Production environment parameter control method and system based on industrial internet of things

Abstract

The application relates to a production environment parameter control method and system based on an industrial Internet of Things. The method comprises the following steps: collecting first real-time production environment parameters and subsequent planned production tasks, performing production environment parameter prediction, obtaining a predicted time-series production environment parameter set, and determining a critical time point; obtaining the response time characteristics of an environment controller, determining a control time point for the critical time point; at the control time point, collecting second real-time production environment parameters of a target workshop, generating environment control parameters of the environment controller, and controlling the environment controller to perform production environment control based on the environment control parameters. Through the Internet of Things sensing network and the production plan, intelligent prediction of production environment parameters, critical point identification and optimal opportunity intervention control are realized, so that the stability of the production environment is ensured and energy consumption is reduced.

Description

Technical Field

[0001] This application relates to the field of industrial Internet of Things (IIoT), and in particular to a method and system for controlling production environment parameters based on IIoT. Background Technology

[0002] With industrial development, higher requirements are being placed on environmental parameters during manufacturing. Currently, environmental parameter control mainly employs continuous tracking control or phased control methods. Continuous tracking control continuously collects environmental parameter data through sensors and adjusts the environmental controller's operation based on real-time data. While this method ensures environmental parameters remain within preset ranges, frequent start-stop and adjustment of the environmental controller leads to excessive energy consumption, increasing production costs. Phased control detects environmental parameters at fixed times or locations, only activating the control device to adjust when parameters deviate from preset ranges. While this method reduces energy consumption to some extent, the lack of intelligent predictive mechanisms often results in control measures being taken only after environmental parameters have exceeded preset ranges, impacting production quality. Summary of the Invention

[0003] The main purpose of this application is to provide a production environment parameter control method and system based on the Industrial Internet of Things, which aims to solve the technical problems in the existing production environment parameter control, such as excessive energy consumption due to continuous tracking control or low level of intelligence in staged control, which makes it impossible to prevent environmental parameters from exceeding the preset range.

[0004] To achieve the above objectives, this application provides a production environment parameter control method based on the Industrial Internet of Things (IIoT). The method includes: receiving first real-time production environment parameters of a target workshop and obtaining subsequent planned production tasks of the target workshop through a production plan list; predicting production environment parameters based on the first real-time production environment parameters and the subsequent planned production tasks to obtain a predicted time-series production environment parameter set; analyzing the predicted time-series production environment parameter set to determine the critical time point in the predicted time-series production environment parameter set that exceeds preset environmental parameter constraints; obtaining the response time characteristics of an environmental controller and determining a control time point for the critical time point based on the response time characteristics; at the control time point, collecting second real-time production environment parameters of the target workshop again through the IoT sensor network; generating environmental control parameters for the environmental controller based on the second real-time production environment parameters, and controlling the environmental controller to perform production environment control based on the environmental control parameters.

[0005] Optionally, the step of predicting production environment parameters based on the first real-time production environment parameters and the subsequent planned production tasks to obtain a predicted time-series production environment parameter set includes: extracting a digital twin model of the target workshop; configuring the initial state of the digital twin model according to the first real-time production environment parameters; simulating the execution of the subsequent planned production tasks through the digital twin model starting from the initial state, and recording the production environment parameter change data during the simulation execution; generating the predicted time-series production environment parameter set based on the production environment parameter change data, wherein the predicted time-series production environment parameter set contains predicted production environment parameter values ​​corresponding to multiple prediction time points.

[0006] Optionally, the production environment parameter types include workshop cleanliness, workshop humidity, and workshop temperature; the step of analyzing the predicted time series production environment parameter set to determine the critical time point where the predicted time series production environment parameter set exceeds the preset environmental parameter constraints includes: extracting the workshop cleanliness range, workshop humidity range, and workshop temperature range from the preset environmental parameter constraints; traversing the multiple predicted time points to determine a first predicted time point, and obtaining the predicted production environment parameter value corresponding to the first predicted time point from the predicted time series production environment parameter set to obtain the first predicted production environment parameter value; extracting the first workshop cleanliness value, the first workshop humidity value, and the first workshop temperature value from the first predicted production environment parameter value; determining whether the first workshop cleanliness value exceeds the workshop cleanliness range, whether the first workshop humidity value exceeds the workshop humidity range, or whether the first workshop temperature value exceeds the workshop temperature range; when the first workshop cleanliness value exceeds the workshop cleanliness range, or the first workshop humidity value exceeds the workshop humidity range, or the first workshop temperature value exceeds the workshop temperature range, marking the first predicted time point as the critical time point.

[0007] Optionally, the environmental controller includes a cleanliness controller, a temperature controller, and a temperature control device; the step of acquiring the response time characteristics of the environmental controller and determining the control time point for the critical time point based on the response time characteristics includes: acquiring the cleanliness response time of the cleanliness controller, the humidity response time of the temperature controller, and the temperature response time of the temperature control device; acquiring the type of critical production environment parameter that exceeds the preset environmental parameter constraints at the critical time point; and determining the control time point based on the type of critical production environment parameter, combined with the cleanliness response time, the humidity response time, and the temperature response time.

[0008] Optionally, generating the environmental control parameters of the environment controller based on the second real-time production environment parameters includes: extracting the real-time workshop cleanliness value, real-time workshop humidity value, and real-time workshop temperature value from the second real-time production environment parameters; obtaining the preset target workshop cleanliness value, target workshop humidity value, and target workshop temperature value from the preset environmental parameter constraints; determining a cleanliness deviation value based on the real-time workshop cleanliness value and the target workshop cleanliness value; determining a humidity deviation value based on the real-time workshop humidity value and the target workshop humidity value; determining a temperature deviation value based on the real-time workshop temperature value and the target workshop temperature value; and generating the environmental control parameters of the environment controller based on the cleanliness deviation value, the humidity deviation value, and the temperature deviation value.

[0009] Optionally, generating environmental control parameters for the environmental controller based on the cleanliness deviation value, the humidity deviation value, and the temperature deviation value includes: obtaining a control parameter generator, the control parameter generator including a cleanliness parameter branch, a humidity parameter branch, a temperature parameter branch, and a parameter influence balance branch; inputting the cleanliness deviation value, the humidity deviation value, and the temperature deviation value into the control parameter generator to generate the environmental control parameters for the environmental controller.

[0010] Optionally, the environmental controller includes a cleanliness controller, a temperature controller, and a temperature control device; the step of inputting the cleanliness deviation value, the humidity deviation value, and the temperature deviation value into the control parameter generator to generate the environmental control parameters of the environmental controller includes: inputting the cleanliness deviation value into the cleanliness parameter branch to generate the initial cleanliness control parameters of the cleanliness controller; inputting the humidity deviation value into the humidity parameter branch to generate the initial humidity control parameters of the temperature controller; inputting the temperature deviation value into the temperature parameter branch to generate the initial temperature control parameters of the temperature control device; inputting the initial cleanliness control parameters, the initial humidity control parameters, and the initial temperature control parameters into the parameter influence balance branch; in the parameter influence balance branch, based on A parameter interaction model is used to determine the first influence of the initial temperature control parameter on workshop humidity and the second influence on workshop cleanliness, as well as the third influence of the initial humidity control parameter on workshop temperature and the fourth influence on workshop cleanliness. The parameter interaction model includes influence coefficients for workshop temperature changes on workshop humidity and cleanliness, and influence coefficients for workshop humidity changes on workshop cleanliness and temperature. Based on the first, second, third, and fourth influences, the initial cleanliness control parameter, the initial humidity control parameter, and the initial temperature control parameter are compensated and adjusted to generate cleanliness control parameters, humidity control parameters, and temperature control parameters. These cleanliness control parameters, humidity control parameters, and temperature control parameters are then used as the environmental control parameters.

[0011] Furthermore, to achieve the above objectives, this application provides a production environment parameter control system based on the Industrial Internet of Things (IIoT). The system includes a management platform, a sensor network platform, and an object platform. The management platform includes: an information receiving module, used to respond to information acquisition commands, acquire first real-time production environment parameters of a target workshop via the IoT sensor network, and obtain subsequent planned production tasks of the target workshop through a production plan list; an environment prediction module, used to predict production environment parameters based on the first real-time production environment parameters and the subsequent planned production tasks, and obtain a predicted time-series production environment parameter set; a critical analysis module, used to analyze the predicted time-series production environment parameter set and determine the critical time point in the predicted time-series production environment parameter set that exceeds preset environmental parameter constraints; a control time module, used to acquire the response time characteristics of the environment controller and determine the control time point for the critical time point based on the response time characteristics; a parameter acquisition module, used to acquire second real-time production environment parameters of the target workshop again via the IoT sensor network at the control time point; and an environment control module, used to generate environmental control parameters for the environment controller based on the second real-time production environment parameters, and control the environment controller to perform production environment control based on the environmental control parameters.

[0012] Furthermore, to achieve the above objectives, this application also provides an electronic device, including a memory and a processor. The memory is used to store computer software programs; the processor is used to read and execute the computer software programs, thereby implementing the industrial Internet of Things-based production environment parameter control method described in any of the above possible implementations.

[0013] In addition, this application also provides a non-transitory computer-readable storage medium storing a computer software program, which, when executed by a processor, implements the production environment parameter control method based on the Industrial Internet of Things in any of the above possible implementations.

[0014] The beneficial effects that this application can achieve are as follows:

[0015] By responding to information collection commands, the IoT sensor network collects the first real-time production environment parameters of the target workshop and obtains the subsequent planned production tasks of the target workshop through the production plan list, providing a basic data source for subsequent predictive analysis. Based on the first real-time production environment parameters and the subsequent planned production tasks, the network predicts production environment parameters to obtain a predicted time-series production environment parameter set, enabling a forward-looking grasp of the future changing trends of production environment parameters. The network analyzes the predicted time-series production environment parameter set to determine the critical time point when the predicted time-series production environment parameters exceed the preset environmental parameter constraints, providing a decision-making basis for timely intervention and avoiding production problems caused by environmental parameters actually exceeding the limits. The network obtains the response time characteristics of the environmental controller and determines the control time point for the critical time point based on the response time characteristics. Considering the time delay required for the environmental controller from startup to actual effect, the network determines the optimal control timing to ensure that the control effect can accurately act on the critical moment. At the control time point, the network collects the second real-time production environment parameters of the target workshop again to ensure that control decisions are made based on the latest situation and improve control accuracy. Based on the second real-time production environment parameters, the network generates environmental control parameters for the environmental controller and controls the environmental controller to execute production environment control based on the environmental control parameters, achieving precise regulation of the production environment. After the environmental controller completes the production environment control, it generates new information acquisition instructions, making the entire control process a closed loop and continuously cyclically executed. This enables dynamic, intelligent, and forward-looking control of production environment parameters, ensuring that the production environment parameters meet production needs while avoiding energy waste. Attached Figure Description

[0016] Figure 1 A flowchart illustrating the production environment parameter control method based on the Industrial Internet of Things provided in this application;

[0017] Figure 2 A schematic diagram of the production environment parameter control system based on the Industrial Internet of Things provided in this application;

[0018] Figure 3 A schematic diagram of the structure of the electronic device provided in this application;

[0019] Figure 4 This is a schematic diagram of the structure of a computer-readable storage medium provided in this application.

[0020] In the attached diagram, the components represented by each number are as follows:

[0021] Information receiving module 11, environmental prediction module 12, critical analysis module 13, control time module 14, parameter acquisition module 15, environmental control module 16, electronic device 200, memory 210, processor 220, computer program 211, and computer-readable storage medium 300.

[0022] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0024] It should be noted that all directional indicators (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationship and movement of each component under a certain preset posture (as shown in the figure). If the preset posture changes, the directional indicator will also change accordingly.

[0025] In this application, unless otherwise expressly specified and limited, the terms "connection," "fixed," etc., should be interpreted broadly. For example, "fixed" can mean a fixed connection, a detachable connection, or an integral part; it can mean a mechanical connection or an electrical connection; it can mean a direct connection or an indirect connection through an intermediate medium; it can mean the internal communication of two components or the interaction between two components, unless otherwise expressly limited. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0026] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the meaning of "and / or" throughout the text includes three parallel solutions; for example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0027] like Figure 1 As shown, the first embodiment of this application provides a method for controlling production environment parameters based on the Industrial Internet of Things, including:

[0028] S1: Responds to the information acquisition command, receives the first real-time production environment parameters of the target workshop collected by the sensor, and obtains the subsequent planned production tasks of the target workshop through the production plan list;

[0029] S2: Based on the first real-time production environment parameters and subsequent planned production tasks, predict the production environment parameters and obtain the predicted time series production environment parameter set;

[0030] S3: Analyze the predicted time series production environment parameter set to determine the critical time point in the predicted time series production environment parameter set that exceeds the preset environmental parameter constraints;

[0031] S4: Obtain the response time characteristics of the environmental controller and determine the control time point for the critical time point based on the response time characteristics;

[0032] S5: At the control time point, receive the second real-time production environment parameters of the target workshop collected by the sensor;

[0033] S6: Based on the second real-time production environment parameters, generate the environment control parameters of the environment controller, and control the environment controller to perform production environment control based on the environment control parameters.

[0034] Specifically, firstly, the system responds to information acquisition commands issued by the production environment parameter control system, triggering the data acquisition process. Specifically, sensors deployed within the target workshop collect key parameters of the current production environment in real time, including but not limited to workshop temperature, humidity, and cleanliness. This data serves as the initial real-time production environment parameters. Simultaneously, the system accesses the production plan list in the production management database to extract subsequent planned production tasks to be executed in the target workshop. These subsequent planned production tasks include data such as task type, estimated start time, duration, and participating personnel. By synchronously acquiring the current real-time environmental status and future production plan information, a data foundation for environmental parameter control is established, providing necessary data support for subsequent environmental parameter prediction and control decisions. The initial real-time production environment parameters collected here serve as the initial state for environmental change prediction, while the subsequent planned production task information serves as the driving factor for the prediction model, together constituting the data source for predictive analysis.

[0035] After acquiring the first real-time production environment parameters and subsequent planned production task information, the system uses the acquired first real-time production environment parameters as the initial state and the subsequent planned production tasks as input conditions to simulate the production process using a digital twin model. During the simulation, the system considers the operating characteristics of various production equipment, process requirements, heat load, and other factors that may affect the environmental parameters, generating a series of predicted environmental parameter values ​​at predicted time points. These predicted environmental parameter values, arranged in chronological order, constitute the predicted time-series production environment parameter set. This predicted time-series production environment parameter set forms a multi-dimensional time-series data structure with time as the horizontal axis and various environmental parameters as the vertical axis. The predicted time-series production environment parameter set clearly shows the possible evolution trajectory of the production environment parameters in the future time period without additional intervention control, providing data support for subsequent critical point identification and control decisions. This achieves a shift from passively responding to environmental changes to actively predicting environmental trends, laying the foundation for intelligent environmental control.

[0036] After obtaining the predicted time-series production environment parameter set, the time points when the production environment parameters are about to exceed the preset environmental parameter constraints are identified from the predicted time-series production environment environment parameter set, providing a time basis for precise intervention. Specifically, firstly, preset environmental parameter constraints are extracted from the production process specifications or quality management system. These constraints define the allowable fluctuation range of various environmental parameters (such as temperature, humidity, cleanliness, etc.). Subsequently, each time point in the predicted time-series production environment parameter set is scanned and analyzed one by one, and the predicted environmental parameter values ​​are compared with the corresponding constraint ranges. When it is detected that the predicted environmental parameter value at a certain time point first exceeds or is about to exceed the preset environmental parameter constraints, that time point is marked as a critical time point. The critical time point indicates that without intervention, the production environment parameters will break through the safety boundary at that moment, which may have an adverse impact on production quality or process stability. By identifying potential risk time points through forward-looking analysis, environmental control is transformed from a traditional post-exceeding correction mode to a predictive prevention and control mode, laying the time foundation for optimal intervention. At the same time, the accurate identification of critical time points also avoids unnecessary frequent adjustments, thereby reducing energy consumption and improving operational efficiency.

[0037] After identifying the critical time point, the response time characteristic parameters of various environmental controllers are acquired. These parameters reflect the time delay required from the issuance of a control command to the effective change of production environment parameters. Subsequently, based on the type of environmental parameter at the critical time point, the response time of the environmental controller is selected. By subtracting the corresponding response time from the critical time point, the control time point is obtained. By fully considering the physical characteristics and time delay of the environmental controllers, precise timing matching between control behavior and demand is achieved, avoiding energy waste caused by premature intervention and control failure caused by late intervention. When the system clock reaches the obtained control time point, the management platform receives the environmental parameter data of the target workshop collected in real time by the sensors through the sensor network platform again, obtaining the latest environmental state at the control time point, which is recorded as the second real-time production environment parameter. By acquiring the latest real-time data before control execution, a more accurate control strategy can be formulated based on environmental parameters that are closest to the actual state. The second real-time production environment parameter serves as the direct input for the generation of subsequent control parameters, providing the latest and most reliable data foundation for precise environmental control.

[0038] After acquiring the second real-time production environment parameters, these parameters are compared with the target values ​​in the preset environmental parameter constraints to calculate the deviation values ​​of each environmental parameter. Subsequently, environmental control parameters for the environmental controller are generated based on these deviation values. These generated environmental control parameters are transmitted to the environmental controller via a sensor network platform, and the environmental controller executes corresponding environmental adjustment actions based on the received parameters. Through precise control parameter settings and coordinated control, the production environment parameters can smoothly transition to the target range while minimizing energy consumption and control fluctuations. After this round of control execution is completed, the system generates new information acquisition instructions and enters the next control cycle, achieving continuous control and management of the production environment parameters.

[0039] The aforementioned production environment parameter control method achieves a shift from passive regulation to proactive intelligent control. First, real-time environmental parameters are collected and planned production tasks are obtained. Potential critical points are identified through environmental parameter predictive analysis. Then, control timing is determined based on the response characteristics of the environmental controller. Finally, the latest environmental data is collected at the control timing point, and precise control commands are generated, forming a complete predictive-identification-intervention control closed loop. Compared with existing technologies, this method solves the problems of excessive energy consumption in continuous tracking control and low intelligence in phased control, achieving stable control of production environment parameters and effective reduction in energy consumption.

[0040] As an optional implementation, production environment parameters are predicted based on first real-time production environment parameters and subsequent planned production tasks to obtain a predicted time-series production environment parameter set, including:

[0041] S21: Extract the digital twin model of the target workshop;

[0042] S22: Configure the initial state of the digital twin model according to the first real-time production environment parameters;

[0043] S23: Starting from the initial state, simulate the execution of subsequent planned production tasks through a digital twin model, and record the changes in production environment parameters during the simulation execution process;

[0044] S24: Generate a predicted time series production environment parameter set based on the production environment parameter change data. The predicted time series production environment parameter set contains predicted production environment parameter values ​​corresponding to multiple predicted time points.

[0045] Specifically, firstly, a digital twin model corresponding to the target workshop is extracted from the database. This digital twin model is a precise virtual mapping of the physical target workshop, including the workshop's geometric layout, equipment distribution, heat source locations, airflow channels, and a precise mathematical model of the impact of related production processes on environmental parameters. Through preliminary modeling work and continuous data calibration, the digital twin model can highly reproduce the physical characteristics and environmental evolution patterns of the real workshop. Then, the first real-time production environmental parameters are imported into the digital twin model to configure its initial state. Specifically, environmental parameters such as workshop temperature, humidity, and cleanliness from the first real-time production environmental parameters are mapped to the corresponding parameter nodes in the digital twin model, ensuring a high degree of consistency between the initial state of the virtual model and the current state of the actual workshop, laying the foundation for the accuracy of subsequent simulations.

[0046] Subsequently, starting from the initial configured state, the subsequent planned production task information obtained from the production plan list is input into the digital twin model to initiate the simulation execution process. During the simulation, the digital twin model simulates the evolution trend of workshop environmental parameters over a future period based on factors such as production task characteristics, equipment operating status, and process characteristics. Simultaneously, environmental parameter values ​​at preset time intervals are recorded during the simulation, forming complete production environmental parameter change data. Next, based on the simulated production environmental parameter change data, a structured predictive time-series production environmental parameter set is constructed. This predictive time-series production environmental parameter set is indexed by time and contains multiple predicted time points and their corresponding predicted production environmental parameter values, forming a complete environmental parameter time-series prediction data structure. Preferably, the predictive time-series production environmental parameter set not only includes the predicted values ​​of environmental parameters but also the confidence intervals and fluctuation trend characteristics of the predicted values, providing comprehensive data support for subsequent critical point analysis.

[0047] By introducing digital twin technology, the accuracy and reliability of environmental parameter prediction have been significantly improved, enabling more precise identification of potential environmental parameter anomalies and thus achieving more efficient environmental control strategies.

[0048] As an optional implementation, the production environment parameter types include workshop cleanliness, workshop humidity, and workshop temperature; the predicted time series production environment parameter set is analyzed to determine the critical time points where the predicted time series production environment parameter set exceeds the preset environmental parameter constraints, including:

[0049] S31: Extract the range of cleanliness, humidity, and temperature of the workshop from the preset environmental parameter constraints.

[0050] S32: Traverse multiple prediction time points, determine the first prediction time point, and obtain the prediction production environment parameter value corresponding to the first prediction time point from the prediction time series production environment parameter set to obtain the first prediction production environment parameter value.

[0051] S33: Extract the first workshop cleanliness value, first workshop humidity value, and first workshop temperature value from the first predicted production environment parameter values;

[0052] S34: Determine whether the cleanliness value of the first workshop exceeds the cleanliness range of the workshop, whether the humidity value of the first workshop exceeds the humidity range of the workshop, or whether the temperature value of the first workshop exceeds the temperature range of the workshop.

[0053] S35: When the cleanliness value of the first workshop exceeds the cleanliness range, or the humidity value of the first workshop exceeds the humidity range, or the temperature value of the first workshop exceeds the temperature range, the first predicted time point is marked as the critical time point.

[0054] Specifically, the types of production environment parameters include workshop cleanliness, workshop humidity, and workshop temperature. First, preset environmental parameter constraints are extracted from a database or production process specifications. These preset environmental parameter constraints include the range of workshop cleanliness, humidity, and temperature. These range values ​​are usually defined in interval form, such as cleanliness level requirements (e.g., the cleanliness level defined in ISO 14644-1), relative humidity percentage range (e.g., 45%~65%), and temperature range (e.g., 20℃~24℃). These constraint ranges are the benchmark standards for judging whether the production environment parameters are in a qualified state. Then, multiple prediction time points in the predicted time series production environment parameter set are traversed chronologically. Starting from the earliest prediction time point, each prediction time point to be evaluated is determined sequentially, called the first prediction time point. For the first prediction time point, the corresponding predicted production environment parameter value is extracted from the predicted time series production environment parameter set and recorded as the first predicted production environment parameter value. The first predicted production environment parameter value contains all environmental parameter status data predicted for the first prediction time point. Subsequently, the cleanliness, humidity, and temperature values ​​of the first workshop were extracted from the first predicted production environment parameter values. These three values ​​reflect the predicted state of the target workshop's production environment at the first predicted time point and serve as the basis for subsequent judgments.

[0055] Subsequently, the extracted cleanliness, humidity, and temperature values ​​of the first workshop were checked separately to determine whether the cleanliness, humidity, and temperature values ​​exceeded the workshop's acceptable range. If any of these three conditions were detected, the current first predicted time point was marked as the critical time point. The critical time point indicates that at that specific moment, without intervention or control, at least one production environment parameter will exceed its safe operating range, potentially adversely affecting production quality.

[0056] By following the steps described above, the earliest possible time point when production environment parameters may exceed limits can be accurately identified, providing a time reference for subsequent precise control. This implementation method considers the combined impact of multiple production environment parameters, ensuring that any parameter exceeding limits can be detected in a timely manner, achieving comprehensive monitoring of multiple production environment parameters.

[0057] As an optional implementation, the environmental controller includes a cleanliness controller, a temperature controller, and a temperature control device; acquiring the response time characteristics of the environmental controller and determining the control time point for the critical time point based on the response time characteristics includes:

[0058] S41: Obtain the cleanliness response time of the cleanliness controller, the humidity response time of the temperature controller, and the temperature response time of the temperature control device;

[0059] S42: Obtain the type of critical production environment parameter that exceeds the preset environmental parameter constraints at the critical time point;

[0060] S43: Determine the control time point based on the type of critical production environment parameters, combined with the cleanliness response time, humidity response time, and temperature response time.

[0061] Specifically, the environmental controller includes cleanliness controllers, temperature controllers, and temperature control devices. First, response time characteristic parameters of various environmental controllers are obtained from an equipment characteristic database or through real-time identification methods. These include the cleanliness response time of cleanliness controllers (such as air filtration systems and purification fans), the humidity response time of temperature controllers (such as humidifiers and dehumidifiers), and the temperature response time of temperature control devices (such as air conditioning systems and heating devices). These response time parameters reflect the time delay required for effective changes in production environmental parameters from the issuance of control commands, serving as the basis for accurate calculation of control time points. Then, the specific type of environmental parameter exceeding preset environmental parameter constraints at the critical time point is determined. This identifies whether it is workshop cleanliness, workshop humidity, workshop temperature, or multiple parameters simultaneously exceeding their respective constraints, and the identification results are recorded as the critical production environmental parameter type.

[0062] Subsequently, based on the types of critical production environment parameters and the response time characteristics of each environmental controller, the control time point is determined. Specifically, when the critical production environment parameter type is only workshop cleanliness, the control time point is obtained by subtracting the cleanliness response time from the critical time point; when the critical production environment parameter type is only workshop humidity, the control time point is obtained by subtracting the humidity response time from the critical time point; when the critical production environment parameter type is only workshop temperature, the control time point is obtained by subtracting the temperature response time from the critical time point; when multiple environmental parameter types simultaneously exceed their respective preset environmental parameter constraints, the response times of all relevant control devices are compared, and the response time corresponding to the environmental parameter type with the longest response time is selected. The control time point is then obtained by subtracting this longest response time from the critical time point. It should be noted that regardless of whether the critical situation involves a single environmental parameter or multiple environmental parameters, the determined control time point is the unified start-up time point of all environmental controllers. At this time point, all environmental controllers, including the cleanliness controller, temperature controller, and temperature control device, will be triggered to perform coordinated control, rather than only activating the control devices directly related to the exceeded parameters.

[0063] By determining the control timing, it is ensured that the control action is initiated at the most appropriate time, enabling the control effect to precisely and effectively impact production environment parameters before the critical time point. This avoids energy waste caused by premature control and parameter exceedances caused by late control. Furthermore, the maximum response time is used as the calculation benchmark for situations where multiple parameters exceed limits simultaneously, ensuring the comprehensive effectiveness of the control.

[0064] As an optional implementation, environmental control parameters for the environmental controller are generated based on the second real-time production environment parameters, including:

[0065] S61: Extract the real-time workshop cleanliness value, real-time workshop humidity value, and real-time workshop temperature value from the second real-time production environment parameters;

[0066] S62: Obtain the preset target workshop cleanliness value, target workshop humidity value, and target workshop temperature value from the preset environmental parameter constraints;

[0067] S63: Determine the cleanliness deviation value based on the real-time cleanliness value of the workshop and the target cleanliness value of the workshop;

[0068] S64: Determine the humidity deviation value based on the real-time workshop humidity value and the target workshop humidity value;

[0069] S65: Determine the temperature deviation value based on the real-time workshop temperature value and the target workshop temperature value;

[0070] S66: Generate environmental control parameters for the environmental controller based on cleanliness deviation, humidity deviation, and temperature deviation.

[0071] Specifically, firstly, the current real-time workshop cleanliness, humidity, and temperature values ​​are precisely extracted from the collected second real-time production environment parameters. These values ​​reflect the precise state of the workshop environment before control execution and form the data foundation for precise control. Then, pre-determined target workshop cleanliness, humidity, and temperature values ​​are obtained from preset environmental parameter constraints. These target values ​​are typically set as the center or optimal value within the allowable range of each parameter, representing the ideal target state for environmental control. Under certain specific process requirements, the target values ​​may also be dynamically adjusted optimized values ​​based on product characteristics or process needs.

[0072] Then, the difference between the real-time cleanliness value of the workshop and the target cleanliness value is calculated to determine the cleanliness deviation value. This deviation value includes both the magnitude and the direction of deviation (positive deviation indicates that the current cleanliness is higher than the target value, and negative deviation indicates that the current cleanliness is lower than the target value), providing a quantitative basis for subsequent cleanliness control. Simultaneously, the difference between the real-time humidity value of the workshop and the target humidity value is calculated to determine the humidity deviation value. This deviation value reflects the degree and direction of deviation between the current humidity state and the target requirement, serving as an important basis for humidity control decisions. Also, the difference between the real-time temperature value of the workshop and the target temperature value is calculated to determine the temperature deviation value. This deviation value quantifies the intensity and direction of temperature control demand, providing a control basis for temperature control. Subsequently, based on the acquired cleanliness deviation value, humidity deviation value, and temperature deviation value, environmental control parameters for the environmental controller are comprehensively generated. The generation process of the control parameters considers not only the magnitude and direction of each deviation value but also the interactive influence relationships between environmental parameters, as well as the dynamic characteristics and energy efficiency features of the control device.

[0073] The control parameter generation method based on deviation analysis described above achieves a precise mapping from environmental state assessment to control strategy formulation, making environmental control more accurate and efficient. Compared to traditional fixed logic control or simple proportional control methods, it can dynamically adjust the control strength and strategy according to the actual deviation, improving control accuracy and reducing energy consumption.

[0074] As an optional implementation, environmental control parameters for the environmental controller are generated based on cleanliness deviation values, humidity deviation values, and temperature deviation values, including:

[0075] S661: Obtain the control parameter generator, which includes cleanliness parameter branch, humidity parameter branch, temperature parameter branch and parameter influence balance branch;

[0076] S662: Input the cleanliness deviation value, humidity deviation value and temperature deviation value into the control parameter generator to generate the environmental control parameters of the environmental controller.

[0077] Specifically, firstly, a specially designed control parameter generator is invoked. This generator employs a multi-branch parallel processing architecture, including branches for cleanliness parameters, humidity parameters, temperature parameters, and parameter influence balancing. The cleanliness parameter branch is specifically responsible for processing the cleanliness control logic and generating cleanliness control parameters; the humidity parameter branch is specifically responsible for processing the humidity control logic and generating humidity control parameters; the temperature parameter branch is specifically responsible for processing the temperature control logic and generating temperature control parameters; and the parameter influence balancing branch is responsible for handling the interaction effects between various environmental parameters, coordinating the control parameters to achieve the overall optimal control effect. This control parameter generator can be implemented using a rule-based expert system, machine learning model, or hybrid intelligent algorithm, and internally contains mathematical models and control strategy libraries for the characteristics of various environmental controllers.

[0078] Then, the obtained cleanliness deviation, humidity deviation, and temperature deviation values ​​are used as input data and fed into the control parameter generator. The control parameter generator first passes these deviation values ​​to their respective parameter branches for preliminary processing, then performs a comprehensive balance analysis in the parameter influence balance branch, and finally outputs the environmental control parameters after interactive influence compensation adjustments. The generated environmental control parameters include the specific control instructions required by each environmental controller, such as the filtration velocity and operating mode of the cleanliness controller, the humidification / dehumidification intensity of the temperature controller, and the cooling / heating power of the temperature control device.

[0079] By employing a multi-branch collaborative control parameter generator, the independent control logic of each production environment parameter is combined with the analysis of the interaction effects between parameters, overcoming the limitation of traditional single-parameter control methods in handling parameter interaction effects. By simultaneously considering the control requirements and mutual influences of multiple environmental parameters, a globally optimal control strategy is generated, improving the accuracy of environmental control and system stability, while reducing energy consumption.

[0080] As an optional implementation, the environmental controller includes a cleanliness controller, a temperature controller, and a temperature control device; cleanliness deviation values, humidity deviation values, and temperature deviation values ​​are input into a control parameter generator to generate environmental control parameters for the environmental controller, including:

[0081] S6621: Input the cleanliness deviation value into the cleanliness parameter branch to generate the initial cleanliness control parameters of the cleanliness controller;

[0082] S6622: Input the humidity deviation value into the humidity parameter branch to generate the initial humidity control parameters for the temperature controller;

[0083] S6623: Input the temperature deviation value into the temperature parameter branch to generate the initial temperature control parameters of the temperature control device;

[0084] S6624: Input parameters of initial cleanliness control parameters, initial humidity control parameters, and initial temperature control parameters affect the balance branch;

[0085] S6625: In the parameter influence balance branch, based on the parameter interaction influence model, determine the first influence of the initial temperature control parameter on workshop humidity and the second influence on workshop cleanliness, as well as the third influence of the initial humidity control parameter on workshop temperature and the fourth influence on workshop cleanliness. The parameter interaction influence model includes the influence coefficients of workshop temperature change on workshop humidity and workshop cleanliness, and the influence coefficients of workshop humidity change on workshop cleanliness and workshop temperature.

[0086] S6626: Based on the first, second, third, and fourth influencing factors, compensate and adjust the initial control parameters for cleanliness, humidity, and temperature to generate the cleanliness control parameters, humidity control parameters, and temperature control parameters.

[0087] S6627: Cleanliness control parameters, humidity control parameters, and temperature control parameters shall be used as environmental control parameters.

[0088] Specifically, firstly, the cleanliness deviation value is input into the cleanliness parameter branch of the control parameter generator. In this branch, a specially designed cleanliness control algorithm (such as a proportional-integral-derivative control algorithm, fuzzy control algorithm, or other intelligent control algorithm) calculates and generates the initial cleanliness control parameters for the cleanliness controller based on the magnitude and trend of the cleanliness deviation value. These initial control parameters include the initial operating instructions for the cleanliness controller, such as the fan speed adjustment parameters of the air filtration system and the selection of the filtration mode. Simultaneously, the humidity deviation value is input into the humidity parameter branch of the control parameter generator. In this branch, the humidity control algorithm calculates and generates the initial humidity control parameters for the temperature controller based on the humidity deviation value and its changing characteristics. These parameters include the initial operating instructions for the temperature controller, such as the humidification intensity of the humidifier and the operating mode of the dehumidifier. Simultaneously, the temperature deviation value is input into the temperature parameter branch of the control parameter generator. In this branch, the temperature control algorithm calculates and generates the initial temperature control parameters for the temperature control device based on the temperature deviation value and its dynamic characteristics. These parameters include the initial setpoints of the temperature control device, such as the temperature setting of the air conditioning system and the cooling / heating power.

[0089] Subsequently, the generated initial control parameters for cleanliness, humidity, and temperature are uniformly input into the parameter influence balance branch of the control parameter generator, entering the interaction influence analysis and balance adjustment stage. In the parameter influence balance branch, based on a pre-established parameter interaction influence model, the potential cross-influences after the execution of each initial control parameter are analyzed. Specifically, four key interaction influences are calculated: the influence of the initial temperature control parameter on workshop humidity (first influence), the influence of the initial temperature control parameter on workshop cleanliness (second influence), the influence of the initial humidity control parameter on workshop temperature (third influence), and the influence of the initial humidity control parameter on workshop cleanliness (fourth influence). These calculations are based on the influence coefficients defined in the parameter interaction influence model, including the influence coefficients of workshop temperature changes on workshop humidity and cleanliness, and the influence coefficients of workshop humidity changes on workshop cleanliness and temperature. This parameter interaction influence model can be obtained through historical data analysis, physical modeling, or machine learning methods.

[0090] Next, based on the calculated first, second, third, and fourth influencing factors, the initial control parameters for cleanliness, humidity, and temperature are adjusted and compensated. The adjustment process considers the interactions between parameters, eliminating or reducing potential negative interference through compensation calculations to achieve synergistic optimization. After compensation and adjustment, the system generates the final cleanliness, humidity, and temperature control parameters. These parameters have undergone interaction balancing, minimizing adverse effects on other environmental parameters while achieving their respective control objectives. Finally, the cleanliness, humidity, and temperature control parameters, adjusted through interaction compensation, are integrated into a complete set of environmental control parameters, which is then passed as the final output to each environmental controller for execution.

[0091] Through step-by-step calculation and interactive compensation mechanisms, coordinated and precise control of environmental parameters is achieved, effectively solving the problem of control oscillation or mutual interference caused by the interaction of multiple parameters. This not only improves the accuracy of control of various production environment parameters, but also ensures the stability of the overall target workshop environment system.

[0092] The second embodiment of this application provides a production environment parameter control system based on the Industrial Internet of Things. The system includes a management platform 101, a sensor network platform 102, and an object platform 103 that are connected in sequence. The object platform 103 includes an environment controller and sensors. The management platform 101 includes an information receiving module 11, an environment prediction module 12, a critical analysis module 13, a control time module 14, a parameter acquisition module 15, and an environment control module 16. The system comprises the following modules: an information receiving module 11, which responds to an information acquisition command, acquires first real-time production environment parameters of the target workshop via an IoT sensor network, and obtains subsequent planned production tasks of the target workshop via a production plan list; an environment prediction module 12, which predicts production environment parameters based on the first real-time production environment parameters and the subsequent planned production tasks, and obtains a predicted time-series production environment parameter set; a critical analysis module 13, which analyzes the predicted time-series production environment parameter set and determines the critical time point in the predicted time-series production environment parameter set that exceeds the preset environmental parameter constraints; a control time module 14, which acquires the response time characteristics of the environment controller and determines the control time point for the critical time point based on the response time characteristics; a parameter acquisition module 15, which again acquires second real-time production environment parameters of the target workshop via the IoT sensor network at the control time point; and an environment control module 16, which generates environmental control parameters for the environment controller based on the second real-time production environment parameters and controls the environment controller to perform production environment control based on the environmental control parameters.

[0093] As an optional implementation, the environment prediction module 12 includes a digital twin model extraction unit, an initial state configuration unit, a simulation execution unit, and a parameter prediction unit. The digital twin model extraction unit extracts a digital twin model of the target workshop; the initial state configuration unit configures the initial state of the digital twin model based on the first real-time production environment parameters; the simulation execution unit simulates the execution of subsequent planned production tasks using the digital twin model, starting from the initial state, and records changes in production environment parameters during the simulation; the parameter prediction unit generates a predicted time-series production environment parameter set based on the changes in production environment parameters, the predicted time-series production environment parameter set containing predicted production environment parameter values ​​corresponding to multiple predicted time points.

[0094] As an optional implementation, the production environment parameter types include workshop cleanliness, workshop humidity, and workshop temperature; the critical analysis module 13 includes a range extraction unit, a time point traversal unit, a parameter extraction unit, a parameter judgment unit, and a critical determination unit. Specifically, the range extraction unit extracts the workshop cleanliness range, workshop humidity range, and workshop temperature range from the preset environmental parameter constraints; the time point traversal unit traverses the multiple predicted time points, determines a first predicted time point, and obtains the predicted production environment parameter value corresponding to the first predicted time point from the predicted time-series production environment parameter set, thus obtaining the first predicted production environment parameter value; the parameter extraction unit extracts the first workshop cleanliness value, the first workshop humidity value, and the first workshop temperature value from the first predicted production environment parameter value; the parameter judgment unit determines whether the first workshop cleanliness value exceeds the workshop cleanliness range, whether the first workshop humidity value exceeds the workshop humidity range, or whether the first workshop temperature value exceeds the workshop temperature range; the critical determination unit marks the first predicted time point as the critical time point when the first workshop cleanliness value exceeds the workshop cleanliness range, or the first workshop humidity value exceeds the workshop humidity range, or the first workshop temperature value exceeds the workshop temperature range.

[0095] As an optional implementation, the environmental controller includes a cleanliness controller, a temperature controller, and a temperature control device; the control time module 14 includes a response time acquisition unit, a parameter type acquisition unit, and a control time point determination unit. Specifically, the response time acquisition unit acquires the cleanliness response time of the cleanliness controller, the humidity response time of the temperature controller, and the temperature response time of the temperature control device; the parameter type acquisition unit acquires the critical production environment parameter type that exceeds the preset environmental parameter constraints at the critical time point; and the control time point determination unit determines the control time point based on the critical production environment parameter type, combined with the cleanliness response time, the humidity response time, and the temperature response time.

[0096] As an optional implementation, the environmental control module 16 includes a real-time value extraction unit, a target value acquisition unit, a cleanliness deviation value determination unit, a humidity deviation value determination unit, a temperature deviation value determination unit, and a control parameter generation unit. Specifically, the real-time value extraction unit extracts the real-time workshop cleanliness value, real-time workshop humidity value, and real-time workshop temperature value from the second real-time production environment parameters; the target value acquisition unit acquires the preset target workshop cleanliness value, target workshop humidity value, and target workshop temperature value from the preset environmental parameter constraints; the cleanliness deviation value determination unit determines the cleanliness deviation value based on the real-time workshop cleanliness value and the target workshop cleanliness value; the humidity deviation value determination unit determines the humidity deviation value based on the real-time workshop humidity value and the target workshop humidity value; the temperature deviation value determination unit determines the temperature deviation value based on the real-time workshop temperature value and the target workshop temperature value; and the control parameter generation unit generates environmental control parameters for the environmental controller based on the cleanliness deviation value, the humidity deviation value, and the temperature deviation value.

[0097] As an optional implementation, the control parameter generation unit includes a generator acquisition unit and a control parameter output unit. The generator acquisition unit acquires a control parameter generator, which includes a cleanliness parameter branch, a humidity parameter branch, a temperature parameter branch, and a parameter influence balance branch. The control parameter output unit inputs the cleanliness deviation value, the humidity deviation value, and the temperature deviation value into the control parameter generator to generate the environmental control parameters of the environmental controller.

[0098] As an optional implementation, the environmental controller includes a cleanliness controller, a temperature controller, and a temperature control device; the generator acquisition unit includes a cleanliness parameter generation unit, a humidity parameter generation unit, a temperature parameter generation unit, a parameter input unit, a parameter balancing unit, a compensation adjustment unit, and a parameter summarization unit. Specifically, the cleanliness parameter generation unit inputs the cleanliness deviation value into the cleanliness parameter branch to generate the initial cleanliness control parameters of the cleanliness controller; the initial humidity parameter generation unit inputs the humidity deviation value into the humidity parameter branch to generate the initial humidity control parameters of the temperature controller; the temperature parameter generation unit inputs the temperature deviation value into the temperature parameter branch to generate the initial temperature control parameters of the temperature control device; the parameter input unit inputs the initial cleanliness control parameters, the initial humidity control parameters, and the initial temperature control parameters into the parameter influence balance branch; and the parameter balancing unit, based on a parameter interaction influence model, determines the first influence of the initial temperature control parameters on the workshop humidity within the parameter influence balance branch. The system includes a first influence quantity and a second influence quantity on workshop cleanliness, as well as a third influence quantity and a fourth influence quantity on workshop temperature and workshop cleanliness, wherein the parameter interaction influence model includes the influence coefficients of workshop temperature change on workshop humidity and workshop cleanliness, and the influence coefficients of workshop humidity change on workshop cleanliness and workshop temperature; the compensation adjustment unit is used to compensate and adjust the initial cleanliness control parameter, the initial humidity control parameter, and the initial temperature control parameter according to the first influence quantity, the second influence quantity, the third influence quantity, and the fourth influence quantity, to generate cleanliness control parameters, humidity control parameters, and temperature control parameters; the parameter aggregation unit is used to use the cleanliness control parameters, the humidity control parameters, and the temperature control parameters as the environmental control parameters.

[0099] The third embodiment of this application provides an electronic device, such as... Figure 3 As shown, the electronic device includes a memory 210, a processor 220, and a computer program 211 stored in the memory 210 and executable on the processor 220. When the processor 220 executes the computer program 211, it implements the production environment parameter control method based on the Industrial Internet of Things in any possible implementation of the first embodiment.

[0100] The fourth embodiment of this application provides a computer-readable storage medium, such as... Figure 4 As shown, a computer program 211 is stored on a computer-readable storage medium 300. When executed by a processor, the computer program 211 implements the production environment parameter control method based on the Industrial Internet of Things in any possible implementation of the first embodiment.

[0101] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for controlling production environment parameters based on the Industrial Internet of Things, characterized in that, The method is applied to a production environment parameter control system, which includes a management platform, a sensor network platform, and an object platform. The method is executed by the management platform and includes: Receive the first real-time production environment parameters of the target workshop, and obtain the subsequent planned production tasks of the target workshop through the production plan list; Based on the first real-time production environment parameters and the subsequent planned production tasks, production environment parameters are predicted to obtain a predicted time series production environment parameter set. The predicted time series production environment parameter set is analyzed to determine the critical time point in the predicted time series production environment parameter set that exceeds the preset environment parameter constraints. The response time characteristics of the environmental controller are obtained, and the control time point for the critical time point is determined based on the response time characteristics. At the controlled time point, the second real-time production environment parameters of the target workshop are received; Based on the second real-time production environment parameters, environmental control parameters for the environment controller are generated, and the environment controller is controlled to perform production environment control based on the environmental control parameters. The production environment parameter types include workshop cleanliness, workshop humidity, and workshop temperature; the predicted time-series production environment parameter set contains predicted production environment parameter values ​​corresponding to multiple predicted time points; the analysis of the predicted time-series production environment parameter set to determine the critical time points in the predicted time-series production environment parameter set that exceed preset environmental parameter constraints includes: Among the preset environmental parameter constraints, the range of workshop cleanliness, the range of workshop humidity, and the range of workshop temperature are extracted; Traverse the multiple prediction time points to determine the first prediction time point, and obtain the prediction production environment parameter value corresponding to the first prediction time point from the prediction time series production environment parameter set to obtain the first prediction production environment parameter value. Extract the first workshop cleanliness value, first workshop humidity value, and first workshop temperature value from the first predicted production environment parameter values; Determine whether the cleanliness value of the first workshop exceeds the cleanliness range of the workshop, whether the humidity value of the first workshop exceeds the humidity range of the workshop, or whether the temperature value of the first workshop exceeds the temperature range of the workshop; When the cleanliness value of the first workshop exceeds the cleanliness range of the workshop, or the humidity value of the first workshop exceeds the humidity range of the workshop, or the temperature value of the first workshop exceeds the temperature range of the workshop, the first predicted time point is marked as the critical time point.

2. The method according to claim 1, characterized in that, The step of predicting production environment parameters based on the first real-time production environment parameters and the subsequent planned production tasks, and obtaining a predicted time-series production environment parameter set, includes: Extract the digital twin model of the target workshop; Configure the initial state of the digital twin model according to the first real-time production environment parameters; Starting from the initial state, the subsequent planned production tasks are simulated and executed using the digital twin model, and the changes in production environment parameters are recorded during the simulation. The predicted time-series production environment parameter set is generated based on the production environment parameter change data.

3. The method according to claim 1, characterized in that, The environmental controller includes a cleanliness controller, a humidity controller, and a temperature controller; the step of acquiring the response time characteristics of the environmental controller and determining the control time point for the critical time point based on the response time characteristics includes: The cleanliness response time of the cleanliness controller, the humidity response time of the temperature controller, and the temperature response time of the temperature control device are obtained. Obtain the type of critical production environment parameter that exceeds the preset environmental parameter constraint at the critical time point; The control time point is determined based on the critical production environment parameter type, combined with the cleanliness response time, the humidity response time, and the temperature response time.

4. The method according to claim 1, characterized in that, The step of generating environmental control parameters for the environment controller based on the second real-time production environment parameters includes: Extract the real-time workshop cleanliness value, real-time workshop humidity value, and real-time workshop temperature value from the second real-time production environment parameters; Obtain the preset target workshop cleanliness value, target workshop humidity value, and target workshop temperature value from the preset environmental parameter constraints; The cleanliness deviation value is determined based on the real-time workshop cleanliness value and the target workshop cleanliness value; The humidity deviation value is determined based on the real-time workshop humidity value and the target workshop humidity value; The temperature deviation value is determined based on the real-time workshop temperature value and the target workshop temperature value; Based on the cleanliness deviation value, the humidity deviation value, and the temperature deviation value, environmental control parameters for the environmental controller are generated.

5. The method according to claim 4, characterized in that, The process of generating environmental control parameters for the environmental controller based on the cleanliness deviation value, the humidity deviation value, and the temperature deviation value includes: Obtain a control parameter generator, which includes a cleanliness parameter branch, a humidity parameter branch, a temperature parameter branch, and a parameter influence balance branch; The cleanliness deviation value, the humidity deviation value, and the temperature deviation value are input into the control parameter generator to generate the environmental control parameters of the environmental controller.

6. The method according to claim 5, characterized in that, The environmental controller includes a cleanliness controller, a temperature controller, and a temperature control device; the step of inputting the cleanliness deviation value, the humidity deviation value, and the temperature deviation value into the control parameter generator to generate the environmental control parameters of the environmental controller includes: The cleanliness deviation value is input into the cleanliness parameter branch to generate the initial cleanliness control parameters of the cleanliness controller; The humidity deviation value is input into the humidity parameter branch to generate the initial humidity control parameters of the temperature controller; The temperature deviation value is input into the temperature parameter branch to generate the initial temperature control parameters of the temperature control device. The initial cleanliness control parameter, the initial humidity control parameter, and the initial temperature control parameter are input into the parameter influence balance branch; In the parameter influence balance branch, based on the parameter interaction influence model, the first influence of the initial temperature control parameter on workshop humidity and the second influence on workshop cleanliness are determined, as well as the third influence of the initial humidity control parameter on workshop temperature and the fourth influence on workshop cleanliness. The parameter interaction influence model includes the influence coefficients of workshop temperature change on workshop humidity and workshop cleanliness, and the influence coefficients of workshop humidity change on workshop cleanliness and workshop temperature. Based on the first, second, third, and fourth influencing factors, the initial cleanliness control parameters, the initial humidity control parameters, and the initial temperature control parameters are compensated and adjusted to generate cleanliness control parameters, humidity control parameters, and temperature control parameters. The cleanliness control parameter, the humidity control parameter, and the temperature control parameter are used as the environmental control parameters.

7. A production environment parameter control system based on the Industrial Internet of Things, characterized in that, For performing the method as described in any one of claims 1 to 6, the system includes a management platform, a sensor network platform, and an object platform, wherein the management platform includes: The information receiving module is used to respond to information collection instructions, collect the first real-time production environment parameters of the target workshop through the object platform, and obtain the subsequent planned production tasks of the target workshop through the production plan list; The environment prediction module is used to predict production environment parameters based on the first real-time production environment parameters and the subsequent planned production tasks, and to obtain a predicted time series production environment parameter set. The critical analysis module is used to analyze the predicted time series production environment parameter set and determine the critical time point in the predicted time series production environment parameter set that exceeds the preset environmental parameter constraints. The control time module is used to acquire the response time characteristics of the environmental controller and determine the control time point for the critical time point based on the response time characteristics. The parameter acquisition module is used to collect the second real-time production environment parameters of the target workshop again through the Internet of Things sensor network at the control time point; The environmental control module is used to generate environmental control parameters for the environmental controller based on the second real-time production environment parameters, and to control the environmental controller to perform production environment control based on the environmental control parameters.

8. An electronic device, characterized in that, include: Memory, used to store computer software programs; A processor for reading and executing the computer software program to implement the method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium, characterized in that, The storage medium stores a computer software program, which, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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