Temperature and humidity regulation and control method and device for rolling and packaging workshop, electronic equipment and storage medium

Abstract

The invention discloses a temperature and humidity regulation and control method and device for a rolling and packaging workshop, electronic equipment and a storage medium. The method comprises the steps of obtaining to-be-used data collected by at least one second temperature and humidity sensor at at least one second detection position associated with a production line in a working state; inputting the to-be-used data into the digital twin system to determine an equipment adjustment strategy of first temperature and humidity control equipment associated with the production line and a temperature and humidity prediction curve of a second detection position point; acquiring a temperature and humidity actual curve acquired by the second temperature and humidity sensor under the condition that the equipment adjustment strategy acts on the first temperature and humidity control equipment within the prediction time length; and based on the temperature and humidity prediction curve, the temperature and humidity actual curve and the energy consumption data of the first temperature and humidity control equipment, performing parameter optimization on the digital twin system, and further continuously determining an equipment adjustment strategy through the digital twin system after parameter optimization. The temperature and humidity control equipment is dynamically regulated and controlled, and the temperature and humidity regulation and control accuracy is improved.

Description

Technical Field

[0001] This invention relates to the field of automatic control technology, and in particular to a method, device, electronic equipment, and storage medium for temperature and humidity control in a packaging workshop. Background Technology

[0002] As a crucial link in tobacco production, the temperature and humidity control in the cigarette packaging workshop directly affects core process parameters such as the moisture content of tobacco shreds and the strength of cigarette paper.

[0003] Currently, fixed-parameter proportional-integral-derivative (PID) control methods are used to regulate air conditioning systems and control the temperature and humidity in packaging workshops. For example, the air conditioning system adjusts based solely on the average temperature and humidity of the workshop, leading to a "heating-cooling-counteracting" phenomenon in adjacent areas. Furthermore, the airflow uniformity is poor, and there is a lack of adaptive control to the dynamic production environment, resulting in poor temperature and humidity control in the packaging workshop. In air conditioning group control systems, excessive fluctuations in chilled water temperature during summer additions or removals of units cause frequent adjustments in terminal controls, resulting in excessive energy consumption. Summary of the Invention

[0004] This invention provides a method, device, electronic equipment, and storage medium for temperature and humidity control in a packaging workshop, in order to solve the problems of poor temperature and humidity control efficiency and high energy consumption in packaging workshops.

[0005] According to one aspect of the present invention, a method for temperature and humidity control in a packaging workshop is provided, wherein the packaging workshop includes at least one production line, the at least one production line includes one or more production devices, and at least one first temperature and humidity sensor is deployed at at least one first detection position associated with the production device, the method comprising:

[0006] For any production line in operation, acquire the data to be used collected by at least one second temperature and humidity sensor at at least one second detection position associated with the production line; wherein the data to be used includes temperature data and / or humidity data;

[0007] The data to be used is input into the digital twin system so that the digital twin system can determine the equipment adjustment strategy of at least one first temperature and humidity control device associated with the production line and the temperature and humidity prediction curve of at least one second detection location point within the prediction time.

[0008] The device adjustment strategy is sent to the corresponding first temperature and humidity control device so that the first temperature and humidity control device operates based on the device adjustment strategy and obtains the actual temperature and humidity curves collected by at least one second temperature and humidity sensor under the action of the device adjustment strategy on the first temperature and humidity control device within the predicted time period.

[0009] Based on the predicted temperature and humidity curves, the actual temperature and humidity curves, and the energy consumption data of at least one first temperature and humidity control device, the parameters of the digital twin system are optimized so that the device adjustment strategy for at least one first temperature and humidity control device can be continued to be executed based on the optimized digital twin system.

[0010] Optionally, the method further includes: determining a first detection location in the packaging workshop for deploying at least one first temperature and humidity sensor based on a thermal imaging method, and deploying a first temperature and humidity sensor with distributed Internet of Things at the first detection location; configuring a sampling frequency for the first temperature and humidity sensor to collect data to be used at the corresponding detection location based on the sampling frequency.

[0011] Optionally, before processing the data to be used based on the digital twin system, the method further includes: constructing a geometric model corresponding to the packaging workshop, and integrating a fluid dynamics simulation module and a module for simulating airflow distribution and heat and moisture transfer processes to obtain a first subsystem in the digital twin system used to determine the control strategy; and determining a second subsystem in the digital twin system used to determine the temperature and humidity prediction curve within the prediction period based on historical prediction data and real-time data.

[0012] Optionally, the first control parameter in the equipment adjustment strategy includes one or more of the valve information of the air supply outlet, the valve information of the air outlet, the fan frequency, and the fan mode of at least one first temperature and humidity control device. The equipment adjustment strategy is sent to the corresponding first temperature and humidity control device so that the first temperature and humidity control device operates based on the equipment adjustment strategy. This includes: determining control correction parameters based on the first control parameter in the equipment adjustment strategy and the actual control parameters of at least one first temperature and humidity control device using a fuzzy PID control algorithm; and controlling the operation of at least one first temperature and humidity control device based on the control correction parameters corresponding to at least one first temperature and humidity control device and the corresponding first control parameters.

[0013] Optionally, it also includes: determining control correction parameters for the first temperature and humidity control device based on the air conditioning unit control mode configured for the first temperature and humidity control device; wherein the air conditioning unit control mode includes a single average temperature and humidity control mode, a cascade PID control mode combining supply air temperature and humidity, and a chiller addition / reduction mode, and in the chiller addition / reduction mode, the control correction parameters include chiller addition / reduction parameters.

[0014] Optionally, the method further includes: acquiring total air volume data corresponding to at least one first temperature and humidity control device; determining the current operating mode and fan operating frequency threshold of at least one first temperature and humidity control device based on the total air volume data; wherein the current operating mode includes single fan mode and dual fan mode.

[0015] Optionally, the parameters of the digital twin system are optimized based on the temperature and humidity prediction curve, the actual temperature and humidity curve, and the energy consumption data of at least one first temperature and humidity control device. This includes: determining the prediction confidence data based on the temperature and humidity prediction curve and the actual temperature and humidity curve; and determining the parameter optimization of the digital twin system based on the energy consumption data, prediction confidence data, and corresponding threshold data of the first temperature and humidity control device for at least one first temperature and humidity control device.

[0016] According to another aspect of the present invention, a temperature and humidity control device for a packaging workshop is provided, characterized in that the packaging workshop includes at least one production line, the at least one production line includes one or more production devices, and at least one first temperature and humidity sensor is deployed at at least one first detection position associated with the production device, the device comprising:

[0017] The data to be used determination module is used to acquire, for any production line in operation, the data to be used collected by at least one second temperature and humidity sensor at at least one second detection position associated with the production line; wherein the data to be used includes temperature data and / or humidity data;

[0018] The equipment adjustment strategy and temperature and humidity prediction curve determination module is used to input the data to be used into the digital twin system so that the digital twin system can determine the equipment adjustment strategy of at least one first temperature and humidity control device associated with the production line and the temperature and humidity prediction curve of at least one second detection location point within the prediction time.

[0019] The temperature and humidity control module is used to send the equipment adjustment strategy to the corresponding first temperature and humidity control device so that the first temperature and humidity control device operates based on the equipment adjustment strategy and obtains the actual temperature and humidity curves collected by at least one second temperature and humidity sensor under the action of the equipment adjustment strategy on the first temperature and humidity control device within the predicted time period.

[0020] The parameter optimization and strategy adjustment module is used to optimize the parameters of the digital twin system based on the temperature and humidity prediction curve, the actual temperature and humidity curve, and the energy consumption data of at least one first temperature and humidity control device, so as to continue to execute the device adjustment strategy of at least one first temperature and humidity control device based on the parameter-optimized digital twin system.

[0021] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0022] At least one processor; and

[0023] A memory that is communicatively connected to at least one processor; wherein,

[0024] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the temperature and humidity control method for a packaging workshop according to any embodiment of the present invention.

[0025] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement a temperature and humidity control method for a packaging workshop according to any embodiment of the present invention.

[0026] The technical solution of this invention includes at least one production line in a packaging workshop. Each production line includes one or more production devices. At least one first temperature and humidity sensor is deployed at at least one first detection position associated with each production device. For any production line in operation, data to be used is acquired from at least one second temperature and humidity sensor at at least one second detection position associated with the production line. This data includes temperature data and / or humidity data. The data is input into a digital twin system to determine the equipment adjustment strategy for at least one first temperature and humidity control device associated with the production line and the predicted temperature and humidity curves for at least one second detection position within a predicted time period. The equipment adjustment strategy is sent to the corresponding first temperature and humidity control device, causing the first temperature and humidity control device to operate based on the strategy and acquire the actual temperature and humidity curves collected by at least one second temperature and humidity sensor under the influence of the equipment adjustment strategy within the predicted time period. Based on the predicted temperature and humidity curves, the actual temperature and humidity curves, and the energy consumption data of at least one first temperature and humidity control device, the parameters of the digital twin system are optimized. The optimized digital twin system then continues to execute the determined equipment adjustment strategy for at least one first temperature and humidity control device. This solution achieves real-time sensing and accurate prediction of workshop temperature and humidity through the joint control of a digital twin system and temperature and humidity control equipment. The dynamic optimization mechanism based on data feedback continuously improves the effectiveness of the control strategy, solving the technical problems of insufficient accuracy of temperature and humidity control strategies in the packaging workshop and the difficulty in balancing energy consumption and temperature control effect. This significantly improves the stability of the production environment to ensure product quality, realizes intelligent and refined temperature and humidity control to improve management efficiency, achieves synergy between energy saving and precise temperature control to reduce operating costs, and also builds a sustainable and iterative management and control system, improving the level of intelligence and refinement of production management in the packaging workshop.

[0027] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a flowchart of a temperature and humidity control method for a roll packaging workshop provided in Embodiment 1 of the present invention;

[0030] Figure 2 This is a flowchart of a temperature and humidity control method for a roll packaging workshop provided in Embodiment 2 of the present invention;

[0031] Figure 3 This is a schematic diagram of a temperature and humidity control device for a packaging workshop provided in Embodiment 3 of the present invention;

[0032] Figure 4 This is a schematic diagram of the structure of an electronic device for implementing the temperature and humidity control method for a packaging workshop according to an embodiment of the present invention. Detailed Implementation

[0033] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0035] Example 1

[0036] Figure 1This is a flowchart of a temperature and humidity control method for a packaging workshop provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where temperature and humidity need to be controlled. The method can be executed by a temperature and humidity control device for a packaging workshop. The temperature and humidity control device for a packaging workshop can be implemented in hardware and / or software. The temperature and humidity control device for a packaging workshop can be configured in electronic devices such as servers, controllers, and computers.

[0037] In this embodiment, the packaging workshop includes at least one production line, and the at least one production line includes one or more production devices. At least one first temperature and humidity sensor is deployed at at least one first detection location associated with the production device. Specifically, the first temperature and humidity sensor can be understood as a device used to collect temperature and humidity data at its location. The first detection location can be understood as a specific monitoring point directly associated with the production device, typically set in a critical area that directly affects the operating status of the production device or product quality, covering as much of the production device and key process areas as possible, such as near core components of the equipment, and areas where raw materials / semi-finished products contact the equipment. The temperature and humidity conditions at these locations are closely related to the stable operation, lifespan, and quality of the produced equipment. By accurately monitoring this location, the first temperature and humidity sensor provides basic environmental data support associated with the production device for the subsequent analysis and control of the digital twin system. In this embodiment, thermal imaging technology can be used to assist in layout optimization, in order to avoid airflow dead zones and equipment heat dissipation interference positions, and to ensure that the measurement point data truly reflects the process requirements. The first detection position associated with the production equipment of each production line in the packaging workshop is determined, and then the first temperature and humidity sensor is installed at the first detection position to accurately and comprehensively detect the temperature and humidity data on the production line, providing a data basis for determining accurate control strategies.

[0038] like Figure 1 As shown, the method includes:

[0039] S110. For any production line in operation, acquire the data to be used collected by at least one second temperature and humidity sensor at at least one second detection position associated with the production line; wherein the data to be used includes temperature data and / or humidity data.

[0040] The second detection location can be specifically understood as a key point associated with a production line in operation that requires monitoring of temperature and humidity. This includes, but is not limited to, critical areas that directly affect the operating status of production equipment or product quality, such as the vicinity of core equipment components or the contact area between raw materials / semi-finished products and equipment. The second temperature and humidity sensor can be specifically understood as a sensor device deployed at the second detection location to collect temperature and / or humidity information at that location. In other words, the second detection location and the second temperature and humidity sensor represent the detection location and corresponding temperature and humidity sensor associated with a production line in operation. Specifically, they are used to distinguish the detection location and corresponding temperature and humidity sensor associated with one or more production devices on at least one production line in the packaging workshop. The first detection location and the first temperature and humidity sensor do not distinguish whether they are deployed on a production line in operation or not. The data to be used specifically represents the set of information collected by the second temperature and humidity sensor at the second detection location, including but not limited to temperature data and / or humidity data.

[0041] Specifically, for any operating production line, at least one second temperature and humidity sensor is deployed at at least one second detection location associated with the operating production line to collect environmental information at the detection location in real time or at regular intervals, thereby obtaining usable data including temperature and / or humidity data, providing accurate data basis for subsequently determining the equipment adjustment strategy of the temperature and humidity control equipment.

[0042] In this embodiment, temperature and humidity data are collected by temperature and humidity sensors at multiple detection locations associated with the production line in operation. This data reflects the temperature and humidity conditions of the production line, enabling accurate and timely capture of the temperature and humidity status of key areas of the production line. This provides a real and reliable basis for determining equipment adjustment strategies based on this data, ensuring real-time perception of the production line environment and laying a data foundation for ensuring stable production conditions and preventing production problems caused by abnormal temperature and humidity.

[0043] Based on the above embodiments, the method further includes: determining a first detection location in the packaging workshop for deploying at least one first temperature and humidity sensor based on a thermal imaging method, and deploying a first temperature and humidity sensor distributed by the Internet of Things at the first detection location; configuring a sampling frequency for the first temperature and humidity sensor to collect data to be used at the corresponding detection location based on the sampling frequency.

[0044] Specifically, in the packaging workshop, before production begins, thermal imaging methods can be used to comprehensively scan and analyze the temperature distribution, heat source location, and other characteristic information within the workshop. This allows for the precise determination of a first detection location suitable for deploying at least one first temperature and humidity sensor. Subsequently, at the determined first detection location, a first temperature and humidity sensor connected to the Internet of Things (IoT) is deployed. Then, an appropriate sampling frequency is configured for the first temperature and humidity sensor connected to the IoT, enabling it to continuously collect data to be used at the corresponding detection location according to the set frequency. The data to be used includes temperature data and / or humidity data.

[0045] In this embodiment, the first detection location determined by thermal imaging is more scientific and targeted, and can accurately capture temperature and humidity changes in key areas of the workshop; the deployment of IoT sensors enables real-time data transmission and centralized management, while reasonable sampling frequency configuration ensures the timeliness and integrity of the data and avoids unnecessary waste of resources, providing high-quality basic data support for subsequent temperature and humidity control and production optimization.

[0046] S120. Input the data to be used into the digital twin system so that the digital twin system can determine the equipment adjustment strategy of at least one first temperature and humidity control device associated with the production line and the temperature and humidity prediction curve of at least one second detection location point within the prediction time.

[0047] A digital twin system is a system that uses digital modeling to map, dynamically simulate, and predict the state, behavior, and environmental characteristics of physical entities (such as a packaging workshop production line) in real time. It can simulate the operation of the entity based on input data and output analysis results. For example, a digital twin system can output equipment adjustment strategies and temperature and humidity prediction curves. The first temperature and humidity control device refers to a device used to regulate the temperature and humidity in relevant areas of the production line, such as an industrial air conditioner, which can affect the ambient temperature and humidity by adjusting its own operating status. The equipment adjustment strategy is a specific operating plan formulated by the digital twin system for the first temperature and humidity control device. The equipment adjustment strategy includes, but is not limited to, valve information at the air inlet and outlet, fan frequency, and fan mode, used to guide the equipment to achieve the target temperature and humidity. The prediction duration refers to the time range preset by the system for predicting future temperature and humidity changes, such as 1 hour or 24 hours. The temperature and humidity prediction curve reflects the trend of temperature and humidity changes at a certain detection location over a future period. The temperature and humidity prediction curve within the prediction duration specifically refers to the trend curve of temperature and humidity changes at that detection location over time within the preset prediction duration, intuitively presenting the system's prediction of future temperature and humidity conditions.

[0048] Specifically, the data to be used, including temperature and / or humidity, collected by the second temperature and humidity sensor at the second detection location associated with the production line, can be transmitted to the pre-built digital twin system. The digital twin system analyzes and processes the input data to be used based on preset model algorithms, such as temperature and humidity change law models and equipment control response models. On the one hand, it calculates the equipment adjustment strategy of at least one first temperature and humidity control device associated with the production line. On the other hand, it generates temperature and humidity prediction curves for at least one second detection location point within the future prediction time.

[0049] In this embodiment, by leveraging the deep computation of real-time data through a digital twin system, it is possible to achieve precise and dynamic control of temperature and humidity control equipment, and to predict the temperature and humidity change trends at the detection location in advance. This provides forward-looking guidance for the stable control of the production line environment, reduces the impact of temperature and humidity fluctuations on production, and improves the intelligence and precision of the control.

[0050] Optionally, before processing the data to be used based on the digital twin system, the method further includes: constructing a geometric model corresponding to the packaging workshop, and integrating a fluid dynamics simulation module and a module for simulating airflow distribution and heat and moisture transfer processes to obtain a first subsystem in the digital twin system used to determine the control strategy; and determining a second subsystem in the digital twin system used to determine the temperature and humidity prediction curve within the prediction period based on historical prediction data and real-time data.

[0051] The first subsystem can be understood as the core module of the digital twin system, focused on determining temperature and humidity control strategies. By constructing a geometric model consistent with the physical space of the packaging workshop and integrating fluid dynamics simulation modules and modules simulating airflow distribution and heat and humidity transfer processes, it can accurately simulate the physical laws governing airflow movement, heat and humidity transfer within the workshop, thereby generating scientifically sound adjustment strategies for the temperature and humidity control equipment related to the production line. The second subsystem can be understood as the module in the digital twin system responsible for predicting temperature and humidity trends. Based on historical prediction data and real-time collected temperature and humidity data, it analyzes the patterns of temperature and humidity changes through a data-driven approach, generating temperature and humidity prediction curves for each detection location within a specified prediction period. This provides support for anticipating environmental changes. The two subsystems work collaboratively to enhance the digital twin system's ability to control and predict the production environment.

[0052] Specifically, before using the digital twin system to process the data to be used, a geometric model that perfectly matches the physical space of the packaging workshop is first constructed. Then, the fluid dynamics simulation module and the module for simulating airflow distribution and heat and moisture transfer processes are integrated into the geometric model to form the first subsystem in the digital twin system responsible for determining the control strategy. At the same time, based on the historical forecast data of the packaging workshop and the real-time collected temperature and humidity data, a second subsystem in the digital twin system is built to generate the temperature and humidity prediction curves within the prediction period, thus completing the core construction of the digital twin system.

[0053] In this embodiment, by combining a geometric model with a professional simulation module, the first subsystem can accurately simulate the airflow and heat and humidity transfer patterns in the workshop, providing scientific physical mechanism support for the control strategy; while the second subsystem, built based on historical and real-time data, can improve the accuracy and adaptability of temperature and humidity prediction. The two work together to enable the digital twin system to have reliable strategy generation and trend prediction capabilities, laying a solid foundation for subsequent intelligent control.

[0054] S130. Send the device adjustment strategy to the corresponding first temperature and humidity control device so that the first temperature and humidity control device operates based on the device adjustment strategy and obtains the actual temperature and humidity curves collected by at least one second temperature and humidity sensor under the action of the device adjustment strategy on the first temperature and humidity control device within the predicted time period.

[0055] Specifically, the first temperature and humidity control device can be understood as a device used to actively regulate the temperature and humidity in the packaging workshop, such as an industrial air conditioner. It can change its operating parameters according to the received equipment adjustment strategy, directly affecting the environment to achieve temperature and humidity control. The actual temperature and humidity curve is a curve formed by real-time temperature and humidity data collected by a temperature and humidity sensor (such as the second temperature and humidity sensor) at the detection location within a certain time period (such as the predicted duration). It intuitively reflects the actual changes in temperature and humidity at that location during the execution of the equipment adjustment strategy, and is an important basis for verifying the effectiveness of the strategy and the true state of the environment.

[0056] Specifically, the device adjustment strategy generated by the digital twin system is sent to the corresponding first temperature and humidity control device through a communication network. For example, the OPC UA protocol can be used for communication to transmit the device adjustment strategy generated by the digital twin system to the corresponding first temperature and humidity control device, so that the corresponding first temperature and humidity control device adjusts its operating status according to the parameters in the device adjustment strategy. At the same time, within a preset prediction time, temperature and humidity data at the second detection position of at least one second temperature and humidity sensor are continuously collected to form the actual temperature and humidity curve under the action of the device adjustment strategy and acquire it.

[0057] In this embodiment, by sending the equipment adjustment strategy to the corresponding first temperature and humidity control device, the first temperature and humidity control device operates based on the equipment adjustment strategy, ensuring the accurate implementation of the equipment adjustment strategy and enabling the temperature and humidity control device to operate according to the optimal scheme. Meanwhile, the synchronously collected actual temperature and humidity curves can reflect the execution effect of the strategy in real time, providing real feedback data for subsequent verification of the prediction accuracy of the digital twin system and optimization of system parameters. This forms a closed loop of "strategy execution - effect feedback", improving the reliability and dynamic adjustment capability of temperature and humidity control in the production line.

[0058] Optionally, the first control parameter in the equipment adjustment strategy includes one or more of the valve information of the air supply outlet, the valve information of the air outlet, the fan frequency, and the fan mode of at least one first temperature and humidity control device. The equipment adjustment strategy is sent to the corresponding first temperature and humidity control device so that the first temperature and humidity control device operates based on the equipment adjustment strategy. This includes: determining control correction parameters based on the first control parameter in the equipment adjustment strategy and the actual control parameters of at least one first temperature and humidity control device using a fuzzy PID control algorithm; and controlling the operation of at least one first temperature and humidity control device based on the control correction parameters corresponding to at least one first temperature and humidity control device and the corresponding first control parameters.

[0059] The first control parameter can be understood as the key parameter used in the equipment adjustment strategy to guide the operation of the first temperature and humidity control equipment. It includes one or more of the following: air supply valve information, air outlet valve information, fan frequency, and fan mode. These are target control values ​​set by the system based on production needs. The actual control parameter can be understood as the parameter used by the first temperature and humidity control equipment during actual operation. It reflects the real-time operating status of the first temperature and humidity control equipment and can be obtained through information acquisition devices within the first temperature and humidity control equipment. The control correction parameter is an adjustment value calculated by comparing and analyzing the first control parameter and the actual control parameter using a fuzzy PID control algorithm. It is used to correct the actual control parameter, making the equipment operating parameters more closely match the requirements of the first control parameter and improving the control accuracy.

[0060] Specifically, after the equipment adjustment strategy is sent to the corresponding first temperature and humidity control device, the first control parameter in the equipment adjustment strategy is compared and analyzed with the current actual control parameter of the first temperature and humidity control device through the fuzzy PID control algorithm, and the control correction parameter is calculated. Then, the equipment is controlled to ensure that the equipment operates according to the adjusted parameters by combining the control correction parameter and the corresponding first control parameter of each first temperature and humidity control device.

[0061] In this embodiment, the introduction of the fuzzy PID control algorithm can effectively solve the problem of parameters being difficult to accurately adapt in traditional control methods. By comparing and generating correction parameters in real time, the control parameters of the device can be dynamically optimized, improving the stability and accuracy of the operation of the first temperature and humidity control device, making temperature and humidity regulation more in line with actual needs, while enhancing the system's adaptability to complex environmental changes and reducing energy waste or poor regulation effect caused by parameter deviation.

[0062] Based on the above embodiments, the method further includes: determining control correction parameters for the first temperature and humidity control device according to the air conditioning unit control mode configured for the first temperature and humidity control device; wherein, the air conditioning unit control mode includes a single average temperature and humidity control mode, a cascade PID control mode combining supply air temperature and humidity, and a chiller addition / reduction mode, and in the chiller addition / reduction mode, the control correction parameters include chiller addition / reduction parameters.

[0063] Specifically, the control mode of the air conditioning unit configured by the first temperature and humidity control device is obtained, including but not limited to a single average temperature and humidity control mode, a cascade PID control mode combining supply air temperature and humidity, and a chiller addition / reduction mode. Then, corresponding control correction parameters are determined based on the characteristics of different modes. In the chiller addition / reduction mode, the control correction parameters specifically include chiller addition / reduction parameters, key control parameters used to dynamically adjust the number of chillers in operation. These parameters can be adjusted based on the deviation between actual load demand and the system's set target to determine whether to increase or decrease the number of chillers in operation, thus achieving precise matching of cooling capacity and load. In the single average temperature and humidity control mode, the control correction parameters are determined based on the average temperature and humidity data of the overall environment to achieve basic adjustment of the overall temperature and humidity. In the cascade PID control mode combining supply air temperature and humidity, the correction parameters are determined through closed-loop adjustment of the PID algorithm, combining the correlation between supply air temperature and humidity and ambient temperature and humidity, thereby improving control accuracy.

[0064] In this embodiment, by accurately determining the corresponding control correction parameters for different control modes, the first temperature and humidity control device can operate efficiently in different scenarios. The exclusive parameters in the refrigeration unit addition / reduction mode can directly guide the reasonable start-up or shutdown or load adjustment of the refrigeration unit, improving the pertinence and effectiveness of the control correction of the first temperature and humidity control device, reducing energy waste, improving the energy efficiency and economy of the system, and thus ensuring more stable and efficient equipment operation, better meeting the temperature and humidity control requirements and energy consumption requirements of the production line.

[0065] S140. Based on the temperature and humidity prediction curve, the actual temperature and humidity curve, and the energy consumption data of at least one first temperature and humidity control device, optimize the parameters of the digital twin system, and continue to execute the device adjustment strategy for determining at least one first temperature and humidity control device based on the parameter-optimized digital twin system.

[0066] Specifically, the system can extract the temperature and humidity prediction curves generated by the digital twin system, the actual temperature and humidity curves collected under the equipment adjustment strategy, and the energy consumption data generated by at least one first temperature and humidity control device during operation. Then, it can use a pre-built multi-objective optimization engine to compare and analyze the deviation between the temperature and humidity prediction curves and the actual curves, and combine the energy consumption data to evaluate the prediction accuracy of the digital twin system under the current parameters and the energy consumption rationality of the control scheme. Based on these analysis results, the model parameters in the digital twin system can be adjusted and optimized. After optimization, the updated digital twin system can be used to continue to determine the equipment adjustment strategy for the first temperature and humidity control device for the production line.

[0067] In this embodiment, by incorporating the deviation between prediction and reality, as well as energy consumption data, into the optimization criteria, the prediction accuracy and scientific nature of the digital twin system can be continuously improved. This allows the generated equipment adjustment strategies to not only ensure the effectiveness of temperature and humidity control but also better align with actual operating conditions and take energy consumption control into account, forming a virtuous cycle of "data acquisition - strategy generation - effect feedback - system optimization," and continuously enhancing the intelligence and efficiency of temperature and humidity control in the production line.

[0068] Based on the above embodiments, the method further includes: acquiring total air volume data corresponding to at least one first temperature and humidity control device; determining the current operating mode and fan operating frequency threshold of at least one first temperature and humidity control device based on the total air volume data; wherein the current operating mode includes single fan mode and dual fan mode.

[0069] Specifically, the total air volume data of at least one first temperature and humidity control device can be obtained through a data acquisition device. Then, based on the size and characteristics of the total air volume data, it can be analyzed to determine whether the current working mode of the first temperature and humidity control device is a single fan mode or a dual fan mode. At the same time, combined with the device performance parameters and total air volume requirements, the fan operating frequency threshold that matches the current working mode can be determined.

[0070] In this embodiment, the total air volume data can accurately identify the operating status of the equipment. Clarifying the current working mode can ensure that the subsequent control strategy is adapted to the actual operating conditions of the equipment. Determining the fan operating frequency threshold can provide a clear boundary for adjusting the equipment operating parameters, avoiding energy waste due to excessively high frequency or affecting the control effect due to excessively low frequency. This improves the stability and energy efficiency of the first temperature and humidity control equipment, providing strong support for the precise control of temperature and humidity in the production line.

[0071] The technical solution of this embodiment acquires, in real time, the temperature and / or humidity data collected by a second temperature and / or humidity sensor at at least one associated second detection location on any operating production line. This data is then input into a digital twin system, which analyzes and generates adjustment strategies for the relevant first temperature and humidity control equipment, as well as temperature and humidity prediction curves for each second detection location within a predicted timeframe. The adjustment strategies are then distributed to the corresponding equipment, enabling it to operate according to the strategies, and simultaneously acquiring the actual temperature and humidity curves for each second detection location within the predicted timeframe under the influence of the strategies. Finally, by combining the predicted curves, actual curves, and energy consumption data of the control equipment, the parameters of the digital twin system are optimized, so that the optimized system continuously provides strategy support for temperature and humidity control of the production line. This solution achieves real-time sensing and accurate prediction of workshop temperature and humidity through the joint control of a digital twin system and temperature and humidity control equipment. The dynamic optimization mechanism based on data feedback continuously improves the effectiveness of the control strategy, solving the technical problems of insufficient accuracy of temperature and humidity control strategies in the packaging workshop and the difficulty in balancing energy consumption and temperature control effect. This significantly improves the stability of the production environment to ensure product quality, realizes intelligent and refined temperature and humidity control to improve management efficiency, achieves synergy between energy saving and precise temperature control to reduce operating costs, and also builds a sustainable and iterative management and control system, improving the level of intelligence and refinement of production management in the packaging workshop.

[0072] Example 2

[0073] Figure 2 This is a flowchart of a temperature and humidity control method for a packaging workshop provided in Embodiment 2 of the present invention. The method in this embodiment is a further optimization of the method in the above embodiments. Optionally, prediction confidence data is determined based on the temperature and humidity prediction curve and the actual temperature and humidity curve; for at least one first temperature and humidity control device, parameter optimization for the digital twin system is determined based on the energy consumption data, prediction confidence data, and corresponding threshold data of the first temperature and humidity control device. Figure 2 As shown, the method includes:

[0074] S210. For any production line in operation, acquire the data to be used collected by at least one second temperature and humidity sensor at at least one second detection position associated with the production line; wherein the data to be used includes temperature data and / or humidity data.

[0075] S220. Input the data to be used into the digital twin system so that the digital twin system can determine the equipment adjustment strategy of at least one first temperature and humidity control device associated with the production line and the temperature and humidity prediction curve of at least one second detection location point within the prediction time.

[0076] S230. Send the device adjustment strategy to the corresponding first temperature and humidity control device so that the first temperature and humidity control device operates based on the device adjustment strategy and obtains the actual temperature and humidity curves collected by at least one second temperature and humidity sensor under the action of the device adjustment strategy on the first temperature and humidity control device within the predicted time period.

[0077] S240. Determine the prediction confidence level data based on the temperature and humidity prediction curve and the actual temperature and humidity curve.

[0078] Among them, prediction confidence data is an indicator used to quantitatively evaluate the degree of agreement between the predicted temperature and humidity curve and the actual curve. It is achieved by statistically analyzing the deviations (such as absolute error, relative error, etc.) of the two at each node within the same time period (including the mean, variance, distribution range of the deviation, etc.), and finally presenting it in the form of specific values ​​(such as values ​​in the range of 0-1) or grades. The higher the value or the higher the grade, the stronger the consistency between the prediction result and the actual situation and the higher the reliability. Conversely, it indicates that there is a large uncertainty in the prediction. It can serve as an important basis for judging the effectiveness of the prediction model, guiding subsequent control decisions (such as whether to carry out operations based on the prediction results), and optimizing the prediction model.

[0079] Specifically, the process first requires obtaining the predicted temperature and humidity curves (based on the temperature and humidity change trends generated by the prediction model) and the actual curves (real temperature and humidity data collected by sensors) for the same time period. The deviation values ​​(such as absolute error, relative error, etc.) of the two curves at each time point are calculated, and then the distribution characteristics of the deviations (such as the mean deviation, variance, and percentage of the maximum deviation) are combined for quantitative analysis. Finally, prediction confidence data (for example, presented as a value in the range of 0-1 or in the form of levels, where a higher value indicates a higher confidence level and a better temperature and humidity control effect) are generated to reflect the degree of agreement between the prediction results and the actual situation.

[0080] In this embodiment, the prediction confidence data can intuitively quantify the degree of agreement between the prediction results of the digital twin system and the actual situation, providing a clear indicator for evaluating the prediction capability of the digital twin system. When the confidence level is high, it can enhance the trust in the subsequent prediction results of the digital twin system and provide a reliable basis for decision-making. If the confidence level is low, it can promptly indicate that the system needs to be optimized and adjusted, which helps to continuously improve the prediction accuracy of the digital twin system and ensure the scientificity and effectiveness of temperature and humidity control in the production line.

[0081] S250. For at least one first temperature and humidity control device, determine the parameter optimization of the digital twin system based on the energy consumption data, prediction confidence data and corresponding threshold data of the first temperature and humidity control device.

[0082] Specifically, for at least one temperature and humidity control device, energy consumption data, prediction confidence data reflecting prediction accuracy, and corresponding threshold data such as preset energy consumption thresholds and confidence thresholds are first collected during its operation. Then, the energy consumption data is compared with the energy consumption thresholds to determine whether the device's energy consumption is within a reasonable range, and the prediction confidence data is compared with the confidence thresholds to assess whether the prediction accuracy of the digital twin system meets the standards. Based on these comparison results, the reasons for excessive energy consumption and / or insufficient prediction accuracy are comprehensively analyzed using preset analysis algorithms, thereby determining the specific parameters that need to be optimized in the digital twin system. These specific parameters include, but are not limited to, physical parameters and algorithm weights in the digital twin system. The preset analysis algorithms include, but are not limited to, bias analysis algorithms, Pearson correlation coefficient analysis algorithms, Spearman rank correlation coefficient analysis algorithms, random forest algorithms, and STL decomposition algorithms.

[0083] In a specific embodiment, for at least one first temperature and humidity control device, the energy consumption data, prediction confidence data, and corresponding threshold data of the first temperature and humidity control device can be analyzed by a multi-objective optimization engine to optimize the parameters of the digital twin system. Specifically, temperature and humidity stability, accuracy, and low energy consumption are taken as optimization objectives, and the optimization objectives are quantified into a multi-objective optimization function, as shown below:

[0084] F = f1 + f2 + f3;

[0085] Where f1 = w1*(σT+σH); f2 = w2*(|Treal-Tset|+|Hreal-Hset|); f3 = w3*Penergy; f1 uses the standard deviation of temperature and humidity (σT, σH) to measure stability, f2 uses the sum of the absolute values ​​of the deviations between the actual temperature and humidity values ​​(Treal, Hreal) and the set temperature and humidity values ​​(Tset, Hset) to reflect accuracy, and f3 directly uses energy consumption (Penergy) as an indicator, using weights (w1, w2, w3) to reflect the target priority under different scenarios. Taking a multi-objective optimization function as the objective, the NSGA-II algorithm is used to generate candidate control strategies. The temperature and humidity field distribution and energy consumption of each scheme are simulated through a digital twin system to select the Pareto optimal solution set. Then, target weights are assigned according to scenario requirements to determine the final control command. At the same time, the model parameters are dynamically corrected through real-time data feedback to achieve rolling time-domain optimization, thereby balancing multi-objective conflicts in closed temperature and humidity control and outputting an equipment adjustment strategy that takes into account stability, accuracy and low energy consumption.

[0086] In this embodiment, by basing parameter optimization on actual operating data and thresholds, the direction of parameter optimization is made more precise, avoiding blind adjustments. By simultaneously considering energy consumption and prediction accuracy, energy consumption control can be taken into account while improving the system's prediction capabilities, forming a closed loop of "data feedback - parameter optimization - efficiency improvement". This continuously enhances the adaptability of the digital twin system to actual scenarios, ultimately outputting a better equipment control strategy, improving the reliability and economy of the digital twin system, and thus enhancing the overall efficiency of temperature and humidity control in the production line.

[0087] S260, The digital twin system based on the optimized parameters continues to execute the equipment adjustment strategy that determines at least one first temperature and humidity control device.

[0088] The technical solution of this embodiment involves acquiring, for any production line in operation, data to be used collected by at least one second temperature and humidity sensor at at least one second detection location associated with the production line; wherein the data to be used includes temperature data and / or humidity data; inputting the data to be used into a digital twin system, so that the digital twin system determines the equipment adjustment strategy of at least one first temperature and humidity control device associated with the production line and the temperature and humidity prediction curve of at least one second detection location within the prediction period; sending the equipment adjustment strategy to the corresponding first temperature and humidity control device, so that the first temperature and humidity control device operates based on the equipment adjustment strategy, and acquiring the actual temperature and humidity curves collected by at least one second temperature and humidity sensor under the action of the equipment adjustment strategy on the first temperature and humidity control device within the prediction period; determining prediction confidence data based on the temperature and humidity prediction curves and the actual temperature and humidity curves; for at least one first temperature and humidity control device, determining parameter optimization for the digital twin system based on the energy consumption data, prediction confidence data, and corresponding threshold data of the first temperature and humidity control device; and continuing to execute the determined equipment adjustment strategy for at least one first temperature and humidity control device based on the parameter optimization of the digital twin system. This solution constructs a complete closed loop of "data acquisition - simulation prediction - strategy execution - effect feedback - model optimization". It drives the digital twin system to generate control strategies by acquiring real-time temperature and humidity data from the second detection position on the production line, ensuring that the strategies fit the actual scenario. By comparing the predicted temperature and humidity curves with the actual curves to calculate the prediction confidence, and combining energy consumption data and thresholds, it achieves precise parameter optimization of the digital twin system, continuously improving the model's prediction accuracy and energy consumption control capabilities. At the same time, based on the optimized model, new equipment adjustment strategies are generated on a rolling basis to dynamically adapt to changes in temperature and humidity on the production line. This ensures the stability and accuracy of temperature and humidity at each detection position, while achieving efficient control through low-energy-consumption equipment adjustment strategies. Ultimately, it achieves a balance between the scientific nature, adaptability, and economy of temperature and humidity control in complex production environments.

[0089] Example 3

[0090] Figure 3 This is a schematic diagram of a temperature and humidity control device for a packaging workshop provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes:

[0091] The data to be used determination module 310 is used to acquire, for any production line in operation, the data to be used collected by at least one second temperature and humidity sensor at at least one second detection position associated with the production line; wherein the data to be used includes temperature data and / or humidity data;

[0092] The equipment adjustment strategy and temperature and humidity prediction curve determination module 320 is used to input the data to be used into the digital twin system so that the digital twin system can determine the equipment adjustment strategy of at least one first temperature and humidity control device associated with the production line and the temperature and humidity prediction curve of at least one second detection location point within the prediction time.

[0093] The temperature and humidity control module 330 is used to send the device adjustment strategy to the corresponding first temperature and humidity control device so that the first temperature and humidity control device operates based on the device adjustment strategy and obtains the actual temperature and humidity curves collected by at least one second temperature and humidity sensor under the action of the device adjustment strategy on the first temperature and humidity control device within the predicted time period.

[0094] The parameter optimization and strategy adjustment module 340 is used to optimize the parameters of the digital twin system based on the temperature and humidity prediction curve, the actual temperature and humidity curve, and the energy consumption data of at least one first temperature and humidity control device, so as to continue to execute the device adjustment strategy of at least one first temperature and humidity control device based on the parameter-optimized digital twin system.

[0095] The technical solution of this embodiment involves a data-to-use determination module acquiring, for any production line in operation, the data to be used collected by at least one second temperature and humidity sensor at at least one second detection location associated with the production line. This data includes temperature data and / or humidity data. An equipment adjustment strategy and temperature / humidity prediction curve determination module inputs the data to be used into a digital twin system, enabling the digital twin system to determine the equipment adjustment strategy for at least one first temperature and humidity control device associated with the production line and the temperature and humidity prediction curve for at least one second detection location within the predicted duration. A temperature and humidity control module sends the equipment adjustment strategy to the corresponding first temperature and humidity control device, causing the first temperature and humidity control device to operate based on the equipment adjustment strategy and acquire the actual temperature and humidity curves collected by at least one second temperature and humidity sensor under the influence of the equipment adjustment strategy within the predicted duration. A parameter optimization and strategy adjustment module optimizes the parameters of the digital twin system based on the temperature and humidity prediction curves, the actual temperature and humidity curves, and the energy consumption data of at least one first temperature and humidity control device, so that the optimized digital twin system continues to execute the determined equipment adjustment strategy for at least one first temperature and humidity control device. This solution achieves real-time sensing and accurate prediction of workshop temperature and humidity through the joint control of a digital twin system and temperature and humidity control equipment. The dynamic optimization mechanism based on data feedback continuously improves the effectiveness of the control strategy, solving the technical problems of insufficient accuracy of temperature and humidity control strategies in the packaging workshop and the difficulty in balancing energy consumption and temperature control effect. This significantly improves the stability of the production environment to ensure product quality, realizes intelligent and refined temperature and humidity control to improve management efficiency, achieves synergy between energy saving and precise temperature control to reduce operating costs, and also builds a sustainable and iterative management and control system, improving the level of intelligence and refinement of production management in the packaging workshop.

[0096] Based on the above embodiments, optionally, the device is further configured to determine a first detection location in the packaging workshop for deploying at least one first temperature and humidity sensor based on a thermal imaging method, and deploy the first temperature and humidity sensor of the distributed Internet of Things at the first detection location; configure a sampling frequency for the first temperature and humidity sensor to collect data to be used at the corresponding detection location based on the sampling frequency.

[0097] Optionally, before processing the data to be used based on the digital twin system, the device is also used to construct a geometric model corresponding to the packaging workshop, and integrate a fluid dynamics simulation module and a module for simulating airflow distribution and heat and moisture transfer processes to obtain a first subsystem in the digital twin system for determining the control strategy; based on historical prediction data and real-time data, a second subsystem in the digital twin system is determined for determining the temperature and humidity prediction curve within the prediction period.

[0098] Optionally, the first control parameter in the equipment adjustment strategy includes one or more of the valve information of the air supply outlet, the valve information of the air outlet, the fan frequency, and the fan mode of at least one first temperature and humidity control device. The temperature and humidity control module 330 is specifically used to determine the control correction parameter based on the first control parameter in the equipment adjustment strategy and the actual control parameter of at least one first temperature and humidity control device using a fuzzy PID control algorithm; and to control the operation of at least one first temperature and humidity control device based on the control correction parameter corresponding to at least one first temperature and humidity control device and the corresponding first control parameter.

[0099] Optionally, the temperature and humidity control module 330 is specifically used to determine the control correction parameters of the first temperature and humidity control device according to the air conditioning unit control mode configured for the first temperature and humidity control device; wherein, the air conditioning unit control mode includes a single average temperature and humidity control mode, a cascade PID control mode combining supply air temperature and humidity, and a chiller addition / reduction mode, and in the chiller addition / reduction mode, the control correction parameters include chiller addition / reduction parameters.

[0100] Optionally, the device is further configured to acquire total air volume data corresponding to at least one first temperature and humidity control device; and determine the current operating mode and fan operating frequency threshold of at least one first temperature and humidity control device based on the total air volume data; wherein the current operating mode includes single fan mode and dual fan mode.

[0101] Optionally, the parameter optimization and strategy adjustment module 340 includes a prediction confidence data determination unit and a parameter optimization unit; the prediction confidence data determination unit is used to determine the prediction confidence data based on the temperature and humidity prediction curve and the actual temperature and humidity curve; the parameter optimization unit is used to determine the parameter optimization for the digital twin system for at least one first temperature and humidity control device based on the energy consumption data, prediction confidence data and corresponding threshold data of the first temperature and humidity control device.

[0102] The temperature and humidity control device for a packaging workshop provided in this embodiment of the invention can execute the temperature and humidity control method for a packaging workshop provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0103] Example 4

[0104] Figure 4This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0105] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0106] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0107] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as temperature and humidity control methods for a packaging workshop.

[0108] In some embodiments, the temperature and humidity control method for a packaging workshop can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the temperature and humidity control method for a packaging workshop described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the temperature and humidity control method for a packaging workshop by any other suitable means (e.g., by means of firmware).

[0109] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0110] Computer programs for implementing the temperature and humidity control method for a packaging workshop according to the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0111] Example 5

[0112] Embodiment 5 of the present invention also provides a computer-readable storage medium storing computer instructions for causing a processor to execute a temperature and humidity control method for a roll packaging workshop, the method comprising:

[0113] For any production line in operation, acquire the data to be used collected by at least one second temperature and humidity sensor at at least one second detection position associated with the production line; wherein the data to be used includes temperature data and / or humidity data;

[0114] The data to be used is input into the digital twin system so that the digital twin system can determine the equipment adjustment strategy of at least one first temperature and humidity control device associated with the production line and the temperature and humidity prediction curve of at least one second detection location point within the prediction time.

[0115] The device adjustment strategy is sent to the corresponding first temperature and humidity control device so that the first temperature and humidity control device operates based on the device adjustment strategy and obtains the actual temperature and humidity curves collected by at least one second temperature and humidity sensor under the action of the device adjustment strategy on the first temperature and humidity control device within the predicted time period.

[0116] Based on the predicted temperature and humidity curves, the actual temperature and humidity curves, and the energy consumption data of at least one first temperature and humidity control device, the parameters of the digital twin system are optimized so that the device adjustment strategy for at least one first temperature and humidity control device can be continued to be executed based on the optimized digital twin system.

[0117] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0118] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0119] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0120] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0121] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0122] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for temperature and humidity control in a packaging workshop, characterized in that, The method includes: a packaging workshop comprising at least one production line, the at least one production line comprising one or more production devices, and at least one first temperature and humidity sensor deployed at at least one first detection location associated with the production device; For any production line in operation, acquire the data to be used collected by at least one second temperature and humidity sensor at at least one second detection location associated with the production line; wherein the data to be used includes temperature data and / or humidity data; The data to be used is input into the digital twin system so that the digital twin system can determine the equipment adjustment strategy of at least one first temperature and humidity control device associated with the production line and the temperature and humidity prediction curve of the at least one second detection location point within the prediction time. The device adjustment strategy is sent to the corresponding first temperature and humidity control device so that the first temperature and humidity control device operates based on the device adjustment strategy and obtains the actual temperature and humidity curves collected by the at least one second temperature and humidity sensor under the action of the device adjustment strategy on the first temperature and humidity control device within the predicted time period. Based on the predicted temperature and humidity curve, the actual temperature and humidity curve, and the energy consumption data of the at least one first temperature and humidity control device, the parameters of the digital twin system are optimized so that the device adjustment strategy for the at least one first temperature and humidity control device can be further executed based on the parameter-optimized digital twin system.

2. The method according to claim 1, characterized in that, The method further includes: The first detection location for deploying at least one first temperature and humidity sensor in the packaging workshop is determined based on a thermal imaging method, and the first temperature and humidity sensor of the distributed Internet of Things is deployed at the first detection location. Configure a sampling frequency for the first temperature and humidity sensor to collect data to be used at the corresponding detection location based on the sampling frequency.

3. The method according to claim 1, characterized in that, Before processing the data to be used based on the digital twin system, the method further includes: A geometric model corresponding to the packaging workshop is constructed, and a fluid dynamics simulation module and a module for simulating airflow distribution and heat and moisture transfer processes are integrated to obtain the first subsystem in the digital twin system used to determine the control strategy. Based on historical forecast data and real-time data, a second subsystem is determined in the digital twin system for determining the temperature and humidity forecast curve within the forecast period.

4. The method according to claim 1, characterized in that, The first control parameter in the equipment adjustment strategy includes one or more of the following: valve information of the air supply outlet of at least one first temperature and humidity control device, valve information of the air outlet, fan frequency, and fan mode. Sending the equipment adjustment strategy to the corresponding first temperature and humidity control device so that the first temperature and humidity control device operates based on the equipment adjustment strategy includes: Based on the fuzzy PID control algorithm, the control correction parameters are determined by the first control parameter in the device adjustment strategy and the actual control parameters of the at least one first temperature and humidity control device. The operation of the at least one first temperature and humidity control device is controlled based on the control correction parameters corresponding to the at least one first temperature and humidity control device and the corresponding first control parameters.

5. The method according to claim 4, characterized in that, Also includes: Based on the air conditioning unit control mode configured for the first temperature and humidity control device, determine the control correction parameters of the first temperature and humidity control device; The control modes of the air conditioning unit include a single average temperature and humidity control mode, a cascade PID control mode combining supply air temperature and humidity, and a chiller addition / reduction mode. In the chiller addition / reduction mode, the control correction parameters include chiller addition / reduction parameters.

6. The method according to claim 1, characterized in that, The method further includes: Obtain the total air volume data corresponding to the at least one first temperature and humidity control device; Based on the total air volume data, determine the current operating mode of the at least one first temperature and humidity control device and the fan operating frequency threshold. The current operating modes include single-fan mode and dual-fan mode.

7. The method according to claim 1, characterized in that, The parameter optimization of the digital twin system based on the predicted temperature and humidity curve, the actual temperature and humidity curve, and the energy consumption data of at least one first temperature and humidity control device includes: Based on the predicted temperature and humidity curve and the actual temperature and humidity curve, determine the prediction confidence level data; For the at least one first temperature and humidity control device, the parameter optimization of the digital twin system is determined based on the energy consumption data of the first temperature and humidity control device, the prediction confidence data, and the corresponding threshold data.

8. A temperature and humidity control device for a packaging workshop, characterized in that, The packaging workshop includes at least one production line, said at least one production line includes one or more production devices, and at least one first temperature and humidity sensor is deployed at at least one first detection location associated with said production device, the device including: The data to be used determination module is used to acquire, for any production line in operation, the data to be used collected by at least one second temperature and humidity sensor at at least one second detection position associated with the production line; wherein, the data to be used includes temperature data and / or humidity data; The equipment adjustment strategy and temperature and humidity prediction curve determination module is used to input the data to be used into the digital twin system so that the digital twin system can determine the equipment adjustment strategy of at least one first temperature and humidity control device associated with the production line and the temperature and humidity prediction curve of the at least one second detection location point within the prediction time. The temperature and humidity control module is used to send the device adjustment strategy to the corresponding first temperature and humidity control device so that the first temperature and humidity control device operates based on the device adjustment strategy and obtains the actual temperature and humidity curves collected by the at least one second temperature and humidity sensor under the device adjustment strategy applied to the first temperature and humidity control device within the predicted time period. The parameter optimization and strategy adjustment module is used to optimize the parameters of the digital twin system based on the temperature and humidity prediction curve, the actual temperature and humidity curve, and the energy consumption data of the at least one first temperature and humidity control device, so as to continue to execute the device adjustment strategy of the at least one first temperature and humidity control device based on the parameter-optimized digital twin system.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the temperature and humidity control method for a packaging workshop as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the temperature and humidity control method for a packaging workshop as described in any one of claims 1-7.

Images