Intelligent control cabinet precise regulation and control method and system based on fusion fuzzy algorithm

Abstract

The invention relates to the technical field of intelligent control cabinets, in particular to an intelligent control cabinet precise regulation and control method and system based on a fusion fuzzy algorithm. The method comprises the steps of obtaining regulation and control data of a control cabinet, performing preprocessing based on the obtained regulation and control data, performing feature extraction according to the preprocessed regulation and control data, taking the extracted features as input to construct a fuzzy control model, and introducing a priority matrix and an energy efficiency optimization strategy. The control quantity obtained through defuzzification is converted into a control signal of an execution mechanism, and the execution mechanism performs intelligent cooperative control; and performing model optimization and fault prediction according to a cooperative control result, and performing accurate regulation and control on the control cabinet based on a prediction result. Model optimization and fault prediction are carried out according to the cooperative control result, the model can continuously adapt to the change of the system, meanwhile, the real-time environment compensation factor is introduced to dynamically adjust the weight of the fuzzy rule, and the adaptability of the control cabinet to the environment change in regulation and control is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control cabinets, and in particular to a precise control method and system for an intelligent control cabinet based on a fusion fuzzy algorithm. Background Art

[0002] In many fields such as industrial automation, new energy, data centers, and intelligent buildings, control cabinets, as core control equipment, play a vital role in ensuring the stable operation of the system. Early control cabinet regulation mainly relied on simple manual operations. Operators adjusted the operating parameters of the equipment based on experience and actual observations. This method is not only inefficient, but also difficult to ensure the control accuracy and is easily affected by human factors. In order to improve the control accuracy and automation of the control cabinet, PID control algorithms are widely used in the control of control cabinets. PID controllers perform proportional, integral, and differential operations on the system errors and output corresponding control signals to adjust the operating state of the controlled object. The PID control algorithm has the advantages of simple structure, easy implementation, and strong robustness, and has achieved good control effects in many industrial process controls.

[0003] In practical applications, the control cabinet needs to control multiple parameters at the same time, such as temperature, humidity, voltage, current, etc. There are often complex coupling relationships between these parameters. The change of one parameter will affect the state of other parameters. The traditional PID control algorithm usually controls a single parameter independently, and it is difficult to deal with the coupling problem between multiple parameters, which can easily lead to poor control effect of the system or even instability. In addition, many actual controlled objects have nonlinear characteristics, and their input-output relationship is not a simple linear relationship. The traditional PID control algorithm is designed based on linear system theory and has poor adaptability to nonlinear systems. When the working point of the controlled object changes or is disturbed by external factors, the traditional PID controller may not be able to adjust the control parameters in time, resulting in a decrease in the control accuracy of the system, and even oscillation or divergence. At this stage, a precise control method and system for intelligent control cabinets based on fusion fuzzy algorithms are needed. Summary of the invention

[0004] In order to solve the problems of poor adaptability in traditional control cabinet regulation and poor system regulation effect due to coupling between parameters, the present invention provides a precise regulation method and system for an intelligent control cabinet based on a fusion fuzzy algorithm.

[0005] In the first aspect, the present invention provides a precise control method of an intelligent control cabinet based on a fusion fuzzy algorithm, which adopts the following technical solution: A precise control method of an intelligent control cabinet based on a fusion fuzzy algorithm, comprising: Obtain control data of the control cabinet, including environmental parameters and equipment operation status data in the control cabinet; Preprocessing is performed based on the acquired control data, including noise reduction using Kalman filtering and the introduction of an adaptive noise covariance adjustment mechanism; Feature extraction is performed based on the preprocessed control data, including extracting single time step deviations and introducing multi-time scale analysis to extract fluctuation rates; The extracted features are used as input to construct a fuzzy control model, including building a fuzzy rule base, a fuzzy reasoning mechanism and a defuzzification module; Priority matrix and energy efficiency optimization strategy are introduced to convert the control quantity obtained by defuzzification into the control signal of the actuator, and the actuator is intelligently coordinated controlled; Model optimization and fault prediction are carried out according to the collaborative control results, and the control cabinet is precisely adjusted based on the prediction results.

[0006] Furthermore, the introduction of the adaptive noise covariance adjustment mechanism includes initializing the Kalman filter parameters according to the acquired control data, calculating the residual sequence of the data, and dynamically adjusting the process noise covariance and the measurement noise covariance using the variance of the residual sequence, and using the adjusted covariance in combination with the new measurement information to correct the state estimation. The dynamic adjustment formula is: , , in, and are the adjustment coefficients for process noise covariance and measurement noise covariance, respectively. is the variance of the residual sequence, and are the process noise covariance and measurement noise covariance at the previous moment, respectively.

[0007] Furthermore, the feature extraction is performed based on the preprocessed control data, including calculating the temperature deviation and the humidity deviation based on the preprocessed control data for extracting the single time step deviation, then setting a sliding window with a length of L, the window containing L continuous time step data, calculating the average voltage fluctuation rate in the window, and finally introducing an exponential weighted average method to calculate the voltage fluctuation rate. The calculation formula of the exponential weighted average voltage fluctuation rate is: , in, For the forgetting factor, For the The exponentially weighted average voltage fluctuation rate in time steps, Expressed as voltage, Expressed as a time difference.

[0008] Furthermore, the feature extraction based on the pre-processed control data also includes setting membership function parameters, fuzzifying the calculated deviation and fluctuation rate, introducing a density clustering algorithm to dynamically determine the number of fuzzy levels, calculating the triangular membership function, Gaussian membership function and trapezoidal membership function according to the fuzzy levels, mixing different membership functions by dynamically adjusting weights, and converting numerical values ​​into fuzzy linguistic variables.

[0009] Furthermore, the construction of the fuzzy rule base, the fuzzy reasoning mechanism and the defuzzification module includes taking the extracted features as input, completely traversing the input data set, counting the frequency of occurrence of each item, and sorting them in descending order of frequency, using the FP-growth algorithm to mine association rules, recursively mining frequent item sets from the bottom of the FP-tree, generating association rules based on the frequent item sets, and screening out association rules with confidence greater than a threshold, and constructing a fuzzy rule base in the form of IF-THEN based on the screened association rules.

[0010] Furthermore, the construction of the fuzzy rule base, fuzzy reasoning mechanism and defuzzification module also includes using the Mamdani reasoning method to search for matching rules in the rule base based on the fuzzified input, calculating the matching degree of each matching rule, and then introducing evidence theory to correct the matching degree, and using the weighted average defuzzification method to calculate the control amount based on the rule matching degree obtained by fuzzy reasoning and the central value of the output membership function.

[0011] Furthermore, the model optimization and fault prediction are performed according to the collaborative control results, including constructing a fitness function based on the regulation error and the control quantity change rate, encoding the association rules in the fuzzy rule base in a binary coding manner, and selecting individuals with higher fitness from the current population as parents for genetic algorithm operations according to the value of the fitness function, and then introducing a real-time environmental compensation factor, dynamically adjusting the weights of the fuzzy rules according to the environmental compensation factor to complete the model optimization.

[0012] In the second aspect, a precise control system of an intelligent control cabinet based on a fusion fuzzy algorithm includes: The data acquisition module is configured to: acquire control data of the control cabinet, including acquiring environmental parameters and equipment operation status data in the control cabinet; The preprocessing module is configured to: perform preprocessing based on the acquired control data, including using Kalman filtering to reduce noise, and introducing an adaptive noise covariance adjustment mechanism; The feature extraction module is configured to: perform feature extraction based on the preprocessed control data, including extracting single time step deviations, and introducing multi-time scale analysis to extract fluctuation rates; The model module is configured to: construct a fuzzy control model using the extracted features as input, including constructing a fuzzy rule base, a fuzzy reasoning mechanism, and a defuzzification module; The conversion module is configured to: introduce a priority matrix and an energy efficiency optimization strategy, convert the control quantity obtained by defuzzification into a control signal of an actuator, and implement intelligent collaborative control of the actuator; The output module is configured to: perform model optimization and fault prediction according to the collaborative control results, and accurately control the control cabinet based on the prediction results.

[0013] In a third aspect, the present invention provides a computer-readable storage medium storing a plurality of instructions, wherein the instructions are suitable for being loaded and executed by a processor of a terminal device, a method for precise control of an intelligent control cabinet based on a fusion fuzzy algorithm.

[0014] In a fourth aspect, the present invention provides a terminal device comprising a processor and a computer-readable storage medium, the processor being used to implement various instructions; the computer-readable storage medium being used to store multiple instructions, the instructions being suitable for being loaded by the processor and executing the method for precise control of an intelligent control cabinet based on a fusion fuzzy algorithm.

[0015] In summary, the present invention has the following beneficial technical effects: 1. The present invention calculates the residual sequence of the data and uses its variance to dynamically adjust the process noise covariance and the measurement noise covariance, so as to more accurately estimate the system state. When there is sudden noise interference in the environment where the control cabinet is located, the dynamically adjusted covariance can enable the Kalman filter to filter the noise more effectively, improve the data quality, and thus improve the accuracy of subsequent control.

[0016] 2. The present invention not only extracts the single time step deviation, but also introduces multi-time scale analysis to extract the fluctuation rate. The single time step deviation can reflect the state difference at the current moment, while the fluctuation rate reflects the change trend of the parameter from the time dimension. By calculating the average voltage fluctuation rate and the exponentially weighted average voltage fluctuation rate, the dynamic change of the voltage can be more comprehensively understood. This multi-dimensional feature extraction method helps to capture the complex dynamic characteristics of the system.

[0017] 3. The present invention utilizes the FP-growth algorithm to mine association rules, automatically discovers the association between input variables and output control quantities from a large amount of data, and constructs a fuzzy rule base. Compared with the traditional method of constructing a rule base based on expert experience, the present invention can more objectively and comprehensively mine the potential rules in the data and generate rules that are more in line with the actual system operation conditions.

[0018] 4. The present invention adopts the Mamdani reasoning method for fuzzy reasoning, and introduces the evidence theory to correct the matching degree. In the defuzzification stage, the weighted average defuzzification method is used to calculate the control quantity according to the rule matching degree and the central value of the output membership function. The fuzzy reasoning result can be converted into an accurate control signal, providing an accurate basis for the coordinated control of the actuator.

[0019] 5. The present invention introduces a priority matrix and an energy efficiency optimization strategy to convert the control quantity obtained by defuzzification into a control signal of the actuator, thereby realizing intelligent collaborative control of the actuator. Through the priority matrix and the energy efficiency optimization strategy, the tasks of each actuator can be reasonably allocated according to the actual needs and operating status of the system, the collaborative work between the actuators can be realized, and the overall regulation efficiency and energy utilization efficiency of the system can be improved.

[0020] 6. The present invention performs model optimization and fault prediction according to the collaborative control results, constructs a fitness function based on the control error and the control quantity change rate, and optimizes the fuzzy rule base using a genetic algorithm, which enables the model to continuously adapt to changes in the system. At the same time, a real-time environmental compensation factor is introduced to dynamically adjust the weights of the fuzzy rules, further improving the adaptability of the control cabinet to environmental changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the overall process of a method for precise control of an intelligent control cabinet based on a fusion fuzzy algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0023] Example 1 Reference Figure 1 , a precise control method of an intelligent control cabinet based on a fusion fuzzy algorithm in this embodiment includes: Obtain control data of the control cabinet, including environmental parameters and equipment operation status data in the control cabinet; Preprocessing is performed based on the acquired control data, including noise reduction using Kalman filtering and the introduction of an adaptive noise covariance adjustment mechanism; Feature extraction is performed based on the preprocessed control data, including extracting single time step deviations and introducing multi-time scale analysis to extract fluctuation rates; The extracted features are used as input to construct a fuzzy control model, including building a fuzzy rule base, a fuzzy reasoning mechanism and a defuzzification module; Priority matrix and energy efficiency optimization strategy are introduced to convert the control quantity obtained by defuzzification into the control signal of the actuator, and the actuator is intelligently coordinated controlled; Optimize the model and predict faults according to the collaborative control results, and accurately regulate the control cabinet based on the prediction results.

[0024] Specifically, an intelligent control cabinet precise regulation method based on a fusion fuzzy algorithm includes the following steps: As Figure 1 shown, S1, obtain the regulation data of the control cabinet, including obtaining the environmental parameters and equipment operation status data inside the control cabinet; Use various sensors to collect the environmental parameters and equipment operation status information inside the control cabinet in real time. The collection frequency is set to once every 100 ms to ensure the real-time nature of the data. In addition to the traditional sensor data such as temperature, humidity, voltage, current, and pressure, introduce a multi-modal data fusion collection mechanism to collect the vibration frequency and sound spectrum data of the equipment inside the control cabinet. These additional data can reflect the equipment operation status from different dimensions. For example, abnormal vibration frequency may indicate wear of mechanical components of the equipment, and changes in the sound spectrum may imply electrical component failures.

[0025] S2, perform preprocessing based on the obtained regulation data, including using Kalman filtering for noise reduction and introducing an adaptive noise covariance adjustment mechanism; Perform noise reduction on the obtained data using Kalman filtering. First, explain the principle of Kalman filtering. Kalman filtering is a recursive algorithm for estimating the state of a dynamic system, which combines the prediction information of the system and the new measurement information to obtain the optimal estimate of the system state. This algorithm is based on two core equations, namely the state equation and the measurement equation. The state equation is: , where represents the system state vector at time k, is the state transition matrix, which describes the evolution law of the system state over time, is the system state vector at time k-1, is the control input matrix, is the control input vector at time k, is the process noise, which follows a Gaussian distribution , is the process noise covariance matrix, which reflects the statistical characteristics of the process noise.

[0026] The measurement equation is expressed as: , where is the measurement vector at time k, that is, the data actually measured by the sensor. H is the measurement matrix, which establishes the connection between the system state and the measurement value, is the measurement noise, which follows a Gaussian distribution , is the measurement noise covariance matrix, which reflects the noise situation in the measurement process.

[0027] Next, the specific process of implementing Kalman filter noise reduction is described: First, initialize the state estimate and the error covariance matrix, and perform a prediction operation at each time step k. Based on the state estimate at the previous moment , use the state equation to predict the state estimate at the current moment. The formula is: , where is the state estimate at the previous moment. Based on the error covariance matrix at the previous moment , calculate the predicted error covariance matrix at the current moment. The formula is: , where is the error covariance matrix at the previous moment. At the current moment k, obtain the measurement value from the sensor. Combine the predicted state estimate and the predicted error covariance matrix obtained in the prediction step, as well as the measurement value obtained in the measurement step, and perform an update operation. Calculate the Kalman gain, the updated state estimate, and the updated error covariance respectively. The calculation of the Kalman gain is expressed as: .

[0028] where is the transpose matrix of the measurement matrix H, is expressed as the Kalman gain.

[0029] The updated state estimate is expressed as: , where is the optimal state estimate value at time k, which is the best estimate of the true state of the system obtained by combining the predicted value and the measurement value, is the predicted state estimate value at time k, which is the state at time k predicted from the state estimate value at time k - 1 through the state equation, is the measurement value at time k.

[0030] The updated error covariance is expressed as: , where is the updated error covariance matrix at time k, which represents the uncertainty of the state estimate after combining the measurement information at time k, It is the unit matrix, which is used to ensure the dimensional matching of matrix operations and to adjust the influence of Kalman gain on the prediction error covariance matrix.

[0031] In traditional Kalman filtering, the process noise covariance and the measurement noise covariance It is usually a fixed value. However, in actual application scenarios, the characteristics of noise often change with time and environment. Therefore, an adaptive noise covariance adjustment mechanism is introduced to dynamically adjust the noise covariance according to the real-time collected data. and At each time step k, the measurement residual is calculated The measurement residual represents the difference between the measured value and the measured value estimated according to the predicted state. The statistical analysis of the residual sequence is mainly to calculate the variance of the residual. The variance of the residual can reflect the degree of deviation between the measured value and the predicted value. When the variance increases, it means that the influence of noise is increasing; when the variance decreases, it means that the influence of noise is decreasing. According to the statistical characteristics of the residual, the process noise covariance is dynamically adjusted. and the measurement noise covariance The value of is adjusted using the following formula: , , in, and are the adjustment coefficients for process noise covariance and measurement noise covariance, respectively. is the variance of the residual sequence, and The process noise covariance and measurement noise covariance of the previous moment are respectively completed. After completing the prediction and update steps of the Kalman filter and adjusting the noise covariance, it enters the next time step k+1 and repeats all the above steps until all the control data are processed. Through continuous iteration, the Kalman filter can always effectively reduce the noise of the data and adaptively adjust the noise covariance to adapt to the complex and changing noise environment.

[0032] S3, extracting features based on the preprocessed control data, including extracting single time step deviations, and introducing multi-time scale analysis to extract fluctuation rates; Feature extraction is performed based on the pre-processed control data. The single time step deviation is the core feature that reflects the real-time status of the system. This is achieved by comparing the difference between the actual value and the set target value. The difference between the current temperature and the set target temperature is calculated to reflect the degree of deviation from the temperature control. For example, if the target temperature is 25°C and the current measured temperature is 26°C, the temperature deviation is +1°C, indicating that cooling control is required. Similarly, the difference between the measured humidity and the target humidity reflects the deviation direction and amplitude of the humidity control. The voltage fluctuation rate in a single time step can reflect the change in voltage between two adjacent time steps. The actual voltage at the kth time step, ) is the actual voltage at the k-1th time step, is the time step, then the voltage fluctuation rate in a single time step is The calculation formula is: , In order to capture the long-term trend of voltage fluctuation, the sliding window technology is introduced to calculate the average voltage fluctuation rate at multiple time scales. A sliding window with a length of L is set, and the window contains L consecutive time step data. For example, if L=10 and the time step is , then the time range covered by the window is 1s. For the kth time step, the average voltage fluctuation rate in the window is calculated. , the formula is: , In order to consider both the short-term impact and long-term trend of voltage fluctuation, the exponential weighted average (EWMA) method is introduced to calculate the voltage fluctuation rate. is the exponentially weighted average voltage fluctuation rate at the kth time step, and the calculation formula of the exponentially weighted average voltage fluctuation rate is: , in, For the forgetting factor, For the The exponentially weighted average voltage fluctuation rate in time steps, Expressed as voltage, It is expressed as time difference. The extracted single time step deviation and multi-time scale fluctuation rate are sorted to form a feature vector or data set for subsequent use in fuzzy controller input calculation. Then, the density clustering algorithm is introduced to dynamically determine the number of fuzzy levels N according to the preprocessed data. The formula is: in, is the preprocessed data, The neighborhood radius determines how far around a data point the data point is considered as its neighborhood. The minimum sample number for the core point, that is, how many data points are required to be around a data point at least to be regarded as a core point. By adjusting these two parameters, the clustering algorithm can divide the data into different numbers of fuzzy levels according to the distribution of the data. For example, when the system is in a stable state, the data distribution is relatively concentrated, and the clustering algorithm may divide the data into 3 - 5 levels to reduce the subsequent calculation amount; when the system is disturbed and the data becomes scattered, the number of levels automatically increases to 7 - 9 to improve the control accuracy.

[0033] After determining the fuzzy levels, update the parameters of the membership function according to the clustering results. For the i-th fuzzy level, calculate its mean value and standard deviation , and then update the parameters of the membership function according to the formula , , , In this way, the membership function can better fit the data distribution of each fuzzy level.

[0034] In order to make full use of the advantages of different types of membership functions, we fuse the triangular membership function, Gaussian membership function, and trapezoidal membership function to define different types of membership functions. Among them, the triangular membership function: , where represents the parameters of different membership functions, defining the shape and position of the triangle. When x < a, the membership degree of x belonging to this fuzzy set is 0, that is, it completely does not belong. When , the membership degree linearly increases from 0 to 1 as x increases, and the increasing rate is determined by . When , the membership degree linearly decreases from 1 to 0 as x increases. When , the membership degree becomes 0 again, that is, it completely does not belong to this fuzzy set.

[0035] Gaussian membership function: , where is the mean value of the Gaussian function, determining the center position of the function, is the standard deviation of the Gaussian function, and its characteristic is that it has a smooth transition characteristic and can handle the continuous change of data. Trapezoidal membership function: , where d is the parameter of the membership function. When x < a, the membership degree of x belonging to this fuzzy set is 0, that is, it completely does not belong. When , the membership degree linearly increases from 0 to 1. When , the membership degree remains 1. When When , the membership degree decreases linearly from 1 to 0 as x increases. When , the membership becomes 0 again, and the three membership functions are combined by linear combination to calculate the mixed membership , the formula is: , in, are the weights of the three membership functions respectively, and the particle swarm optimization algorithm is used to dynamically adjust the weights. The goal is to make the mixed membership As close to the ideal membership as possible , the objective function is ,Through continuous iterative optimization, the optimal weight combination is found, and based on the above, the calculation of the fuzzy control model input is completed to complete the adaptive multi-modal fuzzification processing.

[0036] S4, using the extracted features as input to construct a fuzzy control model, including constructing a fuzzy rule base, a fuzzy reasoning mechanism, and a defuzzification module; Taking the extracted features as input, the historical operation data is analyzed using the data mining algorithm. The input data set is completely traversed, the frequency of occurrence of each item is counted, and it is sorted in descending order of frequency. Then, the data set is scanned again, and the items in each record are inserted into the FP-tree in the sorted order. Starting from the bottom of the FP-tree, frequent item sets are recursively mined. For each item, all its prefix paths in the FP-tree are found, and these prefix paths are merged into a conditional FP-tree. Then, frequent item sets are continued to be mined in the conditional FP-tree. Association rules are generated based on frequent item sets, and association rules with confidence greater than the threshold are screened out to construct a fuzzy rule base in the form of "IF-THEN". The rules describe the relationship between different fuzzy combinations of input variables and output control quantities. For example, “ ”, where PB, PS and NS are fuzzy language variables, representing different fuzzy states. The Mamdani reasoning method is used to find matching rules in the rule base based on the fuzzified input and calculate the matching degree of each matching rule: , in, In order to fuzzify the membership obtained by fuzzification, evidence theory is introduced to correct the matching degree. Evidence theory can handle uncertainty and incomplete information, and adjust the matching degree by fusing evidence from different sources (such as historical data, real-time monitoring data, etc.). For example, when there is a large error in the measurement of certain input variables, evidence theory can correct the matching degree based on other relevant evidence to avoid the deviation of the reasoning result due to the inaccuracy of a single piece of evidence. Finally, the weighted average defuzzification method is used to convert the fuzzy output obtained by fuzzy reasoning into an accurate control quantity. The basic idea of ​​this method is to calculate the accurate control quantity based on the rule matching degree obtained by fuzzy reasoning and the central value of the output membership function: , where n is the number of matching rules, is the rule matching degree obtained based on fuzzy reasoning, It is the central value of the output membership function, thus completing the model construction.

[0037] S5, introduce the priority matrix and energy efficiency optimization strategy, convert the control quantity obtained by defuzzification into the control signal of the actuator, and the actuator is intelligently coordinated controlled; The decision tree is used to classify the importance of the association between different control tasks and actuators. The decision tree classifies the importance of the association between different control tasks and actuators to construct a priority matrix. The priority matrix is ​​a two-dimensional matrix, whose rows represent different control tasks, such as temperature control, humidity control, voltage control, etc.; the columns represent various actuators, such as fans, air conditioners, humidifiers, and voltage stabilizers. Each element in the matrix represents the priority value of the corresponding actuator for a specific control task. For the decision tree classification process, taking the temperature control task as an example, the decision tree algorithm will determine the priority based on the temperature deviation, fluctuation rate, and response effect of actuators such as fans and air conditioners in the historical data. A large amount of historical data is collected, including temperature set values, actual temperatures, temperature fluctuations, and the operating status of each actuator and the effect of regulating temperature at different times. These data are used as the input of the decision tree. The decision tree divides different situations and rules through learning and analyzing the data, thereby determining the priority of each actuator in temperature control. For example, if the fan can quickly and effectively adjust the temperature under certain combinations of temperature deviation and fluctuation rate, then the fan will have a higher priority for the temperature control task in this case; conversely, if the air conditioner works better under certain conditions, the air conditioner will have a higher priority. By performing similar analysis and classification on all control tasks and actuators, a complete priority matrix is ​​finally formed. This matrix will serve as an important basis for the subsequent coordinated control of actuators, ensuring that the system can prioritize the use of appropriate actuators when facing different control tasks.

[0038] For each actuator, the energy consumption data of the actuator under different operating parameters are collected, and regression analysis is used to establish a nonlinear relationship. For example, for a fan, the energy consumption data under different operating parameters such as speed and air volume are collected, and then a nonlinear model between energy consumption and operating parameters is established using regression analysis methods (such as polynomial regression, neural network regression, etc.). Such a model can accurately describe the energy consumption characteristics of the actuator and provide a basis for subsequent energy efficiency optimization.

[0039] The NSGA-II (Non-dominated Sorting Genetic Algorithm-II) algorithm is used to optimize the operating parameters of the actuators. The algorithm aims to reduce energy consumption and meet the control requirements, while taking into account the priority matrix. During the optimization process, the algorithm generates a set of possible operating parameter combinations, and then evaluates and sorts these combinations according to the objective function. The objective function takes into account energy consumption, control effects, and the priority of the actuators. For example, for temperature control tasks, it is necessary to ensure that the temperature can reach the set value quickly, and the exhaustion possibility is low, while giving priority to actuators with high priorities. Through continuous iteration and evolution, the NSGA-II algorithm will find a set of optimal operating parameter combinations and complete the construction of energy efficiency optimization strategies.

[0040] The control quantity obtained by defuzzification is converted into the control signal of the actuator, and a distributed control system architecture is adopted. In this architecture, the control task is distributed to multiple controllers. Each controller is responsible for the control of a part of the actuator, and information sharing and collaborative work between the controllers are achieved through network communication.

[0041] For example, temperature control, humidity control and voltage control tasks are assigned to different controllers. Each controller collaboratively controls the corresponding actuator according to the priority matrix and energy efficiency optimization strategy. When a controller receives the control quantity for a specific control task obtained by defuzzification, it first refers to the priority matrix to determine the priority order of each actuator under the task, and then, according to the optimal operating parameter combination determined in the energy efficiency optimization strategy, the control quantity is converted into a control signal that can be recognized by the specific actuator, such as voltage signal, current signal, pulse signal, etc., and sent to the corresponding actuator.

[0042] At the same time, the controllers share information through the network to understand the execution status of other control tasks and the status of the actuators in real time. If a control task is abnormal or needs to adjust the control strategy, the controllers can make coordinated adjustments based on the shared information to ensure the stable operation and efficient control of the entire control cabinet system. For example, when the temperature control task requires more resources for some reason, the controller can appropriately adjust the operating status of the actuators in the humidity control or voltage control tasks based on the priority matrix and coordination mechanism to give priority to meeting the needs of temperature control.

[0043] S6. Perform model optimization and fault prediction based on the collaborative control results, and accurately adjust the control cabinet based on the prediction results; A fitness function is constructed based on the regulation error and the control amount change rate. The fitness function formula is: , in, It is expressed as the control error, which reflects the deviation between the actual output of the system and the expected output. It is expressed as the rate of change of the control quantity, and m is expressed as the total number of time steps. In fuzzy control, the control error is as small as possible, because a smaller error means that the system can achieve the set goal more accurately. The square treatment of the error is to emphasize the impact of larger errors and avoid the mutual cancellation of positive and negative errors. The drastic change of the control quantity may cause system instability or unnecessary energy consumption. Therefore, we add the rate of change of the control quantity to the fitness function and multiply it by a weight coefficient of 0.1 to balance the influence of the control error and the rate of change of the control quantity. The association rules in the fuzzy rule base are encoded in binary coding. According to the value of the fitness function, individuals with higher fitness are selected from the current population as parents to generate the next generation of individuals. The selected parent individuals are crossovered to generate new offspring individuals. The crossover operation simulates the gene exchange process in biological inheritance. By exchanging some chromosome fragments of the parent individuals, offspring individuals with new characteristics are generated.

[0044] In order to make the fuzzy controller better adapt to the changes in environmental conditions, we introduce a real-time environmental compensation factor : , in, Expressed as ambient temperature, Expressed as ambient humidity, is the target temperature, Expressed as target humidity, when environmental conditions change, The value of will change accordingly, thus providing a basis for subsequent rule weight adjustments.

[0045] The weight of the fuzzy rule is dynamically adjusted according to the environmental compensation factor. The formula is: , in, is the original weight of rule i, is the adjusted weight, It is an adjustment coefficient used to control the influence of the environmental compensation factor on the rule weight. When the environmental conditions change, by adjusting the rule weight, the fuzzy controller can pay more attention to the rules related to the current environmental conditions, thereby improving the control effect. When the ambient temperature and humidity deviate from the set value by a large amount, the corresponding rule weight will be adjusted, so that the controller can control more accurately, thereby completing the precise control of the control cabinet.

[0046] Example 2 The difference between this embodiment and embodiment 1 is that this embodiment provides a precise control system for an intelligent control cabinet based on a fusion fuzzy algorithm, including: The data acquisition module is configured to: acquire control data of the control cabinet, including acquiring environmental parameters and equipment operation status data in the control cabinet; The preprocessing module is configured to: perform preprocessing based on the acquired control data, including using Kalman filtering to reduce noise, and introducing an adaptive noise covariance adjustment mechanism; The feature extraction module is configured to: perform feature extraction based on the preprocessed control data, including extracting single time step deviations, and introducing multi-time scale analysis to extract fluctuation rates; The model module is configured to: construct a fuzzy control model using the extracted features as input, including constructing a fuzzy rule base, a fuzzy reasoning mechanism, and a defuzzification module; The conversion module is configured to: introduce a priority matrix and an energy efficiency optimization strategy, convert the control quantity obtained by defuzzification into a control signal of an actuator, and implement intelligent collaborative control of the actuator; The output module is configured to: perform model optimization and fault prediction according to the collaborative control results, and accurately control the control cabinet based on the prediction results.

[0047] A computer-readable storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded and executed by a processor of a terminal device for a precise control method of an intelligent control cabinet based on a fusion fuzzy algorithm.

[0048] A terminal device includes a processor and a computer-readable storage medium, wherein the processor is used to implement various instructions; the computer-readable storage medium is used to store multiple instructions, and the instructions are suitable for being loaded and executed by the processor to implement a precise control method for an intelligent control cabinet based on a fusion fuzzy algorithm.

[0049] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A precise control method for an intelligent control cabinet based on a fusion fuzzy algorithm, characterized in that: include: Obtain control data of the control cabinet, including environmental parameters and equipment operation status data in the control cabinet; Preprocessing is performed based on the acquired control data, including noise reduction using Kalman filtering and the introduction of an adaptive noise covariance adjustment mechanism; Feature extraction is performed based on the preprocessed control data, including extracting single time step deviations and introducing multi-time scale analysis to extract fluctuation rates; The extracted features are used as input to construct a fuzzy control model, including building a fuzzy rule base, a fuzzy reasoning mechanism and a defuzzification module; Priority matrix and energy efficiency optimization strategy are introduced to convert the control quantity obtained by defuzzification into the control signal of the actuator, and the actuator is intelligently coordinated controlled; Model optimization and fault prediction are carried out according to the collaborative control results, and the control cabinet is precisely adjusted based on the prediction results.

2. According to claim 1, a precise control method of an intelligent control cabinet based on a fusion fuzzy algorithm is characterized in that: The introduction of the adaptive noise covariance adjustment mechanism includes initializing the Kalman filter parameters according to the acquired control data, calculating the residual sequence of the data, and dynamically adjusting the process noise covariance and the measurement noise covariance using the variance of the residual sequence, and using the adjusted covariance combined with the new measurement information to correct the state estimation. The dynamic adjustment formula is: ， ， in, and are the adjustment coefficients for process noise covariance and measurement noise covariance, respectively. is the variance of the residual sequence, and are the process noise covariance and measurement noise covariance at the previous moment, respectively.

3. According to claim 1, a precise control method of an intelligent control cabinet based on a fusion fuzzy algorithm is characterized in that: The feature extraction is performed according to the preprocessed control data, including calculating the temperature deviation and humidity deviation according to the preprocessed control data, which are used to extract the single time step deviation, and then setting a sliding window with a length of L, the window contains L continuous time step data, and calculating the average voltage fluctuation rate in the window. Finally, an exponential weighted average method is introduced to calculate the voltage fluctuation rate. The calculation formula of the exponential weighted average voltage fluctuation rate is: ， in, For the forgetting factor, For the The exponentially weighted average voltage fluctuation rate in time steps, Expressed as voltage, Expressed as a time difference.

4. According to claim 3, a precise control method of an intelligent control cabinet based on a fusion fuzzy algorithm is characterized in that: The feature extraction based on the pre-processed control data also includes setting membership function parameters, fuzzifying the calculated deviation and fluctuation rate, introducing a density clustering algorithm to dynamically determine the number of fuzzy levels, calculating the triangular membership function, Gaussian membership function and trapezoidal membership function according to the fuzzy levels, mixing different membership functions by dynamically adjusting weights, and converting numerical values ​​into fuzzy linguistic variables.

5. According to claim 1, a precise control method of an intelligent control cabinet based on a fusion fuzzy algorithm is characterized in that: The method for constructing a fuzzy rule base, a fuzzy reasoning mechanism and a defuzzification module includes taking the extracted features as input, completely traversing the input data set, counting the occurrence frequency of each item, and sorting them in descending order of frequency, mining association rules using an FP-growth algorithm, recursively mining frequent item sets from the bottom of an FP-tree, generating association rules based on the frequent item sets, screening out association rules with a confidence greater than a threshold, and constructing a fuzzy rule base in the form of IF-THEN based on the screened association rules.

6. According to claim 5, a precise control method of an intelligent control cabinet based on a fusion fuzzy algorithm is characterized in that: The construction of the fuzzy rule base, the fuzzy reasoning mechanism and the defuzzification module also includes using the Mamdani reasoning method to search for matching rules in the rule base according to the fuzzified input, calculating the matching degree of each matching rule, then introducing the evidence theory to correct the matching degree, and using the weighted average defuzzification method to calculate the control amount according to the rule matching degree obtained by fuzzy reasoning and the central value of the output membership function.

7. According to claim 1, a precise control method of an intelligent control cabinet based on a fusion fuzzy algorithm is characterized in that: The method of performing model optimization and fault prediction based on the collaborative control results includes constructing a fitness function based on the regulation error and the control amount change rate, encoding the association rules in the fuzzy rule base in a binary coding manner, selecting individuals with higher fitness from the current population as parents for genetic algorithm operations based on the value of the fitness function, and then introducing a real-time environmental compensation factor, dynamically adjusting the weights of the fuzzy rules based on the environmental compensation factor, and completing the model optimization.

8. An intelligent control cabinet precision control system based on fusion fuzzy algorithm, executed in the method of claim 1, characterized in that: include: The data acquisition module is configured to: acquire control data of the control cabinet, including acquiring environmental parameters and equipment operation status data in the control cabinet; The preprocessing module is configured to: perform preprocessing based on the acquired control data, including using Kalman filtering to reduce noise, and introducing an adaptive noise covariance adjustment mechanism; The feature extraction module is configured to: perform feature extraction based on the preprocessed control data, including extracting single time step deviations, and introducing multi-time scale analysis to extract fluctuation rates; The model module is configured to: construct a fuzzy control model using the extracted features as input, including constructing a fuzzy rule base, a fuzzy reasoning mechanism, and a defuzzification module; The conversion module is configured to: introduce a priority matrix and an energy efficiency optimization strategy, convert the control quantity obtained by defuzzification into a control signal of an actuator, and implement intelligent collaborative control of the actuator; The output module is configured to: perform model optimization and fault prediction according to the collaborative control results, and accurately control the control cabinet based on the prediction results.

9. A computer-readable storage medium storing a plurality of instructions, characterized in that: The instruction is suitable for being loaded by a processor of a terminal device and executing a precise control method for an intelligent control cabinet based on a fusion fuzzy algorithm as described in claim 1.

10. A terminal device, comprising a processor and a computer-readable storage medium, wherein the processor is used to implement each instruction; and the computer-readable storage medium is used to store multiple instructions, characterized in that: The instructions are suitable for being loaded by a processor and executing a precise control method for an intelligent control cabinet based on a fusion fuzzy algorithm as described in claim 1.

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