Intelligent disinfection equipment gasification control method and system based on low-temperature environment self-adaption

Abstract

The invention provides an intelligent disinfection equipment gasification control method and system based on low-temperature environment self-adaption, and the method comprises the steps: monitoring and obtaining the data of a low-temperature environment where disinfection equipment is located, generating a standard temperature equivalent value, and building a dynamic adjustment factor; generating a gasification rate control parameter and a heating power grading parameter according to the dynamic adjustment factor, synchronously adjusting the gasification device, monitoring atomization particle concentration distribution and diffusion uniformity in the process of adjusting the gasification device, and generating a calibration parameter through reverse correction when the deviation amount reaches a set threshold value, feeding back to a dynamic adjustment factor generation process, forming a closed-loop parameter updating link, calculating a temperature attenuation rate based on the temperature data, and correcting a preheating reserve so that the gasification device meets a preset atomization particle concentration threshold value in a low-temperature fluctuation scene; according to the invention, efficient and stable gasification and uniform atomization particle distribution in a low-temperature environment are realized, and the disinfection effect and adaptability under a complex low-temperature condition are improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of disinfection equipment, and in particular to an intelligent disinfection equipment gasification control method and system based on adaptation to low-temperature environments. Background Art

[0002] In multiple fields such as medical treatment, food processing, and public place sanitation management, the application of disinfection equipment is crucial for preventing disease transmission and ensuring environmental hygiene. Especially in low-temperature environments, such as outdoors in winter or inside cold storage, the need for effective disinfection of air and surfaces is particularly urgent. In such an environment, it is required that the disinfection equipment can adapt to environmental temperature changes, maintain a stable gasification rate and a uniform atomized particle distribution to ensure the disinfection effect;

[0003] Most existing disinfection equipment relies on fixed heating power and preset gasification rate parameters to control the gasification process of disinfectants. These devices usually monitor the temperature and humidity of the surrounding environment and adjust their working modes accordingly. However, they mainly use simple linear compensation methods to cope with changes in environmental temperature, lacking comprehensive consideration of complex environmental factors (such as real-time temperature data gradient change trends, humidity weighted correction, etc.) under low-temperature conditions;

[0004] The main defects of the existing solutions are that they cannot effectively adapt to rapid temperature fluctuations and humidity changes in low-temperature environments, resulting in problems such as low gasification efficiency and uneven atomized particle distribution. Especially in extremely low-temperature situations, due to the lack of a dynamic adjustment factor for standard temperature equivalent values and a humidity weighted correction mechanism based on this, traditional disinfection equipment is difficult to maintain an ideal gasification efficiency range. In addition, the existing technology fails to implement the function of dynamically correcting the preheating reserve in the heating power grading parameters based on the attenuation rate, which limits the ability of the equipment to meet the preset atomized particle concentration threshold in low-temperature fluctuation scenarios. Summary of the Invention

[0005] The embodiments of the present application provide an intelligent disinfection equipment gasification control method and system based on adaptation to low-temperature environments, so as to solve the problems of low gasification efficiency, uneven atomized particle distribution, and inability to maintain an ideal gasification efficiency range of disinfection equipment in low-temperature environments due to rapid temperature fluctuations and humidity changes in the prior art.

[0006] In a first aspect, the embodiments of the present application provide an intelligent disinfection equipment gasification control method based on adaptation to low-temperature environments, including:

[0007] Monitoring and acquiring real-time temperature data and real-time humidity data of the low-temperature environment where the disinfection equipment is located, and performing temperature compensation processing on the real-time temperature data to generate a standard temperature equivalent value;

[0008] A dynamic adjustment factor is established based on the correlation between the standard temperature equivalent value and the real-time humidity data. When the standard temperature equivalent value is lower than the preset temperature threshold, humidity weighted correction is triggered to generate a humidity weighted correction result.

[0009] Generate a gasification rate control parameter and a heating power grading parameter according to the dynamic adjustment factor, and synchronously adjust the solenoid valve opening degree and the periodic working mode of the heating element of the gasification device based on the gasification rate control parameter and the heating power grading parameter.

[0010] During the process of adjusting the gasification device, the atomized particle concentration distribution and diffusion uniformity output by the gasification device are monitored in real time. When the deviation amount between the atomized particle concentration distribution and the diffusion uniformity and the preset gasification efficiency interval reaches the set threshold, calibration parameters are generated through the reverse correction of the dynamic adjustment factor, and the calibration parameters are fed back to the generation process of the dynamic adjustment factor to form a closed-loop parameter update link.

[0011] Calculate the temperature decay rate within the time window based on the gradient change trend of the real-time temperature data, and dynamically correct the preheating reserve in the heating power grading parameter according to the decay rate, so that the gasification device meets the preset atomized particle concentration threshold under the low-temperature fluctuation scenario.

[0012] Optionally, during the process of adjusting the gasification device, the atomized particle concentration distribution and diffusion uniformity output by the gasification device are monitored in real time. When the deviation amount between the atomized particle concentration distribution and the diffusion uniformity and the preset gasification efficiency interval reaches the set threshold, calibration parameters are generated through the reverse correction of the dynamic adjustment factor, and the calibration parameters are fed back to the generation process of the dynamic adjustment factor to form a closed-loop parameter update link, including:

[0013] Deploy multiple layers of annular sensor arrays circumferentially around the gasification device. Each group of sensors collects the atomized particle density distribution data of the spatial partition where they are located at a preset sampling frequency, constructs a discretized spatial point cloud set based on the atomized particle density distribution data, and generates an initial three-dimensional concentration distribution field through a radial basis function interpolation algorithm combined with the geometric topology structure of the disinfection area. The initial three-dimensional concentration distribution field is subjected to spatial smoothing processing to generate an enhanced three-dimensional concentration distribution field.

[0014] Extract the axial concentration gradient modulus value and the tangential diffusion uniformity index of the enhanced three-dimensional concentration distribution field, calculate the residual between the gradient modulus value and the theoretical gradient range output by the preset diffusion kinetics model to generate a first deviation amount, and perform a non-linear mapping on the tangential diffusion uniformity index and the boundary value of the preset efficiency interval to generate a second deviation amount.

[0015] Construct an association matrix for the dynamic adjustment factor, the first deviation, and the second deviation. Project the first deviation along the direction of the temperature decay rate to generate an aerodynamic correction component. Project the second deviation along the direction of the humidity-weighted correction result to generate a thermodynamic correction component. Perform dynamic time warping matching on the aerodynamic correction component and the historical change trajectory of the solenoid valve opening compensation coefficient to generate an opening calibration offset. Perform convolution matching on the thermodynamic correction component and the fluctuation spectrum of the heating element power base to generate a power calibration offset;

[0016] Input the opening calibration offset and the power calibration offset into a preset backpropagation model, and use the backpropagation model to update the aerodynamic component weight and the thermodynamic component weight of the dynamic adjustment factor to generate a calibration parameter set;

[0017] Perform phase alignment processing on the offsets of adjacent timestamps in the calibration parameter set to generate a closed-loop parameter increment sequence. Perform time-delay compensation superposition operation on the closed-loop parameter increment sequence and each component of the dynamic adjustment factor to generate an updated dynamic adjustment factor. Feed back the updated dynamic adjustment factor to the generation process of the gasification rate control parameter and the heating power grading parameter to generate a closed-loop parameter update link.

[0018] Optionally, input the opening calibration offset and the power calibration offset into a preset backpropagation model, and use the backpropagation model to update the aerodynamic component weight and the thermodynamic component weight of the dynamic adjustment factor to generate a calibration parameter set, including:

[0019] Decompose the opening calibration offset along the axis of the gasification device into a high-frequency fluctuation component and a low-frequency trend component. Decompose the power calibration offset along the heating element working cycle into a transient response component and a steady-state response component;

[0020] Construct an aerodynamic component weight update model. Use the component weight update model to perform correlation analysis on the spectral characteristics of the high-frequency fluctuation component and the historical spectrum of the solenoid valve opening compensation coefficient to generate an aerodynamic spectrum matching degree. Perform orthogonal projection on the fitting curve of the low-frequency trend component and the gradient change direction of the current standard temperature equivalent value to generate an aerodynamic trend correction coefficient. Based on the aerodynamic spectrum matching degree and the aerodynamic trend correction coefficient, iteratively update the aerodynamic component weight by weighted least squares method to obtain the updated aerodynamic component weight;

[0021] Build a thermodynamic component weight update model, and use the thermodynamic component weight update model to perform dynamic time warping matching on the time constant of the transient response component and the historical transient response data of the heating sheet power base to generate a thermodynamic transient matching degree; perform non-linear regression on the stable value of the steady-state response component and the change rate of the humidity weighted correction result to generate a thermodynamic steady-state correction coefficient. Based on the thermodynamic transient matching degree and the thermodynamic steady-state correction coefficient, use the gradient descent optimization algorithm to iteratively update the thermodynamic component weights to obtain the updated thermodynamic component weights.

[0022] Input the updated pneumatic component weights and the updated thermodynamic component weights into the backpropagation model. The backpropagation model generates a calibration parameter set through an error backpropagation mechanism within a sliding time window based on the time-domain differential characteristics of the current standard temperature equivalent value and the frequency-domain response characteristics of the humidity weighted correction result.

[0023] Optionally, monitor and obtain the real-time temperature data and real-time humidity data of the low-temperature environment where the disinfection device is located, and perform temperature compensation processing on the real-time temperature data to generate a standard temperature equivalent value, including:

[0024] Deploy multiple groups of distributed temperature sensors and distributed humidity sensors on the surface of the disinfection device. Each group of temperature sensors and distributed humidity sensors synchronously collect local environment temperature data and local environment humidity data at a preset time interval.

[0025] Establish a low-temperature drift compensation parameter table based on the historical calibration data of the temperature sensors, match the corresponding temperature drift coefficient according to the interval where the local environment temperature data is located, and perform non-linear superposition operation on the local environment temperature data collected by the distributed temperature sensors and the temperature drift coefficient to generate a preliminary compensated temperature value.

[0026] Build a humidity coupling factor calculation model, and perform exponential decay convolution operation on the local environment humidity data and the historical humidity fluctuation amplitude to generate a dynamic humidity influence weight.

[0027] Perform orthogonal projection on the dynamic humidity influence weight and the preliminary compensated temperature value to generate a humidity compensation correction amount. Perform vector superposition on the preliminary compensated temperature value and the humidity compensation correction amount to generate an intermediate compensated temperature value. Based on the gradient change trend of the intermediate compensated temperature value within a preset time window, match the low-temperature environment temperature decay characteristic curve through the dynamic time warping algorithm, and extract the temperature fluctuation phase offset amount.

[0028] Perform time-domain alignment operation on the phase offset amount and the intermediate compensated temperature value to generate a dynamic compensation factor. Perform piecewise polynomial fitting on the dynamic compensation factor and the intermediate compensated temperature value to generate a standard temperature equivalent value.

[0029] Optionally, a dynamic adjustment factor is established based on the correlation between the standard temperature equivalent value and the real-time humidity data. When the standard temperature equivalent value is lower than the preset temperature threshold, a humidity weighting correction is triggered and a humidity weighting correction result is generated, including:

[0030] Compare the standard temperature equivalent value with the preset temperature threshold. When the standard temperature equivalent value is lower than the preset temperature threshold, trigger the humidity weighting correction mechanism. Based on the fluctuation amplitude of the real-time humidity data within a preset time window, generate a dynamic humidity fluctuation coefficient through the exponential smoothing algorithm. Orthogonally project the dynamic humidity fluctuation coefficient in the gradient change direction of the standard temperature equivalent value to generate a humidity influence weight;

[0031] Construct a temperature-humidity coupling model. Input the standard temperature equivalent value and the real-time humidity data into a preset cluster of phase change characteristic curves in a low-temperature environment. Generate a phase change compensation factor through interpolation calculation. Non-linearly superimpose the phase change compensation factor and the humidity influence weight to generate an initial dynamic adjustment factor;

[0032] Based on the change trend of the initial dynamic adjustment factor within a historical time window, match the temperature-humidity coupling characteristics in a low-temperature environment through the dynamic time warping algorithm. Extract the temperature-humidity phase offset. Perform a time-domain alignment operation on the temperature-humidity phase offset and the initial dynamic adjustment factor to generate an intermediate dynamic adjustment factor;

[0033] Construct a humidity weighting correction model. Perform a convolution operation on the intermediate dynamic adjustment factor and the fluctuation spectrum of the real-time humidity data to generate a humidity correction component. Orthogonally couple the humidity correction component with the gradient change rate of the standard temperature equivalent value to generate a humidity weighting correction result.

[0034] Optionally, generate a vaporization rate control parameter and a heating power grading parameter according to the dynamic adjustment factor, and synchronously adjust the solenoid valve opening degree and the periodic working mode of the heating element of the vaporization device based on the vaporization rate control parameter and the heating power grading parameter, including:

[0035] Decompose the dynamic adjustment factor into a pneumatic component and a thermodynamic component. Map the pneumatic component to a solenoid valve opening degree compensation coefficient through fuzzy control rules, and convert the thermodynamic component into a heating element power base through a dynamic weight distribution algorithm;

[0036] Interpolate and calculate a vaporization rate reference value based on the standard temperature equivalent value and the real-time humidity data in a preset cluster of vaporization characteristic curves. The cluster of vaporization characteristic curves is generated by fitting the phase change experimental data of the disinfectant in a low-temperature environment;

[0037] Taking the difference between the current standard temperature equivalent value and the preset temperature threshold as the abscissa, and the real-time humidity data as the ordinate, locate the dynamic projection point on the three-dimensional characteristic surface constructed by the temperature-pressure coupling relationship, and calculate the non-linear superposition coefficient of the gasification rate reference value and the heating power base according to the curvature radius and the normal vector direction of the dynamic projection point;

[0038] Orthogonally couple the solenoid valve opening compensation coefficient and the non-linear superposition coefficient to generate the gasification rate control parameter, where the parameter value of the gasification rate control parameter generates a dynamic offset with the derivative change of the temperature decay rate;

[0039] Perform a convolution operation on the heating element power base and the historical sliding average of the dynamic adjustment factor to generate a heating power grading parameter including the preheating phase angle, where the size of the phase angle is positively correlated with the quadratic integral value of the temperature gradient change trend;

[0040] Construct a timing cooperative control model for the solenoid valve opening and the periodic working mode of the heating element, use the timing cooperative control model to convert the gasification rate control parameter into a pulse width modulation waveform of the solenoid valve stepper motor, and map the heating power grading parameter to a duty cycle adjustment function of the heating element working cycle, where the leading edge of the solenoid valve opening change forms a preset time delay with the conduction moment of the heating element;

[0041] Write the dynamic offset into the cache queue of the calibration parameter, and associate the phase angle change amount with the calculation process of the correction coefficient of the preheating reserve.

[0042] Optionally, calculate the temperature decay rate within the time window based on the gradient change trend of the real-time temperature data, and dynamically correct the preheating reserve in the heating power grading parameter according to the decay rate, so that the gasification device meets the preset atomized particle concentration threshold in the low-temperature fluctuation scenario, including:

[0043] Based on the change sequence of the real-time temperature data within the preset time window, generate a temperature gradient change sequence through the difference calculation within the sliding time window, input the temperature gradient change sequence into the preset low-temperature decay characteristic curve cluster, and generate the initial temperature decay rate through interpolation calculation;

[0044] Construct a temperature decay rate correction model, perform a convolution operation on the initial temperature decay rate and the fluctuation spectrum of the historical temperature decay data to generate a dynamic decay correction factor, and perform a non-linear superposition operation on the dynamic decay correction factor and the initial temperature decay rate to generate the corrected temperature decay rate;

[0045] Based on the corrected temperature decay rate, match the historical response characteristics of the heating element power base through the dynamic time warping algorithm, extract the heating power response delay amount, perform a time-domain alignment operation on the heating power response delay amount and the corrected temperature decay rate to generate a dynamic preheating compensation coefficient;

[0046] Construct a preheating reserve correction model, perform an orthogonal projection on the dynamic preheating compensation coefficient and the preheating reserve in the heating power grading parameters to generate a preheating reserve correction amount, and perform a vector superposition on the preheating reserve correction amount and the preheating reserve to generate an updated preheating reserve;

[0047] Input the updated preheating reserve into the heating element control module of the gasification device, and by adjusting the duty cycle and power output of the periodic working mode of the heating element, make the gasification device meet the preset atomized particle concentration threshold in the low-temperature fluctuation scenario.

[0048] In a second aspect, an embodiment of the present application provides an intelligent disinfection equipment gasification control system based on low-temperature environment adaptation, including:

[0049] A monitoring module for monitoring and obtaining real-time temperature data and real-time humidity data of the low-temperature environment where the disinfection equipment is located, and performing temperature compensation processing on the real-time temperature data to generate a standard temperature equivalent value;

[0050] A trigger module for establishing a dynamic adjustment factor based on the correlation between the standard temperature equivalent value and the real-time humidity data, and triggering humidity weighted correction and generating a humidity weighted correction result when the standard temperature equivalent value is lower than a preset temperature threshold;

[0051] An adjustment module for generating a gasification rate control parameter and a heating power grading parameter according to the dynamic adjustment factor, and synchronously adjusting the solenoid valve opening degree and the periodic working mode of the heating element of the gasification device based on the gasification rate control parameter and the heating power grading parameter;

[0052] A feedback module for monitoring the atomized particle concentration distribution and diffusion uniformity output by the gasification device in real time during the process of adjusting the gasification device. When the deviation amount between the atomized particle concentration distribution and the diffusion uniformity and the preset gasification efficiency interval reaches a set threshold, generate a calibration parameter through the reverse correction of the dynamic adjustment factor, and feedback the calibration parameter to the generation process of the dynamic adjustment factor to form a closed-loop parameter update link;

[0053] A correction module for calculating the temperature decay rate within a time window based on the gradient change trend of the real-time temperature data, and dynamically correcting the preheating reserve in the heating power grading parameter according to the decay rate, so that the gasification device meets the preset atomized particle concentration threshold in the low-temperature fluctuation scenario.

[0054] In a third aspect, an embodiment of the present application provides a computing device, including a processor and a memory. A computer program is stored in the memory, and the processor is configured to run the computer program to execute any one of the methods for controlling the gasification of an intelligent disinfection device based on adaptation to a low-temperature environment in the first aspect.

[0055] In a fourth aspect, an embodiment of the present application provides a computer storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, they implement any one of the methods for controlling the gasification of an intelligent disinfection device based on adaptation to a low-temperature environment in the first aspect.

[0056] In the embodiment of the present application, by monitoring and obtaining the real-time temperature data and real-time humidity data of the low-temperature environment where the disinfection device is located, temperature compensation processing is performed on the real-time temperature data to generate a standard temperature equivalent value; a dynamic adjustment factor is established based on the correlation between the standard temperature equivalent value and the real-time humidity data. When the standard temperature equivalent value is lower than a preset temperature threshold, humidity weighted correction is triggered to generate a humidity weighted correction result; a gasification rate control parameter and a heating power grading parameter are generated according to the dynamic adjustment factor, and the solenoid valve opening degree and the periodic working mode of the heating element of the gasification device are synchronously adjusted based on the gasification rate control parameter and the heating power grading parameter; during the process of adjusting the gasification device, the concentration distribution and diffusion uniformity of the atomized particles output by the gasification device are monitored in real time. When the deviation amount between the concentration distribution and diffusion uniformity of the atomized particles and a preset gasification efficiency interval reaches a set threshold, a calibration parameter is generated through the reverse correction of the dynamic adjustment factor, and the calibration parameter is fed back to the generation process of the dynamic adjustment factor to form a closed-loop parameter update link; the temperature decay rate within a time window is calculated based on the gradient change trend of the real-time temperature data, and the preheating reserve amount in the heating power grading parameter is dynamically corrected according to the decay rate, so that the gasification device can meet the preset atomized particle concentration threshold in a low-temperature fluctuation scenario.

[0057] The technical solution of the present application monitors and obtains the real-time temperature and humidity data of the low-temperature environment, performs temperature compensation processing to generate a standard temperature equivalent value, and establishes a dynamic adjustment factor based on this, realizing the intelligent adjustment of the gasification rate and heating power of the disinfection device. In particular, when the standard temperature equivalent value is lower than the preset threshold in a low-temperature environment, humidity weighted correction can be triggered to ensure the stability and efficiency of the gasification process. In addition, through the real-time monitoring and feedback correction of the concentration distribution and diffusion uniformity of the atomized particles output by the gasification device, a closed-loop parameter update link is formed, effectively improving the stability and accuracy of the disinfection effect. Finally, the temperature decay rate is calculated according to the temperature gradient change trend, and the preheating reserve amount in the heating power grading parameter is dynamically adjusted, so that the disinfection device can still maintain the best working state in a low-temperature fluctuation scenario, meet the requirements of the preset atomized particle concentration threshold, and thus significantly enhance the adaptability and disinfection efficiency in a complex low-temperature environment;

[0058] Furthermore, by deploying multi-level annular sensor arrays around the gasification device, it is possible to accurately collect the density distribution data of atomized particles in different spatial partitions, and generate an accurate three-dimensional concentration distribution field by combining advanced algorithms (such as radial basis function interpolation algorithm) and the geometric topology of the disinfection area. By performing spatial smoothing on the initial field, the accuracy and reliability of the concentration distribution field are improved. In addition, based on the comparison of the axial concentration gradient modulus and the tangential diffusion uniformity index with the preset efficiency interval, the first and second deviation amounts are generated and mapped into the dynamic adjustment factor, realizing the precise correction of the solenoid valve opening degree and the heating sheet power of the gasification device. This method not only enhances the response speed and accuracy of the system to environmental changes, but also ensures the stability and efficiency under long-term operation through the closed-loop parameter update link, effectively solving the problem that traditional disinfection equipment is difficult to maintain stable gasification efficiency and uniform atomized particle distribution in low-temperature environments, and significantly improving the disinfection effect and adaptability;

[0059] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0061] Figure 1 It is a flowchart of a gasification control method for an intelligent disinfection device based on low-temperature environment adaptation provided by an embodiment of the present application;

[0062] Figure 2 It is a schematic structural diagram of a gasification control system for an intelligent disinfection device based on low-temperature environment adaptation provided by an embodiment of the present application;

[0063] Figure 3 It is a schematic structural diagram of a computing device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.

[0065] In some of the processes described in the specification, claims, and the above-mentioned drawings of this application, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent a sequence, and do not limit that "first" and "second" are of different types.

[0066] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present application.

[0067] Figure 1 The following is a flowchart of an intelligent disinfection equipment gasification control method based on low-temperature environment adaptation provided for the embodiments of the present application. As Figure 1 shown, the method includes:

[0068] Step 101, monitor and obtain the real-time temperature data and real-time humidity data of the low-temperature environment where the disinfection equipment is located, and perform temperature compensation processing on the real-time temperature data to generate a standard temperature equivalent value, including:

[0069] In this step, the real-time temperature data refers to the current temperature information in the environment where the disinfection equipment is located, and the real-time humidity data refers to the humidity level of the air in the same environment. Temperature compensation processing is a technical means used to convert the obtained real-time temperature data into a standard temperature equivalent value to eliminate measurement errors caused by environmental changes and ensure that subsequent operations are based on consistent standard conditions;

[0070] In this step, first, it is necessary to monitor and obtain the real-time temperature and humidity data of the low-temperature environment where the disinfection equipment is located through sensors installed on the disinfection equipment, and then perform temperature compensation processing according to these original data. Considering the variation law of measurement errors under different temperature conditions, a specific algorithm or formula is used to correct the original temperature data, thereby generating a standard temperature equivalent value that is not affected by environmental changes. This standard temperature equivalent value can more accurately reflect the temperature state under actual working conditions and provide a reliable basis for subsequent gasification rate control and heating power adjustment;

[0071] For example, when applying an intelligent disinfection device based on low-temperature environment adaptation in a cold storage of a food processing factory, since the cold storage needs to maintain a constant low temperature (usually between 0°C and 4°C) and a high humidity environment to ensure food safety, this poses special requirements for the disinfection device. The device first monitors the temperature and humidity data at its location in real time through precision temperature and humidity sensors installed on it. Suppose the currently detected temperature is 2°C and the humidity is 95%RH;

[0072] Next, the system performs temperature compensation processing on the collected real-time temperature data. Considering the possible condensation phenomenon of the temperature sensor in a high humidity environment and its impact on measurement accuracy, a specific compensation algorithm (such as a correction formula based on humidity influence factors) is used to correct the original temperature reading. In this example, the standard temperature equivalent value obtained after compensation is 1.8°C. This adjusted standard temperature value not only more accurately reflects the temperature state under actual working conditions but also provides a reliable basis for further calculating the dynamic adjustment factor. Based on this standard temperature equivalent value, the disinfection device can more precisely control the vaporization rate and heating power to ensure efficient disinfection even in a special low-temperature and high-humidity environment like a cold storage, guaranteeing food safety and hygiene.

[0073] Step 102, establish a dynamic adjustment factor based on the correlation between the standard temperature equivalent value and the real-time humidity data, and trigger humidity weighted correction and generate a humidity weighted correction result when the standard temperature equivalent value is lower than a preset temperature threshold, including:

[0074] In this step, the dynamic adjustment factor refers to a parameter calculated based on the standard temperature equivalent value and the real-time humidity data, which is used to guide the adjustment of the vaporization rate and heating power of the disinfection device. Humidity weighted correction is a compensation mechanism for the influence of humidity on the disinfection effect in a low-temperature environment. When the standard temperature equivalent value is lower than the preset temperature threshold, this correction mechanism is triggered to ensure the effectiveness and stability of the disinfection process;

[0075] In this step, first establish a dynamic adjustment factor based on the correlation between the obtained standard temperature equivalent value and the real-time humidity data. This factor reflects the optimal vaporization conditions required to achieve the best disinfection effect under different temperature and humidity combinations. When the detected standard temperature equivalent value is lower than a preset temperature threshold (such as 0°C), the system will automatically start the humidity weighted correction program. This program analyzes the influence degree of the current environmental humidity on the disinfection efficiency and adjusts the working parameters of the disinfection device accordingly, including but not limited to the solenoid valve opening degree and the heating element working mode, to generate a humidity weighted correction result. These adjustments are used to optimize the atomization particle size and distribution uniformity of the disinfectant to ensure efficient disinfection even under extreme low-temperature conditions;

[0076] For example, when the system obtains a standard temperature equivalent value of 1.8 °C, a dynamic adjustment factor is then established based on the correlation between this value and the real-time humidity data (95% RH). Since the standard temperature equivalent value is lower than the preset temperature threshold (e.g., 3 °C), the system automatically triggers the humidity weighted correction mechanism. During this process, the system first analyzes the influence degree of the current humidity on the vaporization rate of the disinfectant and particle diffusion, and adjusts the working parameters of the equipment accordingly. Specifically, the system calculates the humidity weighted correction result through the built-in algorithm. This result indicates that under the current high humidity conditions, in order to achieve the best disinfection effect, it is necessary to increase the heating power to raise the local environmental temperature, and at the same time appropriately increase the opening degree of the solenoid valve to increase the injection amount of the disinfectant. For example, the system may increase the power of the heating element from the default medium level to a higher level to accelerate the vaporization process of the disinfectant, and at the same time increase the opening degree of the solenoid valve from the initial setting of 20% to 30% to ensure that the generated atomized particles are small enough and evenly distributed. In addition, the system continuously monitors the concentration distribution and diffusion uniformity of the output atomized particles, and adjusts the parameters in the dynamic adjustment factor in real time to form a closed-loop control link. For example, in a typical operation cycle, the sensor detects that the concentration of atomized particles in a certain area is slightly lower than the expected value, and the system will fine-tune the heating power and the opening degree of the solenoid valve according to the preset rules until the concentration of atomized particles in the entire cold storage reaches the ideal range. Through such an intelligent adjustment mechanism, even in a complex environment of low temperature and high humidity, the disinfection equipment can maintain high disinfection ability, ensure the hygiene and safety during the food processing process, and effectively prevent potential microbial contamination risks. This method not only improves the reliability and efficiency of the disinfection operation, but also greatly reduces the need for manual intervention, achieving the management goals of automation and intelligence.

[0077] Step 103, generate a vaporization rate control parameter and a heating power grading parameter according to the dynamic adjustment factor, and synchronously adjust the opening degree of the solenoid valve of the vaporization device and the periodic working mode of the heating element based on the vaporization rate control parameter and the heating power grading parameter, including:

[0078] In this step, the vaporization rate control parameter refers to the parameter calculated according to the dynamic adjustment factor for adjusting the speed of the disinfectant converting from liquid to gas, and the heating power grading parameter refers to different levels of heating power set to achieve the best vaporization effect. The opening degree of the solenoid valve determines the flow rate of the disinfectant entering the vaporization device, and the periodic working mode of the heating element refers to the strategy that the heating element works intermittently according to the preset time interval and power level;

[0079] In this step, first, the previously established dynamic adjustment factor is used to generate specific gasification rate control parameters and heating power grading parameters. These parameters take into account factors such as the current ambient temperature, humidity, and the working state of the equipment to ensure the best gasification efficiency and atomized particle distribution. Then, based on the generated gasification rate control parameters, the opening degree of the solenoid valve is adjusted to control the dosage of the disinfectant entering the gasification device. At the same time, according to the heating power grading parameters, the working mode of the heating element is set, including its heating intensity and working cycle, so as to ensure that the disinfectant can be quickly and evenly gasified at an appropriate temperature. By precisely regulating the opening degree of the solenoid valve and the working mode of the heating element, the effective management of the gasification device is achieved, enabling the disinfection equipment to operate efficiently even in low-temperature environments;

[0080] For example, the system established a dynamic adjustment factor based on the determined standard temperature equivalent value of 1.8 °C and the real-time humidity of 95% RH, and generated specific gasification rate control parameters and heating power grading parameters accordingly. Suppose the system calculates that the opening degree of the solenoid valve needs to be increased from the initial 20% to 30%, and the working mode of the heating element is adjusted from continuous low-power heating to intermittent medium-power heating (pausing for 2 minutes every 5 minutes of operation). The purpose of this is to accelerate the gasification process of the disinfectant under low-temperature and high-humidity conditions, while avoiding energy waste or equipment overheating caused by excessive heating. With the adjustment of these parameters, the gasification device can more effectively convert the disinfectant into fine and uniform atomized particles, ensuring that the air and surfaces in the entire cold storage are fully disinfected, maintaining high-standard hygienic conditions, and guaranteeing food safety. It not only improves the disinfection efficiency but also optimizes the energy use, reflecting a high level of adaptability and intelligence.

[0081] Step 104: During the process of adjusting the gasification device, the concentration distribution and diffusion uniformity of the atomized particles output by the gasification device are monitored in real time. When the deviation of the concentration distribution and diffusion uniformity of the atomized particles from the preset gasification efficiency interval reaches the set threshold, calibration parameters are generated through the reverse correction of the dynamic adjustment factor, and the calibration parameters are fed back to the generation process of the dynamic adjustment factor to form a closed-loop parameter update link, including:

[0082] In this step, the concentration distribution of the atomized particles refers to the distribution of the disinfectant atomized particles output by the gasification device in space, and the diffusion uniformity is the standard for measuring whether the distribution of these particles in the target area is uniform. The preset gasification efficiency interval defines the ideal atomized particle concentration range and diffusion uniformity standard. When the actually monitored parameters deviate from this interval and reach the set threshold, reverse correction is performed through the dynamic adjustment factor to generate calibration parameters to adjust the working state of the equipment, ensure that the disinfection effect meets the expectations, and form a closed-loop parameter update link to continuously optimize the operation;

[0083] In this step, during the process of adjusting the gasification device, the concentration distribution and diffusion uniformity of the atomized particles generated by the gasification device are monitored in real time. When it is found that the deviation of these parameters from the preset gasification efficiency range exceeds the set threshold, it indicates that the current gasification effect has not reached the optimal state. At this time, the dynamic adjustment factor is used for reverse correction, and the necessary calibration parameters are calculated based on the current environmental conditions and equipment operation data. These calibration parameters are then fed back into the generation process of the dynamic adjustment factor to adjust key parameters such as the solenoid valve opening and heating power, so as to achieve precise control of the gasification rate and heating mode. In this way, any factors affecting the disinfection effect can be automatically identified and corrected, forming a self-learning and optimizing closed-loop control system to ensure high-efficiency disinfection performance in different environments;

[0084] For example, during the adjustment of the gasification device, the concentration distribution of the output atomized particles and their diffusion uniformity are monitored in real time. Suppose the monitoring results show that the concentration of atomized particles in a certain area is lower than the lower limit of the preset efficiency range and the diffusion uniformity also fails to meet the standard. Immediately, the reverse correction mechanism of the dynamic adjustment factor is activated, and the calibration parameters are recalculated according to the current low temperature (1.8°C) and high humidity (95% RH) conditions. The specific measures include: slightly increasing the opening of the solenoid valve from 30% to 32%, and adjusting the working cycle of the heating element to heat for 4 minutes and then pause for 1 minute to increase the local temperature and promote more effective gasification. These adjusted parameters are fed back to the dynamic adjustment factor generation module of the system to further optimize the subsequent operation strategy. Through this closed-loop parameter update link, not only the initial deficiencies are quickly corrected, but also the overall disinfection efficiency is improved, ensuring that the air quality and surface hygiene in the cold storage are always maintained at a high standard and effectively preventing potential microbial contamination risks.

[0085] Step 105: Calculate the temperature decay rate within the time window based on the gradient change trend of the real-time temperature data, and dynamically correct the preheating reserve in the heating power grading parameters according to the decay rate, so that the gasification device can meet the preset atomized particle concentration threshold under low-temperature fluctuation scenarios, including:

[0086] In this step, the gradient change trend of the real-time temperature data refers to the speed and direction of the temperature change with time in the environment. The temperature decay rate is the rate of temperature drop per unit time calculated based on this trend. The preheating reserve is the additional heating energy value preset to cope with possible future low-temperature environments. By dynamically correcting this reserve, it can be ensured that the gasification device can maintain sufficient heating capacity even when the temperature drops rapidly, ensuring the effective gasification of the disinfectant and meeting the preset atomized particle concentration threshold;

[0087] In this step, first, analyze the changing trend of real-time temperature data, calculate the temperature decay rate within a specific time window. Based on this decay rate, predict the possible low-temperature situations in the next period of time, and dynamically adjust the preheating reserve in the heating power grading parameters accordingly. Specifically, if a high temperature decay rate is detected (i.e., the temperature drops rapidly), increase the preheating reserve to enhance the heating capacity; otherwise, decrease it, ensuring that the gasification device can continuously provide stable heating support in an environment with large temperature fluctuations, thus guaranteeing the efficient gasification of the disinfectant and making the concentration of the generated atomized particles always meet the preset standards.

[0088] For example, the system has already optimized and adjusted the solenoid valve opening and the working mode of the heating element through dynamic adjustment factors (such as increasing the solenoid valve opening from 30% to 32% and adjusting the working cycle of the heating element to heat for 4 minutes and then pause for 1 minute) to ensure the efficient gasification of the disinfectant and achieve an ideal atomized particle concentration distribution and diffusion uniformity. To cope with the possible temperature fluctuations in the cold storage, start analyzing the gradient change trend of real-time temperature data. Suppose that within the past hour, the monitored temperature gradually drops from 2°C to 1.5°C, showing a slow but continuous cooling trend. Based on the temperature change during this period, the system calculates the temperature decay rate within the time window as 0.5°C per hour. According to this decay rate, dynamically correct the preheating reserve in the heating power grading parameters. Since it is predicted that there may be further cooling in the future, appropriately increase the preheating reserve. For example, increase the initial heating power of the heating element by 10%, and increase the continuous working duration and reduce the pause time, thus ensuring that even if the temperature continues to drop, sufficient heating capacity can be maintained to promote the effective gasification of the disinfectant. This approach of pre-enhancing the heating capacity enables the gasification device to still maintain an efficient operating state under low-temperature fluctuations, ensuring that the concentration of the generated atomized particles meets the preset threshold requirements and achieving uniform coverage throughout the cold storage area. In this way, even in the face of complex temperature changes in the cold storage, it can automatically adapt and make corresponding adjustments to ensure that food safety and hygiene conditions are always in the best state. Through such an intelligent adjustment mechanism, not only the disinfection efficiency is improved, but also the response ability and stability of the equipment to environmental changes are enhanced.

[0089] To address the difficulty of achieving precise control of traditional disinfection equipment in complex environments and further improve the stability and accuracy of disinfection effects, in some embodiments, as described in step 104, during the process of adjusting the gasification device, the concentration distribution and diffusion uniformity of the atomized particles output by the gasification device are monitored in real time. When the deviation of the concentration distribution and diffusion uniformity of the atomized particles from the preset gasification efficiency range reaches a set threshold, calibration parameters are generated through the reverse correction of the dynamic adjustment factor, and the calibration parameters are fed back to the generation process of the dynamic adjustment factor to form a closed-loop parameter update link, which specifically includes:

[0090] Deploy multiple layers of annular sensor arrays circumferentially around the gasification device. Each group of sensors collects the atomized particle density distribution data of the spatial partition where it is located at a preset sampling frequency, constructs a discretized spatial point cloud set based on the atomized particle density distribution data, and generates an initial three-dimensional concentration distribution field through a radial basis function interpolation algorithm combined with the geometric topology structure of the disinfection area. The initial three-dimensional concentration distribution field is subjected to spatial smoothing processing to generate an enhanced three-dimensional concentration distribution field; extract the axial concentration gradient modulus value and the tangential diffusion uniformity index of the enhanced three-dimensional concentration distribution field, calculate the residual between the gradient modulus value and the theoretical gradient range output by the preset diffusion kinetic model to generate a first deviation, and perform a non-linear mapping on the tangential diffusion uniformity index and the boundary value of the preset efficiency range to generate a second deviation; construct an association matrix of the dynamic adjustment factor with the first deviation and the second deviation, project the first deviation along the direction of the temperature decay rate to generate an aerodynamic correction component, project the second deviation along the direction of the humidity weighted correction result to generate a thermodynamic correction component, perform dynamic time warping matching on the aerodynamic correction component and the historical change trajectory of the solenoid valve opening compensation coefficient to generate an opening calibration offset, and perform convolution matching on the thermodynamic correction component and the fluctuation spectrum of the heating element power base to generate a power calibration offset; input the opening calibration offset and the power calibration offset into a preset backpropagation model, and use the backpropagation model to update the aerodynamic component weight and the thermodynamic component weight of the dynamic adjustment factor to generate a set of calibration parameters; perform phase alignment processing on the offsets of adjacent timestamps in the set of calibration parameters to generate a closed-loop parameter increment sequence, and perform a time-delay compensation superposition operation on the closed-loop parameter increment sequence and each component of the dynamic adjustment factor to generate an updated dynamic adjustment factor, and feed the updated dynamic adjustment factor back to the generation process of the gasification rate control parameter and the heating power grading parameter to generate a closed-loop parameter update link;

[0091] In this embodiment, the axial concentration gradient modulus value reflects the concentration change rate of the atomized particles in the vertical direction, while the tangential diffusion uniformity index measures the distribution uniformity of the particles in the horizontal direction. These indexes are used to evaluate whether the current disinfection effect meets the expected standard, and accordingly generate the first deviation and the second deviation, which serve as the basic data for adjusting the dynamic adjustment factor;

[0092] In the embodiment of the present application, first, the axial concentration gradient modulus value and the tangential diffusion uniformity index in the enhanced three-dimensional concentration distribution field are extracted, and then they are respectively compared with the ideal ranges calculated by the theoretical model to generate deviation amounts. Next, an association matrix is constructed using these deviation amounts, and an opening calibration offset and a power calibration offset are generated through specific algorithms (such as dynamic time warping matching and convolutional matching). These offsets are input into a preset backpropagation model to update the weights of each item in the dynamic adjustment factor. Finally, a set of calibration parameters is generated. Through phase alignment processing and time-delay compensation superposition operation, an updated dynamic adjustment factor is generated and fed back to the generation process of the gasification rate control parameter and the heating power grading parameter to achieve a closed-loop parameter update link;

[0093] For example, in the application scenario of a cold storage in a food processing factory, not only the challenges brought by the low-temperature and high-humidity environment need to be addressed, but also uniform and efficient disinfection needs to be ensured throughout the space. Therefore, a multi-layer annular sensor array is set in the cold storage to monitor the density distribution of atomized particles in each area in real time. Based on the collected data, a detailed three-dimensional concentration distribution field is constructed, and the accuracy and smoothness of this field are ensured through an optimization algorithm. When it is detected that the atomized particle concentration or diffusion uniformity in some areas does not meet the preset standards, the opening of the solenoid valve and the working mode of the heating element are automatically adjusted, and even fine-tuning can be made according to the specific conditions of different areas. For example, for the area near the cold storage door where the temperature fluctuates greatly, the heating power of this area will be increased and the opening of the solenoid valve will be appropriately increased to ensure that the disinfectant can achieve the best effect at such key positions.

[0094] To further improve the adaptability and accuracy of the disinfection equipment in a complex low-temperature environment, as another embodiment, according to what is described in the previous embodiment, the opening calibration offset and the power calibration offset are input into a preset backpropagation model, and the pneumatic component weight and the thermodynamic component weight of the dynamic adjustment factor are updated using the backpropagation model to generate a set of calibration parameters, specifically including:

[0095] Decompose the opening calibration offset into a high-frequency fluctuation component and a low-frequency trend component along the axial direction of the gasification device, and decompose the power calibration offset into a transient response component and a steady-state response component along the working cycle of the heating element; construct a pneumatic component weight update model, and use the component weight update model to perform a correlation analysis on the spectral characteristics of the high-frequency fluctuation component and the historical spectrum of the solenoid valve opening compensation coefficient to generate a pneumatic spectrum matching degree. Perform an orthogonal projection on the fitting curve of the low-frequency trend component and the gradient change direction of the current standard temperature equivalent value to generate a pneumatic trend correction coefficient. Based on the pneumatic spectrum matching degree and the pneumatic trend correction coefficient, iteratively update the pneumatic component weight through the weighted least squares method to obtain the updated pneumatic component weight; construct a thermodynamic component weight update model, and use the thermodynamic component weight update model to perform a dynamic time warping match on the time constant of the transient response component and the historical transient response data of the heating element power base to generate a thermodynamic transient matching degree; perform a non-linear regression on the stable value of the steady-state response component and the change rate of the humidity weighted correction result to generate a thermodynamic steady-state correction coefficient. Based on the thermodynamic transient matching degree and the thermodynamic steady-state correction coefficient, iteratively update the thermodynamic component weight through the gradient descent optimization algorithm to obtain the updated thermodynamic component weight; input the updated pneumatic component weight and the updated thermodynamic component weight into the backpropagation model. The backpropagation model generates a calibration parameter set through the error backpropagation mechanism within the sliding time window based on the time-domain differential characteristics of the current standard temperature equivalent value and the frequency-domain response characteristics of the humidity weighted correction result;

[0096] In this embodiment, the high-frequency fluctuation component reflects the rapidly changing part of the solenoid valve opening compensation coefficient, while the low-frequency trend component represents the long-term change trend. The transient response component describes the ability of the heating element power base to respond to environmental changes in a short period of time, and the steady-state response component measures its ability to maintain a stable output over a long period of time. These components are used to evaluate the performance of the system on different time scales and optimize the pneumatic and thermodynamic component weights accordingly;

[0097] In the embodiment of the present application, first, perform a spectral analysis on the opening calibration offset, extract the high-frequency fluctuation component and the low-frequency trend component therein, and compare them with the historical data of the solenoid valve respectively to generate a pneumatic spectrum matching degree and a pneumatic trend correction coefficient. Then, perform a time constant and stable value analysis on the power calibration offset to generate a thermodynamic transient matching degree and a thermodynamic steady-state correction coefficient. Based on these matching degrees and correction coefficients, use the weighted least squares method and the gradient descent optimization algorithm to update the pneumatic and thermodynamic component weights respectively. Finally, combine the current standard temperature equivalent value and its change rate, and generate a new calibration parameter set through the backpropagation model to ensure that each parameter can adapt to environmental changes in real time and maintain the best disinfection effect;

[0098] For example, when it is detected that the temperature in the area near the cold storage door fluctuates greatly due to frequent door openings, the heating power in this area is automatically increased and the solenoid valve opening is adjusted to compensate for the impact of temperature drop. By decomposing the opening calibration offset and power calibration offset into different response components and using advanced data analysis techniques (such as spectrum analysis, time warping matching, etc.) to optimize the adjustment strategy, the environmental changes can be predicted and responded to more accurately, ensuring that the disinfectant can reach the ideal atomized particle concentration and diffusion uniformity in each area, improving the disinfection efficiency, enhancing the equipment's adaptability to complex environmental changes, and ensuring that food safety and hygiene conditions are always in the best state.

[0099] To solve the problems that traditional disinfection equipment is difficult to accurately measure temperature in a complex low-temperature environment and the influence of humidity on temperature measurement, and to further improve the accuracy and reliability of temperature compensation processing, as another embodiment, according to what is described in step 101, monitor and obtain the real-time temperature data and real-time humidity data of the low-temperature environment where the disinfection equipment is located, and perform temperature compensation processing on the real-time temperature data to generate a standard temperature equivalent value, which specifically includes:

[0100] Deploy multiple groups of distributed temperature sensors and distributed humidity sensors on the surface of the disinfection equipment. Each group of temperature sensors and distributed humidity sensors synchronously collect local environmental temperature data and local environmental humidity data at a preset time interval; establish a low-temperature drift compensation parameter table based on the historical calibration data of the temperature sensors, match the corresponding temperature drift coefficient according to the interval where the local environmental temperature data is located, and perform a non-linear superposition operation on the local environmental temperature data collected by the distributed temperature sensors and the temperature drift coefficient to generate a preliminary compensated temperature value; construct a humidity coupling factor calculation model, perform an exponential decay convolution operation on the local environmental humidity data and the historical humidity fluctuation amplitude to generate a dynamic humidity influence weight; perform an orthogonal projection on the dynamic humidity influence weight and the preliminary compensated temperature value to generate a humidity compensation correction amount, perform a vector superposition on the preliminary compensated temperature value and the humidity compensation correction amount to generate an intermediate compensated temperature value, based on the gradient change trend of the intermediate compensated temperature value within a preset time window, match the low-temperature environment temperature decay characteristic curve through the dynamic time warping algorithm, and extract the temperature fluctuation phase offset; perform a time-domain alignment operation on the phase offset and the intermediate compensated temperature value to generate a dynamic compensation factor, and perform a piecewise polynomial fitting on the dynamic compensation factor and the intermediate compensated temperature value to generate a standard temperature equivalent value;

[0101] In this embodiment, the low-temperature drift compensation parameter table contains temperature drift coefficients corresponding to different temperature ranges, which are used to correct the measurement errors of the temperature sensor caused by low temperature. The dynamic humidity influence weight reflects the degree of influence of the current humidity on the temperature measurement result and is generated through exponential decay convolution operation combined with historical humidity fluctuation data. The humidity compensation correction amount is the result of further adjusting the preliminary compensated temperature value based on the humidity influence weight to ensure that the temperature reading is not disturbed by humidity changes;

[0102] In the embodiment of the present application, first, the ambient temperature and humidity data are synchronously collected by the distributed temperature sensor and the humidity sensor. Then, the temperature drift coefficient in the low-temperature drift compensation parameter table is used to preliminarily compensate the original temperature data to generate a preliminary compensated temperature value. Next, a humidity coupling factor calculation model is constructed to analyze the influence of the current humidity on the temperature measurement, generate a dynamic humidity influence weight, and generate a humidity compensation correction amount accordingly. The preliminary compensated temperature value and the humidity compensation correction amount are vectorially superimposed to obtain an intermediate compensated temperature value. Next, based on the time series of the intermediate compensated temperature value, the dynamic time warping algorithm is used to match the typical temperature decay characteristic curve in the low-temperature environment, extract the phase offset of the temperature fluctuation, and perform a time-domain alignment operation on it. Finally, through the piecewise polynomial fitting method, combined with the phase offset and the intermediate compensated temperature value, an accurate standard temperature equivalent value is generated to ensure reliable temperature measurement results even in complex low-temperature and high-humidity environments;

[0103] For example, in the area near the cold storage door, due to frequent personnel entry and exit, the temperature and humidity change greatly. Traditional temperature measurement methods are prone to errors. Therefore, multiple groups of distributed temperature sensors and humidity sensors are deployed in this area to monitor the temperature and humidity changes in real time. Based on the low-temperature drift compensation parameter table and the humidity coupling factor calculation model generated from historical data, the system can accurately compensate the temperature measurement value, eliminate the influence of humidity on the temperature reading, match the temperature decay characteristic curve through the dynamic time warping algorithm, predict the temperature change trend in the next period of time, and adjust the heating power and the opening degree of the solenoid valve in advance to ensure that the disinfectant can be vaporized under the best conditions, achieving an ideal atomization particle concentration and diffusion uniformity. This not only improves the disinfection effect but also enhances the equipment's response ability to environmental changes, ensuring that food safety and hygiene conditions are always in the best state.

[0104] To solve the problem that traditional disinfection equipment is difficult to accurately adjust the vaporization rate and heating power in a low-temperature and high-humidity environment and to further improve the utilization efficiency of the correlation between temperature and humidity data, as another embodiment, according to what is described in step 102, a dynamic adjustment factor is established based on the correlation between the standard temperature equivalent value and the real-time humidity data. When the standard temperature equivalent value is lower than the preset temperature threshold, humidity weighted correction is triggered and a humidity weighted correction result is generated, which specifically includes:

[0105] Compare the standard temperature equivalent value with a preset temperature threshold. When the standard temperature equivalent value is lower than the preset temperature threshold, trigger the humidity-weighted correction mechanism. Based on the fluctuation amplitude of the real-time humidity data within a preset time window, generate a dynamic humidity fluctuation coefficient through the exponential smoothing algorithm. Orthogonally project the dynamic humidity fluctuation coefficient onto the gradient change direction of the standard temperature equivalent value to generate a humidity influence weight. Construct a temperature-humidity coupling model. Input the standard temperature equivalent value and the real-time humidity data into a preset cluster of phase change characteristic curves for low-temperature environments, and generate a phase change compensation factor through interpolation calculation. Nonlinearly superimpose the phase change compensation factor and the humidity influence weight to generate an initial dynamic adjustment factor. Based on the change trend of the initial dynamic adjustment factor within a historical time window, match the temperature-humidity coupling characteristics in the low-temperature environment through the dynamic time warping algorithm, extract the temperature-humidity phase offset, and perform a time-domain alignment operation on the temperature-humidity phase offset and the initial dynamic adjustment factor to generate an intermediate dynamic adjustment factor. Construct a humidity-weighted correction model. Convolve the intermediate dynamic adjustment factor with the fluctuation spectrum of the real-time humidity data to generate a humidity correction component, and orthogonally couple the humidity correction component with the gradient change rate of the standard temperature equivalent value to generate a humidity-weighted correction result.

[0106] In this embodiment, the dynamic humidity fluctuation coefficient reflects the change rate and fluctuation range of the real-time humidity data over time, and is used to evaluate the degree of influence of the current humidity condition on temperature measurement. The humidity influence weight is the result of correcting the standard temperature equivalent value based on the humidity fluctuation coefficient, ensuring that the temperature reading is not interfered by humidity changes. The phase change compensation factor provides a compensation mechanism to adapt to the phase change characteristics under different conditions by analyzing the interaction between temperature and humidity in a low-temperature environment.

[0107] In the embodiment of the present application, first compare the standard temperature equivalent value with a preset temperature threshold. If the former is lower than the latter, start the humidity-weighted correction mechanism. Next, use the exponential smoothing algorithm to process the real-time humidity data to generate a dynamic humidity fluctuation coefficient, and orthogonally project it onto the gradient change direction of the standard temperature equivalent value to obtain a humidity influence weight. Then, construct a temperature-humidity coupling model, input the standard temperature equivalent value and the real-time humidity data into a cluster of phase change characteristic curves for low-temperature environments, generate a phase change compensation factor through interpolation calculation, and nonlinearly superimpose it with the humidity influence weight to generate an initial dynamic adjustment factor. Based on the historical change trend of the initial dynamic adjustment factor, use the dynamic time warping algorithm to match the temperature-humidity coupling characteristics, extract the temperature-humidity phase offset, and perform a time-domain alignment operation on it to generate an intermediate dynamic adjustment factor. Finally, through the humidity-weighted correction model, convolve the intermediate dynamic adjustment factor with the fluctuation spectrum of the real-time humidity data to generate a humidity correction component, and orthogonally couple it with the gradient change rate of the standard temperature equivalent value to generate the final humidity-weighted correction result.

[0108] For example, in the area near the cold storage door, due to frequent personnel entry and exit, the temperature and humidity change greatly, which may cause the traditional temperature control method to fail. Therefore, first, monitor and obtain the standard temperature equivalent value and real-time humidity data. Once it is found that the standard temperature equivalent value is lower than the preset threshold (such as 3°C), the humidity-weighted correction mechanism is triggered. The exponential smoothing algorithm is used to process the real-time humidity data to generate a dynamic humidity fluctuation coefficient, and based on this, a humidity influence weight is generated. Then, through the temperature-humidity coupling model, combined with the phase change characteristic curve cluster in the low-temperature environment, the phase change compensation factor is calculated and superimposed with the humidity influence weight to generate an initial dynamic adjustment factor. Based on the historical change trend of this factor, the dynamic time warping algorithm is used to match the temperature-humidity coupling characteristics, extract the phase offset amount, and generate an intermediate dynamic adjustment factor. Finally, through the humidity-weighted correction model, the system generates a humidity correction component, and orthogonally couples it with the gradient change rate of the standard temperature equivalent value to generate a humidity-weighted correction result, thereby precisely adjusting the solenoid valve opening and the heating element power to ensure that the disinfectant can be vaporized under the best conditions, maintain the ideal atomization particle concentration and diffusion uniformity, and ensure that food safety and hygiene conditions are always in the best state.

[0109] To solve the problem that traditional disinfection equipment is difficult to accurately control the vaporization rate and heating power in a complex low-temperature environment, and to further improve the utilization efficiency of the influence of temperature and humidity data on the vaporization efficiency, as another embodiment, according to what is described in step 103, the vaporization rate control parameter and the heating power grading parameter are generated based on the dynamic adjustment factor, and the solenoid valve opening of the vaporization device and the periodic working mode of the heating element are synchronously adjusted based on the vaporization rate control parameter and the heating power grading parameter, specifically including:

[0110] Decompose the dynamic adjustment factor into an aerodynamic component and a thermodynamic component. Map the aerodynamic component to the solenoid valve opening compensation coefficient through fuzzy control rules, and convert the thermodynamic component into the heating element power base through the dynamic weight distribution algorithm. Interpolate and calculate the gasification rate reference value based on the standard temperature equivalent value and real-time humidity data in a preset cluster of gasification characteristic curves, which are generated by fitting the phase change experimental data of the disinfectant in a low-temperature environment. Use the difference between the current standard temperature equivalent value and the preset temperature threshold as the abscissa and the real-time humidity data as the ordinate to locate the dynamic projection point on the three-dimensional characteristic surface constructed by the temperature-pressure coupling relationship. Calculate the non-linear superposition coefficient between the gasification rate reference value and the heating power base according to the curvature radius and normal vector direction of the dynamic projection point. Orthogonally couple the solenoid valve opening compensation coefficient and the non-linear superposition coefficient to generate the gasification rate control parameter, where the parameter value of the gasification rate control parameter generates a dynamic offset with the derivative of the temperature decay rate. Perform a convolution operation on the heating element power base and the historical sliding average of the dynamic adjustment factor to generate the heating power grading parameter including the preheating phase angle, where the size of the phase angle is positively correlated with the second integral value of the temperature gradient change trend. Construct a sequential cooperative control model for the solenoid valve opening and the periodic working mode of the heating element. Use the sequential cooperative control model to convert the gasification rate control parameter into the pulse width modulation waveform of the solenoid valve stepper motor, and map the heating power grading parameter to the duty cycle adjustment function of the heating element working cycle, where a preset time delay is formed between the leading edge of the solenoid valve opening change and the conduction moment of the heating element. Write the dynamic offset into the cache queue of the calibration parameter, and associate the phase angle change amount with the calculation process of the correction coefficient of the preheating reserve amount.

[0111] In this embodiment, the solenoid valve opening compensation coefficient reflects the influence degree of the aerodynamic component on the solenoid valve opening, and is used to adjust the dosage of the disinfectant entering the gasification device. The heating element power base represents the basic contribution of the thermodynamic component to the heating element power to ensure that the disinfectant can be quickly gasified at an appropriate temperature. The non-linear superposition coefficient is an adjustment factor calculated based on temperature and humidity data, and is used to optimize the balance relationship between the gasification rate reference value and the heating power base. The preheating phase angle refers to the pre-set heating angle to cope with the possible low-temperature environment in the future, and its size is positively correlated with the second integral value of the temperature gradient change trend.

[0112] In the embodiments of the present application, first, the dynamic adjustment factor is decomposed into an aerodynamic component and a thermodynamic component, which are respectively mapped to the solenoid valve opening compensation coefficient and the heating element power base value. Then, based on the standard temperature equivalent value and the real-time humidity data, the gasification rate reference value is interpolated and calculated in the preset gasification characteristic curve cluster. Next, the three-dimensional characteristic surface constructed by the temperature-pressure coupling relationship is used to locate the dynamic projection point, and the non-linear superposition coefficient is calculated according to its radius of curvature and the direction of the normal vector. Then, the solenoid valve opening compensation coefficient and the non-linear superposition coefficient are orthogonally coupled to generate the gasification rate control parameter, and the dynamic offset is generated by considering the derivative change of the temperature decay rate. For the heating element power base value, the heating power grading parameter including the preheating phase angle is generated through convolution operation combined with the historical moving average. In addition, a timing cooperative control model of the solenoid valve opening and the periodic working mode of the heating element is constructed, the gasification rate control parameter is converted into the pulse width modulation waveform of the solenoid valve stepping motor, and the heating power grading parameter is mapped to the duty cycle adjustment function of the heating element working cycle to ensure that the time delay between the two meets the preset requirements. Finally, the dynamic offset is written into the cache queue of the calibration parameter, and the phase angle change amount is associated with the calculation process of the correction coefficient of the preheating reserve amount to achieve intelligent adjustment;

[0113] For example, in the application scenario of the cold storage in a food processing factory, first, the dynamic adjustment factor is decomposed into an aerodynamic component and a thermodynamic component, which are respectively mapped to the solenoid valve opening compensation coefficient and the heating element power base value. Then, based on the standard temperature equivalent value and the real-time humidity data, the gasification rate reference value is interpolated and calculated in the preset gasification characteristic curve cluster. Next, the three-dimensional characteristic surface constructed by the temperature-pressure coupling relationship is used to locate the dynamic projection point, and the non-linear superposition coefficient is calculated according to its radius of curvature and the direction of the normal vector. Then, the solenoid valve opening compensation coefficient and the non-linear superposition coefficient are orthogonally coupled to generate the gasification rate control parameter, and the dynamic offset is generated by considering the derivative change of the temperature decay rate. For the heating element power base value, the heating power grading parameter including the preheating phase angle is generated through convolution operation combined with the historical moving average. In addition, a timing cooperative control model of the solenoid valve opening and the periodic working mode of the heating element is constructed, the gasification rate control parameter is converted into the pulse width modulation waveform of the solenoid valve stepping motor, and the heating power grading parameter is mapped to the duty cycle adjustment function of the heating element working cycle to ensure that the time delay between the two meets the preset requirements. Finally, through the intelligent adjustment mechanism, the solenoid valve opening and the heating element power can be accurately adjusted to ensure that the disinfectant can be gasified under the best conditions, maintain the ideal atomized particle concentration and diffusion uniformity, and ensure that the food safety and hygiene conditions are always in the best state.

[0114] To solve the problem that traditional disinfection equipment is difficult to accurately predict and respond to temperature changes in a low-temperature fluctuating environment, and to further improve the adaptability and response speed of the gasification device to environmental temperature changes, as another embodiment, as described in step 105, calculate the temperature decay rate within a time window based on the gradient change trend of real-time temperature data, and dynamically correct the preheating reserve in the heating power grading parameters according to the decay rate, so that the gasification device meets the preset atomized particle concentration threshold in a low-temperature fluctuating scenario, specifically including:

[0115] Based on the change sequence of real-time temperature data within a preset time window, generate a temperature gradient change sequence through differential calculation within a sliding time window, input the temperature gradient change sequence into a preset low-temperature decay characteristic curve cluster, and generate an initial temperature decay rate through interpolation calculation; construct a temperature decay rate correction model, perform a convolution operation on the initial temperature decay rate and the fluctuation spectrum of historical temperature decay data to generate a dynamic decay correction factor, perform a non-linear superposition operation on the dynamic decay correction factor and the initial temperature decay rate to generate a corrected temperature decay rate; based on the corrected temperature decay rate, match the historical response characteristics of the heating element power base through the dynamic time warping algorithm, extract the heating power response delay, perform a time-domain alignment operation on the heating power response delay and the corrected temperature decay rate to generate a dynamic preheating compensation coefficient; construct a preheating reserve correction model, perform an orthogonal projection on the dynamic preheating compensation coefficient and the preheating reserve in the heating power grading parameters to generate a preheating reserve correction amount, perform a vector superposition on the preheating reserve correction amount and the preheating reserve to generate an updated preheating reserve; input the updated preheating reserve into the heating element control module of the gasification device, and by adjusting the duty cycle and power output of the periodic working mode of the heating element, in this embodiment, the temperature gradient change sequence reflects the change rate of real-time temperature data within a specific time window and is used to analyze the trend of temperature decrease or increase. The dynamic decay correction factor is generated based on the fluctuation spectrum of historical temperature decay data and is used to correct the initial temperature decay rate to make it more accurately reflect the temperature change characteristics under the current environmental conditions. The preheating reserve correction amount is the result obtained by performing an orthogonal projection on the dynamic preheating compensation coefficient and the preheating reserve and is used to adjust the working intensity of the heating element to cope with possible future low-temperature situations;

[0116] In the embodiments of the present application, first, based on the change sequence of real-time temperature data within a preset time window, a temperature gradient change sequence is generated through differential calculation within a sliding time window, and it is input into a cluster of low-temperature attenuation characteristic curves. An initial temperature attenuation rate is generated through interpolation calculation. Then, a temperature attenuation rate correction model is constructed. The initial temperature attenuation rate is convolved with the fluctuation spectrum of historical temperature attenuation data to generate a dynamic attenuation correction factor, and the dynamic attenuation correction factor is non-linearly superimposed with the initial temperature attenuation rate to generate a corrected temperature attenuation rate. Then, based on the corrected temperature attenuation rate, the historical response characteristics of the heating sheet power base are matched through the dynamic time warping algorithm, and the heating power response delay amount is extracted. The heating power response delay amount is combined with the corrected temperature attenuation rate after time-domain alignment operation to generate a dynamic preheating compensation coefficient. Next, a preheating reserve correction model is constructed. The dynamic preheating compensation coefficient is orthogonally projected onto the preheating reserve in the heating power grading parameters to generate a preheating reserve correction amount, and the preheating reserve correction amount is vectorially superimposed with the preheating reserve to generate an updated preheating reserve. Finally, the updated preheating reserve is input into the heating sheet control module of the gasification device. By adjusting the duty cycle and power output of the periodic working mode of the heating sheet, it is ensured that the gasification device can maintain the best working state under low-temperature fluctuation scenarios and meet the preset atomized particle concentration threshold. Meet the preset atomized particle concentration threshold under low-temperature fluctuation scenarios;

[0117] In this embodiment, the temperature gradient change sequence reflects the change rate of real-time temperature data within a specific time window and is used to analyze the trend of temperature decrease or increase. The dynamic attenuation correction factor is generated based on the fluctuation spectrum of historical temperature attenuation data and is used to correct the initial temperature attenuation rate to make it more accurately reflect the temperature change characteristics under the current environmental conditions. The preheating reserve correction amount is the result obtained by orthogonally projecting the dynamic preheating compensation coefficient onto the preheating reserve and is used to adjust the working intensity of the heating sheet to cope with possible future low-temperature situations;

[0118] In the embodiment of the present application, first, based on the change sequence of real-time temperature data within a preset time window, a temperature gradient change sequence is generated through differential calculation within a sliding time window and input into a cluster of low-temperature attenuation characteristic curves. An initial temperature attenuation rate is generated through interpolation calculation. Then, a temperature attenuation rate correction model is constructed, and the initial temperature attenuation rate is convolved with the fluctuation spectrum of historical temperature attenuation data to generate a dynamic attenuation correction factor, which is non-linearly superimposed with the initial temperature attenuation rate to generate a corrected temperature attenuation rate. Then, based on the corrected temperature attenuation rate, the historical response characteristics of the heating element power base are matched through the dynamic time warping algorithm, and the heating power response delay is extracted and combined with the corrected temperature attenuation rate after time-domain alignment operation to generate a dynamic preheating compensation coefficient. Next, a preheating reserve correction model is constructed, and the dynamic preheating compensation coefficient is orthogonally projected onto the preheating reserve in the heating power grading parameters to generate a preheating reserve correction amount, which is vectorially superimposed with the preheating reserve to generate an updated preheating reserve. Finally, the updated preheating reserve is input into the heating element control module of the gasification device, and by adjusting the duty cycle and power output of the periodic working mode of the heating element, it is ensured that the gasification device can maintain the best working state under low-temperature fluctuation scenarios and meet the preset atomized particle concentration threshold.

[0119] Figure 2 FIG. is a schematic structural diagram of a gasification control system of an intelligent disinfection device based on low-temperature environment adaptation provided by an embodiment of the present application, as Figure 2 shown, the system includes:

[0120] A monitoring module 21, configured to monitor and obtain real-time temperature data and real-time humidity data of the low-temperature environment where the disinfection device is located, and perform temperature compensation processing on the real-time temperature data to generate a standard temperature equivalent value;

[0121] A trigger module 22, configured to establish a dynamic adjustment factor based on the correlation between the standard temperature equivalent value and the real-time humidity data, and trigger humidity weighted correction and generate a humidity weighted correction result when the standard temperature equivalent value is lower than a preset temperature threshold;

[0122] An adjustment module 23, configured to generate a gasification rate control parameter and a heating power grading parameter according to the dynamic adjustment factor, and synchronously adjust the solenoid valve opening degree of the gasification device and the periodic working mode of the heating element based on the gasification rate control parameter and the heating power grading parameter;

[0123] A feedback module 24 is configured to monitor in real time the concentration distribution and diffusion uniformity of the atomized particles output by the gasification device during the process of adjusting the gasification device. When the deviation of the concentration distribution and diffusion uniformity of the atomized particles from the preset gasification efficiency range reaches a set threshold, calibration parameters are generated through the reverse correction of the dynamic adjustment factor, and the calibration parameters are fed back to the generation process of the dynamic adjustment factor to form a closed-loop parameter update link;

[0124] A correction module 25 is configured to calculate the temperature decay rate within a time window based on the gradient change trend of real-time temperature data, and dynamically correct the preheating reserve in the heating power grading parameters according to the decay rate, so that the gasification device meets the preset atomized particle concentration threshold under low-temperature fluctuation scenarios.

[0125] Figure 2 The described intelligent disinfection equipment gasification control system based on low-temperature environment adaptability can execute Figure 1 The described intelligent disinfection equipment gasification control method based on low-temperature environment adaptability in the illustrated embodiment, its implementation principle and technical effects will not be elaborated. For the intelligent disinfection equipment gasification control system based on low-temperature environment adaptability in the above embodiment, the specific ways in which each module and unit perform operations have been described in detail in the embodiment related to the method, and will not be elaborated here.

[0126] In a possible design, Figure 2 The intelligent disinfection equipment gasification control system based on low-temperature environment adaptability in the illustrated embodiment can be implemented as a computing device, such as Figure 3 shown, and the computing device may include a storage component 31 and a processing component 32;

[0127] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32.

[0128] The processing component 32 is configured to: monitor and obtain the real-time temperature data and real-time humidity data of the low-temperature environment where the disinfection device is located, perform temperature compensation processing on the real-time temperature data to generate a standard temperature equivalent value; establish a dynamic adjustment factor based on the correlation between the standard temperature equivalent value and the real-time humidity data, trigger humidity weighted correction and generate a humidity weighted correction result when the standard temperature equivalent value is lower than a preset temperature threshold; generate a gasification rate control parameter and a heating power grading parameter according to the dynamic adjustment factor, and synchronously adjust the solenoid valve opening degree and the periodic working mode of the heating element of the gasification device based on the gasification rate control parameter and the heating power grading parameter; during the process of adjusting the gasification device, monitor the concentration distribution and diffusion uniformity of the atomized particles output by the gasification device in real time, and when the deviation amount between the concentration distribution and diffusion uniformity of the atomized particles and the preset gasification efficiency interval reaches a set threshold, generate a calibration parameter through the reverse correction of the dynamic adjustment factor, and feedback the calibration parameter to the generation process of the dynamic adjustment factor to form a closed-loop parameter update link; calculate the temperature decay rate within a time window based on the gradient change trend of the real-time temperature data, and dynamically correct the preheating reserve amount in the heating power grading parameter according to the decay rate, so that the gasification device meets the preset atomized particle concentration threshold in a low-temperature fluctuation scenario.

[0129] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0130] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0131] Of course, the computing device may also necessarily include other components, such as input / output interfaces, display components, communication components, etc.

[0132] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above peripheral interface module may be an output device, an input device, etc.

[0133] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0134] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from a cloud computing platform.

[0135] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above-mentioned Figure 1 intelligent disinfection device gasification control method based on adaptation to low temperature environment shown in the embodiment.

[0136] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0137] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0138] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0139] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A gasification control method for intelligent disinfection equipment based on low temperature environment adaptation, characterized in that: include: Monitor and obtain the real-time temperature data and real-time humidity data of the low-temperature environment where the disinfection equipment is located, and perform temperature compensation processing on the real-time temperature data to generate a standard temperature equivalent value; A dynamic adjustment factor is established based on the correlation between the standard temperature equivalent value and the real-time humidity data, and when the standard temperature equivalent value is lower than a preset temperature threshold, a humidity weighted correction is triggered and a humidity weighted correction result is generated; Generate a gasification rate control parameter and a heating power classification parameter according to the dynamic adjustment factor, and synchronously adjust the opening of the solenoid valve of the gasification device and the periodic working mode of the heating plate based on the gasification rate control parameter and the heating power classification parameter; During the process of adjusting the gasification device, the concentration distribution and diffusion uniformity of the atomized particles output by the gasification device are monitored in real time. When the deviation of the concentration distribution and diffusion uniformity of the atomized particles from the preset gasification efficiency range reaches a set threshold, a calibration parameter is generated by reverse correction of the dynamic adjustment factor, and the calibration parameter is fed back to the generation process of the dynamic adjustment factor to form a closed-loop parameter update link; The temperature decay rate within the time window is calculated based on the gradient change trend of the real-time temperature data, and the pre-heating reserve in the heating power classification parameter is dynamically corrected according to the decay rate, so that the gasification device can meet the preset atomization particle concentration threshold under the low temperature fluctuation scenario.

2. The method according to claim 1, characterized in that During the process of adjusting the gasification device, the concentration distribution and diffusion uniformity of the atomized particles output by the gasification device are monitored in real time. When the deviation of the concentration distribution and diffusion uniformity of the atomized particles from the preset gasification efficiency range reaches a set threshold, the calibration parameters are generated by reverse correction of the dynamic adjustment factor, and the calibration parameters are fed back to the generation process of the dynamic adjustment factor to form a closed-loop parameter update link, including: A multi-layer annular sensor array is deployed around the gasification device, and each group of sensors collects atomized particle density distribution data of the spatial partition at a preset sampling frequency. A discretized spatial point cloud set is constructed based on the atomized particle density distribution data, and an initial three-dimensional concentration distribution field is generated by combining the geometric topological structure of the disinfection area with a radial basis function interpolation algorithm. The initial three-dimensional concentration distribution field is spatially smoothed to generate an enhanced three-dimensional concentration distribution field; Extracting the axial concentration gradient modulus and the tangential diffusion uniformity index of the enhanced three-dimensional concentration distribution field, performing residual calculation on the gradient modulus and the theoretical gradient range output by the preset diffusion dynamics model to generate a first deviation, and performing nonlinear mapping on the tangential diffusion uniformity index and the boundary value of the preset performance interval to generate a second deviation; Constructing a correlation matrix of the dynamic adjustment factor, the first deviation and the second deviation, projecting the first deviation along the direction of the temperature attenuation rate to generate an aerodynamic correction component, projecting the second deviation along the direction of the humidity weighted correction result to generate a thermodynamic correction component, dynamically time-warping the aerodynamic correction component with the historical change trajectory of the solenoid valve opening compensation coefficient to generate an opening calibration offset, and convolving the thermodynamic correction component with the fluctuation spectrum of the heating plate power base to generate a power calibration offset; Inputting the opening calibration offset and the power calibration offset into a preset back propagation model, and using the back propagation model to update the aerodynamic component weight and the thermodynamic component weight of the dynamic adjustment factor to generate a calibration parameter set; The offsets of adjacent timestamps in the calibration parameter set are phase-aligned to generate a closed-loop parameter increment sequence, and the closed-loop parameter increment sequence is subjected to a time-delay compensation superposition operation with each component of the dynamic adjustment factor to generate an updated dynamic adjustment factor. The updated dynamic adjustment factor is fed back to the generation process of the gasification rate control parameter and the heating power grading parameter to generate a closed-loop parameter update link.

3. The method according to claim 2, characterized in that Input the opening calibration offset and the power calibration offset into a preset back propagation model, use the back propagation model to update the aerodynamic component weight and the thermodynamic component weight of the dynamic adjustment factor, and generate a calibration parameter set, including: Decomposing the opening calibration offset along the axial direction of the gasification device into a high-frequency fluctuation component and a low-frequency trend component, and decomposing the power calibration offset along the working cycle of the heating plate into a transient response component and a steady-state response component; A pneumatic component weight update model is constructed. The component weight update model is used to perform correlation analysis between the spectrum characteristics of the high-frequency fluctuation component and the historical spectrum of the solenoid valve opening compensation coefficient to generate the pneumatic spectrum matching degree. The fitting curve of the low-frequency trend component is orthogonally projected with the gradient change direction of the current standard temperature equivalent value to generate the pneumatic trend correction coefficient. Based on the pneumatic spectrum matching degree and the pneumatic trend correction coefficient, the pneumatic component weight is iteratively updated by the weighted least squares method to obtain the updated pneumatic component weight. A thermodynamic component weight update model is constructed, and the time constant of the transient response component is dynamically time-warped and matched with the historical transient response data of the heating plate power base using the thermodynamic component weight update model to generate a thermodynamic transient matching degree; the stable value of the steady-state response component and the rate of change of the humidity weighted correction result are nonlinearly regressed to generate a thermodynamic steady-state correction coefficient; based on the thermodynamic transient matching degree and the thermodynamic steady-state correction coefficient, the thermodynamic component weight is iteratively updated through a gradient descent optimization algorithm to obtain an updated thermodynamic component weight; The updated aerodynamic component weights and the updated thermodynamic component weights are input into the back propagation model. The back propagation model generates a calibration parameter set through the error back propagation mechanism within the sliding time window based on the time domain differential characteristics of the current standard temperature equivalent value and the frequency domain response characteristics of the humidity weighted correction result.

4. The method according to claim 1, characterized in that: Monitor and obtain the real-time temperature data and real-time humidity data of the low-temperature environment where the disinfection equipment is located, and perform temperature compensation processing on the real-time temperature data to generate a standard temperature equivalent value, including: Deploy multiple groups of distributed temperature sensors and distributed humidity sensors on the surface of the disinfection equipment, and each group of temperature sensors and distributed humidity sensors synchronously collects local environmental temperature data and local environmental humidity data at preset time intervals; A low temperature drift compensation parameter table is established based on the historical calibration data of the temperature sensor, the corresponding temperature drift coefficient is matched according to the interval where the local ambient temperature data is located, and the local ambient temperature data collected by the distributed temperature sensor is nonlinearly superimposed with the temperature drift coefficient to generate a preliminary compensation temperature value; A humidity coupling factor calculation model was constructed to perform an exponential decay convolution operation on the local environmental humidity data and the historical humidity fluctuation amplitude to generate a dynamic humidity impact weight. Orthogonally project the dynamic humidity influence weight and the preliminary compensation temperature value to generate a humidity compensation correction, perform vector superposition of the preliminary compensation temperature value and the humidity compensation correction to generate an intermediate compensation temperature value, and based on the gradient change trend of the intermediate compensation temperature value within a preset time window, match the low temperature environment temperature attenuation characteristic curve through a dynamic time warping algorithm to extract the temperature fluctuation phase offset; The phase offset and the intermediate compensation temperature value are aligned in time domain to generate a dynamic compensation factor, and the dynamic compensation factor and the intermediate compensation temperature value are fitted with a piecewise polynomial to generate a standard temperature equivalent value.

5. The method according to claim 1, characterized in that A dynamic adjustment factor is established based on the correlation between the standard temperature equivalent value and the real-time humidity data. When the standard temperature equivalent value is lower than a preset temperature threshold, a humidity weighted correction is triggered and a humidity weighted correction result is generated, including: The standard temperature equivalent value is compared with the preset temperature threshold. When the standard temperature equivalent value is lower than the preset temperature threshold, the humidity weighted correction mechanism is triggered. Based on the fluctuation amplitude of the real-time humidity data within the preset time window, a dynamic humidity fluctuation coefficient is generated through an exponential smoothing algorithm. The dynamic humidity fluctuation coefficient is orthogonally projected with the gradient change direction of the standard temperature equivalent value to generate a humidity influence weight. Construct a temperature-humidity coupling model, input the standard temperature equivalent value and real-time humidity data into the preset low-temperature environment phase change characteristic curve cluster, generate the phase change compensation factor through interpolation calculation, perform nonlinear superposition operation on the phase change compensation factor and the humidity influence weight, and generate the initial dynamic adjustment factor; Based on the changing trend of the initial dynamic adjustment factor in the historical time window, the temperature and humidity coupling characteristics in the low-temperature environment are matched by the dynamic time warping algorithm, the temperature and humidity phase offset is extracted, and the temperature and humidity phase offset is aligned with the initial dynamic adjustment factor in the time domain to generate an intermediate dynamic adjustment factor; A humidity weighted correction model is constructed, and the intermediate dynamic adjustment factor is convolved with the fluctuation spectrum of the real-time humidity data to generate a humidity correction component. The humidity correction component is orthogonally coupled with the gradient change rate of the standard temperature equivalent value to generate a humidity weighted correction result.

6. The method according to claim 1, characterized in that Generate a gasification rate control parameter and a heating power classification parameter according to the dynamic adjustment factor, and synchronously adjust the solenoid valve opening of the gasification device and the periodic working mode of the heating plate based on the gasification rate control parameter and the heating power classification parameter, including: The dynamic adjustment factor is decomposed into a pneumatic component and a thermodynamic component, and the pneumatic component is mapped to the electromagnetic valve opening compensation coefficient through fuzzy control rules, and the thermodynamic component is converted into the heating plate power base through a dynamic weight distribution algorithm; Based on the standard temperature equivalent value and the real-time humidity data, the vaporization rate reference value is calculated by interpolation in a preset vaporization characteristic curve cluster, wherein the vaporization characteristic curve cluster is generated by fitting the phase change experimental data of the disinfectant in a low temperature environment; The difference between the current standard temperature equivalent value and the preset temperature threshold is used as the horizontal coordinate, and the real-time humidity data is used as the vertical coordinate. The dynamic projection point is located on the three-dimensional characteristic surface constructed by the temperature-pressure coupling relationship, and the nonlinear superposition coefficient of the vaporization rate reference value and the heating power base is calculated according to the curvature radius and normal vector direction of the dynamic projection point. The electromagnetic valve opening compensation coefficient and the nonlinear superposition coefficient are orthogonally coupled to generate a gasification rate control parameter, wherein the parameter value of the gasification rate control parameter changes with the derivative of the temperature decay rate to generate a dynamic offset; The heating plate power base and the historical sliding mean of the dynamic adjustment factor are convolved to generate a heating power grading parameter including a preheating phase angle, wherein the phase angle is positively correlated with the quadratic integral value of the temperature gradient change trend; A timing coordinated control model of the solenoid valve opening and the periodic working mode of the heating plate is constructed. The gasification rate control parameter is converted into a pulse width modulation waveform of the solenoid valve stepper motor using the timing coordinated control model, and the heating power classification parameter is mapped into a duty cycle adjustment function of the heating plate working cycle, wherein a preset time delay is formed between the front edge of the solenoid valve opening change and the heating plate conduction moment; The dynamic offset is written into the cache queue of the calibration parameters, and the phase angle change is associated with the correction coefficient calculation process of the preheating reserve.

7. The method according to claim 1, characterized in that The temperature decay rate in the time window is calculated based on the gradient change trend of the real-time temperature data, and the preheating reserve in the heating power classification parameter is dynamically corrected according to the decay rate, so that the gasification device meets the preset atomization particle concentration threshold in the low temperature fluctuation scenario, including: Based on the change sequence of real-time temperature data within a preset time window, a temperature gradient change sequence is generated by differential calculation within a sliding time window, the temperature gradient change sequence is input into a preset low-temperature decay characteristic curve cluster, and an initial temperature decay rate is generated by interpolation calculation; Constructing a temperature decay rate correction model, performing a convolution operation on the initial temperature decay rate and the fluctuation spectrum of the historical temperature decay data to generate a dynamic decay correction factor, performing a nonlinear superposition operation on the dynamic decay correction factor and the initial temperature decay rate to generate a corrected temperature decay rate; Based on the corrected temperature decay rate, the historical response characteristics of the heating plate power base are matched through a dynamic time warping algorithm, the heating power response delay is extracted, and the heating power response delay is aligned with the corrected temperature decay rate in the time domain to generate a dynamic preheating compensation coefficient; Constructing a preheating reserve correction model, orthogonally projecting the dynamic preheating compensation coefficient and the preheating reserve in the heating power classification parameter to generate a preheating reserve correction, and vector-superimposing the preheating reserve correction with the preheating reserve to generate an updated preheating reserve; The updated preheating reserve is input into the heating plate control module of the gasifier, and the duty cycle and power output of the periodic working mode of the heating plate are adjusted so that the gasifier can meet the preset atomized particle concentration threshold in the low temperature fluctuation scenario.

8. A gasification control system for intelligent disinfection equipment based on low temperature environment adaptation, characterized in that: include: A monitoring module is used to monitor and obtain real-time temperature data and real-time humidity data of the low-temperature environment in which the disinfection equipment is located, and to perform temperature compensation processing on the real-time temperature data to generate a standard temperature equivalent value; A trigger module, which establishes a dynamic adjustment factor based on the correlation between the standard temperature equivalent value and the real-time humidity data, triggers humidity weighted correction when the standard temperature equivalent value is lower than a preset temperature threshold, and generates a humidity weighted correction result; A regulating module, used for generating a gasification rate control parameter and a heating power classification parameter according to a dynamic adjustment factor, and synchronously regulating the opening of the solenoid valve of the gasification device and the periodic working mode of the heating plate based on the gasification rate control parameter and the heating power classification parameter; A feedback module is used to monitor the concentration distribution and diffusion uniformity of the atomized particles output by the gasification device in real time during the process of adjusting the gasification device. When the deviation of the concentration distribution and diffusion uniformity of the atomized particles from the preset gasification efficiency range reaches a set threshold, a calibration parameter is generated by reverse correction of the dynamic adjustment factor, and the calibration parameter is fed back to the generation process of the dynamic adjustment factor to form a closed-loop parameter update link; The correction module is used to calculate the temperature decay rate within the time window based on the gradient change trend of the real-time temperature data, and dynamically correct the pre-heating reserve in the heating power classification parameter according to the decay rate, so that the gasification device meets the preset atomization particle concentration threshold in the low temperature fluctuation scenario.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a gasification control method for intelligent disinfection equipment based on low-temperature environment adaptation as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a gasification control method for intelligent disinfection equipment based on low-temperature environment adaptation as described in any one of claims 1 to 7 is implemented.

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