Intelligent control method and system of multi-stage temperature change test box

Abstract

The present application relates to the technical field of temperature change control, and discloses an intelligent control method and system for a multi-stage temperature change test box, which obtains setting information of a temperature change control network and a temperature sensing network of the test box, divides the internal space of the test box into regions, obtains a corresponding digital feedback model, collects temperature data of the test box through the temperature sensing network, and substitutes the temperature data into the digital feedback model to analyze the temperature distribution information and temperature time-varying characteristics of the internal space of the test box, obtain temperature environment information required by the current test, generate a temperature change control target in combination with the temperature distribution information, the temperature time-varying characteristics, and the temperature environment information, select a temperature control region and analyze a temperature control mode for the temperature change control target based on the digital feedback model, obtain the best temperature control region and the best temperature control strategy, place a test object in the best temperature control region, and drive the temperature change control network to control the temperature according to the best temperature control strategy.

Description

Technical Field

[0001] This invention relates to the technical field of temperature change control, and in particular to an intelligent control method and system for a multi-stage temperature change test chamber. Background Technology

[0002] In the field of materials science, the performance research of many new materials requires precise temperature control. In addition, the performance and reliability of electronic and electrical products are also affected by different temperature environments. By simulating different temperature conditions through multi-level temperature change test chambers, we can study the changes in phase transformation, thermal expansion, electrical properties, etc. of materials during temperature changes, thus providing a basis for the research and development and application of materials. Traditional test chamber temperature control methods are difficult to meet the requirements of high precision, rapid temperature change, and complex temperature curves. Therefore, intelligent control methods are needed to achieve precise temperature regulation. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent control method and system for a multi-stage temperature change test chamber, aiming to solve the problem that existing technologies cannot meet the requirements for high-precision and rapid temperature change.

[0004] The present invention is implemented as follows: Firstly, the present invention provides an intelligent control method for a multi-stage temperature change test chamber, comprising: The setting information of the temperature change control network and temperature sensing network of the test chamber is obtained to divide the internal space of the test chamber into regions and obtain the corresponding digital feedback model. Temperature data from the test chamber is collected by a temperature sensing network and fed into the digital feedback model to analyze the temperature distribution and time-varying characteristics of the internal space of the test chamber. Obtain the temperature environment information required for the current experiment, and generate a temperature change control target by combining the temperature distribution information, the temperature time-varying characteristics, and the temperature environment information; Based on the digital feedback model, the temperature control target is selected for temperature control region and temperature control method is analyzed to obtain the optimal temperature control region and optimal temperature control strategy. The test subject is placed in the optimal temperature control zone, and the temperature control network is driven to control the temperature according to the optimal temperature control strategy.

[0005] Secondly, the present invention provides an intelligent control system for a multi-stage temperature change test chamber, used to implement the intelligent control method for a multi-stage temperature change test chamber as described in any one of the first aspects, comprising: The region division module is used to obtain the setting information of the temperature change control network and temperature sensing network of the test chamber, so as to divide the internal space of the test chamber into regions and obtain the corresponding digital feedback model. The temperature analysis module is used to collect temperature data of the test chamber through the temperature sensing network and input it into the digital feedback model to analyze the temperature distribution information and time-varying characteristics of the internal space of the test chamber. The target analysis module is used to obtain the temperature environment information required for the current experiment, and generate a temperature change control target by combining the temperature distribution information, the temperature time-varying characteristics, and the temperature environment information. The strategy analysis module is used to select the temperature control area and analyze the temperature control method based on the digital feedback model to obtain the optimal temperature control area and the optimal temperature control strategy. The temperature control module is used to place the test object in the optimal temperature control area and drive the temperature change control network to control the temperature according to the optimal temperature control strategy.

[0006] This invention provides an intelligent control method for a multi-stage temperature change test chamber, which has the following beneficial effects: This invention, by constructing a digital feedback model, can accurately grasp the temperature distribution and variation characteristics of the test chamber, generate control targets based on test requirements, and determine the optimal temperature control area and strategy through analysis. This enables precise temperature control, placing the test object in the optimal area and controlling the temperature according to the strategy, providing a stable and compliant temperature environment for the test, improving the accuracy and reliability of test results, reducing errors caused by temperature fluctuations, improving temperature control efficiency, and reducing energy consumption and costs. Attached Figure Description

[0007] Figure 1 This is a schematic diagram illustrating the steps of an intelligent control method for a multi-stage temperature change test chamber provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of an intelligent control system for a multi-stage temperature change test chamber provided in an embodiment of the present invention. Detailed Implementation

[0008] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0009] The implementation of the present invention will be described in detail below with reference to specific embodiments.

[0010] Reference Figure 1 , Figure 2 The diagram shows a preferred embodiment of the present invention.

[0011] In a first aspect, the present invention provides an intelligent control method for a multi-stage temperature change test chamber, comprising: S1: Obtain the setting information of the temperature change control network and temperature sensing network of the test chamber to divide the internal space of the test chamber into regions and obtain the corresponding digital feedback model. S2: Collect temperature data of the test chamber through a temperature sensing network and input it into the digital feedback model to analyze the temperature distribution information and time-varying characteristics of the internal space of the test chamber. S3: Obtain the temperature environment information required for the current experiment, and generate a temperature change control target by combining the temperature distribution information, the temperature time-varying characteristics, and the temperature environment information; S4: Based on the digital feedback model, perform temperature control area selection and temperature control method analysis on the temperature change control target to obtain the optimal temperature control area and optimal temperature control strategy; S5: Place the test object in the optimal temperature control area and drive the temperature change control network to control the temperature according to the optimal temperature control strategy.

[0012] Specifically, in step S1 of the embodiment provided by the present invention, the setting information of the temperature change control network and temperature sensing network of the test chamber is obtained, and the temperature change control unit and temperature sensing unit are set according to the setting information of the pre-constructed original digital model, thereby obtaining the digital model of the temperature change control network and temperature sensing network. For example, the temperature change control network includes multiple heating or cooling devices, and the temperature sensing network consists of multiple temperature sensors. Their positions, performance parameters and other information are accurately set in the original digital model.

[0013] More specifically, the original digital model is just a basic framework. By setting up temperature control units and temperature sensing units, the actual hardware devices can be mapped into the digital model, providing a foundation for subsequent analysis and simulation. Only by accurately reflecting the characteristics and locations of these devices in the digital model can the temperature control and sensing situation inside the test chamber be more realistically reflected.

[0014] More specifically, based on the digital model of the temperature control network and the temperature sensing network, the action space of temperature control and temperature sensing is analyzed on the original digital model. Then, the temperature control range of each temperature control unit and the sensing performance range of each temperature sensing unit are marked on the original digital model. For example, the effective heating range of each heating device and the area where each temperature sensor can accurately sense the temperature are determined through theoretical calculations or experimental data.

[0015] More specifically, understanding the effective range of each temperature control unit and temperature sensing unit is key to dividing the test chamber into zones. Different devices have different effective ranges, and marking these ranges can clearly show which areas inside the test chamber are affected by which devices, providing a basis for subsequent zone division and data fusion.

[0016] More specifically, the original digital model is superimposed based on the sensing performance range of each temperature sensing unit to obtain the data fusion relationship between the temperature sensing units. For example, when the sensing ranges of multiple temperature sensors overlap, their data are analyzed to complement and verify each other, and the data fusion algorithm and rules are determined.

[0017] More specifically, in practical applications, the measurement of a single temperature sensor has errors or limitations. By using data fusion relationships, data from multiple temperature sensors can be combined to improve the accuracy and reliability of temperature measurement. At the same time, data fusion relationships also help with subsequent analysis and processing of temperature data.

[0018] More specifically, the original digital model is superimposed with the temperature change effects according to the temperature change range of each temperature change control unit, and the original digital model is divided into several temperature control areas. The temperature control correlation between each temperature control area and the temperature change control network is obtained, and finally a digital feedback model is formed. For example, according to the heating range of the heating equipment and the cooling range of the cooling equipment, the inside of the test chamber is divided into different temperature control areas, and it is clear which temperature change control units control each area is controlled by.

[0019] More specifically, dividing the interior of the test chamber into temperature-controlled zones allows for more targeted temperature control. Different temperature-controlled zones have different temperature requirements. By determining the temperature control correlation, it is possible to know how to adjust the temperature of each zone through a temperature change control network. The digital feedback model can link temperature sensing and temperature control to achieve intelligent feedback control of the test chamber temperature.

[0020] Specifically, in step S2 of the embodiment provided by the present invention, temperature data of the test chamber is collected by each temperature sensing unit of the temperature sensing network to obtain temperature sensing data of each temperature sensing unit. The temperature sensing units are usually temperature sensors distributed in different positions in the test chamber. They measure the temperature at their location at certain time intervals (e.g., every 1 minute) and transmit the measurement data to the control system.

[0021] More specifically, temperature sensing data is the foundation for subsequent analysis. Temperatures vary at different locations within the test chamber. By placing temperature sensors at multiple locations, the temperature conditions inside the test chamber can be comprehensively obtained. Only by accurately collecting temperature data at each location can the temperature distribution and time-varying characteristics be further analyzed.

[0022] More specifically, the temperature sensing data of each temperature sensing unit is substituted into the digital feedback model. The temperature sensing data is traced through the data fusion relationship between the temperature sensing units in the digital feedback model. The temperature prediction data of each specific location is marked in the internal space of the digital feedback model. For example, by using a data fusion algorithm (such as the weighted average method) and combining the data of adjacent temperature sensors, the temperature at the location between the two sensors can be predicted.

[0023] More specifically, a single temperature sensor can only measure the temperature at its location. However, we need to understand the temperature conditions in various parts of the test chamber. Through temperature tracing and data fusion, we can more accurately infer the temperature at other locations inside the test chamber based on limited temperature sensor data, thereby obtaining more comprehensive temperature distribution information.

[0024] More specifically, by combining the temperature projection data at various locations, temperature distribution information can be obtained. This temperature projection data can be presented in the form of charts (such as isotherm diagrams) or tables to intuitively show the temperature distribution inside the test chamber. Temperature distribution information can help us understand the overall temperature distribution pattern inside the test chamber, such as which areas have higher temperatures and which areas have lower temperatures. This is of great significance for judging the temperature uniformity of the test chamber and formulating subsequent temperature control strategies.

[0025] More specifically, temperature distribution information at each moment is recorded, and the characteristics of temperature changes at specific locations are analyzed to obtain the time-varying characteristics of temperature at each location. By plotting the temperature change curve over time, the rising and falling trends of temperature, as well as the amplitude and frequency of temperature changes, can be analyzed. The time-varying characteristics of temperature reflect the changes in temperature inside the test chamber over time. In actual experiments, temperature will change due to the influence of external environment, experimental operation, and other factors. Understanding the time-varying characteristics of temperature helps to predict future temperature change trends, adjust temperature control strategies in a timely manner, and ensure that the temperature inside the test chamber meets the experimental requirements.

[0026] Specifically, in step S3 of the embodiment provided by the present invention, based on the temperature distribution information and the time-varying temperature characteristics of specific locations, the overall temperature conditions of the internal space of the test chamber at the current moment are analyzed. The internal space of the test chamber can be divided into multiple small areas. Based on the temperature values ​​of each area obtained from the previous analysis and the temperature change pattern over time, the temperature state of the entire test chamber at the current moment is comprehensively evaluated. Using mathematical models or data structures, these analytical results are integrated to establish an initial temperature condition model. For example, a matrix can be used to represent the temperature values ​​of different areas. At the same time, combined with the parameters of temperature change trend, a model that can describe the current temperature condition of the test chamber is formed.

[0027] More specifically, the initial temperature condition model is the basis for generating subsequent temperature change control targets. By accurately grasping the current overall temperature conditions of the test chamber, we can clearly understand the actual temperature situation inside the test chamber before the test begins, providing a reference for subsequent comparison and analysis with the target temperature. Only by clarifying the initial state can we formulate targeted temperature change strategies to achieve the temperature environment required for the test.

[0028] More specifically, the temperature environment information required for the current experiment is obtained. This can usually be obtained from the experiment design documents, operating instructions, etc. For example, the experiment requires maintaining a specific temperature range in a certain area or throughout the entire test chamber. The temperature environment information is analyzed to determine the required space and temperature, clarifying the specific spatial ranges and temperature levels required by the experiment, whether it is local high or low temperature or overall uniform temperature. Then, this analyzed information is converted into mathematical form to establish a temperature target model corresponding to the temperature environment information. For example, parameters such as the temperature range that meets the experiment requirements and the temperature value of a specific area are defined.

[0029] More specifically, the temperature target model represents the temperature conditions that the experiment is expected to achieve. It clarifies the temperature requirements of the experiment and is the target direction for temperature control. By establishing this model, the specific requirements of the experiment can be transformed into an operable and analyzable mathematical form, which is convenient for comparison and calculation with the initial temperature condition model, thereby determining the amount and manner of temperature change that needs to be carried out.

[0030] More specifically, by combining the temperature target model with the initial temperature condition model and comparing the temperature parameters and spatial information in the two models, the required temperature adjustment amount and direction for each area within the test chamber are determined. For example, it calculates which areas need to be heated and by what magnitude; which areas need to be cooled and by what specific value. By combining these calculation results, a temperature change control target is generated. This target can be represented by a set of instructions or parameters, which clarifies the specific requirements for temperature change control, such as the target temperature for each area and the time limit for reaching the target temperature.

[0031] More specifically, the temperature change control target is the core guide for the entire temperature control process. By combining the initial temperature conditions and the target temperature required for the test, the temperature adjustment measures that need to be taken can be accurately determined, providing a clear basis for selecting the optimal temperature control area and formulating temperature control strategies. Only by generating a reasonable temperature change control target can we ensure that the temperature inside the test chamber can be precisely adjusted according to the test requirements, thereby ensuring the smooth progress of the test and the accuracy of the test results.

[0032] Specifically, in step S4 of the embodiment provided by the present invention, the temperature condition parameters of the digital feedback model are deployed according to the initial temperature condition model of the temperature change control target. This means that the various temperature parameters in the initial temperature condition model (such as the current temperature value of each area, temperature change trend, etc.) are accurately input into the digital feedback model so that the digital feedback model can accurately represent the initial temperature condition model. For example, if the initial temperature condition model shows that the current temperature of a certain area of ​​the test chamber is 20°C and shows a slow temperature rise trend, the same temperature and change trend parameters are set in the corresponding area of ​​the digital feedback model.

[0033] More specifically, the digital feedback model is the foundation for subsequent analysis. Only by accurately deploying the initial temperature condition parameters can the digital feedback model truly reflect the current temperature state of the test chamber. Only then will the subsequent analysis and simulation based on the model have practical significance and provide a reliable basis for selecting temperature control zones and strategies.

[0034] More specifically, the temperature target model of the temperature change control target is projected onto each temperature control region in the digital feedback model to obtain several forms of temperature control target. That is, each temperature control region in the digital feedback model is assumed to be set as a region that meets the temperature target model, thus forming different temperature control target setting methods. For example, if the temperature target model requires a uniform temperature of 30°C in the test chamber, then the cases of adjusting each different temperature control region to 30°C are considered separately, and each consideration method is a form of temperature control target.

[0035] More specifically, by projecting the temperature target model onto different temperature control zones, various possible temperature control schemes can be comprehensively considered. Different temperature control zones have different locations and different relationships with temperature change control units, resulting in different levels of difficulty and effectiveness in achieving the temperature target. Obtaining multiple forms of temperature control targets helps to select the optimal scheme in the future.

[0036] More specifically, based on the temperature control correlation between each temperature control region and the temperature change control network in the digital feedback model, the temperature change execution mode of each temperature control target form is analyzed. According to the connection and interaction between different temperature control regions and temperature change control units (such as heating equipment, cooling equipment, etc.), the specific operations required to make the region reach the temperature target model are determined, such as which heating or cooling equipment to turn on, the operating power and duration of the equipment, etc., thereby obtaining the temperature control strategy of the temperature change control network corresponding to each temperature control target form.

[0037] More specifically, different temperature control zones and temperature control targets require different execution methods to achieve them. Analyzing the temperature control strategies for each type of temperature control target can clarify the specific operation methods of the temperature change control network under each scheme, providing specific operational content for subsequent evaluation and selection of the best strategy. Only by understanding the various possible temperature control strategies can we compare their advantages and disadvantages.

[0038] More specifically, the simulation parameters of the temperature control network of the digital feedback model are deployed according to each temperature control strategy. The digital feedback model is then used to digitally simulate the temperature change effect of each temperature control unit in the temperature control network, thereby obtaining temperature change records at specific locations inside the test chamber. For example, the power and running time of the heating equipment are set according to a certain temperature control strategy, and its influence on the temperature inside the test chamber is simulated in the digital feedback model, and the temperature changes at different locations at different times are recorded.

[0039] More specifically, based on temperature change records, the temperature control efficiency and stability of the temperature control area are analyzed to obtain the efficiency value parameters and stability parameters of the temperature control strategy. For example, the time required to reach the target temperature is calculated to evaluate the temperature control efficiency, and the temperature fluctuation range is analyzed to evaluate the temperature control stability. Then, the efficiency value parameters and stability value parameters of the temperature control strategy are weighted and fused according to the preset selection weights to obtain the execution value information of the temperature control strategy. Based on the execution value information, the optimal temperature control area and the optimal temperature control strategy are determined. The temperature control area corresponding to the temperature control strategy with the best execution value information (such as the highest comprehensive score) is selected as the optimal temperature control area, and this strategy is the optimal temperature control strategy.

[0040] More specifically, digital simulation can predict the effects of various temperature control strategies in advance without actually operating the temperature control network. Analyzing temperature control efficiency and stability can evaluate the advantages and disadvantages of strategies from different perspectives. The execution value information obtained by weighted fusion takes into account multiple factors, making the selection more scientific and reasonable. Finally, determining the optimal temperature control area and strategy can ensure that the temperature environment required for the experiment is achieved in the most effective way, thereby improving the efficiency and accuracy of the experiment.

[0041] Specifically, in step S5 of the embodiment provided by the present invention, based on the precise location information of the optimal temperature control area obtained from the previous analysis, the boundary and center position of the area are clearly defined inside the test chamber. The specific position can be determined by marking on the inner wall of the test chamber, using a positioning device, or using coordinate information in a digital model. Appropriate tools (such as robotic arms, manual operating devices, etc.) are used to finely adjust the position of the test object to ensure that the test object is accurately placed within the optimal temperature control area. During the placement process, care should be taken to avoid collisions between the test object and other components inside the test chamber, while ensuring that the test object is in a stable state.

[0042] More specifically, the optimal temperature control zone is determined through a series of analyses and simulations. It provides the test object with the temperature environment that best meets the test requirements. Placing the test object in this zone can minimize the impact of temperature deviation on the test results, ensuring that the test object is under uniform, stable, and expected temperature conditions, thereby improving the accuracy and reliability of the test. Placing the test object in the optimal temperature control zone allows the temperature change control network to act more directly and effectively on the test object, reducing energy waste and temperature adjustment time, improving temperature control efficiency, and reaching the required temperature environment for the test more quickly.

[0043] More specifically, based on the parameters determined in the optimal temperature control strategy (such as the on-time, off-time, operating power, and temperature regulation rate of the temperature control unit), the control system of the temperature control network is set accordingly. These parameters can be programmed, manually input, or loaded into the control system from the stored strategy file. After the settings are completed, the temperature control network is started, so that each temperature control unit starts to run according to the preset parameters. During the operation, the working status of the temperature control unit and the temperature change inside the test chamber are monitored in real time to ensure that the temperature control process proceeds as expected.

[0044] More specifically, based on temperature monitoring feedback, the operating parameters of the temperature control unit are dynamically adjusted. If the temperature changes too quickly or too slowly, or if the temperature deviates from the expected target value, the power, running time, and other parameters of the temperature control unit can be adjusted in a timely manner to ensure that the temperature in the optimal temperature control area can accurately change to the temperature environment required for the test.

[0045] More specifically, the optimal temperature control strategy is formulated based on the temperature distribution information of the test chamber, the time-varying characteristics of temperature, and the temperature environment information required for the experiment, and is targeted and optimal. Driving the temperature control network according to this strategy can ensure that the temperature in the optimal temperature control area within the test chamber accurately reaches and is maintained within the required temperature range for the experiment, meeting the precise temperature control requirements of the experiment. Precise temperature control is crucial for many experiments, and different experiments have strict requirements for temperature stability and accuracy. By controlling the temperature according to the optimal temperature control strategy, the interference of temperature fluctuations on the experimental process and results can be effectively avoided, ensuring the smooth progress of the experiment and the accuracy of the results, and improving the quality and repeatability of the experiment.

[0046] This invention provides an intelligent control method for a multi-stage temperature change test chamber, which has the following beneficial effects: This invention, by constructing a digital feedback model, can accurately grasp the temperature distribution and variation characteristics of the test chamber, generate control targets based on test requirements, and determine the optimal temperature control area and strategy through analysis. This enables precise temperature control, placing the test object in the optimal area and controlling the temperature according to the strategy, providing a stable and compliant temperature environment for the test, improving the accuracy and reliability of test results, reducing errors caused by temperature fluctuations, improving temperature control efficiency, and reducing energy consumption and costs.

[0047] Preferably, the steps of acquiring the setting information of the temperature change control network and temperature sensing network of the test chamber, in order to divide the internal space of the test chamber into regions and obtain the corresponding digital feedback model include: S11: Obtain the setting information of the temperature change control network and temperature sensing network of the test chamber, and set each temperature change control unit and each temperature sensing unit in the pre-constructed original digital model according to the setting information, so as to obtain the digital model of the temperature change control network and temperature sensing network. S12: Based on the digital model of the temperature control network and the temperature sensing network, analyze the action space of temperature control and temperature sensing in the original digital model, so as to mark the temperature action range of each temperature control unit and the sensing performance range of each temperature sensing unit on the original digital model. S13: The original digital model is subjected to superposition processing of sensing effects according to the sensing performance range of each temperature sensing unit to obtain the data fusion relationship between each temperature sensing unit. S14: The original digital model is superimposed with temperature change effects according to the temperature change range of each temperature change control unit to divide the original digital model into several temperature control regions, and the temperature control correlation between each temperature control region and the temperature change control network is obtained to form a digital feedback model.

[0048] Specifically, the setting information of the temperature control network and temperature sensing network is obtained from the design documents, equipment specifications, or control system of the test chamber. This information includes the number, model, installation location, and performance parameters of the temperature control units (such as heaters and coolers) and temperature sensing units (such as temperature sensors). In the pre-built original digital model (usually a three-dimensional model based on the geometry and physical characteristics of the test chamber), each temperature control unit and each temperature sensing unit is precisely set according to the obtained setting information. For example, the specific coordinate position of each temperature sensor is determined in the model, and corresponding power, control mode, and other parameters are assigned to each temperature control unit.

[0049] More specifically, by presenting the actual temperature control network and temperature sensing network in the form of a digital model, their operation can be simulated and analyzed in a virtual environment. This provides a foundation for subsequent action space analysis and area division, enabling research on temperature control and sensing inside the test chamber to be conducted on the digital model, thus improving the efficiency and accuracy of the research. By setting these units on the original digital model, a unified analysis platform is established, allowing relevant information on temperature control and temperature sensing to be integrated and processed in the same model, facilitating subsequent comprehensive analysis and decision-making.

[0050] More specifically, based on the digital model of the temperature control network and temperature sensing network, combined with physical principles and mathematical models, the temperature change range of each temperature control unit and the sensing performance range of each temperature sensing unit are theoretically calculated. For example, the heating range of the heater is calculated according to the heat conduction equation, and its sensing range is determined according to the sensitivity and measurement accuracy of the sensor. At the same time, the calculation results are verified and corrected through actual experimental measurements. The calculated and verified temperature change range and sensing performance range are marked on the original digital model. Different colors, lines or marks can be used to distinguish the range of action of different units, intuitively showing the influence area of ​​each unit in the internal space of the test chamber.

[0051] More specifically, marking the temperature change range and sensing performance range can clearly define the effective operating area of ​​each temperature change control unit and temperature sensing unit inside the test chamber. This helps to understand the distribution of temperature control and sensing inside the test chamber, providing a basis for subsequent area division and data fusion. By observing the operating range of each unit, the rationality of the equipment layout can be evaluated. If blind spots or overlaps in temperature control or sensing are found in certain areas, the installation position or parameters of the equipment can be adjusted in a timely manner to optimize the performance of the test chamber.

[0052] More specifically, based on the sensing performance range of each temperature sensing unit, the original digital model is superimposed to achieve the desired sensing effect. For example, when the sensing ranges of multiple temperature sensors overlap, the relationship between their measurement data in the overlapping area is analyzed. Through statistical analysis, machine learning, and other methods, the superimposed sensing data is processed to obtain the data fusion relationship between each temperature sensing unit. For example, it is possible to determine how to integrate the data from multiple sensors under different conditions to improve the accuracy and reliability of temperature measurement, and to establish data fusion algorithms and models.

[0053] More specifically, measurements from a single temperature sensor have errors or limitations. Data fusion can integrate information from multiple sensors, complementing and verifying each other, thereby improving the accuracy and reliability of temperature measurements. Data fusion relationships enable us to obtain more comprehensive and accurate temperature information from data from multiple sensors, providing a more reliable basis for subsequent temperature distribution analysis and temperature change control.

[0054] More specifically, based on the temperature change range of each temperature control unit, the original digital model is superimposed with the temperature change effects. The interaction between the effects of different temperature control units is analyzed to determine which regions are affected by the combined effects of which temperature control units. Based on the superposition results of the temperature change effects, the original digital model is divided into several temperature control regions. Each temperature control region has relatively consistent temperature control characteristics, that is, it is affected by the same or similar combinations of temperature control units. The correlation between each temperature control region and each temperature control unit in the temperature change control network is analyzed to clarify which temperature control units can adjust the temperature of each temperature control region, as well as the adjustment method and degree. These correlations are recorded to form part of the digital feedback model.

[0055] More specifically, by dividing the interior of the test chamber into multiple temperature control zones and determining the correlation between each zone and the temperature change control unit, precise temperature control of the test chamber can be achieved. Different temperature control strategies can be formulated for different temperature control zones to improve the efficiency and accuracy of temperature control. The temperature control correlation in the digital feedback model provides the basis for subsequent temperature feedback adjustment. By monitoring the temperature of each temperature control zone and adjusting the working state of the corresponding temperature change control unit according to the correlation, real-time and dynamic adjustment of the temperature inside the test chamber can be achieved to ensure that the temperature inside the test chamber always meets the test requirements.

[0056] Preferably, the step of collecting temperature data from the test chamber through a temperature sensing network and inputting it into the digital feedback model to analyze and obtain the temperature distribution information and time-varying temperature characteristics of the internal space of the test chamber includes: S21: Temperature data of the test chamber is collected by each temperature sensing unit of the temperature sensing network to obtain the temperature sensing data of each temperature sensing unit. S22: Substitute the temperature sensing data of each temperature sensing unit into the digital feedback model, and use the data fusion relationship between each temperature sensing unit in the digital feedback model to trace the temperature of each temperature sensing data, so as to mark the temperature prediction data of each specific location in the internal space of the digital feedback model. S23: Combine the temperature prediction data at specific locations to obtain temperature distribution information; S24: Record the temperature distribution information at each time point and perform feature analysis on the temperature changes at specific locations to obtain the time-varying characteristics of temperature at each specific location.

[0057] Specifically, based on the experimental requirements and the general laws of temperature change, a reasonable acquisition frequency is determined. For example, for experiments with relatively slow temperature changes, the acquisition frequency can be set to once per minute; while for experiments with rapid temperature changes, the acquisition frequency needs to be increased to once per second. Each temperature sensing unit (such as a temperature sensor) in the temperature sensing network measures the temperature at different locations in the test chamber in real time according to the set acquisition frequency. The measured temperature data is transmitted to the data acquisition system in the form of electrical or digital signals. The data acquisition system converts these signals into corresponding temperature values ​​and records them to form the temperature sensing data of each temperature sensing unit.

[0058] More specifically, temperature sensing data is the foundation for subsequent analysis of temperature distribution and time-varying characteristics. Only by comprehensively and accurately collecting temperature data from different locations within the test chamber can we gain a preliminary understanding of the temperature conditions inside the test chamber, providing reliable data support for subsequent in-depth analysis. Temperature sensing units at different locations can measure the temperature in different areas within the test chamber. Combining these data can more realistically reflect the actual temperature distribution inside the test chamber and avoid information bias caused by single-point measurements.

[0059] More specifically, the temperature sensing data collected from each temperature sensing unit is input into the digital feedback model. The digital feedback model is constructed based on the settings of the temperature change control network and the temperature sensing network. It includes the data fusion relationship between each temperature sensing unit. Using the data fusion relationship in the digital feedback model, temperature tracing is performed on each temperature sensing data. For example, if there is no direct temperature sensor measuring a certain location, but it is within the influence range of multiple sensors with measurement data, the temperature value at that location can be inferred by combining the measurement data of these sensors through data fusion algorithms (such as weighted average method, interpolation method, etc.). In the internal space of the digital feedback model, the inferred temperature data of each specific location is labeled. Three-dimensional visualization technology can be used to display the temperature of each location with different colors or values, intuitively presenting the temperature distribution.

[0060] More specifically, due to the limited number and distribution of temperature sensing units, there are some areas in the test chamber without direct measurement data. Through temperature tracing and data fusion, the temperature of these areas can be inferred using data from existing sensors, thereby obtaining more comprehensive temperature distribution information. The data fusion relationship takes into account the mutual influence and correlation between various temperature sensing units, which can optimize the measurement data, reduce the impact of measurement errors, improve the accuracy of temperature inference, and provide a more reliable basis for subsequent temperature distribution and time-varying characteristic analysis.

[0061] More specifically, the temperature prediction data at various locations in the digital feedback model can be collected and organized. This data can be stored in a database or table, classified and sorted according to location information, and the integrated temperature data can be analyzed and processed to generate temperature distribution information. This can be done by drawing isotherm maps, temperature cloud maps, etc., to visually display the temperature levels and distribution in different areas inside the test chamber. Alternatively, statistical parameters such as temperature gradient and temperature standard deviation can be calculated to quantitatively describe the characteristics of the temperature distribution.

[0062] More specifically, temperature distribution information can intuitively display the overall temperature situation inside the test chamber, helping researchers quickly understand the spatial variation of temperature, identify areas with high or low temperatures, and areas with large temperature gradients. This is of great significance for evaluating the performance of the test chamber and optimizing test conditions. Temperature distribution information is the basis for further analysis of temperature time-varying characteristics and the formulation of temperature control strategies. By understanding the current temperature distribution, temperature monitoring and adjustment can be carried out in a targeted manner in different areas to meet the requirements of the test.

[0063] More specifically, temperature distribution information at each time point is recorded according to a preset time interval. This allows the temperature distribution data at different times to be stored in a historical database for subsequent querying and analysis. Time series analysis can be performed on the recorded temperature data at specific locations to extract the characteristics of temperature changes, such as calculating the rate of temperature rise or fall, the periodicity of temperature changes, and the amplitude of temperature fluctuations. Statistical analysis methods, machine learning algorithms, or signal processing techniques can be used for feature extraction and analysis.

[0064] More specifically, the time-varying temperature characteristics reflect how the temperature inside the test chamber changes over time. By analyzing these characteristics, we can understand the trends, stability, and periodicity of temperature changes. This is of great guiding significance for predicting future temperature changes and adjusting temperature control strategies. In many experiments, temperature stability is a key factor in ensuring the accuracy and reliability of test results. By analyzing the time-varying temperature characteristics, we can promptly detect abnormal temperature changes and take corresponding measures to adjust them, ensuring that the temperature environment inside the test chamber always meets the test requirements.

[0065] Preferably, the step of obtaining the temperature environment information required for the current experiment, and generating a temperature change control target by combining the temperature distribution information, the temperature time-varying characteristics, and the temperature environment information includes: S31: Based on the temperature distribution information and the time-varying temperature characteristics at specific locations, analyze the overall temperature conditions of the internal space of the test chamber at the current moment to establish an initial temperature condition model. S32: Obtain the temperature environment information required for the current experiment, and analyze the required space and required temperature of the temperature environment information to establish a temperature target model corresponding to the temperature environment information; S33: Combine the temperature target model with the initial temperature condition model to generate a temperature change control target.

[0066] Specifically, the previously obtained temperature distribution information and temperature time-varying characteristic data at various locations are collected. This data is then cleaned and preprocessed to remove outliers and noise interference, ensuring the accuracy and reliability of the data. Based on the processed data, the overall temperature conditions inside the test chamber at the current moment are analyzed from multiple dimensions. For example, the average temperature, maximum temperature, and minimum temperature of different areas are calculated, the temperature gradient distribution is analyzed, and the temperature stability is evaluated.

[0067] More specifically, based on the analysis results, an appropriate mathematical model or data structure is selected to establish the initial temperature condition model. Matrices, vectors, or other multidimensional data structures can be used to represent the temperature state and change trend at different locations within the test chamber. For example, a three-dimensional matrix can be used to represent information such as the current temperature value, temperature change rate, and temperature fluctuation amplitude of each small area within the test chamber.

[0068] More specifically, the initial temperature condition model can comprehensively and accurately reflect the temperature status of the test chamber at the current moment, providing a basis for the subsequent development of temperature change control strategies. By analyzing the overall temperature conditions, the distribution and change characteristics of the temperature inside the test chamber can be clearly understood, and areas of temperature inhomogeneity or instability that may exist can be identified. As a benchmark for comparison with the temperature target model, this model helps to determine the direction and magnitude of the temperature adjustment that needs to be made. Only by clarifying the initial temperature state of the test chamber can a targeted temperature change control scheme be developed to achieve the temperature environment required for the test.

[0069] More specifically, obtain the temperature environment information required for the current test from the test design documents, test standards, or operators. This information includes the target temperature value, temperature range, and temperature change rate requirements for a specific area. Analyze the obtained temperature environment information in detail to determine the required space and required temperature. The required space refers to the specific area where the test requires specific temperature conditions. It can be the entire interior of the test chamber or a specific area. The required temperature specifies the temperature value or temperature range that these areas need to reach. Based on the results of the requirement analysis, construct a temperature target model corresponding to the temperature environment information. This can also be represented using mathematical models or data structures. For example, use a matrix with the same dimensions as the initial temperature condition model to represent the target temperature value and allowable temperature deviation range at each location within the required space.

[0070] More specifically, the temperature target model presents the temperature environment information required for the experiment in a specific model form, making the temperature change control target more explicit and quantifiable. By analyzing the required space and required temperature, the area to be controlled and the temperature conditions to be achieved can be accurately defined, providing a clear direction for the subsequent generation of temperature change control targets. This model is an important basis for formulating temperature change control strategies. Only by clearly knowing the temperature environment required for the experiment can appropriate temperature adjustment measures be determined based on the initial temperature condition model, and the optimal temperature control area and temperature control method be selected to achieve the successful conduct of the experiment.

[0071] More specifically, the temperature target model is compared point by point with the initial temperature condition model to calculate the temperature difference at each location, i.e., the amount of temperature adjustment required. At the same time, the time-varying characteristics of temperature are considered to analyze the trend and rate of temperature change, determine the time requirement for temperature adjustment, and integrate the temperature adjustment requirements of each location to form a unified temperature change control target. The temperature change control target can be represented by a set of parameters, including the target temperature to be achieved in each temperature control zone, the allowable temperature deviation range, the time limit for reaching the target temperature, and the required rate of temperature change.

[0072] More specifically, by combining the temperature target model and the initial temperature condition model, the direction (heating or cooling) and degree of temperature adjustment required at each location within the test chamber can be clearly defined, providing specific guidance for the operation of the temperature change control network. The temperature change control target is the core of the entire temperature control process, combining the temperature requirements of the test with the actual temperature state of the test chamber to ensure that the temperature change control network can operate according to precise requirements, thereby ensuring that the temperature inside the test chamber accurately reaches the environmental conditions required for the test, guaranteeing the smooth progress of the test and the accuracy of the test results.

[0073] Preferably, the steps of selecting the temperature control region and analyzing the temperature control method based on the digital feedback model to obtain the optimal temperature control region and the optimal temperature control strategy include: S41: Deploy temperature condition parameters for the digital feedback model based on the initial temperature condition model of the temperature change control target, so as to represent the initial temperature condition model through the digital feedback model. S42: Project the temperature target model of the temperature change control target onto each temperature control region in the digital feedback model to obtain several temperature control target forms; wherein, the temperature control target form is used to describe setting a specified temperature control region as the temperature target model; S43: Based on the temperature control correlation between each temperature control region in the digital feedback model and the temperature change control network, analyze the temperature change execution mode of each temperature control target form to obtain the temperature control strategy of the temperature change control network corresponding to each temperature control target form; S44: Based on the digital feedback model, perform digital simulation on each of the temperature control strategies to obtain the execution value information of each of the temperature control strategies, and determine the optimal temperature control area and the optimal temperature control strategy based on the execution value information.

[0074] Specifically, various temperature-related parameters, such as the current temperature value at each location, temperature change trend, and temperature gradient, are extracted from the initial temperature condition model of the temperature change control target. The extracted parameters are then accurately deployed to the corresponding locations and attributes in the digital feedback model. For example, if the initial temperature condition model indicates that the current temperature in a corner of the test chamber is 25℃, the temperature of the node corresponding to that corner in the digital feedback model is set to 25℃. If there is temperature change trend information, such as the temperature in a certain area rising at a rate of 0.5℃ / minute, this rate of change is also reflected in the digital feedback model.

[0075] More specifically, by deploying the parameters of the initial temperature condition model into the digital feedback model, the digital feedback model can accurately simulate the current actual temperature conditions of the test chamber. This allows for subsequent analysis and simulation based on real initial conditions in a virtual environment, improving the reliability and accuracy of the analysis results. It provides an accurate starting point for subsequent temperature control zone selection and strategy formulation, ensuring the consistency between the digital feedback model and the actual test chamber state. This ensures that all subsequent operations and analyses performed on the digital model are closely related to the actual situation, avoiding erroneous decisions caused by discrepancies between the model and reality.

[0076] More specifically, the pre-defined temperature control zones in the digital feedback model are clearly defined. These zones are derived from the previous analysis of the operating range of the temperature control unit. Each zone has relatively independent temperature control characteristics. The temperature target model of the temperature control objective is applied to each temperature control zone. That is, each temperature control zone is assumed to be set to achieve the temperature state specified by the temperature target model, thus obtaining various different temperature control target forms. For example, if the temperature target model requires a specific temperature range of 30-35℃, applying this range to each temperature control zone yields the corresponding temperature control target form for each zone.

[0077] More specifically, obtaining several temperature control target forms allows for a comprehensive consideration of the possibility of achieving the temperature target in different temperature control regions. Different temperature control regions have different locations and relationships with temperature control units, resulting in significant differences in the ease and effectiveness of achieving the temperature target. In this way, all possible temperature control schemes can be initially screened and evaluated, providing more options for selecting the best temperature control region and strategy, and increasing the possibility of finding the optimal solution. If only a few regions are considered, better temperature control schemes may be missed, while comprehensive temperature control target forms can more comprehensively explore various potential control methods.

[0078] More specifically, this study delves into the temperature control relationships between each temperature control zone and the temperature change control network in the digital feedback model. This includes understanding which temperature change control units (such as heaters and coolers) affect each temperature control zone, as well as the temperature regulation capabilities and methods (such as power and adjustment time) of these control units in that zone. For each type of temperature control target, based on the aforementioned temperature control relationships, the study analyzes the specific temperature change execution methods that the temperature change control network should adopt. For example, it determines which temperature change control units need to be activated, the operating power of each unit should be set, and the operating time, thereby obtaining the temperature control strategy corresponding to each type of temperature control target.

[0079] More specifically, clarifying the temperature control strategy corresponding to each temperature control target form can transform the abstract temperature target into specific temperature change control network operation instructions. Only by determining the detailed execution method can the temperature of each temperature control zone be adjusted by controlling the temperature change control unit in actual operation, thereby achieving the temperature target of the entire test chamber. By analyzing the temperature control strategy, the feasibility and effectiveness of each scheme can be preliminarily evaluated. For example, some strategies require excessively high power or excessively long time, which is not feasible in practical applications. By analyzing in advance, these unreasonable strategies can be eliminated, reducing the range of subsequent selections.

[0080] More specifically, for each temperature control strategy, corresponding simulation parameters are set for the temperature change control network in the digital feedback model. For example, parameters such as the power of the heater and the switching time of the cooler are set. The digital model is then started to simulate the temperature change control process. During the simulation, the temperature changes at specific locations inside the test chamber are recorded, including the temperature change curve over time, the time to reach the target temperature, and the temperature fluctuation. These data are then analyzed to calculate the efficiency value parameters (such as the time to reach the target temperature and energy consumption) and stability parameters (such as the temperature fluctuation amplitude) of each temperature control strategy.

[0081] More specifically, based on preset selection weights, the efficiency value parameter and stability value parameter of each temperature control strategy are weighted and fused to obtain the execution value information of each strategy. For example, if more emphasis is placed on the efficiency of temperature control, the weight of the efficiency value parameter can be set higher. By comparing the execution value information of each temperature control strategy, the temperature control region corresponding to the strategy with the best execution value information is selected as the optimal temperature control region, which is the optimal temperature control strategy.

[0082] More specifically, digital simulation allows for the prediction and evaluation of the effects of various temperature control strategies before their actual implementation. This avoids resource waste and experimental failures caused by using unreasonable strategies in actual operation, improving the efficiency and accuracy of temperature control. The execution value information comprehensively considers multiple factors such as temperature control efficiency and stability. Through weighted fusion, the importance of each factor can be flexibly adjusted according to actual needs. Therefore, selecting the strategy with the optimal execution value information can ensure a balance in multiple aspects, finding the best temperature control area and the best temperature control strategy for the current experimental requirements.

[0083] Preferably, the step of performing digital simulations on each of the temperature control strategies based on the digital feedback model to obtain the execution value information of each of the temperature control strategies includes: S51: Based on the temperature control strategy, the simulation parameters of the temperature change control network of the digital feedback model are deployed to drive the digital feedback model to digitally simulate the temperature change effect of each temperature change control unit of the temperature change control network, so as to obtain the temperature change record of each specific location in the internal space of the test chamber. S52: Based on the temperature change records, analyze the temperature control efficiency and temperature control stability of the temperature control area to obtain the efficiency value parameters and stability parameters of the temperature control strategy. S53: The efficiency value parameter and stability value parameter of the temperature control strategy are weighted and fused according to the preset selection weights to obtain the execution value information of the temperature control strategy.

[0084] Specifically, key simulation parameters are extracted from each temperature control strategy. These parameters typically include the start-up time, shutdown time, operating power, and adjustment frequency of the temperature control unit (such as heaters and coolers). For example, if a temperature control strategy requires a 500W heater to start 5 minutes after the start of the test and run continuously for 30 minutes, these specific time and power parameters are extracted and accurately deployed into the temperature control network of the digital feedback model. In the digital feedback model, each temperature control unit has corresponding attributes and control interfaces. By modifying these attributes and calling the control interfaces, the simulation operation of the temperature control unit is realized. The digital feedback model is started, allowing it to digitally simulate the temperature change effect of the temperature control unit according to the deployed simulation parameters. During the simulation, the model calculates the temperature changes at specific locations inside the test chamber based on physical laws (such as heat conduction and heat convection) and records these temperature data in real time, forming a temperature change record.

[0085] More specifically, by simulating on a digital feedback model, the effect of each temperature control strategy in practical applications can be predicted without actually operating the test chamber. This helps to identify potential problems in advance, avoid adopting unreasonable strategies in actual tests, reduce resource waste and test risks. Temperature change records are the basic data for subsequent analysis of temperature control efficiency and stability. Only by obtaining detailed temperature change information through accurate simulation can the performance of each temperature control strategy be objectively and accurately evaluated.

[0086] More specifically, based on temperature change records, the time required for the temperature-controlled area to reach the target temperature is calculated. For example, if the temperature target is to raise a certain area from 20°C to 30°C, the time from the start of the simulation to the temperature stabilizing at 30°C is recorded. At the same time, factors such as energy consumption can be considered to calculate the energy consumed by the temperature control unit during the process of reaching the target temperature. These data are used as efficiency value parameters to reflect the speed and energy consumption of the temperature control strategy in achieving the temperature target. The temperature fluctuation of the temperature-controlled area in the temperature change records is analyzed, and statistical indicators such as the maximum fluctuation amplitude and standard deviation of the temperature are calculated to evaluate whether the temperature can remain stable after reaching the target temperature. These indicators serve as stability parameters, reflecting the performance of the temperature control strategy in maintaining the target temperature.

[0087] More specifically, temperature control efficiency and stability are important indicators for evaluating the quality of temperature control strategies. By analyzing these two aspects and quantifying them into specific parameters, the performance differences of different temperature control strategies can be compared intuitively. For example, a strategy that can quickly reach the target temperature with small temperature fluctuations is obviously better. Efficiency and stability parameters provide specific decision-making basis for selecting the best temperature control strategy. In practical applications, different experiments have different emphases on temperature control efficiency and stability. By clarifying these two parameters, a comprehensive consideration and selection can be made according to actual needs.

[0088] More specifically, based on the specific needs and priorities of the experiment, the selection weights of efficiency value parameters and stability value parameters are pre-assigned. For example, for some experiments with high time requirements, the weight of efficiency value parameters can be set higher; while for some experiments with strict temperature stability requirements, the weight of stability value parameters is set higher. The efficiency value parameter and stability value parameter of each temperature control strategy are multiplied by their corresponding weights, and then the two weighted parameters are added together to obtain the execution value information of the temperature control strategy.

[0089] More specifically, different test scenarios have varying requirements for temperature control efficiency and stability. Simply comparing efficiency or stability parameters alone cannot comprehensively evaluate the merits of a temperature control strategy. By using a weighted fusion approach, these two important factors can be considered together to obtain execution value information that comprehensively reflects the performance of the temperature control strategy. This execution value information provides a unified standard for selecting the optimal temperature control strategy. By comparing the execution value information of different temperature control strategies, it is possible to directly determine which strategy is optimal in terms of overall performance, thus providing clear guidance for actual temperature control operations.

[0090] Preferably, the step of placing the test subject in the optimal temperature control zone and driving the temperature change control network to control the temperature according to the optimal temperature control strategy includes: S51: Adjust the placement of the test object to place it in the optimal temperature control zone; S52: Drive each temperature control unit of the temperature control network to perform temperature control according to the optimal temperature control strategy, so as to change the temperature of the optimal temperature control area to the temperature environment information required for the test.

[0091] Specifically, based on the previously determined location information of the optimal temperature control zone, the boundary and center position of the zone are clearly defined inside the test chamber. This can be determined by using the coordinate information recorded in the digital feedback model, or by pre-marking the inner wall of the test chamber.

[0092] More specifically, based on the size, shape, weight, and other characteristics of the test object, select appropriate tools and methods to adjust its placement. For small and lightweight test objects, a robotic arm can be used for precise operation. During the movement, operate slowly and steadily to avoid shaking or collision of the test object. Carefully move the test object to the optimal temperature control area and ensure that its position is accurate. This can be confirmed using positioning sensors or manual measurement to ensure that the test object is completely within the effective range of the optimal temperature control area.

[0093] More specifically, the optimal temperature control zone is determined through a series of analyses and simulations. It provides the temperature environment that best meets the test requirements. Placing the test object in this zone ensures that the temperature around the test object is uniform and stable, minimizing the impact of temperature deviations on the test results and ensuring that the test is conducted under ideal temperature conditions. This improves the accuracy and reliability of the test. When the test object is in the optimal temperature control zone, the temperature regulation network can act more directly and effectively on the test object. This shortens the time to reach the target temperature, reduces energy consumption, improves temperature control efficiency, and ensures that the temperature inside the test chamber can quickly and accurately meet the test requirements.

[0094] More specifically, based on the parameters determined in the optimal temperature control strategy, such as the start-up time, shutdown time, operating power, and temperature regulation rate of the temperature control unit, the control system of the temperature control network is configured in detail. Simultaneously, a stable communication connection is established between the temperature control network and the control system to ensure that control commands are accurately transmitted to each temperature control unit. After configuration, each temperature control unit in the temperature control network is started sequentially according to the requirements of the optimal temperature control strategy. For example, if the strategy requires starting the heater first to raise the temperature, the heater is started according to the set time and power. During startup, the operating status of each temperature control unit is closely monitored to ensure their normal operation.

[0095] More specifically, during the temperature control process, the temperature changes in the optimal temperature control zone are monitored in real time. Temperature data can be obtained through a temperature sensing network and compared with the temperature environment information required for the experiment. If the temperature deviation is found to exceed the allowable range, the operating parameters of the temperature control unit are dynamically adjusted in a timely manner according to the optimal temperature control strategy. For example, if the temperature rise rate is too slow, the power of the heater can be appropriately increased; if the temperature is close to the target value and there is an overshoot trend, the power is reduced or the heater is turned off in a timely manner.

[0096] More specifically, the optimal temperature control strategy is formulated based on the temperature distribution information of the test chamber, the time-varying characteristics of temperature, and the temperature environment information required for the test. It is targeted and optimal. Driving the temperature control network according to this strategy can ensure that the temperature in the optimal temperature control area accurately and stably reaches the temperature environment required for the test, meet the precise temperature control requirements of the test, and ensure the reliability of the test results. Precise temperature control is crucial for the success of many tests. Different tests have strict requirements for temperature stability and accuracy. Any temperature fluctuation may affect the test process and results. By monitoring and dynamically adjusting the operating parameters of the temperature control unit in real time, temperature deviations can be corrected in a timely manner, ensuring that the test can be carried out smoothly in a stable temperature environment and avoiding test failure or inaccurate data due to temperature problems.

[0097] Preferably, the test chamber is equipped with a mechanical moving mechanism, which is used to adjust the placement position of the test object.

[0098] Specifically, the mechanical movement structure can be a robotic arm, an automatic gripper, or a design with similar functions.

[0099] Reference Figure 2 As shown, in a second aspect, the present invention provides an intelligent control system for a multi-stage temperature change test chamber, used to implement the intelligent control method for a multi-stage temperature change test chamber as described in any one of the first aspects, comprising: The region division module is used to obtain the setting information of the temperature change control network and temperature sensing network of the test chamber, so as to divide the internal space of the test chamber into regions and obtain the corresponding digital feedback model. The temperature analysis module is used to collect temperature data of the test chamber through the temperature sensing network and input it into the digital feedback model to analyze the temperature distribution information and time-varying characteristics of the internal space of the test chamber. The target analysis module is used to obtain the temperature environment information required for the current experiment, and generate a temperature change control target by combining the temperature distribution information, the temperature time-varying characteristics, and the temperature environment information. The strategy analysis module is used to select the temperature control area and analyze the temperature control method based on the digital feedback model to obtain the optimal temperature control area and the optimal temperature control strategy. The temperature control module is used to place the test object in the optimal temperature control area and drive the temperature change control network to control the temperature according to the optimal temperature control strategy.

[0100] In this embodiment, the specific implementation of each module in the above system embodiment is described in the above method embodiment, and will not be repeated here.

[0101] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent control method for a multi-stage temperature change test chamber, characterized in that, include: The setting information of the temperature change control network and temperature sensing network of the test chamber is obtained to divide the internal space of the test chamber into regions and obtain the corresponding digital feedback model. Temperature data from the test chamber is collected by a temperature sensing network and fed into the digital feedback model to analyze the temperature distribution and time-varying characteristics of the internal space of the test chamber. Obtain the temperature environment information required for the current experiment, and generate a temperature change control target by combining the temperature distribution information, the temperature time-varying characteristics, and the temperature environment information; Based on the digital feedback model, the temperature control target is selected for temperature control region and temperature control method is analyzed to obtain the optimal temperature control region and optimal temperature control strategy. The test subject is placed in the optimal temperature control zone, and the temperature control network is driven to control the temperature according to the optimal temperature control strategy.

2. The intelligent control method for the multi-stage temperature change test chamber as described in claim 1, characterized in that, The steps for obtaining the setting information of the temperature control network and temperature sensing network of the test chamber, dividing the internal space of the test chamber into regions, and obtaining the corresponding digital feedback model include: The setting information of the temperature change control network and temperature sensing network of the test chamber is obtained, and the temperature change control unit and temperature sensing unit are set according to the setting information to obtain the digital model of the temperature change control network and temperature sensing network. Based on the digital models of the temperature control network and the temperature sensing network, the action space of temperature control and temperature sensing is analyzed on the original digital model to mark the temperature action range of each temperature control unit and the sensing performance range of each temperature sensing unit on the original digital model. The original digital model is superimposed based on the sensing performance range of each temperature sensing unit to obtain the data fusion relationship between the temperature sensing units, so as to initially establish a digital feedback model. The original digital model is superimposed with the temperature change effects of each temperature change control unit to divide the original digital model into several temperature control regions, and the temperature control correlation between each temperature control region and the temperature change control network is obtained to form a digital feedback model.

3. The intelligent control method for the multi-stage temperature change test chamber as described in claim 1, characterized in that, The steps of collecting temperature data from the test chamber through a temperature sensing network and inputting it into the digital feedback model to analyze the temperature distribution information and time-varying characteristics of the internal space of the test chamber include: Temperature data of the test chamber is collected by each temperature sensing unit of the temperature sensing network to obtain the temperature sensing data of each temperature sensing unit. The temperature sensing data of each temperature sensing unit is substituted into the digital feedback model. The temperature sensing data is traced through the data fusion relationship between each temperature sensing unit in the digital feedback model. The temperature prediction data of each specific location is marked in the internal space of the digital feedback model. By combining the temperature prediction data at specific locations, temperature distribution information is obtained; Temperature distribution information at each time point is recorded, and the characteristics of temperature changes at specific locations are analyzed to obtain the time-varying characteristics of temperature at each specific location.

4. The intelligent control method for the multi-stage temperature change test chamber as described in claim 1, characterized in that, The steps of obtaining the temperature environment information required for the current experiment, and generating a temperature change control target by combining the temperature distribution information, the time-varying temperature characteristics, and the temperature environment information include: Based on the temperature distribution information and the time-varying temperature characteristics at specific locations, the overall temperature conditions inside the test chamber at the current moment are analyzed to establish an initial temperature condition model. Obtain the temperature environment information required for the current experiment, and analyze the required space and required temperature of the temperature environment information to establish a temperature target model corresponding to the temperature environment information; The temperature target model is combined with the initial temperature condition model to generate a temperature change control target.

5. The intelligent control method for a multi-stage temperature change test chamber as described in claim 4, characterized in that, The steps for selecting the optimal temperature control region and analyzing the optimal temperature control method based on the digital feedback model to obtain the optimal temperature control region and optimal temperature control strategy include: Based on the initial temperature condition model of the temperature change control target, the temperature condition parameters of the digital feedback model are deployed so as to represent the initial temperature condition model through the digital feedback model. The temperature target model of the temperature change control target is projected onto each temperature control region in the digital feedback model to obtain several temperature control target forms; wherein, the temperature control target form is used to describe setting a specified temperature control region as the temperature target model; Based on the temperature control correlation between each temperature control region and the temperature change control network in the digital feedback model, the temperature change execution mode of each temperature control target form is analyzed to obtain the temperature control strategy of the temperature change control network corresponding to each temperature control target form. Based on the digital feedback model, each of the temperature control strategies is digitally simulated to obtain the execution value information of each temperature control strategy, and the optimal temperature control region and the optimal temperature control strategy are determined based on the execution value information.

6. The intelligent control method for a multi-stage temperature change test chamber as described in claim 5, characterized in that, The step of performing digital simulations on each of the temperature control strategies based on the digital feedback model to obtain the execution value information of each of the temperature control strategies includes: Based on the temperature control strategy, the simulation parameters of the temperature change control network of the digital feedback model are deployed to drive the digital feedback model to digitally simulate the temperature change effect of each temperature change control unit of the temperature change control network, so as to obtain the temperature change record of each specific location in the internal space of the test chamber. Based on the temperature change records, the temperature control efficiency and stability of the temperature control area are analyzed to obtain the efficiency value parameters and stability parameters of the temperature control strategy. The efficiency value parameter and stability value parameter of the temperature control strategy are weighted and fused according to the preset selection weights to obtain the execution value information of the temperature control strategy.

7. The intelligent control method for a multi-stage temperature change test chamber as described in claim 1, characterized in that, The steps of placing the test subject in the optimal temperature control zone and driving the temperature change control network to control the temperature according to the optimal temperature control strategy include: The placement of the test subject is adjusted to place it in the optimal temperature control zone; According to the optimal temperature control strategy, each temperature control unit of the temperature control network is driven to perform temperature control to change the temperature of the optimal temperature control area to the temperature environment information required for the test.

8. The intelligent control method for a multi-stage temperature change test chamber as described in claim 7, characterized in that, The test chamber is equipped with a mechanical moving mechanism, which is used to adjust the placement position of the test object.

9. An intelligent control system for a multi-stage temperature change test chamber, characterized in that, A method for implementing an intelligent control of a multi-stage temperature change test chamber as described in any one of claims 1-8 includes: The region division module is used to obtain the setting information of the temperature change control network and temperature sensing network of the test chamber, so as to divide the internal space of the test chamber into regions and obtain the corresponding digital feedback model. The temperature analysis module is used to collect temperature data of the test chamber through the temperature sensing network and input it into the digital feedback model to analyze the temperature distribution information and time-varying characteristics of the internal space of the test chamber. The target analysis module is used to obtain the temperature environment information required for the current experiment, and generate a temperature change control target by combining the temperature distribution information, the temperature time-varying characteristics, and the temperature environment information. The strategy analysis module is used to select the temperature control area and analyze the temperature control method based on the digital feedback model to obtain the optimal temperature control area and the optimal temperature control strategy. The temperature control module is used to place the test object in the optimal temperature control area and drive the temperature change control network to control the temperature according to the optimal temperature control strategy.

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