Production environment parameter control method and system based on industrial Internet of Things

Abstract

The invention relates to a production environment parameter control method and system based on industrial Internet of Things. The method comprises the following steps: collecting a first real-time production environment parameter and a subsequent planned production task, carrying out production environment parameter prediction, obtaining a prediction time sequence production environment parameter set, and determining a critical time point; acquiring response time characteristics of the environment controller, and determining a control time point for the critical time point; and at the control time point, collecting a second real-time production environment parameter of the target workshop, generating an environment control parameter of the environment controller, and controlling the environment controller to execute production environment control based on the environment control parameter. Intelligent prediction of production environment parameters, critical point identification and optimal opportunity intervention control are realized through an Internet of Things sensing network and a production plan, so that the stability of the production environment is ensured, and meanwhile, the energy consumption is reduced.

Description

Technical Field

[0001] The present application relates to the field of industrial Internet of Things, and in particular to a method and system for controlling production environment parameters based on the industrial Internet of Things. Background Art

[0002] With the development of industry, higher requirements are being placed on production environment parameters during the manufacturing process. Currently, production environment parameter control mainly adopts continuous tracking control or staged control methods. Among them, the continuous tracking control method continuously collects environmental parameter data through sensors and continuously adjusts the working status of the environmental controller based on real-time data. Although this method can ensure that the production environment parameters are always within the preset range, the frequent start-stop and adjustment of the environmental controller leads to excessive energy consumption, which increases production costs. The staged control method detects environmental parameters at regular or fixed points, and only activates the control device to make adjustments when the parameters deviate from the preset range. Although this method reduces energy consumption to a certain extent, due to the lack of an intelligent prediction mechanism, control measures are often not taken until the production environment parameters have exceeded the preset range, affecting 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, aiming to solve the technical problems in the existing technology of production environment parameter control that continuous tracking control leads to excessive energy consumption or the low level of intelligence of staged control leads to the inability to prevent environmental parameters from exceeding the preset range.

[0004] To achieve the above-mentioned objectives, the present application provides a production environment parameter control method based on the industrial Internet of Things, the method comprising: receiving a first real-time production environment parameter of a target workshop, and obtaining a subsequent planned production task of the target workshop through a production plan list; predicting the production environment parameter based on the first real-time production environment parameter and the subsequent planned production task, and obtaining a predicted time series production environment parameter set; analyzing the predicted time series production environment parameter set, and determining a critical time point in the predicted time series production environment parameter set that exceeds a preset environment parameter constraint; obtaining a response time characteristic of an environment controller, and determining a control time point for the critical time point based on the response time characteristic; at the control time point, again collecting a second real-time production environment parameter of the target workshop through the Internet of Things sensor network; generating an environment control parameter of the environment controller based on the second real-time production environment parameter, and controlling the environment controller to perform production environment control based on the environment control parameter.

[0005] Optionally, the production environment parameter prediction is performed based on the first real-time production environment parameter and the subsequent planned production task to obtain a predicted time series production environment parameter set, including: extracting the digital twin model of the target workshop; configuring the initial state of the digital twin model according to the first real-time production environment parameter; taking the initial state as the starting point, simulating the execution of the subsequent planned production task through the digital twin model, 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, the predicted time series production environment parameter set containing 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 analysis of 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 environment parameter constraints includes: extracting the workshop cleanliness range, workshop humidity range and workshop temperature range in the preset environment parameter constraints; traversing the multiple prediction time points to determine the first prediction time point, and obtaining the predicted production environment parameter value corresponding to the first prediction time point in 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 in the first predicted production environment parameter value; judging 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 prediction time point as the critical time point.

[0007] Optionally, the environmental controller includes a cleanliness controller, a temperature controller and a temperature control device; obtaining 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: obtaining 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; obtaining the type of critical production environment parameter that exceeds the preset environmental parameter constraint at the critical time point; 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 environmental 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 target workshop cleanliness value, target workshop humidity value and target workshop temperature value preset in 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 environmental controller based on the cleanliness deviation value, the humidity deviation value and the temperature deviation value.

[0009] Optionally, generating the environmental control parameters of 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 of the environmental controller.

[0010] Optionally, the environmental controller includes a cleanliness controller, a temperature controller and a temperature control device; the 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 cleanliness initial control parameter of the cleanliness controller; inputting the humidity deviation value into the humidity parameter branch to generate the humidity initial control parameter of the temperature controller; inputting the temperature deviation value into the temperature parameter branch to generate the temperature initial control parameter of the temperature control device; inputting the cleanliness initial control parameter, the humidity initial control parameter and the temperature initial control parameter 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 temperature initial control parameter on the workshop humidity and the second influence on the workshop cleanliness, as well as the third influence of the humidity initial control parameter on the workshop temperature and the fourth influence on the workshop cleanliness, wherein the parameter interaction model includes the influence coefficient of the workshop temperature change on the workshop humidity and the workshop cleanliness, and the influence coefficient of the workshop humidity change on the workshop cleanliness and the workshop temperature; according to the first influence, the second influence, the third influence and the fourth influence, the cleanliness initial control parameter, the humidity initial control parameter and the temperature initial control parameter are compensated and adjusted to generate the cleanliness control parameter, the humidity control parameter and the temperature control parameter; the cleanliness control parameter, the humidity control parameter and the temperature control parameter are used as the environmental control parameters.

[0011] In addition, to achieve the above-mentioned purpose, the present application provides a production environment parameter control system based on the industrial Internet of Things, the system including a management platform, a sensor network platform and an object platform, the management platform including: an information receiving module for responding to an information collection instruction, collecting a first real-time production environment parameter of a target workshop through the Internet of Things sensor network, and obtaining a subsequent planned production task of the target workshop through a production plan list; an environment prediction module for predicting the production environment parameter based on the first real-time production environment parameter and the subsequent planned production task, and obtaining a predicted time series production environment parameter set; a critical analysis module for analyzing the predicted time series production environment parameter set, and determining a critical time point in the predicted time series production environment parameter set at which the preset environment parameter constraint is exceeded; a control time module for obtaining a response time characteristic of an environment controller, and determining a control time point for the critical time point based on the response time characteristic; a parameter acquisition module for collecting a second real-time production environment parameter of the target workshop again through the Internet of Things sensor network at the control time point; an environment control module for generating an environment control parameter of the environment controller based on the second real-time production environment parameter, and controlling the environment controller to perform production environment control based on the environment control parameter.

[0012] Furthermore, to achieve the above-mentioned objectives, the present application further provides an electronic device comprising a memory and a processor. The memory is used to store a computer software program; the processor is used to read and execute the computer software program, thereby implementing any of the above-mentioned possible implementations of the production environment parameter control method based on the Industrial Internet of Things.

[0013] In addition, the present application also provides a non-transitory computer-readable storage medium, which stores a computer software program. When the computer software program is executed by a processor, it implements the production environment parameter control method based on the industrial Internet of Things in any of the above possible implementation methods.

[0014] The beneficial effects that this application can achieve are as follows: In response to information collection instructions, 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. The production environment parameters are predicted based on the first real-time production environment parameters and the subsequent planned production tasks to obtain a set of predicted time-series production environment parameters, enabling forward-looking understanding of future trends in the production environment parameters. The predicted time-series production environment parameter set is analyzed to determine the critical time point at which the predicted time-series production environment parameter set exceeds the preset environmental parameter constraints, providing a decision-making basis for timely intervention to avoid production problems caused by actual environmental parameter violations. The response time characteristics of the environmental controller are obtained and, based on the response time characteristics, the control time point for the critical time point is determined. The optimal control timing is determined based on the time delay from the start of the environmental controller to the actual effect, ensuring that the control effect can be accurately applied at the critical moment. At the control time point, the IoT sensor network again collects the second real-time production environment parameters of the target workshop, ensuring that control decisions are based on the latest situation and improving control accuracy. Based on the second real-time production environment parameters, environmental control parameters are generated for the environmental controller, and the environmental controller is controlled 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 collection instructions, so that the entire control process forms a closed loop and is continuously executed in a cycle, thereby achieving dynamic, intelligent, and forward-looking control of production environment parameters, ensuring that production environment parameters meet production needs and avoiding energy waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flow chart of the production environment parameter control method based on the Industrial Internet of Things provided in this application; Figure 2 A schematic diagram of the structure of the production environment parameter control system based on the Industrial Internet of Things provided in this application; Figure 3 A schematic diagram of the structure of the electronic device provided for this application; Figure 4 A schematic diagram of the structure of a computer-readable storage medium provided in this application.

[0016] In the accompanying drawings, the components represented by the reference numerals are as follows: Information receiving module 11, environment prediction module 12, criticality analysis module 13, control time module 14, parameter acquisition module 15, environment control module 16, electronic device 200, memory 210, processor 220, computer program 211, computer readable storage medium 300.

[0017] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] 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 of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0019] It should be noted that all directional indications in the embodiments of the present application (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the various components under a certain preset posture (as shown in the accompanying drawings). If the preset posture changes, the directional indication will also change accordingly.

[0020] In this application, unless otherwise specified or limited, the terms "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can mean fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. For those skilled in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0021] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the meaning of "and / or" appearing throughout the text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or a scheme in which A and B are satisfied at the same time. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0022] like Figure 1 As shown, the first embodiment of the present application provides a production environment parameter control method based on the industrial Internet of Things, including: S1: responding to the information collection instruction, receiving the first real-time production environment parameters of the target workshop collected by the sensor, and obtaining the subsequent planned production tasks of the target workshop through the production plan list; S2: Predicting production environment parameters based on the first real-time production environment parameters and subsequent planned production tasks to obtain a predicted time series production environment parameter set; S3: Analyze the predicted time series production environment parameter set to determine the critical time point at which the predicted time series production environment parameter set exceeds the preset environment parameter constraints; S4: Obtaining the response time characteristics of the environmental controller, and determining the control time point for the critical time point according to the response time characteristics; S5: At the control time point, receiving the second real-time production environment parameter of the target workshop collected by the sensor; S6: Generate an environment control parameter for the environment controller according to the second real-time production environment parameter, and control the environment controller to perform production environment control based on the environment control parameter.

[0023] Specifically, first, in response to the information collection instruction issued by the production environment parameter control system, the data acquisition process is triggered. Specifically, the key parameter data of the current production environment, including but not limited to production environment parameters such as workshop temperature, workshop humidity and workshop cleanliness, are collected in real time through sensors deployed in the target workshop, and these data are used as the first real-time production environment parameters. At the same time, the system accesses the production plan list in the production management database and extracts the subsequent planned production tasks to be executed by the target workshop. The subsequent planned production tasks include data such as the type of subsequent production tasks, expected start time, duration, and participants. 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 first 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 of the prediction model, together constituting the data source for predictive analysis.

[0024] After acquiring the first real-time production environment parameters and subsequent planned production task information, the digital twin model simulates the production process, using the first real-time production environment parameters as the initial state and the subsequent planned production tasks as input conditions. During the simulation, the system considers the operating characteristics of various production equipment, process requirements, heat load, and other factors that may affect environmental parameters, generating a series of predicted environmental parameter values ​​at predicted time points. These chronologically arranged predicted environmental parameter values ​​constitute a predicted time-series production environment parameter set. This predicted time-series production environment parameter set forms a multidimensional time-series data structure with time as the horizontal axis and the various environmental parameters as the vertical axis. This predicted time-series production environment parameter set clearly illustrates the likely evolution of production environment parameters over a future time period if no additional control intervention is implemented. This provides data support for subsequent critical point identification and control decisions, enabling a shift from passively responding to environmental changes to proactively predicting environmental trends and laying the foundation for intelligent environmental control.

[0025] After obtaining a set of predicted time-series production environmental parameters, the system identifies time points at which production environmental parameters are about to exceed preset environmental parameter constraints, providing a time basis for precise intervention. Specifically, the system first extracts preset environmental parameter constraints from production process specifications or quality management systems. These constraints define the permissible fluctuation ranges for various environmental parameters (such as temperature, humidity, and cleanliness). Each time point in the set of predicted time-series production environmental parameters is then scanned and analyzed, and the predicted environmental parameter values ​​are compared with the corresponding constraint ranges. When a predicted environmental parameter value at a given time point first exceeds or is about to exceed a preset environmental parameter constraint, that time point is marked as a critical time point. A critical time point indicates that, without intervention, the production environmental parameter will exceed safety limits, potentially impacting production quality or process stability. By proactively identifying potential risk points, the system shifts environmental control from a traditional post-exception correction model to a predictive prevention and control model, laying the foundation for optimally timed intervention. Accurately identifying critical time points also avoids unnecessary frequent adjustments, thereby reducing energy consumption and improving operational efficiency.

[0026] After identifying critical time points, the response time characteristic parameters of various environmental controllers are obtained. These response time characteristic parameters reflect the time delay from when a control command is issued until the production environment parameters begin to effectively change. Subsequently, the environmental controller response time is selected based on the environmental parameter type at the critical time point. The control time point is determined by subtracting the corresponding response time from the critical time point. By fully considering the physical characteristics and time delays of the environmental controllers, precise timing matching of control actions with demand is achieved, avoiding energy waste caused by premature intervention and control failure caused by late intervention. When the system clock reaches the determined control time point, the management platform receives the target workshop's environmental parameter data collected in real time by sensors through the sensor network platform again, obtaining the latest environmental status at the control time point and recording it as the second real-time production environment parameter. By obtaining the latest real-time data before control execution, more precise control strategies can be formulated based on environmental parameters that are closest to the actual state. The second real-time production environment parameter serves as a direct input for the generation of subsequent control parameters, providing the most up-to-date and reliable data foundation for precise environmental control.

[0027] After acquiring the second real-time production environment parameters, they are compared with the target values ​​within the preset environmental parameter constraints, and the deviation values ​​for each environmental parameter are calculated. Subsequently, environmental control parameters for the environmental controller are generated based on these deviation values. The generated environmental control parameters are transmitted to the environmental controller via the sensor network platform, and the environmental controller performs corresponding environmental adjustment actions based on the received environmental control parameters. Through precise control parameter setting and coordinated control, the production environment parameters can be smoothly transitioned to the target range while minimizing energy consumption and control fluctuations. After this round of control execution is completed, the system generates new information collection instructions and enters the next control cycle, achieving continuous control and management of production environment parameters.

[0028] The above-mentioned production environment parameter control method achieves a transition from passive regulation to forward-looking intelligent control. First, real-time environmental parameters are collected and planned production tasks are obtained. Potential critical points are identified through environmental parameter prediction and analysis. Then, the control time point is determined based on the response characteristics of the environmental controller. Finally, the latest environmental data is collected at the control time point and precise control instructions are generated, forming a complete prediction-identification-intervention control closed loop. Compared with existing technologies, this method solves the problems of excessive energy consumption in continuous tracking control and the low level of intelligence in staged control, achieving stable control of production environment parameters and effectively reducing energy consumption.

[0029] As an optional implementation, performing production environment parameter prediction based on the first real-time production environment parameter and the subsequent planned production task to obtain a predicted time series production environment parameter set includes: S21: Extract the digital twin model of the target workshop; S22: configuring an initial state of the digital twin model according to the first real-time production environment parameters; S23: Starting from the initial state, the digital twin model is used to simulate the execution of subsequent planned production tasks, and the change data of production environment parameters is recorded during the simulation execution; S24: Generate a predicted time series production environment parameter set based on the production environment parameter change data, where the predicted time series production environment parameter set includes predicted production environment parameter values ​​corresponding to multiple prediction time points.

[0030] Specifically, first, a digital twin model corresponding to the target workshop is extracted from the database. The digital twin model is an accurate virtual mapping of the physical target workshop, which contains the geometric layout of the workshop, equipment distribution, heat source location, air flow channels, and an accurate 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 restore the physical characteristics and environmental evolution laws of the real workshop. Then, the first real-time production environment parameters collected are imported into the digital twin model to configure the initial state of the model. Specifically, the environmental parameters such as workshop temperature, workshop humidity, and workshop cleanliness in the first real-time production environment parameters collected in real time are mapped to the corresponding parameter nodes of the digital twin model, so that the initial state of the virtual model is highly consistent with the current state of the actual workshop, laying the foundation for the accuracy of subsequent simulations.

[0031] Subsequently, starting from the initial state after configuration, the subsequent planned production task information obtained from the production plan list is input into the digital twin model to start the simulation execution process. During the simulation execution process, the digital twin model simulates the evolution trend of the workshop environmental parameters in the future period of time based on factors such as the characteristics of the production task, the operating status of the equipment, and the characteristics of the process. At the same time, during the simulation process, the environmental parameter values ​​​​at each key time point are recorded at preset time intervals to form complete production environment parameter change data. Then, based on the production environment parameter change data obtained by simulation, a structured prediction time series production environment parameter set is constructed. The prediction time series production environment parameter set is indexed by time and contains multiple prediction time points and their corresponding prediction production environment parameter values, forming a complete environmental parameter time series prediction data structure. Preferably, the prediction time series production environment parameter set not only includes the predicted values ​​of the environmental parameters, but also includes the confidence intervals and fluctuation trend characteristics of the predicted values, providing comprehensive data support for subsequent critical point analysis.

[0032] By introducing digital twin technology, the accuracy and reliability of environmental parameter predictions have been significantly improved, enabling more accurate identification of potential environmental parameter anomalies, thereby achieving more efficient environmental control strategies.

[0033] As an optional implementation, the production environment parameter types include workshop cleanliness, workshop humidity, and workshop temperature; analyzing the predicted time series production environment parameter set to determine the critical time point at which the predicted time series production environment parameter set exceeds the preset environmental parameter constraints includes: S31: Extracting the workshop cleanliness range, workshop humidity range, and workshop temperature range from the preset environmental parameter constraints; S32: traverse multiple prediction time points, determine a first prediction time point, and obtain a prediction production environment parameter value corresponding to the first prediction time point in the prediction time series production environment parameter set to obtain a first prediction production environment parameter value; S33: 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; S34: Determine whether the cleanliness value of the first workshop exceeds the workshop cleanliness range, whether the humidity value of the first workshop exceeds the workshop humidity range, or whether the temperature value of the first workshop exceeds the workshop temperature range; S35: 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 a critical time point.

[0034] Specifically, production environment parameter types 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 workshop cleanliness ranges, workshop humidity ranges, and workshop temperature ranges. These ranges are typically defined in interval form, such as cleanliness level requirements (e.g., the cleanliness levels defined in ISO 14644-1), relative humidity percentage ranges (e.g., 45% to 65%), and temperature ranges (e.g., 20°C to 24°C). These constraint ranges serve as benchmarks for evaluating whether production environment parameters meet acceptable standards. Next, multiple prediction time points in the prediction time series production environment parameter set are traversed in chronological order. Starting with the earliest prediction time point, each prediction time point to be evaluated is identified, referred to as the first prediction time point. For the first prediction time point, the predicted production environment parameter value corresponding to that time point is extracted from the prediction 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. Next, the first workshop cleanliness value, first workshop humidity value, and first workshop temperature value are extracted from the first predicted production environment parameter values. These three values ​​reflect the expected state of the target workshop production environment at the first predicted time point and serve as the basis for subsequent judgments.

[0035] Subsequently, the extracted values ​​for the first workshop's cleanliness, humidity, and temperature are individually checked to determine whether the first workshop's cleanliness, humidity, and temperature exceed the workshop's cleanliness, humidity, and temperature ranges. If any of the following conditions are detected: the first workshop's cleanliness, humidity, or temperature exceed the workshop's cleanliness, humidity, or temperature ranges, the first predicted time point is marked as a critical time point. A critical time point indicates that, at that specific moment, without intervention and control, at least one production environment parameter will exceed its safe operating range, potentially adversely affecting production quality.

[0036] Through the above steps, the earliest point at which a production environment parameter might exceed its limit 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 its limit can be detected promptly, achieving comprehensive monitoring of multiple production environment parameters.

[0037] As an optional embodiment, the environmental controller includes a cleanliness controller, a temperature controller, and a temperature control device; obtaining a response time characteristic of the environmental controller and determining a control time point for a critical time point based on the response time characteristic include: S41: Obtaining 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; S42: Obtaining the type of critical production environment parameter that exceeds the preset environment parameter constraint at the critical time point; S43: Determine the control time point based on the type of critical production environment parameters and in combination with the cleanliness response time, humidity response time and temperature response time.

[0038] Specifically, environmental controllers include cleanliness controllers, temperature controllers, and temperature control devices. First, the response time characteristic parameters of each type of environmental controller are obtained from a device 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 from the issuance of a control command to the actual effective change in production environmental parameters and serve as the basis for the precise calculation of control time points. Next, the specific environmental parameter type that exceeded the preset environmental parameter constraints at the critical time point is determined. This identification identifies whether it is the workshop cleanliness, humidity, temperature, or multiple parameters simultaneously exceeding their respective constraints. The identification result is recorded as the critical production environmental parameter type.

[0039] Subsequently, a control time point is determined based on the critical production environmental parameter type and the response time characteristics of each environmental controller. Specifically, when the critical production environmental parameter type is only workshop cleanliness, the cleanliness response time is subtracted from the critical time point to obtain the control time point. When the critical production environmental parameter type is only workshop humidity, the humidity response time is subtracted from the critical time point to obtain the control time point. When the critical production environmental parameter type is only workshop temperature, the temperature response time is subtracted from the critical time point to obtain the control 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. This longest response time is subtracted from the critical time point to obtain the control 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 activation time point for all environmental controllers. At this time point, all environmental controllers, including the cleanliness controller, temperature controller, and temperature control devices, are triggered for coordinated control, rather than only activating the control devices directly related to the exceeded parameters.

[0040] By determining the control time point, we ensure that control actions are initiated at the most appropriate time, ensuring that the control effect can effectively affect production environment parameters before the critical time point, avoiding energy waste caused by premature control and parameter overruns caused by late control. At the same time, when multiple parameters exceed the limit at the same time, the maximum response time is used as the calculation basis to ensure the comprehensive effectiveness of the control.

[0041] As an optional implementation, generating an environmental control parameter of the environmental controller according to the second real-time production environment parameter includes: S61: extracting a real-time workshop cleanliness value, a real-time workshop humidity value, and a real-time workshop temperature value from the second real-time production environment parameter; S62: Obtaining a target workshop cleanliness value, a target workshop humidity value, and a target workshop temperature value preset in the preset environmental parameter constraints; S63: Determine a cleanliness deviation value based on the real-time workshop cleanliness value and the target workshop cleanliness value; S64: Determine a humidity deviation value based on the real-time workshop humidity value and the target workshop humidity value; S65: Determine a temperature deviation value based on the real-time workshop temperature value and the target workshop temperature value; S66: Generate environmental control parameters of the environmental controller based on the cleanliness deviation value, the humidity deviation value, and the temperature deviation value.

[0042] Specifically, first, the current real-time workshop cleanliness value, real-time workshop humidity value, and real-time workshop temperature value are accurately extracted from the collected second real-time production environment parameters. These values ​​reflect the precise state of the workshop environment before control is executed and are the data basis for precise control. Then, from the preset environmental parameter constraints, the predetermined target workshop cleanliness value, target workshop humidity value, and target workshop temperature value are obtained. These target values ​​are usually set as the center value or optimal value of the allowable range of each parameter and are the ideal target state for environmental control. Under certain specific process requirements, the target value may also be an optimized value that is dynamically adjusted according to product characteristics or process requirements.

[0043] Next, the difference between the real-time workshop cleanliness value and the target workshop cleanliness value is calculated to determine the cleanliness deviation value. This deviation value includes both the magnitude and direction of the deviation (a positive deviation indicates that the current cleanliness is above the target value, while a negative deviation indicates that the current cleanliness is below the target value), providing a quantitative basis for subsequent cleanliness control. Simultaneously, the difference between the real-time workshop humidity value and the target workshop 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 and is an important basis for humidity control decisions. Furthermore, the difference between the real-time workshop temperature value and the target workshop temperature value is calculated to determine the temperature deviation value. This deviation value quantifies the intensity and direction of temperature control requirements and provides a basis for temperature control. Subsequently, based on the obtained cleanliness deviation value, humidity deviation value, and temperature deviation value, the environmental control parameters of the environmental controller are comprehensively generated. The control parameter generation process not only considers the magnitude and direction of each deviation value, but also the interactive influence relationship between environmental parameters, as well as the dynamic characteristics and energy efficiency characteristics of the control device.

[0044] This deviation analysis-based control parameter generation method 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, this method can dynamically adjust control intensity and strategy based on actual deviations, improving control accuracy and reducing energy consumption.

[0045] As an optional implementation, generating environmental control parameters of the environmental controller based on the cleanliness deviation value, the humidity deviation value, and the temperature deviation value includes: S661: Acquire a control parameter generator, which includes a cleanliness parameter branch, a humidity parameter branch, a temperature parameter branch, and a parameter impact balance branch; S662: Input the cleanliness deviation value, humidity deviation value, and temperature deviation value into a control parameter generator to generate environmental control parameters of the environmental controller.

[0046] Specifically, a specially designed control parameter generator is first called. This generator uses a multi-branch parallel processing architecture and includes a cleanliness parameter branch, a humidity parameter branch, a temperature parameter branch, and a parameter impact balance branch. The cleanliness parameter branch is responsible for processing the cleanliness control logic and generating cleanliness control parameters; the humidity parameter branch is responsible for processing the humidity control logic and generating humidity control parameters; the temperature parameter branch is responsible for processing the temperature control logic and generating temperature control parameters; and the parameter impact balance branch is responsible for handling the interactions between the various environmental parameters and coordinating the various control parameters to achieve the overall optimal control effect. This control parameter generator can be implemented using a rule-based expert system, a machine learning model, or a hybrid intelligent algorithm. It contains mathematical models of the characteristics of various environmental controllers and a control strategy library.

[0047] The resulting cleanliness, humidity, and temperature deviations are then passed as input to the control parameter generator. The control parameter generator first passes these deviations to the corresponding parameter branches for preliminary processing. A comprehensive balance analysis is then performed in the parameter impact balance branch, ultimately outputting environmental control parameters adjusted for interaction effects. The generated environmental control parameters contain the specific control instructions required by each environmental controller, such as the filtration air speed 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.

[0048] By employing a multi-branch collaborative control parameter generator, the independent control logic for each production environment parameter is combined with analysis of the interaction between these parameters, overcoming the limitations of traditional single-parameter control methods, which struggle to handle parameter interactions. By simultaneously considering the control requirements and interactions of multiple environmental parameters, a globally optimal control strategy is generated, improving environmental control accuracy and system stability while reducing energy consumption.

[0049] As an optional embodiment, the environmental controller includes a cleanliness controller, a temperature controller, and a temperature control device; the cleanliness deviation value, the humidity deviation value, and the temperature deviation value are input into a control parameter generator to generate environmental control parameters of the environmental controller, including: S6621: Input the cleanliness deviation value into the cleanliness parameter branch to generate the cleanliness initial control parameters of the cleanliness controller; S6622: Input the humidity deviation value into the humidity parameter branch to generate the initial humidity control parameter of the temperature controller; S6623: Input the temperature deviation value into the temperature parameter branch to generate the initial temperature control parameters of the temperature control device; S6624: Input the cleanliness initial control parameter, humidity initial control parameter, and temperature initial control parameter into the parameter influence balance branch; S6625: In the parameter influence balance branch, based on the parameter interaction influence model, determine a first influence of the initial temperature control parameter on the workshop humidity and a second influence on the workshop cleanliness, as well as a third influence of the initial humidity control parameter on the workshop temperature and a fourth influence on the workshop cleanliness. The parameter interaction influence model includes the influence coefficients of the workshop temperature change on the workshop humidity and the workshop cleanliness, and the influence coefficients of the workshop humidity change on the workshop cleanliness and the workshop temperature. S6626: Compensate and adjust the cleanliness initial control parameter, the humidity initial control parameter, and the temperature initial control parameter based on the first influence quantity, the second influence quantity, the third influence quantity, and the fourth influence quantity to generate the cleanliness control parameter, the humidity control parameter, and the temperature control parameter. S6627: Cleanliness control parameters, humidity control parameters, and temperature control parameters are used as environmental control parameters.

[0050] Specifically, the cleanliness deviation value is first input into the cleanliness parameter branch of the control parameter generator. Within 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 initial cleanliness control parameters for the cleanliness controller based on the magnitude and changing trend of the cleanliness deviation value. These initial control parameters include preliminary operating instructions for the cleanliness controller, such as wind speed adjustment parameters and filter mode selection for the air filtration system. Simultaneously, the humidity deviation value is input into the humidity parameter branch of the control parameter generator. Within this branch, the humidity control algorithm calculates and generates initial humidity control parameters for the temperature controller based on the humidity deviation value and its changing characteristics. These parameters include preliminary 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. Within this branch, the temperature control algorithm calculates and generates initial temperature control parameters for the temperature control device based on the temperature deviation value and its dynamic characteristics. These parameters include preliminary setpoints for the temperature control device, such as the temperature setting and cooling / heating power of the air conditioning system.

[0051] Subsequently, the generated initial cleanliness, humidity, and temperature control parameters are uniformly input into the parameter impact balance branch of the control parameter generator, entering the interaction impact analysis and balance adjustment phase. In this branch, the potential cross-influences resulting from the implementation of each initial control parameter are analyzed based on a pre-established parameter interaction model. Specifically, four key interaction effects are calculated: the impact of the initial temperature control parameter on workshop humidity (the first impact variable), the impact of the initial temperature control parameter on workshop cleanliness (the second impact variable), the impact of the initial humidity control parameter on workshop temperature (the third impact variable), and the impact of the initial humidity control parameter on workshop cleanliness (the fourth impact variable). These calculations are based on the influence coefficients defined in the parameter interaction model, including the impact coefficients of changes in workshop temperature on workshop humidity and workshop cleanliness, and the impact coefficients of changes in workshop humidity on workshop cleanliness and workshop temperature. This parameter interaction model can be derived through historical data analysis, physical modeling, or machine learning methods.

[0052] Then, based on the calculated first, second, third and fourth influencing quantities, the initial cleanliness control parameters, humidity initial control parameters and temperature initial control parameters are compensated and adjusted. The adjustment process takes into account the interaction between the parameters, and eliminates or reduces possible negative interference between the parameters through compensation calculation to achieve collaborative optimization. After compensation adjustment, the system generates the final cleanliness control parameters, humidity control parameters and temperature control parameters. These parameters have been processed for interactive influence balance, and can minimize the adverse effects on other environmental parameters while achieving their respective control objectives. Afterwards, the cleanliness control parameters, humidity control parameters and temperature control parameters that have undergone interactive compensation adjustment are integrated into a complete set of environmental control parameters and passed to each environmental controller for execution as the final output.

[0053] Through step-by-step calculation and interactive compensation mechanism, coordinated and precise control of environmental parameters is achieved, effectively solving the control oscillation or mutual interference problems caused by the interaction of multiple parameters. It not only improves the accuracy of control of various production environment parameters, but also ensures the stability of the overall target workshop environment system.

[0054] The second embodiment of the present 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 communicatively connected in sequence. The object platform 103 includes an environment controller and a sensor. 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. Among them, the information receiving module 11 is used to respond to the information collection instruction, collect the first real-time production environment parameters of the target workshop through the Internet of Things sensor network, and obtain the subsequent planned production tasks of the target workshop through the production plan list; the environment prediction module 12 is used to predict the production environment parameters based on the first real-time production environment parameters and the subsequent planned production tasks, and obtain the predicted time series production environment parameter set; the critical analysis module 13 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 environment parameter constraint; the control time module 14 is used to obtain 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; the parameter acquisition module 15 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 environment control module 16 is used to generate the environment control parameters of 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 environment control parameters.

[0055] As an optional implementation, the environment prediction module 12 includes a twin model extraction unit, an initial state configuration unit, a simulation execution unit, and a parameter prediction unit. The twin model extraction unit is used to extract the digital twin model of the target workshop; the initial state configuration unit is used to configure the initial state of the digital twin model according to the first real-time production environment parameter; the simulation execution unit is used to simulate the execution of the subsequent planned production tasks using the digital twin model with the initial state as the starting point, and record the production environment parameter change data during the simulation execution process; the parameter prediction unit is used to generate the predicted time series production environment parameter set based on the production environment parameter change data, and the predicted time series production environment parameter set includes the predicted production environment parameter values ​​corresponding to multiple prediction time points.

[0056] As an optional embodiment, 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. The range extraction unit is used to extract the workshop cleanliness range, workshop humidity range, and workshop temperature range from the preset environmental parameter constraints; the time point traversal unit is used to traverse the multiple prediction time points, determine the first prediction time point, and obtain the predicted production environment parameter value corresponding to the first prediction time point from the predicted time series production environment parameter set to obtain the first predicted production environment parameter value; the parameter extraction unit is used to extract 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 is used to determine 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; and the critical determination unit is used to mark the first prediction time point as the critical time point when the first workshop cleanliness value exceeds the workshop cleanliness range, the first workshop humidity value exceeds the workshop humidity range, or the first workshop temperature value exceeds the workshop temperature range.

[0057] As an optional embodiment, 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. The response time acquisition unit is used to acquire 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 is used to acquire the type of critical production environmental parameter that exceeds the preset environmental parameter constraint at the critical time point; and the control time point determination unit is used to determine the control time point based on the critical production environmental parameter type, the cleanliness response time, the humidity response time, and the temperature response time.

[0058] As an optional embodiment, 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. The real-time value extraction unit is used to 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 parameter; the target value acquisition unit is used to obtain the target workshop cleanliness value, target workshop humidity value, and target workshop temperature value preset in the preset environmental parameter constraints; the cleanliness deviation value determination unit is used to determine the cleanliness deviation value based on the real-time workshop cleanliness value and the target workshop cleanliness value; the humidity deviation value determination unit is used to determine the humidity deviation value based on the real-time workshop humidity value and the target workshop humidity value; the temperature deviation value determination unit is used to determine the temperature deviation value based on the real-time workshop temperature value and the target workshop temperature value; and the control parameter generation unit is used to generate environmental control parameters for the environmental controller based on the cleanliness deviation value, the humidity deviation value, and the temperature deviation value.

[0059] As an optional embodiment, the control parameter generation unit includes a generator acquisition unit and a control parameter output unit. The generator acquisition unit is used to obtain a control parameter generator, wherein the control parameter generator includes a cleanliness parameter branch, a humidity parameter branch, a temperature parameter branch, and a parameter influence balance branch; and the control parameter output unit is used to input 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.

[0060] As an optional embodiment, 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 balance unit, a compensation adjustment unit and a parameter summary unit. Among them, the cleanliness parameter generation unit is used to input the cleanliness deviation value into the cleanliness parameter branch to generate the cleanliness initial control parameter of the cleanliness controller; the humidity initial parameter generation unit is used to input the humidity deviation value into the humidity parameter branch to generate the humidity initial control parameter of the temperature controller; the temperature parameter generation unit is used to input the temperature deviation value into the temperature parameter branch to generate the temperature initial control parameter of the temperature control device; the parameter input unit is used to input the cleanliness initial control parameter, the humidity initial control parameter and the temperature initial control parameter into the parameter influence balance branch; the parameter balance unit is used to determine the first influence of the temperature initial control parameter on the workshop humidity in the parameter influence balance branch based on the parameter interaction model. The parameter interaction model includes the influence coefficient of the change of workshop temperature on the workshop humidity and the workshop cleanliness, and the influence coefficient of the change of workshop humidity on the workshop cleanliness and the workshop temperature; the compensation adjustment unit is used to compensate and adjust the cleanliness initial control parameter, the humidity initial control parameter and the temperature initial control parameter according to the first influence quantity, the second influence quantity, the third influence quantity and the fourth influence quantity to generate the cleanliness control parameter, the humidity control parameter and the temperature control parameter; the parameter summary unit is used to use the cleanliness control parameter, the humidity control parameter and the temperature control parameter as the environmental control parameter.

[0061] A third embodiment of the present 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, the production environment parameter control method based on the industrial Internet of Things in any possible implementation method of the first embodiment is implemented.

[0062] The fourth embodiment of the present application provides a computer-readable storage medium, such as Figure 4 As shown, a computer program 211 is stored on the computer-readable storage medium 300. When the computer program 211 is executed by the processor, the production environment parameter control method based on the industrial Internet of Things in any possible implementation manner in the first embodiment is implemented.

[0063] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A production environment parameter control method based on 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: 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; Performing production environment parameter prediction based on the first real-time production environment parameter and the subsequent planned production task to obtain a predicted time series production environment parameter set; Analyze the predicted time series production environment parameter set to determine a critical time point at which the predicted time series production environment parameter set exceeds a preset environment parameter constraint; Acquiring a response time characteristic of the environmental controller, and determining a control time point for the critical time point according to the response time characteristic; At the control time point, receiving a second real-time production environment parameter of the target workshop; According to the second real-time production environment parameter, an environment control parameter of the environment controller is generated, and based on the environment control parameter, the environment controller is controlled to perform production environment control.

2. The method according to claim 1, characterized in that The performing of production environment parameter prediction based on the first real-time production environment parameter and the subsequent planned production task to obtain a predicted time series production environment parameter set includes: Extracting a digital twin model of the target workshop; Configuring an initial state of the digital twin model according to the first real-time production environment parameters; Taking the initial state as the starting point, simulating the execution of the subsequent planned production tasks through the digital twin model, and recording the production environment parameter change data during the simulation execution process; The predicted time series production environment parameter set is generated based on the production environment parameter change data, and the predicted time series production environment parameter set includes predicted production environment parameter values ​​corresponding to multiple prediction time points.

3. The method according to claim 2, characterized in that The production environment parameter types include workshop cleanliness, workshop humidity, and workshop temperature; and analyzing the predicted time series production environment parameter set to determine a critical time point at which the predicted time series production environment parameter set exceeds a preset environment parameter constraint includes: Extracting the workshop cleanliness range, workshop humidity range, and workshop temperature range from the preset environmental parameter constraints; Traversing the multiple prediction time points, determining a first prediction time point, and obtaining a prediction production environment parameter value corresponding to the first prediction time point in the prediction time series production environment parameter set to obtain a first prediction production environment parameter value; Extracting a first workshop cleanliness value, a first workshop humidity value, and a first workshop temperature value from the first predicted production environment parameter values; Determining 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 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, the first predicted time point is marked as the critical time point.

4. The method according to claim 1, wherein The environmental controller includes a cleanliness controller, a humidity controller, and a temperature controller; obtaining the response time characteristics of the environmental controller and determining the control time point for the critical time point according to the response time characteristics include: Obtaining 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; Obtaining the type of critical production environment parameter that exceeds the preset environment parameter constraint at the critical time point; The control time point is determined according to the type of the critical production environment parameter and in combination with the cleanliness response time, the humidity response time, and the temperature response time.

5. The method according to claim 1, wherein Generating the environmental control parameters of the environmental controller according to the second real-time production environment parameters includes: Extracting the real-time workshop cleanliness value, the real-time workshop humidity value, and the real-time workshop temperature value from the second real-time production environment parameter; Obtaining a target workshop cleanliness value, a target workshop humidity value, and a target workshop temperature value preset in the preset environmental parameter constraints; Determining a cleanliness deviation value according to the real-time workshop cleanliness value and the target workshop cleanliness value; Determining a humidity deviation value according to the real-time workshop humidity value and the target workshop humidity value; Determining a temperature deviation value according to the real-time workshop temperature value and the target workshop temperature value; An environmental control parameter of an environmental controller is generated based on the cleanliness deviation value, the humidity deviation value, and the temperature deviation value.

6. The method according to claim 5, characterized in that The step of generating an environmental control parameter of an environmental controller based on the cleanliness deviation value, the humidity deviation value, and the temperature deviation value includes: Obtaining a control parameter generator, wherein the control parameter generator includes a cleanliness parameter branch, a humidity parameter branch, a temperature parameter branch, and a parameter impact 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.

7. The method according to claim 6, characterized in that The environmental controller includes a cleanliness controller, a temperature controller, and a temperature control device; 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, including: Inputting the cleanliness deviation value into the cleanliness parameter branch to generate the cleanliness initial control parameter of the cleanliness controller; Inputting the humidity deviation value into the humidity parameter branch to generate the humidity initial control parameter of the temperature controller; Inputting the temperature deviation value into the temperature parameter branch to generate the initial temperature control parameter of the temperature control device; Inputting the cleanliness initial control parameter, the humidity initial control parameter, and the temperature initial control parameter into the parameter influence balance branch; In the parameter influence balance branch, based on the parameter interaction influence model, a first influence of the temperature initial control parameter on the workshop humidity and a second influence on the workshop cleanliness, as well as a third influence of the humidity initial control parameter on the workshop temperature and a fourth influence on the workshop cleanliness are determined, wherein the parameter interaction influence model includes an influence coefficient of the workshop temperature change on the workshop humidity and the workshop cleanliness, and an influence coefficient of the workshop humidity change on the workshop cleanliness and the workshop temperature; Compensate and adjust the cleanliness initial control parameter, the humidity initial control parameter, and the temperature initial control parameter according to the first influence amount, the second influence amount, the third influence amount, and the fourth influence amount to generate a cleanliness control parameter, a humidity control parameter, and a temperature control parameter; The cleanliness control parameter, the humidity control parameter, and the temperature control parameter are used as the environmental control parameters.

8. A production environment parameter control system based on industrial Internet of Things, characterized in that: For executing the method according to any one of claims 1 to 7, the system comprises a management platform, a sensor network platform and an object platform, wherein the management platform comprises: An information receiving module, configured to respond to an information collection instruction, collect first real-time production environment parameters of a target workshop through an object platform, and obtain subsequent planned production tasks of the target workshop through a production plan list; An environment prediction module, configured to predict production environment parameters based on the first real-time production environment parameters and the subsequent planned production tasks, and obtain a set of predicted time-series production environment parameters; A criticality analysis module, configured to analyze the predicted time series production environment parameter set and determine a critical time point at which the predicted time series production environment parameter set exceeds a preset environment parameter constraint; A control time module, configured to obtain a response time characteristic of the environmental controller and determine a control time point for the critical time point according to the response time characteristic; A parameter acquisition module, configured to collect, at the control time point, a second real-time production environment parameter of the target workshop through the Internet of Things sensor network again; The environment control module is used to generate environment control parameters of the environment controller according to the second real-time production environment parameters, and control the environment controller to perform production environment control based on the environment control parameters.

9. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor, configured to read and execute the computer software program, thereby implementing the method according to any one of claims 1 to 7.

10. 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 according to any one of claims 1 to 7.

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