Intelligent control cabinet adjusting method and system based on environment regulation and control

Abstract

The invention belongs to the technical field of intelligent regulation and control, and discloses an intelligent control cabinet regulation method and system based on environment regulation and control, and the method comprises the steps: obtaining an environment parameter region and corresponding three-dimensional environment data; environment regulation sensitivity and environment stability are obtained through parameter coupling analysis and airflow state distribution analysis; evaluating airflow interaction influence based on environmental stability, and obtaining airflow balance intensity and adjustment prediction indexes; constructing an adaptive control framework, and forming a dynamic fusion control strategy; constructing an anomaly detection model, analyzing abnormal fluctuation of the environmental parameters and generating an anomaly coping strategy; performing parameter optimization and energy efficiency adjustment in combination with a dynamic fusion control strategy and an abnormality coping strategy, and generating an optimal environment regulation and control scheme; and finally, executing the scheme and carrying out real-time monitoring and feedback adjustment. According to the invention, precise environment regulation and control of the intelligent control cabinet are realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and more specifically, to an intelligent control cabinet adjustment method and system based on environmental control. Background Art

[0002] During operation, the internal equipment of an intelligent control cabinet generates heat, leading to temperature and humidity fluctuations, which in turn cause fluctuations in the environmental parameters inside the cabinet. Under normal circumstances, the environmental control system of the intelligent control cabinet maintains the internal environment within the parameter range required for normal equipment operation through cooling, ventilation, and other means. However, due to factors such as changes in external temperature and humidity and the operating load of the equipment inside the control cabinet, the internal environmental parameters may exceed the safe range, thereby affecting the normal operation of the equipment and even shortening its service life. Therefore, it is necessary to accurately control the changes in the environmental parameters of the intelligent control cabinet to help maintenance personnel optimize control strategies and improve the reliability and operating efficiency of the equipment.

[0003] Existing technologies typically employ a single control strategy to regulate the overall environment of the control cabinet. However, in real-world scenarios, the distribution of equipment and the characteristics of heat sources vary across different areas within the control cabinet, resulting in differences in environmental parameters such as temperature and humidity. Consequently, the degree of change in the local environment within the control cabinet varies. Traditional environmental control methods can only assess the overall control cabinet environment and cannot identify the key local environmental areas that significantly impact the control effect, leading to substantial errors in the optimization of the control strategy.

[0004] In view of this, the present invention proposes an intelligent control cabinet adjustment method and system based on environmental regulation to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: An intelligent control cabinet adjustment method based on environmental regulation includes: Step S1: Obtain several environmental parameter regions, collect data on the environmental parameters within the environmental parameter regions at different times, obtain the corresponding three-dimensional environmental data, and construct the corresponding environmental parameter regions based on it; Step S2: Based on the constructed environmental parameter region, perform regional sensitivity analysis and regional stability analysis respectively to obtain the corresponding environmental regulation sensitivity and environmental stability; Step S3: Based on environmental stability and combined with three-dimensional environmental data, conduct an assessment of airflow interaction effects, obtain the airflow balance intensity of the environmental parameter region at different times, and conduct environmental fluctuation change analysis based on it to obtain regulation prediction indicators. Step S4: Construct an adaptive control framework and combine it with environmental regulation sensitivity and regulation prediction indicators to construct control strategies and obtain corresponding dynamic fusion control strategies; Step S5: Construct an anomaly detection model based on the environmental parameter region, and combine it with a dynamic fusion control strategy to analyze the abnormal fluctuations of environmental parameters within the environmental parameter region, and obtain corresponding anomaly response strategies; Step S6: Based on the dynamic fusion control strategy and the anomaly response strategy, perform adaptive parameter optimization and energy efficiency adjustment to generate the optimal environmental control scheme for the intelligent control cabinet; Step S7: Execute the optimal environmental control plan, and combine it with dynamic fusion control strategy and abnormal response strategy to conduct real-time effect monitoring and feedback adjustment.

[0006] Furthermore, the process of acquiring regional environmental parameter data includes: Several environmental control zones are set up inside the intelligent control cabinet; environmental parameters in each environmental parameter zone are continuously collected based on pre-deployed sensor acquisition nodes to obtain three-dimensional environmental data, and corresponding environmental parameter zones are constructed based on this data, with the pixels in each environmental parameter zone serving as environmental parameter pixels.

[0007] Furthermore, the process of conducting regional sensitive area analysis includes the following steps: Step S211: Perform coupling relationship analysis based on the obtained environmental parameter region to obtain the corresponding parameter coupling coefficient; Step S212: Based on the parameter coupling coefficient and combined with the three-dimensional environmental data in different environmental parameter regions, perform parameter synergy analysis to obtain the corresponding environmental regulation sensitivity.

[0008] Furthermore, the process of conducting regional stability analysis includes the following steps: Step S221: Perform airflow state distribution analysis on the environmental parameter region to obtain the airflow distribution density at different times in the corresponding environmental parameter region; Step S222: Based on the airflow distribution density and the uniformity of the overall airflow distribution between different environmental parameter regions, obtain the environmental stability of the environmental parameter region at different times.

[0009] Furthermore, the process of obtaining the forecast indicators includes: Based on 3D environmental data, the overlap and continuity of airflow at adjacent temperatures within an environmental parameter region are analyzed, and several airflow convergence points are selected from environmental parameter pixels within the same region. The distribution trend of these convergence points is analyzed to obtain the airflow equilibrium intensity within the environmental parameter region. The environmental stability termination degree of the environmental parameter region at different times is obtained. Based on the airflow balance intensity, the environmental fluctuation change of the environmental parameter region is analyzed to obtain the regulation prediction index of the environmental parameter region.

[0010] Furthermore, the process of acquiring the dynamic fusion control strategy includes: An adaptive control framework is constructed, which matches operating conditions based on the current environmental parameter time-series data and corresponding equipment operating data to identify several historical operating conditions with the highest similarity. The framework then obtains the performance and adaptability of each basic control algorithm under these historical operating conditions and quantifies them as an algorithm performance score. A weight coefficient calculation model is established based on environmental regulation sensitivity and regulation prediction indicators. This model assigns corresponding weight coefficients to each basic control algorithm based on its performance score. Finally, a corresponding dynamic fusion control strategy is constructed based on these weight coefficients.

[0011] The basic control algorithms include classical PID control, fuzzy control, and predictive control.

[0012] Furthermore, the process of acquiring anomaly response strategies includes: A multi-dimensional anomaly detection model is constructed based on the collected environmental parameter time series data, and corresponding anomaly thresholds and alarm levels are set according to the environmental sensitivity of different environmental control areas. Based on the aforementioned anomaly detection model, real-time monitoring of the time-series data of environmental parameters is performed, and anomaly pattern recognition is conducted in conjunction with the set anomaly threshold to obtain potential anomalies; the probability distribution of the corresponding potential anomalies belonging to different fault types is obtained; and the data collected by multiple sensor nodes are cross-verified to determine the type, location, and severity of the fault. Anomaly response strategies are generated based on the fault type and the regional priority of each environmental control area, combined with the obtained dynamic fusion control strategy.

[0013] Furthermore, the process of obtaining the optimal environmental control scheme includes: A multi-objective optimization function is constructed, and a strategy construction model is established by combining historical environmental parameter time series data and equipment operation data. The parameters of the basic control algorithm within the corresponding dynamic fusion control strategy are adaptively adjusted simultaneously. The collected environmental time series parameter data is input into the constructed strategy construction model to obtain the optimal environmental control scheme.

[0014] Furthermore, the process of real-time effect monitoring and feedback adjustments includes: Based on the adjusted dynamic fusion control strategy, the corresponding optimal environmental control scheme is converted into specific equipment instructions and sent to the execution equipment in the target environmental control area. At the same time, based on the sensor acquisition nodes, environmental status data in the corresponding environmental control area is continuously collected, and the data is compared with the expected parameter target for difference analysis and performance evaluation to obtain preliminary performance evaluation results. Meanwhile, based on the anomaly detection model, the collected environmental status parameters are used to identify anomaly patterns and obtain potential anomalies. Based on the preliminary performance evaluation results and the identified potential anomalies, it is determined whether there are obvious anomalies or environmental parameter change trends deviating from expectations within the target environmental control area. If so, compensation control is carried out according to the constructed anomaly response strategy.

[0015] An intelligent control cabinet regulation system based on environmental control includes: The data acquisition module acquires several environmental parameter regions, collects environmental parameters within these regions at different times, obtains corresponding three-dimensional environmental data, and constructs corresponding environmental parameter regions based on this data. The parameter analysis module performs regional sensitivity analysis and regional stability analysis based on the constructed environmental parameter region to obtain the corresponding environmental regulation sensitivity and environmental stability. The regional analysis module assesses the interaction effects of airflow based on environmental stability and combined with three-dimensional environmental data. It obtains the airflow balance intensity of the region at different times and analyzes environmental fluctuations based on it to obtain regulation prediction indicators. The control strategy module constructs an adaptive control framework and combines environmental regulation sensitivity and regulation prediction indicators to build control strategies, thereby obtaining corresponding dynamic fusion control strategies. The anomaly identification module constructs an anomaly detection model based on the environmental parameter region, and combines a dynamic fusion control strategy to analyze the abnormal fluctuations of environmental parameters within the environmental parameter region, thereby obtaining corresponding anomaly response strategies. The scheme optimization module performs adaptive parameter optimization and energy efficiency adjustment based on dynamic fusion control strategy and anomaly response strategy to generate the optimal environmental control scheme for the intelligent control cabinet. The feedback adjustment module is used to execute the optimal environmental control plan and combine dynamic fusion control strategy and anomaly response strategy to perform real-time effect monitoring and feedback adjustment.

[0016] The present invention discloses an intelligent control cabinet adjustment method and system based on environmental regulation, and its technical effects and advantages are as follows: 1. This embodiment innovatively introduces a parameter coupling analysis mechanism, combining Pearson correlation coefficient and Granger causality test to accurately quantify the mutual influence relationships between different environmental parameters, solving the problem of traditional methods neglecting complex interactions between parameters. Simultaneously, through dual assessment of environmental control sensitivity and environmental stability, it achieves accurate identification of key influence areas within the control cabinet, providing a scientific basis for the formulation of subsequent control strategies.

[0017] 2. In terms of airflow dynamic characteristics analysis, this embodiment uses temperature gradient analysis and airflow intersection point identification technology to reveal the airflow distribution pattern and balance state inside the control cabinet. Combined with environmental fluctuation analysis, a high-precision regulation prediction index is constructed, enabling environmental control to transform from passive response to active prediction.

[0018] 3. The adaptive control framework in this embodiment integrates multiple basic control algorithms and realizes intelligent selection and dynamic adjustment of control strategies through algorithm-scenario matching matrix, which significantly enhances the system's adaptability to complex working conditions. The introduction of anomaly detection model and response strategy enables the system to identify and effectively respond to abnormal situations such as sensor failure, actuator failure or sudden environmental changes in a timely manner, which greatly improves the reliability and robustness of the control system.

[0019] Overall, the intelligent control cabinet environmental control method proposed in this embodiment not only significantly improves the accuracy of environmental state perception and control response speed, but also achieves precise control of the internal environment of the control cabinet through parameter coupling analysis and airflow dynamic characteristic evaluation, which significantly improves system stability, energy utilization efficiency and equipment reliability, and provides an innovative solution for the application of intelligent control cabinets in high-requirement scenarios such as data centers and precision manufacturing. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of an intelligent control cabinet adjustment method based on environmental regulation according to the present invention; Figure 2 This is a schematic diagram of an intelligent control cabinet adjustment system based on environmental regulation according to the present invention. Detailed Implementation

[0021] 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.

[0022] Example 1 Please see Figure 1As shown in this embodiment, an intelligent control cabinet adjustment method based on environmental regulation includes: Step S1: Obtain several environmental control areas, collect data on environmental parameters within the environmental control areas at different times, obtain corresponding three-dimensional environmental data, and construct corresponding environmental parameter areas based on them; It should be noted that existing technologies use temperature and humidity sensors at fixed locations to collect data and predict environmental control parameters by comparing the differences in temperature and humidity inside and outside the intelligent control cabinet. In real-world scenarios, the airflow distribution structure, equipment distribution, and heat source characteristics differ in different areas within the intelligent control cabinet, resulting in variations in the collected values ​​and stability of environmental parameters in different areas. Consequently, the degree of local environmental change within the control cabinet varies. Traditional environmental control methods can only adjust the temperature and humidity changes at the overall control cabinet level and cannot understand the core local environmental areas inside the control cabinet that affect environmental stability, leading to significant errors in the predicted environmental control parameters.

[0023] It should be further explained that, in the specific implementation process, the acquisition of the corresponding environmental parameter area includes: In the intelligent control cabinet to be monitored, several sub-regions of the same size are evenly set up according to equipment density, importance, and heat source distribution, and these sub-regions are used as environmental control areas. Sensor acquisition nodes are deployed within the corresponding environmental control areas to continuously collect environmental parameters for each area, obtaining corresponding three-dimensional environmental data, and constructing the corresponding environmental parameter areas based on this data. The specific process is as follows: The sensing and acquisition node continuously monitors the corresponding environmental control area through its built-in infrared thermal imaging device, and scans the corresponding environmental control area based on the spectral sensing array to obtain three-dimensional environmental data at continuous time. Taking a specific moment as an example, a heatmap of the three-dimensional environmental data collected at that moment is obtained and used as an environmental parameter region, with each pixel in the environmental parameter region serving as an environmental parameter pixel. Each environmental parameter region corresponds to several environmental parameter pixels, and each environmental pixel parameter point corresponds to a three-dimensional environmental data point, which includes environmental parameter information such as temperature, humidity, air pressure, and gas flow rate. The collection frequency of the corresponding three-dimensional environmental data can be determined according to the specific implementation.

[0024] Step S2: Based on the constructed environmental parameter region, perform regional sensitivity analysis and regional stability analysis respectively to obtain the corresponding environmental regulation sensitivity and environmental stability; It should be further explained that, in the specific implementation process, the analysis of sensitive areas includes the following steps: Step S211: Perform coupling relationship analysis based on the obtained environmental parameter region to obtain the corresponding parameter coupling coefficient; Step S212: Based on the parameter coupling coefficient and combined with the three-dimensional environmental data in different environmental parameter regions, perform parameter synergy analysis to obtain the corresponding environmental regulation sensitivity; It should be noted that in traditional environmental control processes, each control parameter is usually treated as an independent variable, ignoring the coupling relationship between parameters, which leads to poor results in multi-parameter coordinated control; this invention achieves a more precise control strategy by identifying the mutual influence between parameters. It should be further explained that, in the specific implementation process, the process of obtaining the parameter coupling coefficients includes: Taking any environmental parameter region as an example, the three-dimensional environmental data corresponding to the environmental parameter pixels within the environmental parameter region is obtained, and data preprocessing is performed on it. The data preprocessing includes outlier detection and processing, missing value imputation, and data standardization to ensure data quality. Among them, outlier detection is implemented using the Z-score method; missing value imputation is implemented using a linear interpolation algorithm, and the min-max method is used to standardize each environmental parameter in the three-dimensional environmental data to the [0,1] interval to facilitate subsequent calculation and eliminate dimensions. Based on the preprocessed 3D environmental data, the interrelationships and Granger causality tests among various environmental parameters are analyzed to obtain the corresponding parameter coupling values. The normalized parameter coupling values ​​are then used as parameter coupling coefficients. The specific process is as follows: The Pearson correlation coefficient and Granger causality coefficient between different environmental parameters within each environmental parameter similarity point are obtained based on the Pearson correlation coefficient algorithm and the Granger causality test algorithm, respectively. Then, the mean values ​​of the corresponding Pearson correlation coefficient and Granger causality coefficient between different environmental parameters are obtained, and they are weighted and summed to obtain the corresponding parameter coupling values. The obtained parameter coupling values ​​are normalized and labeled as parameter coupling coefficients. The parameter coupling coefficients can be used to reflect the degree of mutual influence between different environmental parameters. It should be noted that the embodiment uses normalization to limit the parameter coupling values ​​to the range [0,1], where 0 represents no correlation and 1 represents complete correlation; implementers can choose other correlation measurement methods and normalization functions according to the actual situation.

[0025] It should be noted that the equipment distribution and heat source characteristics differ in different environmental parameter areas within the intelligent control cabinet. Therefore, the impact of changes in environmental parameters in different environmental parameter areas on the overall environmental control also varies. Thus, based on the parameter coupling degree and the synergistic effect of overall environmental parameter changes among different environmental parameter areas, the environmental control sensitivity of an environmental parameter area at different times can be obtained. A higher environmental control sensitivity indicates a more significant impact of that environmental parameter area on the overall environmental control, reflecting a greater influence of environmental changes within that area on the overall environmental stability of the control cabinet. It should be further explained that, in the specific implementation process, the process of obtaining the environmental control sensitivity coefficient includes: Based on the degree of synchronous change of various environmental parameters within regions with different environmental parameters, the spatial coupling strength between regions with different environmental parameters is obtained; the mathematical formula for calculating the spatial coupling strength is as follows: In the formula, Indicates the area of ​​environmental parameters and Spatial coupling strength between them and These represent the indices of the environmental parameter categories within the corresponding environmental parameter regions (i.e., (or ) indicates the environmental parameter area (or The first one in ) (or (environmental parameters) Indicates the total number of environmental parameter categories; Indicates the area of ​​environmental parameters Inner Class environment parameters and environment parameter regions The The time-delay correlation coefficient between environmental parameters; where, In the formula, Indicates the first Class environment parameters in Parameter values ​​at time, Indicates the first Class environment parameters in Parameter values ​​at time, Indicates the first and the The time delay between the acquisition of environmental parameters; and They represent the first Class and First The average value of environmental parameters over the corresponding collection period; and They represent the first Class and First Standard deviation of environmental parameters over the corresponding data collection period; This represents the expected operation; since there are several environmental parameter pixels in different environmental parameter regions, and each environmental parameter pixel corresponds to a three-dimensional environmental data; therefore, in the calculation of the corresponding time-delay related data, the average summation of the collected values ​​of each environmental parameter at different environmental parameter pixels will be used as the parameter value of the environmental parameter at the corresponding time in the corresponding environmental parameter region; The obtained spatial coupling strength and parameter coupling coefficient are weighted and summed to obtain the corresponding comprehensive coupling index, which reflects the degree of correlation that needs to be given priority when carrying out environmental regulation. At the same time, the differences between the coupling coefficients of corresponding parameters in different environmental parameter regions are obtained, and the environmental regulation sensitivity of the corresponding environmental parameter region at the same time is obtained by combining the corresponding comprehensive coupling index; the specific implementation process is as follows: Taking a certain environmental parameter region as an example, the comprehensive coupling index between the corresponding environmental parameter region and other environmental parameter regions at a certain time is obtained, and the average value is calculated to obtain the corresponding average coupling index. Obtain the difference in parameter coupling coefficients between the corresponding environmental parameter region and other environmental parameter regions, and mark it as... , In the formula, and They represent the first The and the first The first environmental parameter region Class and the The parameter coupling coefficient between class environment parameters; ; Then, the weighted sum of the differences between the obtained average coupling index and the parameter coupling coefficient is used to obtain the corresponding environmental control sensitivity; among which, the environmental control sensitivity can be used to measure the degree of influence of the corresponding environmental parameter area on the overall environment of the intelligent control cabinet.

[0026] It should be further explained that, in the specific implementation process, the regional stability analysis includes the following steps: Step S221: Perform airflow state distribution analysis on the environmental parameter region to obtain the airflow distribution density at different times in the corresponding environmental parameter region; Step S222: Based on the airflow distribution density and the uniformity of the overall airflow distribution between different environmental parameter regions, obtain the environmental stability of the environmental parameter region at different times. It should be noted that the distribution pattern of airflow within any given environmental parameter region is not necessarily the same; that is, the region may contain multiple airflow distribution patterns. Furthermore, the temperature and humidity gradients formed by different airflow distribution patterns will also differ, resulting in more areas where environmental parameters vary. Therefore, based on the environmental parameter pixels, the distribution state of airflow within the same environmental parameter region can be analyzed to obtain the airflow distribution density of the environmental parameter region at different times.

[0027] It should be further explained that, in the specific implementation process, the method for obtaining the airflow distribution density includes the following steps: Take any given moment as the target moment; for any given environmental parameter region, analyze the temperature gradient between pixels with different environmental parameters within the environmental parameter region at the target moment to obtain the airflow distribution density of the environmental parameter region at the target moment; the specific process is as follows: Each environmental parameter pixel within the environmental parameter region is used as a reference point, and the temperature difference between the reference point and its neighboring environmental parameter pixels is calculated. For each reference point, the absolute value of its temperature difference with all neighboring points is calculated, and these absolute values ​​are summed and divided by the number of neighboring points to obtain the local temperature gradient of the reference point. The local temperature gradients of all environmental parameter pixels within the environmental parameter region are averaged to obtain the average temperature gradient value of the environmental parameter region. The average temperature gradient value is normalized using an inverse proportional relationship to obtain the airflow distribution density of the environmental parameter region at the target time. In this embodiment, the exp(-x) model is used to present the inverse proportional relationship and normalization process, where x is the input of the model. The implementer can choose the inverse proportional function and the normalization function according to the actual situation.

[0028] It should be noted that all environmental parameter areas are uniformly arranged in the same intelligent control cabinet. In the same intelligent control cabinet, there are usually not many types of airflow patterns, so the corresponding airflow distribution structures are not very different from each other. Therefore, based on the airflow distribution density and the uniformity of the overall airflow distribution between different environmental parameter areas, the environmental stability of the environmental parameter area at different times can be obtained. It should be further explained that, in the specific implementation process, the process of obtaining environmental stability includes: At the target time, the airflow distribution density values ​​of all environmental parameter areas within the intelligent control cabinet are acquired, and the average value is calculated. The average value is used as the average airflow distribution density at the corresponding target time. For any environmental parameter area, the absolute value of the difference between the airflow distribution density of that area at the target time and the average airflow distribution density is calculated. The absolute value of the difference is divided by the sum of the average airflow distribution density and the preset hyperparameter μ to obtain the relative deviation value of that environmental parameter area at the target time. In this embodiment, the preset hyperparameter is μ=1 to prevent the denominator from being zero during the calculation of the relative deviation value. The relative deviation value is normalized to obtain the environmental stability of the environmental parameter region at the target time.

[0029] Step S3: Based on environmental stability and combined with three-dimensional environmental data, conduct an assessment of airflow interaction effects, obtain the airflow balance intensity of the environmental parameter region at different times, and conduct environmental fluctuation analysis based on it to obtain corresponding regulation prediction indicators. It should be further explained that, in the specific implementation process, the process of obtaining the adjustment and forecasting indicators includes: It should be noted that each environmental parameter region is formed by multiple airflow channels intersecting each other according to certain rules to create different temperature and humidity gradients. These airflow channels intertwine and will generate different pressures due to their respective airflow distribution patterns. Therefore, based on three-dimensional environmental data and environmental stability, the balance of airflow interaction within the same environmental parameter region can be analyzed to obtain the airflow balance intensity of the environmental parameter region at different times.

[0030] In some implementations of this invention, the method for obtaining the airflow balance intensity is as follows: at the same time, based on three-dimensional environmental data, the overlap and continuity of airflow at adjacent temperatures within the same environmental parameter region are analyzed, and several airflow convergence points are selected from the environmental parameter pixels within the same environmental parameter region based on this; the distribution trend of the airflow convergence points is analyzed to obtain the airflow balance intensity of the environmental parameter region at the same time; the specific process is as follows: Within the same environmental parameter region, the temperature gradient distance between pixels with different environmental parameters is analyzed to obtain the airflow convergence degree of pixels with different environmental parameters. Based on the airflow convergence degree, several airflow convergence points are selected from the environmental parameter region. The specific process is as follows: Take any environmental parameter pixel within any environmental parameter region at any given time as the target environmental parameter pixel. Take the temperature information in the 3D environmental data of the target environmental parameter pixel as the ambient temperature of the target environmental parameter pixel. Within the eight neighborhoods of the target environmental parameter pixel, take the absolute value of the difference between the ambient temperature of the target environmental parameter pixel and each other as the local temperature difference of the target environmental parameter pixel. Take the normalized value of the mean of all local temperature differences of the target environmental parameter pixel as the airflow convergence degree of the target environmental parameter pixel. Obtain the airflow convergence degree of all environmental parameter pixels within this environmental parameter region. A preset airflow convergence threshold T4 is set, and environmental parameter pixels with an airflow convergence greater than T4 within the environmental parameter area are taken as airflow convergence points; where T4 is a fixed value, which can be determined according to the specific implementation.

[0031] Furthermore, the product of the mean Euclidean distance between all airflow convergence points within the environmental parameter region and the environmental stability of the environmental parameter region at the target time is taken as the airflow balance intensity of the environmental parameter region at that time. Furthermore, by analyzing the stability trend of environmental parameter fluctuations in the same environmental parameter region over consecutive time periods, the environmental stability termination degree of the environmental parameter region at different time periods is obtained; based on the environmental stability termination degree, environmentally stable times are selected from all time periods; based on the airflow balance intensity, the airflow balance limit at the environmentally stable time is obtained; based on the airflow balance limit, the environmental fluctuation changes within the same environmental parameter region are analyzed to obtain the regulation prediction index of the environmental parameter region; the specific process is as follows: By comparing the changes in airflow balance intensity between environmental parameter regions at adjacent time points, the environmental stability termination degree of the environmental parameter region at different time points is obtained; the specific process is as follows: Taking any environmental parameter region and any two adjacent moments as an example, the inversely proportional normalized value of the absolute value of the difference in airflow balance intensity between the second moment and the first moment is taken as the environmental stability termination degree of the second moment; the environmental stability termination degree of the environmental parameter region at all moments is obtained; the environmental stability termination degree can be used to measure whether the changing trend of environmental parameters in the corresponding environmental parameter region tends to a stable state; An environmentally stable moment is selected from all moments based on the environmental stability termination degree; the airflow balance difference between the environmentally stable moment and the starting moment is compared, and the airflow balance limit of the environmentally stable moment is obtained based on the comparison results; the specific process is as follows: A preset environmental stability termination threshold T5 is defined, and the moment when the environmental stability termination first exceeds T5 is defined as the environmental stability moment. The absolute value of the difference in the number of airflow intersection points between the environmental parameter region at the environmental stability moment and the start moment is defined as the airflow balance limit of the environmental parameter region. Using the same method as the environmental stability acquisition process, the ratio between the airflow balance linearity of the corresponding environmental parameter region and the overall average airflow balance linearity is obtained and marked as the regulation prediction index. The larger the regulation prediction index, the more significant the impact of the environmental parameter region on the overall environmental stability during the environmental regulation process, and the better the region can reflect the environmental regulation capability of the entire intelligent control cabinet.

[0032] Step S4: Construct an adaptive control framework and combine it with environmental regulation sensitivity and regulation prediction indicators to construct control strategies and obtain corresponding dynamic fusion control strategies; It should be further explained that, in the specific implementation process, the construction process of the adaptive control framework includes: A candidate set of algorithms is constructed, which includes various basic control algorithms such as classical PID control, fuzzy control, model predictive control (MPC), and adaptive control. Performance evaluation and adaptability analysis are performed on the corresponding candidate algorithm sets to obtain the corresponding algorithm-scenario matching matrix. The performance evaluation refers to assessing indicators such as rise time, overshoot, steady-state error, anti-interference capability, and robustness, and measuring the performance of the corresponding basic control algorithm under different operating conditions based on the evaluation. The adaptability analysis examines the applicability of the basic control algorithm in different intelligent control cabinet application scenarios, including special cases such as rapid environmental changes and abnormal interference. Based on the algorithm-scenario matching matrix, a corresponding adaptive control framework is constructed. The adaptive control framework can be used to measure the performance and adaptability of different basic control algorithms under different operating conditions based on environmental state data and equipment operation data. It should be further explained that, in the specific implementation process, the acquisition process of the dynamic fusion control strategy includes: Taking a specific environmental parameter region as an example, the corresponding environmental parameter region and the corresponding equipment operating data at the current moment are obtained and imported into an adaptive control framework. The adaptive control framework performs operating condition matching based on the environmental parameter region and the corresponding equipment operating data, identifies several historical operating conditions with the highest similarity, and obtains the performance and adaptability of each basic control algorithm under the corresponding historical operating conditions, quantifying them as algorithm performance scores. Then, a weight coefficient calculation model is established based on environmental regulation sensitivity and regulation prediction indicators. The weight coefficient calculation model assigns corresponding weight coefficients to each basic control algorithm based on its algorithm performance score. Finally, a corresponding dynamic fusion control strategy is constructed based on the weight coefficients. For example, the basic control algorithm with the highest weight coefficient is used as the master control algorithm, and the algorithm with the second highest weight coefficient is used as the auxiliary algorithm, participating in control decisions according to their respective weight ratios. It should be further explained that, in the specific implementation process, the basic control algorithm will be adaptively modified in real-world use; for example, dynamically optimizing the proportional, integral, and derivative coefficients of PID control and the membership function of fuzzy control, etc. Step S5: Construct an anomaly detection model based on the environmental parameter region, and combine it with a dynamic fusion control strategy to analyze the abnormal fluctuations of environmental parameters within the environmental parameter region, and obtain corresponding anomaly response strategies; It should be noted that in actual operation, intelligent control cabinets may face abnormal operating conditions such as sensor failure, actuator failure, or sudden environmental changes. Traditional systems lack effective abnormal detection and response mechanisms. Therefore, an abnormal detection model can be constructed based on the dynamic fusion control strategy to analyze the abnormal fluctuations of environmental parameters in each area and obtain the abnormal response strategy for the intelligent control cabinet. The abnormal response strategy refers to the backup control scheme or degraded operation mechanism activated when an abnormal operating condition is detected to ensure the reliability and safety of the system.

[0033] It should be further explained that, in the specific implementation process, the process of obtaining the anomaly response strategy includes: The collected environmental parameter regions are read, and a multi-dimensional anomaly detection model is constructed based on them. The anomaly detection model identifies abnormal fluctuations in environmental parameters by combining statistical analysis methods and machine learning algorithms. Among them, the statistical analysis methods mainly include moving average method, exponential weighted moving average method, and CUSUM algorithm, etc.; the machine learning algorithms mainly include isolated forest algorithm, single-class support vector machine, and autoencoder, etc. The construction process of the anomaly detection model is existing technology and will not be described in detail in this application. Furthermore, based on the anomaly detection model, and combined with the environmental sensitivity corresponding to different environmental parameter regions, corresponding anomaly thresholds and alarm levels are set; among them, the environmental parameter regions with higher environmental control sensitivity have more stringent anomaly thresholds and higher alarm levels. Based on the aforementioned anomaly detection model, real-time monitoring of the environmental parameter area is performed, and anomaly pattern recognition is conducted in conjunction with the set anomaly threshold to obtain corresponding potential anomalies. A Bayesian network-based fault diagnosis model is used to calculate the probability distribution of each potential anomaly belonging to different fault types. Data from multiple sensor acquisition nodes is then cross-validated to determine the type, location, and severity of the fault. Based on this, the fault is isolated into three main categories: sensor faults, actuator faults, or environmental mutations. The construction process of the fault diagnosis model is existing technology and will not be elaborated upon in this application. Based on the fault type and the regional priority of each environmental parameter area, and combined with the obtained dynamic fusion control strategy, anomaly response strategies are automatically generated. These strategies include various measures such as adjusting control parameters, switching control algorithms, activating backup equipment, or reducing load. For example, for sensor faults, a sensor data reconstruction mechanism is activated to perform virtual sensing using sensor data from adjacent areas and historical data. For actuator faults, backup execution paths are activated or control strategies are adjusted to bypass the faulty actuator. For sudden environmental changes, a rapid response mode is activated to temporarily increase the control frequency and intensity of high-priority areas. The regional priority is determined by the comprehensive coupling index corresponding to the relevant environmental control parameters and the importance of the equipment within the region.

[0034] Step S6: Based on the dynamic fusion control strategy and the anomaly response strategy, perform adaptive parameter optimization and energy efficiency adjustment to generate the optimal environmental control scheme for the intelligent control cabinet; It should be noted that traditional environmental control systems typically employ fixed parameters and control strategies, lacking adaptive optimization capabilities and exhibiting low energy efficiency. Therefore, adaptive parameter optimization and energy efficiency adjustment can be performed based on dynamic fusion control strategies and anomaly response strategies to generate the optimal environmental control scheme for the intelligent control cabinet. The optimal environmental control scheme refers to a control scheme that maximizes energy efficiency, extends equipment life, and maintains system stability while meeting the environmental parameter requirements of each region.

[0035] It should be further explained that, in the specific implementation process, the process of obtaining the optimal environmental control scheme includes: A multi-objective optimization function was constructed with the objectives of improving equipment control accuracy, energy utilization efficiency, and equipment lifespan. A strategy construction model was then established by combining historical environmental parameters and equipment operating data. The specific process is as follows: First, feature extraction and normalization are performed on environmental state data and equipment operation data within each environmental parameter region to form a state vector. Second, an action space is constructed, which includes the adjustment amounts of control parameters corresponding to the execution equipment such as cooling, heating, ventilation, and dehumidification. Then, a reward value is calculated based on a multi-objective optimization function. The reward value is used to comprehensively reflect the balance between environmental control accuracy, energy utilization efficiency, and equipment lifespan. Subsequently, a deep Q-learning algorithm with a dual-network structure (DQN) or a policy gradient algorithm (such as PPO) is adopted. Through temporal difference learning, the network parameters are continuously optimized so that, given the input state vector, the long-term cumulative reward value can be maximized based on the action space. To improve learning efficiency, an experience playback mechanism and a target network update mechanism can be introduced. At the same time, an exploration-utilization balance strategy (such as an ε-greedy strategy) is adopted to ensure that the algorithm can achieve a balance between utilizing historical experience and exploring new strategies. Finally, through environmental interaction sampling and iterative optimization, a strategy construction model that can adapt to different environmental conditions is obtained. The strategy construction model can output the optimal environmental control scheme based on the current environmental state data and equipment operation data. Meanwhile, the parameters of the basic control algorithm within the corresponding dynamic fusion control strategy are adaptively adjusted. For example, the PID parameters, MPC prediction time domain, fuzzy rules and other control parameters are dynamically adjusted based on environmental parameter regions and equipment operation data. The corresponding adjustment process is existing technology and will not be described in this application. Then, the collected three-dimensional environmental data is input into the constructed strategy model to obtain the corresponding optimal environmental control scheme; the optimal environmental control scheme includes temperature adjustment strategy, humidity adjustment strategy and energy adjustment strategy, etc.

[0036] Step S7: Execute the optimal environmental control plan, and combine it with dynamic fusion control strategy and abnormal response strategy to conduct real-time effect monitoring and feedback adjustment.

[0037] It should be further explained that the process of real-time effect monitoring and feedback adjustment during implementation includes: Based on the adjusted dynamic fusion control strategy, each control strategy within the corresponding optimal environmental control scheme is converted into specific equipment instructions. These instructions include specific information such as the type of executing equipment, adjustment parameters, execution sequence, and duration. According to the area priority corresponding to each environmental parameter area, the obtained equipment instructions are sent to the executing equipment within the target environmental parameter area. Simultaneously, environmental state data within the corresponding environmental parameter area is continuously collected by sensor acquisition nodes. The collected environmental state parameters are compared with the expected parameter targets using difference analysis and performance evaluation to obtain preliminary performance evaluation results. The difference analysis refers to calculating the deviation between the environmental state parameters and the expected parameter targets, such as mean square error, mean absolute error, and maximum deviation. Performance evaluation comprehensively considers indicators such as control accuracy, response speed, and stability. Furthermore, based on an anomaly detection model, anomaly pattern recognition is performed on the collected environmental state parameters to obtain corresponding potential anomalies. Furthermore, based on the preliminary performance evaluation results and the identified potential anomalies, it is determined whether there are obvious anomalies or whether the trend of environmental parameter changes deviates from expectations within the target environmental parameter range. If so, an anomaly control strategy for feedback adjustment is dynamically generated according to the constructed anomaly response strategy; and compensation control is performed based on it; for example, fine-tuning PID controller parameters, adjusting target setpoints, changing the operating mode of the execution equipment, or activating backup control channels, etc., to ensure that environmental parameters can operate stably within the allowable fluctuation range, thereby ensuring the normal working status of the equipment in the control cabinet.

[0038] It should be noted that one embodiment of the present invention further includes: setting a three-level control strategy, including normal mode, energy-saving mode and emergency mode; for example: in normal mode, the system balances control accuracy and energy efficiency; in energy-saving mode, the system prioritizes energy efficiency and appropriately relaxes the parameter fluctuation range of non-critical areas; in emergency mode, the system prioritizes the stability of environmental parameters in high-priority areas, and sacrifices control accuracy and energy efficiency in low-priority areas when necessary.

[0039] Example 2 Please see Figure 2 As shown, parts not described in detail in this embodiment are described in Embodiment 1. An intelligent control cabinet adjustment system based on environmental regulation is provided, including: The data acquisition module acquires several environmental parameter regions, collects environmental parameters within these regions at different times, obtains corresponding three-dimensional environmental data, and constructs corresponding environmental parameter regions based on this data. The parameter analysis module performs regional sensitivity analysis and regional stability analysis based on the constructed environmental parameter region to obtain the corresponding environmental regulation sensitivity and environmental stability. The regional analysis module assesses the interaction effects of airflow based on environmental stability and combined with three-dimensional environmental data. It obtains the airflow balance intensity of the region at different times and analyzes environmental fluctuations based on it to obtain regulation prediction indicators. The control strategy module constructs an adaptive control framework and combines environmental regulation sensitivity and regulation prediction indicators to build control strategies, thereby obtaining corresponding dynamic fusion control strategies. The anomaly identification module constructs an anomaly detection model based on the environmental parameter region, and combines a dynamic fusion control strategy to analyze the abnormal fluctuations of environmental parameters within the environmental parameter region, thereby obtaining corresponding anomaly response strategies. The scheme optimization module performs adaptive parameter optimization and energy efficiency adjustment based on dynamic fusion control strategy and anomaly response strategy to generate the optimal environmental control scheme for the intelligent control cabinet. The feedback adjustment module is used to execute the optimal environmental control plan and combine dynamic fusion control strategy and anomaly response strategy to perform real-time effect monitoring and feedback adjustment.

[0040] The modules are connected via wired and / or wireless means to enable data transmission between them.

[0041] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0042] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0043] In the description of this invention, it should be understood that the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0044] In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0045] In the description of this invention, "several" means one or more, and "a large number" means two or more.

[0046] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0047] All formulas in this manual are dimensionless and calculated numerically. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0048] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for adjusting an intelligent control cabinet based on environmental regulation, characterized in that, include: Step S1: Obtain several environmental parameter regions, collect data on the environmental parameters within the environmental parameter regions at different times, obtain the corresponding three-dimensional environmental data, and construct the corresponding environmental parameter regions based on it; Step S2: Based on the constructed environmental parameter region, perform regional sensitivity analysis and regional stability analysis respectively to obtain the corresponding environmental regulation sensitivity and environmental stability; Step S3: Based on environmental stability and combined with three-dimensional environmental data, conduct an assessment of airflow interaction effects, obtain the airflow balance intensity of the environmental parameter region at different times, and conduct environmental fluctuation change analysis based on it to obtain regulation prediction indicators. Step S4: Construct an adaptive control framework and combine it with environmental regulation sensitivity and regulation prediction indicators to construct control strategies and obtain corresponding dynamic fusion control strategies; Step S5: Construct an anomaly detection model based on the environmental parameter region, and combine it with a dynamic fusion control strategy to analyze the abnormal fluctuations of environmental parameters within the environmental parameter region, and obtain corresponding anomaly response strategies; Step S6: Based on the dynamic fusion control strategy and the anomaly response strategy, perform adaptive parameter optimization and energy efficiency adjustment to generate the optimal environmental control scheme for the intelligent control cabinet; Step S7: Execute the optimal environmental control plan, and combine it with dynamic fusion control strategy and abnormal response strategy to conduct real-time effect monitoring and feedback adjustment.

2. The intelligent control cabinet adjustment method based on environmental regulation according to claim 1, characterized in that, The process of acquiring regional environmental parameter data includes: Several environmental control zones are set up inside the intelligent control cabinet; environmental parameters in each environmental parameter zone are continuously collected based on pre-deployed sensor acquisition nodes to obtain three-dimensional environmental data, and corresponding environmental parameter zones are constructed based on this data, with the pixels in each environmental parameter zone serving as environmental parameter pixels.

3. The intelligent control cabinet adjustment method based on environmental regulation according to claim 1, characterized in that, The process of conducting regional sensitive area analysis includes the following steps: Step S211: Perform coupling relationship analysis based on the obtained environmental parameter region to obtain the corresponding parameter coupling coefficient; Step S212: Based on the parameter coupling coefficient and combined with the three-dimensional environmental data in different environmental parameter regions, perform parameter synergy analysis to obtain the corresponding environmental regulation sensitivity.

4. The intelligent control cabinet adjustment method based on environmental regulation according to claim 1, characterized in that, The process of conducting regional stability analysis includes the following steps: Step S221: Perform airflow state distribution analysis on the environmental parameter region to obtain the airflow distribution density at different times in the corresponding environmental parameter region; Step S222: Based on the airflow distribution density and the uniformity of the overall airflow distribution between different environmental parameter regions, obtain the environmental stability of the environmental parameter region at different times.

5. The intelligent control cabinet adjustment method based on environmental regulation according to claim 1, characterized in that, The process of obtaining the adjustment forecast indicators includes: Based on 3D environmental data, the overlap and continuity of airflow at adjacent temperatures within an environmental parameter region are analyzed, and several airflow convergence points are selected from environmental parameter pixels within the same region. The distribution trend of these convergence points is analyzed to obtain the airflow equilibrium intensity within the environmental parameter region. The environmental stability termination degree of the environmental parameter region at different times is obtained. Based on the airflow balance intensity, the environmental fluctuation change of the environmental parameter region is analyzed to obtain the regulation prediction index of the environmental parameter region.

6. The intelligent control cabinet adjustment method based on environmental regulation according to claim 1, characterized in that, The process of acquiring the dynamic fusion control strategy includes: An adaptive control framework is constructed, which matches operating conditions based on the current environmental parameter time-series data and corresponding equipment operating data to identify several historical operating conditions with the highest similarity. The framework then obtains the performance and adaptability of each basic control algorithm under these historical operating conditions and quantifies them as an algorithm performance score. A weight coefficient calculation model is established based on environmental regulation sensitivity and regulation prediction indicators. This model assigns corresponding weight coefficients to each basic control algorithm based on its performance score. Finally, a corresponding dynamic fusion control strategy is constructed based on these weight coefficients. The basic control algorithms include classical PID control, fuzzy control, and predictive control.

7. The intelligent control cabinet adjustment method based on environmental regulation according to claim 1, characterized in that, The process of obtaining anomaly response strategies includes: A multi-dimensional anomaly detection model is constructed based on the collected environmental parameter time series data, and corresponding anomaly thresholds and alarm levels are set according to the environmental sensitivity of different environmental control areas. Based on the aforementioned anomaly detection model, real-time monitoring of the time-series data of environmental parameters is performed, and anomaly pattern recognition is conducted in conjunction with the set anomaly threshold to obtain potential anomalies; the probability distribution of the corresponding potential anomalies belonging to different fault types is obtained; and the data collected by multiple sensor nodes are cross-verified to determine the type, location, and severity of the fault. Anomaly response strategies are generated based on the fault type and the regional priority of each environmental control area, combined with the obtained dynamic fusion control strategy.

8. The intelligent control cabinet adjustment method based on environmental regulation according to claim 1, characterized in that, The process of obtaining the optimal environmental control scheme includes: A multi-objective optimization function is constructed, and a strategy construction model is established by combining historical environmental parameter time series data and equipment operation data. The parameters of the basic control algorithm within the corresponding dynamic fusion control strategy are adaptively adjusted simultaneously. The collected environmental time series parameter data is input into the constructed strategy construction model to obtain the optimal environmental control scheme.

9. The intelligent control cabinet adjustment method based on environmental regulation according to claim 1, characterized in that, The process of real-time effect monitoring and feedback adjustment includes: Based on the adjusted dynamic fusion control strategy, the corresponding optimal environmental control scheme is converted into specific equipment instructions and sent to the execution equipment in the target environmental control area. At the same time, based on the sensor acquisition nodes, environmental status data in the corresponding environmental control area is continuously collected, and the data is compared with the expected parameter target for difference analysis and performance evaluation to obtain preliminary performance evaluation results. Meanwhile, based on the anomaly detection model, the collected environmental status parameters are used to identify anomaly patterns and obtain potential anomalies. Based on the preliminary performance evaluation results and the identified potential anomalies, it is determined whether there are obvious anomalies or environmental parameter change trends deviating from expectations within the target environmental control area. If so, compensation control is carried out according to the constructed anomaly response strategy.

10. An intelligent control cabinet adjustment system based on environmental regulation, used to implement the intelligent control cabinet adjustment method based on environmental regulation as described in any one of claims 1 to 9, characterized in that, include: The data acquisition module is used to acquire several environmental parameter regions, collect environmental parameters within these regions at different times, obtain corresponding three-dimensional environmental data, and construct corresponding environmental parameter regions based on this data. The parameter analysis module performs regional sensitivity analysis and regional stability analysis based on the constructed environmental parameter region to obtain the corresponding environmental regulation sensitivity and environmental stability. The regional analysis module assesses the interaction effects of airflow based on environmental stability and combined with three-dimensional environmental data. It obtains the airflow balance intensity of the region at different times and analyzes environmental fluctuations based on it to obtain regulation prediction indicators. The control strategy module constructs an adaptive control framework and combines environmental regulation sensitivity and regulation prediction indicators to build control strategies, thereby obtaining corresponding dynamic fusion control strategies. The anomaly identification module constructs an anomaly detection model based on the environmental parameter region, and combines a dynamic fusion control strategy to analyze the abnormal fluctuations of environmental parameters within the environmental parameter region, thereby obtaining corresponding anomaly response strategies. The scheme optimization module performs adaptive parameter optimization and energy efficiency adjustment based on dynamic fusion control strategy and anomaly response strategy to generate the optimal environmental control scheme for the intelligent control cabinet. The feedback adjustment module is used to execute the optimal environmental control plan and combine dynamic fusion control strategy and anomaly response strategy to perform real-time effect monitoring and feedback adjustment.

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