding safety risk coefficient in the historical data measurement cycle is more unstable, and the comprehensive safety degree of the RV is evaluated lower; when the safety risk coefficient is higher, the comprehensive safety degree of the RV is evaluated lower, and the comprehensive safety index Com of the RV is lower; Set the evaluation interval of the RV comprehensive safety index Com, and evaluate the comprehensive safety level of the RV through interval comparison; S4, RV security energy consumption monitoring through AI energy consumption prediction sub-model: Collect and analyze equipment energy data, evaluate the security energy consumption of the RV, and conduct equipment energy consumption forecast analysis to generate a monitoring equipment call plan, thereby reducing the energy consumption of the monitoring equipment and ensuring the sustainable stability of the RV energy. The specific process of analyzing equipment energy data is as follows: By analyzing and processing equipment energy data, we can obtain the RV security energy consumption index and evaluate the RV security energy consumption level; The equipment energy data includes the power consumption of M0 monitoring devices. The power consumption of any monitoring device p is marked as Wp. The power consumption of M0 monitoring devices is combined to obtain the RV security energy consumption index Eneg: ,in, is the power consumption conversion coefficient, which refers to the preset constant of the RV security energy consumption index Eneg by combining the power consumption of M0 monitoring devices; when the power consumption Wp of M0 monitoring devices is higher, the RV security energy consumption index Eneg is higher, and the security energy consumption level of the RV is evaluated to be higher; wherein, the monitoring devices include infrared cameras and sensors set in the security monitoring module; Extract the RV comprehensive safety index Com and RV security energy consumption index Eneg corresponding to N2 data measurement cycles, so as to conduct equipment energy consumption prediction analysis: By calculating the standard deviation of the RV comprehensive safety index Com corresponding to N2 data measurement cycles, the floating coefficient of the RV comprehensive safety index Com is obtained. : ,in, is the average value of the RV comprehensive safety index Com corresponding to N2 data measurement cycles; By calculating the difference between the RV security energy consumption index Eneg corresponding to two adjacent data calculation cycles, the change range of the RV security energy consumption index Eneg within a data calculation cycle is obtained, thereby obtaining the change range of the RV security energy consumption index corresponding to [(N2)-1] data calculation cycles, and calculating the average value to obtain the average increase Kg of the RV security energy consumption index Eneg: ,in, It refers to the change range of the RV security energy consumption index in any data calculation period q. It refers to the change range of the RV security energy consumption index in the previous data calculation cycle adjacent to the data calculation cycle q; Through the RV comprehensive safety index Com and its floating coefficient , and the RV security energy consumption index Eneg and its average increase Kg to obtain the security energy consumption demand forecast index Ec:

[0019] Among them, η1 and η2 are the weight coefficients of the RV comprehensive safety index and the RV security energy consumption index, respectively, and both η1 and η2 are preset to be greater than 0; Set Cd as the comparison factor of the RV comprehensive safety index Com. When the RV comprehensive safety index Com is lower than or equal to the comparison factor Cd, it is determined that the RV comprehensive safety level is low; otherwise, it is determined that the RV comprehensive safety level is normal; Set Eh as the comparison factor of the RV security energy consumption index Eneg. When the RV security energy consumption index Eneg is higher than the comparison factor Eh, it is determined that the RV security energy consumption level is high; otherwise, it is determined that the RV security energy consumption level is normal; When the comprehensive safety level of the RV is low, it is necessary to increase the security monitoring of the RV; when the energy consumption level of the RV security is high, it is necessary to reduce the energy consumption of the RV security equipment; the security energy consumption demand forecast index Ec represents the direction and magnitude of the energy consumption of the RV monitoring equipment that needs to be regulated; By setting control schemes for M0 monitoring devices of the security monitoring module and integrating them into a monitoring device calling scheme, a monitoring device calling scheme is generated and output; S5, alarm response to abnormal safety components of the RV: The corresponding alarm devices are activated through the alarm device management solution, so as to deal with the abnormal safety components in a targeted manner and improve the safety and intelligence level of RV applications; For example, the vehicle safety alarm signal may be an alarm of low / high tire pressure, then the corresponding alarm prompt scheme is preset to immediately stop the car and check the tire condition to ensure that the tire pressure is within the normal range. If the tire pressure is abnormal, the tire pressure should be adjusted appropriately, and long-term high-speed driving or high-intensity driving activities should be avoided. Further, the car repair shop or emergency rescue service can be contacted for inspection and repair; the anti-theft safety alarm signal may be an alarm of suspicious persons prying doors and windows, then the corresponding alarm prompt scheme is preset to automatically send out voice prompts through the microphone to drive away the suspicious persons, and further to automatically make a 110 communication alarm; the fire safety alarm signal may be a high concentration of flammable gas and an impending fire, then the corresponding alarm prompt scheme is preset to immediately make a fire risk voice text prompt and automatically open the doors and windows, and further to automatically perform cooling spray, and automatically make a 119 communication alarm when the fire is uncontrollable; the environmental safety alarm signal may be abnormal temperature and humidity in the RV room, then the corresponding alarm prompt scheme is preset to immediately start the air conditioning system for temperature and humidity control, and open the doors and windows for proper ventilation to avoid the accumulation of harmful gases and affect human comfort and health; S6, optimize and update the RV security management model: Adjust the RV safety management model through the monitoring equipment calling plan, regulate and manage the monitoring equipment of the safety monitoring module, so as to optimize and refresh the RV safety management model; The security energy consumption demand forecast index Ec represents the direction and amplitude of the energy consumption of the RV monitoring equipment that needs to be regulated. The regulation direction is determined by the positive and negative values ​​of the security energy consumption demand forecast index Ec. When the security energy consumption demand forecast index Ec is positive, the regulation direction of the energy consumption of the RV monitoring equipment is to increase; when the security energy consumption demand forecast index Ec is negative, the regulation direction of the energy consumption of the RV monitoring equipment is to decrease. The control range is determined by the absolute value of the security energy demand forecast index Ec. The higher the absolute value of the security energy demand forecast index Ec, the higher the control range. The specific control operation needs to be set by technical personnel based on the actual situation. For example, the operating time of the monitoring equipment and the data collection cycle or frequency are adjusted. When the monitoring equipment runs at a high frequency for a long time, the energy consumption of the monitoring equipment will increase. By reducing the operating time and collection frequency of the monitoring equipment, the energy consumption of the monitoring equipment will be reduced. When the comprehensive safety level of the RV is high and the security energy consumption level of the RV is low, it means that the RV security management model is in the best state at this time, and there is no need to manage the energy consumption of the RV security equipment. The security energy consumption demand prediction index Ec is set to 0, and no processing is done; Through the monitoring device calling plan, the M0 monitoring devices of the security monitoring module are started and stopped, achieving efficient and low-energy RV security management, and realizing adaptive training optimization of artificial intelligence.

[0020] In summary, the present invention constructs an RV security management model to perform RV security anomaly monitoring and RV safety in-depth analysis, performs RV security monitoring from local to overall, generates an alarm device management plan, realizes multiple security guarantees for RV security, and locates the security anomaly components in a targeted manner for alarm processing, thereby improving the efficiency and accuracy of the alarm work, and then deeply analyzes and evaluates the comprehensive safety level of the RV, thereby performing overall safety monitoring, improving risk response efficiency, and enhancing the safety and intelligence level of RV applications; The present invention combines the comprehensive safety level of the RV with the security energy consumption level through an AI energy consumption prediction sub-model, predicts the security energy consumption demand, generates a monitoring equipment calling plan, and optimizes and refreshes the RV security management model, thereby reducing the energy consumption of the monitoring equipment, ensuring the sustainable stability of the RV energy, and realizing efficient and low-energy RV security management and adaptive training optimization of artificial intelligence.

[0021] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technical personnel in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0022] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A RV security system based on artificial intelligence technology, characterized by: It includes a security monitoring module, an energy consumption monitoring module, a data processor, an alarm response module and a call management module, wherein the security monitoring module, the energy consumption monitoring module, the data processor, the alarm response module and the call management module are communicatively connected; The security monitoring module is used to monitor the security of the RV: monitor the abnormality of the RV safety through monitoring equipment, and collect and obtain security monitoring data; the security monitoring data includes vehicle safety parameters, anti-theft safety parameters, fire safety parameters and environmental safety parameters; The energy consumption monitoring module is used to monitor the energy of the monitoring equipment: the energy of the monitoring equipment is monitored through the power meter, and the energy data of the equipment is collected; The data processor is used to construct an RV security management model for data processing, and manage and call the monitoring equipment of the security monitoring module through the RV security management model: the RV security management model includes a local analysis sub-model, an overall analysis sub-model and an AI energy consumption prediction sub-model; wherein, the security monitoring data is analyzed through the local analysis sub-model to evaluate the abnormality of vehicle safety, the abnormality of anti-theft safety, the abnormality of fire safety and the abnormality of environmental safety, and integrate them into a safety abnormality parameter vector to generate an alarm device management plan; and then the safety abnormality parameter vector is deeply analyzed through the overall analysis sub-model to evaluate the comprehensive safety level of the RV; and the equipment energy data is analyzed through the AI ​​energy consumption prediction sub-model to evaluate the security energy consumption level of the RV, and the equipment energy consumption prediction analysis is performed to generate a monitoring equipment call plan; The alarm response module is used to receive the alarm device management plan and make corresponding alarm response to the security abnormality parameter vector; The call management module is used to receive the monitoring device call plan to manage the monitoring device of the safety monitoring module, thereby adjusting the RV safety management model to achieve optimization and refresh of the model.

2. The RV security system based on artificial intelligence technology according to claim 1 is characterized by: The RV security management model includes a local analysis sub-model, an overall analysis sub-model, and an AI energy consumption prediction sub-model. The specific process is as follows: A1, analyze the safety monitoring data through the local analysis sub-model: By analyzing vehicle safety parameters, the vehicle safety risk coefficient is obtained and the degree of vehicle safety abnormality is evaluated; By analyzing anti-theft security parameters, the anti-theft security risk coefficient is obtained and the degree of anti-theft security abnormality is evaluated; By analyzing fire safety parameters, the fire safety risk coefficient is obtained and the degree of fire safety abnormality is evaluated; By analyzing environmental safety parameters, we can obtain environmental safety risk factors and assess the degree of environmental safety anomalies; The vehicle safety risk factor, the anti-theft safety risk factor, the fire safety risk factor and the environmental safety risk factor are integrated and marked as a safety anomaly parameter vector; Set the evaluation interval of each component of the safety anomaly parameter vector, determine the risk level of the safety anomaly component by comparing the intervals, and generate an alarm device management plan; A2, conducts in-depth analysis of safety monitoring data through the overall analysis sub-model: By analyzing the safety anomaly parameter vector, the vehicle safety risk factor, the anti-theft safety risk factor, the fire safety risk factor and the environmental safety risk factor are combined to obtain the RV comprehensive safety index and evaluate the comprehensive safety level of the RV. A3, RV security energy consumption monitoring through AI energy consumption prediction management model: By analyzing and processing equipment energy data, we can obtain the RV security energy consumption index and evaluate the RV security energy consumption level; The RV comprehensive safety index and RV security energy consumption index are used to conduct equipment energy consumption forecast analysis to obtain the security energy consumption demand forecast index, thereby generating a monitoring equipment calling plan.

3. The RV security system based on artificial intelligence technology according to claim 2 is characterized by: The specific process of analyzing vehicle safety parameters is as follows: Vehicle safety parameters include tire pressure a1, engine oil level a2, and brake sound frequency a3; Construct a parameter analysis model, input parameter index a, set the standard interval [Da, Ga] of parameter index a, obtain and output the evaluation coefficient Xa of parameter index a by combining parameter index a and its standard interval [Da, Ga]; Set the standard ranges of tire pressure a1, engine oil volume a2, and brake sound frequency a3, and substitute them into the parameter analysis model to obtain the tire quality assessment coefficient Xa1, engine status assessment coefficient Xa2, and brake system assessment coefficient Xa3 in turn, and then comprehensively obtain the vehicle safety risk coefficient Risk1; Set the evaluation interval of the vehicle safety risk factor Risk1, evaluate the degree of vehicle safety abnormality through interval comparison, and generate a corresponding vehicle safety alarm signal.

4. The RV security system based on artificial intelligence technology according to claim 3 is characterized by: The specific process of analyzing anti-theft security parameters is: The anti-theft safety parameters include the door and window vibration amplitude b1, the door and window vibration frequency b2 and the number of people outside the vehicle b3; The anti-theft safety risk factor Risk2 is obtained by combining the door and window vibration amplitude b1, the door and window vibration frequency b2 and the number of people outside the vehicle b3; An assessment interval for the anti-theft safety risk factor Risk2 is set, the degree of anti-theft safety abnormality is assessed through interval comparison, and a corresponding anti-theft safety alarm signal is generated.

5. The RV security system based on artificial intelligence technology according to claim 4 is characterized by: The specific process of analyzing fire safety parameters is: Fire safety parameters include smoke particle concentration c1 and combustion gas concentration assessment coefficient c2; There are n0 types of combustion gases to be detected by the gas detector, and the concentration of any combustion gas q is marked as Cq. By combining the concentrations Cq of the n0 types of combustion gases q, the combustion gas concentration evaluation coefficient c2 is obtained; By combining the smoke particle concentration c1 and the combustion gas concentration assessment coefficient c2, the fire safety risk factor Risk3 is obtained; Set the evaluation interval of the fire safety risk factor Risk3, evaluate the degree of fire safety abnormality through interval comparison, and generate a corresponding fire safety alarm signal.

6. The RV security system based on artificial intelligence technology according to claim 5 is characterized by: The specific process of analyzing environmental safety parameters is: Environmental safety parameters include ambient temperature d1, ambient humidity d2 and harmful gas concentration assessment coefficient d3; The ambient temperature d1 and ambient humidity d2 are collected by the temperature and humidity sensor, and the standard ranges of the ambient temperature d1 and ambient humidity d2 are set respectively; Substitute the ambient temperature d1 and its standard range into the parameter analysis model to obtain the ambient temperature evaluation coefficient Xd1; Substitute the ambient humidity d2 and its standard range into the parameter analysis model to obtain the ambient humidity assessment coefficient Xd2; The environmental safety risk factor Risk4 is obtained by combining the environmental temperature assessment coefficient Xd1, the environmental humidity assessment coefficient Xd2 and the harmful gas concentration assessment coefficient d3; Set the assessment interval of the environmental safety risk factor Risk4, evaluate the degree of environmental safety abnormality through interval comparison, and generate corresponding environmental safety alarm signals.

7. The RV security system based on artificial intelligence technology according to claim 6 is characterized by: The specific generation process of the alarm device management plan is as follows: The vehicle safety risk factor Risk1, the anti-theft safety risk factor Risk2, the fire safety risk factor Risk3 and the environmental safety risk factor Risk4 are integrated and marked as the safety anomaly parameter vector Rsafe: ； Set the evaluation interval of each component of the safety anomaly parameter vector, and determine the risk level of the safety anomaly component by comparing the intervals; Mark any safety anomaly component of the safety anomaly parameter vector Rsafe as , set the safety anomaly component There are Ng evaluation intervals, and the safety anomaly component Any evaluation interval is marked as Qr, when the safety abnormal component When it is in the evaluation interval Qr, the safety abnormal component is determined The risk level is R level, and the output safety abnormal component Rg-level safety alarm signal; The risk levels of the four components of the safety anomaly parameter vector are determined, and the safety alarm signals of the corresponding levels are output. The R1-level vehicle safety alarm signal, the R2-level anti-theft safety alarm signal, the R3-level fire safety alarm signal and the R3-level environmental safety alarm signal are integrated and marked as an alarm signal group, thereby generating an alarm equipment management plan.

8. The RV security system based on artificial intelligence technology according to claim 7 is characterized by: The specific process of in-depth analysis of safety monitoring data is as follows: Set a data calculation cycle to perform regular calculations on the safety anomaly parameter vector Rsafe, combine the vehicle safety risk factor Risk1, the anti-theft safety risk factor Risk2, the fire safety risk factor Risk3 and the environmental safety risk factor Risk4 of the safety anomaly parameter vector Rsafe to obtain the RV comprehensive safety index Com; Set the evaluation range of the RV comprehensive safety index Com, and evaluate the comprehensive safety level of the RV through interval comparison.

9. The RV security system based on artificial intelligence technology according to claim 8 is characterized by: The specific process of analyzing equipment energy data is as follows: The equipment energy data includes the power consumption of M0 monitoring devices. The power consumption of any monitoring device p is marked as Wp. The RV security energy consumption index Eneg is obtained by combining the power consumption of M0 monitoring devices. Extract the RV comprehensive safety index Com and RV security energy consumption index Eneg corresponding to N2 data measurement cycles, so as to conduct equipment energy consumption prediction analysis: By calculating the standard deviation of the RV comprehensive safety index Com corresponding to N2 data measurement cycles, the floating coefficient of the RV comprehensive safety index Com is obtained. ; By calculating the difference between the RV security energy consumption index Eneg corresponding to two adjacent data calculation cycles, the change range of the RV security energy consumption index Eneg within a data calculation cycle is obtained, thereby obtaining the average increase Kg of the RV security energy consumption index Eneg; Through the RV comprehensive safety index Com and its floating coefficient , and the RV security energy consumption index Eneg and its average increase Kg to obtain the security energy consumption demand forecast index Ec; The control scheme is set for the M0 monitoring devices of the security monitoring module through the security energy consumption demand prediction index Ec, and it is integrated into a monitoring device calling scheme, thereby generating and outputting the monitoring device calling scheme.

10. A RV security method based on artificial intelligence technology, characterized in that: The method applies the RV security system based on artificial intelligence technology as described in any one of claims 1 to 9 above, and comprises the following steps: Step 1: Build the initial RV security management model: The RV security management model includes a local analysis sub-model, an overall analysis sub-model, and an AI energy consumption prediction sub-model; Step 2: Monitor the abnormality of RV security through the local analysis sub-model: Collect and analyze security monitoring data to construct security anomaly parameter vectors, evaluate security anomaly components, and generate alarm device management plans; Step 3: Conduct an in-depth safety analysis of the RV through the overall analysis sub-model: Evaluate the comprehensive safety level of the RV through the safety abnormality parameter vector; Step 4: Monitor RV security energy consumption through the AI ​​energy consumption prediction sub-model: Collect and analyze equipment energy data, evaluate the security energy consumption of the RV, and conduct equipment energy consumption forecast analysis to generate monitoring equipment call plans; Step 5: Response to the abnormal safety component of the RV: Respond to and enable corresponding alarm devices through the alarm device management solution, so as to deal with security anomalies in a targeted manner; Step 6: Optimize and refresh the RV security management model: The RV safety management model is adjusted through the monitoring device calling plan, and the corresponding monitoring devices of the security monitoring module are called and managed, so as to achieve model optimization and refresh, and realize RV security management with high efficiency and low energy consumption.

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