Positive-pressure poison filtration and ventilation control system and method for anti-riot dispersing vehicle

Abstract

The invention relates to the technical field of ventilation control, in particular to a positive-pressure poison filtering ventilation control system and method for an anti-riot dispersing truck. According to chemical characteristics and concentration of poison gas, the working priority and the distribution operation sequence of a filtering layer are dynamically adjusted by adopting a gradient lifting decision tree; the method realizes selection and priority arrangement of targeted filtering, avoids excessive consumption of filtering resources, and enables the filtering device to monitor the running state in real time, adjust the working sequence in time and prolong the effective life of the filtering device in long-term running by monitoring the adsorption saturation and the working temperature of the filtering layer and combining with dynamic operation optimization decision. The fan speed and the positive pressure maintainer setting are adjusted through a fuzzy control method, the in-vehicle positive pressure and ventilation effect is optimized in real time, the in-vehicle air flowing efficiency and the air quality distribution uniformity are optimized by simulating the ventilation path and adjusting the fan power output and the air valve state, and the overall dynamic response capacity and operation stability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ventilation control for dispersal vehicles, and in particular to a positive pressure gas filtering ventilation control system and method for riot control dispersal vehicles. Background Art

[0003] Spinosaurus riot control and dispersal vehicles are often used in the People's Armed Police Force, mainly in the performance of duties, handling emergencies, anti-terrorism operations and other tasks; Generally speaking, Spinosaurus riot control and dispersal vehicles are mainly composed of three parts: riot control and strike, media transmission, and vehicle-mounted mobility. Among them, Spinosaurus riot control and dispersal vehicles are usually equipped with a gas filtration and ventilation system to ensure that the air breathed by people in the vehicle meets safety standards in the event of poisonous gas, smoke and other harmful gas invasion scenarios, avoid external polluted gases from infiltrating the interior of the vehicle, enhance the protection capabilities of the vehicle, and ensure the life safety and continuity of action of people in the vehicle; At present, the main function of the gas filtration and ventilation system is to achieve the exchange, purification and pressure regulation of air in a specific environment to ensure air quality and environmental safety. It has been widely used in industrial production, civil buildings, vehicle equipment and special scene protection, including core functions such as air filtration, flow regulation and pressure control.

[0004] However, existing anti-riot dispersal vehicles are prone to identification bias when faced with scenes where multiple toxic gases are mixed or the concentrations vary greatly. The configuration and operation of the filtering device mostly use static settings or a single preset logic, and the operating status and adsorption performance of the filter layer are insufficiently monitored, resulting in the risk of filtration failure and leakage of air pollutants into the safe space. In addition, the existing technology mostly uses a fixed fan speed and pressure adjustment method, and is unable to adjust the fan speed and positive pressure settings in real time according to changes in the environment inside and outside the vehicle, resulting in reduced ventilation efficiency and excessive energy consumption. At the same time, the optimization of the airflow distribution in the vehicle is insufficient, resulting in uneven air flow. The air quality in some areas cannot meet safety requirements, reducing the overall protection effect and operating efficiency.

[0005] Therefore, how to automatically control the ventilation of the riot dispersal vehicle is a technical problem that technicians need to solve at present. Summary of the invention

[0007] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a positive pressure gas filtering and ventilation control system and method for a riot control and dispersal vehicle.

[0008] In order to achieve the above-mentioned object, the first aspect of the present invention proposes a positive pressure gas filtering and ventilation control system for a riot control and dispersal vehicle, comprising: Gas type and concentration identification module: detects the toxic gas composition and concentration in the external environment based on chemical sensors and optical sensors, records the chemical properties of the gas, and generates gas property data; Dynamic configuration module of filter device: based on the gas characteristic data, adopts gradient boosting decision tree, selects appropriate filter layer according to the chemical characteristics of poisonous gas, adjusts the working priority and working status of activated carbon layer, HEPA layer and ionosphere, allocates operation sequence, and generates filter configuration plan; Optimizing the filtering operation module: according to the filtering configuration scheme, combined with the working priority and chemical characteristics of the filtering layer, determining the timing of activating and shutting down the filtering device, and adjusting the working sequence of the filtering device to match the actual needs, generating an operation optimization decision; Filter efficiency monitoring module: monitors the adsorption saturation and operating temperature of each filter layer according to the operation optimization decision, checks the operating efficiency of each filter layer, and generates efficiency monitoring records; Ventilation effect real-time adjustment module: Based on the performance monitoring records, the fuzzy control method is used to adjust the fan speed and the setting of the positive pressure maintainer in real time, analyze the real-time data to maintain the positive pressure in the vehicle and optimize the ventilation effect, and generate ventilation parameter adjustments; Ventilation path optimization module: Based on the ventilation parameter adjustment, simulate the airflow distribution in the vehicle, adjust the fan output power and air valve status according to the real-time ventilation effect and the space layout in the vehicle, and generate an optimized airflow path.

[0009] Preferably, the gas type and concentration identification module includes a chemical data acquisition submodule, an optical property analysis submodule and a gas property extraction submodule, wherein: Chemical data acquisition submodule: Based on the detection of toxic gas components and concentrations in the external environment by chemical sensors, the chemical sensor electrical signal is collected, the changes in electrical signal intensity are extracted, the chemical reaction characteristics of the toxic gas components are analyzed, the electrical signal characteristic values ​​related to the toxic gas components are extracted, and the chemical response characteristic data are generated; Optical characteristic analysis submodule: Based on the chemical response characteristic data, the optical sensor detects the absorption characteristics of light of a specific wavelength, collects the changes in the absorption light intensity of the optical sensor, analyzes the wavelength absorption characteristics of the poison gas molecules to light, extracts the characteristic information of the poison gas molecules on the spectrum, and generates optical absorption characteristic data; Gas characteristic extraction submodule: Based on the optical absorption characteristic data, combined with the chemical response characteristic data, by matching the optical characteristics and chemical response characteristic values, the chemical properties and concentration information of the poisonous gas are extracted, and the gas characteristic data is output.

[0010] Preferably, the filtering device dynamic configuration module includes a filtering demand evaluation submodule, a filtering layer priority adjustment submodule and a filtering configuration generation submodule, wherein: Filtration demand assessment submodule: Based on the gas characteristic data, a gradient boosting decision tree is used to analyze the chemical properties of the toxic gas, calculate the reaction performance of the toxic gas components and the filter material, evaluate the adsorption capacity and load demand of the filter layer, extract the demand parameters of the filter material, and generate the filtration demand parameters; Filter layer priority adjustment submodule: based on the filtering demand parameters, adjust the working mode of the filter layer, set the activation conditions of the activated carbon layer, the HEPA layer and the ionization layer, optimize the filtering priority sequence of each layer, establish the dynamic operation sequence configuration of the filter layer, and generate the filter layer priority configuration; Filter configuration generation submodule: Based on the filter layer priority configuration, allocate the running tasks of the filter device, set the working order of each layer in the filter device, set the operation logic and association rules during dynamic switching, generate the task operation table of the filter device, and output the filter configuration plan.

[0011] Preferably, the calculation formula for the reaction performance of the toxic gas component and the filter material is:

[0012] in: is the gradient value of the left branch filter material, is the gradient value of the right branch filter material, is the second-order gradient value of the left branch filter material, is the second-order gradient value of the right branch filter material, is the regularization parameter, T is the chemical reaction time between the poison gas and the filter material, is the chemical reaction time between the left branch poison gas and the filter material, is the chemical reaction time between the right branch poison gas and the filter material, P is the affinity coefficient between the poison gas molecules and the filter material molecules, M is the surface molecular density of the filter material, is the surface molecular density of the left branch filter material, is the surface molecular density of the right branch filter material, is the weight coefficient of chemical reaction time, is the weight of the affinity coefficient, is the weight of the molecular density.

[0013] Preferably, the optimized filtering operation module includes a filtering timing control submodule, a work sequence adjustment submodule and an operation decision generation submodule, wherein: Filter timing control submodule: Based on the filtering configuration scheme, by detecting the current state of the filter layer in the filter device and combining the environmental toxic gas concentration, extracting environmental parameters and filter layer operation data, calculating the filtering conditions required for the device to start, extracting the filter layer switching time, and generating a filter timing determination result; Working sequence adjustment submodule: based on the filtering timing determination result, combined with the chemical characteristics of each filtering layer, by analyzing the working conditions of the filtering layer and the internal parameters of the device, setting the priority switching rules of the filtering layer, optimizing the operating order of the filtering layer, determining the working mode of each layer, and generating the filtering operation sequence; Operation decision generation submodule: Based on the filtering operation sequence, by extracting the dynamic switching rules and operation logic of the filtering layer, setting the overall task allocation of the filtering device and the start-up conditions of each layer, generating the device operation plan and synchronizing it to all filtering modules, and outputting the operation optimization decision.

[0014] Preferably, the filtration efficiency monitoring module includes an adsorption saturation monitoring submodule, an operating temperature monitoring submodule and an operation efficiency evaluation submodule, wherein: Adsorption saturation monitoring submodule: Based on the above operation optimization decision, the adsorption performance of the filter layer is collected by real-time data, the working record and chemical adsorption amount of the adsorption layer are extracted, the saturation change of the adsorption layer is analyzed, and the remaining adsorption capacity is calculated in combination with the toxic gas concentration to generate the adsorption state monitoring result; Working temperature monitoring submodule: Based on the adsorption state monitoring results, by real-time monitoring of the temperature change of the filter layer, extracting the heat data recorded by the temperature sensor, analyzing the impact of temperature change on the filtration performance, determining the temperature parameters in combination with the saturation state, and generating working temperature parameter records; Operation efficiency evaluation submodule: Based on the operating temperature parameter records, the overall operation data of the filter device under different temperatures and adsorption states is analyzed, and by extracting efficiency parameters and remaining filtration capacity, the operation index of the filter device is calculated, and the performance monitoring record is output.

[0015] Preferably, the ventilation effect real-time adjustment module includes a fan speed adjustment submodule, a positive pressure setting adjustment submodule and a ventilation parameter analysis submodule, wherein: Fan speed adjustment submodule: Based on the performance monitoring records, extract the airflow data inside and outside the vehicle and the fan operation status data, read the current fan speed in real time and compare the current air pressure inside the vehicle with the external air pressure, analyze the impact of the air pressure difference on the ventilation demand, calculate the fan speed adjustment amount in combination with the real-time toxic gas concentration, determine the adjusted fan operation parameters, and generate the fan speed adjustment result; Positive pressure setting adjustment submodule: Based on the fan speed adjustment result, a fuzzy control method is used to extract the positive pressure maintainer operation data, and the positive pressure maintainer output parameters are analyzed by calculating the positive pressure difference in the vehicle and the change of the external environmental pressure, combined with the fan speed adjustment amount, and the operation mode and control logic of the positive pressure maintainer are adjusted to generate positive pressure setting parameters; Ventilation parameter analysis submodule: Based on the positive pressure setting parameters, extract the real-time ventilation effect related data and the air flow change state in the vehicle, calculate the combined effect of positive pressure and fan speed on the air flow in the vehicle, extract the air exchange rate and air flow distribution data in the vehicle, and output the ventilation parameter adjustment.

[0016] Preferably, the positive pressure maintainer operation data is extracted. Extracting the positive pressure maintainer operation data requires collecting the maintainer output pressure value, target pressure setting value, real-time output power, pressure adjustment range and valve opening and closing state. The output pressure is collected in real time by a pressure sensor, and the target pressure and adjustment range are extracted in combination with the setting value. The current power output is recorded, and the valve opening and closing angle and action state are recorded at the same time. All data are synchronously extracted through the real-time control system of the positive pressure maintainer. The extraction of real-time ventilation effect related data needs to include pollutant concentration, temperature, humidity, air pressure, air flow speed and direction. The pollutant concentration is obtained through an air quality sensor, the temperature and humidity distribution is obtained through a temperature and humidity sensor, the air pressure difference inside and outside the vehicle is recorded through an air pressure sensor, the flow speed and direction data of different areas in the vehicle are obtained through an air flow sensor, and the air flow distribution characteristics in the vehicle are comprehensively analyzed in combination with the operating status of the fan and the air valve.

[0017] Preferably, the ventilation path optimization module includes an airflow distribution simulation submodule, a fan power adjustment submodule and a valve state optimization submodule, wherein: Airflow distribution simulation submodule: Based on the ventilation parameter adjustment, extract the space layout data in the vehicle and the physical position relationship between the fan and the air valve, read the real-time ventilation status data and combine it with the temperature and humidity distribution in the vehicle, analyze the air flow trajectory of each area in the vehicle, identify the air flow obstruction area and optimize the airflow path, and generate the airflow distribution path; Fan power adjustment submodule: extracts fan power output data based on the airflow distribution path, adjusts the fan power output level and operation mode by comparing the airflow distribution uniformity and air exchange rate of each area, optimizes the fan operation parameters to improve the airflow distribution, and generates fan output power parameters; Valve state optimization submodule: Based on the fan output power parameters, extract the real-time opening and closing state data of the valve, analyze the linkage relationship between the valve opening and closing and the fan output, optimize the airflow channel by adjusting the valve opening angle and dynamic switching strategy, establish a ventilation path optimization plan, and output the airflow optimization path.

[0018] A second aspect of the present invention provides a positive pressure gas filtering ventilation control method for a riot control and dispersal vehicle, which is applied to the positive pressure gas filtering ventilation control system for a riot control and dispersal vehicle described above, and comprises the following steps: Step 1: Based on the output data of chemical sensors and optical sensors, extract the chemical reaction signals and optical absorption signals of the poisonous gas, analyze their characteristic values, and combine the chemical reaction time, conductivity and light absorption spectrum to classify the types of poisonous gas and quantify the concentration to generate gas characteristic data; Step 2: Based on the gas characteristic data, combined with the chemical composition category and concentration of the poisonous gas, the reaction performance of the poisonous gas and the filter material is analyzed, the adsorption adaptability of the activated carbon layer to organic poisonous gas, the efficiency of the HEPA layer to particulate matter filtration and the removal capacity of the ionosphere to chemical reaction poisonous gas are analyzed, and the priority and operation timing rules of each layer of filter units are dynamically adjusted through the gradient boosting decision tree, the filtering tasks are allocated and the required units are activated, and the filtering configuration plan is generated; Step 3: Based on the filtering configuration scheme, the current operating load, task priority and toxic gas characteristics of the filtering unit are dynamically analyzed. By judging the saturation and chemical adaptability of the filtering unit, the filtering unit is started or shut down according to the task priority, the operating time of the low-load unit is extended, and the operating time of the high-load unit is reduced. The working sequence is optimized and dynamic switching is completed to generate an operation optimization decision; Step 4: Based on the operation optimization decision, collect the adsorption saturation, current working temperature and filtration time of each filter unit, determine the remaining capacity of the adsorption layer according to the chemical reaction rate, record the filtration saturation and operation efficiency of the HEPA layer for particulate matter, dynamically adjust the thermal impact of the high-temperature filter layer, record the current performance status of all layers, and generate performance monitoring records; Step 5: Based on the performance monitoring record, by analyzing the pressure difference between the inside and outside of the vehicle, the fan output speed and the current power of the positive pressure maintainer, the fan speed is dynamically adjusted to stabilize the positive pressure state, and the power level of the positive pressure maintainer is adjusted according to the fuzzy control method combined with the positive pressure output range, the ventilation effect is optimized and the fan and the positive pressure device are synchronously adjusted to generate ventilation parameter adjustment; Step six: Based on the ventilation parameter adjustment, analyze the linkage relationship between the fan output power and the airflow distribution, calculate the adaptive adjustment plan of the airflow path through the air pressure distribution at different positions in the vehicle, dynamically adjust the opening and closing angle of the air valve and the fan output power, perform real-time correction on the distribution area of ​​the airflow in the vehicle, and generate an optimized airflow path.

[0019] Compared with the prior art, the advantages and positive effects of the present invention are: 1. In the present invention, by combining the chemical characteristics and concentration of the poison gas, a gradient boosting decision tree is used to dynamically adjust the working priority of the filter layer and the allocation operation sequence, thereby realizing the selection and priority arrangement of targeted filtering and avoiding excessive consumption of filtering resources.

[0020] 2. In the present invention, by monitoring the adsorption saturation and working temperature of the filter layer and combining dynamic operation optimization decision-making, the filter device can monitor the operating status in real time during long-term operation, adjust the working sequence in time, and extend the effective life of the filter device.

[0021] 3. In the present invention, the fan speed and the positive pressure maintainer setting are adjusted by the fuzzy control method to optimize the positive pressure and ventilation effect in the vehicle in real time. By simulating the ventilation path and adjusting the fan power output and the air valve state, the flow efficiency of the air in the vehicle and the uniformity of the air quality distribution are optimized, and the overall dynamic response capability and operation stability are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a system flow chart of the present invention.

[0024] Figure 2 Schematic diagram of the system framework of the present invention.

[0025] Figure 3 It is a schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0028] See also Figure 1 The present invention provides a technical solution: a positive pressure gas filtering and ventilation control system for a riot control and dispersal vehicle comprises: Gas type and concentration identification module: detects the toxic gas composition and concentration in the external environment based on chemical sensors and optical sensors, records the chemical properties of the gas, and generates gas property data; Dynamic configuration module of filter device: Based on gas characteristic data, gradient boosting decision tree is used to select appropriate filter layers according to the chemical characteristics of poisonous gas, adjust the working priority and working status of activated carbon layer, HEPA layer and ionosphere, assign operation sequence, and generate filter configuration plan; Optimize filtration operation module: According to the filtration configuration plan, combined with the working priority and chemical characteristics of the filter layer, determine the timing of activating and shutting down the filter device, and adjust the working sequence of the filter device to match the actual needs to generate operation optimization decisions; Filtration efficiency monitoring module: monitors the adsorption saturation and operating temperature of each filter layer according to the operation optimization decision, checks the operating efficiency of each filter layer, and generates efficiency monitoring records; Real-time ventilation effect adjustment module: Based on the performance monitoring records, the fuzzy control method is used to adjust the fan speed and the setting of the positive pressure maintainer in real time, analyze the real-time data to maintain the positive pressure in the car and optimize the ventilation effect, and generate ventilation parameter adjustments; Ventilation path optimization module: Based on the ventilation parameter adjustment, simulate the airflow distribution in the car, adjust the fan output power and air valve status according to the real-time ventilation effect and the space layout in the car, and generate the airflow optimization path.

[0029] See also Figure 2 The gas type and concentration identification module includes a chemical data acquisition submodule, an optical property analysis submodule and a gas property extraction submodule, wherein: Chemical data acquisition submodule: Based on the detection of toxic gas components and concentrations in the external environment by chemical sensors, the chemical sensor electrical signal is collected, the changes in electrical signal intensity are extracted, the chemical reaction characteristics of the toxic gas components are analyzed, the electrical signal characteristic values ​​related to the toxic gas components are extracted, and the chemical response characteristic data are generated; Optical characteristic analysis submodule: Based on the chemical response characteristic data, the optical sensor detects the absorption characteristics of light of a specific wavelength, collects the changes in the absorption light intensity of the optical sensor, analyzes the wavelength absorption characteristics of the poison gas molecules to light, extracts the characteristic information of the poison gas molecules on the spectrum, and generates optical absorption characteristic data; Gas characteristic extraction submodule: Based on the optical absorption characteristic data and combined with the chemical response characteristic data, the chemical properties and concentration information of the poisonous gas are extracted by matching the optical characteristics and chemical response characteristic values, and the gas characteristic data is output; Chemical data acquisition submodule: Based on chemical sensors to detect the composition and concentration of toxic gas, the constant current potential method is used to set the constant current value to 1 microampere for electrochemical detection of toxic gas components, and the electrical signal is collected for the redox reaction between the toxic gas components and the sensor electrode material. The collected electrical signal is converted into the frequency domain by using a signal decoding algorithm, specifically a Fourier transform algorithm, and the number of transformation points is set to 1024. The spectrum amplitude changes of the electrical signal intensity are collected, and the converted data are windowed, each window is set to 256 points, and a sliding window processing with a superposition of 50% of the window size is performed, so as to extract the characteristic spectrum peaks associated with the toxic gas components and their corresponding frequencies, and generate chemical response characteristic data; Optical property analysis submodule: Based on the chemical response characteristic data, an ultraviolet-visible spectrophotometer is used to detect the absorption characteristics of light of a specific wavelength. The detection wavelength range is set to 200 nanometers to 800 nanometers, and the scanning step length is 2 nanometers. The absorption light intensity generated by the optical sensor is collected, and a spectral decomposition algorithm is used, specifically a least squares curve fitting algorithm. By setting the fitting order to 4, the light absorption curve is analyzed, and the absorption peak of the specific wavelength light and its corresponding wavelength are extracted. The change data of the light absorption intensity is normalized, and the normalization range is set to 0 to 1, and the absorption spectrum peak characteristic data is extracted to generate optical absorption characteristic data; Gas characteristic extraction submodule: Based on the optical absorption characteristic data and combined with the chemical response characteristic data, a correlation analysis algorithm is used, specifically the Pearson correlation coefficient calculation method. By calculating the correlation coefficient between the chemical response characteristic spectrum and the optical absorption characteristic data, the correlation threshold is set to 0.85, and the highly matching characteristics of the poison gas molecules in chemical and optical properties are extracted. The concentration value of the poison gas molecules is calculated based on the matching characteristic data through a linear regression algorithm. The loss function of the linear regression is set to the least squares error function and the number of iterations is optimized to 1000 times through the gradient descent method, and finally the gas characteristic data is output.

[0030] See also Figure 2 The dynamic configuration module of the filtering device includes a filtering demand evaluation submodule, a filtering layer priority adjustment submodule and a filtering configuration generation submodule, wherein: Filtration demand assessment submodule: Based on gas characteristic data, the gradient boosting decision tree is used to analyze the chemical properties of the toxic gas, calculate the reaction performance of the toxic gas components and the filter material, evaluate the adsorption capacity and load demand of the filter layer, extract the demand parameters of the filter material, and generate the filtration demand parameters; Filter layer priority adjustment submodule: Based on the filtering demand parameters, adjust the working mode of the filter layer, set the activation conditions of the activated carbon layer, HEPA layer and ionization layer, optimize the filtering priority sequence of each layer, establish the dynamic operation sequence configuration of the filter layer, and generate the filter layer priority configuration; Filter configuration generation submodule: Based on the filter layer priority configuration, allocate the operation tasks of the filter device, set the working order of each layer in the filter device, set the operation logic and association rules during dynamic switching, generate the task operation table of the filter device, and output the filter configuration plan; Filtration demand assessment submodule: Based on gas characteristic data, a gradient boosting decision tree is used to analyze the chemical properties of the toxic gas. The maximum depth is set to 10, the learning rate of each tree is set to 0.1, and the subsample ratio is set to 0.8. The reaction performance of the toxic gas components and the filter material is calculated. The adsorption parameters of the filter material are defined, including the adsorption constant is set to 0.005 mol / l and the reaction time constant is set to 10 seconds. The adsorption capacity of the filter material is evaluated, and the adsorption load of the filter layer is calculated. The material adsorption demand analysis is performed by defining the load threshold as 500 mg / g of the material adsorption upper limit, extracting the filter material demand parameters, and generating the filtration demand parameters; Filter layer priority adjustment submodule: Based on the filtering demand parameters, the dynamic priority adjustment algorithm is used to optimize the working mode of the filter layer. The activation condition for the activated carbon layer is set to an input gas concentration greater than 50 mg per cubic meter, the activation condition for the HEPA layer is set to an input particle diameter less than 2.5 microns and a concentration greater than 35 micrograms per cubic meter, and the activation condition for the ionosphere is set to an input gas type containing components with charged ionization characteristics. The priority order of the filter layers is defined by the priority adjustment function as follows: the activated carbon layer priority value is 1, the HEPA layer priority value is 2, and the ionosphere priority value is 3. The operation order of the filter layers is dynamically adjusted according to the characteristics of the toxic gas, the dynamic operation order configuration of the filter layers is established, and the filter layer priority configuration is generated; Filter configuration generation submodule: Based on the filter layer priority configuration, the task allocation algorithm is used to allocate the filter device operation tasks, and the task allocation parameters of each layer of the filter device are defined, including the task processing time is set to no more than 10 minutes each time, the dynamic switching condition is set to the task execution time reaching the set value or the adsorption layer load exceeds the set threshold, and the working order of each layer in the filter device is set to switch in order from low to high priority. The operation logic and association rules of dynamic switching are set, including the switching condition between tasks is set to automatically trigger the activation of the lower layer when the current layer cannot meet the task requirements. The switching rules of each layer are defined through functions, including task suspension rules and switching activation rules, and the task operation table of the filter device is generated, and the filter configuration plan is output.

[0031] The calculation formula for the reaction performance of toxic gas components and filter materials is:

[0032] in: is the gradient value of the left branch filter material, is the gradient value of the right branch filter material, is the second-order gradient value of the left branch filter material, is the second-order gradient value of the right branch filter material, is the regularization parameter, T is the chemical reaction time between the poison gas and the filter material, is the chemical reaction time between the left branch poison gas and the filter material, is the chemical reaction time between the right branch poison gas and the filter material, P is the affinity coefficient between the poison gas molecules and the filter material molecules, M is the surface molecular density of the filter material, is the surface molecular density of the left branch filter material, is the surface molecular density of the right branch filter material, is the weight coefficient of chemical reaction time, is the weight of the affinity coefficient, is the weight of molecular density; Execution process: First, the gradient values ​​of the left and right branch filter materials are calculated by the gradient boosting decision tree method. and The gradient value indicates the effect of the gas characteristics on the filter material in the two branches. Then the second-order gradient value is calculated. and , which represents the curvature of the performance change of the poison gas on the filter material. The chemical reaction time T between the poison gas and the filter material is determined by experiment, and the chemical reaction time of the left branch and the right branch is further obtained. and Then the affinity coefficient P between the poison gas molecules and the filter material molecules is calculated by molecular dynamics method, and then the surface molecular density M of the filter material is calculated by material surface analysis experiment, including the molecular density of the left and right branches and , the molecular density is determined by the number of molecules per unit area, and finally the weight coefficient of the chemical reaction time is determined by fitting the data through multiple experiments , weight of affinity coefficient and the weight of the molecular density The weight coefficients are , , , substitute all parameters into the formula, calculate the reaction performance gain value between the toxic gas component and the filter material, and use it as the key parameter for evaluating the adsorption capacity and load demand of the filter layer, and finally generate the filtration demand parameters of the system.

[0033] See also Figure 2 The optimization filtering operation module includes a filtering timing control submodule, a work sequence adjustment submodule and an operation decision generation submodule, wherein: Filter timing control submodule: Based on the filter configuration scheme, by detecting the current state of the filter layer in the filter device and combining it with the environmental toxic gas concentration, extracting environmental parameters and filter layer operation data, calculating the filter conditions required for the device to start, extracting the filter layer switching time, and generating the filter timing determination result; Working sequence adjustment submodule: Based on the results of filtration timing judgment, combined with the chemical characteristics of each filter layer, by analyzing the working conditions of the filter layer and the internal parameters of the device, the priority switching rules of the filter layer are set, the operating order of the filter layer is optimized, the working mode of each layer is determined, and the filtration operation sequence is generated; Operation decision generation submodule: Based on the filtering operation sequence, by extracting the dynamic switching rules and operation logic of the filtering layer, setting the overall task allocation of the filtering device and the start conditions of each layer, generating the device operation plan and synchronizing it to all filtering modules, and outputting the operation optimization decision; Filter timing control submodule: Based on the filter configuration scheme, the current state of the filter layer in the filter device is detected using a state monitoring algorithm. The state monitoring parameters are defined, including the filter layer load value set to 500 mg / g, the filter layer adsorption efficiency threshold set to 80%, and the toxic gas concentration data is collected through an environmental monitor in combination with the environmental toxic gas concentration. The toxic gas concentration data is smoothed using a sliding window averaging algorithm. The window size is set to 10 seconds for real-time concentration mean calculation. The filter layer operation data is subjected to filter condition determination using a logistic regression model in the state monitoring algorithm. The filter start threshold is set to activate the filter device when the state determination value is greater than 0.8. The filter layer switching time is extracted, and the switching timing is determined by calculating the toxic gas concentration change rate and setting the change rate threshold to 5 mg / s, generating a filter timing determination result. Working sequence adjustment submodule: Based on the results of the filtration timing judgment and in combination with the chemical characteristics of each filter layer, the working conditions of the filter layer are sorted by a priority sorting algorithm through a dynamic weighted model. The weight parameters are set, including the weight of the filter layer adsorption capacity is 0.5, the weight of the filter layer working temperature adaptability is 0.3, and the weight of the filter layer life is 0.2. The internal parameters of the device, including the current operating load and temperature data, are collected in real time and used as model inputs. The priority switching rules of the filter layer are determined through iterative weighted calculations. The filter layer operation sequence is optimized through a dynamic adjustment module. The filter layer operation sequence is generated through the adjusted operation sequence, the working mode of each layer is determined, and the filtration operation sequence is generated; Operation decision generation submodule: Based on the filtering operation sequence, the task allocation algorithm is used to analyze the dynamic switching rules and operation logic of the filter layer. The overall task allocation of the filtration device is performed by defining the task priority weight, setting the activated carbon layer weight to 0.6, the HEPA layer weight to 0.3, and the ionosphere weight to 0.1. The start-up conditions of each layer are determined by the logic judgment module. The start-up conditions are set to include the concentration of toxic gas exceeding the set threshold or the filter layer priority trigger. The operation tasks are allocated and the device operation plan is generated through the dynamic task scheduling module. The operation plan is broadcast to all filter modules through the synchronization module, and the operation optimization decision is output.

[0034] See also Figure 2The filtration efficiency monitoring module includes an adsorption saturation monitoring submodule, an operating temperature monitoring submodule and an operation efficiency evaluation submodule, wherein: Adsorption saturation monitoring submodule: Based on the operation optimization decision, the real-time data is used to collect the adsorption performance of the filter layer. By extracting the working records and chemical adsorption amount of the adsorption layer, the saturation change of the adsorption layer is analyzed, and the remaining adsorption capacity is calculated in combination with the toxic gas concentration to generate the adsorption state monitoring results; Working temperature monitoring submodule: Based on the adsorption state monitoring results, the temperature change of the filter layer is monitored in real time, the heat data recorded by the temperature sensor is extracted, the impact of temperature change on the filtration performance is analyzed, the temperature parameters are determined in combination with the saturation state, and the working temperature parameter records are generated; Operation efficiency evaluation submodule: Based on the operating temperature parameter records, analyze the overall operation data of the filter device under different temperatures and adsorption states, calculate the filter device operation indicators by extracting efficiency parameters and remaining filtration capacity, and output efficiency monitoring records; Adsorption saturation monitoring submodule: Based on the operation optimization decision, the real-time data acquisition algorithm is used to monitor the adsorption performance of the filter layer, and the working records of the adsorption layer are extracted. The extracted parameters include the working time record in seconds and the adsorption amount in milligrams. The linear regression model is used to analyze the saturation change trend of the chemical adsorption amount. The loss function of the model is set to the least squares error function, and the number of iterations is set to 1000 times. The saturation of the adsorption layer is curve fitted, and the current adsorption state is calculated by the fitting results. Combined with the toxic gas concentration data, the residual adsorption capacity calculation formula is set to be the residual adsorption capacity equal to the total adsorption capacity minus the current adsorption amount, and the adsorption state monitoring results are generated; Working temperature monitoring submodule: Based on the adsorption state monitoring results, the temperature change analysis algorithm is used to monitor the temperature change of the filter layer. The temperature change record is collected through the real-time temperature sensor data. The parameters are extracted, including real-time data of heat units in joules and temperature units in degrees Celsius. The heat data is analyzed for the change of filtration performance using a formula based on the laws of thermodynamics. The temperature change rate calculation formula is set to be the temperature change rate equal to the temperature difference divided by the time difference. The temperature parameters are determined by the cross analysis model of saturation and temperature change in combination with the adsorption state, and the working temperature parameter record is generated; Operation efficiency evaluation submodule: Based on the working temperature parameter records, the operation efficiency analysis algorithm is used to analyze the overall operation data of the filter device under different temperatures and adsorption states. The filtration efficiency parameters are extracted, including the amount of toxic gas processed per minute in milligrams per minute and the remaining filtration capacity in milligrams. The K-means clustering algorithm is used to group the extracted data for operation status analysis. The clustering centers are set to 3 groups, corresponding to high-efficiency operation, low-efficiency operation and critical operation states, respectively. The operation indicators of the filter device are calculated through the clustering results, and the performance monitoring records are output.

[0035] See also Figure 2 The ventilation effect real-time adjustment module includes a fan speed adjustment submodule, a positive pressure setting adjustment submodule and a ventilation parameter analysis submodule, wherein: Fan speed adjustment submodule: Based on the performance monitoring records, extract the airflow data inside and outside the vehicle and the fan operation status data, read the current fan speed in real time and compare the current air pressure inside the vehicle with the external air pressure, analyze the impact of the air pressure difference on the ventilation demand, calculate the fan speed adjustment amount in combination with the real-time toxic gas concentration, determine the adjusted fan operation parameters, and generate the fan speed adjustment result; Positive pressure setting adjustment submodule: Based on the fan speed adjustment result, the fuzzy control method is used to extract the positive pressure maintainer operation data. By calculating the positive pressure difference in the vehicle and the change of the external environmental pressure, the output parameters of the positive pressure maintainer are analyzed in combination with the fan speed adjustment amount, and the operation mode and control logic of the positive pressure maintainer are adjusted to generate the positive pressure setting parameters. Ventilation parameter analysis submodule: Based on the positive pressure setting parameters, extract the real-time ventilation effect related data and the air flow change status in the car. By calculating the combined effect of positive pressure and fan speed on the air flow in the car, extract the air exchange rate and air flow distribution data in the car, and output the ventilation parameter adjustment; Fan speed adjustment submodule: Based on the performance monitoring records, the real-time data acquisition algorithm is used to extract the airflow data inside and outside the vehicle and the fan operation status data. The PID control algorithm is used to compare the real-time air pressure inside the vehicle and the external air pressure by reading the current speed of the fan. The parameters of the PID control algorithm are set as a proportional coefficient of 0.5, an integral coefficient of 0.1, and a differential coefficient of 0.05. The ventilation demand analysis is performed on the air pressure difference data. The fan operation speed adjustment amount is calculated by the interpolation algorithm in combination with the toxic gas concentration data. The interpolation step size is set to 0.1. The adjusted fan speed data is combined with the PID control parameters to update the speed, determine the adjusted fan operation parameters, and generate the fan speed adjustment result; Positive pressure setting adjustment submodule: Based on the fan speed adjustment result, the fuzzy control algorithm is used to extract the positive pressure maintainer operation data. The positive pressure calculation model is set to pressure difference equal to the pressure inside the vehicle minus the external pressure by using the pressure difference formula by calculating the positive pressure difference inside the vehicle. The external environment pressure change is monitored in real time. The fuzzy set is set to three output levels of low, medium and high respectively through the fuzzy control rules in combination with the fan speed adjustment amount. The output parameters of the positive pressure maintainer are reasoned by rules. The adjusted output value setting range is 0 to 100 generated by the fuzzy rules. The positive pressure maintainer operation mode and control logic are adjusted to generate positive pressure setting parameters. Ventilation parameter analysis submodule: Based on the positive pressure setting parameters, a joint analysis algorithm is used to extract real-time ventilation effect related data and the airflow change state in the vehicle. The positive pressure parameters and fan speed data are jointly analyzed through a multivariate linear regression model. The linear regression model parameters are set to include independent variables such as positive pressure and fan speed, and the dependent variable is the air exchange rate in the vehicle. The air flow state in the vehicle is extracted through a wind speed sensor to extract the air flow distribution data. The data collection frequency is set to 10 times per second. The air exchange rate and airflow distribution data in the vehicle are extracted through the joint regression results, and the ventilation parameter adjustment is output.

[0036] Extract the operating data of the positive pressure maintainer. To extract the operating data of the positive pressure maintainer, it is necessary to collect the output pressure value, target pressure setting value, real-time output power, pressure adjustment range and valve opening and closing status of the maintainer. The output pressure is collected in real time through the pressure sensor, and the target pressure and adjustment range are extracted in combination with the setting value. The current power output is recorded, and the opening and closing angle and action status of the valve are recorded at the same time. All data are synchronously extracted through the real-time control system of the positive pressure maintainer. Extracting real-time ventilation effect-related data requires pollutant concentration, temperature, humidity, air pressure, air flow speed and direction. The pollutant concentration is obtained through air quality sensors, the temperature and humidity distribution is obtained through temperature and humidity sensors, the air pressure difference inside and outside the vehicle is recorded through air pressure sensors, the flow rate and direction data of different areas in the vehicle are obtained through air flow sensors, and the air flow distribution characteristics in the vehicle are comprehensively analyzed in combination with the operating status of the fan and air valve.

[0037] See also Figure 2 The ventilation path optimization module includes an airflow distribution simulation submodule, a fan power adjustment submodule, and a valve state optimization submodule, among which: Airflow distribution simulation submodule: Based on ventilation parameter adjustment, extract the space layout data in the vehicle and the physical position relationship between the fan and the air valve, read the real-time ventilation status data and combine it with the temperature and humidity distribution in the vehicle, analyze the air flow trajectory of each area in the vehicle, identify the air flow obstruction area and optimize the airflow path, and generate the airflow distribution path; Fan power adjustment submodule: extracts fan power output data based on the airflow distribution path, adjusts the fan power output level and operation mode by comparing the airflow distribution uniformity and air exchange rate in each area, optimizes the fan operation parameters to improve the airflow distribution, and generates the fan output power parameters; Valve state optimization submodule: Based on the fan output power parameters, extract the real-time opening and closing state data of the valve, analyze the linkage relationship between the valve opening and closing and the fan output, optimize the airflow channel by adjusting the valve opening angle and dynamic switching strategy, establish a ventilation path optimization plan, and output the airflow optimization path; Airflow distribution simulation submodule: Based on the ventilation parameter adjustment, the numerical fluid dynamics simulation algorithm is used to simulate and analyze the interior space layout data and the physical position relationship between the fan and the air valve. The parameters extracted from the interior space layout data include geometric information with length, width and height in meters and the cross-sectional area of ​​the ventilation channel in square meters. The real-time ventilation status data is read and the temperature and humidity gradients are smoothed using an interpolation algorithm in combination with the temperature and humidity distribution data in the vehicle. The interpolation step is set to 0.01 meters for spatial temperature and humidity distribution grid processing. The air flow trajectory in each area of ​​the vehicle is calculated by using a fluid dynamics calculation model with the flow velocity in meters per second and the turbulence intensity in 10% for flow path calculation. The air flow obstruction area is identified by setting the obstruction judgment condition to mark the area with a flow velocity below 0.1 meters per second as an obstruction area and adjusting the path to generate the airflow distribution path. Fan power adjustment submodule: Based on the airflow distribution path, a linear regression algorithm is used to analyze the fan power output data. By extracting the fan output power in watts and comparing it with the airflow distribution uniformity in percentage and air exchange rate in ventilation times per hour in each area, the fan power adjustment amount is set as the power adjustment amount through a regression model, and the independent variables are airflow uniformity and exchange rate. The number of model iterations is set to 1000 times. The fan power output level is adjusted through the fitting calculation results. The adjusted fan operation modes, including variable frequency operation and constant operation modes, are used to set the operation parameters and generate the fan output power parameters. Valve state optimization submodule: Based on the fan output power parameters, a genetic algorithm is used to optimize the real-time opening and closing state data of the valve. By extracting the valve opening and closing state data including the opening angle in degrees and the dynamic switching time in seconds, the linkage relationship between the valve opening and closing and the fan output is analyzed. The genetic algorithm sets the population size to 50, the crossover probability to 0.8, and the mutation probability to 0.1 to optimize the valve opening angle and dynamic switching strategy. The ventilation path is adjusted for the optimized valve state through the control logic module, the optimized ventilation path is established, and the airflow optimization path is generated.

[0038] A positive pressure gas filtering ventilation control method for a riot control and dispersal vehicle is applied to the positive pressure gas filtering ventilation control system of the riot control and dispersal vehicle, and comprises the following steps: Step 1: Based on the output data of chemical sensors and optical sensors, extract the chemical reaction signals and optical absorption signals of the poisonous gas, analyze their characteristic values, and combine the chemical reaction time, conductivity and light absorption spectrum to classify the types of poisonous gas and quantify the concentration to generate gas characteristic data; Step 2: Based on the gas characteristic data, combined with the chemical composition category and concentration of the poisonous gas, the reaction performance of the poisonous gas and the filter material is analyzed, the adsorption adaptability of the activated carbon layer to organic poisonous gas, the efficiency of the HEPA layer to particulate matter filtration and the removal capacity of the ionosphere to chemical reaction poisonous gas are analyzed, and the priority and operation timing rules of each layer of filtration unit are dynamically adjusted through the gradient boosting decision tree, the filtration tasks are assigned and the required units are activated to generate the filtration configuration plan; Step 3: Based on the filter configuration scheme, the current operating load, task priority and toxic gas characteristics of the filter unit are dynamically analyzed. By judging the saturation and chemical adaptability of the filter unit, the filter unit is started or shut down according to the task priority, the operating time of the low-load unit is extended, and the operating time of the high-load unit is reduced. The working sequence is optimized and dynamic switching is completed to generate operation optimization decisions; Step 4: Based on the operation optimization decision, collect the adsorption saturation, current working temperature and filtration time of each filter unit, determine the remaining capacity of the adsorption layer according to the chemical reaction rate, record the filtration saturation and operating efficiency of the HEPA layer for particulate matter, dynamically adjust the thermal impact of the high-temperature filter layer, record the current performance status of all layers, and generate performance monitoring records; Step 5: Based on the performance monitoring records, by analyzing the pressure difference between the inside and outside of the vehicle, the fan output speed and the current power of the positive pressure maintainer, the fan speed is dynamically adjusted to stabilize the positive pressure state, and the power level of the positive pressure maintainer is adjusted according to the fuzzy control method combined with the positive pressure output range to optimize the ventilation effect and synchronously adjust the fan and the positive pressure device to generate ventilation parameter adjustments; Step 6: Based on the ventilation parameter adjustment, analyze the linkage relationship between the fan output power and the airflow distribution, calculate the adaptation adjustment plan of the airflow path through the air pressure distribution at different positions in the car, dynamically adjust the opening and closing angle of the air valve and the fan output power, perform real-time correction on the airflow distribution area in the car, and generate an optimized airflow path.

[0039] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A positive pressure gas filtering and ventilation control system for a riot control and dispersal vehicle, characterized in that: The system comprises: Gas type and concentration identification module: used to detect the composition and concentration of toxic gases in the external environment based on chemical sensors and optical sensors, record the chemical properties of the gases, and generate gas property data; Dynamic configuration module of filter device: used for selecting appropriate filter layer according to the chemical characteristics of poisonous gas by using gradient boosting decision tree based on the gas characteristic data, adjusting the working priority and working status of activated carbon layer, HEPA layer and ionosphere, and allocating operation sequence, and generating filter configuration scheme; Optimizing filtering operation module: used to determine the timing of activating and deactivating the filtering device according to the filtering configuration scheme, combined with the working priority and chemical characteristics of the filtering layer, and adjust the working sequence of the filtering device to match the actual needs, so as to generate an operation optimization decision; Filter efficiency monitoring module: used to optimize the decision according to the operation, monitor the adsorption saturation and working temperature of each filter layer, check the operating efficiency of each filter layer, and generate efficiency monitoring records; Ventilation effect real-time adjustment module: used to adjust the fan speed and the setting of the positive pressure maintainer in real time according to the performance monitoring record, using the fuzzy control method, analyze the real-time data to maintain the positive pressure in the vehicle and optimize the ventilation effect, and generate ventilation parameter adjustments; Ventilation path optimization module: used to simulate the airflow distribution in the vehicle based on the ventilation parameter adjustment, adjust the fan output power and air valve status according to the real-time ventilation effect and the space layout in the vehicle, and generate an optimized airflow path.

2. The positive pressure gas filtering and ventilation control system for riot control and dispersal vehicles according to claim 1 is characterized in that: The gas type and concentration identification module includes a chemical data acquisition submodule, an optical property analysis submodule and a gas property extraction submodule, wherein: Chemical data acquisition submodule: Based on the detection of toxic gas components and concentrations in the external environment by chemical sensors, the chemical sensor electrical signal is collected, the changes in electrical signal intensity are extracted, the chemical reaction characteristics of the toxic gas components are analyzed, the electrical signal characteristic values ​​related to the toxic gas components are extracted, and the chemical response characteristic data are generated; Optical characteristic analysis submodule: Based on the chemical response characteristic data, the optical sensor detects the absorption characteristics of light of a specific wavelength, collects the changes in the absorption light intensity of the optical sensor, analyzes the wavelength absorption characteristics of the poison gas molecules to light, extracts the characteristic information of the poison gas molecules on the spectrum, and generates optical absorption characteristic data; Gas characteristic extraction submodule: Based on the optical absorption characteristic data, combined with the chemical response characteristic data, by matching the optical characteristics and chemical response characteristic values, the chemical properties and concentration information of the poisonous gas are extracted, and the gas characteristic data is output.

3. The positive pressure gas filtering and ventilation control system for a riot control and dispersal vehicle according to claim 1 is characterized in that: The filtering device dynamic configuration module includes a filtering demand evaluation submodule, a filtering layer priority adjustment submodule and a filtering configuration generation submodule, wherein: Filtration demand assessment submodule: Based on the gas characteristic data, a gradient boosting decision tree is used to analyze the chemical properties of the toxic gas, calculate the reaction performance of the toxic gas components and the filter material, evaluate the adsorption capacity and load demand of the filter layer, extract the demand parameters of the filter material, and generate the filtration demand parameters; Filter layer priority adjustment submodule: based on the filtering demand parameters, adjust the working mode of the filter layer, set the activation conditions of the activated carbon layer, the HEPA layer and the ionization layer, optimize the filtering priority sequence of each layer, establish the dynamic operation sequence configuration of the filter layer, and generate the filter layer priority configuration; Filter configuration generation submodule: Based on the filter layer priority configuration, allocate the running tasks of the filter device, set the working order of each layer in the filter device, set the operation logic and association rules during dynamic switching, generate the task operation table of the filter device, and output the filter configuration plan.

4. The positive pressure gas filtering and ventilation control system for a riot control and dispersal vehicle according to claim 3 is characterized in that: The calculation formula of the reaction performance of the poisonous gas component and the filter material is: Where: Gain is the gain value of the reaction performance between the toxic gas component and the filter material, is the gradient value of the left branch filter material, is the gradient value of the right branch filter material, is the second-order gradient value of the left branch filter material, is the second-order gradient value of the right branch filter material, is the regularization parameter, T is the chemical reaction time between the poison gas and the filter material, is the chemical reaction time between the left branch poison gas and the filter material, is the chemical reaction time between the right branch poison gas and the filter material, P is the affinity coefficient between the poison gas molecules and the filter material molecules, M is the surface molecular density of the filter material, is the surface molecular density of the left branch filter material, is the surface molecular density of the right branch filter material, is the weight coefficient of chemical reaction time, is the weight of the affinity coefficient, is the weight of the molecular density.

5. The positive pressure gas filtering and ventilation control system for a riot control and dispersal vehicle according to claim 1 is characterized in that: The optimized filtering operation module includes a filtering timing control submodule, a work sequence adjustment submodule and an operation decision generation submodule, wherein: Filter timing control submodule: Based on the filtering configuration scheme, by detecting the current state of the filter layer in the filter device and combining the environmental toxic gas concentration, extracting environmental parameters and filter layer operation data, calculating the filtering conditions required for the device to start, extracting the filter layer switching time, and generating a filter timing determination result; Working sequence adjustment submodule: based on the filtering timing determination result, combined with the chemical characteristics of each filtering layer, by analyzing the working conditions of the filtering layer and the internal parameters of the device, setting the priority switching rules of the filtering layer, optimizing the operating order of the filtering layer, determining the working mode of each layer, and generating the filtering operation sequence; Operation decision generation submodule: Based on the filtering operation sequence, by extracting the dynamic switching rules and operation logic of the filtering layer, setting the overall task allocation of the filtering device and the start-up conditions of each layer, generating the device operation plan and synchronizing it to all filtering modules, and outputting the operation optimization decision.

6. The positive pressure gas filtering and ventilation control system for a riot control and dispersal vehicle according to claim 1 is characterized in that: The filtration efficiency monitoring module includes an adsorption saturation monitoring submodule, an operating temperature monitoring submodule and an operation efficiency evaluation submodule, wherein: Adsorption saturation monitoring submodule: Based on the above operation optimization decision, the adsorption performance of the filter layer is collected by real-time data, the working record and chemical adsorption amount of the adsorption layer are extracted, the saturation change of the adsorption layer is analyzed, and the remaining adsorption capacity is calculated in combination with the toxic gas concentration to generate the adsorption state monitoring result; Working temperature monitoring submodule: Based on the adsorption state monitoring results, by real-time monitoring of the temperature change of the filter layer, extracting the heat data recorded by the temperature sensor, analyzing the impact of temperature change on the filtration performance, determining the temperature parameters in combination with the saturation state, and generating working temperature parameter records; Operation efficiency evaluation submodule: Based on the operating temperature parameter records, the overall operation data of the filter device under different temperatures and adsorption states is analyzed, and by extracting efficiency parameters and remaining filtration capacity, the operation index of the filter device is calculated, and the performance monitoring record is output.

7. The positive pressure gas filtering and ventilation control system for a riot control and dispersal vehicle according to claim 1 is characterized in that: The ventilation effect real-time adjustment module includes a fan speed adjustment submodule, a positive pressure setting adjustment submodule and a ventilation parameter analysis submodule, wherein: Fan speed adjustment submodule: Based on the performance monitoring records, extract the airflow data inside and outside the vehicle and the fan operation status data, read the current fan speed in real time and compare the current air pressure inside the vehicle with the external air pressure, analyze the impact of the air pressure difference on the ventilation demand, calculate the fan speed adjustment amount in combination with the real-time toxic gas concentration, determine the adjusted fan operation parameters, and generate the fan speed adjustment result; Positive pressure setting adjustment submodule: Based on the fan speed adjustment result, a fuzzy control method is used to extract the positive pressure maintainer operation data, and the positive pressure maintainer output parameters are analyzed by calculating the positive pressure difference in the vehicle and the change of the external environmental pressure, combined with the fan speed adjustment amount, and the operation mode and control logic of the positive pressure maintainer are adjusted to generate positive pressure setting parameters; Ventilation parameter analysis submodule: Based on the positive pressure setting parameters, extract the real-time ventilation effect related data and the air flow change state in the vehicle, calculate the combined effect of positive pressure and fan speed on the air flow in the vehicle, extract the air exchange rate and air flow distribution data in the vehicle, and output the ventilation parameter adjustment.

8. The positive pressure gas filtering and ventilation control system for a riot control and dispersal vehicle according to claim 7 is characterized in that: The extraction of the positive pressure maintainer operation data requires the collection of the maintainer's output pressure value, target pressure setting value, real-time output power, pressure adjustment range and valve opening and closing state. The output pressure is collected in real time by a pressure sensor, and the target pressure and adjustment range are extracted in combination with the setting value. The current power output is recorded, and the valve opening and closing angle and action state are recorded at the same time. All data are synchronously extracted through the real-time control system of the positive pressure maintainer. The extraction of real-time ventilation effect related data needs to include pollutant concentration, temperature, humidity, air pressure, air flow speed and direction. The pollutant concentration is obtained through an air quality sensor, the temperature and humidity distribution is obtained through a temperature and humidity sensor, the air pressure difference inside and outside the vehicle is recorded through an air pressure sensor, the flow speed and direction data of different areas in the vehicle are obtained through an air flow sensor, and the air flow distribution characteristics in the vehicle are comprehensively analyzed in combination with the operating status of the fan and the air valve.

9. The positive pressure gas filtering and ventilation control system for a riot control and dispersal vehicle according to claim 1, characterized in that: The ventilation path optimization module includes an airflow distribution simulation submodule, a fan power adjustment submodule and a valve state optimization submodule, wherein: Airflow distribution simulation submodule: Based on the ventilation parameter adjustment, extract the space layout data in the vehicle and the physical position relationship between the fan and the air valve, read the real-time ventilation status data and combine it with the temperature and humidity distribution in the vehicle, analyze the air flow trajectory of each area in the vehicle, identify the air flow obstruction area and optimize the airflow path, and generate the airflow distribution path; Fan power adjustment submodule: extracts fan power output data based on the airflow distribution path, adjusts the fan power output level and operation mode by comparing the airflow distribution uniformity and air exchange rate of each area, optimizes the fan operation parameters to improve the airflow distribution, and generates fan output power parameters; Valve state optimization submodule: Based on the fan output power parameters, extract the real-time opening and closing state data of the valve, analyze the linkage relationship between the valve opening and closing and the fan output, optimize the airflow channel by adjusting the valve opening angle and dynamic switching strategy, establish a ventilation path optimization plan, and output the airflow optimization path.

10. A positive pressure gas filtering and ventilation control method for a riot control and dispersal vehicle, applied to the positive pressure gas filtering and ventilation control system for a riot control and dispersal vehicle as claimed in any one of claims 1 to 9, characterized in that: The method comprises the following steps: Step 1: Based on the output data of chemical sensors and optical sensors, extract the chemical reaction signals and optical absorption signals of the poisonous gas, analyze their characteristic values, and combine the chemical reaction time, conductivity and light absorption spectrum to classify the types of poisonous gas and quantify the concentration to generate gas characteristic data; Step 2: Based on the gas characteristic data, combined with the chemical composition category and concentration of the poisonous gas, the reaction performance of the poisonous gas and the filter material is analyzed, the adsorption adaptability of the activated carbon layer to organic poisonous gas, the efficiency of the HEPA layer to particulate matter filtration and the removal capacity of the ionosphere to chemical reaction poisonous gas are analyzed, and the priority and operation timing rules of each layer of filter units are dynamically adjusted through the gradient boosting decision tree, the filtering tasks are allocated and the required units are activated, and the filtering configuration plan is generated; Step 3: Based on the filtering configuration scheme, the current operating load, task priority and toxic gas characteristics of the filtering unit are dynamically analyzed. By judging the saturation and chemical adaptability of the filtering unit, the filtering unit is started or shut down according to the task priority, the operating time of the low-load unit is extended, and the operating time of the high-load unit is reduced. The working sequence is optimized and dynamic switching is completed to generate an operation optimization decision; Step 4: Based on the operation optimization decision, collect the adsorption saturation, current working temperature and filtration time of each filter unit, determine the remaining capacity of the adsorption layer according to the chemical reaction rate, record the filtration saturation and operation efficiency of the HEPA layer for particulate matter, dynamically adjust the thermal impact of the high-temperature filter layer, record the current performance status of all layers, and generate performance monitoring records; Step 5: Based on the performance monitoring record, by analyzing the pressure difference between the inside and outside of the vehicle, the fan output speed and the current power of the positive pressure maintainer, the fan speed is dynamically adjusted to stabilize the positive pressure state, and the power level of the positive pressure maintainer is adjusted according to the fuzzy control method combined with the positive pressure output range, the ventilation effect is optimized and the fan and the positive pressure device are synchronously adjusted to generate ventilation parameter adjustment; Step six: Based on the ventilation parameter adjustment, analyze the linkage relationship between the fan output power and the airflow distribution, calculate the adaptive adjustment plan of the airflow path through the air pressure distribution at different positions in the vehicle, dynamically adjust the opening and closing angle of the air valve and the fan output power, perform real-time correction on the distribution area of ​​the airflow in the vehicle, and generate an optimized airflow path.

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