Intelligent automatic temperature adjusting method and system of temperature controller

Abstract

The invention discloses an intelligent automatic temperature adjustment method and system for a temperature controller, and relates to the technical field of temperature control, and the method comprises the steps: dividing a deviation value range in a historical period, testing the adjustment degree of the temperature controller under different deviation interval conditions, and precisely constructing a plurality of groups of electric power Dgz, Dgz, Dgz, Dgz, Dgz, Dgz, Dgz, Dgz, Dgz, Dgz and Dgz; the electric power data provides a reliable basis for screening optimal temperature control parameters, the accuracy of temperature control is effectively improved, and energy waste caused by excessive or untimely adjustment is reduced. The deep learning technology is utilized to predict the temperature change condition in the future time period, predictive response is carried out on the temperature controller based on the prediction result, the adjustment method based on the future temperature value can pre-judge the temperature change trend in advance and carry out optimization adjustment, and the adjustment efficiency is improved. Therefore, temperature out-of-control or energy waste caused by response lag in a traditional control method is avoided, and the corresponding demand satisfaction degree is matched according to the electric power Dgz value output by the temperature controller in the future time period.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control, and in particular to an intelligent automatic temperature adjustment method and system for a temperature controller. Background Art

[0002] Temperature controllers are core devices in many fields such as industrial automation and smart home. Their main function is to monitor and adjust the temperature of a specific space in real time to ensure that the equipment or environment is maintained within a suitable temperature range. With the development of Internet of Things technology and artificial intelligence technology, traditional temperature control methods are gradually moving towards intelligence. In this context, intelligent temperature controllers have become key components in many application scenarios such as industrial production, smart homes, and precision instruments. Especially in the field of intelligent automatic temperature control, improving the response speed and energy-saving effect of temperature control through accurate temperature prediction and adaptive adjustment methods has become the focus of industry attention.

[0003] Current temperature control systems usually rely on preset temperature thresholds and simple on-off control methods, and lack the ability to deeply learn and predict ambient temperature changes. This traditional control method often faces the following problems: First, the control response is delayed or overreacts, resulting in large temperature fluctuations; second, due to the lack of effective use of historical data, the system cannot make predictive adjustments based on long-term temperature change trends.

[0004] These existing defects arise from the working principle of traditional temperature control systems, the core of which mainly relies on setting fixed temperature thresholds or simple feedback mechanisms, and fails to comprehensively consider multi-dimensional information such as the environment, user needs and historical data. Due to the lack of learning and prediction of temperature fluctuation patterns, temperature control systems often cannot respond accurately at the right time, resulting in over-adjustment or slow response. Specifically, over-adjustment may lead to energy waste because the control system frequently starts and stops equipment, increasing energy consumption, while slow response may cause the temperature to deviate from the set target, affecting comfort and production efficiency. In addition, the lack of personalized and adaptive control strategies makes it difficult for the system to cope with diverse environmental needs, thereby reducing user satisfaction and may have an adverse impact on temperature-sensitive equipment or processes. Ultimately, the performance bottleneck of the temperature control system not only increases operating costs, but also affects the stability and efficiency of the overall system. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an intelligent automatic temperature adjustment method and system for a temperature controller, which solves the problems in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a temperature controller intelligent automatic temperature adjustment method, comprising the following steps:

[0007] S1. Extract relevant temperature data of historical periods from the cloud platform in advance based on the Internet of Things technology, and generate a historical temperature set after data preprocessing;

[0008] S2. According to the historical temperature set and in combination with user needs, the deviation value range in the historical period is obtained, and the deviation value range in the historical period is divided into several groups of deviation intervals, and the different adjustment degrees output by the temperature controller when performing temperature control under the corresponding deviation interval conditions are tested, so as to construct multiple groups of electric power Dgz under the corresponding deviation interval conditions, and based on the multiple groups of electric power Dgz, the optimal parameter group in the temperature controller is screened out;

[0009] S3. Based on the content of S2, a parameter set is constructed, and the temperature change in the future period is predicted by using deep learning technology and the historical temperature set to generate a future temperature value Wwz. According to the future temperature value Wwz and in combination with the parameter set, a predictive response is made to the automatic temperature control of the temperature controller to obtain the electric power Dgz output by the temperature controller in the future period;

[0010] S4. According to the value of the electric power Dgz output by the temperature controller in the future period, the corresponding demand satisfaction is matched.

[0011] Preferably, the specific steps of S1 include:

[0012] S11. Obtain temperature changes in historical periods from the cloud platform through Internet of Things technology and database query to obtain relevant temperature data of the historical periods, wherein the relevant temperature data of the historical periods include actual monitored temperature values ​​Wd and deviation values ​​Wc at different spatial locations in each historical period;

[0013] S12. Preprocess the extracted relevant temperature data of the historical period to remove outliers, fill in missing values ​​and perform data standardization. Then, aggregate the relevant data after data preprocessing into a historical temperature set, and combine the dimensionless processing technology to remove the dimensional influence of the relevant data in the historical temperature set.

[0014] Preferably, the specific steps of S2 include:

[0015] S21, obtaining user requirements in advance, the user requirements including the target temperature Mwz and the target electric power range Scope, and combining the historical temperature set, sorting the multiple groups of deviation values ​​Wc in the historical temperature set by size to construct a sequence group, and evenly dividing the temperature difference in the sequence group into multiple groups of deviation intervals;

[0016] S211, wherein the order of the size is: sorting the groups of temperature differences Wc in order from small to large according to the numerical value;

[0017] S212. The deviation value Wc is obtained as follows: Wc=|Lwz-Mwz|; the deviation value range within the historical period refers to the range of several groups of deviation values ​​Wc within the historical temperature set.

[0018] Preferably, the specific step S2 also includes:

[0019] S22. According to several groups of deviation intervals, different adjustment degrees output by the temperature controller when performing temperature control under the corresponding deviation interval conditions are tested to construct multiple groups of electric power Dgz under the corresponding deviation interval conditions. Specifically, the electric power Dgz is obtained by the following formula:

[0020]

[0021] Wherein, Dgz(t) represents the output of the controller at the historical period t under the corresponding deviation interval conditions, that is, the electric power; Wc(t) represents the deviation value at the historical period t under the corresponding deviation interval conditions; Wc(τ) represents the deviation value at the historical time point τ under the corresponding deviation interval conditions; α1 represents the proportional gain; α2 represents the integral gain; α3 represents the differential gain;

[0022] S23, wherein a parameter group is formed by proportional gain α1, integral gain α2 and differential gain α3, several parameter groups are randomly generated, and the formula for obtaining electric power Dgz in S22 is used, and several parameter groups are substituted into the formula to obtain multiple groups of electric power Dgz under corresponding deviation range conditions.

[0023] Preferably, the specific step S2 also includes:

[0024] S24, according to the multiple groups of electric power Dgz under the corresponding deviation interval conditions obtained in S23, monitoring the influence of each group of parameter groups on temperature control under the corresponding deviation interval conditions to obtain relevant debugging data, wherein the relevant debugging data includes the average overshoot of the corresponding group of parameter groups under the corresponding deviation interval conditions. Under the corresponding deviation range conditions, test the average steady-state error of the corresponding group of parameter groups And under the corresponding deviation interval conditions, test the average response time of the corresponding group parameter group

[0025] S25. Based on the relevant debugging data, the performance of the temperature controller is measured, and after dimensionless processing, the adaptability Syd of each parameter group to temperature control under the corresponding deviation range is calculated and obtained, which is specifically obtained according to the following formula:

[0026]

[0027] In the formula, It indicates the average deviation value of the corresponding group of parameter groups tested under the corresponding deviation interval conditions; It indicates the average overshoot of the corresponding group of parameters under the corresponding deviation interval conditions; It indicates the average response time of the corresponding group parameter group under the corresponding deviation interval conditions; It represents the average steady-state error of the corresponding group of parameters under the corresponding deviation range conditions; w1, w2, w3 and w4 all represent weight values; among them, the specific values ​​of w1, w2, w3 and w4 are set by the user according to the situation.

[0028] Preferably, the specific step S2 also includes:

[0029] S26. According to the conditions of the corresponding deviation interval, the fitness Syd of each parameter group to the temperature control is tested, the fitness Syd with the maximum value is extracted, and the parameter group corresponding to the fitness Syd with the maximum value is used as the optimal parameter group in the temperature controller.

[0030] Preferably, the specific steps of S3 include:

[0031] S31, according to the content in S26, respectively obtain the optimal parameter groups in the temperature controller under the corresponding deviation interval conditions, and after summarizing, construct a parameter set;

[0032] S32. Use deep learning technology, combined with historical temperature sets and temperature heat transfer, to predict temperature changes in future time periods to construct the rate of change of temperature relative to time. The specific method is to obtain it according to the following formula:

[0033]

[0034] Where Wd represents the actual monitored temperature value, and Wd(x, y, z) represents the temperature value at the spatial position (x, y, z) and time t; It expresses the rate of change of temperature with respect to time; and They represent the second-order partial derivatives of temperature with respect to the spatial coordinates x, y, and z, respectively; β represents the thermal diffusivity;

[0035] S33. Convert the formula involved in S32 into a discrete form by numerical method to obtain the future temperature value Wwz.

[0036] Preferably, the specific step S3 also includes:

[0037] S34. Determine the future deviation value WPc based on the future temperature value Wwz. Lock the deviation interval corresponding to the future deviation value Wpc based on the numerical value of the future deviation value Wpc, and record the deviation interval corresponding to the future deviation value Wpc as the future deviation interval. Perform a predictive response to the automatic temperature control of the temperature controller based on the optimal parameter group in the temperature controller in the future deviation interval. Substitute the optimal parameter group in the temperature controller in the future deviation interval into the formula involved in S22 to obtain the electric power Dgz output by the temperature controller in the future time period.

[0038] Preferably, the specific steps of S4 include:

[0039] S41. According to the value of the electric power Dgz output by the temperature controller in the future period, and in combination with the target electric power range Scope, the corresponding demand satisfaction is matched. The specific contents are as follows:

[0040] If the electric power Dgz value output by the temperature controller in the future period falls within the target electric power range Scope, it is judged that the electric power Dgz output by the temperature controller in the future period is in a qualified state, and the temperature control operation will be performed according to the current optimal parameter group;

[0041] If the value of the electric power Dgz output by the temperature controller in the future time period does not fall within the target electric power range Scope, it is determined that the electric power Dgz output by the temperature controller in the future time period is not in a qualified state. At this time, the S23 content will be re-executed to regenerate the parameter set and again determine whether the value of the electric power Dgz output by the temperature controller in the future time period falls within the target electric power range Scope.

[0042] An intelligent automatic temperature control system for a temperature controller, comprising a data module, a test module, a prediction module and an automatic temperature control module;

[0043] The data module is used to extract relevant temperature data of historical periods from the cloud platform in advance according to the Internet of Things technology, and generate a historical temperature set after data preprocessing;

[0044] The test module is used to obtain the deviation value range in the historical period according to the historical temperature set and in combination with user needs, by dividing the deviation value range in the historical period into several groups of deviation intervals, and testing the different adjustment degrees output by the temperature controller when performing temperature control under the corresponding deviation interval conditions, so as to construct multiple groups of electric power Dgz under the corresponding deviation interval conditions, and based on the multiple groups of electric power Dgz, screen out the best parameter group in the temperature controller;

[0045] The prediction module is used to construct a parameter set, and use deep learning technology and historical temperature sets to predict the temperature change in the future period to generate a future temperature value Wwz. According to the future temperature value Wwz and in combination with the parameter set, a predictive response is made to the automatic temperature control of the temperature controller to obtain the electric power Dgz output by the temperature controller in the future period;

[0046] The automatic temperature control module is used to match the corresponding demand satisfaction according to the value of the electric power Dgz output by the temperature controller in the future period.

[0047] The present invention provides a temperature controller intelligent automatic temperature adjustment method and system, which has the following beneficial effects:

[0048] (1) By extracting and preprocessing the temperature data of the historical period from the cloud platform, generating a historical temperature set, and optimizing the temperature controller in a targeted manner based on user needs and environmental characteristics, it is possible to provide personalized adjustment strategies for different usage scenarios. This method solves the problem that the traditional temperature control system relies on fixed thresholds and simple feedback mechanisms, making the temperature control more intelligent and adaptable. This method divides the deviation value range within the historical period and tests the adjustment degree of the thermostat under different deviation interval conditions to accurately construct multiple groups of electric power Dgz. These electric power data provide a reliable basis for screening the best temperature control parameter group, effectively improving the accuracy of temperature control and reducing energy waste caused by excessive or untimely adjustment. Deep learning technology is used to predict temperature changes in the future period, and the temperature controller is predicted to respond based on the prediction results. This adjustment method based on future temperature values ​​can predict the temperature change trend in advance and make optimization adjustments, thereby avoiding temperature out of control or energy waste caused by response lag in traditional control methods. By matching the corresponding demand satisfaction according to the value of the electric power Dgz output by the temperature controller in the future period, the efficiency of the temperature control system and the user's satisfaction are ensured. This automatic temperature adjustment method based on precise adjustment and prediction not only meets the user's comfort needs, but also effectively reduces energy consumption and improves the overall operation efficiency of the system. In general, this method can improve the adaptability, responsiveness and energy efficiency of the system while ensuring precise temperature control, and has important practical application value.

[0049] (2) By conducting detailed tests on several groups of deviation intervals, the electric power Dgz output by the temperature controller under different deviation interval conditions can be accurately constructed. This process establishes a refined adjustment model of the temperature controller through a formula, in which the proportional gain, integral gain and differential gain are used as key parameters, and the power output by the controller can be adjusted according to the actual deviation situation. The specific formula calculation method makes the temperature control more refined and personalized, ensuring that the controller can provide appropriate electric power adjustment under different deviation conditions. By randomly generating several groups of parameter groups (including proportional gain, integral gain and differential gain) and substituting them into the formula to calculate the electric power Dgz, the present invention can obtain multiple different electric power combinations, which can be used to meet different temperature control requirements and adapt to changing environmental conditions. Based on the output of these multiple groups of electric power, the temperature controller can flexibly adjust its own adjustment strategy to achieve a relatively good temperature control effect and improve the adaptability and flexibility of the system.

[0050] (3) By testing the influence of each parameter group on temperature control under different deviation interval conditions, detailed debugging data can be collected, including key indicators such as average overshoot, average steady-state error and average response time. In this way, the specific influence of each parameter group on temperature control can be fully understood, ensuring that the temperature control system can show stable and accurate temperature control performance in practical applications. This method effectively overcomes the traditional system that only relies on a single feedback adjustment or an insufficiently refined debugging process, and improves the intelligent adjustment capability of the system. Based on the collected relevant debugging data, the present invention calculates the fitness Syd of each parameter group through dimensionless processing. The fitness Syd combines performance indicators of multiple dimensions, such as average deviation, average overshoot, average response time and average steady-state error, and comprehensively evaluates the performance of the temperature controller from multiple angles. Through this comprehensive evaluation method, the influence of each parameter combination on system performance can be more objectively reflected, and a relatively better control strategy can be selected.

[0051] (4) Based on the predicted future deviation value, the system can select a relatively better control parameter group within the expected future deviation range, and calculate the corresponding electric power Dgz through the parameter group. This predictive electric power regulation method based on the future deviation range and the best parameter group can achieve more accurate temperature control. By grasping the temperature change trend in advance, the temperature controller can reduce unnecessary energy waste, reduce energy consumption while meeting temperature requirements, and thus achieve efficient energy saving effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A schematic diagram of a flow chart of an intelligent automatic temperature adjustment method of a temperature controller according to the present invention;

[0053] Figure 2 The present invention is a block diagram of an intelligent automatic temperature control system of a temperature controller. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Example 1

[0056] See also Figure 1 The present invention provides an intelligent automatic temperature adjustment method of a temperature controller, comprising the following steps:

[0057] S1. Extract relevant temperature data of historical periods from the cloud platform in advance based on the Internet of Things technology, and generate a historical temperature set after data preprocessing;

[0058] S2. According to the historical temperature set and in combination with user needs, the deviation value range in the historical period is obtained, and the deviation value range in the historical period is divided into several groups of deviation intervals, and the different adjustment degrees output by the temperature controller when performing temperature control under the corresponding deviation interval conditions are tested, so as to construct multiple groups of electric power Dgz under the corresponding deviation interval conditions, and based on the multiple groups of electric power Dgz, the optimal parameter group in the temperature controller is screened out;

[0059] S3. Based on the content of S2, a parameter set is constructed, and the temperature change in the future period is predicted by using deep learning technology and the historical temperature set to generate a future temperature value Wwz. According to the future temperature value Wwz and in combination with the parameter set, a predictive response is made to the automatic temperature control of the temperature controller to obtain the electric power Dgz output by the temperature controller in the future period;

[0060] S4. According to the value of the electric power Dgz output by the temperature controller in the future period, the corresponding demand satisfaction is matched.

[0061] In this embodiment, by extracting the temperature data of the historical period based on the Internet of Things technology and performing data preprocessing, a historical temperature set is generated, so that the system can fully understand the historical laws of temperature changes. Combined with user needs, the historical temperature deviation values ​​are accurately divided and analyzed, and the controller parameters are optimized using the adjustment power Dgz in the corresponding deviation interval, so that the temperature controller can achieve more accurate temperature control in actual applications, reduce unnecessary power consumption, and thus significantly improve the energy-saving effect of the system. With the help of deep learning technology and deep mining of historical temperature data, the present invention can predict the temperature change trend in the future period and generate the future temperature value Wwz. Based on this prediction result, the temperature controller can optimize the adjustment parameters in advance to achieve a predictable temperature adjustment response. This ability significantly improves the response speed of the temperature controller to complex environmental changes and avoids the temperature adjustment deviation caused by hysteresis in the traditional temperature control method. An accurate mapping relationship is established between the temperature control electric power output and the demand satisfaction, so that the temperature controller can flexibly adjust the output power according to different user needs to meet personalized temperature control requirements. At the same time, by matching the electric power Dgz output by the temperature controller in the future period with the user's satisfaction with demand, a refined evaluation and optimization of the temperature control results is achieved, which improves the user's overall satisfaction with the temperature control effect and further enhances the practicality of the system and user experience.

[0062] Example 2

[0063] Please refer to Figure 1 , specifically: S1 specific steps include:

[0064] S11. Through the Internet of Things technology and using the database query method, the temperature changes in the historical period are obtained from the cloud platform to obtain the relevant temperature data of the historical period, where the relevant temperature data of the historical period includes the actual monitored temperature value Wd and the deviation value Wc at different spatial positions in each historical time period; the cloud platform is connected to the Internet of Things device (such as MQTT protocol, HTTP API, etc.) to pull data and extract the temperature data of a specific time period (such as the past week, month, etc.).

[0065] S12. Preprocess the extracted relevant temperature data of the historical period to remove outliers, fill in missing values ​​and perform data standardization, then aggregate the relevant data after data preprocessing into a historical temperature set, and combine the dimensionless processing technology to remove the dimension (unit) influence of the relevant data in the historical temperature set.

[0066] The specific steps of S2 include:

[0067] S21, obtaining user requirements in advance, the user requirements including the target temperature Mwz and the target electric power range Scope, and combining the historical temperature set, sorting the multiple groups of deviation values ​​Wc in the historical temperature set by size to construct a sequence group, and evenly dividing the temperature difference in the sequence group into multiple groups of deviation intervals;

[0068] S211, wherein the order of the size is: sorting the groups of temperature differences Wc in order from small to large according to the numerical value;

[0069] S212. The deviation value Wc is obtained as follows: Wc=|Lwz-Mwz|; the deviation value range within the historical period refers to the range of several groups of deviation values ​​Wc within the historical temperature set.

[0070] In this embodiment, through the Internet of Things technology, combined with the cloud platform and database query method, the actual monitored temperature values ​​and deviation values ​​Wc at different spatial positions in the historical period can be obtained. This data collection method can ensure that a large amount of real and accurate temperature data is obtained, providing a solid data foundation for subsequent temperature control. At the same time, through the efficient connection of Internet of Things devices (such as MQTT protocol, HTTP API, etc.), real-time pulling of temperature data can be achieved, avoiding time delays and human errors that may occur in traditional manual data collection processes. In the data preprocessing stage, the present invention ensures the high quality and availability of historical temperature set data by removing outliers, filling missing values, and data standardization. Further combined with dimensionless processing technology, the influence of dimensions on data analysis and processing is removed, ensuring the consistency and accuracy of different data sources. This processing method can effectively solve the control errors caused by data quality problems and improve the reliability and accuracy of temperature control. By obtaining user needs in advance and combining historical temperature data, the present invention can build a flexible temperature control strategy according to different target temperatures Mwz and target power range Scope. On this basis, the deviation values ​​Wc in the historical temperature concentration are sorted by size, and the temperature difference is evenly divided into several deviation intervals to ensure that the temperature regulation can be accurately adjusted for different temperature fluctuations. The implementation of this step can customize the temperature control strategy according to the specific needs of the user, and improve the user experience and system operation efficiency. By sorting the deviation values ​​Wc by size and dividing the deviation intervals, the present invention can identify the regulation rules of different temperature intervals, so as to more accurately construct a temperature control strategy that adapts to different environments and needs. This method is of great significance in solving the temperature fluctuation problem in different usage scenarios, and can significantly improve the response speed and adaptability of the temperature control system, avoid the situation of over-adjustment or untimely adjustment of temperature, and ensure that the temperature runs stably within the set range. In summary, this method not only improves the overall performance of the system through accurate data collection, intelligent data preprocessing, flexible user demand adaptation and accurate temperature adjustment strategy, but also significantly improves the efficiency and stability of the temperature control process, and has broad application prospects and practical value.

[0071] Example 3

[0072] Please refer to Figure 1 Specifically: S2 includes the following specific steps:

[0073] S22. According to several groups of deviation intervals, different adjustment degrees output by the temperature controller when performing temperature control under the corresponding deviation interval conditions are tested to construct multiple groups of electric power Dgz under the corresponding deviation interval conditions. Specifically, the electric power Dgz is obtained by the following formula:

[0074]

[0075] Wherein, Dgz(t) represents the output of the controller at the historical period t under the corresponding deviation interval, that is, the electric power; Wc(t) represents the deviation value at the historical period t under the corresponding deviation interval; Wc(τ) represents the deviation value at the historical time point τ under the corresponding deviation interval; α1 represents the proportional gain, which is the gain coefficient directly related to the deviation in the controller. The role of the proportional term is to adjust the control amount based on the current deviation. A large proportional gain will make the system respond faster, but may cause overshoot or oscillation; a smaller proportional gain will make the system respond slower. α1*Wc(t) represents the proportional term; α2 represents the integral gain, which is a gain coefficient proportional to the integral of the deviation. The integral term takes into account the cumulative effect of the error and helps eliminate the static error in the system. If the system has a continuous deviation, the integral term will gradually increase the output of the controller, thereby eliminating the steady-state error. The integral action may sometimes cause the system to overshoot or oscillate (due to excessive accumulation of errors). Indicates the accumulation of the deviation value Wc from time 0 to time t; α3 represents the differential gain, which is a gain coefficient proportional to the rate of change of the error (i.e., the derivative of the error). The differential term predicts the trend of the error change and is used to reduce the overshoot and oscillation of the system. The differential term helps to reflect the trend of the error change in advance, especially for suppressing the drastic change and oscillation of the error. However, it may be sensitive to noise because the differential term is very sensitive to the rapid change of the error; represents the differential term, that is, the rate of change of the deviation with respect to time; τ represents the historical time point;

[0076] S23, wherein a parameter group is formed by proportional gain α1, integral gain α2 and differential gain α3, several parameter groups are randomly generated, and the formula for obtaining electric power Dgz in S22 is used, and several parameter groups are substituted into the formula to obtain multiple groups of electric power Dgz under corresponding deviation range conditions.

[0077] In this embodiment, the method uses the PID control strategy to accurately adjust the output of the controller by testing the electric power Dgz output by the temperature controller under different deviation interval conditions. The formula takes into account the deviation value, proportional gain, integral gain and differential gain to ensure that the output of the electric power can be adjusted in real time according to the current deviation and historical error. By constructing multiple groups of electric power Dgz, the present invention can flexibly adjust the power output of the controller under complex environmental conditions to achieve accurate temperature control effects. This precise control strategy can effectively reduce the unstable factors caused by temperature fluctuations and improve the stability and accuracy of the system. The present invention dynamically adjusts the system through a parameter group composed of proportional gain, integral gain and differential gain. In a specific implementation, the system adjusts the gain coefficient according to the current deviation and its change trend, thereby responding to temperature changes. The proportional gain helps to quickly respond to temperature deviations, the integral gain can eliminate long-term static errors, and the differential gain helps to predict temperature change trends and avoid over-adjustment or oscillation. This adaptive gain adjustment mechanism ensures that the system can maintain relatively good regulation performance in different deviation intervals, thereby improving the adaptability of the temperature control system, especially for environments with large temperature fluctuations. By randomly generating multiple groups of parameter groups and substituting them into the PID control formula, the impact of different gain configurations on the output power of the temperature control system can be comprehensively evaluated. The randomly generated parameter groups provide the system with a rich selection space and further optimize the configuration of the controller parameters. This method enables the system to quickly screen out relatively optimal parameter groups under different control requirements and deviation ranges, thereby achieving the best performance of the temperature control system. Through this optimization strategy, the temperature controller can provide personalized and flexible adjustment solutions in complex environments. The PID control strategy can not only effectively respond to temperature deviations, but also reduce long-term errors and predict system fluctuations through integral and differential terms. The application of this method enhances the robustness of the temperature controller in non-ideal environments, especially in the face of sudden temperature fluctuations or system noise. It can effectively suppress over-adjustment through differential gain to avoid temperature runaway or oscillation. Therefore, during long-term operation, the system can stably maintain within the set temperature range, reducing energy waste and equipment damage. In summary, the present invention, by introducing PID control strategy and multi-group parameter group optimization method, not only improves the response speed and adjustment accuracy of the temperature controller, but also enhances the adaptability, stability and robustness of the system, and has the ability to intelligently and efficiently adjust the temperature under changing environmental conditions, which brings significant technical advantages and practical benefits to the application of temperature control systems.

[0078] Example 4

[0079] Please refer to Figure 1 Specifically: S2 includes the following specific steps:

[0080] S24, according to the multiple groups of electric power Dgz under the corresponding deviation interval conditions obtained in S23, monitoring the influence of each group of parameter groups on temperature control under the corresponding deviation interval conditions to obtain relevant debugging data, wherein the relevant debugging data includes the average overshoot of the corresponding group of parameter groups under the corresponding deviation interval conditions. Under the corresponding deviation range conditions, test the average steady-state error of the corresponding group of parameter groups And under the corresponding deviation interval conditions, test the average response time of the corresponding group parameter group

[0081] S25. Based on the relevant debugging data, the performance of the temperature controller is measured, and after dimensionless processing, the adaptability Syd of each parameter group to temperature control under the corresponding deviation range is calculated and obtained, which is specifically obtained according to the following formula:

[0082]

[0083] In the formula, It indicates the average deviation value of the corresponding group of parameter groups tested under the corresponding deviation interval conditions; It indicates the average overshoot of the corresponding group of parameters under the corresponding deviation interval conditions; It indicates the average response time of the corresponding group parameter group under the corresponding deviation interval conditions; It represents the average steady-state error of the corresponding group of parameters under the corresponding deviation range conditions; w1, w2, w3 and w4 all represent weight values; among them, 0<w1<1, 0<w2<1, 0<w3<1, 0<w4<1, and the specific values ​​of w1, w2, w3 and w4 are set by the user according to the situation.

[0084] Among them, overshoot: the percentage by which the maximum temperature in the control process exceeds the target temperature; response time: the time required for the system to start heating / cooling and approach the target temperature; steady-state error: the final error in the control process, defined as the difference between the final system temperature and the target temperature.

[0085] The specific steps of S2 also include:

[0086] S26. According to the conditions of the corresponding deviation interval, the fitness Syd of each parameter group to the temperature control is tested, the fitness Syd with the maximum value is extracted, and the parameter group corresponding to the fitness Syd with the maximum value is used as the optimal parameter group in the temperature controller.

[0087] In this embodiment, the method obtains multi-dimensional debugging data by testing different parameter groups under corresponding deviation interval conditions, including key performance indicators such as average overshoot, average steady-state error and average response time. Through comprehensive monitoring of these data, the adjustment performance of the temperature controller can be evaluated from multiple angles. This multi-dimensional optimization method enables the temperature controller to achieve relatively good performance under different usage environments, avoiding the imbalance and inadaptability that may be caused by single indicator optimization. Based on the obtained debugging data, the present invention introduces a calculation method for fitness Syd, combines dimensionless processing technology, and comprehensively evaluates the performance of each group of parameters through a formula. Fitness Syd takes into account multiple key factors such as deviation, overshoot, steady-state error and response time in temperature control, and combines weight values ​​for weighted summation to ensure that the evaluation result is more in line with actual control requirements. This multi-dimensional weighted evaluation method enables the temperature controller to achieve more accurate adjustment and avoids problems such as temperature fluctuations and response lag. By evaluating the fitness Syd of each parameter group, the optimized best parameter group enables the temperature controller to respond quickly and accurately when facing different deviation intervals, thereby improving the stability and efficiency of temperature control. By selecting the best parameter group, the present invention ensures the efficient adaptability of the temperature controller in complex environments. Under different temperature fluctuation ranges and user demand conditions, the controller can automatically adjust and quickly enter a stable state, avoiding problems such as overshoot, overshoot or slow adjustment. This optimized temperature control system has stronger robustness and can effectively cope with the challenges of complex environments, such as industrial scenarios with drastic temperature changes or changing demands in smart home environments.

[0088] Example 5

[0089] Please refer to Figure 1 , specifically: S3 specific steps include:

[0090] S31, according to the content in S26, respectively obtain the optimal parameter groups in the temperature controller under the corresponding deviation interval conditions, and after summarizing, construct a parameter set;

[0091] S32. Use deep learning technology, combined with historical temperature sets and temperature heat transfer, to predict temperature changes in future time periods to construct the rate of change of temperature relative to time. The specific method is to obtain it according to the following formula:

[0092]

[0093] Where Wd represents the actual monitored temperature value, and Wd(x, y, z) represents the temperature value at the spatial position (x, y, z) and time t; Represents the rate of change of temperature with respect to time (i.e., the time derivative of temperature); and They represent the second-order partial derivatives of temperature with respect to the spatial coordinates x, y, and z, respectively, reflecting the distribution and change of temperature in different directions in space; β represents thermal diffusivity, which is a physical property of the material and is defined as: Where k is thermal conductivity, ρ is the density of the material, C is the specific heat capacity, and thermal diffusivity describes how fast heat spreads in an object. This formula is used to describe how temperature changes over time in three-dimensional space in order to optimize energy efficiency and reduce system energy consumption.

[0094] S33. The formula involved in S32 is converted into a discrete form by a numerical method, and then solved by a computer to obtain the future temperature value Wwz.

[0095] Among them, common numerical methods include finite difference method (FDM), finite element method (FEM) and finite volume method (FVM). The finite difference method (FDM) approximates the solution of the above formula by discretizing space and time. In the heat conduction equation, the derivatives in time and space can be approximated by differences. Among them, common time discretization methods include explicit methods and implicit methods.

[0096] S34. Determine the future deviation value WPc based on the future temperature value Wwz. Lock the deviation interval corresponding to the future deviation value Wpc based on the numerical value of the future deviation value Wpc, and record the deviation interval corresponding to the future deviation value Wpc as the future deviation interval. Perform a predictive response to the automatic temperature control of the temperature controller based on the optimal parameter group in the temperature controller in the future deviation interval. Substitute the optimal parameter group in the temperature controller in the future deviation interval into the formula involved in S22 to obtain the electric power Dgz output by the temperature controller in the future time period.

[0097] Among them, the future deviation value WPc = |Wwz-Mwz|;

[0098] In this embodiment, by combining historical temperature data, spatial distribution information and heat transfer laws, deep learning technology and numerical solutions to heat transfer equations can accurately predict temperature changes in future time periods. This process not only takes into account temperature changes over time, but also reflects temperature changes in space through the partial derivatives of temperature in spatial coordinates, thereby improving the accuracy of prediction. By numerical methods (such as finite difference methods), complex partial differential equations are converted into discrete forms that can be solved by computers, achieving efficient and accurate calculation of future temperatures. After predicting future temperature values ​​through steps S32 and S33, the future deviation value WPc can be determined based on the prediction results, and the corresponding future deviation interval can be locked. The future deviation interval is matched with the optimal parameter group in the historical temperature controller to provide a predictive response strategy for the temperature control system. This method can dynamically adjust the temperature control strategy according to future temperature trends, ensuring that the temperature controller can adjust the output power in a timely and effective manner, avoiding the situation of delayed response or inadaptability to future temperature changes that may occur in traditional methods. Intelligent temperature control predictive response, by substituting the optimal parameter group of the future deviation interval into the electric power calculation formula, the temperature controller can respond to temperature changes in advance and accurately control the electric power output. This predictive response not only improves the accuracy of temperature control, but also significantly improves the response speed of the system. Compared with the traditional adjustment method based on the current temperature deviation, the present invention adjusts the temperature more intelligently and efficiently through the prediction and optimization of the future deviation interval, thereby effectively avoiding problems such as overshoot, overshoot and system oscillation. Since the prediction of the future deviation interval enables the temperature controller to adopt a relatively optimal electric power output strategy within a predetermined time, the temperature control system avoids over-adjustment or slow response, reducing energy waste. Through the accurate prediction of future temperature changes, the time and power of the heating / cooling process can be more accurately controlled to ensure efficient use of energy and maintain stable operation of the system.

[0099] Example 6

[0100] Please refer to Figure 1 Specifically: S41, according to the value of the electric power Dgz output by the temperature controller in the future period, and combined with the target electric power range Scope, the corresponding demand satisfaction is matched, and the specific contents are as follows:

[0101] If the electric power Dgz value output by the temperature controller in the future period falls within the target electric power range Scope, it is judged that the electric power Dgz output by the temperature controller in the future period is in a qualified (satisfactory) state, and the temperature control operation will be performed according to the current optimal parameter group;

[0102] If the value of the electric power Dgz output by the temperature controller in the future time period does not fall within the target electric power range Scope, it is determined that the electric power Dgz output by the temperature controller in the future time period is not in a qualified (unsatisfactory) state. At this time, the S23 content will be re-executed to regenerate the parameter set and again determine whether the value of the electric power Dgz output by the temperature controller in the future time period falls within the target electric power range Scope.

[0103] In this embodiment, by comparing the output electric power Dgz of the temperature controller with the target electric power range Scope, the present invention can determine in real time whether the output of the control system meets the preset requirements. If the electric power Dgz falls into the target range, the system considers that the temperature control is in a satisfactory state and can continue to operate stably according to the current optimal parameter group. This precise electric power control mechanism ensures that the temperature control system avoids energy waste and over-adjustment while meeting user needs, thereby improving the stability and accuracy of the system operation. The adaptive adjustment ability of the system is optimized. If the electric power Dgz fails to fall into the target electric power range Scope in the future period, the system will re-execute step S23 according to the feedback, regenerate the parameter set and optimize the temperature control strategy. This mechanism enables the temperature control system to adjust in time when encountering abnormalities or changes in demand, ensuring the adaptability of the system. Through this dynamic adjustment mechanism, the system can flexibly respond to environmental changes or external interference, ensuring that a more suitable temperature control solution can be provided under any circumstances. Improve energy efficiency and energy saving effects. By accurately matching the electric power Dgz with the target electric power range Scope, the present invention avoids energy waste caused by excessive or low electric power. When the electric power Dgz value does not meet the target range, the system will automatically re-optimize the control parameters to ensure the effective use of energy. This energy-saving effect not only helps to reduce energy consumption, but also reduces the system operating costs, and realizes efficient and green operation of the temperature control system. In summary, the present invention not only improves the accuracy, energy-saving effect and adaptive adjustment ability of temperature control through precise electric power control and automatic adjustment mechanism, but also greatly improves the intelligence level of the system and user satisfaction, providing an efficient, reliable, green and intelligent solution for the temperature control system.

[0104] Example 7

[0105] Please refer to Figure 2 ,Specifically: A temperature controller intelligent automatic temperature adjustment system includes a data module, a test module, a foresight module and an automatic temperature adjustment module;

[0106] The data module is used to extract relevant temperature data of historical periods from the cloud platform in advance according to the Internet of Things technology, and generate a historical temperature set after data preprocessing;

[0107] The test module is used to obtain the deviation value range in the historical period according to the historical temperature set and in combination with user needs, by dividing the deviation value range in the historical period into several groups of deviation intervals, and testing the different adjustment degrees output by the temperature controller when performing temperature control under the corresponding deviation interval conditions, so as to construct multiple groups of electric power Dgz under the corresponding deviation interval conditions, and based on the multiple groups of electric power Dgz, screen out the best parameter group in the temperature controller;

[0108] The prediction module is used to construct a parameter set, and use deep learning technology and historical temperature sets to predict the temperature change in the future period to generate a future temperature value Wwz. According to the future temperature value Wwz and in combination with the parameter set, a predictive response is made to the automatic temperature control of the temperature controller to obtain the electric power Dgz output by the temperature controller in the future period;

[0109] The automatic temperature control module is used to match the corresponding demand satisfaction according to the value of the electric power Dgz output by the temperature controller in the future period.

[0110] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent automatic temperature adjustment method for a temperature controller, characterized in that: The following steps are included: S1. Extract relevant temperature data of historical periods from the cloud platform in advance based on the Internet of Things technology, and generate a historical temperature set after data preprocessing; S2. According to the historical temperature set and in combination with user needs, the deviation value range in the historical period is obtained, and the deviation value range in the historical period is divided into several groups of deviation intervals, and the different adjustment degrees output by the temperature controller when performing temperature control under the corresponding deviation interval conditions are tested, so as to construct multiple groups of electric power Dgz under the corresponding deviation interval conditions, and based on the multiple groups of electric power Dgz, the optimal parameter group in the temperature controller is screened out; S3. Based on the content of S2, a parameter set is constructed, and the temperature change in the future period is predicted by using deep learning technology and the historical temperature set to generate a future temperature value Wwz. According to the future temperature value Wwz and in combination with the parameter set, a predictive response is made to the automatic temperature control of the temperature controller to obtain the electric power Dgz output by the temperature controller in the future period; S4. According to the value of the electric power Dgz output by the temperature controller in the future period, the corresponding demand satisfaction is matched.

2. The intelligent automatic temperature adjustment method of a temperature controller according to claim 1, characterized in that: The specific steps of S1 include: S11. Obtain temperature changes in historical periods from the cloud platform through Internet of Things technology and database query to obtain relevant temperature data of the historical periods, wherein the relevant temperature data of the historical periods include actual monitored temperature values ​​Wd and deviation values ​​Wc at different spatial locations in each historical period; S12. Preprocess the extracted relevant temperature data of the historical period to remove outliers, fill in missing values ​​and perform data standardization. Then, aggregate the relevant data after data preprocessing into a historical temperature set, and combine the dimensionless processing technology to remove the dimensional influence of the relevant data in the historical temperature set.

3. The intelligent automatic temperature adjustment method of a temperature controller according to claim 2, characterized in that: The specific steps of S2 include: S21, obtaining user requirements in advance, the user requirements including the target temperature Mwz and the target electric power range Scope, and combining the historical temperature set, sorting the multiple groups of deviation values ​​Wc in the historical temperature set by size to construct a sequence group, and evenly dividing the temperature difference in the sequence group into multiple groups of deviation intervals; S211, wherein the order of the size is: sorting the groups of temperature differences Wc in order from small to large according to the numerical value; S212. The deviation value Wc is obtained as follows: Wc=|Lwz-Mwz|; the deviation value range within the historical period refers to the range of several groups of deviation values ​​Wc within the historical temperature set.

4. The intelligent automatic temperature adjustment method of a temperature controller according to claim 3, characterized in that: The specific steps of S2 also include: S22. According to several groups of deviation intervals, different adjustment degrees output by the temperature controller when performing temperature control under the corresponding deviation interval conditions are tested to construct multiple groups of electric power Dgz under the corresponding deviation interval conditions. Specifically, the electric power Dgz is obtained by the following formula: Wherein, Dgz(t) represents the output of the controller at the historical period t under the corresponding deviation interval conditions, that is, the electric power; Wc(t) represents the deviation value at the historical period t under the corresponding deviation interval conditions; Wc(τ) represents the deviation value at the historical time point τ under the corresponding deviation interval conditions; α1 represents the proportional gain; α2 represents the integral gain; α3 represents the differential gain; S23, wherein a parameter group is formed by proportional gain α1, integral gain α2 and differential gain α3, several parameter groups are randomly generated, and the formula for obtaining electric power Dgz in S22 is used, and several parameter groups are substituted into the formula to obtain multiple groups of electric power Dgz under corresponding deviation range conditions.

5. The intelligent automatic temperature adjustment method of a temperature controller according to claim 4, characterized in that: The specific steps of S2 also include: S24, according to the multiple groups of electric power Dgz under the corresponding deviation interval conditions obtained in S23, monitoring the influence of each group of parameter groups on temperature control under the corresponding deviation interval conditions to obtain relevant debugging data, wherein the relevant debugging data includes the average overshoot of the corresponding group of parameter groups under the corresponding deviation interval conditions. Under the corresponding deviation range conditions, test the average steady-state error of the corresponding group of parameter groups And under the corresponding deviation interval conditions, test the average response time of the corresponding group parameter group S25. Based on the relevant debugging data, the performance of the temperature controller is measured, and after dimensionless processing, the adaptability Syd of each parameter group to temperature control under the corresponding deviation range is calculated and obtained, which is specifically obtained according to the following formula: In the formula, It indicates the average deviation value of the corresponding group of parameter groups tested under the corresponding deviation interval conditions; It indicates the average overshoot of the corresponding group of parameters under the corresponding deviation interval conditions; It indicates the average response time of the corresponding group parameter group under the corresponding deviation interval conditions; It represents the average steady-state error of the corresponding group of parameters under the corresponding deviation interval conditions; w1, w2, w3 and w4 all represent weight values; The specific values ​​of w1, w2, w3 and w4 are set by the user according to the actual situation.

6. The intelligent automatic temperature adjustment method of a temperature controller according to claim 5, characterized in that: The specific steps of S2 also include: S26. According to the conditions of the corresponding deviation interval, the fitness Syd of each parameter group to the temperature control is tested, the fitness Syd with the maximum value is extracted, and the parameter group corresponding to the fitness Syd with the maximum value is used as the optimal parameter group in the temperature controller.

7. The intelligent automatic temperature adjustment method of a temperature controller according to claim 6, characterized in that: The specific steps of S3 include: S31, according to the content in S26, respectively obtain the optimal parameter groups in the temperature controller under the corresponding deviation interval conditions, and after summarizing, construct a parameter set; S32. Use deep learning technology, combined with historical temperature sets and temperature heat transfer, to predict temperature changes in future time periods to construct the rate of change of temperature relative to time. The specific method is to obtain it according to the following formula: Where Wd represents the actual monitored temperature value, and Wd(x, y, z) represents the temperature value at the spatial position (x, y, z) and time t; It represents the rate of change of temperature with respect to time; and They represent the second-order partial derivatives of temperature with respect to the spatial coordinates x, y, and z, respectively; β represents the thermal diffusivity; S33. Convert the formula involved in S32 into a discrete form by numerical method to obtain the future temperature value Wwz.

8. The intelligent automatic temperature adjustment method of a temperature controller according to claim 7, characterized in that: The specific steps of S3 also include: S34. Determine the future deviation value WPc based on the future temperature value Wwz. Lock the deviation interval corresponding to the future deviation value Wpc based on the numerical value of the future deviation value Wpc, and record the deviation interval corresponding to the future deviation value Wpc as the future deviation interval. Perform a predictive response to the automatic temperature control of the temperature controller based on the optimal parameter group in the temperature controller in the future deviation interval. Substitute the optimal parameter group in the temperature controller in the future deviation interval into the formula involved in S22 to obtain the electric power Dgz output by the temperature controller in the future time period.

9. The intelligent automatic temperature adjustment method of a temperature controller according to claim 8, characterized in that: The specific steps of S4 include: S41. According to the value of the electric power Dgz output by the temperature controller in the future period, and in combination with the target electric power range Scope, the corresponding demand satisfaction is matched. The specific contents are as follows: If the electric power Dgz value output by the temperature controller in the future period falls within the target electric power range Scope, it is judged that the electric power Dgz output by the temperature controller in the future period is in a qualified state, and the temperature control operation will be performed according to the current optimal parameter group; If the value of the electric power Dgz output by the temperature controller in the future time period does not fall within the target electric power range Scope, it is determined that the electric power Dgz output by the temperature controller in the future time period is not in a qualified state. At this time, the S23 content will be re-executed to regenerate the parameter set and again determine whether the value of the electric power Dgz output by the temperature controller in the future time period falls within the target electric power range Scope.

10. An intelligent automatic temperature adjustment system of a temperature controller, used to implement an intelligent automatic temperature adjustment method of a temperature controller as claimed in any one of claims 1 to 9, characterized in that: Including data module, test module, prediction module and automatic temperature control module; The data module is used to extract relevant temperature data of historical periods from the cloud platform in advance according to the Internet of Things technology, and generate a historical temperature set after data preprocessing; The test module is used to obtain the deviation value range in the historical period according to the historical temperature set and in combination with user needs, by dividing the deviation value range in the historical period into several groups of deviation intervals, and testing the different adjustment degrees output by the temperature controller when performing temperature control under the corresponding deviation interval conditions, so as to construct multiple groups of electric power Dgz under the corresponding deviation interval conditions, and based on the multiple groups of electric power Dgz, screen out the best parameter group in the temperature controller; The prediction module is used to construct a parameter set, and use deep learning technology and historical temperature sets to predict the temperature change in the future period to generate a future temperature value Wwz. According to the future temperature value Wwz and in combination with the parameter set, a predictive response is made to the automatic temperature control of the temperature controller to obtain the electric power Dgz output by the temperature controller in the future period; The automatic temperature control module is used to match the corresponding demand satisfaction according to the value of the electric power Dgz output by the temperature controller in the future period.

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