High-precision, fast-response industrial thermostat temperature control system

Abstract

The present invention relates to the field of industrial automation control technology, and discloses a high-precision, fast-response industrial thermostat temperature control system, which includes temperature acquisition, fluctuation analysis, dynamic calibration, gradient optimization, threshold management, and adjustment execution modules. The temperature acquisition module acquires real-time temperature and environmental thermal inertia parameters and sets the temperature adjustment range; the fluctuation analysis module divides temperature nodes and generates fluctuation feature vectors; the dynamic calibration module extracts temperature deviation indicators and establishes adjustment compensation rules; the gradient optimization module identifies the temperature change level and calculates the fluctuation density difference; the threshold management module derives the optimal adjustment threshold and generates a threshold deviation sequence; and the adjustment execution module integrates and forms a temperature adjustment execution plan. Through the collaboration of multiple modules, the system achieves in-depth analysis and dynamic compensation of temperature fluctuations, improves adjustment accuracy and response speed, and is suitable for high-precision temperature control industrial scenarios such as chemical industry and machinery manufacturing.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation control, and in particular to a high-precision, fast-response industrial thermostat temperature regulation system. Background Art

[0002] In industrial production, temperature is a critical process parameter, and its precise control directly impacts equipment operational stability, production efficiency, and product quality. Industrial thermostats, as core temperature control components, are widely used in the chemical industry, machinery manufacturing, energy and power, and other fields. They are responsible for real-time temperature regulation of target equipment to ensure that the equipment operates within a safe and efficient temperature range. However, existing industrial thermostat temperature control systems generally suffer from insufficient adjustment accuracy, slow response speed, and poor environmental adaptability, making them unable to meet the modern industry's demand for high-precision, high-reliability temperature control.

[0003] From a technical implementation perspective, traditional temperature control systems typically employ fixed-threshold PID control strategies. Their core flaw lies in their inability to dynamically adapt to changes in equipment operating conditions and environmental thermal inertia. For example, when equipment loads suddenly change or ambient temperatures fluctuate dramatically, fixed control parameters can easily lead to temperature regulation lags, resulting in significant temperature overshoots or blind spots, which can in turn cause equipment failures or production accidents. Furthermore, traditional systems analyze temperature fluctuation data at a simple statistical level, lacking the ability to deeply analyze and recognize temperature node fluctuation characteristics, making it difficult to predict temperature trends and develop targeted regulation strategies.

[0004] In practical applications, the complexity of industrial scenarios further exacerbates the difficulty of temperature regulation. Different devices have unique thermal characteristics at different stages of operation, with parameters such as thermal inertia strength and temperature change rate varying significantly. Traditional systems often adopt a "one-size-fits-all" regulation mode, which cannot accurately match the operating status of the device. Furthermore, when multiple devices operate in coordination, the thermal coupling effect between each device can lead to uneven temperature distribution. Traditional systems lack the ability to dynamically calibrate and optimize gradients for global temperature fluctuations, which can easily lead to systematic temperature control deviations.

[0005] With the advancement of industrial automation and intelligentization, high-precision, fast-response temperature control has become a key requirement for industry upgrades. For example, in temperature-sensitive fields such as semiconductor manufacturing and precision instrument processing, temperature fluctuations must be controlled within ±0.1°C. Traditional temperature control systems are no longer able to meet such stringent requirements. Therefore, developing a new temperature control system that can sense device status and environmental changes in real time, deeply analyze temperature fluctuation characteristics, and dynamically optimize control strategies has become a pressing technical challenge in the field of industrial thermostat technology.

[0006] While some existing research has attempted to incorporate machine learning algorithms to analyze temperature data, these approaches have largely remained at the laboratory stage. Practical applications face challenges such as high algorithm complexity, poor real-time performance, and expensive hardware. Furthermore, existing systems lack an effective linkage mechanism between threshold management and control execution, resulting in a significant disconnect between control instructions and actual temperature deviations, making it difficult to achieve optimal closed-loop control. Summary of the Invention

[0007] The object of the present invention is to provide a high-precision, fast-response industrial thermostat temperature control system to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a high-precision, fast-response industrial thermostat temperature control system, the system comprising:

[0009] The temperature acquisition module is used to obtain the real-time temperature data of the target device and the environmental thermal inertia parameters, and set the temperature adjustment range that matches the operating status of the device. The temperature adjustment range is the temperature fluctuation data to be processed;

[0010] The fluctuation analysis module is used to divide the temperature adjustment range into multiple temperature nodes, perform pattern recognition on the fluctuation data of each temperature node, and generate the fluctuation feature vector corresponding to the temperature node;

[0011] A dynamic calibration module is used to extract temperature deviation indicators from the fluctuation feature vector, establish adjustment and compensation rules associated with temperature nodes, and obtain threshold control parameters corresponding to the adjustment and compensation rules;

[0012] Gradient optimization module, used to identify the temperature change level in the threshold control parameter, dynamically compensate the temperature deviation index according to the temperature change level, and calculate the fluctuation density difference of each temperature node under different adjustment strategies;

[0013] The threshold management module is used to derive the optimal adjustment threshold according to the fluctuation density difference and generate a threshold deviation sequence by comparing the current temperature fluctuation density with the optimal adjustment threshold;

[0014] The regulation execution module is used to parse the threshold deviation sequence and integrate the threshold deviation sequence into a temperature regulation execution plan based on the fluctuation density characteristics of the temperature nodes.

[0015] Preferably, the implementation of the fluctuation analysis module includes: constructing a device feature library corresponding to the temperature node, the device feature library containing real-time temperature data and temperature parameter vectors mapped by environmental thermal inertia parameters;

[0016] The temperature parameter vector is matched with similar fluctuations, and the temperature parameter vector is divided into fluctuation cluster groups according to the matching results; the distribution balance point of the temperature data is extracted from the fluctuation cluster group, and the distribution balance point is set as the temperature node.

[0017] Preferably, the fluctuation clustering groups for dividing the temperature parameter vector further include:

[0018] According to the temperature control frequency and thermal inertia fluctuation index in the temperature parameter vector, the temperature change rate, adjustment interval and thermal inertia strength parameters are extracted, and the fluctuation feature label is generated based on the temperature change rate, adjustment interval and thermal inertia strength parameters;

[0019] The fluctuation feature labels are associated with the temperature parameter vectors. By calculating the fluctuation similarity between the feature labels, the parameter vectors with similarity higher than the preset fluctuation threshold are screened to form a fluctuation cluster group.

[0020] Preferably, the implementation method of generating the fluctuation characteristic vector corresponding to the temperature node includes:

[0021] For each temperature node, according to the time series distribution of the temperature node in the temperature adjustment range, the fluctuation difference parameter of the temperature node within the preset period is obtained, and the temperature difference coefficient of the node is calculated;

[0022] When the temperature difference coefficient exceeds the first calibration threshold, the node is marked as an unbalanced node, and its temperature data is extracted to form a fluctuation feature vector; when the temperature difference coefficient is lower than the first calibration threshold, the node is marked as a stable node, and the temperature data of the node's adjacent nodes are gradient superimposed, and the superimposed data are reconstructed into a fluctuation feature vector.

[0023] Preferably, the implementation of the dynamic calibration module includes:

[0024] Separate the temperature data proportion, abnormal fluctuation proportion and thermal inertia fluctuation parameters from the fluctuation characteristic vector, and generate the adjustment and compensation rules of the temperature nodes based on the above parameters;

[0025] If the number of temperature nodes covered by the current adjustment and compensation rule is less than the preset fluctuation threshold, the fluctuation feature vectors of adjacent temperature nodes are traversed, and the temperature indicators not included in the adjustment and compensation rules of the adjacent nodes are added to the current rule.

[0026] Preferably, the implementation of the gradient optimization module includes: obtaining a timing parameter for adjusting the validity period in the temperature change level and a fluctuation parameter for the gradient compensation intensity;

[0027] A compensation state network associated with timing parameters and fluctuation parameters is constructed. According to the switching probability of various paths in the compensation state network, the fluctuation density difference under different adjustment strategies is determined.

[0028] Preferably, constructing the compensation state network further includes:

[0029] Identify the cycle pattern of the timing parameters. If the current cycle pattern completely matches the preset temperature control cycle, set the timing parameters as the starting point of the compensation state network.

[0030] Calculate the gradient correlation between the timing parameters and the fluctuation parameters, and generate the intermediate points and end points of the compensation state network in descending order according to the gradient correlation;

[0031] The state of the termination point is reversely verified. When the gradient correlation of the termination point is lower than the preset gradient threshold, it is output as the final link of the compensation state network.

[0032] Preferably, the implementation of calculating the fluctuation density difference includes:

[0033] Statistically calculate the mean value and range of the timing parameters of each termination point in the compensation state network, and calculate the global covariance of all node parameters;

[0034] The difference between the mean of the timing parameters of a single termination point and the mean of the timing parameters of the adjacent nodes is subtracted, and the difference obtained is divided by the global covariance to obtain the timing difference coefficient; at the same time, the ratio of the fluctuation parameter range to the global covariance is calculated, and the weighted sum of the ratio and the timing difference coefficient is taken as the fluctuation density difference of the node.

[0035] Preferably, the implementation of deriving the optimal adjustment threshold includes:

[0036] Extracting the adjustment mode closest to the current fluctuation density difference in the historical data, and calculating the Euclidean distance between the fluctuation density difference in the closest adjustment mode and the current fluctuation density difference in the time series distribution as the first threshold reference value;

[0037] Counting the difference in the number of peak points between the fluctuation density difference in the closest adjustment mode and the current fluctuation density difference, and using the difference as a second threshold reference value;

[0038] Based on the linear combination of the first threshold reference value and the second threshold reference value, the optimal adjustment threshold in the preset adjustment threshold table is matched.

[0039] Preferably, the implementation of the adjustment execution module includes: dividing the temperature fluctuation domain and the reverse fluctuation domain according to the temperature fluctuation trend of each node in the threshold deviation sequence;

[0040] The convergence frequency of the threshold deviation in the positive fluctuation domain and the diffusion frequency of the threshold deviation in the reverse fluctuation domain are extracted, and the two are dynamically reconciled according to the adjustment weight of the temperature node to generate the configuration parameters of the temperature adjustment execution plan.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The temperature acquisition module acquires the target device's temperature data and ambient thermal inertia parameters in real time, and sets a matching temperature adjustment range based on the device's operating status, enabling precise location and preprocessing of temperature fluctuation data. This design overcomes the limitations of traditional systems with fixed thresholds, enabling the adjustment system to dynamically adapt to the thermal characteristics of the device at different operating stages, laying the data foundation for subsequent high-precision adjustments.

[0043] The Fluctuation Analysis Module achieves a deep deconstruction of temperature parameter vectors by building a device feature library, performing similar fluctuation matching, and cluster analysis. This module not only divides temperature nodes and generates corresponding fluctuation feature vectors, but also generates fluctuation feature labels by extracting parameters such as temperature change rate and adjustment interval, effectively identifying the patterns and regularities of temperature fluctuations. This refined analysis capability enables the system to predict temperature trends in advance, providing a key basis for dynamic calibration and gradient optimization, significantly improving the system's adaptability to complex temperature fluctuation scenarios.

[0044] The dynamic calibration module extracts temperature deviation indicators from the fluctuation feature vector, establishes adjustment and compensation rules associated with temperature nodes, and dynamically expands the coverage of these rules by traversing the data of adjacent nodes. This mechanism ensures that the adjustment strategy can respond to subtle changes in temperature fluctuations in real time. Especially when there are few temperature nodes or the fluctuation characteristics are not obvious, the supplementary analysis of adjacent node data avoids the one-sidedness and lag of the adjustment rules, and improves the comprehensiveness and accuracy of adjustment compensation.

[0045] The gradient optimization module constructs a compensation state network and combines the gradient correlation between timing parameters and fluctuation parameters to dynamically calculate the fluctuation density difference under different control strategies. This module not only identifies the level of temperature change but also optimizes the control strategy based on the path switching probability of the compensation state network, achieving dynamic compensation for temperature deviation indicators. This calculation method, based on global covariance and timing difference coefficients, enables the system to select the optimal solution from multiple control strategies, significantly improving the dynamic response speed and adjustment accuracy of temperature control.

[0046] The threshold management module analyzes adjustment patterns in historical data, combines Euclidean distance and peak point differences to generate the optimal adjustment threshold. It then compares the current fluctuation density with the threshold to generate a deviation sequence. This design enables dynamic threshold updates and adaptive adjustment, avoiding the failure of traditional fixed thresholds in complex scenarios. This allows the system to maintain optimal adjustment across diverse environments and device states.

[0047] The regulation execution module divides the fluctuation domain based on the threshold deviation sequence, extracts the convergence and diffusion frequencies, and dynamically reconciles these to generate configuration parameters. This module transforms abstract deviation data into a specific regulation execution plan, precisely matching regulation instructions with temperature fluctuation trends and ensuring the effectiveness and timeliness of regulation actions. The closed-loop linkage mechanism between multiple modules forms a complete control loop from data acquisition to regulation execution, significantly improving the system's overall control performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a working principle diagram of the high-precision, fast-response industrial thermostat temperature control system of the present invention;

[0049] Figure 2 Flowchart for the division of fluctuation cluster groups;

[0050] Figure 3 Flowchart for the generation of fluctuation eigenvectors;

[0051] Figure 4 Flowchart for the calculation of the fluctuation density difference. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0053] See also Figure 1-Figure 4 The present invention relates to a high-precision, fast-response industrial thermostat temperature control system, which includes a temperature acquisition module, a fluctuation analysis module, a dynamic calibration module, a gradient optimization module, a threshold management module, and a control execution module. Specifically, the system includes the following steps:

[0054] The temperature acquisition module uses a temperature sensor array to collect real-time temperature data from the target device. It also uses the environmental parameter monitoring unit to obtain environmental thermal inertia parameters (such as ambient temperature, humidity, and airflow velocity). Based on device operating parameters (such as power output and load level), a preset state-temperature mapping algorithm is used to set a temperature adjustment range that matches the device's operating state. Temperature fluctuation data within this range is then processed.

[0055] Fluctuation Analysis Module: This module divides the temperature control range into multiple temperature nodes. It performs pattern recognition on the fluctuation data at each node and generates a corresponding fluctuation feature vector. This module uses algorithms such as Fourier transform and wavelet analysis to extract time and frequency domain features and construct a feature vector containing parameters such as mean, variance, and peak frequency.

[0056] Dynamic Calibration Module: This module extracts temperature deviation indicators (such as absolute deviation, relative deviation, and cumulative deviation) from the fluctuation characteristic vector and establishes adjustment and compensation rules associated with the temperature nodes. Based on the PID control principle and incorporating fuzzy logic algorithms, these rules dynamically adjust the compensation coefficient based on the deviation indicators. Simultaneously, it obtains threshold control parameters (such as proportional coefficient, integral time, and differential coefficient) corresponding to the rules.

[0057] Gradient Optimization Module: This module identifies the temperature variation level (e.g., slow, medium, or high) within the threshold control parameter and dynamically compensates for temperature deviations based on the level. Using a particle swarm optimization (PSO) or genetic algorithm, it calculates the fluctuation density difference (i.e., the difference in the number of temperature fluctuations per unit time) for each temperature node under different regulation strategies (e.g., segmented control or adaptive control).

[0058] Threshold Management Module: Based on the fluctuation density difference, the optimal adjustment threshold is derived through regression analysis or neural network model. The current temperature fluctuation density is compared with the optimal threshold to generate a threshold deviation sequence containing the deviation values of each node.

[0059] Regulation execution module: This module analyzes the threshold deviation sequence and, based on the fluctuation density characteristics of each node, converts the deviation sequence into specific regulation actions through actuators (such as electric control valves and cooling fans), such as adjusting the coolant flow rate and starting and stopping the cooling device, thus forming a closed-loop control.

[0060] The present invention will be further described below in conjunction with Examples 1 to 5:

[0061] Example 1:

[0062] The specific implementation of the fluctuation analysis module includes the construction of the equipment feature library, temperature parameter vector processing, fluctuation clustering group division and temperature node setting. Each link realizes the structured analysis of temperature fluctuation through technical means such as data mapping, pattern matching and feature extraction. The details are as follows:

[0063] Build a device feature library corresponding to the temperature nodes. The device feature library is a comprehensive database established based on the historical operating data and real-time monitoring data of the target device. Its core function is to store multi-dimensional data information related to the temperature node. At the data acquisition level, the temperature data of the target device is collected in real time through temperature sensors (such as thermocouples and thermal resistors) distributed in key parts of the equipment. The acquisition frequency can be set to once per second or higher according to the operating characteristics of the equipment to ensure the timeliness and continuity of the data. At the same time, the environmental thermal inertia parameters, including ambient temperature, humidity, airflow velocity, thermal conductivity, etc., are synchronously obtained through environmental parameter monitoring units (such as temperature and humidity sensors, anemometers). These parameters are used to characterize the degree of influence of the environment on the temperature change of the equipment.

[0064] In the data mapping phase, the real-time temperature data is associated with the environmental thermal inertia parameters to generate a temperature parameter vector. Each temperature parameter vector corresponds to a specific time point and contains dimensions such as timestamp, real-time temperature value, and environmental thermal inertia parameter value. For example, the temperature parameter vector at a certain moment can be expressed as [May 29, 2025, 10:00:00, 45°C, ambient temperature 25°C, humidity 60%, airflow velocity 0.5m / s, thermal conductivity 0.3W / (m·K)], where the timestamp is used to identify the data collection moment, the real-time temperature value reflects the current temperature status of the device, and the environmental thermal inertia parameter is used for subsequent fluctuation analysis.

[0065] Similar fluctuation matching is performed on the temperature parameter vectors. The purpose of similar fluctuation matching is to classify temperature parameter vectors with similar fluctuation patterns for subsequent cluster analysis. In this process, the dynamic time warping (DTW) algorithm is used to calculate the similarity between different temperature parameter vectors. The DTW algorithm is a method used to measure the similarity of two time series data. Its core idea is to align the time points of the two sequences by bending the time axis to find the best matching path. Specifically, for two temperature parameter vector sequences, the algorithm constructs a distance matrix, where each element in the matrix represents the distance between the corresponding dimensions of the two vectors (such as the Euclidean distance), and then uses dynamic programming to find the shortest path from the starting point to the end point of the matrix. The length of this path is the similarity measure of the two sequences. The smaller the similarity measure, the more similar the fluctuation patterns of the two temperature parameter vectors are.

[0066] Based on the results of similar fluctuation matching, temperature parameter vectors are divided into different fluctuation clusters. A fluctuation cluster is a collection of vectors with similar temperature fluctuation patterns. The vectors within each cluster have high consistency in temperature change trends, fluctuation amplitudes, and frequencies. When dividing clusters, a preset fluctuation threshold (e.g., a similarity measure less than 0.5) is set. Temperature parameter vectors with similarity above this threshold are grouped together. For example, if the similarity measure between vectors A and B is 0.3, which is less than the preset threshold of 0.5, they are grouped together. However, if the similarity measure between vectors C and A is 0.6, which is greater than the preset threshold, they are grouped together in different clusters.

[0067] After completing the fluctuation clustering, the distribution equilibrium points of the temperature data need to be extracted from each cluster and set as temperature nodes. The distribution equilibrium points are characteristic values that characterize the concentration trend of the temperature data within the cluster. They can be determined by calculating the mean, median, or mode of the temperature data within the cluster. For example, for a temperature data sequence within a cluster [42°C, 43°C, 45°C, 44°C, 43°C], the mean is 43.4°C and the median is 43°C. One of these values can be used as the distribution equilibrium point for the cluster. Once the distribution equilibrium points are set as temperature nodes, they serve as key reference points for subsequent temperature fluctuation analysis and adjustment, dividing the temperature adjustment range and generating the corresponding fluctuation characteristic vectors.

[0068] Furthermore, when dividing the temperature parameter vector into fluctuation clusters, two parameters need to be considered: temperature control frequency and thermal inertia fluctuation index. Temperature control frequency refers to the number of times the temperature control system performs adjustment actions per unit time, reflecting the system's response frequency to temperature fluctuations. The thermal inertia fluctuation index measures the rate of change of the ambient thermal inertia parameter. The calculation formula is (current thermal inertia parameter value - previous thermal inertia parameter value) / time interval, which is used to indicate the speed of change of the ambient thermal inertia.

[0069] Based on the temperature control frequency, thermal inertia fluctuation index, and other parameters in the temperature parameter vector, the temperature change rate, adjustment interval, and thermal inertia strength parameters are extracted. The temperature change rate is the change in device temperature per unit time, calculated as (current temperature value - previous temperature value) / time interval, and is used to describe the speed of temperature fluctuations. The adjustment interval is the time difference between two consecutive temperature adjustment actions, reflecting the time interval characteristics of system adjustments. The thermal inertia strength parameter is the absolute value of the environmental thermal inertia parameter, which is used to indicate the degree of environmental resistance to device temperature changes.

[0070] Fluctuation signatures are generated based on the three parameters mentioned above (temperature change rate, adjustment interval, and thermal inertia strength parameter). Fluctuation signatures are abstract descriptions of temperature fluctuation patterns. For example, when the temperature change rate is low (e.g., less than 1°C / min), the adjustment interval is long (e.g., greater than 10 minutes), and the thermal inertia strength parameter is large (e.g., greater than 0.5 W / (m·K)), a "low-frequency, low-speed, high-inertia" fluctuation signature might be generated. When the temperature change rate is high (e.g., greater than 5°C / min), the adjustment interval is short (e.g., less than 2 minutes), and the thermal inertia strength parameter is small, a "high-frequency, high-speed, low-inertia" fluctuation signature might be generated.

[0071] After the fluctuation feature label is generated, it is associated with the temperature parameter vector so that each vector carries the corresponding fluctuation feature label. Then, by calculating the fluctuation similarity between the feature labels, the parameter vectors with similarity higher than the preset fluctuation threshold are screened to form a fluctuation clustering group. The fluctuation similarity can be calculated using the cosine similarity algorithm, which measures the similarity between two feature label vectors by calculating the cosine value of the angle between them. For example, for the two feature labels "low frequency, low speed, high inertia" and "low frequency, medium speed, high inertia", the cosine value is calculated after converting them into vector form. If the cosine value is greater than the preset threshold (such as 0.7), it is considered that their similarity is high, and the corresponding temperature parameter vectors can be classified into the same fluctuation clustering group.

[0072] Through these steps, the fluctuation analysis module effectively performs structured analysis on temperature fluctuation data within the temperature control range, dividing complex temperature fluctuation patterns into multiple temperature nodes with distinct characteristics. This provides accurate input data for subsequent modules such as dynamic calibration and gradient optimization, thereby achieving high precision and rapid response for the industrial thermostat temperature control system. This entire process, driven by data and combining pattern recognition and feature extraction techniques, ensures the scientific and rational division of temperature nodes, laying the foundation for improving the system's overall performance.

[0073] Example 2:

[0074] The implementation method for generating the fluctuation feature vector corresponding to the temperature node is based on the time series distribution of the temperature node within the temperature adjustment range. Through the steps of fluctuation difference parameter collection, temperature difference coefficient calculation, node state determination and feature vector generation, the feature extraction and reconstruction of temperature data under different fluctuation states are realized. The specific process is as follows:

[0075] For each temperature node, based on its temporal distribution within the temperature regulation range (i.e., a chronological sequence of nodes), the fluctuation difference parameter for that node is obtained within a preset period. The preset period can be set based on the frequency characteristics of the device's temperature fluctuations. For example, for devices with relatively slow temperature changes, the preset period can be set to 10 minutes; for devices with relatively rapid temperature changes, the preset period can be shortened to 1 minute. During the preset period, the device temperature is continuously sampled via a temperature sensor. The sampling interval matches the sensor's accuracy and the device's control requirements, typically once per second or every few seconds.

[0076] The fluctuation difference parameter is the absolute difference between temperature values at adjacent moments within a preset period, reflecting the magnitude of temperature fluctuations over a short period. For example, if the sampled data for a temperature node within a preset period is [40°C, 42°C, 41°C, 43°C, 42°C], the fluctuation difference parameters at adjacent moments are 2°C, 1°C, 2°C, and 1°C, respectively. By statistically analyzing the distribution of these difference parameters, we can further analyze the fluctuation characteristics of the temperature node.

[0077] Based on the obtained fluctuation difference parameters, the temperature difference coefficient of the node is calculated. The temperature difference coefficient is a key indicator for measuring the stability of temperature fluctuations. It is calculated as the ratio of the standard deviation of the fluctuation difference parameter to the mean. The standard deviation reflects the degree of dispersion of the fluctuation difference parameter, while the mean reflects the average level of the fluctuation difference. The ratio of the two effectively represents the relative stability of temperature fluctuations. For example, if the fluctuation difference parameter of a node has a mean of 1.5°C and a standard deviation of 0.5°C, the temperature difference coefficient is 0.5 / 1.5, which is ≈ 0.33. If the fluctuation difference parameter of another node has a mean of 1.0°C and a standard deviation of 0.8°C, the temperature difference coefficient is 0.8 / 1.0, which is 0.8. The latter has lower fluctuation stability than the former.

[0078] The temperature node status is determined based on the comparison of the temperature difference coefficient with the first calibration threshold. The first calibration threshold is a pre-set critical value used to distinguish the node's fluctuation state. Its value can be determined based on the device's temperature control accuracy requirements and historical data statistics, for example, it can be set to 1.0 or 1.5. When the temperature difference coefficient exceeds the first calibration threshold, it indicates that the node's temperature fluctuation amplitude is large and its stability is poor. In this case, the node is marked as unbalanced. When the temperature difference coefficient is lower than the first calibration threshold, it indicates that the node's temperature fluctuation amplitude is small and its stability is good. In this case, the node is marked as stable.

[0079] For temperature nodes marked as unbalanced, their temperature data is directly extracted to form a fluctuation feature vector. This extracted temperature data is typically continuous sampling values within a preset period, such as the temperature values of 20 consecutive sampling points. This data contains detailed information about the node's recent temperature fluctuations and can intuitively reflect its abnormal fluctuation status. The dimensions of the fluctuation feature vector can be determined based on subsequent analysis requirements. For example, it can include timestamps, temperature values, and fluctuation difference parameters to facilitate in-depth analysis by the dynamic calibration module and gradient optimization module.

[0080] For temperature nodes marked as stable nodes, it is necessary to perform gradient superposition processing on the temperature data of its adjacent nodes. Adjacent nodes refer to the previous node and the next node adjacent to the stable node in the time series distribution of the temperature adjustment interval. Gradient superposition is to perform weighted summation of the temperature data of adjacent nodes according to preset weights. For example, set the weight of the previous node to 0.3, the weight of the current stable node to 0.4, and the weight of the next node to 0.3, and perform superposition calculation on the temperature data of the three nodes to obtain the superimposed data sequence. This processing method can make full use of the temperature data information around the stable node and make up for the lack of data features of a single stable node.

[0081] After gradient superposition, the superimposed data needs to be reconstructed to generate a fluctuation feature vector. This reconstruction process utilizes data dimensionality reduction techniques, such as principal component analysis (PCA). By mapping high-dimensional temperature data into a low-dimensional space, redundant information is removed from the data, and the principal components that best reflect the temperature fluctuation characteristics are extracted. For example, PCA can be used to reduce the superimposed three-dimensional temperature data (previous node, current node, next node) into a two-dimensional or one-dimensional feature vector. This vector contains the main fluctuation trends and correlation information of the original data, effectively characterizing the overall fluctuation characteristics of a stable node and its adjacent nodes.

[0082] During the entire implementation process, the following points need to be noted: First, the setting of the preset period should be flexible and can be dynamically adjusted according to changes in the equipment operating status. For example, when the equipment load suddenly changes, the preset period is automatically shortened to increase the frequency of data collection; Second, the determination of the first calibration threshold should be based on a large amount of historical data statistics and equipment control demand analysis to avoid inaccurate node status judgment due to improper threshold setting; Third, the weight distribution of gradient superposition should be reasonably designed according to the position and importance of the node in the time series distribution. For example, the weight close to the current stable node can be appropriately increased to highlight its dominant role; Fourth, sufficient feature information should be retained during the data dimensionality reduction process to ensure that the reconstructed fluctuation feature vector can accurately reflect the fluctuation pattern of the original data.

[0083] The fluctuation feature vectors generated through the above method can effectively distinguish the temperature fluctuation characteristics of unbalanced nodes and stable nodes. For unbalanced nodes, the original temperature data is directly used to extract features to ensure the integrity of abnormal fluctuation information. For stable nodes, the normal fluctuation pattern is effectively characterized through gradient superposition and dimensionality reduction reconstruction of adjacent node data. This differentiated processing method can not only capture abnormal conditions in temperature fluctuations, but also refine the laws of normal fluctuations, providing accurate input data for the subsequent dynamic calibration module. It helps the industrial thermostat temperature control system formulate corresponding adjustment strategies based on different fluctuation characteristics, thereby achieving high-precision and fast-response temperature control goals. The entire process ensures the scientific nature and effectiveness of the fluctuation feature vector through data processing and feature engineering technology, providing strong support for improving the overall performance of the system.

[0084] Example 3:

[0085] The implementation of the dynamic calibration module revolves around the parameter analysis of the fluctuation characteristic vector, the generation of adjustment compensation rules, and the rule expansion mechanism. Through data separation, logical judgment, and rule fusion technology, it realizes the dynamic optimization of the temperature node adjustment strategy. The specific process is as follows:

[0086] Separate the temperature data ratio, abnormal fluctuation ratio, and thermal inertia fluctuation parameters from the fluctuation feature vector. The fluctuation feature vector is a multidimensional data set output by the temperature acquisition module and the fluctuation analysis module, and contains dimensions such as temperature value, timestamp, environmental thermal inertia parameters, and fluctuation difference. The temperature data ratio is the ratio of the sum of the values of the temperature value dimension in the vector to the sum of the values of all dimensions of the vector, which is used to measure the weight of the temperature value in the overall fluctuation feature; the abnormal fluctuation ratio is the ratio of the number of temperature fluctuations in the vector that exceed the preset warning threshold to the total number of fluctuations. The preset warning threshold is determined based on the temperature range of normal operation of the equipment. For example, if the normal temperature range of the equipment is [30℃, 50℃], the warning threshold can be set below 30℃ or above 50℃; the thermal inertia fluctuation parameter is the change in the environmental thermal inertia parameters (such as ambient temperature, humidity, and airflow velocity) within a preset time period. The calculation formula is:

[0087]

[0088] in, represents the thermal inertia fluctuation parameter, Indicates the current environmental thermal inertia parameter value, express The value of the environmental thermal inertia parameter time units ago, The number of time units for the preset time period (e.g. represents the first 10 minutes).

[0089] Based on the separated temperature data proportion, abnormal fluctuation proportion and thermal inertia fluctuation parameters, the adjustment compensation rules associated with the temperature node are generated. The adjustment compensation rules adopt the condition-action logic structure. For example, when the temperature data proportion is greater than 60% and the abnormal fluctuation proportion is greater than 20%, the proportional coefficient is triggered. Increase 20% of the compensation action; when the thermal inertia fluctuation parameters When it is greater than 0.5 and the temperature data accounts for less than 40%, the integration time is triggered. Compensation actions are shortened by 15%. These rules are generated based on historical equipment operation data and control theory, such as the combination of PID control principles and fuzzy logic algorithms. By analyzing the temperature regulation effects under different parameter combinations, a mapping relationship between parameter thresholds and compensation actions is established.

[0090] After generating an adjustment and compensation rule, it's necessary to determine whether the number of temperature nodes covered by the current rule is less than the preset fluctuation threshold. This threshold is determined based on the total number of temperature nodes that the system requires adjustment. For example, if the system has 20 temperature nodes, the preset fluctuation threshold can be set to 6 (30% of the total number of nodes). If the number of nodes covered by the current rule is less than this threshold, it indicates that the existing rule is too narrow to meet the adjustment requirements of complex temperature scenarios. In this case, it's necessary to traverse the fluctuation feature vectors of adjacent temperature nodes and extract temperature indicators not included in the adjustment and compensation rules for these adjacent nodes, such as peak temperature, valley temperature, and maximum and minimum temperature change rates.

[0091] The temperature index extraction process is as follows: For each adjacent node's fluctuation feature vector, the time series distribution of its temperature data is analyzed to determine the peak and valley points of the temperature curve. The peak temperature is the temperature value at the peak point, and the valley temperature is the temperature value at the valley point. The maximum and minimum values of the temperature change rate are calculated, that is, the maximum value of the temperature increase rate and the minimum value of the temperature decrease rate per unit time. These indicators can further characterize the detailed characteristics of temperature fluctuations. For example, the peak temperature reflects the upper limit of the temperature fluctuation, and the maximum value of the temperature change rate reflects a sharp temperature increase.

[0092] After extracting the new temperature indicator, it is added to the current adjustment and compensation rules through the rule fusion algorithm. The rule fusion algorithm adopts the decision tree integration method, takes the existing rules and the newly extracted indicators as input features, and generates a composite rule containing the new indicator by training multiple decision tree models. For example, the existing rule is "When the temperature data accounts for more than 60% and the abnormal fluctuation accounts for more than 20%, Increase by 20%", the newly extracted indicator is peak temperature > 55℃, then the fused rule can be expanded to "when the temperature data ratio is > 60% and the abnormal fluctuation ratio is > 20% and the peak temperature is > 55℃, Increased by 30%” by increasing the compensation amplitude to cope with higher temperature peaks.

[0093] In the process of rule expansion, the following points need to be noted: First, the extraction of temperature indicators should be targeted, and priority should be given to indicators related to scenarios that are not adequately covered by the current rules. For example, when the existing rules do not adequately handle high-temperature peak scenarios, the peak temperature indicators should be extracted. Second, the training data of the rule fusion algorithm should contain sufficient historical cases to ensure that the generated composite rules are statistically significant. Third, the compensation action amplitude of the new rule should be determined through sensitivity analysis to avoid deterioration of the temperature regulation effect due to excessive or insufficient compensation. Fourth, the expanded rules need to be checked for consistency to ensure that there is no logical conflict between different rules. For example, the same parameter combination cannot trigger both increase and decrease at the same time. action.

[0094] Through the above steps, the dynamic calibration module generates initial adjustment and compensation rules based on the parameter characteristics of the fluctuation feature vector. When the rule coverage is insufficient, the rules are dynamically refined by expanding the indicators of adjacent nodes. This mechanism enables the system to adapt to the fluctuation characteristics of different temperature nodes, especially for providing precise adjustment responses to complex and changing temperature scenarios (such as sudden changes in equipment load and drastic changes in environmental thermal inertia). The separation of temperature data proportion, abnormal fluctuation proportion, and thermal inertia fluctuation parameters provides multi-dimensional input for rule generation. The rule fusion algorithm and indicator expansion mechanism ensure the comprehensiveness and flexibility of the adjustment strategy, avoiding adjustment lags or inadequacies caused by a single rule. The entire process combines data-driven and logical reasoning to achieve dynamic optimization of the adjustment and compensation rules, laying the foundation for high-precision control of industrial thermostat temperature control systems.

[0095] Example 4:

[0096] The implementation of the gradient optimization module is based on the temperature change level in the threshold control parameter. By extracting the timing parameters of the adjustment validity period and the fluctuation parameters of the gradient compensation intensity, a compensation state network is constructed and the fluctuation density difference is calculated to achieve quantitative evaluation of different adjustment strategies. The specific process is as follows:

[0097] Get the timing parameters and fluctuation parameters in the temperature change level. The temperature change level is divided into low-speed changes (such as ≤1℃ / min), medium-speed changes (1-5℃ / min), and high-speed changes (≥5℃ / min) according to the rate of temperature fluctuation of the device. Each level corresponds to a different adjustment strategy. The timing parameter of the adjustment validity period refers to the duration from the execution of the adjustment action to the occurrence of obvious temperature changes. For example, after a cooling fan is started, the effective time for the device temperature to start to drop is 2 minutes; the fluctuation parameter of the gradient compensation intensity refers to the change range of the compensation coefficient in the adjustment strategy. For example, at the high-speed change level, the proportional coefficient It may be adjusted from 0.5 to 0.8, with a fluctuation parameter of 0.3.

[0098] When building a compensation state network associated with the above parameters, the first step is to identify the cyclical pattern of the timing parameters. A cyclical pattern refers to the regularity with which a timing parameter changes over time. For example, a daily cyclical pattern of rising temperature from 9:00 AM to 11:00 AM due to increased load, or an hourly cyclical pattern of temperature fluctuations occurring every hour. By analyzing the temporal distribution of historical data, if the current cyclical pattern exactly matches the preset temperature control cycle (such as the typical operating cycle of the device) (e.g., a fluctuation cycle of two hours for both), then that timing parameter is set as the starting point of the compensation state network. For example, the network starts at the moment when the temperature begins to rise.

[0099] Calculate the gradient correlation between the timing parameter and the fluctuation parameter. The gradient correlation is used to measure the correlation between the two parameters. For example, in a scenario where the adjustment validity period is long, whether the gradient compensation strength needs to be adjusted accordingly. During the calculation, a statistical method is used to analyze the changing trends of the two. For example, when the adjustment validity period is extended, if the gradient compensation strength also shows an increasing trend, the two are considered to be positively correlated; if it shows a decreasing trend, it is considered to be negatively correlated. The middle point and end point of the compensation state network are generated in descending order according to the correlation degree. The middle point represents the key state in the parameter change process (such as the compensation strength change node when the validity period is extended from 2 minutes to 5 minutes), and the end point represents the final state of the parameter change (such as the compensation strength value when the validity period is stable at 10 minutes).

[0100] When performing reverse verification of the state of the termination point, it is necessary to trace back from the termination point to the starting point to check whether the gradient correlation of the entire path is logical. For example, if the compensation intensity corresponding to a certain termination point is inconsistent with the trend of the validity period change during the backtracking process (such as the validity period is shortened but the compensation intensity continues to increase), and the correlation is lower than the preset gradient threshold (such as lower than 0.4), then the path is determined to be invalid and needs to be regenerated; if the correlation meets the requirements, it is output as the final link of the compensation state network. The final link clearly shows the parameter change path from the starting point to the termination point, such as "starting point (validity period 2 minutes, compensation intensity 0.2) → intermediate point (validity period 5 minutes, compensation intensity 0.5) → termination point (validity period 10 minutes, compensation intensity 0.8)", providing a path basis for the subsequent fluctuation density difference calculation.

[0101] When calculating the fluctuation density difference, we first calculate the mean and range of the timing parameters for each endpoint in the compensation state network. The mean timing parameter is the average value of all timing parameters in the path corresponding to that endpoint. For example, if a path for a given endpoint contains validity periods of 2 minutes, 5 minutes, and 10 minutes, its mean is (2+5+10) / 3, which is approximately 5.67 minutes. The range of the fluctuation parameter is the difference between the maximum and minimum values of the fluctuation parameter in that path. For example, if the compensation intensity changes from 0.2 to 0.8, the range is 0.6. At the same time, the global covariance of all node parameters (including the timing parameters and fluctuation parameters of the starting point, intermediate points, and endpoints) is calculated. The global covariance is used to measure the overall correlation between all parameters and reflects the degree of coordination of parameter changes under different adjustment strategies.

[0102] Next, the mean of the timing parameters of a single endpoint is subtracted from the mean of the timing parameters of the adjacent nodes to obtain the timing difference. For example, the mean of endpoint A is 5.67 minutes, while the mean of the adjacent endpoint B is 8 minutes, resulting in a difference of -2.33 minutes. This difference is divided by the global covariance to obtain the timing difference coefficient, which is used to standardize timing differences and eliminate dimensional effects. Simultaneously, the ratio of the fluctuation parameter range to the global covariance is calculated to obtain the fluctuation difference coefficient, which reflects the significance of the fluctuation parameter change relative to the overall correlation. The timing difference coefficient and the fluctuation difference coefficient are weighted and summed according to preset weights (e.g., 60% for timing and 40% for fluctuation) to obtain the fluctuation density difference for that node. For example, if the timing difference coefficient is -0.5, the fluctuation difference coefficient is 0.8, and the weights are 0.6 and 0.4 respectively, then the fluctuation density difference is (-0.5×0.6)+(0.8×0.4)=-0.3+0.32=0.02, which indicates the degree of difference between the temperature fluctuation density under this adjustment strategy and that of the adjacent strategies.

[0103] For example, a piece of industrial equipment has a medium-speed temperature change (3°C / min) and a preset temperature control cycle of one hour. During the construction of the compensation state network, it was identified that the current timing parameters exhibited a distinct hourly cycle pattern, and the starting point was set to the 10th minute of the temperature rise phase (the start of the validity period). By analyzing historical data, three main paths were extracted:

[0104] Path 1: Starting point (validity period 10 minutes, compensation intensity 0.3) → midpoint (validity period 25 minutes, compensation intensity 0.5) → ending point (validity period 40 minutes, compensation intensity 0.7), the time series mean is 25 minutes, and the fluctuation range is 0.4;

[0105] Path 2: Starting point (validity period 10 minutes, compensation intensity 0.3) → midpoint (validity period 30 minutes, compensation intensity 0.6) → ending point (validity period 50 minutes, compensation intensity 0.9), the time series mean is 30 minutes, and the fluctuation range is 0.6;

[0106] Path three: starting point (validity period 10 minutes, compensation intensity 0.3) → middle point (validity period 15 minutes, compensation intensity 0.4) → end point (validity period 20 minutes, compensation intensity 0.5), the time series mean is 15 minutes, and the fluctuation range is 0.2.

[0107] When calculating the global covariance, the timing parameters (10, 25, 40, 30, 50, 15, 20) and the volatility parameters (0.3, 0.5, 0.7, 0.6, 0.9, 0.4, 0.5) of all nodes are combined, resulting in a covariance of 12.5. For example, the mean difference between the timing of the endpoint of path one and the endpoint of path two is 25-30 = -5 minutes. Dividing this by the covariance of 12.5 yields a timing difference coefficient of -0.4. The ratio of the volatility range of 0.4 to the covariance is 0.4 / 12.5 = 0.032. Using weights of 0.6 and 0.4, the volatility density difference is (-0.4 × 0.6) + (0.032 × 0.4) = -0.24 + 0.0128 = -0.2272, indicating that the volatility density of path one is lower than that of path two, potentially corresponding to a more stable regulatory effect.

[0108] In practical applications, the construction of the compensation state network needs to be dynamically updated. For example, when the equipment batch changes or the environmental parameters change, the node parameters and link relationships are adjusted through the new data collected in real time. The calculation of the gradient correlation needs to be combined with the physical characteristics of the equipment. For example, in equipment with high thermal conductivity efficiency, the correlation between the adjustment validity period and the compensation intensity may be higher, and the weight of the timing parameters needs to be increased. The comparison of the fluctuation density difference can help the system automatically select the optimal adjustment strategy. For example, in the above example, if the goal is to reduce the fluctuation density, the system can give priority to path three with a smaller fluctuation density difference. Because its time series mean is shorter and the fluctuation amplitude is smaller, it may be more suitable for the need for rapid response.

[0109] Through parameter correlation analysis and network modeling, the entire process transforms abstract regulation strategies into quantifiable fluctuation density differences, providing a scientific basis for decision-making in the threshold management module. The dynamic construction and verification of the compensation state network ensures the timeliness and accuracy of the model, while the weighted summation difference calculation method comprehensively considers the dual effects of timing and fluctuations, enabling the system to dynamically optimize regulation strategies based on real-time data, ultimately achieving high-precision control of industrial thermostats at varying temperature levels.

[0110] Example 5:

[0111] The optimal adjustment threshold is derived and the implementation method of generating the temperature adjustment execution plan is based on historical data matching, parameter difference analysis and dynamic reconciliation algorithm to achieve accurate mapping from fluctuation density difference to specific adjustment action. The specific process is as follows:

[0112] To derive the optimal adjustment threshold, we need to extract the adjustment pattern from historical data that is closest to the current fluctuation density difference. Historical data is stored in the system database. Each adjustment pattern includes the fluctuation density difference, the corresponding adjustment threshold, and the temporal distribution characteristics of temperature fluctuations. For example, a historical adjustment pattern is recorded as: a fluctuation density difference of 0.15, an adjustment threshold of 45°C, and a temporal trend of high temperature fluctuations in the morning and low temperature fluctuations in the afternoon. The similarity between the current fluctuation density difference and the historical pattern is calculated using Euclidean distance. The Euclidean distance formula is the square root of the sum of the squares of the differences in each dimension. In this scenario, there is only one dimension (the fluctuation density difference), so the distance is the absolute difference. Assume that the current difference is 0.18, and there are three similar historical patterns: 0.17, 0.19, and 0.21. The pattern 0.19 has the smallest distance (0.01), so this pattern is selected as the closest reference.

[0113] To calculate the Euclidean distance between the closest adjustment pattern and the current fluctuation density difference in the time series distribution (i.e., the first threshold reference value), the temperature fluctuation data for both must be aligned by time point. For example, if the current data records the hourly fluctuation density difference sequence [0.15, 0.18, 0.20, …] for 24 hours, and the corresponding sequence for the historical pattern is [0.16, 0.19, 0.21, …], the first threshold reference value is obtained by calculating the sum of the squares of the differences at each time point and then taking the square root. Simultaneously, the difference in the number of peak points between the two is calculated (i.e., the second threshold reference value). A peak point is defined as a local maximum (i.e., the difference at a given moment is greater than the difference at the preceding and following moments). If the current data has five peak points and the historical pattern has four, the difference is 1.

[0114] Based on a linear combination of the first and second threshold reference values (e.g., weighted in a 7:3 ratio), the optimal adjustment threshold is matched to the preset adjustment threshold table. This table, generated through orthogonal experimental design, covers thresholds corresponding to different combinations of fluctuation density difference, temporal distance, and peak difference. For example, when the linear combination value is 0.05, the optimal adjustment threshold is 46°C. This threshold is used to determine whether the current temperature fluctuation requires adjustment and the adjustment range.

[0115] For example, consider an industrial boiler with a temperature control range of [40°C, 60°C] and a current fluctuation density difference of 0.22. The system retrieves the closest control pattern from historical data with a difference of 0.23. The corresponding historical control threshold for this pattern is 48°C. The time series distribution exhibits a fluctuation peak every two hours, while the current data exhibits a peak every hour. To calculate the Euclidean distance of the time series distribution, the 24-hour data is divided into 24 time points. The difference at each point is the difference in fluctuation density between the current and historical patterns. The square root of the sum of squares yields a first threshold reference value of 0.08. The difference in the number of peaks is 12 (current) - 6 (historical) = 6, which serves as the second threshold reference value. The linear combination yields a value of 0.08 × 0.7 + 6 × 0.3 = 0.056 + 1.8 = 1.856, which matches the optimal control threshold to 47°C.

[0116] When the regulation execution module analyzes the threshold deviation sequence, it first divides each node's temperature fluctuation trend into a positive fluctuation domain and a negative fluctuation domain. The positive fluctuation domain refers to the set of nodes with an upward temperature trend, such as the node sequence [42°C, 44°C, 46°C], where the differences between adjacent nodes are all positive. The negative fluctuation domain refers to the set of nodes with a downward temperature trend, such as [48°C, 45°C, 43°C], where the differences are all negative. Within the positive fluctuation domain, the threshold deviation convergence frequency is calculated, i.e., the number of times the deviation value (current temperature minus regulation threshold) decreases per unit time. Within the negative fluctuation domain, the threshold deviation diffusion frequency is calculated, i.e., the number of times the deviation value expands.

[0117] For example, if the deviation at a node in the positive fluctuation domain is +3°C (currently 48°C, threshold 45°C), then +2°C the next moment, and +1°C the next, the convergence frequency is 2 times per unit time. If the deviation at a node in the negative fluctuation domain is -2°C (currently 43°C, threshold 45°C), then -3°C the next moment, and -4°C the next, the diffusion frequency is 2 times per unit time. The convergence and diffusion frequencies are dynamically reconciled using the adjustment weights of the temperature nodes. These weights are preset based on the thermal sensitivity of each device component. For example, nodes near the heating element have a weight of 0.6, and nodes in the heat dissipation area have a weight of 0.4.

[0118] Continuing with the boiler example, the threshold deviation sequence is: Node 1 (46°C, deviation +1°C, positive), Node 2 (48°C, deviation +3°C, positive), Node 3 (47°C, deviation +2°C, positive), Node 4 (45°C, deviation 0°C, stable), Node 5 (43°C, deviation -2°C, negative), and Node 6 (41°C, deviation -4°C, negative). The positive fluctuation range is nodes 1-3, with a convergence frequency of 1 (the deviation from node 2 to node 3 decreases from +3°C to +2°C); the negative fluctuation range is nodes 5-6, with a diffusion frequency of 1 (the deviation from node 5 to node 6 increases from -2°C to -4°C). The adjustment weights for each node are: Node 1 (heating zone, 0.5), Node 2 (heating zone, 0.6), Node 3 (heating zone, 0.5), Node 5 (heating zone, 0.3), and Node 6 (heating zone, 0.3).

[0119] During dynamic reconciliation, the weighted convergence frequency in the forward domain is calculated: (1 × 0.5 + 1 × 0.6 + 0 × 0.5) / (0.5 + 0.6 + 0.5) = 1.1 / 1.6, which is approximately 0.6875 times per unit time. The weighted diffusion frequency in the reverse domain is calculated: (0 × 0.3 + 1 × 0.3) / (0.3 + 0.3) = 0.3 / 0.6, which is approximately 0.5 times per unit time. The two are then summed according to preset weights (e.g., 0.7 for the forward domain and 0.3 for the reverse domain), resulting in a comprehensive adjustment parameter: 0.6875 × 0.7 + 0.5 × 0.3 = 0.48125 + 0.15 = 0.63125. Based on these parameters, an implementation plan is generated: in the forward domain, the cooling fan is activated, reducing heating power by 10%; in the reverse domain, the fuel supply is increased, increasing heating power by 15%, while simultaneously closing some cooling dampers.

[0120] In practical applications, the update frequency of historical data must match the equipment's operating cycle. For example, the latest 24-hour data should be automatically synchronized at dawn each day to ensure the timeliness of the adjustment mode. The preset adjustment threshold table should be maintained in conjunction with equipment maintenance records. When key components (such as heat exchangers) are replaced, orthogonal testing should be repeated through manual intervention to update the parameters in the table. The setting of adjustment weights should take into account the equipment's heat conduction path. For example, nodes far from the heat source should have a lower weight, while nodes close to the sensor should have a higher weight, to prioritize temperature accuracy at the measurement point.

[0121] The entire process transforms abstract fluctuation characteristics into specific adjustment thresholds and execution parameters through similarity matching of historical data and multi-dimensional parameter analysis, achieving closed-loop control from data to action. Differentiated processing of forward and reverse fluctuation domains, combined with dynamic coordination of node weights, ensures the targeted and effective adjustment scheme, enabling the system to quickly and accurately respond to complex temperature fluctuation scenarios, ultimately achieving the goal of high-precision temperature control for industrial thermostats.

[0122] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0123] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A high-precision, fast-response industrial thermostat temperature control system, characterized in that: include: The temperature acquisition module is used to obtain the real-time temperature data of the target device and the environmental thermal inertia parameters, and set the temperature adjustment range that matches the operating status of the device. The temperature adjustment range is the temperature fluctuation data to be processed; The fluctuation analysis module is used to divide the temperature adjustment range into multiple temperature nodes, perform pattern recognition on the fluctuation data of each temperature node, and generate the fluctuation feature vector corresponding to the temperature node; A dynamic calibration module is used to extract temperature deviation indicators from the fluctuation feature vector, establish adjustment and compensation rules associated with temperature nodes, and obtain threshold control parameters corresponding to the adjustment and compensation rules; Gradient optimization module, used to identify the temperature change level in the threshold control parameter, dynamically compensate the temperature deviation index according to the temperature change level, and calculate the fluctuation density difference of each temperature node under different adjustment strategies; The threshold management module is used to derive the optimal adjustment threshold according to the fluctuation density difference and generate a threshold deviation sequence by comparing the current temperature fluctuation density with the optimal adjustment threshold; The regulation execution module is used to analyze the threshold deviation sequence and integrate the threshold deviation sequence into a temperature regulation execution plan based on the fluctuation density characteristics of the temperature nodes; The implementation of the gradient optimization module includes: obtaining the timing parameters of the adjustment validity period in the temperature change level and the fluctuation parameters of the gradient compensation intensity; A compensation state network associated with timing parameters and fluctuation parameters is constructed. Based on the switching probabilities of various paths in the compensation state network, the fluctuation density differences under different adjustment strategies are determined. Building a compensation state network also includes: Identify the cycle pattern of the timing parameters. If the current cycle pattern completely matches the preset temperature control cycle, set the timing parameters as the starting point of the compensation state network. Calculate the gradient correlation between the timing parameters and the fluctuation parameters, and generate the intermediate points and end points of the compensation state network in descending order according to the gradient correlation; Perform state reverse verification on the termination point. When the gradient correlation of the termination point is lower than the preset gradient threshold, it is output as the final link of the compensation state network. The implementation methods for calculating the fluctuation density difference include: Statistically calculate the mean value and range of the timing parameters of each termination point in the compensation state network, and calculate the global covariance of all node parameters; The mean of the timing parameters of a single termination point is subtracted from the mean of the timing parameters of the adjacent nodes, and the difference obtained is divided by the global covariance to obtain the timing difference coefficient; at the same time, the ratio of the fluctuation parameter range to the global covariance is calculated, and the weighted sum of the ratio and the timing difference coefficient is taken as the fluctuation density difference of the node; The implementation methods for deriving the optimal adjustment threshold include: Extracting the adjustment mode closest to the current fluctuation density difference in the historical data, and calculating the Euclidean distance between the fluctuation density difference in the closest adjustment mode and the current fluctuation density difference in the time series distribution as the first threshold reference value; Counting the difference in the number of peak points between the fluctuation density difference in the closest adjustment mode and the current fluctuation density difference, and using the difference as a second threshold reference value; Matching an optimal adjustment threshold in a preset adjustment threshold table based on a linear combination of the first threshold reference value and the second threshold reference value; The implementation method of the regulation execution module includes: dividing the temperature fluctuation domain and the reverse fluctuation domain according to the temperature fluctuation trend of each node in the threshold deviation sequence; The convergence frequency of the threshold deviation in the positive fluctuation domain and the diffusion frequency of the threshold deviation in the reverse fluctuation domain are extracted, and the two are dynamically reconciled according to the adjustment weight of the temperature node to generate the configuration parameters of the temperature adjustment execution plan.

2. The high-precision, fast-response industrial thermostat temperature control system according to claim 1 is characterized in that: The implementation of the fluctuation analysis module includes: building a device feature library corresponding to the temperature node, the device feature library contains real-time temperature data and temperature parameter vectors mapped by environmental thermal inertia parameters; The temperature parameter vector is matched with similar fluctuations, and the temperature parameter vector is divided into fluctuation cluster groups according to the matching results; the distribution balance point of the temperature data is extracted from the fluctuation cluster group, and the distribution balance point is set as the temperature node.

3. The high-precision, fast-response industrial thermostat temperature control system according to claim 2, characterized in that: The fluctuation clustering groups that divide the temperature parameter vector also include: According to the temperature control frequency and thermal inertia fluctuation index in the temperature parameter vector, the temperature change rate, adjustment interval and thermal inertia strength parameters are extracted, and the fluctuation feature label is generated based on the temperature change rate, adjustment interval and thermal inertia strength parameters; The fluctuation feature labels are associated with the temperature parameter vectors. By calculating the fluctuation similarity between the feature labels, the parameter vectors with similarity higher than the preset fluctuation threshold are screened to form a fluctuation cluster group.

4. The high-precision, fast-response industrial thermostat temperature control system according to claim 1, characterized in that: The implementation methods for generating the fluctuation characteristic vector corresponding to the temperature node include: For each temperature node, according to the time series distribution of the temperature node in the temperature adjustment range, the fluctuation difference parameter of the temperature node within the preset period is obtained, and the temperature difference coefficient of the node is calculated; When the temperature difference coefficient exceeds the first calibration threshold, the node is marked as an unbalanced node, and its temperature data is extracted to form a fluctuation feature vector; when the temperature difference coefficient is lower than the first calibration threshold, the node is marked as a stable node, and the temperature data of the node's adjacent nodes are gradient superimposed, and the superimposed data are reconstructed into a fluctuation feature vector.

5. The high-precision, fast-response industrial thermostat temperature control system according to claim 1, characterized in that: The implementation of the dynamic calibration module includes: Separate the temperature data proportion, abnormal fluctuation proportion and thermal inertia fluctuation parameters from the fluctuation characteristic vector, and generate the adjustment and compensation rules of the temperature nodes based on the above parameters; If the number of temperature nodes covered by the current adjustment and compensation rule is less than the preset fluctuation threshold, the fluctuation feature vectors of adjacent temperature nodes are traversed, and the temperature indicators not included in the adjustment and compensation rules of the adjacent nodes are added to the current rule.

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