Intelligent temperature control method and system for ceramic electric heating element

Abstract

The invention discloses an intelligent temperature control method and system for a ceramic electric heating element, and relates to the technical field of heating control, and the method comprises the steps: loading a multi-dimensional preset temperature control scene index; according to the multi-dimensional preset temperature control scene indexes, temperature control scene parameters of the target ceramic electric heating element are collected, and a target temperature control operation scene is obtained; performing temperature control data registration mining on the target temperature control operation scene based on a predetermined temperature control registration constraint, and constructing an element temperature control registration space; performing temperature control parameter optimization according to a temperature control optimization algorithm to obtain a target temperature control scheme; executing control of the target ceramic electric heating element, and performing real-time monitoring to obtain element control feedback monitoring data and real-time heating scene data; and based on the target temperature control scheme, performing temperature control compensation adjustment on the target ceramic electric heating element according to the element control feedback monitoring data and the real-time heating scene data. Therefore, the technical effects of accurate temperature control and self-adaptive control of the ceramic electric heating element are realized.

Description

Technical Field

[0001] The present invention relates to the field of heating control technology, and in particular to an intelligent temperature control method and system for ceramic electric heating elements. Background Art

[0002] With the advancement of science and technology and the development of industry, ceramic electric heating elements are increasingly used in various fields, especially in heating, drying, heat preservation, etc. Traditional temperature control methods usually use simple temperature sensors and controllers to perform simple on-off control of the temperature, which cannot meet the needs of precise temperature control and energy saving. Existing ceramic electric heating elements have technical problems in temperature control, such as large temperature fluctuations and low control accuracy. Summary of the invention

[0003] The present invention provides an intelligent temperature control method and system for ceramic electric heating elements, so as to solve the technical problems of large temperature fluctuation and low control precision in the prior art, and achieve the technical effects of precise temperature control and adaptive control of ceramic electric heating elements.

[0004] In a first aspect, the present invention provides an intelligent temperature control method for a ceramic electric heating element, wherein the method comprises: Loading multi-dimensional preset temperature control scene indicators, wherein the multi-dimensional preset temperature control scene indicators include heating objects, heating requirements and heating environment; According to the multi-dimensional preset temperature control scene indicators, the temperature control scene parameters of the target ceramic electric heating element are collected to obtain the target temperature control operation scene; Performing temperature control data registration mining on the target temperature control operation scene based on predetermined temperature control registration constraints to construct a component temperature control registration space; Based on the component temperature control registration space and the target temperature control operation scenario, optimizing the temperature control parameters of the target ceramic electric heating element according to the temperature control optimization algorithm, and obtaining a target temperature control solution that meets the predetermined temperature control optimization constraints; Based on the target temperature control operation scenario, the target ceramic electric heating element is controlled according to the target temperature control scheme, and the target ceramic electric heating element is monitored in real time through an Internet of Things monitoring module to obtain element control feedback monitoring data and real-time heating scenario data; Based on the target temperature control scheme, the temperature control compensation adjustment is performed on the target ceramic electric heating element according to the element control feedback monitoring data and the real-time heating scene data.

[0005] In this method, multi-dimensional preset temperature control scenarios and corresponding temperature control solutions are obtained through big data, and the configuration mining of temperature control data is performed based on the predetermined temperature control registration constraints, so as to obtain the basic range and requirements of temperature control parameters. Then, combined with the temperature control optimization algorithm, the intelligent analysis and control of the target temperature control operation scenario is realized, and the efficiency and accuracy of the temperature control operation are improved. In addition, based on the temperature control compensation adjustment method, the feedback adjustment and automatic control of the temperature control operation are further realized, which improves the efficiency and quality of the temperature control operation, reduces energy consumption and cost, and also improves the safety and reliability of the temperature control operation.

[0006] In a feasible implementation, the target temperature control operation scene is subjected to temperature control data registration mining based on a predetermined temperature control registration constraint to construct a component temperature control registration space, including: Based on big data, the temperature control record data of the target ceramic electric heating element is collected to obtain a set of element temperature control operation records, wherein the set of element temperature control operation records includes multiple groups of temperature control operation records corresponding to the target ceramic electric heating element, and each group of temperature control operation records includes historical temperature control operation scenes and historical temperature control plans; Based on big data, the temperature control record data of similar ceramic electric heating elements of the target ceramic electric heating element are collected to obtain a temperature control operation record set of similar elements; Performing data cleaning based on the component temperature control operation record set and the temperature control operation record set of the same type of components to obtain a temperature control operation record data set; Based on the predetermined temperature control registration constraint, performing temperature control scheme registration screening on the temperature control operation record data set according to the target temperature control operation scene, and generating a scene-registered temperature control scheme set; Calibrate the temperature control parameter trigger interval based on the scene registration temperature control solution set to obtain a multi-level temperature control parameter trigger interval; The multi-level temperature control parameter trigger interval is traversed to perform multiple random value acquisitions to obtain multiple component temperature control registration schemes, and the multiple component temperature control registration schemes are added to the component temperature control registration space.

[0007] The method in the above implementation expands the collection scope of component temperature control operation record sets and improves the diversity of recorded data through information collection and data acquisition based on big data, thereby facilitating the diversity of subsequent temperature control operation record sets of similar components, ensuring the comprehensive accuracy of the multi-level temperature control parameter trigger intervals that are screened and set according to the target temperature control operation scenario. This is conducive to ensuring the comprehensive coverage of the component temperature control registration space.

[0008] In a feasible implementation, based on the predetermined temperature control registration constraint, the temperature control scheme registration screening is performed on the temperature control operation record data set according to the target temperature control operation scene to generate a scene registration temperature control scheme set, including: Traversing the temperature control operation record data set, extracting a first group of temperature control operation records, wherein the first group of temperature control operation records includes a first historical temperature control operation scene and a first historical temperature control solution; Performing similarity identification on the first historical temperature control operation scene and the target temperature control operation scene to obtain a first temperature control scene registration coefficient; Determining whether the first temperature control scene registration coefficient satisfies the predetermined temperature control registration constraint; If the first temperature control scene registration coefficient satisfies the predetermined temperature control registration constraint, adding the first historical temperature control solution to the scene registration temperature control solution set; If the first temperature control scene registration coefficient does not satisfy the predetermined temperature control registration constraint, the first historical temperature control solution is eliminated.

[0009] Among them, the first historical temperature control operation scene and the target temperature control operation scene are similarly identified to obtain the first temperature control scene registration coefficient, which is used to measure the similarity between the two scenes. If the first temperature control scene registration coefficient meets the predetermined temperature control registration constraint, it means that the historical solution is suitable for the current target scene. If it does not meet the predetermined temperature control registration constraint, the historical solution is eliminated because it is not similar enough to the current target scene. In this way, a historical solution similar to the current target temperature control operation scene is found to improve the efficiency and accuracy of the temperature control solution.

[0010] In a feasible implementation, based on the component temperature control registration space and the target temperature control operation scenario, the temperature control parameters of the target ceramic electric heating element are optimized according to the temperature control optimization algorithm to obtain a target temperature control solution that meets the predetermined temperature control optimization constraints, including: Extracting a first component temperature control registration scheme according to the component temperature control registration space; Based on the target temperature control operation scenario, performing a temperature control quality analysis on the first component temperature control registration scheme according to a pre-built temperature control quality prediction and evaluation model to obtain a first temperature control quality prediction and evaluation coefficient; Extracting a second component temperature control registration scheme according to the component temperature control registration space; Based on the target temperature control operation scenario, performing a temperature control quality analysis on the second component temperature control registration scheme according to the temperature control quality prediction and evaluation model to obtain a second temperature control quality prediction and evaluation coefficient; Perform maximum value screening based on the first temperature control quality prediction evaluation coefficient and the second temperature control quality prediction evaluation coefficient to determine the current optimal temperature control quality coefficient; Based on the first component temperature control registration scheme and the second component temperature control registration scheme, matching the current optimal temperature control registration scheme corresponding to the current optimal temperature control quality coefficient; Based on the current optimal temperature control quality coefficient and the current optimal temperature control alignment scheme, the component temperature control alignment space is continuously iterated and optimized in combination with the temperature control quality prediction and evaluation model until the predetermined temperature control optimization constraint is met, thereby generating the target temperature control scheme.

[0011] That is to say, firstly, based on the target temperature control operation scenario, a preliminary screening is performed in the component temperature control registration space to obtain the optimal temperature control registration scheme, which is the temperature control scheme that best adapts to the current temperature control operation scenario in the component temperature control registration space. Then, based on this scheme, the output of the temperature control quality prediction and evaluation model is used as the objective function to iteratively optimize the current optimal temperature control registration scheme, thereby achieving the maximum adaptability of the temperature control scheme to the scenario. This ensures the temperature control effect.

[0012] In a feasible implementation, based on the target temperature control operation scenario, the temperature control quality analysis of the first component temperature control registration scheme is performed according to a pre-built temperature control quality prediction and evaluation model to obtain a first temperature control quality prediction and evaluation coefficient, including: Loading basic parameters of the target ceramic electric heating element to obtain target element information; The temperature control quality prediction and evaluation model includes a temperature control prediction and evaluation sub-model and a temperature control quality calculation sub-model; Input the target temperature control operation scenario, the first component temperature control registration scheme and the target component information into the temperature control prediction and evaluation sub-model to obtain a first temperature control prediction and evaluation result, wherein the first temperature control prediction and evaluation result includes a first temperature control stability prediction and evaluation coefficient, a first temperature control safety prediction and evaluation coefficient, a first temperature control operation efficiency prediction and evaluation coefficient, a first temperature control energy consumption prediction and evaluation coefficient and a first temperature control component failure probability prediction and evaluation coefficient; Normalizing the first temperature control prediction evaluation result to generate a first temperature control standard prediction result, wherein the first temperature control standard prediction result includes a first temperature control stability standard prediction coefficient, a first temperature control safety standard prediction coefficient, a first temperature control operation efficiency standard prediction coefficient, a first temperature control energy consumption standard prediction coefficient, and a first temperature control element failure probability standard prediction coefficient; The first temperature control standard prediction result is input into the temperature control quality calculation sub-model to generate the first temperature control quality prediction evaluation coefficient, wherein the temperature control quality calculation sub-model includes a temperature control quality calculation function, and the temperature control quality calculation function is: ; Among them, TCQ represents the temperature control quality prediction evaluation coefficient, TCS represents the temperature control stability standard prediction coefficient, TCA represents the temperature control safety standard prediction coefficient, TCE represents the temperature control operation efficiency standard prediction coefficient, TCF represents the temperature control energy consumption standard prediction coefficient, TCU represents the temperature control component failure probability standard prediction coefficient, α, β, γ, δ, ε are the preset temperature control quality calculation weight constraints, and the sum of α, β, γ, δ, ε is 1.

[0013] In a feasible implementation, based on the target temperature control scheme, the target ceramic electric heating element is subjected to temperature control compensation adjustment according to the element control feedback monitoring data and the real-time heating scene data, including: Perform parameter mapping comparison between the component control feedback monitoring data and the target temperature control solution to generate temperature control offset characteristic data; Performing a temperature control offset level evaluation based on the temperature control offset characteristic data to generate a temperature control offset level parameter; If the temperature control offset level parameter is greater than / equal to the temperature control offset level threshold, a temperature control offset compensation instruction is generated; Based on the temperature control offset compensation instruction, a temperature control compensation analysis is performed according to the temperature control offset characteristic data to generate temperature control offset compensation control information, and temperature control adjustment of the target ceramic electric heating element is performed according to the temperature control offset compensation control information.

[0014] In a feasible implementation, based on the target temperature control scheme, the target ceramic electric heating element is subjected to temperature control compensation adjustment according to the element control feedback monitoring data and the real-time heating scene data, including: Perform scene mapping comparison on the target temperature control operation scene and the real-time heating scene data to generate a temperature control scene mapping comparison result; Performing a temperature control scene offset evaluation based on the temperature control scene mapping comparison result, generating a temperature control scene offset coefficient, and determining whether the temperature control scene offset coefficient is greater than / equal to a temperature control scene offset threshold; If the temperature control scene offset coefficient is greater than / equal to the temperature control scene offset threshold, a scene temperature control adjustment instruction is generated; Based on the scene temperature control adjustment instruction, temperature control optimization is performed according to the real-time heating scene data and the temperature control optimization algorithm, an adjustment temperature control scheme is generated, and temperature control optimization of the target ceramic electric heating element is performed based on the adjustment temperature control scheme.

[0015] In a second aspect, the present invention further provides an intelligent temperature control system for a ceramic electric heating element, wherein the system comprises: A scene acquisition module, wherein the scene acquisition module is used to load multi-dimensional preset temperature control scene indicators, wherein the multi-dimensional preset temperature control scene indicators include a heating object, a heating demand, and a heating environment; A temperature control parameter acquisition module, the temperature control parameter acquisition module is used to collect the temperature control scene parameters of the target ceramic electric heating element according to the multi-dimensional preset temperature control scene indicators to obtain the target temperature control operation scene; A temperature control space mining module, the temperature control space mining module is used to perform temperature control data registration mining on the target temperature control operation scene based on a predetermined temperature control registration constraint, and construct a component temperature control registration space; A parameter optimization module, which is used to optimize the temperature control parameters of the target ceramic electric heating element according to the temperature control optimization algorithm based on the element temperature control registration space and the target temperature control operation scenario, so as to obtain a target temperature control solution that meets the predetermined temperature control optimization constraints; A control feedback module, which is used to control the target ceramic electric heating element according to the target temperature control scheme based on the target temperature control operation scenario, and to monitor the target ceramic electric heating element in real time through an Internet of Things monitoring module to obtain element control feedback monitoring data and real-time heating scenario data; A compensation control module is used to perform temperature control compensation adjustment on the target ceramic electric heating element based on the target temperature control scheme, according to the element control feedback monitoring data and the real-time heating scene data.

[0016] The present invention discloses an intelligent temperature control method and system for ceramic electric heating elements, comprising: loading multi-dimensional preset temperature control scene indicators; collecting temperature control scene parameters of a target ceramic electric heating element according to the multi-dimensional preset temperature control scene indicators to obtain a target temperature control operation scene; performing temperature control data registration mining on the target temperature control operation scene based on a preset temperature control registration constraint to construct an element temperature control registration space; optimizing temperature control parameters of a target ceramic electric heating element according to a temperature control optimization algorithm based on the element temperature control registration space and the target temperature control operation scene to obtain a target temperature control scheme that meets the preset temperature control optimization constraints; based on the target temperature control operation scene, executing control of the target ceramic electric heating element according to the target temperature control scheme, and monitoring the target ceramic electric heating element in real time through an Internet of Things monitoring module to obtain element control feedback monitoring data and real-time heating scene data; based on the target temperature control scheme, performing temperature control compensation adjustment on the target ceramic electric heating element according to the element control feedback monitoring data and the real-time heating scene data. The intelligent temperature control method and system for ceramic electric heating elements disclosed in the present invention solve the technical problems of large temperature fluctuation and low control accuracy, and achieve the technical effects of precise temperature control and adaptive control of ceramic electric heating elements. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic flow chart of the intelligent temperature control method for ceramic electric heating elements of the present invention; Figure 2The figure is a schematic diagram of the structure of the intelligent temperature control system for ceramic electric heating elements of the present invention.

[0018] Explanation of reference numerals: scene acquisition module 11 , temperature control parameter acquisition module 12 , temperature control space mining module 13 , parameter optimization module 14 , control feedback module 15 , compensation control module 16 . DETAILED DESCRIPTION

[0019] The technical solution provided in the embodiments of the present invention is to solve the technical problems of large temperature fluctuation and low control accuracy in the prior art. The overall idea adopted is as follows: First, load the multi-dimensional preset temperature control scene indicators. Then, according to the multi-dimensional preset temperature control scene indicators, collect the temperature control scene parameters of the target ceramic electric heating element to obtain the target temperature control operation scene. Then, based on the predetermined temperature control registration constraints, the target temperature control operation scene is subjected to temperature control data registration mining to construct the component temperature control registration space. Next, based on the component temperature control registration space and the target temperature control operation scene, the temperature control optimization algorithm is used to optimize the temperature control parameters of the target ceramic electric heating element to obtain the target temperature control scheme that meets the predetermined temperature control optimization constraints. Subsequently, based on the target temperature control operation scene, the control of the target ceramic electric heating element is executed, and the target ceramic electric heating element is monitored in real time through the Internet of Things monitoring module to obtain the component control feedback monitoring data and real-time heating scene data. Finally, based on the target temperature control scheme, the target ceramic electric heating element is temperature controlled and compensated according to the component control feedback monitoring data and real-time heating scene data. Thereby achieving the technical effect of precise temperature control and adaptive control of the ceramic electric heating element.

[0020] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention are shown in the drawings, rather than all of them.

[0021] Embodiment 1 Figure 1 The figure is a flow chart of the intelligent temperature control method for ceramic electric heating elements of the present invention; wherein, it includes: Loading multi-dimensional preset temperature control scene indicators, wherein the multi-dimensional preset temperature control scene indicators include heating objects, heating requirements and heating environment; That is to say, the temperature control scene indicators include the heating object, heating demand and heating environment, where the heating object is the object of the heating operation, and the type and nature of the heating object determine the heating power and total heat required to achieve the target heating effect; the heating demand includes the target temperature, heating duration, temperature-time curve, etc. The heating environment includes environmental factors of the heating operation, such as ambient temperature, humidity, etc. The heating object, heating demand and heating environment together form a multi-dimensional preset temperature control scene indicator.

[0022] According to the multi-dimensional preset temperature control scene indicators, the temperature control scene parameters of the target ceramic electric heating element are collected to obtain the target temperature control operation scene; The target temperature control operation scenario refers to the actual operation scenario integrated with the multi-dimensional preset temperature control scenario indicators and the collected temperature control scenario parameters of the ceramic electric heating element. This scenario includes information such as the object to be heated, the heating requirements, and the heating environment, which can be used as the basis for setting and adjusting the temperature control system to achieve precise temperature control of the ceramic electric heating element.

[0023] Exemplarily, a target temperature control operation scenario is obtained. First, the ceramic electric heating element that needs to be temperature controlled is determined. Then, the temperature, heating power and other parameters of the ceramic electric heating element are collected in real time through sensors and other equipment. The collected temperature control scene parameters are compared and integrated with the multi-dimensional preset temperature control scene indicators to determine the target temperature control operation scenario. Through these steps, the actual temperature control scenario of the target ceramic electric heating element can be obtained for subsequent temperature control and management.

[0024] Performing temperature control data registration mining on the target temperature control operation scene based on predetermined temperature control registration constraints to construct a component temperature control registration space; In some embodiments, performing temperature control data registration mining on the target temperature control operation scene based on a predetermined temperature control registration constraint to construct a component temperature control registration space includes: Based on big data, the temperature control record data of the target ceramic electric heating element is collected to obtain a set of element temperature control operation records, wherein the set of element temperature control operation records includes multiple groups of temperature control operation records corresponding to the target ceramic electric heating element, and each group of temperature control operation records includes historical temperature control operation scenes and historical temperature control plans; Based on big data, the temperature control record data of similar ceramic electric heating elements of the target ceramic electric heating element are collected to obtain a temperature control operation record set of similar elements; Performing data cleaning based on the component temperature control operation record set and the temperature control operation record set of the same type of components to obtain a temperature control operation record data set; Based on the predetermined temperature control registration constraint, performing temperature control scheme registration screening on the temperature control operation record data set according to the target temperature control operation scene, and generating a scene-registered temperature control scheme set; Calibrate the temperature control parameter trigger interval based on the scene registration temperature control solution set to obtain a multi-level temperature control parameter trigger interval; The multi-level temperature control parameter trigger interval is traversed to perform multiple random value acquisitions to obtain multiple component temperature control registration schemes, and the multiple component temperature control registration schemes are added to the component temperature control registration space.

[0025] Specifically, the historical temperature control scheme includes heating voltage, heating current, heating temperature, heating time, etc. The historical temperature control operation scene corresponds to the historical temperature control scheme. The historical temperature control scheme is a control scheme solution for a historical temperature control operation scene. Multiple groups of temperature control operation records constitute a component temperature control operation record set, which constitutes a control scheme solution set under different operation scenarios.

[0026] Exemplarily, similar ceramic heating elements refer to ceramic heating elements that have similar operating parameters or heating performance to the target ceramic heating elements. By collecting the temperature control record data of similar ceramic heating elements of the target ceramic heating elements, a set of temperature control operation records of similar elements is obtained, thereby expanding the basic space that can be used for subsequent solution optimization, which is conducive to obtaining the optimal temperature control solution.

[0027] Optionally, the acquired temperature control operation record data is cleaned to ensure data quality.

[0028] Optionally, the temperature control parameter trigger interval is calibrated based on the scene-aligned temperature control solution set. This includes determining the upper and lower limits of the temperature trigger point, as well as other parameter ranges that may affect the temperature control operation. Through calibration, a multi-level trigger interval can be obtained, that is, a trigger interval set for different temperature ranges or other conditions. This allows for more precise temperature control and improved accuracy and stability of temperature control. Exemplarily, different degrees of temperature offset correspond to different heating powers, and the greater the temperature offset between the real-time temperature and the desired temperature, the greater the required heating power to achieve rapid temperature control adjustment.

[0029] Optionally, by randomly selecting from the multi-level temperature control parameter trigger interval, the temperature control schemes to be selected are expanded, thereby avoiding the subsequent optimization from falling into the local optimal solution due to the singleness of the scene-matched temperature control scheme set of the sample, thereby improving the global optimization capability.

[0030] In some implementations, based on the predetermined temperature control registration constraint, the temperature control scheme registration screening is performed on the temperature control operation record data set according to the target temperature control operation scene to generate a scene-registered temperature control scheme set, including: Traversing the temperature control operation record data set, extracting a first group of temperature control operation records, wherein the first group of temperature control operation records includes a first historical temperature control operation scene and a first historical temperature control solution; Performing similarity identification on the first historical temperature control operation scene and the target temperature control operation scene to obtain a first temperature control scene registration coefficient; Determining whether the first temperature control scene registration coefficient satisfies the predetermined temperature control registration constraint; If the first temperature control scene registration coefficient satisfies the predetermined temperature control registration constraint, adding the first historical temperature control solution to the scene registration temperature control solution set; If the first temperature control scene registration coefficient does not satisfy the predetermined temperature control registration constraint, the first historical temperature control solution is eliminated.

[0031] Among them, the temperature control scene registration coefficient quantitatively reflects the direct similarity between the target temperature control operation scene and the historical temperature control operation scene. If the temperature control scene registration coefficient is greater than or equal to the predetermined temperature control registration constraint, it means that the historical temperature control operation scene corresponding to the temperature control scene registration coefficient has a sufficiently high similarity with the target temperature control operation scene, and the corresponding temperature control operation record can be used to generate and optimize the temperature control plan for the target temperature control operation scene.

[0032] Optionally, a similarity calculation method (such as cosine similarity, Euclidean distance, etc.) is used to calculate the similarity between the first historical temperature control operation scene and the target temperature control operation scene to obtain a similarity measurement value. Then, based on the similarity measurement value, a scene registration coefficient is normalized to reflect the similarity between the first historical temperature control operation scene and the target temperature control operation scene.

[0033] Based on the component temperature control registration space and the target temperature control operation scenario, optimizing the temperature control parameters of the target ceramic electric heating element according to the temperature control optimization algorithm, and obtaining a target temperature control solution that meets the predetermined temperature control optimization constraints; In some embodiments, based on the element temperature control registration space and the target temperature control operation scenario, the temperature control parameters of the target ceramic electric heating element are optimized according to the temperature control optimization algorithm to obtain a target temperature control solution that meets the predetermined temperature control optimization constraints, including: Extracting a first component temperature control registration scheme according to the component temperature control registration space; Based on the target temperature control operation scenario, performing a temperature control quality analysis on the first component temperature control registration scheme according to a pre-built temperature control quality prediction and evaluation model to obtain a first temperature control quality prediction and evaluation coefficient; Extracting a second component temperature control registration scheme according to the component temperature control registration space; Based on the target temperature control operation scenario, performing a temperature control quality analysis on the second component temperature control registration scheme according to the temperature control quality prediction and evaluation model to obtain a second temperature control quality prediction and evaluation coefficient; Perform maximum value screening based on the first temperature control quality prediction evaluation coefficient and the second temperature control quality prediction evaluation coefficient to determine the current optimal temperature control quality coefficient; Based on the first component temperature control registration scheme and the second component temperature control registration scheme, matching the current optimal temperature control registration scheme corresponding to the current optimal temperature control quality coefficient; Based on the current optimal temperature control quality coefficient and the current optimal temperature control alignment scheme, the component temperature control alignment space is continuously iterated and optimized in combination with the temperature control quality prediction and evaluation model until the predetermined temperature control optimization constraint is met, thereby generating the target temperature control scheme.

[0034] Optionally, the first component temperature control registration scheme and the second component temperature control registration scheme are selected based on the temperature control quality prediction and evaluation model, wherein the first component temperature control registration scheme refers to any native temperature control registration scheme of the target temperature control operation scenario, and the second component temperature control registration scheme refers to the temperature control scheme obtained by performing multiple random value sampling to obtain one of the multiple component temperature control registration schemes. The temperature control quality prediction and evaluation model takes the component temperature control registration scheme and the target temperature control operation scenario as input, and outputs the first temperature control quality prediction evaluation coefficient and the second temperature control quality prediction evaluation coefficient. The component temperature control registration scheme corresponding to a higher coefficient value is considered to have a higher temperature control quality, and can be used as a benchmark scheme for iterative optimization.

[0035] Optionally, starting from the current optimal temperature control alignment scheme, the current optimal temperature control quality coefficient is used as the initial temperature control quality coefficient to enter the iterative process. In each iteration, according to the current temperature control alignment scheme and the temperature control quality coefficient, combined with the temperature control quality prediction and evaluation model, the component temperature control alignment space is adjusted to improve the temperature control quality. Involving operations such as adjusting temperature control parameters and changing temperature control strategies. Then, based on the temperature control quality prediction and evaluation model, the temperature control quality of each adjusted temperature control scheme is evaluated to obtain a new temperature control quality coefficient. Determine whether the new temperature control quality coefficient meets the predetermined temperature control optimization constraints. If it does, the final target temperature control scheme is generated; if it does not, continue to iterate and adjust. Until the predetermined temperature control optimization constraints are met, in other words, the upper limit of the number of iterations is reached.

[0036] In some implementations, based on the target temperature control operation scenario, a temperature control quality analysis is performed on the first component temperature control registration scheme according to a pre-built temperature control quality prediction and evaluation model to obtain a first temperature control quality prediction and evaluation coefficient, including: Loading basic parameters of the target ceramic electric heating element to obtain target element information; The temperature control quality prediction and evaluation model includes a temperature control prediction and evaluation sub-model and a temperature control quality calculation sub-model; Input the target temperature control operation scenario, the first component temperature control registration scheme and the target component information into the temperature control prediction and evaluation sub-model to obtain a first temperature control prediction and evaluation result, wherein the first temperature control prediction and evaluation result includes a first temperature control stability prediction and evaluation coefficient, a first temperature control safety prediction and evaluation coefficient, a first temperature control operation efficiency prediction and evaluation coefficient, a first temperature control energy consumption prediction and evaluation coefficient and a first temperature control component failure probability prediction and evaluation coefficient; Normalizing the first temperature control prediction evaluation result to generate a first temperature control standard prediction result, wherein the first temperature control standard prediction result includes a first temperature control stability standard prediction coefficient, a first temperature control safety standard prediction coefficient, a first temperature control operation efficiency standard prediction coefficient, a first temperature control energy consumption standard prediction coefficient, and a first temperature control element failure probability standard prediction coefficient; The first temperature control standard prediction result is input into the temperature control quality calculation sub-model to generate the first temperature control quality prediction evaluation coefficient, wherein the temperature control quality calculation sub-model includes a temperature control quality calculation function, and the temperature control quality calculation function is: ; Among them, TCQ represents the temperature control quality prediction evaluation coefficient, TCS represents the temperature control stability standard prediction coefficient, TCA represents the temperature control safety standard prediction coefficient, TCE represents the temperature control operation efficiency standard prediction coefficient, TCF represents the temperature control energy consumption standard prediction coefficient, TCU represents the temperature control component failure probability standard prediction coefficient, α, β, γ, δ, ε are the preset temperature control quality calculation weight constraints, and the sum of α, β, γ, δ, ε is 1.

[0037] Optionally, target element information is obtained by loading and analyzing basic parameters of the target ceramic electric heating element, wherein the basic parameters of the ceramic electric heating element include element model, size, material, operating voltage, power and other information.

[0038] Optionally, the collected basic parameter data is processed, organized into a unified data format, and cleaned to ensure the accuracy and completeness of the data, and stored as target component information.

[0039] Optionally, design the structure of the temperature control quality prediction and evaluation model, including the composition and relationship between the temperature control prediction and evaluation sub-model and the temperature control quality calculation sub-model. Then, prepare the data set required for training, including the target temperature control operation scenario, sample temperature control registration scheme and target component information, as well as the corresponding sample evaluation results. Then use the prepared data set to train the temperature control prediction and evaluation sub-model to learn the complex mapping rules of the target temperature control operation scenario, temperature control registration scheme, target component information and sample evaluation results. Realize end-to-end temperature control prediction and evaluation.

[0040] Optionally, the first temperature control prediction and evaluation result includes multiple aspects, covering the stability, safety, efficiency, energy consumption and component failure probability of the temperature control operation. Among them, the temperature control stability prediction evaluation coefficient is used to evaluate the stability of the temperature during the temperature control operation, that is, whether the temperature can remain stable within a predetermined range; the temperature control safety prediction evaluation coefficient is used to evaluate the safety during the temperature control operation, including whether there is a risk of excessively high or low temperatures, and whether there are other safety hazards; the temperature control operation efficiency prediction evaluation coefficient is used to evaluate the efficiency of the temperature control operation, that is, the time and resources required to complete the temperature control task; the temperature control energy consumption prediction evaluation coefficient is used to evaluate the energy consumption during the temperature control operation, including energy consumption and cost; the temperature control component failure probability prediction evaluation coefficient is used to evaluate the probability of failure of the temperature control component, that is, the possibility of problems with the component during the temperature control operation. These evaluation coefficients ensure the stability, safety and efficiency of the temperature control operation by evaluating and optimizing various aspects of the temperature control solution.

[0041] Optionally, after normalizing the first temperature control prediction evaluation result, the first temperature control standard prediction result is obtained. The temperature control quality calculation sub-model calculates the first temperature control quality prediction evaluation coefficient according to the first temperature control standard prediction result through the embedded temperature control quality calculation function. The temperature control quality prediction evaluation coefficient is a weighted calculation of the above-mentioned TCS temperature control stability standard prediction coefficient, TCA temperature control safety standard prediction coefficient, TCE temperature control operation efficiency standard prediction coefficient, TCF temperature control energy consumption standard prediction coefficient, and TCU temperature control component failure probability standard prediction coefficient, thereby achieving a comprehensive performance evaluation of the temperature control prediction evaluation results.

[0042] Based on the target temperature control operation scenario, the target ceramic electric heating element is controlled according to the target temperature control scheme, and the target ceramic electric heating element is monitored in real time through an Internet of Things monitoring module to obtain element control feedback monitoring data and real-time heating scenario data; In other words, based on the target temperature control scheme, the target ceramic electric heating element is controlled, and the element is monitored in real time through the IoT monitoring module, thereby obtaining two types of data: Component control feedback monitoring data is used to feedback the actual control status of the target ceramic electric heating element. It includes control-related data such as current temperature, current, voltage, etc., which are used to verify the execution of the target temperature control scheme; real-time heating scene data is used to describe the heating scene of the target ceramic electric heating element during actual use, including temperature distribution, heating rate, heating effect and other information. By monitoring these data, the actual situation of the target temperature control operation scene can be understood, and reference can be provided for the improvement of the temperature control scheme.

[0043] Real-time acquisition and monitoring of the above data can help ensure the smooth execution of target temperature control operations and provide important data support for subsequent optimization of temperature control solutions.

[0044] Based on the target temperature control scheme, the temperature control compensation adjustment is performed on the target ceramic electric heating element according to the element control feedback monitoring data and the real-time heating scene data.

[0045] In some embodiments, based on the target temperature control scheme, the temperature control compensation adjustment of the target ceramic electric heating element is performed according to the element control feedback monitoring data and the real-time heating scene data, including: Perform parameter mapping comparison between the component control feedback monitoring data and the target temperature control solution to generate temperature control offset characteristic data; Performing a temperature control offset level evaluation based on the temperature control offset characteristic data to generate a temperature control offset level parameter; If the temperature control offset level parameter is greater than / equal to the temperature control offset level threshold, a temperature control offset compensation instruction is generated; Based on the temperature control offset compensation instruction, a temperature control compensation analysis is performed according to the temperature control offset characteristic data to generate temperature control offset compensation control information, and temperature control adjustment of the target ceramic electric heating element is performed according to the temperature control offset compensation control information.

[0046] Optionally, the component control feedback monitoring data is compared with the parameters in the target temperature control scheme to generate temperature control offset characteristic data. The temperature control offset characteristic data describes the difference between the actual control state and the expected control state. Exemplary differences include the delay between the temperature control instruction and the actual control.

[0047] Optionally, based on the temperature control offset level evaluation rules, a temperature control offset level parameter is obtained. The temperature control offset level evaluation reflects the deviation of the control scheme in actual application. If the temperature control offset level parameter is greater than / equal to the temperature control offset level threshold, it means that the actual application deviation of the control scheme is large and corresponding compensation control is required.

[0048] Exemplarily, a temperature control compensation analysis is performed based on the temperature control offset characteristic data to determine how to adjust the parameters of the temperature control system to achieve temperature control compensation, and generate temperature control offset compensation control information. The temperature control offset compensation control information includes intensity compensation information and timing compensation information. The intensity compensation information describes the temperature control intensity that needs to be adjusted. It involves increasing or decreasing the power or working time of the heating element to correct the temperature offset. The timing compensation information describes the timing law of the temperature control adjustment. It includes adjusting the working cycle of the heating element or periodically applying temperature control adjustments to maintain temperature stability.

[0049] In some embodiments, based on the target temperature control scheme, the target ceramic electric heating element is temperature-controlled and compensated according to the element control feedback monitoring data and the real-time heating scene data, further comprising: Perform scene mapping comparison on the target temperature control operation scene and the real-time heating scene data to generate a temperature control scene mapping comparison result; Performing a temperature control scene offset evaluation based on the temperature control scene mapping comparison result, generating a temperature control scene offset coefficient, and determining whether the temperature control scene offset coefficient is greater than / equal to a temperature control scene offset threshold; If the temperature control scene offset coefficient is greater than / equal to the temperature control scene offset threshold, a scene temperature control adjustment instruction is generated; Based on the scene temperature control adjustment instruction, temperature control optimization is performed according to the real-time heating scene data and the temperature control optimization algorithm, an adjustment temperature control scheme is generated, and temperature control optimization of the target ceramic electric heating element is performed based on the adjustment temperature control scheme.

[0050] Optionally, perform a scene mapping comparison to generate a temperature control scene mapping comparison result, which describes the difference between the actual heating scene and the expected temperature control scene. Perform a temperature control scene offset evaluation to generate a temperature control scene offset coefficient. This coefficient is used to describe the degree of offset of the temperature control scene. If the temperature control scene offset coefficient is greater than or equal to the preset temperature control scene offset threshold, a scene temperature control adjustment instruction is generated. This instruction indicates that the temperature control scene needs to be adjusted to ensure the temperature control effect.

[0051] Optionally, the temperature control optimization of the target ceramic electric heating element is performed, including performing temperature control optimization according to the real-time heating scene data and the temperature control optimization algorithm. To determine how to adjust the temperature control parameters to achieve scene temperature control adjustment. Exemplarily, the data of the target heating scene data is updated based on the real-time heating scene data, and the temperature control optimization is performed according to the updated scene data using the temperature control optimization algorithm.

[0052] In summary, the intelligent temperature control method for ceramic electric heating elements provided by the present invention has the following technical effects: By loading multi-dimensional preset temperature control scene indicators; according to the multi-dimensional preset temperature control scene indicators, the temperature control scene parameters of the target ceramic electric heating element are collected to obtain the target temperature control operation scene; based on the predetermined temperature control registration constraints, the temperature control data registration mining is performed on the target temperature control operation scene to build the component temperature control registration space; the temperature control parameters are optimized according to the temperature control optimization algorithm to obtain the target temperature control plan; the control of the target ceramic electric heating element is executed, and real-time monitoring is performed to obtain the component control feedback monitoring data and real-time heating scene data; based on the target temperature control plan, the temperature control compensation adjustment of the target ceramic electric heating element is performed according to the component control feedback monitoring data and real-time heating scene data. The technical problems of large temperature fluctuations and low control accuracy are solved, and the technical effects of precise temperature control and adaptive control of ceramic electric heating elements are achieved.

[0053] Embodiment 2 Figure 2 Schematic diagram of the structure of the intelligent temperature control system for ceramic electric heating elements of the present invention. For example, Figure 1 The flow chart of the intelligent temperature control method for ceramic electric heating elements of the present invention can be shown as follows: Figure 2 The structure shown is implemented.

[0054] Based on the same concept as the intelligent temperature control method for ceramic electric heating elements in the above embodiment, the present invention also provides an intelligent temperature control system for ceramic electric heating elements, comprising: A scene acquisition module 11 is used to load multi-dimensional preset temperature control scene indicators, wherein the multi-dimensional preset temperature control scene indicators include heating objects, heating requirements and heating environment; The temperature control parameter acquisition module 12 is used to acquire the temperature control scene parameters of the target ceramic electric heating element according to the multi-dimensional preset temperature control scene indicators to obtain the target temperature control operation scene; A temperature control space mining module 13 is used to perform temperature control data registration mining on the target temperature control operation scene based on a predetermined temperature control registration constraint to construct a component temperature control registration space; A parameter optimization module 14 is used to optimize the temperature control parameters of the target ceramic electric heating element according to the temperature control optimization algorithm based on the element temperature control registration space and the target temperature control operation scenario, so as to obtain a target temperature control solution that meets the predetermined temperature control optimization constraints; A control feedback module 15 is used to control the target ceramic electric heating element according to the target temperature control scheme based on the target temperature control operation scenario, and to monitor the target ceramic electric heating element in real time through an Internet of Things monitoring module to obtain element control feedback monitoring data and real-time heating scenario data; The compensation control module 16 is used to perform temperature control compensation adjustment on the target ceramic electric heating element based on the target temperature control scheme, according to the element control feedback monitoring data and the real-time heating scene data.

[0055] The temperature control space mining module 13 includes: A big data acquisition unit, used to acquire temperature control record data of a target ceramic electric heating element based on big data, and obtain a set of element temperature control operation records, wherein the set of element temperature control operation records includes multiple groups of temperature control operation records corresponding to the target ceramic electric heating element, and each group of temperature control operation records includes a historical temperature control operation scene and a historical temperature control scheme; A homologous record collection unit is used to collect temperature control record data of similar ceramic electric heating elements of the target ceramic electric heating element based on big data, and obtain a temperature control operation record set of similar elements; A data fusion unit, used for performing data cleaning based on the component temperature control operation record set and the temperature control operation record set of the same type of component to obtain a temperature control operation record data set; A constraint screening unit, configured to perform temperature control scheme registration screening on the temperature control operation record data set based on the predetermined temperature control registration constraint and according to the target temperature control operation scene, and generate a scene-registered temperature control scheme set; A control interval marking unit, used to calibrate the temperature control parameter trigger interval based on the scene registration temperature control solution set to obtain a multi-level temperature control parameter trigger interval; A random generation unit is used to traverse the multi-level temperature control parameter trigger interval to perform multiple random value acquisitions, obtain multiple component temperature control registration schemes, and add the multiple component temperature control registration schemes to the component temperature control registration space.

[0056] In some implementations, the constraint screening unit includes: a record extraction unit, configured to traverse the temperature control operation record data set and extract a first group of temperature control operation records, wherein the first group of temperature control operation records includes a first historical temperature control operation scene and a first historical temperature control solution; a similarity matching unit, configured to identify similarities between the first historical temperature control operation scene and the target temperature control operation scene, and obtain a first temperature control scene registration coefficient; a constraint determination unit, configured to determine whether the first temperature control scene registration coefficient satisfies the predetermined temperature control registration constraint; a solution intake unit, configured to add the first historical temperature control solution to the scene registration temperature control solution set if the first temperature control scene registration coefficient satisfies the predetermined temperature control registration constraint; A solution elimination unit is used to eliminate the first historical temperature control solution if the first temperature control scene registration coefficient does not meet the predetermined temperature control registration constraint.

[0057] The parameter optimization module 14 includes: A first sequence extraction unit, configured to extract a first component temperature control registration scheme according to the component temperature control registration space; A first priority evaluation unit is used to perform a temperature control quality analysis on the first component temperature control registration scheme based on the target temperature control operation scenario and according to a pre-built temperature control quality prediction and evaluation model to obtain a first temperature control quality prediction and evaluation coefficient; A second sequence extraction unit, used for extracting a second component temperature control registration scheme according to the component temperature control registration space; A second priority evaluation unit is used to perform a temperature control quality analysis on the second component temperature control registration scheme based on the target temperature control operation scenario and according to the temperature control quality prediction and evaluation model to obtain a second temperature control quality prediction and evaluation coefficient; a maximum value screening unit, configured to screen the maximum value based on the first temperature control quality prediction evaluation coefficient and the second temperature control quality prediction evaluation coefficient to determine the current optimal temperature control quality coefficient; an optimal calling unit, configured to match a current optimal temperature control registration scheme corresponding to the current optimal temperature control quality coefficient based on the first component temperature control registration scheme and the second component temperature control registration scheme; An iterative optimization unit is used to continue to iteratively optimize the component temperature control alignment space based on the current optimal temperature control quality coefficient and the current optimal temperature control alignment scheme in combination with the temperature control quality prediction and evaluation model until the predetermined temperature control optimization constraint is met, thereby generating the target temperature control scheme.

[0058] In some implementations, the first priority evaluation unit includes: A parameter acquisition unit, used to load the basic parameters of the target ceramic electric heating element and obtain target element information; A model calling unit, used to obtain the temperature control quality prediction and evaluation model including a temperature control prediction and evaluation sub-model and a temperature control quality calculation sub-model; A prediction and evaluation unit, configured to input the target temperature control operation scenario, the first component temperature control registration scheme, and the target component information into the temperature control prediction and evaluation sub-model to obtain a first temperature control prediction and evaluation result, wherein the first temperature control prediction and evaluation result includes a first temperature control stability prediction and evaluation coefficient, a first temperature control safety prediction and evaluation coefficient, a first temperature control operation efficiency prediction and evaluation coefficient, a first temperature control energy consumption prediction and evaluation coefficient, and a first temperature control component failure probability prediction and evaluation coefficient; a normalization unit, configured to perform normalization processing on the first temperature control prediction evaluation result to generate a first temperature control standard prediction result, wherein the first temperature control standard prediction result includes a first temperature control stability standard prediction coefficient, a first temperature control safety standard prediction coefficient, a first temperature control operation efficiency standard prediction coefficient, a first temperature control energy consumption standard prediction coefficient, and a first temperature control element failure probability standard prediction coefficient; A fusion evaluation unit is used to input the first temperature control standard prediction result into the temperature control quality calculation sub-model to generate the first temperature control quality prediction evaluation coefficient, wherein the temperature control quality calculation sub-model includes a temperature control quality calculation function, and the temperature control quality calculation function is: ; Among them, TCQ represents the temperature control quality prediction evaluation coefficient, TCS represents the temperature control stability standard prediction coefficient, TCA represents the temperature control safety standard prediction coefficient, TCE represents the temperature control operation efficiency standard prediction coefficient, TCF represents the temperature control energy consumption standard prediction coefficient, TCU represents the temperature control component failure probability standard prediction coefficient, α, β, γ, δ, ε are the preset temperature control quality calculation weight constraints, and the sum of α, β, γ, δ, ε is 1.

[0059] Furthermore, the compensation control module 16 includes: A temperature mapping comparison unit, used for performing parameter mapping comparison between the component control feedback monitoring data and the target temperature control scheme to generate temperature control offset characteristic data; a temperature offset evaluation unit, configured to perform a temperature control offset level evaluation based on the temperature control offset characteristic data and generate a temperature control offset level parameter; a temperature compensation determination unit, configured to generate a temperature control offset compensation instruction if the temperature control offset level parameter is greater than / equal to a temperature control offset level threshold; A temperature compensation control unit is used to perform temperature control compensation analysis based on the temperature control offset compensation instruction and the temperature control offset characteristic data, generate temperature control offset compensation control information, and perform temperature control adjustment of the target ceramic electric heating element according to the temperature control offset compensation control information.

[0060] Furthermore, the compensation control module 16 also includes: A scene mapping comparison unit, used to perform scene mapping comparison on the target temperature control operation scene and the real-time heating scene data to generate a temperature control scene mapping comparison result; A scene offset evaluation unit, configured to perform a temperature control scene offset evaluation based on the temperature control scene mapping comparison result, generate a temperature control scene offset coefficient, and determine whether the temperature control scene offset coefficient is greater than / equal to a temperature control scene offset threshold; A scene compensation determination unit, configured to generate a scene temperature control adjustment instruction if the temperature control scene offset coefficient is greater than / equal to the temperature control scene offset threshold; A scene compensation control unit is used to perform temperature control optimization based on the scene temperature control adjustment instruction, according to the real-time heating scene data and the temperature control optimization algorithm, generate an adjustment temperature control scheme, and perform temperature control optimization of the target ceramic electric heating element based on the adjustment temperature control scheme.

[0061] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the aforementioned embodiment one are also applicable to the intelligent temperature control system for ceramic electric heating elements described in embodiment two. For the sake of brevity of the specification, they will not be further expanded here.

[0062] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the above-mentioned embodiments. It should be understood that those skilled in the art can still modify the technical solutions recorded in the above-mentioned embodiments, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. An intelligent temperature control method for a ceramic electric heating element, characterized in that: The method comprises: Loading multi-dimensional preset temperature control scene indicators, wherein the multi-dimensional preset temperature control scene indicators include heating objects, heating requirements and heating environment; According to the multi-dimensional preset temperature control scene indicators, the temperature control scene parameters of the target ceramic electric heating element are collected to obtain the target temperature control operation scene; Based on the predetermined temperature control registration constraints, the temperature control data registration mining is performed on the target temperature control operation scene to construct the component temperature control registration space; Based on the component temperature control registration space and the target temperature control operation scenario, optimizing the temperature control parameters of the target ceramic electric heating element according to the temperature control optimization algorithm, and obtaining a target temperature control solution that meets the predetermined temperature control optimization constraints; Based on the target temperature control operation scenario, the target ceramic electric heating element is controlled according to the target temperature control scheme, and the target ceramic electric heating element is monitored in real time through an Internet of Things monitoring module to obtain element control feedback monitoring data and real-time heating scenario data; Based on the target temperature control scheme, the temperature control compensation adjustment is performed on the target ceramic electric heating element according to the element control feedback monitoring data and the real-time heating scene data.

2. The method according to claim 1, characterized in that Based on the predetermined temperature control registration constraint, the temperature control data registration mining is performed on the target temperature control operation scene to construct the component temperature control registration space, including: Based on big data, the temperature control record data of the target ceramic electric heating element is collected to obtain a set of element temperature control operation records, wherein the set of element temperature control operation records includes multiple groups of temperature control operation records corresponding to the target ceramic electric heating element, and each group of temperature control operation records includes historical temperature control operation scenes and historical temperature control plans; Based on big data, the temperature control record data of similar ceramic electric heating elements of the target ceramic electric heating element are collected to obtain a temperature control operation record set of similar elements; Performing data cleaning based on the component temperature control operation record set and the temperature control operation record set of the same type of components to obtain a temperature control operation record data set; Based on the predetermined temperature control registration constraint, performing temperature control scheme registration screening on the temperature control operation record data set according to the target temperature control operation scene, and generating a scene-registered temperature control scheme set; Calibrate the temperature control parameter trigger interval based on the scene registration temperature control solution set to obtain a multi-level temperature control parameter trigger interval; The multi-level temperature control parameter trigger interval is traversed to perform multiple random value acquisitions to obtain multiple component temperature control registration schemes, and the multiple component temperature control registration schemes are added to the component temperature control registration space.

3. The method according to claim 2, characterized in that Based on the predetermined temperature control registration constraint, the temperature control operation record data set is screened for temperature control scheme registration according to the target temperature control operation scene to generate a scene registration temperature control scheme set, including: Traversing the temperature control operation record data set, extracting a first group of temperature control operation records, wherein the first group of temperature control operation records includes a first historical temperature control operation scene and a first historical temperature control solution; Performing similarity identification on the first historical temperature control operation scene and the target temperature control operation scene to obtain a first temperature control scene registration coefficient; Determining whether the first temperature control scene registration coefficient satisfies the predetermined temperature control registration constraint; If the first temperature control scene registration coefficient satisfies the predetermined temperature control registration constraint, adding the first historical temperature control solution to the scene registration temperature control solution set; If the first temperature control scene registration coefficient does not satisfy the predetermined temperature control registration constraint, the first historical temperature control solution is eliminated.

4. The method according to claim 1, characterized in that Based on the component temperature control registration space and the target temperature control operation scenario, the temperature control parameters of the target ceramic electric heating element are optimized according to the temperature control optimization algorithm to obtain a target temperature control solution that meets the predetermined temperature control optimization constraints, including: Extracting a first component temperature control registration scheme according to the component temperature control registration space; Based on the target temperature control operation scenario, performing a temperature control quality analysis on the first component temperature control registration scheme according to a pre-built temperature control quality prediction and evaluation model to obtain a first temperature control quality prediction and evaluation coefficient; Extracting a second component temperature control registration scheme according to the component temperature control registration space; Based on the target temperature control operation scenario, performing a temperature control quality analysis on the second component temperature control registration scheme according to the temperature control quality prediction and evaluation model to obtain a second temperature control quality prediction and evaluation coefficient; Perform maximum value screening based on the first temperature control quality prediction evaluation coefficient and the second temperature control quality prediction evaluation coefficient to determine the current optimal temperature control quality coefficient; Based on the first component temperature control registration scheme and the second component temperature control registration scheme, matching the current optimal temperature control registration scheme corresponding to the current optimal temperature control quality coefficient; Based on the current optimal temperature control quality coefficient and the current optimal temperature control alignment scheme, the component temperature control alignment space is continuously iterated and optimized in combination with the temperature control quality prediction and evaluation model until the predetermined temperature control optimization constraint is met, thereby generating the target temperature control scheme.

5. The method according to claim 4, characterized in that Based on the target temperature control operation scenario, a temperature control quality analysis is performed on the first component temperature control registration scheme according to a pre-built temperature control quality prediction and evaluation model to obtain a first temperature control quality prediction and evaluation coefficient, including: Loading basic parameters of the target ceramic electric heating element to obtain target element information; The temperature control quality prediction and evaluation model includes a temperature control prediction and evaluation sub-model and a temperature control quality calculation sub-model; Input the target temperature control operation scenario, the first component temperature control registration scheme and the target component information into the temperature control prediction and evaluation sub-model to obtain a first temperature control prediction and evaluation result, wherein the first temperature control prediction and evaluation result includes a first temperature control stability prediction and evaluation coefficient, a first temperature control safety prediction and evaluation coefficient, a first temperature control operation efficiency prediction and evaluation coefficient, a first temperature control energy consumption prediction and evaluation coefficient and a first temperature control component failure probability prediction and evaluation coefficient; Normalizing the first temperature control prediction evaluation result to generate a first temperature control standard prediction result, wherein the first temperature control standard prediction result includes a first temperature control stability standard prediction coefficient, a first temperature control safety standard prediction coefficient, a first temperature control operation efficiency standard prediction coefficient, a first temperature control energy consumption standard prediction coefficient, and a first temperature control element failure probability standard prediction coefficient; The first temperature control standard prediction result is input into the temperature control quality calculation sub-model to generate the first temperature control quality prediction evaluation coefficient, wherein the temperature control quality calculation sub-model includes a temperature control quality calculation function, and the temperature control quality calculation function is: ； Among them, TCQ represents the temperature control quality prediction evaluation coefficient, TCS represents the temperature control stability standard prediction coefficient, TCA represents the temperature control safety standard prediction coefficient, TCE represents the temperature control operation efficiency standard prediction coefficient, TCF represents the temperature control energy consumption standard prediction coefficient, TCU represents the temperature control component failure probability standard prediction coefficient, α, β, γ, δ, ε are the preset temperature control quality calculation weight constraints, and the sum of α, β, γ, δ, ε is 1.

6. The method according to claim 1, characterized in that Based on the target temperature control scheme, the target ceramic electric heating element is subjected to temperature control compensation adjustment according to the element control feedback monitoring data and the real-time heating scene data, including: Perform parameter mapping comparison between the component control feedback monitoring data and the target temperature control solution to generate temperature control offset characteristic data; Performing a temperature control offset level evaluation based on the temperature control offset characteristic data to generate a temperature control offset level parameter; If the temperature control offset level parameter is greater than / equal to the temperature control offset level threshold, a temperature control offset compensation instruction is generated; Based on the temperature control offset compensation instruction, a temperature control compensation analysis is performed according to the temperature control offset characteristic data to generate temperature control offset compensation control information, and temperature control adjustment of the target ceramic electric heating element is performed according to the temperature control offset compensation control information.

7. The method according to claim 1, characterized in that Based on the target temperature control scheme, the target ceramic electric heating element is subjected to temperature control compensation adjustment according to the element control feedback monitoring data and the real-time heating scene data, including: Perform scene mapping comparison on the target temperature control operation scene and the real-time heating scene data to generate a temperature control scene mapping comparison result; Performing a temperature control scene offset evaluation based on the temperature control scene mapping comparison result, generating a temperature control scene offset coefficient, and determining whether the temperature control scene offset coefficient is greater than / equal to a temperature control scene offset threshold; If the temperature control scene offset coefficient is greater than / equal to the temperature control scene offset threshold, a scene temperature control adjustment instruction is generated; Based on the scene temperature control adjustment instruction, temperature control optimization is performed according to the real-time heating scene data and the temperature control optimization algorithm, an adjustment temperature control scheme is generated, and temperature control optimization of the target ceramic electric heating element is performed based on the adjustment temperature control scheme.

8. An intelligent temperature control system for ceramic electric heating elements, characterized in that: The system is used to perform the method according to any one of claims 1 to 7, and the system comprises: A scene acquisition module, wherein the scene acquisition module is used to load multi-dimensional preset temperature control scene indicators, wherein the multi-dimensional preset temperature control scene indicators include a heating object, a heating demand, and a heating environment; A temperature control parameter acquisition module, the temperature control parameter acquisition module is used to collect the temperature control scene parameters of the target ceramic electric heating element according to the multi-dimensional preset temperature control scene indicators to obtain the target temperature control operation scene; A temperature control space mining module, the temperature control space mining module is used to perform temperature control data registration mining on the target temperature control operation scene based on a predetermined temperature control registration constraint, and construct a component temperature control registration space; A parameter optimization module, which is used to optimize the temperature control parameters of the target ceramic electric heating element according to the temperature control optimization algorithm based on the element temperature control registration space and the target temperature control operation scenario, so as to obtain a target temperature control solution that meets the predetermined temperature control optimization constraints; A control feedback module, which is used to control the target ceramic electric heating element according to the target temperature control scheme based on the target temperature control operation scenario, and to monitor the target ceramic electric heating element in real time through an Internet of Things monitoring module to obtain element control feedback monitoring data and real-time heating scenario data; A compensation control module is used to perform temperature control compensation adjustment on the target ceramic electric heating element based on the target temperature control scheme, according to the element control feedback monitoring data and the real-time heating scene data.

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