Micro-piezoelectric oscillation type mustard gas supply device for toxic agent detection and calibration

Abstract

The invention provides a micro-piezoelectric oscillation type mustard gas supply device for toxic agent detection and calibration, and belongs to the technical field of toxic agent detection.The micro-piezoelectric oscillation type mustard gas supply device comprises a dynamic generation module, a gas mixing cabin module, a flow control module, a temperature control module, a pressure control module and an interaction control module, and a control chip is arranged in the dynamic generation module; the control chip is internally provided with a dynamic generation control module, and is characterized in that the dynamic generation module comprises a liquid tank chamber assembly, a circuit driving assembly, a micro-piezoelectric device and a spray head substrate, the micro-piezoelectric device converts an electric signal into mechanical oscillation, and the spray head substrate releases liquid mustard gas in an aerosol form through a micropore nozzle; the dynamic generation control module is used for calculating parameters required by micro-droplet formation through a Laplace pressure equation and analyzing a liquid jet fracture process in combination with a Kelvin Helmholtz interface instability model, so that high-precision stable generation and precise control of the mustard gas standard gas concentration are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of toxic agent detection, and in particular relates to a micro-piezoelectric oscillation mustard gas supply device for toxic agent detection calibration. Background Art

[0002] In the fields of national defense security, environmental monitoring, and toxic agent detection, mustard gas, as a representative substance of highly toxic chemical weapons, requires a high-precision standard gas source for the calibration and calibration of detection equipment in the development of its monitoring and protection technology. Traditional methods for producing mustard gas standard gas mainly rely on static gas distribution and inflation methods. These methods inject a known amount of mustard gas liquid into a standard container and use the volatilization principle to obtain a gas sample of a specific concentration. In addition, Figure 10 As shown, common dynamic generation technologies such as the permeation tube method and the capillary tube method achieve gas generation by controlling the cavity temperature to adjust the liquid vapor pressure.

[0003] However, these traditional technologies have significant drawbacks in practical applications. The static gas distribution method is limited by pipeline adsorption and cabin wall adhesion effects, resulting in significant deviations between the actual concentration and the theoretical calculated value. The inflation method struggles to precisely control the concentration and suffers from poor concentration stability. The permeation tube and capillary tube methods, which rely on temperature control to achieve gas release, suffer from response lags, long response times, and large concentration fluctuations, making them unsuitable for high-precision detection and calibration. In other words, the existing technology faces the technical challenge of achieving high-precision and stable generation of mustard gas standard concentrations. Summary of the Invention

[0004] In view of this, the present invention provides a micro-piezoelectric oscillation mustard gas supply device for toxic agent detection and calibration, which can solve the technical problem in the prior art that it is difficult to achieve high-precision and stable generation of mustard gas standard gas concentration.

[0005] The present invention is implemented as follows: The present invention provides a micro-piezoelectric oscillation mustard gas supply device for toxic agent detection and calibration. The dynamic generation module includes a liquid tank chamber component, a circuit drive component, a micro-piezoelectric device and a nozzle substrate. The micro-piezoelectric device converts electrical signals into mechanical oscillations, and the nozzle substrate releases liquid mustard gas in the form of an aerosol through a microporous nozzle; the dynamic generation control module calculates the parameters required for micro-droplet formation through the Laplace pressure equation, and combines the Kelvin-Helmholtz interface instability model to analyze the liquid jet fracture process, thereby achieving high-precision, stable generation and precise control of the mustard gas standard gas concentration.

[0006] Among them, the dynamic generation control module is used to perform the following steps: system initialization and environmental parameter regulation; fluid dynamics calculation and atomization parameter generation; micro-piezoelectric oscillation drive and aerosol generation; multi-physics field coupling monitoring and feedback adjustment; concentration closed-loop control and mass balance maintenance; system status evaluation and process optimization.

[0007] Among them, the system initialization and environmental parameter control steps specifically initialize the system parameters and set the target concentration value of mustard gas generation, detect the standard liquid temperature of mustard gas in the liquid tank chamber and the pressure of the mixing chamber, and adjust the working environment to the optimal working state through the temperature control component and the flow control module.

[0008] Among them, the fluid dynamics calculation and atomization parameter generation steps are specifically to calculate the standard mustard gas liquid injection volume required for the target concentration based on the Laplace pressure equation, and to generate the optimal driving frequency parameters of the micro piezoelectric device based on the Rayleigh liquid jet instability theory.

[0009] Among them, the micro-piezoelectric oscillation drive and aerosol generation steps specifically send a drive signal to the circuit drive component to control the micro-piezoelectric device to oscillate according to the calculated frequency, and use the inverse effect of the piezoelectric material to convert electrical energy into mechanical energy to achieve precise atomization of the standard liquid.

[0010] Among them, the multi-physical field coupling monitoring and feedback adjustment step specifically monitors the spray status of the nozzle substrate in real time and collects aerosol distribution data, applies the Kelvin Helmholtz interface instability model to analyze the micro-droplet formation process, and dynamically adjusts the driving parameters through the feedback control algorithm.

[0011] Among them, the concentration closed-loop control and mass balance maintenance steps specifically include continuously monitoring the mustard gas concentration at the outlet of the mixing chamber and comparing it with the target concentration, and using a predictive control algorithm to adaptively adjust the driving frequency of the micro-piezoelectric device to ensure long-term stable output.

[0012] Among them, the system status assessment and process optimization steps are specifically to conduct real-time assessment of the system operation status based on the state transition network model, optimize working parameters to adapt to environmental changes, and execute system safety shutdown and cleaning procedures when the preset working time is reached or the abnormal state triggers the protection mechanism.

[0013] The Laplace pressure equation is used to describe the relationship between the pressure difference and the surface curvature under the action of liquid surface tension. The Laplace pressure equation is used in the atomization system to accurately calculate the minimum surface energy required for the formation of micro-droplets.

[0014] Among them, the dynamic generation control module further adopts a two-layer game theory model to optimize the mustard gas concentration control strategy. The two-layer game theory model is composed of an upper-layer resource allocation game model and a lower-layer operation parameter game model; the upper-layer resource allocation game model serves as the leader, responsible for macro resource allocation and working mode selection, and the objective function of the upper-layer resource allocation game model is to maximize the long-term stability and calibration accuracy of the system, while minimizing energy consumption and standard liquid usage; the lower-layer operation parameter game model serves as the follower, responsible for micro parameter regulation and real-time response, and the objective function of the lower-layer operation parameter game model is to minimize the deviation between the target concentration and the actual concentration, while maximizing the system response speed.

[0015] The Kelvin-Helmholtz interface instability model is used to analyze the wave characteristics during the breakup of liquid jets and the formation of microdroplets. The Kelvin-Helmholtz interface instability model determines the most unstable wavelength and growth rate by solving the dispersion relationship equation.

[0016] The upper-level resource allocation game model and the lower-level operating parameter game model achieve overall optimal control through coupling constraints and information interaction, and iteratively solve until a Nash equilibrium state is reached. At this time, the upper-level resource allocation strategy and the lower-level operating parameter strategy reach local optimality under their respective constraints.

[0017] The system cleaning steps include turning off the micro-piezoelectric device drive signal, opening the flow controller valve to the disinfection state, passing clean gas to purge all pipelines for 90 seconds, and closing the flow controller valve.

[0018] The micro-piezoelectric oscillation mustard gas supply device proposed in this invention uses the principle of micro-piezoelectric oscillation to directly convert liquid mustard gas into aerosol, which is then mixed with a carrier gas to form a precisely concentrated standard gas. The device calculates the parameters required for microdroplet formation using the Laplace pressure equation and analyzes the liquid jet breakup process using the Kelvin-Helmholtz interface instability model. This allows for precise control of aerosol particle size and distribution, ensuring highly consistent gas concentration. The device employs a two-layer game theory model to optimize and control complex systems under multiple objectives and constraints. The upper-layer resource allocation game model manages macro-resources, while the lower-layer operating parameter game model regulates micro-parameters. Through information exchange and coupled constraints, a Nash equilibrium is achieved, effectively addressing the challenges of unstable concentration, delayed response, and insufficient precision encountered in conventional technologies. The system implements fully closed-loop control, adaptively adjusting operating parameters based on environmental changes to ensure long-term stable output. This invention successfully addresses the technical challenges of generating high-precision and stable standard mustard gas concentrations, achieving a fast-response, precisely controllable, and long-term stable supply of standard mustard gas, providing reliable technical support for the high-precision calibration of toxic agent detection equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a schematic diagram of the overall structure of the micro-piezoelectric oscillation mustard gas supply device of the present invention;

[0020] Figure 2 This is a schematic diagram of the structure of the dynamic generation module of the present invention;

[0021] Figure 3 This is a schematic diagram of the structure of the gas mixing cabin module of the present invention;

[0022] Figure 4 This is a schematic structural diagram of the flow control module of the present invention;

[0023] Figure 5 This is a schematic structural diagram of the temperature control module of the present invention;

[0024] Figure 6 This is a schematic diagram of the micro-hole nozzle structure of the nozzle substrate of the present invention;

[0025] Figure 7 Schematic diagram of a circuit driving component in Example 2 of the present invention;

[0026] Figure 8 This is a diagram of the flowchart of the dynamic generation control module of the present invention.

[0027] Figure 9 This is a partial cross-sectional view of the gas mixing chamber module in Example 2;

[0028] Figure 10 It is a schematic diagram of the structure of the dynamic generation module of the prior art in the background technology. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0030] like Figure 1 FIG. 1 is a flow chart of a micro-piezoelectric oscillating mustard gas supply device for toxic agent detection and calibration provided by the present invention. The method includes the following steps:

[0031] A micro-piezoelectric oscillation mustard gas supply device for toxic agent detection and calibration, comprising a dynamic generation module, a gas mixing chamber module, a flow control module, a temperature control module, a pressure control module and an interactive control module. The dynamic generation module, the gas mixing chamber module, the flow control module, the temperature control module, the pressure control module and the interactive control module are electrically connected to each other. A control chip is provided in the dynamic generation module, and a dynamic generation control module is provided in the control chip. The dynamic generation module is used to generate mustard gas standard gas, the gas mixing chamber module is used to achieve effective and precise mixing of standard gas and distribution gas, the flow control module is used to achieve precise control of the flow of standard gas, the temperature control module is used to achieve precise control and feedback of the temperature of standard gas, the pressure control module is used to achieve precise control and feedback of the pressure of standard gas, and the interactive control module is used to receive manually input instructions and execute operations.

[0032] like Figure 2 As shown, the dynamic generation module includes a liquid tank chamber assembly, a circuit driving assembly, a micro piezoelectric device and a nozzle substrate. The liquid tank chamber assembly, the circuit driving assembly, the micro piezoelectric device and the nozzle substrate are interconnected to form an integrated structure. The liquid tank chamber assembly is used to contain mustard gas standard liquid, the circuit driving assembly is used to provide a driving signal to the micro piezoelectric device, the micro piezoelectric device is used to convert the electrical signal into mechanical oscillation, and the nozzle substrate is used to release liquid mustard gas in the form of an aerosol through a microporous nozzle.

[0033] like Figure 3 As shown, the mixing chamber module is used to achieve effective and precise mixing of mustard gas standard gas and distribution gas. The mixing chamber module is designed for airtightness with reference to GB / T 6455-2020 "National Standard for Container Airtightness Testing". The mixing chamber module includes a cavity, a gas inlet connector, a gas outlet connector and a sealing assembly. The cavity, the gas inlet connector, the gas outlet connector and the sealing assembly are interconnected to form a sealing structure.

[0034] The flow control module includes a system solenoid valve group and a corrosion-resistant mass flow controller group. The system solenoid valve group includes 3 groups of solenoid valves, and the corrosion-resistant mass flow controller group includes 7 groups of flow controllers. The system solenoid valve group is used to control the on-off of the gas circuit, and the corrosion-resistant mass flow controller group is used to accurately control the gas flow.

[0035] The temperature control module includes a corrosion-resistant system temperature sensor group, a system temperature and humidity sensor group, a system temperature sensor group, an ambient temperature and humidity sensor group, a system temperature control component group and a system radiator group. The corrosion-resistant system temperature sensor group includes 4 groups of sensors, the system temperature and humidity sensor group includes 3 groups of sensors, the system temperature sensor group includes 3 groups of sensors, the ambient temperature and humidity sensor group includes 2 groups of sensors, the system temperature control component group includes 3 groups of heating components, and the system radiator group includes 2 groups of radiators.

[0036] The pressure control module includes a corrosion-resistant system pressure sensor group and an environmental pressure sensor group. The corrosion-resistant system pressure sensor group includes one group of sensors, and the environmental pressure sensor group includes two groups of sensors. The pressure control module is used to achieve precise control and feedback of the pressure of mustard gas standard gas.

[0037] The interactive control module is used to receive manual input instructions and perform corresponding operations. The interactive control module is electrically connected to the dynamic generation module, the mixing chamber module, the flow control module, the temperature control module and the pressure control module.

[0038] like Figure 8 As shown, the control chip is arranged in the dynamic generation module, and the control chip is electrically connected to the mixing chamber module, the flow control module, the temperature control module, the pressure control module and the interactive control module respectively, and the control chip is used to control the operation of the entire system.

[0039] The dynamic generation control module is provided in the control chip, and is used to perform the following steps:

[0040] S01. System initialization and environmental parameter control: Initialize system parameters and set the target concentration of mustard gas. Detect the standard mustard gas liquid temperature in the liquid tank chamber and the pressure of the mixing chamber. Adjust the working environment to the optimal working state through the temperature control component and flow control module.

[0041] S02. Fluid dynamics calculation and atomization parameter generation: Calculate the standard mustard gas liquid injection volume required for the target concentration based on the Laplace pressure equation, and generate the optimal driving frequency parameters of the micro-piezoelectric device based on the Rayleigh liquid jet instability theory;

[0042] S03. Micro-piezoelectric oscillation drive and aerosol generation: Sending a drive signal to the circuit drive component controls the micro-piezoelectric device to oscillate according to the calculated frequency, using the inverse effect of the piezoelectric material to convert electrical energy into mechanical energy, achieving precise atomization of the standard liquid;

[0043] S04, Multi-physics field coupling monitoring and feedback adjustment: Real-time monitoring of the nozzle substrate spraying status and collection of aerosol distribution data, application of the Kelvin-Helmholtz interface instability model to analyze the micro-droplet formation process, and dynamic adjustment of driving parameters through feedback control algorithms;

[0044] S05, Concentration Closed-Loop Control and Mass Balance Maintenance: Continuously monitor the mustard gas concentration at the mixing chamber outlet and compare it with the target concentration. Adopt a predictive control algorithm to adaptively adjust the micro-piezoelectric device drive frequency to ensure long-term stable output.

[0045] S06. System status assessment and process optimization: Based on the state transition network model, the system operation status is evaluated in real time, and the working parameters are optimized to adapt to environmental changes. When the preset working time is reached or the abnormal state triggers the protection mechanism, the system safety shutdown and cleaning procedures are executed.

[0046] The Laplace pressure equation is used to describe the relationship between the pressure difference and the surface curvature under the action of liquid surface tension. The Laplace pressure equation is used in the atomization system to accurately calculate the minimum surface energy required for the formation of micro-droplets. The input of the Laplace pressure equation includes the liquid surface tension coefficient σ, the liquid density ρ, the nozzle diameter D, and the ambient temperature T. The output of the Laplace pressure equation is the critical pressure difference ΔP required to form stable droplets. crit , the calculation formula of the Laplace pressure equation is:

[0047]

[0048] Among them, r eq is the equilibrium droplet radius, D eq is the equilibrium droplet diameter, the droplet volume V d With D eq The relationship is:

[0049]

[0050] The Kelvin Helmholtz interface instability model is used to analyze the wave characteristics of liquid jet breakup and micro-droplet formation. The Kelvin Helmholtz interface instability model determines the most unstable wavelength and growth rate by solving the dispersion relationship equation. The input of the Kelvin Helmholtz interface instability model includes the liquid density ρ l , gas density ρ g , relative velocity U, surface tension σ, liquid viscosity μ l , the output of the Kelvin-Helmholtz interface instability model is the liquid jet breakup frequency f break With the droplet size distribution function P(D), the core equation of the Kelvin-Helmholtz interface instability model is:

[0051]

[0052] Among them, ω is the fluctuation growth rate, k = 2π / λ is the wave number, λ is the wavelength, and the wave number k corresponding to the most unstable wavelength is max Determines the main size distribution of droplets.

[0053] In the system state assessment and process optimization steps, the Dijkstra shortest path algorithm is used to optimize and make decisions on system parameters. The Dijkstra shortest path algorithm represents each state of the system as a node in graph theory, and the state conversion cost is represented as the weight of the edge. By constructing a weighted directed graph and solving the single-source shortest path problem, the optimal parameter adjustment path from the current state to the target state is determined, and the energy consumption and concentration fluctuations during the adjustment process are minimized. The Dijkstra shortest path algorithm has global optimization capabilities in complex multi-parameter systems and is particularly suitable for aerosol generation control systems that require multi-objective trade-offs.

[0054] The dynamic generation control module further adopts a two-layer game theory model to optimize the mustard gas concentration control strategy. The two-layer game theory model is composed of an upper-layer resource allocation game model and a lower-layer operating parameter game model. The upper-layer resource allocation game model and the lower-layer operating parameter game model achieve overall optimal control through coupling constraints and information interaction. The two-layer game theory model is suitable for complex system optimization control under multi-objective and multi-constraint conditions.

[0055] The upper-level resource allocation game model acts as the leader, responsible for macro-resource allocation and working mode selection. The objective function of the upper-level resource allocation game model is to maximize the long-term stability and calibration accuracy of the system, while minimizing energy consumption and standard liquid usage. The upper-level resource allocation game model obtains the resource allocation strategy matrix by solving the optimization problem. The objective function of the upper-level resource allocation game model comprehensively considers four key indicators: system stable operation time, concentration accuracy, energy efficiency and standard liquid consumption. The constraints of the upper-level resource allocation game model include total power limit, liquid storage limit, minimum concentration accuracy requirement and maximum continuous working time requirement.

[0056] The lower-level operating parameter game model acts as a follower, responsible for micro-parameter regulation and real-time response. The objective function of the lower-level operating parameter game model is to minimize the deviation between the target concentration and the actual concentration, and at the same time maximize the system response speed. The lower-level operating parameter game model receives the resource constraints allocated by the upper-level resource allocation game model, and obtains the optimal micro-piezoelectric drive parameters and flow control parameters by solving the multi-parameter nonlinear optimization problem. The objective function of the lower-level operating parameter game model comprehensively considers four dynamic indicators: the sum of squares of concentration deviations, concentration volatility, parameter adjustment rate, and system steady-state error. The constraints of the lower-level operating parameter game model include the safe operating range of the micro-piezoelectric device, the adjustment range of the flow controller, the temperature change rate limit, and the pressure fluctuation limit.

[0057] The mathematical expression of the objective function of the upper-level resource allocation game model is:

[0058]

[0059] Among them, X u is the upper-level decision variable vector, which includes the power allocation coefficient α, the liquid supply rate β, the working mode selection variable γ and the backup resource ratio δ; T stable is the system stable operation time function; P accuracy is the concentration accuracy function; E consumption is the energy consumption function; V liquid is the liquid consumption function; ω1, ω2, ω3, ω4 are the weight coefficients of each target, satisfying And ω i >0.

[0060] The constraints of the upper-level resource allocation game model are expressed as follows:

[0061] g1(X u ):

[0062] g2(X u ):β·T work ≤V total ;

[0063] g3(X u ):P accuracy (X u )≥P min ;

[0064] g4(X u ):T work ≤T max ;

[0065] Among them, P i is the rated power of the i-th component, Pmax is the maximum power limit of the system, T work is working time, V total is the total liquid reserves, P min For the minimum accuracy requirement, T max The maximum continuous working time limit.

[0066] The mathematical expression of the objective function of the lower layer operation parameter game model is:

[0067]

[0068] Among them, X l is the lower-level decision variable vector, which includes the micro-piezoelectric driving frequency f, driving voltage V, flow control valve opening θ and mixing ratio η; C target is the target concentration; C actual is the actual concentration; is the concentration variance function; R response is the response rate function; E steady is the steady-state error function; λ1, λ2, λ3, λ4 are the weight coefficients of each target, satisfying And λ i >0.

[0069] The constraints of the lower-level operating parameter game model are expressed as follows:

[0070] h1(X l ):f min ≤f≤f max ;

[0071] h2(X l ):V min ≤V≤V max ;

[0072]

[0073] h5(X l , X u ):X l ∈Ω(X u );

[0074] Among them, f min and f max is the micro piezoelectric driving frequency range, V min and V max is the driving voltage range, ΔT max is the maximum temperature change rate limit, ΔP max is the maximum pressure change rate limit, Ω(X u ) is the upper-level decision variable vector X u Determine the feasible domain.

[0075] The upper-layer resource allocation game model and the lower-layer operation parameter game model achieve information exchange through the following coupling relationship:

[0076] X l ∈Ω(X u ):Ω(X u )={X l |G(X l , X u )≤0};

[0077]

[0078] Among them, G(X l , X u ) is the coupling constraint function, Given the upper decision variable vector X u The optimal solution of the current bottom-level operation parameter game model, F u (X u ) is the objective function of the upper-level resource allocation game model.

[0079] The two-layer game theory model iteratively solves the optimization problem of the upper-layer resource allocation game model and the lower-layer operating parameter game model until a Nash equilibrium state is reached. At this time, the upper-layer resource allocation strategy and the lower-layer operating parameter strategy each reach a local optimum under their respective constraints, and neither layer can obtain a better target value by adjusting the strategy alone, and the system as a whole reaches a balanced operating state.

[0080] like Figure 6 As shown, the preset temperature range of the mustard gas standard liquid is 25℃~35℃; the preset pressure range of the mixing chamber is 101.3kPa~105kPa; the diameter D of the micro-hole nozzle on the nozzle substrate is 5.0μm; the driving voltage range is 0V~5V; the working flow rate is 0.17m / s; the main physical and chemical parameters of the mustard gas standard liquid are: density 1.15g / cm 3 , surface tension 30 mN / m, viscosity 1.0 mPa·s;

[0081] The system cleaning steps include: turning off the micro-piezoelectric device drive signal, opening the flow controller valve to the decontamination state, passing clean gas to purge all pipelines for 90 seconds, and closing the flow controller valve;

[0082] Among them, the Rayleigh liquid jet instability theory is a theory that describes the physical process of a liquid jet breaking into droplets under the action of surface tension; the state transition network model is a mathematical model that describes the changes and transformations of the system state; the Nash equilibrium state is the state in which the strategy combinations of all participants in game theory reach equilibrium, and in the Nash equilibrium state, any participant will not gain greater benefits by changing their strategy alone.

[0083] The Laplace pressure equation plays a key role in accurately predicting the liquid surface morphology in micro-piezoelectric oscillating mustard gas delivery devices. By rigorously describing the relationship between liquid surface tension and curvature radius, the equation effectively calculates the critical pressure difference required to form stable microdroplets, thus providing a theoretical basis for the aerosol generation process. In practical applications, the Laplace equation enables the system to precisely control droplet size based on the physical properties of the mustard gas standard liquid, ensuring that the generated aerosol particle distribution meets the stringent requirements of detection calibration. This significantly improves concentration stability and repeatability, which are difficult to achieve with traditional osmotic or thermal evaporation methods.

[0084] The method of generating the optimal driving frequency parameters for micro-piezoelectric devices based on Rayleigh's theory of liquid jet instability has significantly improved the controllability of aerosol generation. By analyzing the physical process of a liquid jet breaking into droplets under the action of surface tension, the system can accurately calculate the most unstable wavelength and the liquid jet breakup frequency, thereby determining the optimal driving frequency of the micro-piezoelectric device. Compared with traditional empirical methods or trial-and-error methods, this theoretically guided parameter generation method makes the size distribution of aerosol particles more concentrated and the particle size consistency significantly improved. This is directly reflected in the uniform and stable concentration of the standard gas, and the concentration fluctuation is significantly reduced, providing a more reliable calibration benchmark for toxic agent detection equipment.

[0085] The two-layer game theory model achieves the rational allocation of system resources and precise adjustment of control parameters by dividing the system control strategy into two levels: upper-level resource allocation and lower-level parameter regulation. The upper-level game model, as the leader, is responsible for macro-resource planning and work mode selection, optimizing long-term stability and energy consumption; the lower-level game model, as the follower, is responsible for micro-parameter adjustment and real-time response, optimizing concentration accuracy and system response speed. This hierarchical decision-making mechanism has significant advantages over traditional single control strategies. Through the coordinated interaction of the two-layer strategy, the system can find the optimal performance balance under resource constraints while maintaining the ability to adapt to complex environmental changes, greatly improving the stability and efficiency of the system during long-term operation.

[0086] The state transition network model discretizes the system's operating state space through graph theory, establishing transition relationships and a cost assessment system between state nodes. This model enables the system to predict the state change paths resulting from different operational decisions and, through a shortest path algorithm, find the optimal state transition sequence, thereby minimizing energy consumption and concentration fluctuations while ensuring system stability. Compared to traditional fixed-rule control, the state transition network model possesses adaptive decision-making capabilities, dynamically adjusting control strategies based on real-time conditions, effectively addressing uncertainties in complex operating conditions and improving system robustness and reliability.

[0087] like Figure 10As shown, in conventional techniques, the target substance to be generated is generated by the oscillation of a micro-piezoelectric device, which then permeates the nozzle substrate membrane. This is combined with an appropriate carrier gas flow to achieve calibration gas generation. Specifically, a standard liquid of known purity is placed in the liquid chamber of the dynamic gas distribution module. A circuit driver controls the oscillation of the micro-piezoelectric device, enclosed by a special permeable membrane, to emit a trace amount of the target substance to be measured through the nozzle substrate. After purging with the standard gas, relatively controllable dynamic production of the standard target substance is achieved. However, these conventional techniques fail to achieve precise regulation. The device of the present invention, however, forms a complete intelligent control system by integrating multiple mathematical models within the control chip. This system constructs a multi-level, multi-scale control framework, from the microscopic droplet formation mechanism to the macroscopic system resource allocation. Compared to conventional toxic agent standard gas generation devices that rely on empirical parameters or simple closed-loop control, this multi-model fusion-based control strategy significantly improves the system's accuracy, stability, and intelligence, achieving efficient utilization of system resources and adaptive optimization, providing more reliable technical support for the calibration of toxic agent detection equipment.

[0088] Specifically, the principle of the present invention is as follows: The core principle of the present micro-piezoelectric oscillation mustard gas supply device, which can solve the technical problems of high-precision stable generation and precise control, lies in the innovative combination of micro-electromechanical system (MEMS) technology and fluid dynamics theory, achieving efficient and precise conversion and control from liquid to gas. Specifically, the micro-piezoelectric oscillation principle adopted by the device utilizes the characteristic of piezoelectric materials to produce deformation under the action of an electric field. The circuit drive component provides an electrical signal with the optimal driving frequency and amplitude, causing the micro-piezoelectric device to produce precise mechanical oscillations, thereby driving the liquid mustard gas through the micro-pore nozzle to form uniform micro-droplets.

[0089] The device's core control algorithm is based on the Laplace pressure equation and the Kelvin-Helmholtz interface instability model. The former accurately calculates the critical pressure difference required for microdroplet formation, while the latter analyzes the fluctuation characteristics of the liquid jet breakup and microdroplet formation process. Combining these two methods determines the optimal micro-piezoelectric drive frequency, ensuring uniform droplet size distribution and consistent gas concentration. This approach overcomes the limitations of traditional temperature-controlled evaporation and enables direct control of the gas generation process.

[0090] The dual-layer game theory model employed in this invention is a key mechanism for achieving high-precision control. The upper-layer resource allocation game model optimizes resource allocation by comprehensively considering four indicators: system stable operating time, generated concentration accuracy, energy efficiency, and standard liquid consumption. The lower-layer operating parameter game model, subject to the upper-layer constraints, adjusts microscopic parameters by minimizing the deviation between target and actual concentrations and maximizing system response speed. The two-layer model achieves information exchange through a coupling relationship, iterating the solution until a Nash equilibrium is reached, forming an adaptive, self-optimizing closed-loop control system.

[0091] like Figure 4 As shown, the overall design of the device conforms to the basic principles of fluid mechanics and analytical chemistry. The mixing chamber module is designed according to national standards to ensure airtightness. The flow control module and pressure control module, together with the temperature control module, form a complete multi-parameter coordinated control system. The system uses the Dykstra shortest path algorithm for parameter optimization and decision-making. The system represents each state as a graph node and solves the single-source shortest path problem to determine the optimal parameter adjustment path, effectively reducing energy consumption and concentration fluctuations.

[0092] In summary, the present invention solves the problem of high-precision and stable generation of mustard gas standard gas concentration in principle by combining micro-piezoelectric oscillation technology with advanced control algorithms, significantly improving the accuracy and reliability of toxic agent detection calibration.

[0093] A specific embodiment 1 of the present invention is provided below. The specific implementation of each step in this embodiment 1 is described in detail as follows.

[0094] The hardware part of the micro-piezoelectric oscillation mustard gas supply device used for toxic agent detection and calibration consists of six main modules, namely, a dynamic generation module, a mixing chamber module, a flow control module, a temperature control module, a pressure control module and an interactive control module, among which the dynamic generation module is the core component of the present invention.

[0095] The dynamic generator module primarily consists of a liquid tank chamber assembly, a circuit drive assembly, a micro-piezoelectric device, and a nozzle substrate. The liquid tank chamber assembly is constructed of 316L stainless steel, offering excellent corrosion resistance and high-temperature strength. A 2% to 3% addition of molybdenum further enhances its corrosion resistance to reducing salts and various inorganic and organic acids, bases, and salts. The liquid tank chamber is used to store standard mustard gas liquid, with a maximum capacity of 1.5 mL. The tank cover is equipped with a modified PTFE gasket filled with 25% glass fiber, resulting in a compression ratio of 15% to 20%. The system offers a temperature resistance range of -200°C to 260°C and excellent chemical compatibility, capable of withstanding highly corrosive media with a pH of 0 to 14. The sealing structure of the liquid tank chamber and the liquid tank cover adopts a tapered pipe thread sealing design. The thread processing adopts CNC whirlwind milling technology with an accuracy of IT7 level. At the same time, it adopts an end face sealing composite structure, that is, a 45° sealing bevel is set at the root of the thread, and a 0.5mm deep annular groove is processed on the end face to accommodate the sealing material, ensuring that the system has no visible leakage within 30 minutes under a pressure of 0.5MPa.

[0096] The circuit driver component is responsible for providing the drive signal for the micro-piezoelectric device. It adopts a synchronous buck converter circuit design with an input voltage of 24V DC, an allowable range of 18-30V, an output voltage of 5V±1%, a maximum output current of 3A, and a switching frequency of 200kHz. The main switching devices are SI 7850DP high-side field-effect transistors and SI 7336ADP low-side field-effect transistors. Both have a withstand voltage of 30V and an on-resistance of 9mΩ and 13mΩ respectively, ensuring that the system voltage regulation rate is less than 1% under full load and the current step response time does not exceed 92μs. In terms of circuit layout, the input capacitor is controlled within 5mm from the field-effect transistor pins, and the switch node area is minimized to less than 20mm. 2 The ground plane adopts a split design, with the power ground and digital ground connected at a single point, and the sampling ground connected to the digital ground via a 0Ω resistor, effectively suppressing the conduction and radiation of switching noise. Regarding thermal design, the FET heatsink pad is constructed of 2oz copper with an area of ​​at least 15mm x 15mm. Ten 0.3mm thermal vias are added to the back copper layer to ensure that the device temperature rise is controlled within 12°C when operating at full load.

[0097] The micro-piezoelectric device is manufactured from piezoelectric ceramic material, doped with 2% niobium ions and 1% manganese ions. This increases the dielectric constant of the material to 4500, reduces the dielectric loss to 0.01, and enhances the electromechanical coupling performance. The mechanical resonance frequency of the micro-piezoelectric device is 12.05kHz, and the designed operating frequency is 10.87kHz, which is 30% lower than the resonance frequency, avoiding mechanical damage caused by resonance. The bottom of the micro-piezoelectric device uses gold-plated electrodes to form a closed loop with the circuit drive component, and the top is in direct contact with the nozzle substrate. When the drive signal is applied, the piezoelectric material deforms, converting electrical energy into mechanical vibration energy, prompting the mustard gas standard liquid in the liquid tank chamber to be ejected through the nozzle substrate.

[0098] The nozzle substrate adopts a multi-layer composite structure design. The bottom layer is a silicon-based material that has been silanized, the middle layer is a polytetrafluoroethylene isolation layer, and the upper layer is a 316L stainless steel film layer. The surface is treated with a diamond-like carbon coating for corrosion resistance. A 5.0μm diameter micro-hole nozzle array is designed on the nozzle substrate. The nozzle adopts a 3:1 aspect ratio design, an inlet cone angle of 60°±10°, and an R2μm chamfer at the outlet to optimize the liquid flow separation characteristics and suppress the generation of satellite droplets. The micro-hole nozzle is manufactured using a laser processing solution. During the processing, a spiral scanning method is used to ensure the smoothness of the hole wall. At the same time, inert gas protection is used to prevent oxidation and debris accumulation. After processing, the aperture, roundness and surface quality are tested by scanning electron microscopy to ensure that the processing accuracy is within the range of ±0.5μm.

[0099] The gas mixing chamber module is made of 316L stainless steel, with an internal volume of 50mL and an inner wall surface roughness of Ra ≤ 0.4μm. It is tested using a white light interferometer. The module design refers to GB / T 6455-2020 "National Standard for Container Air Tightness Testing". The helium mass spectrometer leak detection standard is 1×10 -4 mbar·L / s. The interior of the mixing chamber module is designed with a streamlined structure to reduce dead zones and eddy currents, ensuring thorough mixing of the mustard gas standard gas and the carrier gas. The inlet and outlet are designed with conical diffusion and contraction sections, respectively, to optimize flow field distribution and reduce pressure loss. A vortex-generating structure is also incorporated into the inner wall to promote uniform gas mixing.

[0100] The flow control module includes three sets of system solenoid valves and seven sets of corrosion-resistant mass flow controllers. The system solenoid valves adopt a direct-acting electromagnetic drive structure with a drive voltage of 24V DC, a flow rate of 50 standard liters per minute, an operating pressure difference range of 0.05-0.5MPa, and a single-point leakage rate of less than 1×10 -8 Pa·m 3 / s. The mass flow controller uses the thermal mass flow measurement principle and is available in three specifications: 1L / min, 5L / min, and 10L / min. It has an accuracy of ±1.0% of full scale, a linear accuracy of ±0.5% of full scale, a repeatability of ±0.2% of full scale, and a response time of 2 seconds. All gas-contacting materials of the controller are made of 316L stainless steel and polytetrafluoroethylene to ensure long-term stable operation in corrosive environments.

[0101] like Figure 5 As shown, the temperature control module includes multiple sets of temperature sensors and temperature control components. Four sets of corrosion-resistant system temperature sensors are placed in key gas path locations. They utilize the platinum resistance temperature measurement principle, with a temperature transmission range of -40°C to 125°C and a measurement accuracy of ±0.1°C. The sensor housing is made of stainless steel, and the seal is a perfluoroelastomer O-ring. The system temperature control component consists of a nickel-chromium alloy heater and a thermocouple temperature transmitter. Each set has a heating power of 50W, a temperature control range of room temperature to 200°C, and a temperature measurement accuracy of ±1°C. A fin-structured heat dissipation layer is installed between the heater and the heated object to increase the heat dissipation area. A dual-mode temperature control strategy, combining position control and proportional control, ensures accurate and stable temperature control.

[0102] The pressure control module includes one corrosion-resistant system pressure sensor and two ambient pressure sensors. The corrosion-resistant system pressure sensor is a piezoresistive sensor with a maximum pressure resistance of 1500 kPa, an accuracy of ±3%, and a linearity error of ±2%. The sensor's pressure-sensing diaphragm is made of 316L stainless steel and is laser-welded to ensure a seal in corrosive environments. The ambient pressure sensor has a range of 0 to 120 kPa and an accuracy of ±0.15 kPa. It monitors changes in ambient atmospheric pressure in real time and provides a reference for system pressure control.

[0103] The interactive control module uses an industrial-grade embedded computing platform equipped with an ARM Cortex-A72 quad-core processor with a main frequency of 1.5GHz, 4GB RAM and 32GB eMMC storage. It communicates with each functional module via the RS485 bus with a sampling period of 100ms. The human-machine interface uses a 7-inch capacitive touch screen with a resolution of 1024×600 pixels and a brightness of 400cd / m 2 The system boasts a contrast ratio of 800:1, a viewing angle of 170°, and an LVDS display interface that supports 10-point touch. The system software utilizes a real-time operating system platform and establishes a layered control system, including a hardware abstraction layer, driver layer, communication layer, application layer, and user interface layer, enabling precise control and status monitoring of the entire device.

[0104] Through the coordinated work of the above hardware modules, the micro-piezoelectric oscillation mustard gas supply device of the present invention can stably produce mustard gas standard gas with precise concentration, meeting the strict requirements for the calibration of toxic agent detection equipment. The overall design of the system fully considers key factors such as corrosion resistance, high precision, and high reliability to ensure long-term stable operation in harsh environments.

[0105] The dynamic generation control module is used to perform the following steps:

[0106] The specific implementation of step S01 is based on parameter initialization and environmental control strategy to achieve full parameter initialization and optimal working state establishment of the system. First, the interactive control module receives the mustard gas target concentration value C input by the operator. target , the value range is 0.01~10.00mg / m 3 ; Then the adaptive parameter initialization algorithm is used to generate the initial system parameter set P init , specifically expressed as follows: P init ={T set , P set , f init , V init ,θ init , η init}; where T set P is the set temperature, the value range is 25~35℃;set is the set pressure, the value range is 101.3~105.0kPa; f init is the initial driving frequency, which is 10.87kHz; V init is the initial driving voltage, which is 3.0V; θ init is the initial flow control valve opening, which is 50%; η init is the initial mixing ratio, which is set to 0.5. Then the actual temperature T of the mustard gas standard liquid in the liquid tank chamber is read by the temperature sensor. actual , and calculate the temperature deviation ΔT=T set -T actual When |ΔT|>2.0℃, the temperature closed-loop control process is triggered and the heating power P is calculated by the proportional integral differential control algorithm. heat , specifically expressed as follows: Where K p is the proportional coefficient, which is 5.0W / ℃; K i is the integral coefficient, which is 0.5W / (℃·s); K d is the differential coefficient, which is set to 1.0W·s / ℃. At the same time, the actual pressure P in the mixing chamber is read by the pressure sensor. actual , calculate the pressure deviation ΔP=P set -P actual When |ΔP|>0.5kPa, the flow control algorithm is called to adjust the flow control valve opening θ, which is specifically expressed as follows: θ=θ init +K θp ΔP+K θi ∫ΔPdt; where K θp is the pressure proportional coefficient, which is 10% / kPa; K θi is the pressure integral coefficient, with a value of 2% / (kPa·s). Through the above environmental parameter control process, the system can reach the preset optimal working state within 180 seconds, laying the foundation for the subsequent precise aerosol generation process.

[0107] The specific implementation of step S02 is based on fluid mechanics theory and numerical calculation methods to achieve accurate mapping from target concentration to micro-droplet injection parameters. First, a relationship model between target concentration and liquid injection volume is established based on the principle of mass conservation, which is specifically expressed as follows: Where m liquid is the total amount of liquid required, in mg; C target is the target concentration in mg / m 3 ;Q gas is the carrier gas flow rate, in m 3 / s;t work is the working time, in seconds; η convis the liquid-to-gas conversion efficiency, dimensionless, ranging from 0.85 to 0.95. Next, the critical pressure difference required for micro-droplet formation is calculated based on the Laplace pressure equation, which is specifically expressed as follows: Where, σ is the surface tension of the liquid, which is 30mN / m; r eq is the equilibrium droplet radius, in m; D eq is the equilibrium droplet diameter in m, which is experimentally determined to be 8.5 μm. Then, combined with the Rayleigh liquid jet instability theory, the droplet volume V is calculated. d The relationship between nozzle diameter D, flow velocity v and physical properties is as follows: Where D is the nozzle diameter, which is 5.0 μm; ρ is the liquid density, which is 1.15 g / cm 3 ; v is the flow velocity, which is 0.17 m / s. Further, calculate the mass m of each microdroplet d , specifically expressed as follows: Based on the number of droplets N required per unit time d , specifically expressed as follows: Finally, calculate the optimal driving frequency f of the micro piezoelectric device opt , specifically expressed as follows: Where n nozzle is the number of nozzles, which is 144. In practical applications, by considering the influence factor ξ of liquid viscosity μ Correct the frequency, specifically expressed as follows: f actual =f opt ·(1+ξ μ ); where k μ is the viscosity correction coefficient, which is set to 0.25; μ is the actual viscosity, which is set to 1.0 mPa·s; and μ0 is the standard viscosity, which is set to 1.0 mPa·s. Through the above fluid dynamics calculation process, an accurate correspondence between the target concentration and the driving parameters of the micro piezoelectric device is achieved.

[0108] The specific implementation of step S03 is based on the piezoelectric effect and current control theory to achieve accurate conversion of the drive signal to mechanical oscillation. First, based on the calculated drive frequency and voltage parameters, a pulse waveform control signal is generated, which is specifically expressed as follows: Where V amp is the pulse amplitude, ranging from 0 to 5V; rect() is the rectangular pulse function; is the pulse period; T width =D duty ·T period is the pulse width; D dutyis the duty cycle, which is 0.4; n is an integer. The control signal is then converted into a drive current through the circuit driver component, which is specifically expressed as follows: Where Z piezo (f) is the complex impedance of the micro piezoelectric device at frequency f, R piezo is the equivalent resistance, which is 50Ω; L piezo is the equivalent inductance, which is 10mH; C piezo is the equivalent capacitance, which is 50nF. The micro piezoelectric device generates a displacement d(t) under the action of the driving current, which is specifically expressed as follows: Where, d 33 is the piezoelectric strain constant, which is 650pm / V; h piezo is the thickness of the piezoelectric device, which is 0.5 mm. Furthermore, the displacement generates a pressure wave P wave (t), specifically expressed as follows: Where, ρ l is the liquid density, which is 1.15 g / cm 3 ;c l The speed of sound in liquid is 1450m / s. The pressure wave acts on the micro-hole nozzle of the nozzle substrate. When the pressure exceeds the critical pressure difference ΔP crit During the micro-piezoelectric oscillation, the energy conversion efficiency is evaluated by monitoring the current and voltage phase difference φ, which is specifically expressed as follows: η energy =cosφ; when η energy When the frequency is less than 0.85, the micro-piezoelectric device's resonance point automatic tracking algorithm is triggered, and the optimal energy conversion is achieved by slightly adjusting the frequency. Through this micro-piezoelectric oscillation drive process, efficient conversion of electrical energy to mechanical energy is achieved, ensuring the stability of aerosol generation.

[0109] The specific implementation of step S04 is based on multi-physics field coupling theory and real-time feedback control strategy to achieve accurate monitoring and regulation of the aerosol generation process. First, a high-speed image acquisition system is used to acquire the spray state image sequence I (x, y, t), with an acquisition frequency of 1000 frames per second and an image resolution of 1024 × 768 pixels. Then, an image processing algorithm is used to extract droplet parameters, including diameter distribution D dist , velocity distribution v dist , quantity N actual and spatial distribution S dist , specifically expressed as follows: D dist ={(D i , p i )|i=1,2,...,n D};v dist ={(v j ,q j)|j=1, 2, ..., n v}; S dist ={(x k ,y k , z k , r k )|k=1,2,...,N actual}; where p i and q j are the probability densities of diameter and velocity respectively; n(t) is the number of droplets per unit time; (x k ,y k , z k ) is the spatial coordinate of the kth droplet; r k is the droplet radius. The Kelvin-Helmholtz interface instability model is then applied to analyze the droplet formation process, which is specifically expressed as follows: Where ω is the fluctuation growth rate; ρ l and ρ g are the densities of liquid and gas respectively; U l and U g are the liquid and gas velocities, respectively; is the wave number; λ is the wavelength; μ l and μ g are the viscosity of liquid and gas respectively. By analyzing the wave characteristics, the most unstable wavelength λ is determined max , specifically expressed as follows: Where k max The wave number that maximizes ω is constructed based on the monitoring results. The state deviation vector ΔX is specifically expressed as follows: Where, and σ D are the mean and standard deviation of the droplet diameter, respectively; and σ v are the mean and standard deviation of the droplet velocity, respectively. Then the feedback control algorithm is applied to calculate the drive parameter adjustment ΔP, which is specifically expressed as follows: ΔP = K·ΔX; where K is the feedback gain matrix, k ij Determined by system identification experiment. Finally, the driving parameters are updated, which are specifically expressed as follows: new =f old +Δf;V new =V old +ΔV; where Δf and ΔV are the two components of ΔP. Through the above multi-physics field coupled monitoring and feedback adjustment process, precise control of the aerosol generation process is achieved.

[0110] The specific implementation of step S05 is based on the concentration closed-loop control theory and adaptive control algorithm to achieve accurate control of mustard gas concentration and long-term stable output. First, the mustard gas concentration C at the outlet of the mixing chamber is monitored in real time by a gas concentration detector. actual (t), the sampling frequency is 1 Hz, and the measurement accuracy is ±0.5%. Then calculate the concentration deviation ΔC(t) = C target -C actual (t) and the rate of change of deviation Based on the deviation data, the concentration state vector is constructed The predictive control algorithm is used to calculate the future concentration trend, which is specifically expressed as follows: Where, and are the estimated values ​​of the first and second derivatives of the concentration, respectively, obtained by Kalman filter, and are expressed as follows: Where γ1 and γ2 are filter gains; ε1(t) and ε2(t) are estimation errors. Based on the prediction results, the dynamic matrix control algorithm is used to calculate the control quantity, which is specifically expressed as follows: Δu(t) = (G T G+λI) -1 G T (Y ref -Y pred ); where G is the system step response matrix; λ is the suppression factor, which is 0.1; Y ref is the reference trajectory; Y pred is the predicted output; Δu(t) is the control increment. The control increment is converted into the micro piezoelectric device drive frequency adjustment Δf(t), which is specifically expressed as follows: Δf(t) = K f Δu(t); where K f is the frequency adjustment gain, which is set to 0.5kHz. When the concentration deviation exceeds the allowable range, that is, |ΔC(t)|>0.03·C target When the duration exceeds 30 seconds, the adaptive control algorithm is triggered to dynamically adjust the control parameters, as shown below: f (t+1)=K f (t)·(1+α·sign(ΔC(t))·|ΔC(t)|); where α is the adaptive coefficient, set to 0.02, and sign() is the sign function. Through this closed-loop concentration control process, precise control and long-term stable output of mustard gas standard gas concentration are achieved, with the relative standard deviation kept within 1.5%.

[0111] The specific implementation of step S06 is based on the state transition network theory and multi-objective optimization strategy to achieve real-time evaluation of the system operation status and process optimization. First, the system state vector S(t) is constructed, which is specifically expressed as follows: S(t) = [T(t), P(t), C(t), f(t), V(t), θ(t), η(t), E(t)] T ; Where T(t) is temperature; P(t) is pressure; C(t) is concentration; f(t) is frequency; V(t) is voltage; θ(t) is valve opening; η(t) is mixing ratio; E(t) is energy consumption. Then a state transition network model is established. The system state space is discretized into a finite number of nodes using graph theory. Each node represents a system state. The edges between nodes represent the state transition path. The edge weight represents the conversion cost. Specifically, it is expressed as follows: G = (V, E, W); Where V = {v i |i=1,2,...,n} is a node set; E={e ij =(v i , v j )|v i , v j ∈V} is the edge set; W={w ij |e ij ∈E} is a weight set. The conversion cost calculation formula is: w ij =α E ·ΔE ij +α T ΔT ij +α C ΔC ij +α S ΔS ij Where, ΔE ij is the energy consumption increment; ΔT ij is the time increment; ΔC ij is the concentration fluctuation; ΔS ij is the impact on system stability; α E , α T , α C and α S is the weight coefficient, which takes values ​​of 0.3, 0.2, 0.4 and 0.1 respectively. The Dykstra shortest path algorithm is applied to solve the optimal state migration path, which is specifically expressed as follows: first initialize the distance array dist[i] = ∞, dist[src] = 0; then select the current minimum distance node u, update its adjacent node distance dist[v] = min(dist[v], dist[u] + w(u, v)); repeat until all nodes are processed. Based on the optimal path, a system parameter adjustment sequence is generated to reduce energy consumption and concentration fluctuations. During the operation of the system, key parameters are continuously monitored and the state evaluation index I is constructed. state , specifically expressed as follows: Istate =β1·I T +β2·I P +β3·I C +β4·I E Where, I T , I P , I C and I E are temperature, pressure, concentration and energy consumption evaluation indicators respectively; β1, β2, β3 and β4 are weight coefficients, and the sum is 1. state <0.6, the system abnormal protection mechanism is triggered; when the system running time reaches the preset value T preset Or, if a shutdown command is received, the system executes a safety shutdown procedure, including shutting off the micro-piezoelectric device drive signal, switching the flow controller valve to the decontamination state, introducing clean gas at a flow rate of 20 L / min to purge all pipes for 90 seconds, and closing the flow controller valve to ensure system safety and residue-free operation. This system status assessment and process optimization process enables real-time monitoring and intelligent management of system operating status, improving equipment operational efficiency and safety.

[0112] In order to better understand and implement the present invention, the following provides an embodiment 2 of a specific application scenario of the present invention: Figure 7 , Figure 9 As shown in the figure, in a chemical defense research laboratory, researchers implemented a micro-piezoelectric oscillation mustard gas supply device to improve the calibration accuracy of toxic agent detection equipment. The device consists of six main modules: a dynamic generation module, a gas mixing chamber module, a flow control module, a temperature control module, a pressure control module, and an interactive control module. The dynamic generation module, as the core component, uses micro-piezoelectric oscillation technology to achieve precise generation of mustard gas standard gas.

[0113] The dynamic generation module in this embodiment is made of 316L stainless steel, with a maximum liquid chamber volume of 1.5mL and a modified polytetrafluoroethylene gasket to ensure system airtightness. The circuit drive component uses a synchronous buck converter circuit with an input voltage of 24V, an output voltage of 5V±1%, and a maximum output current of 3A. The micro-piezoelectric device uses a piezoelectric ceramic material doped with 2% niobium ions and 1% manganese ions. Its characteristic parameters are shown in Table 1:

[0114] Table 1 Main characteristic parameters of micro piezoelectric devices

[0115] Parameter name Parameter value unit Piezoelectric strain constant 650 pm / V Dielectric constant 4500 dimensionless Dielectric loss 0.01 dimensionless Mechanical resonance frequency 12.05 kHz Equivalent resistance 50 Ω Equivalent capacitance 50 nF Equivalent inductance 10 mH Device thickness 0.5 mm

[0116] The nozzle substrate adopts a multi-layer composite structure, the micropore nozzle has a diameter of 5.0μm, an aspect ratio of 3:1, an inlet cone angle of 60°, and an outlet chamfer of R2μm. The physical and chemical parameters of the mustard gas standard liquid are shown in Table 2:

[0117] Table 2 Physical and chemical parameters of mustard gas standard liquid

[0118] Parameter name Parameter value unit density 1.15 <![CDATA[g / cm 3 ]]> surface tension 30 mN / m Viscosity 1.0 mPa·s Speed ​​of sound 1450 m / s Refractive index 1.48 dimensionless Volatility 0.072 <![CDATA[mg / (m 2 ·s)]]>

[0119] The mixing chamber module is molded from 316L stainless steel in one piece, with an internal volume of 50mL and an inner wall surface roughness of Ra ≤ 0.4μm. The flow control module includes three system solenoid valves and seven corrosion-resistant mass flow controllers with an accuracy of ±1.0% of full scale. The temperature control module is equipped with four corrosion-resistant system temperature sensors with a temperature measurement accuracy of ±0.1°C. Each of the three system temperature control components has a heating power of 50W. The pressure control module is equipped with one corrosion-resistant system pressure sensor with a pressure measurement range of 0 to 1500kPa and an accuracy of ±3%.

[0120] In actual application, the interactive control module first receives the mustard gas target concentration value 0.5 mg / m 3 During the system initialization process, the temperature is set to 30°C, the pressure is set to 102.5kPa, the initial driving frequency is 10.87kHz, the initial driving voltage is 3.0V, the initial flow control valve opening is 50%, and the initial mixing ratio is 0.5. After the system is started, the temperature sensor detects that the actual temperature of the mustard gas standard liquid in the liquid tank chamber is 26.3°C, and the calculated temperature deviation is 3.7°C. Since the absolute value of the deviation is greater than 2.0°C, the temperature closed-loop control process is triggered. The system calculates the heating power to be 20.5W based on the proportional-integral-differential control algorithm and starts the corresponding temperature control component. At the same time, the system detects that the actual pressure of the mixing chamber is 103.1kPa, and the calculated pressure deviation is -0.6kPa. Since the absolute value of the deviation is greater than 0.5kPa, the system adjusts the flow control valve opening to 44%.

[0121] After about 90 seconds of adjustment, the system reached a stable working state, with the liquid chamber temperature at 29.8°C and the gas mixture chamber pressure at 102.6 kPa. Entering the fluid dynamics calculation stage, the system first calculated the total amount of liquid required for 30 minutes of operation based on the principle of conservation of mass, which was 10.5 mg. The liquid-gas conversion efficiency was set to 0.92. Using the Laplace pressure equation, the critical pressure difference required to form a stable droplet was calculated to be 14.1 kPa. Applying the Rayleigh liquid jet instability theory, the volume of a single droplet was calculated to be 3.15 aL (3.15×10 -18 L), the droplet diameter is 8.5 μm. Further calculations show that the mass of a single microdroplet is 3.62 pg, and the number of droplets required per unit time is 1.62×10 6 The number of nozzles in the system is 144, and the optimal driving frequency of the micro-piezoelectric device is calculated to be 11.25 kHz. The actual driving frequency after correction of the viscosity influence factor is 11.25 kHz.

[0122] The drive signal waveform parameters generated by the system are shown in Table 3:

[0123] Table 3 Micro piezoelectric device driving signal parameters

[0124] Parameter name Parameter value unit Pulse amplitude 4.2 V Pulse period 88.9 μs Duty cycle 0.4 dimensionless Pulse width 35.6 μs Rise time 2.5 μs Fall time 2.0 μs Drive current peak 84 mA Energy conversion efficiency 0.93 dimensionless

[0125] After initiating the micro-piezoelectric oscillation drive process, the system captured images of the jetting state using a high-speed image acquisition system at a frequency of 1000 frames per second. Image processing and analysis results showed that the average droplet diameter was 8.6 μm, with a standard deviation of 0.4 μm, the average droplet velocity was 0.17 m / s, with a standard deviation of 0.02 m / s, and the droplet count reached 96.5% of the expected value. Applying the Kelvin-Helmholtz interface instability model, the system found that the most unstable wavelength was 29.7 μm, consistent with theoretical expectations. Based on the monitoring results, the system calculated the state deviation vector [0.012, -0.035, 0.047, 0.118]. T , the feedback control algorithm is applied to fine-tune the driving frequency to 11.18kHz and the driving voltage to 4.3V.

[0126] After the system had been running for 15 minutes, the concentration detector showed that the actual concentration of mustard gas at the outlet of the mixing chamber was 0.487 mg / m 3 , the concentration deviation is 0.013mg / m 3 The deviation percentage was 2.6%, which was lower than the preset threshold of 3%. The system continued to operate stably, and the concentration fluctuation range was controlled within ±0.015mg / m 3 During operation, the system status evaluation index always remained above 0.85, far exceeding the abnormal protection threshold of 0.6. After completing the scheduled 30-minute operation, the system automatically executed the safety shutdown procedure, turned off the micro-piezoelectric device drive signal, switched the flow controller valve to the decontamination state, introduced clean gas at a flow rate of 20L / min to purge all pipelines for 90 seconds, and closed the flow control valve. The key performance indicators during system operation are shown in Table 4:

[0127] Table 4 Key performance indicators of system operation

[0128]

[0129]

[0130] Traditional gas generators for toxic gas calibration primarily use the permeation tube method and dynamic dilution method. The permeation tube method relies on constant temperature heating to allow the toxic agent to diffuse naturally through a porous membrane to form gas. This method is difficult to precisely control concentration and has a long response time, typically with a concentration accuracy of ±10% and a response time of 2 to 4 hours to reach stability. The dynamic dilution method achieves concentration adjustment by mixing high-concentration gas with a carrier gas, but this suffers from uneven mixing and adsorption losses, resulting in actual concentration fluctuations of up to ±8%. In contrast, the micro-piezoelectric oscillating mustard gas supply device of this embodiment utilizes direct liquid atomization technology, combined with precise fluid dynamics control and multi-physics field coupled monitoring, to improve concentration accuracy to ±2.6%, shorten response time to 90 seconds, and significantly enhance system stability. Traditional devices often lack real-time feedback control mechanisms, making them difficult to cope with environmental changes and performance drift during long-term operation. However, this embodiment, by introducing a predictive control algorithm and a two-level game theory model, achieves adaptive adjustment and optimal decision-making in the system, ensuring long-term stable operation. This embodiment also offers significant advantages in system safety and liquid utilization, increasing it from the traditional 65% to 92%, significantly reducing the consumption and potential risks of highly toxic liquids. Overall, this embodiment, through the deep integration of micro-piezoelectric technology and advanced control algorithms, successfully solves the challenge of precise concentration control in toxic agent detection calibration, providing a more accurate and reliable calibration method for toxic agent detection equipment.

[0131] It should be noted that the variables involved in the present invention are explained in detail as shown in Table 5 (a) and (b) below.

[0132] Table 5(a) Variable Explanation Table

[0133]

[0134]

[0135] Table 5(b) Variable Explanation Table

[0136]

[0137]

[0138] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A micro-piezoelectric oscillating mustard gas supply device for toxic agent detection and calibration, comprising a dynamic generation module, a gas mixing chamber module, a flow control module, a temperature control module, a pressure control module, and an interactive control module. The dynamic generation module is provided with a control chip, and the control chip is provided with a dynamic generation control module. The device is characterized in that: The dynamic generation module includes a liquid tank chamber component, a circuit drive component, a micro-piezoelectric device and a nozzle substrate. The micro-piezoelectric device converts electrical signals into mechanical oscillations, and the nozzle substrate releases liquid mustard gas in the form of an aerosol through a microporous nozzle; the dynamic generation control module calculates the parameters required for micro-droplet formation through the Laplace pressure equation, and combines the Kelvin-Helmholtz interface instability model to analyze the liquid jet fracture process, thereby achieving high-precision, stable generation and precise control of the standard mustard gas concentration.

2. The device according to claim 1, characterized in that The dynamic generation control module is used to perform the following steps: system initialization and environmental parameter regulation; fluid dynamics calculation and atomization parameter generation; micro-piezoelectric oscillation drive and aerosol generation; multi-physics field coupling monitoring and feedback adjustment; concentration closed-loop control and mass balance maintenance; system state evaluation and process optimization.

3. The device according to claim 2, characterized in that The system initialization and environmental parameter control steps specifically include initializing system parameters and setting the target concentration value for mustard gas generation, detecting the standard mustard gas liquid temperature in the liquid tank chamber and the mixing chamber pressure, and adjusting the working environment to the optimal working state through the temperature control component and the flow control module.

4. The device according to claim 3, characterized in that The fluid dynamics calculation and atomization parameter generation steps specifically calculate the standard mustard gas liquid injection volume required for the target concentration based on the Laplace pressure equation, and generate the optimal driving frequency parameters of the micro piezoelectric device based on the Rayleigh liquid jet instability theory.

5. The device according to claim 4, characterized in that The micro-piezoelectric oscillation drive and aerosol generation step specifically sends a drive signal to the circuit drive component to control the micro-piezoelectric device to oscillate according to the calculated frequency, utilizes the inverse effect of the piezoelectric material to convert electrical energy into mechanical energy, and realizes the precise atomization of the standard liquid.

6. The device according to claim 5, characterized in that The multi-physics field coupling monitoring and feedback adjustment step specifically monitors the spray state of the nozzle substrate in real time and collects aerosol distribution data, applies the Kelvin Helmholtz interface instability model to analyze the micro-droplet formation process, and dynamically adjusts the driving parameters through the feedback control algorithm.

7. The device according to claim 6, characterized in that The concentration closed-loop control and mass balance maintenance steps specifically include continuously monitoring the mustard gas concentration at the outlet of the mixing chamber and comparing it with the target concentration, and using a predictive control algorithm to adaptively adjust the driving frequency of the micro-piezoelectric device to ensure long-term stable output.

8. The device according to claim 7, characterized in that The system status assessment and process optimization steps specifically include real-time assessment of the system operating status based on the state transition network model, optimizing working parameters to adapt to environmental changes, and executing system safety shutdown and cleaning procedures when the preset working time is reached or the abnormal state triggers the protection mechanism.

9. The device according to claim 8, characterized in that The Laplace pressure equation is used to describe the relationship between the pressure difference and the surface curvature under the action of liquid surface tension. The Laplace pressure equation is used in an atomization system to accurately calculate the minimum surface energy required for the formation of micro-droplets.

10. The device according to claim 9, characterized in that The dynamic generation control module adopts a two-layer game theory model to optimize the mustard gas concentration control strategy. The two-layer game theory model consists of an upper-layer resource allocation game model and a lower-layer operation parameter game model. The upper-layer resource allocation game model serves as the leader, responsible for macro-resource allocation and working mode selection. The objective function of the upper-layer resource allocation game model is to maximize the long-term stability and calibration accuracy of the system while minimizing energy consumption and standard liquid usage. The lower-layer operation parameter game model serves as the follower, responsible for micro-parameter regulation and real-time response. The objective function of the lower-layer operation parameter game model is to minimize the deviation between the target concentration and the actual concentration while maximizing the system response speed.

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