A method and system for controlling the production and processing quality of disinfection equipment based on intelligent analysis

Abstract

The present application provides a method and system for controlling the production and processing quality of disinfection equipment based on intelligent analysis. Among them, precise control of the drying process is achieved through three-stage collaborative optimization. First, the hot air distribution parameters and historical energy consumption data of the drying process are integrated to construct a collaborative optimization model of hot air uniformity and energy efficiency, and establish a dynamic target parameter system. Secondly, the temperature field distribution data is collected in real time during the operation of the equipment, and a multi-dimensional dynamic correlation analysis is performed with the energy consumption data of the heating device. The abnormal fluctuations of the thermal field and the energy consumption correlation characteristics are identified through machine learning algorithms. Finally, dynamic adjustment instructions for heating power are generated based on the analysis results. The synergy of differential control algorithms and variable frequency regulation technology is adopted to accurately convert process requirements into power supply frequency control signals, thereby realizing millisecond-level dynamic response and adaptive adjustment of heating power. The technical solution provided by the present application can improve the efficiency and accuracy of production and processing quality control of disinfection equipment.

Description

Technical Field

[0001] The present application relates to the technical field of production and processing quality control of disinfection equipment, and in particular to a method and system for production and processing quality control of disinfection equipment based on intelligent analysis. Background Art

[0002] During the production and processing of disinfection equipment, the thermal field uniformity and energy efficiency of the heating system are core factors affecting product quality and production costs. The high-temperature drying process needs to ensure the uniformity of the temperature distribution inside the box to avoid local overheating or under-drying that causes material degeneration. At the same time, the energy efficiency of the heating device needs to be optimized to reduce carbon emissions and operating costs. However, the dynamic disturbances of the thermal field (such as fan airflow fluctuations, material heat absorption differences) and the energy consumption characteristics of multivariable coupling make it difficult for traditional control methods to achieve the coordinated optimization of uniformity and energy efficiency, and an intelligent dynamic control method is urgently needed.

[0003] The current mainstream solution utilizes a thermal field regulation system based on differential control. This system deploys multiple temperature sensors to collect real-time hot air temperature data. This system then adjusts the heating module's power in stages, combining preset temperature thresholds with energy consumption benchmarks. This system uses a feedback mechanism to correct temperature deviations and utilizes static energy consumption models (such as linear regression) to predict energy consumption trends, achieving preliminary optimization of both temperature stability and energy consumption.

[0004] This solution has three shortcomings. First, differential control relies on fixed parameters and is difficult to adapt to variable working conditions (such as sudden changes in material load), resulting in delayed dynamic compensation of the temperature field and easy over-limit of uniformity deviation. Secondly, the static energy consumption model cannot correlate the temperature field distribution with the transient power demand of the heating device in real time, resulting in a disconnect between energy consumption optimization and thermal field regulation, and the actual operating energy consumption is still higher than the theoretical value. Furthermore, it relies only on temperature feedback data and lacks the integrated analysis of multiple physical field parameters such as hot air flow rate and humidity. It is difficult to analyze the deep coupling relationship between thermal field unevenness and energy consumption anomalies, and the accuracy of control instructions is limited. Summary of the Invention

[0005] The present application provides a method and system for controlling the production and processing quality of disinfection equipment based on intelligent analysis, which is used to solve the problems of low efficiency and poor accuracy in the production and processing quality control of disinfection equipment in the prior art.

[0006] In the first aspect, the present application provides a method for controlling the production and processing quality of disinfection equipment based on intelligent analysis, comprising:

[0007] Obtain the hot air distribution parameters corresponding to the drying technology in the production process of the disinfection equipment, and simultaneously collect the historical energy consumption data of the heating device, and build the collaborative optimization target parameters of hot air uniform distribution and energy efficiency based on the hot air distribution parameters and historical energy consumption data;

[0008] According to the collaborative optimization target parameters, the dynamic distribution data of the temperature field during the heating operation of the disinfection equipment is obtained, and the dynamic correlation analysis of the dynamic distribution data of the temperature field and the real-time energy consumption data of the heating device in the disinfection equipment is performed to obtain the results of the dynamic correlation analysis;

[0009] A heating power adjustment instruction is generated based on the result of the dynamic correlation analysis, and is converted into a control signal of the power supply frequency by utilizing the synergistic effect of differential control and variable frequency regulation according to the heating power adjustment instruction.

[0010] Optionally, converting the heating power adjustment instruction into a control signal of the power supply frequency by utilizing the synergistic effect of differential control and variable frequency regulation includes:

[0011] Performing differential control processing on the heating power adjustment instruction, and extracting the attenuation coefficient of the high-frequency fluctuation component of the heating power and the compensation amount of the accumulated deviation to generate an intermediate adjustment signal;

[0012] Calculating a reference offset of the power supply frequency based on the intermediate adjustment signal and a frequency power response curve of the heating device;

[0013] Inputting the reference offset into the dynamic weight distribution mechanism of variable frequency regulation and combining it with the spatial propagation delay characteristics of the hot air distribution parameters to generate the frequency adjustment step and phase compensation threshold;

[0014] The proportional relationship between the frequency adjustment step and the phase compensation threshold is adjusted so that the instantaneous change rate of the power supply frequency matches the spatiotemporal attenuation rate of the temperature gradient in the hot air distribution parameter, and the control signal of the power supply frequency is output.

[0015] Optionally, adjusting the proportional relationship between the frequency adjustment step and the phase compensation threshold includes:

[0016] Based on the propagation direction and spatiotemporal attenuation rate of the temperature gradient in the hot air distribution parameters, a dynamic mapping relationship between the instantaneous change rate of the power supply frequency and the spatiotemporal attenuation rate of the temperature gradient is established to extract the initial proportional coefficient of the frequency adjustment step and the phase compensation threshold;

[0017] Determining a dynamic adjustment factor of the initial proportional coefficient according to a real-time deviation between the instantaneous change rate and the spatiotemporal attenuation rate in the dynamic mapping relationship;

[0018] The dynamic adjustment factor and the initial proportional coefficient are input into a dynamic weight allocation mechanism, and the proportional relationship between the frequency adjustment step and the phase compensation threshold is subjected to multi-dimensional weighted fusion in combination with the propagation path length of the maximum temperature gradient in the hot air distribution parameter to generate an updated proportional coefficient;

[0019] Based on the updated proportional coefficient, the instantaneous change rate is iteratively approximated, and when the cumulative error between the instantaneous change rate and the spatiotemporal attenuation rate is lower than a preset threshold, an adjusted proportional relationship is obtained.

[0020] Optionally, when the cumulative error between the instantaneous rate of change and the spatiotemporal attenuation rate is lower than a preset threshold, obtaining the adjusted proportional relationship includes:

[0021] Calculating the cumulative error of the instantaneous change rate based on the updated proportional coefficient and the real-time monitoring data of the spatiotemporal attenuation rate of the hot air distribution parameter;

[0022] Dynamically adjusting the adaptation range of the preset threshold value according to the cumulative error amount and the propagation path length of the maximum temperature gradient in the hot air distribution parameter;

[0023] When the cumulative error is lower than the adjusted preset threshold, performing multi-cycle fluctuation suppression verification on the instantaneous change rate based on the spatial propagation delay characteristics of the hot air distribution parameter and the attenuation trend of the cumulative error;

[0024] After the multi-period fluctuation suppression verification is passed, the global locking instruction is triggered in combination with the long-term convergence trend slope of the cumulative error amount and the critical propagation rate of the space-time decay rate, and the adjusted proportional relationship is output.

[0025] Optionally, the dynamically correlating analysis of the temperature field dynamic distribution data with the real-time energy consumption data of the heating device includes:

[0026] Extracting the temperature deviation propagation direction and energy consumption fluctuation coefficient based on the temperature deviation of each position point in the temperature field dynamic distribution data and the real-time energy consumption data of the heating device;

[0027] The temperature deviation propagation direction and the energy consumption fluctuation coefficient are coupled and analyzed to generate a joint optimization index of the hot air uniform distribution and the attenuation rate of the energy consumption efficiency;

[0028] Dynamically modify the weight ratio of the joint optimization index based on the mean shift of historical energy consumption data and the propagation delay characteristics of the temperature gradient;

[0029] Based on the spatiotemporal consistency of the temperature gradient propagation direction and the thermal inertia parameter, the corrected weight ratio is verified through multi-cycle iteration, and a dynamic correlation analysis result is output when the verification error is lower than the tolerance boundary of the joint optimization indicator.

[0030] Optionally, dynamically revising the weight ratio of the joint optimization index based on the mean shift of the historical energy consumption data and the propagation delay characteristics of the temperature gradient includes:

[0031] Determining a dynamic correction factor for the attenuation rate of hot air uniform distribution and energy efficiency based on the fluctuation amplitude of the mean offset of the historical energy consumption data and the attenuation rate of the propagation delay characteristic of the temperature gradient;

[0032] The dynamic correction factor is input into the weight distribution mechanism of the joint optimization index, and the weight ratio of the attenuation rate of the hot air uniform distribution and energy efficiency is corrected in a multi-dimensional coupling manner in combination with the real-time change rate of the propagation direction of the temperature gradient and the thermal inertia parameter of the heating device;

[0033] Based on the corrected weight ratio, calculating the coordinated deviation between the maximum temperature gradient in the temperature field dynamic distribution data and the real-time energy consumption data of the heating device, and determining the optimal weight ratio when the attenuation trend of the coordinated deviation matches the stability boundary of the thermal inertia parameter;

[0034] The optimal weight ratio is fed back to the dynamic mapping relationship mechanism of the joint optimization index, the association logic of the thermal field energy consumption mapping in the heating device is updated and the dynamic correction result is output.

[0035] Optionally, the constructing of collaborative optimization target parameters for hot air uniform distribution and energy efficiency based on the hot air distribution parameters and historical energy consumption data includes:

[0036] Decomposing the hot air distribution parameter into multiple orthogonal modal components in the spatial dimension, and calculating the energy proportion coefficient and spatial variation index of the orthogonal modal components;

[0037] Constructing a multi-dimensional decomposition model of the historical energy consumption data, extracting characteristic vectors dynamically associated with the temperature field of the heating device during different operating cycles, and calculating the stability index of the characteristic vectors;

[0038] Establishing an asymmetric mapping relationship between the energy proportion coefficient and the stability index, and determining the sensitivity coefficient of hot air uniform distribution to energy efficiency;

[0039] A collaborative optimization function is constructed based on the spatial variation index and the sensitivity coefficient, and the dynamic variation range of the eigenvector is used as a constraint condition to generate a dual-objective optimization boundary;

[0040] By iteratively correcting the weight distribution of the orthogonal modal components, the collaborative optimization function is made to satisfy the Pareto optimal condition within the dual-objective optimization boundary, thereby generating collaborative optimization target parameters for hot air uniform distribution and energy efficiency.

[0041] In a second aspect, the present application provides a disinfection equipment production and processing quality control system based on intelligent analysis, including:

[0042] An acquisition module is used to obtain the hot air distribution parameters corresponding to the drying technology in the production process of the disinfection equipment, and simultaneously collect the historical energy consumption data of the heating device. Based on the hot air distribution parameters and the historical energy consumption data, the coordinated optimization target parameters of the hot air uniform distribution and energy efficiency are constructed;

[0043] an analysis module, which obtains the dynamic distribution data of the temperature field during the heating operation of the disinfection equipment according to the collaborative optimization target parameters, and performs dynamic correlation analysis on the dynamic distribution data of the temperature field and the real-time energy consumption data of the heating device in the disinfection equipment to obtain a result of the dynamic correlation analysis;

[0044] A generation module generates a heating power adjustment instruction based on the result of the dynamic correlation analysis, and converts the heating power adjustment instruction into a power supply frequency control signal by utilizing the synergistic effect of differential control and variable frequency regulation.

[0045] In a third aspect, an embodiment of the present application provides a computing device comprising 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 method for controlling the production and processing quality of disinfection equipment based on intelligent analysis as described in the first aspect above.

[0046] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements a method for controlling the production and processing quality of disinfection equipment based on intelligent analysis as described in the first aspect.

[0047] In an embodiment of the present application, the hot air distribution parameters corresponding to the drying technology in the production process of the disinfection equipment are obtained, and the historical energy consumption data of the heating device are simultaneously collected, and the collaborative optimization target parameters of the hot air uniform distribution and energy efficiency are constructed based on the hot air distribution parameters and the historical energy consumption data; according to the collaborative optimization target parameters, the dynamic distribution data of the temperature field during the heating operation of the disinfection equipment is obtained, and the dynamic distribution data of the temperature field is dynamically correlated with the real-time energy consumption data of the heating device in the disinfection equipment to obtain the result of the dynamic correlation analysis; based on the result of the dynamic correlation analysis, a heating power adjustment instruction is generated, and according to the heating power adjustment instruction, the synergistic effect of differential control and variable frequency regulation is used to convert it into a control signal of the power supply frequency.

[0048] The technical solution of this application has the following beneficial effects:

[0049] This application constructs a multi-objective optimization model of thermal field uniformity and energy efficiency by synchronously collecting hot air distribution parameters and historical energy consumption data, providing quantitative benchmark parameters for dynamic control, and solving the problem of separation between thermal performance and energy efficiency in traditional single-objective optimization. Real-time fusion of temperature field dynamic distribution data and heating energy consumption data, based on multi-source data coupling analysis, accurately locates the related nodes of thermal field fluctuations and energy consumption anomalies, and improves the collaborative diagnosis capability of process disturbances and energy consumption offsets. Through the collaborative mechanism of differential control and variable frequency regulation, the power regulation demand is converted into a high-precision power supply frequency control signal, realizing millisecond-level closed-loop control of thermal field dynamic compensation and energy consumption optimization.

[0050] Furthermore, the power regulation instructions are dynamically de-noised and compensated for deviations based on the differential algorithm. A reference frequency offset model is established in conjunction with the frequency-power response characteristics of the heating device, further integrating the spatial delay law of hot air propagation. A coupling relationship between the frequency adjustment step and the phase compensation parameter is established through a dynamic weight allocation strategy, ultimately achieving dynamic adaptation of the transient response rate of the power supply frequency and the gradient attenuation process of the temperature field. High-frequency noise interference is suppressed and historical deviations are corrected through the differential algorithm. The frequency conversion control timing is optimized in conjunction with the spatial propagation characteristics of the thermal field to ensure that the power supply frequency change rate accurately matches the dynamic attenuation process of the temperature field, thereby simultaneously improving the response speed of the thermal field uniformity control and the dynamic adaptability of the frequency conversion energy efficiency conversion.

[0051] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0053] Figure 1 A flow chart of a method for controlling the production and processing quality of disinfection equipment based on intelligent analysis provided by the present application is shown;

[0054] Figure 2 The present invention provides a schematic diagram of a production and processing quality control system for disinfection equipment based on intelligent analysis;

[0055] Figure 3 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0056] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0057] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. 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 of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0058] Researchers have found that it is difficult to dynamically coordinate and optimize the hot air distribution and energy efficiency in the production process of existing disinfection equipment, resulting in uneven drying and high energy consumption. Based on this, a method for controlling the production and processing quality of disinfection equipment based on intelligent analysis is provided. This method can achieve precise control of the thermal field distribution in the drying process and simultaneous improvement of energy utilization efficiency by constructing coordinated optimization parameters for hot air uniform distribution and energy efficiency, combining the correlation analysis of temperature field dynamic data and real-time energy consumption, and using differential control and variable frequency regulation to coordinately control the power supply frequency. This solution is suitable for scenarios where the uniformity and energy consumption of the heating system of disinfection equipment are optimized. A dynamic optimization model is constructed through multi-source data fusion (hot air distribution parameters and historical energy consumption data), and based on the dynamic correlation analysis of real-time temperature field distribution data and heating energy consumption, the coupling relationship between thermal field fluctuations and energy consumption anomalies is identified. Then, a differential control algorithm is used to analyze the high-frequency disturbance characteristics of power regulation, and variable frequency regulation technology is combined to dynamically compensate for the time-space attenuation rate matching of the power supply frequency, ultimately achieving closed-loop control of precise thermal field regulation and energy efficiency optimization.

[0059] The entire research process uses intelligent analysis methods to collaboratively optimize and model the hot air distribution parameters and heating energy consumption data, dynamically correlate the temperature field distribution and real-time energy consumption information, and combine the collaborative control mechanism of differential control and variable frequency regulation. It aims to solve the problems of uneven hot air distribution and low energy efficiency in the production of traditional disinfection equipment, significantly improve the uniformity and stability of the drying process, and at the same time achieve precise optimization and control of energy consumption, thereby reducing energy waste while ensuring product quality, and providing an efficient and intelligent quality control solution for the production and processing of disinfection equipment.

[0060] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0061] Figure 1 The present invention provides a flowchart of a method for controlling the production and processing quality of disinfection equipment based on intelligent analysis, as shown in FIG. Figure 1 As shown, the method includes:

[0062] 101. Obtain hot air distribution parameters corresponding to the drying technology in the production process of the disinfection equipment, and simultaneously collect historical energy consumption data of the heating device, and establish collaborative optimization target parameters for hot air uniform distribution and energy efficiency based on the hot air distribution parameters and historical energy consumption data;

[0063] In this step, drying technology refers to the process of removing moisture from the surface and interior of the material through hot air circulation, radiation heat transfer or conduction heating during the production of disinfection equipment.

[0064] Hot air distribution parameters refer to the spatial distribution characteristic data such as hot air temperature, flow rate, uniformity, etc. in the box collected by multi-point temperature sensors, anemometers and other equipment during the drying process.

[0065] Historical energy consumption data refers to the recorded data on power consumption, thermal efficiency, carbon emissions, etc. of heating equipment (such as electric heating tubes and gas boilers) during their historical operating cycles.

[0066] Uniform distribution of hot air means that in the drying process of the disinfection equipment, by regulating the heating device and the air circulation system, the hot air temperature, flow rate and heat transfer rate in each area of ​​the box are spatially consistent, avoiding material denaturation or uneven drying caused by local overheating or under-drying.

[0067] Energy efficiency refers to the ratio of a heating device to convert input energy (electricity, gas, etc.) into effective heat energy.

[0068] Collaborative optimization target parameters refer to quantitative indicators generated by multi-objective optimization algorithms, which are used to balance the conflicting requirements of thermal field uniformity and energy efficiency.

[0069] In an embodiment of the present application, a high-precision temperature sensor array (such as an infrared thermal imager) and a wind speed probe are deployed in the drying box to collect hot air distribution parameters in real time, and the historical energy consumption data of the heating device is recorded through the energy consumption metering module. A multi-objective optimization algorithm (such as a non-dominated sorting genetic algorithm) is used, with the minimization of the hot air temperature standard deviation and the maximization of energy efficiency as the objective function. The model weights are trained in combination with historical data to generate collaborative optimization target parameters (such as temperature uniformity threshold and energy efficiency weight coefficient). Finally, the optimized target parameters (such as temperature uniformity ±1.5℃, energy efficiency weight 0.7) are input into the control system as a benchmark for subsequent regulation.

[0070] In a certain disinfection equipment production line, a 16-channel temperature sensor and ultrasonic anemometer were installed in the drying oven to simultaneously collect hot air temperature distribution data (e.g., the temperature range at each point was 120-125°C). Simultaneously, historical energy consumption data for the heating device over the past 30 days (e.g., an average daily power consumption of 500kW·h) was extracted from the energy management system. Through calculations using a multi-objective optimization model, collaborative optimization target parameters were generated, temperature uniformity was controlled within ±1.5°C, and energy efficiency was significantly improved.

[0071] 102. According to the collaborative optimization target parameters, obtain dynamic distribution data of the temperature field during the heating operation of the disinfection equipment, and perform dynamic correlation analysis on the dynamic distribution data of the temperature field and the real-time energy consumption data of the heating device in the disinfection equipment to obtain a result of the dynamic correlation analysis;

[0072] In this step, the temperature field dynamic distribution data refers to the real-time monitoring data of the temperature changes of various areas in the box over time during the heating process.

[0073] Real-time energy consumption data refers to the energy consumption information collected instantly during the operation of the heating device, including power (such as current power 80kW), fuel flow (such as natural gas flow rate 10m 3 / h) and thermal efficiency (such as real-time thermal efficiency 82%) and other parameters.

[0074] Dynamic correlation analysis refers to the use of statistical or machine learning methods to analyze the real-time coupling relationship between temperature field fluctuations and energy consumption changes, and identify abnormal correlation nodes.

[0075] In an embodiment of the present application, the dynamic distribution data of the temperature field and the real-time energy consumption data of the heating device are transmitted to the edge computing node in real time through the Internet of Things protocol, and the timestamps are aligned to millisecond-level accuracy. The dynamic time warping algorithm or Granger causality analysis is used to calculate the correlation coefficient between the standard deviation of the temperature field, the gradient change rate and the real-time energy consumption fluctuation, and identify key correlation features (such as a sudden change in the temperature gradient causing a surge in energy consumption). Based on cluster analysis, normal working conditions and abnormal working conditions are divided, and a dynamic correlation analysis report is generated (such as a sudden drop in temperature of 2°C in a certain area causing an increase in energy consumption).

[0076] Continuing with the previous example, during production line operation, the system detected a 2°C drop in the temperature of the left front zone of the drying oven (from 123°C to 121°C) over a period of 5 minutes. Simultaneously, the heating unit power increased from 80kW to 92kW. Dynamic correlation analysis determined that the temperature drop was caused by abnormal fan speed, which hindered hot air circulation. The abnormal increase in energy consumption was strongly correlated with temperature field non-uniformity (correlation coefficient 0.89).

[0077] 103. Generate a heating power adjustment instruction based on the result of the dynamic correlation analysis, and convert the heating power adjustment instruction into a power supply frequency control signal by utilizing the synergistic effect of differential control and variable frequency regulation.

[0078] In this step, heating power refers to the total amount of heat energy output by the heating device per unit time, measured in kilowatts, which directly affects the drying rate and temperature field stability.

[0079] Differential control refers to a control algorithm that predicts the future deviation of the system by calculating the rate of change of the error signal, and is used to suppress high-frequency disturbances.

[0080] Variable frequency regulation refers to changing the output power of the motor or heating element by adjusting the power supply frequency to achieve energy efficiency optimization.

[0081] The power supply frequency refers to the periodic frequency of alternating current. By adjusting the power supply frequency of the motor or heating element through the frequency converter (such as reducing 50Hz to 45Hz), the speed or power output of the equipment can be controlled.

[0082] The control signal is a command signal generated by the control system, which is used to drive the actuator (such as inverter, relay) to adjust the operating status of the equipment.

[0083] In the embodiment of the present application, based on the results of dynamic correlation analysis, a fuzzy differential control algorithm is used to generate a heating power adjustment instruction, for example, to adjust the target power from 90kW to 85kW. The heating power adjustment instruction is differentiated, the high-frequency fluctuation component is extracted and the attenuation coefficient is calculated to generate a smoothed intermediate adjustment signal. According to the frequency power response curve of the inverter (such as 50Hz corresponding to 100kW), combined with the hot air propagation delay time (such as 0.5s), the frequency adjustment step size (such as 0.2Hz / step) and the phase compensation amount are dynamically allocated, and the control signal is output to the inverter.

[0084] When the system detects an abnormal temperature gradient in the right rear area (heating rate too fast), it generates a command to reduce the heating power (from 95kW to 88kW). The differential control algorithm suppresses high-frequency noise in the command (such as fluctuations caused by fan vibration). The inverter adjusts the power supply frequency from 49Hz to 47.5Hz based on the control signal, smoothly reducing the heating tube power to the target value while compensating for the hot air circulation delay, restoring temperature uniformity to ±1.2°C within 5 seconds.

[0085] In summary, steps 101 to 103 achieve the simultaneous optimization of thermal field uniformity and energy efficiency in the drying process of disinfection equipment through multi-objective dynamic optimization that integrates thermal field distribution and energy consumption data, combined with temperature field energy consumption correlation analysis and differential frequency conversion collaborative control technology. At the same time, process disturbances are suppressed through millisecond-level dynamic response, ensuring product quality stability and reducing production costs.

[0086] In order to solve the problem of coordinated optimization of high-frequency disturbance suppression and thermal field response delay in dynamic heating power regulation, this solution uses a differential control algorithm to filter out high-frequency noise in the power instruction and compensate for historical deviations, combines the frequency power response curve to quantify the frequency offset requirements, and further integrates the hot air propagation delay characteristics to design a dynamic weight distribution mechanism, ultimately matching the power supply frequency change rate with the temperature gradient attenuation process to achieve spatiotemporal synchronization optimization of thermal field regulation and variable frequency energy efficiency. In some embodiments, the conversion of the heating power regulation instruction into a power supply frequency control signal using the synergistic effect of differential control and variable frequency regulation in step 103 also includes:

[0087] 201. Perform differential control processing on the heating power adjustment instruction, extract the attenuation coefficient of the high-frequency fluctuation component of the heating power and the compensation amount of the accumulated deviation, and generate an intermediate adjustment signal;

[0088] In step 201, the differential control process is a control algorithm based on the error change rate. It predicts the future deviation trend of the system by calculating the instantaneous change rate of the target parameter (such as heating power) in real time, and generates a corresponding compensation signal. The attenuation coefficient of the high-frequency fluctuation component refers to the suppression intensity parameter of the fast fluctuation component caused by equipment vibration or power supply interference in the heating power signal, which characterizes the control system's ability to filter noise. The compensation amount of the cumulative deviation refers to the cumulative difference in power demand caused by historical control errors (such as long-term uncorrected temperature deviation), which needs to be dynamically compensated through integral operations. The intermediate adjustment signal is the power adjustment instruction after differential control processing, which includes the target power value and deviation compensation value after noise reduction.

[0089] In the embodiment of the present application, differential control processing is used to perform time domain decomposition of the heating power adjustment command, and a high-pass filter is used to extract the high-frequency fluctuation component (such as ±2kW / s), and its attenuation coefficient (such as 0.8) is calculated. At the same time, an integrator is used to accumulate historical power deviations (such as 3kW underadjustment in the past 5 minutes) to generate a compensation amount. The attenuation coefficient and compensation amount are superimposed on the target power reference value (such as 90kW to 87kW), and a smoothed intermediate adjustment signal is output.

[0090] 202. Calculate a reference offset of the power supply frequency based on the intermediate adjustment signal and a frequency power response curve of the heating device;

[0091] In step 202, the intermediate regulation signal is a power regulation command processed by differential control, including a noise-reduced target power value and a deviation compensation value. The frequency-power response curve represents the relationship between the power supply frequency (e.g., 50 Hz) and the output power (e.g., 100 kW) of a heating device, and is obtained through experimental calibration or data fitting. The baseline offset is the change in power supply frequency required to achieve target power regulation (e.g., from 50 Hz to 48 Hz).

[0092] In the embodiment of the present application, based on the target power value of the intermediate adjustment signal (e.g., 87kW), a pre-stored frequency-power response curve is queried (e.g., if the frequency-power relationship is linear, 1Hz is approximately equal to 2kW), and the reference frequency offset is calculated (e.g., if a 3kW reduction is required, the frequency is lowered by 1.5Hz). The frequency-power nonlinear error (e.g., curve offset caused by decreased thermal efficiency at high temperatures) is calibrated in combination with real-time operating conditions, and the reference offset is corrected to 1.8Hz.

[0093] 203. Input the reference offset into the dynamic weight distribution mechanism of the variable frequency regulation, and generate a frequency adjustment step and a phase compensation threshold in combination with the spatial propagation delay characteristics of the hot air distribution parameters;

[0094] In step 203, the dynamic weight allocation mechanism is an adaptive algorithm that dynamically adjusts the parameter weight ratio according to the real-time working conditions, and is used to solve the priority conflict problem in multi-variable coupling control. It performs differentiated weight allocation on the control parameters (such as frequency adjustment step and phase compensation threshold) by integrating the spatial propagation delay characteristics of the thermal field (such as the diffusion time difference of hot air from the heating end to the end of the box), ensuring that the control instructions match the dynamic response characteristics of the thermal field. The spatial propagation delay characteristic refers to the time difference for hot air to diffuse from the outlet of the heating device to each area of ​​the box (such as a delay of 0.3 seconds in the left area and a delay of 0.5 seconds in the right area). The frequency adjustment step is the amplitude of the change in the power supply frequency in a single control (such as 0.2Hz each adjustment). The phase compensation threshold is the trigger condition for predicting the effective moment of the frequency adjustment based on the delay time (such as issuing a command 0.5 seconds in advance).

[0095] In the embodiment of the present application, based on the spatial propagation delay characteristics of the hot air distribution parameters (such as a delay of 0.3 seconds in the left area and a delay of 0.5 seconds in the right area), the reference offset (1.8Hz) is input into the dynamic weight allocation mechanism. A fuzzy logic algorithm is used to adjust the step weight according to the regional delay time allocation frequency (such as a step of 0.1Hz / time in the left area and a step of 0.25Hz / time in the right area), and a phase compensation threshold is generated (such as the right area instruction is executed 0.5 seconds in advance). Finally, the regional differentiated control parameters are output.

[0096] 204. Adjust the proportional relationship between the frequency adjustment step and the phase compensation threshold so that the instantaneous change rate of the power supply frequency matches the spatiotemporal attenuation rate of the temperature gradient in the hot air distribution parameter, and output the power supply frequency control signal.

[0097] In step 204, the instantaneous rate of change of the power supply frequency refers to the adjustment rate of the power supply frequency per unit time (such as a change of 0.5 Hz per second), which represents the response speed of the inverter to the control command. This parameter needs to be dynamically matched with the spatiotemporal decay rate of the temperature gradient in the thermal field (such as the rate of diffusion from the high temperature area to the low temperature area) to ensure that the frequency adjustment is synchronized with the thermal field change and avoid temperature fluctuations or energy waste caused by inconsistent response speed. The spatiotemporal decay rate of the temperature gradient refers to the heat diffusion rate in the temperature-inhomogeneous area (such as the high temperature area to the low temperature area) in the box, which is calculated by the heat conduction equation.

[0098] In this embodiment, the frequency adjustment step size (0.25 Hz / step) is adjusted in proportion to the phase compensation threshold (0.5 seconds) based on the temperature gradient decay rate (e.g., the right rear zone cools at 0.5°C / s). This allows the instantaneous rate of change of the power supply frequency (e.g., 0.5 Hz / s) to match the spatiotemporal decay rate of the temperature gradient in the hot air distribution parameters. The proportional coefficient is calibrated in real time by a differential controller, ultimately generating a power supply frequency control signal that is dynamically synchronized with the thermal field (e.g., the frequency in the right zone changes from 49 Hz to 47.2 Hz, and in the left zone from 49 Hz to 48.5 Hz).

[0099] Here's a specific example:

[0100] In a certain sterilization equipment production line, the temperature gradient in the right rear area of ​​the drying oven is abnormal due to material accumulation (130°C to 122°C, cooling rate 0.6°C / s). The system performs the following control and detection: power command fluctuations (target power from 95kW to 88kW), differential control filters out high-frequency noise (±1.2kW) caused by fan vibration, and compensates for historical cumulative deviations (2.5kW underadjustment in the past 10 minutes), generating an intermediate adjustment signal of 89.5kW. Query the frequency power curve (1Hz is approximately equal to 2.2kW), calculate the reference frequency offset of -3.4Hz (from 50Hz to 46.6Hz), and correct it to -3.6Hz after calibration. According to the 0.6-second delay characteristic of the right area, the frequency adjustment step size is 0.3Hz / time and the phase compensation threshold is 0.6 seconds, and the step size of the left area is 0.15Hz / time. The temperature decay rate of the right zone is matched to 0.6°C / s, and the frequency change rate is adjusted to 0.5Hz / s. Finally, the control signals with a frequency of 46.6Hz in the right zone and 48.4Hz in the left zone are output, and the temperature uniformity is restored to ±1.3°C within 10 seconds.

[0101] In summary, steps 201 to 204 suppress high-frequency disturbances and compensate for historical deviations through differential control. By combining the frequency-power response characteristics with the spatial delay pattern of the thermal field, the frequency adjustment step size and phase compensation threshold are dynamically allocated to achieve a precise match between the rate of change of the power supply frequency and the temperature gradient attenuation process. This method improves the accuracy of thermal field uniformity control, simultaneously reduces ineffective energy consumption caused by control lag, and shortens the recovery time from temperature anomalies to the 10-second level, significantly improving the quality stability and energy efficiency of the disinfection equipment drying process.

[0102] To address the temperature uniformity fluctuation problem caused by the mismatch between power supply frequency regulation and the dynamic attenuation rate of the thermal field, the solution establishes a dynamic mapping model between the temperature gradient propagation direction and the frequency change rate by adapting the ratio of the frequency adjustment step size to the phase compensation threshold. This model generates a proportional coefficient adjustment factor based on the real-time deviation, and combines the propagation path length for multi-dimensional weighted fusion and iterative approximation to ensure that the proportional relationship adaptively matches the dynamic attenuation characteristics of the thermal field. In some embodiments, adjusting the proportional relationship between the frequency adjustment step size and the phase compensation threshold in step 204 also includes:

[0103] 301. Based on the propagation direction and spatiotemporal attenuation rate of the temperature gradient in the hot air distribution parameters, a dynamic mapping relationship between the instantaneous change rate of the power supply frequency and the spatiotemporal attenuation rate of the temperature gradient is established to extract the initial proportional coefficient of the frequency adjustment step and the phase compensation threshold;

[0104] In step 301 , the propagation direction of the temperature gradient is the direction of the heat diffusion path from the high temperature area to the low temperature area in the box (eg, propagating from left to right).

[0105] The spatiotemporal decay rate refers to the rate of heat diffusion in a specific direction of the temperature gradient (e.g., the high temperature area decays toward the low temperature area at a rate of 0.5°C per second).

[0106] The initial proportional coefficient refers to the initial proportional relationship parameter between the frequency adjustment step (single frequency change) and the phase compensation threshold (control instruction advance time).

[0107] In the embodiment of the present application, a spatiotemporal decay rate model of the temperature gradient is established based on the heat conduction equation, and the propagation direction of the temperature gradient is fitted in combination with data from multiple temperature sensors (e.g., the right rear area is the main direction of heat diffusion). A dynamic mapping relationship between the instantaneous rate of change of the power supply frequency (e.g., 0.6 Hz / s) and the spatiotemporal decay rate of the temperature gradient (0.5°C / s) is constructed through regression analysis, and the initial proportional coefficient is extracted (e.g., a step size of 0.2 Hz / time corresponds to a compensation threshold of 0.4 seconds).

[0108] 302. Determine a dynamic adjustment factor of the initial proportional coefficient according to a real-time deviation between the instantaneous change rate and the spatiotemporal attenuation rate in the dynamic mapping relationship;

[0109] In step 302, the dynamic mapping relationship refers to the real-time correspondence between the instantaneous rate of change of the power supply frequency (such as adjusting 0.5 Hz per second) and the spatiotemporal attenuation rate of the temperature gradient (such as decreasing 0.6°C per second) established by a mathematical model or algorithm. Its essence is to describe the matching rules between the control response speed and the dynamic attenuation characteristics of the thermal field, and is used to guide the proportional distribution of the frequency adjustment step and the phase compensation threshold. The real-time deviation is the real-time difference between the instantaneous rate of change and the temperature attenuation rate (such as a frequency change rate of 0.7 Hz / s corresponding to a temperature attenuation of 0.5°C / s, and a deviation of 0.2 Hz·s / °C). The dynamic adjustment factor refers to a dynamic parameter used to correct the initial proportional coefficient, reflecting the weight of the deviation on the control timing.

[0110] In this embodiment, the real-time deviation (0.17 Hz·s / °C) is calculated by monitoring the spatiotemporal decay rate (e.g., 0.48°C / s) and the instantaneous rate of change of frequency (e.g., 0.65 Hz / s) in real time. A proportional-integral algorithm is used to generate a dynamic adjustment factor (e.g., 1.2 times the correction factor) to correct the initial proportional coefficient (step size from 0.2 Hz to 0.24 Hz, and compensation threshold from 0.4 seconds to 0.33 seconds).

[0111] 303. Input the dynamic adjustment factor and the initial proportional coefficient into a dynamic weight allocation mechanism, combine the propagation path length of the maximum temperature gradient in the hot air distribution parameter, perform multi-dimensional weighted fusion on the proportional relationship between the frequency adjustment step and the phase compensation threshold, and generate an updated proportional coefficient;

[0112] In step 303, the propagation path length is the maximum diffusion distance of the temperature gradient from the heat source to the target area (eg, the path length of the right rear area is 2 meters).

[0113] The frequency adjustment step refers to the change in power supply frequency within a single control cycle (such as 0.2Hz each adjustment), which is used to approach the target frequency value in stages. Its size needs to be dynamically set in combination with the hot air propagation delay characteristics (such as 0.5 seconds for hot air to diffuse in a certain area) to ensure that the timing of the control command taking effect matches the thermal field changes.

[0114] Multi-dimensional weighted fusion refers to the dynamic allocation of weights based on multiple factors such as path length and thermal inertia parameters to optimize the proportional coefficient.

[0115] In this embodiment, a fuzzy logic algorithm is used to assign weights (e.g., a path length weight of 0.6 and a thermal inertia weight of 0.4) based on the propagation path length (2 meters) of the maximum temperature gradient in the right rear area and thermal inertia parameters (e.g., heat capacity coefficient). The dynamic adjustment factor (1.2 times) and the initial proportional coefficient are input into the dynamic weight allocation mechanism to generate an updated proportional coefficient (step size 0.24Hz to 0.26Hz, compensation threshold 0.33 seconds to 0.28 seconds).

[0116] 304. Based on the updated proportional coefficient, iteratively approximate the instantaneous change rate, and when the cumulative error between the instantaneous change rate and the spatiotemporal attenuation rate is lower than a preset threshold, obtain an adjusted proportional relationship.

[0117] In step 304, iterative approximation is an optimization method that gradually approaches the target value through multiple parameter corrections. During the control, by calculating the cumulative error between the instantaneous rate of change and the temperature decay rate (such as 0.1 Hz·s / °C), the proportional coefficient (such as the ratio of the step size to the compensation threshold) is dynamically adjusted until the error is lower than the preset threshold (such as 0.05 Hz·s / °C). The cumulative error is the cumulative value of the deviation between the instantaneous rate of change and the temperature decay rate over multiple control cycles. The preset threshold refers to the maximum allowable cumulative error range, which is used to determine whether the proportional relationship has converged.

[0118] In this embodiment, a gradient descent method is used to iteratively update the proportional coefficient and calculate the cumulative error between the instantaneous rate of change and the spatiotemporal decay rate (e.g., after five adjustments, the error drops to 0.05 Hz·s / °C). When the error falls below a preset threshold (e.g., 0.1 Hz·s / °C), a multi-cycle verification is triggered (e.g., the error remains stable for three consecutive cycles), and the proportional relationship is finally determined (with a step size of 0.26 Hz / time and a compensation threshold of 0.28 seconds).

[0119] Here's a specific example:

[0120] In a certain sterilization equipment production line, the right front area of ​​the drying oven has an abnormal temperature gradient due to poor hot air circulation (the high temperature area of ​​130°C diffuses to the left at a rate of 0.4°C / s). The system performs the following control: a dynamic mapping relationship between the temperature gradient attenuation rate (0.4°C / s) and the frequency change rate (0.5Hz / s) in the right front area is established, and the initial proportional coefficient is extracted (step size 0.18Hz / time, compensation threshold 0.45 seconds). Real-time deviation is detected (frequency change 0.55Hz / s, temperature attenuation 0.38°C / s, deviation 0.17Hz·s / °C), an adjustment factor of 1.15 times is generated, the step size is corrected to 0.207Hz, and the compensation threshold is adjusted to 0.39 seconds. Combined with the weighted fusion of the path length (1.8 meters) in the right front area, the proportional coefficient is updated to a step size of 0.22Hz / time and a compensation threshold of 0.35 seconds. After five iterations, the cumulative error dropped to 0.08 Hz·s / °C, triggering the locking mechanism and outputting the final proportional relationship (step size 0.22 Hz / time, compensation threshold 0.35 s). Within 15 s, the temperature uniformity returned to ±1.4°C.

[0121] In summary, steps 301 to 304 establish a dynamic mapping relationship between temperature gradient attenuation and frequency change, combine it with real-time deviations to generate a dynamic adjustment factor, and perform multi-dimensional weighted fusion based on the propagation path length to achieve adaptive iterative optimization of the proportional coefficient. This method shortens the lag time of temperature gradient control and reduces the cumulative error to approximately half of the preset threshold. At the same time, a locking mechanism is used to avoid overshoot oscillation, stabilizing temperature uniformity within a ±1.5°C range and reducing ineffective energy consumption due to frequency mismatch, significantly improving the dynamic response accuracy and energy efficiency balance of the disinfection equipment drying process.

[0122] To address the collaborative optimization problem of dynamically correcting cumulative errors and verifying control stability, the solution dynamically adjusts the threshold range based on the cumulative error amount and propagation path length. Through multi-cycle fluctuation suppression verification and long-term convergence trend analysis, a global locking mechanism is triggered to address the stability verification problem of the instantaneous rate of change and the temperature gradient decay rate, thereby avoiding control overshoot or oscillation. In some embodiments, when the cumulative error between the instantaneous rate of change and the spatiotemporal decay rate is lower than a preset threshold, the adjusted proportional relationship is obtained, and the following further comprises:

[0123] 401. Calculate the cumulative error of the instantaneous change rate based on the updated proportional coefficient and the real-time monitoring data of the spatiotemporal attenuation rate in the hot air distribution parameter;

[0124] In step 401, the hot air distribution parameters refer to the spatial distribution characteristics of the hot air temperature, flow rate, and uniformity within the drying chamber, collected by multiple temperature sensors, anemometers, and other equipment during the drying process. The cumulative error is the cumulative deviation between the instantaneous rate of change (e.g., frequency adjustment rate) and the temperature gradient decay rate over multiple control cycles, reflecting the total amount of error caused by historical control precision.

[0125] In an embodiment of the present application, based on the updated proportional coefficient (such as a step size of 0.25 Hz / time and a compensation threshold of 0.3 seconds) and the real-time monitoring data of the spatiotemporal attenuation rate (such as 0.45°C / s), the instantaneous deviation within each control cycle is calculated (such as a frequency change rate of 0.5 Hz / s corresponding to a temperature attenuation of 0.45°C / s, and a single deviation of 0.05 Hz·s / °C), and the total error of the past 10 controls is accumulated through a moving average algorithm (such as a cumulative error of 0.4 Hz·s / °C).

[0126] 402. Dynamically adjust the adaptation range of the preset threshold value according to the accumulated error amount and the propagation path length of the maximum temperature gradient in the hot air distribution parameter;

[0127] In step 402, the cumulative error refers to the cumulative value of the deviation between the instantaneous rate of change (such as the frequency adjustment rate) and the temperature gradient attenuation rate within multiple control cycles, reflecting the total amount of error caused by insufficient historical control accuracy. The maximum temperature gradient refers to the maximum temperature difference between different areas in the drying oven of the disinfection equipment at a certain moment (such as 130°C in the left area and 122°C in the right area, with a gradient of 8°C), or the extreme value of the temperature change rate (such as a certain area heating up at a rate of 1°C / s). It characterizes the severity of the thermal field inhomogeneity and is the core parameter for determining the control priority. The preset threshold adaptation range refers to the allowable cumulative error boundary that is dynamically adjusted according to the propagation path length of the maximum temperature gradient (such as 2 meters) (such as the original threshold of 0.5Hz·s / °C is adjusted to 0.6Hz·s / °C).

[0128] In an embodiment of the present application, a fuzzy logic control algorithm is used to dynamically adjust the preset threshold range (such as relaxing the original threshold of 0.5 Hz·s / °C to 0.65 Hz·s / °C) based on the propagation path length of the maximum temperature gradient (such as 2 meters) and the thermal diffusion resistance coefficient (such as 0.8) to adapt to the delay tolerance requirements of long-path thermal field regulation.

[0129] 403. When the cumulative error is lower than the adjusted preset threshold, based on the spatial propagation delay characteristics of the hot air distribution parameter and the attenuation trend of the cumulative error, perform multi-cycle fluctuation suppression verification on the instantaneous change rate;

[0130] In step 403, the preset threshold refers to the maximum allowable cumulative error range, which is used to determine whether the proportional relationship has converged. The spatial propagation delay characteristic refers to the time difference between the hot air from the outlet of the heating device to the various areas of the cabinet (such as a delay of 0.3 seconds in the left area and a delay of 0.5 seconds in the right area). Multi-cycle fluctuation suppression verification refers to verifying whether the fluctuation of the instantaneous change rate has converged over multiple consecutive control cycles after the cumulative error is lower than the threshold, to avoid misjudgment caused by occasional errors.

[0131] In an embodiment of the present application, when the cumulative error amount drops to 0.55 Hz·s / °C (lower than the adjusted threshold value of 0.65 Hz·s / °C), based on the spatial propagation delay characteristics of the hot air distribution parameters (such as a delay of 0.6 seconds in the right area), the instantaneous change rate of the subsequent five control cycles is subjected to multi-cycle fluctuation suppression verification (such as standard deviation calculation), and combined with the error attenuation trend prediction model (such as the exponential smoothing method), it is verified whether the fluctuation has stably converged (such as the standard deviation ≤ 0.02 Hz / s).

[0132] 404. After the multi-period fluctuation suppression verification is passed, a global locking instruction is triggered based on the long-term convergence trend slope of the cumulative error amount and the critical propagation rate of the spatiotemporal attenuation rate, and the adjusted proportional relationship is output.

[0133] In step 404, the long-term convergence trend slope refers to the decay rate of the cumulative error over time (e.g., a decrease of 0.1 Hz·s / °C per day). The critical propagation rate is a safety upper limit for the thermal field diffusion rate (e.g., the temperature gradient decay rate must not exceed 1°C / s).

[0134] In this embodiment, if the multi-cycle fluctuation suppression verification passes (e.g., the standard deviation of fluctuations within 5 cycles is ≤ 0.02 Hz / s), the long-term convergence trend slope of the cumulative error (e.g., -0.08 Hz·s / °C / cycle) is further analyzed to see if it satisfies the critical propagation rate constraint (e.g., the decay rate is ≤ 1°C / s). If the long-term convergence trend slope is compatible with the critical propagation rate, a global lock instruction is triggered, fixing the proportional relationship (e.g., a step size of 0.25 Hz / time and a compensation threshold of 0.3 seconds).

[0135] Here's a specific example:

[0136] In a sterilization equipment production line, the left rear area of ​​the drying oven experienced a maximum temperature gradient (12°C) and a long propagation path (2.5 meters) due to uneven hot air circulation. The system implemented the following control measures: Based on the proportional coefficient (step size 0.28 Hz / time, compensation threshold 0.35 seconds) and the real-time attenuation rate (0.4°C / s), the cumulative error was calculated to be 0.58 Hz·s / °C. Based on the path length of 2.5 meters, the threshold was expanded to 0.7 Hz·s / °C, and 0.58 Hz·s / °C was determined to meet the requirements. The instantaneous rate of change fluctuation (standard deviation 0.018 Hz / s) was verified over five consecutive cycles, and the error trend was predicted to converge. The trend slope (-0.07 Hz·s / °C / cycle) was found to be below the critical propagation rate (1°C / s), triggering a global lock and outputting a proportional relationship (step size 0.28 Hz / time, compensation threshold 0.35 seconds). Within 20 seconds, temperature uniformity was restored to ±1.3°C.

[0137] In summary, steps 401 to 404 achieve global stability locking of the proportional relationship through dynamic calculation of the cumulative error and adjustment of the threshold adaptation range, combined with multi-cycle fluctuation suppression verification and long-term convergence trend analysis. This method improves the accuracy of cumulative error control by more than 30%, expands the fault tolerance threshold for long-path thermal field regulation, and significantly improves the multi-cycle verification pass rate. At the same time, a global locking mechanism is used to prevent parameter drift, ensuring long-term temperature uniformity stability within ±1.5°C. It also reduces the number of invalid frequency adjustments caused by error accumulation, significantly enhancing the robustness and energy efficiency sustainability of the disinfection equipment drying process.

[0138] To address the issue of insufficient accuracy in dynamically correlating decisions between thermal field uniformity and energy efficiency, the solution constructs a joint optimization index through coupled analysis of the temperature deviation propagation direction and the energy consumption fluctuation coefficient. This solution dynamically modifies the weight ratio based on historical energy consumption offsets and propagation delay characteristics, and performs multi-cycle iterative verification based on spatiotemporal consistency to achieve accurate correlation decisions between thermal field uniformity and energy efficiency decay rate. In some embodiments, the dynamic correlation analysis of the temperature field dynamic distribution data with the real-time energy consumption data of the heating device in step 102 also includes:

[0139] 501. Extracting the temperature deviation propagation direction and energy consumption fluctuation coefficient based on the temperature deviation of each location point in the temperature field dynamic distribution data and the real-time energy consumption data of the heating device;

[0140] In step 501, the dynamic distribution data of the temperature field refers to the spatiotemporal distribution information of the temperature changes of various areas in the box over time, which is collected in real time by a sensor array (such as an infrared thermal imager, a thermocouple) during the drying process of the disinfection equipment, including parameters such as temperature value, temperature gradient (temperature difference per unit distance) and temperature change rate. The temperature deviation refers to the difference between the actual temperature and the target temperature (or the temperature of the adjacent area), which is used to characterize the degree of local overheating or under-drying. The direction of temperature deviation propagation is the direction of the heat diffusion path from the abnormal temperature area (such as the high temperature area) in the box to the adjacent area (such as diffusion from the center to the edge). The energy consumption fluctuation coefficient refers to the degree of deviation of the real-time energy consumption of the heating device from the historical mean.

[0141] In this embodiment, a temperature sensor array collects temperature data at each point in the dynamic distribution of the temperature field (e.g., 125°C in the left zone and 118°C in the right zone), calculates the temperature deviation (+5°C in the left zone and -2°C in the right zone), and combines this with thermal imaging analysis to determine the direction of temperature deviation propagation (from the left zone to the top). Real-time energy consumption data (e.g., current power of 92kW) is simultaneously collected and compared with the historical average (80kW) to calculate the energy consumption fluctuation coefficient (15%).

[0142] 502. Couple the temperature deviation propagation direction with the energy consumption fluctuation coefficient to generate a joint optimization index of hot air uniform distribution and energy efficiency attenuation rate;

[0143] In step 502, coupling analysis is a multi-source data joint modeling method that uses statistical or machine learning algorithms (such as Granger causality tests and neural networks) to explore the correlation between temperature field parameters (such as temperature gradient and flow rate) and energy consumption data (such as power and thermal efficiency), revealing the synergistic mechanism between thermal field uniformity and energy efficiency. The joint optimization index is a collaborative optimization objective function that quantifies the decay rate of hot air uniformity (such as a temperature gradient decrease of 0.3°C per second) and the decay rate of energy efficiency (such as a decrease of 0.5% per second in energy efficiency).

[0144] In an embodiment of the present application, a dynamic time warping algorithm is used to align the temperature deviation propagation direction and the energy consumption fluctuation coefficient, establish a coupling model of the hot air uniform distribution attenuation rate (0.3°C / s) and the energy consumption efficiency attenuation rate (0.5% / s), and generate a joint optimization index (such as the weight ratio: thermal field uniformity accounts for 60%, and energy consumption efficiency accounts for 40%).

[0145] 503. Dynamically modify the weight ratio of the joint optimization index based on the mean shift of the historical energy consumption data and the propagation delay characteristics of the temperature gradient;

[0146] In step 503, mean shift refers to the systematic deviation of historical energy consumption data from the long-term mean (e.g., energy consumption has been consistently 8% higher over the past week). Propagation delay refers to the time difference in the diffusion of the temperature gradient from the heat source to the target area (e.g., a 0.7 second delay in the top area). Dynamic correction refers to adjusting control parameters (e.g., weight ratios, control thresholds) based on real-time monitoring data and historical trends through adaptive algorithms (e.g., fuzzy logic control, rolling horizon optimization) to respond to changes in operating conditions (e.g., material load fluctuations, ambient temperature and humidity disturbances).

[0147] In an embodiment of the present application, the weights of the joint optimization indicators are corrected based on the mean offset (+8%) of the historical energy consumption data and the propagation delay characteristics of the temperature gradient (the thermal field weight is reduced to 55%, and the energy consumption weight is increased to 45%). Combined with the propagation delay characteristics of the temperature gradient in the top area (0.7 seconds), a fuzzy logic algorithm is used to dynamically adjust the weight distribution ratio (such as a thermal field weight of 52% and an energy consumption weight of 48%).

[0148] 504. Based on the spatiotemporal consistency of the temperature gradient propagation direction and the thermal inertia parameter, perform multi-cycle iterative verification on the corrected weight ratio, and output a dynamic correlation analysis result when the verification error is lower than the tolerance limit of the joint optimization indicator.

[0149] In step 504, the thermal inertia parameter is the hysteresis characteristic of the temperature change after the material absorbs heat (for example, the material temperature rise needs to lag the heat supply by 3 seconds). Spatiotemporal consistency refers to the distribution uniformity and dynamic stability of the temperature field in the time dimension (for example, the entire drying process) and the spatial dimension (for example, each area of ​​the chamber). It is comprehensively evaluated through indicators such as the standard deviation of the time series (for example, the temperature fluctuation per hour is ≤1.5°C) and the spatial coefficient of variation (for example, the temperature difference between each point is ≤5%). The tolerance boundary is the maximum verification error range allowed by the joint optimization indicator (for example, the temperature uniformity error is ±1.5°C).

[0150] In this embodiment, the revised weight ratio (52%:48%) was iteratively verified for five control cycles based on the temperature gradient propagation direction and thermal inertia parameters (with a 3-second lag). A Kalman filter was used to predict the error trend. When the verification error (e.g., ±1.2°C) fell below the tolerance limit of the joint optimization indicator (±1.5°C), a dynamic correlation analysis result (requiring a 10% power reduction in the left zone and a 20% wind speed increase at the top) was output.

[0151] Here's a specific example:

[0152] In a certain sterilization equipment production line, the center of the drying oven experienced temperature deviations (135°C in the center and 120°C in the edge) and abnormal energy consumption (power 110kW, historical average 95kW) due to material accumulation. The direction of temperature deviation propagation (center to edge) was extracted, and the energy consumption fluctuation coefficient of +15.8% was calculated. A joint optimization index was then generated (thermal field weight 65%, energy consumption weight 35%). Combining the historical energy consumption offset (+10%) and a 0.8-second delay in the edge area, the weights were adjusted to 58% for the thermal field and 42% for energy consumption. After three cycles of verification, the error was reduced to ±1.3°C, and control instructions were output (power reduction of 12% in the center area and frequency increase of 2Hz in the edge area). Within 15 seconds, temperature uniformity returned to ±1.4°C, and energy consumption dropped back to 98kW.

[0153] In summary, steps 501 to 504 achieve precise coordinated optimization of thermal field uniformity and energy efficiency by coupling analysis of the temperature deviation propagation direction and the energy consumption fluctuation coefficient, combining historical offset and propagation delay characteristics to dynamically modify the weight ratio. Multi-cycle iterative verification is then performed based on thermal inertia parameters. This method reduces temperature uniformity control errors and energy consumption fluctuation coefficients, while simultaneously improving energy efficiency through dynamic weight allocation. This significantly enhances the multi-objective coordinated control capabilities and long-term operational stability of the disinfection equipment drying process.

[0154] To address the problem of uneven hot air distribution and energy efficiency attenuation being difficult to coordinately optimize, the solution generates a dynamic correction factor based on the historical energy consumption mean offset and the temperature gradient attenuation rate, combines the real-time rate of change with the thermal inertia parameter to perform multi-dimensional coupled corrections on the weight ratio, and determines the final weight by matching the coordinated deviation attenuation trend with the stability boundary, thereby improving the adaptability and robustness of the joint optimization indicator. In some embodiments, the dynamic correction of the weight ratio of the joint optimization indicator by combining the mean offset of the historical energy consumption data and the propagation delay characteristics of the temperature gradient in step 503 also includes:

[0155] 601. Determine a dynamic correction factor for the attenuation rate of hot air uniform distribution and energy efficiency based on the fluctuation amplitude of the mean offset of the historical energy consumption data and the attenuation rate of the propagation delay characteristic of the temperature gradient;

[0156] In step 601, the mean offset refers to the absolute deviation between the historical energy consumption data sequence and its moving average. The fluctuation amplitude refers to the deviation of the mean energy consumption per unit time (such as per hour) relative to the long-term average value in the historical energy consumption data. It is calculated through the sliding window standard deviation to reflect the intensity of short-term fluctuations in energy consumption. The temperature gradient propagation delay characteristic represents the hysteresis effect of the heat transfer rate in the temperature field attenuating with distance. The decay rate describes the degree to which this hysteresis effect weakens over time. The dynamic correction factor is an adjustment coefficient that comprehensively considers the relationship between thermal field uniformity and energy efficiency attenuation.

[0157] In the present embodiment, a sliding average algorithm is first used to calculate the mean curve of the historical energy consumption data over the past 24 hours. The fluctuation amplitude of the mean offset at each time point is obtained through point-by-point differentiation. Simultaneously, a time series analysis method is used to perform a Fourier transform on the temperature gradient propagation data collected by the temperature sensor network to extract the amplitude decay rate of the main frequency component. The two types of parameters are input into a multivariate regression model, and a coupling equation for the fluctuation amplitude and decay rate is established through partial least squares regression. Ultimately, a dynamic correction factor with time-varying characteristics is determined.

[0158] 602. Input the dynamic correction factor into the weight distribution mechanism of the joint optimization index, and combine the real-time change rate of the temperature gradient propagation direction and the thermal inertia parameter of the heating device to perform multi-dimensional coupling correction on the weight ratio of the hot air uniform distribution and the attenuation rate of energy efficiency;

[0159] In step 602, the joint optimization index is a composite evaluation function of thermal field uniformity and energy efficiency, and the weight allocation mechanism is constructed using the fuzzy analytic hierarchy process. The real-time rate of change in the propagation direction is obtained through temperature field vector analysis. Thermal inertia parameters include physical properties of the heating device, such as specific heat capacity and thermal conductivity. Multidimensional coupling correction refers to the coordinated adjustment of weight ratios based on multiple dimensions, including spatial distribution, temporal variation, and parameter correlation.

[0160] In this embodiment, the dynamic correction factor is input into a pre-trained weight allocation mechanism, which includes three hidden layers and an adaptive activation function. Combined with the calculation results of the spatial derivative of the temperature field gradient in the propagation direction and the thermal inertia parameters obtained through material property testing, the weight allocation mechanism is constructed with joint constraints of the heat conduction equation and the energy flow equation. A particle swarm optimization algorithm is used to perform multi-dimensional coupling correction, and the final output is the weight ratio divided by spatial dimension.

[0161] 603. Based on the corrected weight ratio, calculate the coordinated deviation between the maximum temperature gradient in the temperature field dynamic distribution data and the real-time energy consumption data of the heating device, and determine the optimal weight ratio when the attenuation trend of the coordinated deviation matches the stability boundary of the thermal inertia parameter;

[0162] In step 603, the coordinated deviation is the combined deviation between the maximum temperature gradient and the real-time energy consumption in a standardized dimension. The stability boundary is determined by the Lyapunov function, which defines the system's safe operation threshold. Thermal inertia parameters characterize the response of a heating device or heated object to temperature changes. These parameters include specific heat capacity (the amount of heat required to raise a unit mass of material by 1°C), thermal conductivity (heat transfer rate), thermal diffusivity (rate of change of temperature field), and other physical properties.

[0163] In the embodiment of the present application, based on the corrected weight ratio, the gradient modulus of each grid point in the temperature field dynamic distribution data is calculated, and the maximum temperature gradient area is extracted through Gaussian filtering. After the real-time energy consumption data is processed by wavelet noise reduction, the covariance is calculated with the maximum gradient value. An exponential decay model of the collaborative deviation is constructed, and its characteristic roots are spectrally matched with the stability criterion matrix composed of thermal inertia parameters. When the decay rate enters the convergence domain, the optimal weight ratio is locked.

[0164] 604. Feedback the optimal weight ratio to the dynamic mapping relationship mechanism of the joint optimization index, update the association logic of the thermal field energy consumption mapping in the heating device and output a dynamic correction result.

[0165] In step 604, the dynamic mapping mechanism is a knowledge graph describing the nonlinear relationship between thermal field parameters and energy consumption indicators. The association logic is updated using incremental learning. The thermal field energy consumption mapping model is a correlation model between the temperature field (thermal field) distribution characteristics and the energy consumption of the heating device. Specifically, it is manifested in the energy consumption value changes corresponding to different temperature gradient distribution patterns.

[0166] In this embodiment, the determined optimal weight ratio is input into a dynamic mapping relationship mechanism built on a graph database, and the associated paths of the thermal field energy consumption mapping are reconstructed through a node attribute update algorithm. A long-short-term memory network is used to extract features from historical mapping relationships. Combined with the real-time thermal field energy consumption data stream, the edge weights of the knowledge graph are dynamically adjusted through an online learning mechanism, ultimately outputting a dynamically corrected result that incorporates spatiotemporal characteristics.

[0167] Here's a specific example:

[0168] To optimize the hot air distribution in a sterilization equipment production line, energy consumption time-series data and temperature field distribution records for 216 units over the past 30 days were collected. Hilbert-Huang transform analysis revealed an 8-minute natural oscillation period for the hot air circulation system. Based on this, a moving average window was set, and the mean shift fluctuation for each sterilization chamber zone was calculated to be within ±12%. The propagation delay attenuation coefficient of the temperature gradient from the air inlet to the discharge outlet was also found to be 0.35 / min. Combining these two factors, a dynamic correction factor matrix was generated, indicating that increased regulation intensity was required in the core area. Based on the thermal inertia parameters of the 316L stainless steel chamber (thermal conductivity 16.3 W / m·K, specific heat capacity 500 J / kg·K), a weight allocation model adjusted the thermal uniformity weight from an initial 0.6 to 0.72. When changes in the loading density of a batch of products were detected, resulting in a change in the hot air flow direction, the model completed the spatial redistribution of the weight coefficients within 5 seconds. When a local temperature gradient exceeding the standard was detected at the discharge outlet, real-time energy consumption increased by 15% compared to the baseline value. After collaborative deviation calculations revealed that the deviation between the two reached a threshold, the system automatically triggered a weight adjustment, increasing the weight of the temperature gradient by 18%, ensuring that the deviation converged within the allowable range of thermal inertia parameters (stability boundary). By updating the mapping relationship, it was identified that the correlation between fan speed and bottom heater power needed to be strengthened by 22%. After implementation, the temperature uniformity of the sterilization chamber increased by 37%, the fluctuation range of energy consumption decreased by 29% during the same period, and the system could automatically adapt to production switching for different product specifications.

[0169] In summary, steps 601 to 604 achieve the coordinated optimization of thermal field distribution and energy efficiency by constructing a dynamic correction factor and a multi-dimensional weight adjustment mechanism. Using spatiotemporal coupling analysis technology, closed-loop control is established between the temperature gradient propagation characteristics and the equipment's thermal inertia parameters. Practical applications have demonstrated that while ensuring thermal field uniformity, the system's energy efficiency decay rate is reduced, and it can dynamically adapt to changing production conditions, significantly improving the equipment's overall energy efficiency. Continuously optimizing the correlation model through an online learning mechanism ensures the system maintains optimal operating conditions, addressing the energy efficiency loss caused by static parameter settings in traditional methods.

[0170] In order to solve the problem of the difficulty in achieving a dynamic balance between uniform hot air distribution and energy efficiency, the solution decomposes the hot air distribution parameters into orthogonal modal components and quantifies the energy proportion, constructs an asymmetric mapping relationship based on the historical energy consumption feature vector, defines the dual-objective optimization boundary through the sensitivity coefficient and the spatial variation index, and iteratively corrects the modal weight to achieve Pareto optimality, solving the problem of generating multi-objective collaborative parameters for thermal field uniformity and energy efficiency. In some embodiments, the collaborative optimization target parameters for hot air uniform distribution and energy efficiency based on the hot air distribution parameters and historical energy consumption data in step 101 also include:

[0171] 701. Decompose the hot air distribution parameter into multiple orthogonal modal components in the spatial dimension, and calculate the energy proportion coefficient and spatial variation index of the orthogonal modal components;

[0172] In step 701, orthogonal modal components are decomposed into independent spatial modes using intrinsic orthogonal decomposition. Each mode represents the thermal field distribution characteristics of a specific direction or region. The energy contribution coefficient refers to the contribution ratio of each modal component to the total energy, reflecting its dominance over the overall thermal field distribution. The spatial variation index describes the intensity of the fluctuation of the modal component in the spatial dimension and is calculated as the ratio of the standard deviation to the mean.

[0173] In the embodiment of the present application, the hot air distribution parameters collected by the temperature sensor network are processed using the intrinsic orthogonal decomposition algorithm, and the first N orthogonal modal components are extracted (N is determined by exceeding the set threshold according to the energy accumulation ratio). The square of the amplitude of each orthogonal modal component is normalized to obtain the energy ratio coefficient of each orthogonal modal component. The thermal field area is divided into grid cells, and the difference between the maximum and minimum values ​​of each orthogonal modal component in each grid is calculated, and then divided by the mean of the orthogonal modal component state to obtain the spatial variation index. The energy ranking and spatial heterogeneity analysis results of each mode are finally output.

[0174] 702. Construct a multi-dimensional decomposition model of the historical energy consumption data, extract characteristic vectors dynamically associated with the temperature field of the heating device during different operating cycles, and calculate a stability index of the characteristic vectors;

[0175] In step 702, the multi-dimensional decomposition model is a mathematical model that decomposes historical energy consumption data by time, load, ambient temperature, and other dimensions. The eigenvector is a combination of key parameters that reflect the relationship between the operating status of the heating device and the temperature field, such as the heating power fluctuation frequency and fan speed gradient. The stability index refers to the fluctuation amplitude of the eigenvector over a continuous operating cycle, calculated using a combination of variance and autocorrelation coefficient.

[0176] In this embodiment, time-frequency analysis techniques are applied to historical energy consumption data to decompose the eigenvectors dynamically associated with the temperature field during different operating cycles of the heating device, constructing a 16-dimensional eigenvector set. The variance and autocorrelation coefficient of each eigenvector are calculated, and a stability index score is generated through weighted fusion. Principal component analysis is used to select core eigenvectors that are highly sensitive to temperature field changes, and an energy consumption-thermal field correlation model is established.

[0177] 703. Establish an asymmetric mapping relationship between the energy proportion coefficient and the stability index, and determine the sensitivity coefficient of hot air uniform distribution to energy efficiency;

[0178] In step 703, the energy contribution coefficient refers to the ratio of the energy contained in each orthogonal modal component to the total thermal field energy, reflecting the weighted contribution of that component to the overall thermal field distribution. The asymmetric mapping relationship is a characteristic of the directional difference in the correlation between changes in thermal field uniformity and changes in energy efficiency. The sensitivity coefficient reflects the degree of change in energy efficiency caused by a unit change in thermal field uniformity.

[0179] In the embodiment of the present application, the grey correlation analysis method is used to calculate the asymmetric mapping relationship between the energy proportion coefficient of each mode and the characteristic vector stability index. The sensitivity coefficients of different intervals are calculated by fitting the curve of energy proportion and stability index through piecewise regression. Highly sensitive modes (such as the second mode corresponding to the upper air supply area) are identified and their priority for regulating energy efficiency is determined.

[0180] 704. Construct a collaborative optimization function based on the spatial variation index and the sensitivity coefficient, use the dynamic variation range of the eigenvector as a constraint, and generate a dual-objective optimization boundary;

[0181] In step 704, the spatial variation index (SVI) describes the degree of fluctuation of the modal component in the spatial dimension and is calculated by the ratio of the difference (range) between the local maximum and minimum values ​​to the modal mean. The collaborative optimization function is a mathematical expression with the dual objectives of reducing the SVI and improving the sensitivity coefficient. The dual-objective optimization boundary refers to the feasible solution region defined by the allowable fluctuation range of the eigenvectors.

[0182] In this embodiment, the collaborative optimization function is constructed by minimizing the sum of the spatial variation indices and maximizing the sum of the sensitivity coefficients. Constraints are then set such that the variance of the eigenvectors does not exceed a set proportion of historical extreme values, and the autocorrelation coefficient does not fall below a critical value. Finally, a constrained optimization algorithm is used to generate a dual-objective optimization boundary containing a feasible solution.

[0183] 705. By iteratively correcting the weight distribution of the orthogonal modal components, the collaborative optimization function is made to satisfy the Pareto optimal condition within the dual-objective optimization boundary, thereby generating collaborative optimization target parameters for hot air uniform distribution and energy efficiency.

[0184] In step 705, iterative correction is an optimization process that gradually approaches the optimal solution by adjusting the weight parameters in multiple cycles. Orthogonal modal components refer to the decomposition of thermal field distribution parameters into multiple independent spatial mode components through mathematical decomposition methods (such as intrinsic orthogonal decomposition), each of which represents the thermal field distribution characteristics of a specific area or direction. The Pareto optimal condition refers to the optimal solution set where it is impossible to further optimize either objective without compromising the other objective within the dual-objective optimization boundary.

[0185] In this embodiment, a multi-objective evolutionary algorithm is used to iteratively optimize the weight distribution of orthogonal modal components and dynamically adjust the search direction. Within the dual-objective optimization boundary, a set of non-inferior solutions that simultaneously improves hot air uniformity and energy efficiency is selected. Based on a comprehensive scoring model, the optimal collaborative optimization target parameters are selected to balance thermal field performance and energy efficiency requirements.

[0186] Here's a specific example:

[0187] In the optimization of the high-temperature sterilization chamber of a certain disinfection equipment production line: first, 6 orthogonal modes were decomposed, among which the energy of the second mode (hot air reflow at the bottom) accounted for 35%, but the spatial variation index was as high as 1.2, indicating that the distribution in this area was extremely unstable. The eigenvector extracted from the energy consumption data showed that the correlation coefficient between the fan frequency conversion cycle and the bottom temperature fluctuation was 0.91, and the variance exceeded the limit and triggered the constraint alarm. The sensitivity coefficient of the second mode was 0.65, indicating that improving the bottom uniformity had a significant impact on energy efficiency. By optimizing the boundaries, the fan speed fluctuation must be <±15%, and the heating power adjustment interval must be >30 seconds. Finally, after 5 rounds of iterative optimization, the weight distribution scheme was selected to reduce the standard deviation of the bottom area temperature from 4.3°C to 2.8°C, and the energy consumption of single batch sterilization was reduced during the same period.

[0188] In summary, steps 701 to 705 achieve precise coordinated control of thermal field distribution and energy efficiency through the deep integration of modal decomposition and multi-objective optimization. In the disinfection equipment production line, the system can automatically identify high-sensitivity areas and dynamically adjust operating parameters to improve temperature uniformity while reducing energy consumption fluctuations. The use of asymmetric mapping relationships and Pareto optimal solution screening mechanisms effectively avoids the problem of local overshoot caused by traditional single-objective optimization. Through the dual guarantee of constraints and stability indicators, the reliable operation of the equipment under complex working conditions is ensured, the comprehensive energy efficiency ratio is improved, and an innovative solution is provided for high-precision thermal field control.

[0189] Figure 2 The present invention provides a structural diagram of a disinfection equipment production and processing quality control system based on intelligent analysis, as shown in FIG. Figure 2 As shown, the system includes:

[0190] Acquisition module 21, obtains hot air distribution parameters corresponding to the drying technology in the production process of the disinfection equipment, and simultaneously collects historical energy consumption data of the heating device, and constructs collaborative optimization target parameters for hot air uniform distribution and energy efficiency based on the hot air distribution parameters and historical energy consumption data;

[0191] The analysis module 22 obtains the dynamic distribution data of the temperature field during the heating operation of the disinfection equipment according to the collaborative optimization target parameters, and performs dynamic correlation analysis on the dynamic distribution data of the temperature field and the real-time energy consumption data of the heating device in the disinfection equipment to obtain the results of the dynamic correlation analysis;

[0192] The generating module 23 generates a heating power adjustment instruction based on the result of the dynamic correlation analysis, and converts the heating power adjustment instruction into a control signal of the power supply frequency by utilizing the synergistic effect of differential control and variable frequency regulation.

[0193] Figure 2 The disinfection equipment production and processing quality control system based on intelligent analysis can be executed Figure 1 The implementation principle and technical effects of the intelligent analysis-based production and processing quality control method for disinfection equipment described in the illustrated embodiment will not be repeated here. The specific manner in which each module and unit performs operations in the intelligent analysis-based production and processing quality control system for disinfection equipment in the above embodiment has been described in detail in the embodiments of the method and will not be elaborated on here.

[0194] In one possible design, Figure 2 The embodiment shown is a sterilization equipment production and processing quality control system based on intelligent analysis, which can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0195] 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 .

[0196] The processing component 32 is used for the above Figure 1 The embodiment provides a method for controlling the production and processing quality of disinfection equipment based on intelligent analysis.

[0197] The processing component 32 may include one or more processors to execute computer instructions to perform all or part of the steps in the above method. Of course, the processing component may also be implemented as 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 to perform the above method.

[0198] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory 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 disk.

[0199] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0200] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0201] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0202] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, 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 the cloud computing platform.

[0203] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is a method for controlling the production and processing quality of disinfection equipment based on intelligent analysis.

[0204] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0205] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0206] 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, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology 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, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling 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 certain parts of the embodiments.

[0207] 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for controlling the production and processing quality of disinfection equipment based on intelligent analysis, characterized in that: include: Obtain the hot air distribution parameters corresponding to the drying technology in the production process of the disinfection equipment, and simultaneously collect the historical energy consumption data of the heating device, and build the collaborative optimization target parameters of hot air uniform distribution and energy efficiency based on the hot air distribution parameters and historical energy consumption data; According to the collaborative optimization target parameters, the dynamic distribution data of the temperature field during the heating operation of the disinfection equipment is obtained, and the dynamic correlation analysis of the dynamic distribution data of the temperature field and the real-time energy consumption data of the heating device in the disinfection equipment is performed to obtain the results of the dynamic correlation analysis; generating a heating power adjustment instruction based on the result of the dynamic correlation analysis, and converting the heating power adjustment instruction into a control signal of the power supply frequency by utilizing the synergistic effect of differential control and variable frequency regulation; The method of converting the heating power adjustment instruction into a power supply frequency control signal by utilizing the synergistic effect of differential control and variable frequency regulation includes: Performing differential control processing on the heating power adjustment instruction, and extracting the attenuation coefficient of the high-frequency fluctuation component of the heating power and the compensation amount of the accumulated deviation to generate an intermediate adjustment signal; Calculating a reference offset of the power supply frequency based on the intermediate adjustment signal and a frequency power response curve of the heating device; Inputting the reference offset into the dynamic weight distribution mechanism of variable frequency regulation and combining it with the spatial propagation delay characteristics of the hot air distribution parameters to generate the frequency adjustment step and phase compensation threshold; The proportional relationship between the frequency adjustment step and the phase compensation threshold is adjusted so that the instantaneous change rate of the power supply frequency matches the spatiotemporal attenuation rate of the temperature gradient in the hot air distribution parameter, and the control signal of the power supply frequency is output.

2. The method according to claim 1, characterized in that The adjusting the proportional relationship between the frequency adjustment step and the phase compensation threshold includes: Based on the propagation direction and spatiotemporal attenuation rate of the temperature gradient in the hot air distribution parameters, a dynamic mapping relationship between the instantaneous change rate of the power supply frequency and the spatiotemporal attenuation rate of the temperature gradient is established to extract the initial proportional coefficient of the frequency adjustment step and the phase compensation threshold; Determining a dynamic adjustment factor of the initial proportional coefficient according to a real-time deviation between the instantaneous change rate and the spatiotemporal attenuation rate in the dynamic mapping relationship; The dynamic adjustment factor and the initial proportional coefficient are input into a dynamic weight allocation mechanism, and the proportional relationship between the frequency adjustment step and the phase compensation threshold is subjected to multi-dimensional weighted fusion in combination with the propagation path length of the maximum temperature gradient in the hot air distribution parameter to generate an updated proportional coefficient; Based on the updated proportional coefficient, the instantaneous change rate is iteratively approximated, and when the cumulative error between the instantaneous change rate and the spatiotemporal attenuation rate is lower than a preset threshold, an adjusted proportional relationship is obtained.

3. The method according to claim 2, characterized in that When the cumulative error between the instantaneous rate of change and the spatiotemporal attenuation rate is lower than a preset threshold, obtaining the adjusted proportional relationship includes: Calculating the cumulative error of the instantaneous change rate based on the updated proportional coefficient and the real-time monitoring data of the spatiotemporal attenuation rate of the hot air distribution parameter; Dynamically adjusting the adaptation range of the preset threshold value according to the cumulative error amount and the propagation path length of the maximum temperature gradient in the hot air distribution parameter; When the cumulative error is lower than the adjusted preset threshold, performing multi-cycle fluctuation suppression verification on the instantaneous change rate based on the spatial propagation delay characteristics of the hot air distribution parameter and the attenuation trend of the cumulative error; After the multi-period fluctuation suppression verification is passed, the global locking instruction is triggered in combination with the long-term convergence trend slope of the cumulative error amount and the critical propagation rate of the space-time decay rate, and the adjusted proportional relationship is output.

4. The method according to claim 1, wherein The dynamic correlation analysis of the temperature field dynamic distribution data and the real-time energy consumption data of the heating device includes: Extracting the temperature deviation propagation direction and energy consumption fluctuation coefficient based on the temperature deviation of each position point in the temperature field dynamic distribution data and the real-time energy consumption data of the heating device; The temperature deviation propagation direction and the energy consumption fluctuation coefficient are coupled and analyzed to generate a joint optimization index of the hot air uniform distribution and the attenuation rate of the energy consumption efficiency; Dynamically modify the weight ratio of the joint optimization index based on the mean shift of historical energy consumption data and the propagation delay characteristics of the temperature gradient; Based on the spatiotemporal consistency of the temperature gradient propagation direction and the thermal inertia parameter, the corrected weight ratio is verified through multi-cycle iteration, and a dynamic correlation analysis result is output when the verification error is lower than the tolerance boundary of the joint optimization indicator.

5. The method according to claim 4, characterized in that The method of dynamically correcting the weight ratio of the joint optimization index by combining the mean shift of the historical energy consumption data and the propagation delay characteristics of the temperature gradient includes: Determining a dynamic correction factor for the attenuation rate of hot air uniform distribution and energy efficiency based on the fluctuation amplitude of the mean offset of the historical energy consumption data and the attenuation rate of the propagation delay characteristic of the temperature gradient; The dynamic correction factor is input into the weight distribution mechanism of the joint optimization index, and the weight ratio of the attenuation rate of the hot air uniform distribution and energy efficiency is corrected in a multi-dimensional coupling manner in combination with the real-time change rate of the propagation direction of the temperature gradient and the thermal inertia parameter of the heating device; Based on the corrected weight ratio, calculating the coordinated deviation between the maximum temperature gradient in the temperature field dynamic distribution data and the real-time energy consumption data of the heating device, and determining the optimal weight ratio when the attenuation trend of the coordinated deviation matches the stability boundary of the thermal inertia parameter; The optimal weight ratio is fed back to the dynamic mapping relationship mechanism of the joint optimization index, the association logic of the thermal field energy consumption mapping in the heating device is updated and the dynamic correction result is output.

6. The method according to claim 1, wherein The collaborative optimization target parameters of hot air uniform distribution and energy efficiency are constructed based on the hot air distribution parameters and historical energy consumption data, including: Decomposing the hot air distribution parameter into multiple orthogonal modal components in the spatial dimension, and calculating the energy proportion coefficient and spatial variation index of the orthogonal modal components; Constructing a multi-dimensional decomposition model of the historical energy consumption data, extracting characteristic vectors dynamically associated with the temperature field of the heating device during different operating cycles, and calculating the stability index of the characteristic vectors; Establishing an asymmetric mapping relationship between the energy proportion coefficient and the stability index, and determining the sensitivity coefficient of hot air uniform distribution to energy efficiency; A collaborative optimization function is constructed based on the spatial variation index and the sensitivity coefficient, and the dynamic variation range of the eigenvector is used as a constraint condition to generate a dual-objective optimization boundary; By iteratively correcting the weight distribution of the orthogonal modal components, the collaborative optimization function is made to satisfy the Pareto optimal condition within the dual-objective optimization boundary, thereby generating collaborative optimization target parameters for hot air uniform distribution and energy efficiency.

7. A disinfection equipment production and processing quality control system based on intelligent analysis, characterized in that: include: An acquisition module is used to obtain the hot air distribution parameters corresponding to the drying technology in the production process of the disinfection equipment, and simultaneously collect the historical energy consumption data of the heating device. Based on the hot air distribution parameters and the historical energy consumption data, the coordinated optimization target parameters of the hot air uniform distribution and energy efficiency are constructed; an analysis module, which obtains the dynamic distribution data of the temperature field during the heating operation of the disinfection equipment according to the collaborative optimization target parameters, and performs dynamic correlation analysis on the dynamic distribution data of the temperature field and the real-time energy consumption data of the heating device in the disinfection equipment to obtain a result of the dynamic correlation analysis; a generating module for generating a heating power adjustment instruction based on the result of the dynamic correlation analysis, and converting the heating power adjustment instruction into a power supply frequency control signal by utilizing the synergistic effect of differential control and variable frequency regulation; The method of converting the heating power adjustment instruction into a power supply frequency control signal by utilizing the synergistic effect of differential control and variable frequency regulation includes: Performing differential control processing on the heating power adjustment instruction, and extracting the attenuation coefficient of the high-frequency fluctuation component of the heating power and the compensation amount of the accumulated deviation to generate an intermediate adjustment signal; Calculating a reference offset of the power supply frequency based on the intermediate adjustment signal and a frequency power response curve of the heating device; Inputting the reference offset into the dynamic weight distribution mechanism of variable frequency regulation and combining it with the spatial propagation delay characteristics of the hot air distribution parameters to generate the frequency adjustment step and phase compensation threshold; The proportional relationship between the frequency adjustment step and the phase compensation threshold is adjusted so that the instantaneous change rate of the power supply frequency matches the spatiotemporal attenuation rate of the temperature gradient in the hot air distribution parameter, and the control signal of the power supply frequency is output.

8. A computing device, characterized in that It includes 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 disinfection equipment production and processing quality control method based on intelligent analysis as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, the method for controlling the production and processing quality of disinfection equipment based on intelligent analysis as described in any one of claims 1 to 6 is implemented.

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