Fuzzy-control-based energy-saving optimization control method and system for fresh air handling unit of air conditioner

Abstract

The invention discloses an air conditioner fresh air handling unit energy-saving optimization control method and system based on fuzzy control, and belongs to the technical field of air conditioner system control, and the method specifically comprises the steps that environmental parameters and unit state information are collected in real time through sensors arranged indoors and outdoors, and then the information is subjected to fuzzy processing and converted into fuzzy variables; establishing a fuzzy control rule base by combining actual data according to the characteristics and energy-saving requirements of the air conditioning system; a fuzzy reasoning method is utilized, a fuzzy control quantity is obtained through calculation according to the fuzzy variable and the rule base and then converted into an accurate control value through clearness processing, and the fresh air handling unit is controlled to operate; and the system monitors the environment change and the unit state, and the steps are periodically repeated to form closed-loop feedback control, so that energy-saving optimization is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of air conditioning system control, and in particular to an energy-saving optimization control method and system for an air conditioning fresh air unit based on fuzzy control. Background Art

[0002] In modern buildings, air conditioning units are widely used to maintain indoor air quality and comfort. However, traditional air conditioning units often use fixed control strategies, such as operating according to a predetermined schedule or a simple temperature setting value, which may lead to energy waste. On the one hand, in actual operation, indoor and outdoor environmental parameters such as temperature, humidity, and air quality are constantly changing. Fixed control strategies cannot be accurately adjusted in real time according to these changes, which may cause the air conditioning units to over-operate or under-operate in some periods. On the other hand, traditional control methods are difficult to balance the relationship between indoor air quality and energy consumption. While ensuring that the air quality meets the standard, they often consume too much energy.

[0003] For example, a Chinese patent with the authorization announcement number CN102003770B discloses a communication base station air conditioning energy-saving control method and device, including: connecting an energy-saving board to the base station air conditioning, and setting up a host computer to communicate remotely with the energy-saving board. The energy-saving board performs cruise detection on the air conditioning parameters. When it is detected that the air conditioning on-site parameters are inconsistent with the parameters set in the energy-saving board program, the energy-saving board sends the set parameters to the air conditioning to make the on-site air conditioning parameters consistent with the set parameters. The outdoor environment is detected. When the outdoor environment meets the requirements, the fresh air energy-saving unit is turned on, and the base station is cooled using the outdoor free cold source to maximize energy saving. The energy-saving board is provided with a maintenance key, which can make the energy-saving board lose control of the air conditioning and only maintain data communication with the host computer. The energy-saving board internally times the start time of the maintenance mode, and the energy-saving board automatically resumes control after maintenance. This technical solution performs cruise detection on the temperature of the air conditioner in the base station, adopts a remote monitoring method, and uses free cold sources to optimize the energy saving of the base station and reduce the pressure of use costs.

[0004] For example, a Chinese patent with authorization announcement number CN115164378B discloses a fresh air unit control method and related devices based on digital twins, including: constructing a physical model of the target fresh air unit, and then building a twin heat and moisture treatment mechanism model, and then adding a twin control model, and outputting control instructions to the corresponding actuator to achieve the needs of dynamically debugging the fresh air unit system based on the mechanism model. At the same time, by optimizing the control parameters, it effectively overcomes the problem that the control system of the relevant fresh air unit is mainly based on experience control and lacks dynamic adaptive control capabilities for variable operating conditions of the unit, and meets control requirements such as energy saving, thereby improving the working efficiency of the fresh air unit.

[0005] The above existing technologies all have the following problems: lack of refined control and inability to adapt to complex environmental changes; mainly rely on pre-set parameters and simple conditional judgments, lack intelligent decision-making mechanisms and comprehensive evaluation of operating status; when faced with rapidly changing indoor and outdoor environmental parameters and operating status, the response speed is slow and the flexibility is limited. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention proposes an energy-saving optimization control method and system for an air-conditioning fresh air unit based on fuzzy control. Environmental parameters and unit status information are collected in real time by sensors arranged indoors and outdoors, and then the information is fuzzified and converted into fuzzy variables. A fuzzy control rule base is established based on the characteristics and energy-saving requirements of the air-conditioning system and in combination with actual data. The fuzzy reasoning method is used to calculate the fuzzy control quantity based on the fuzzy variables and the rule base, and then the fuzzy control quantity is converted into a precise control value through clarification processing to control the operation of the fresh air unit. The system monitors environmental changes and unit status, and repeats the above steps at regular intervals to form a closed-loop feedback control, thereby achieving energy-saving optimization.

[0007] To achieve the above object, the present invention provides the following technical solutions: The energy-saving optimization control method of the air-conditioning fresh air unit based on fuzzy control includes: Step S1: Collect indoor and outdoor environmental parameter information and operating status parameter information of the fresh air unit in real time through sensors arranged indoors and outdoors; Step S2: fuzzy process the collected indoor and outdoor environmental parameter information and fresh air unit operating status parameter information and convert them into corresponding fuzzy variable sets; Step S3: Establish a fuzzy control rule base based on the operating characteristics and energy-saving requirements of the air-conditioning system and in combination with actual test data; Step S4: according to the fuzzy variables and the established fuzzy rule base, the fuzzy output variables are calculated by using the fuzzy reasoning method to obtain the fuzzy set of the control quantity of the fresh air unit; Step S5: converting the fuzzy control quantity obtained by fuzzy reasoning into an accurate control value through a clarification method, and sending the clarified control value to the controller of the fresh air unit to perform a control operation; Step S6: Monitor indoor and outdoor environmental parameters and the operating status of the fresh air unit, and repeat the above steps at predetermined time intervals to form a closed-loop feedback control system.

[0008] Specifically, the specific steps of step S2 include: S2.1: Obtain indoor and outdoor environmental parameter information and fresh air unit operating status parameter information , and preprocessing is performed, where represents the nth indoor and outdoor environmental parameter, represents the mth fresh air unit operating status parameter, n represents the number of indoor and outdoor environmental parameters, and m represents the number of fresh air unit operating status parameters; S2.2: Collect and organize the energy-saving optimization control targets and actual operation characteristic data of the air conditioning fresh air unit, and and Conduct analysis, combine energy-saving optimization control objectives and actual operation characteristic data, and preliminarily determine the candidate list of fuzzy parameters; S2.3: The final fuzzy parameters are selected from the fuzzy parameter candidate list based on the correlation analysis. At the same time, for each selected fuzzy parameter, the domain range of the corresponding fuzzy parameter is determined in combination with the statistical characteristics of the actual operation data. and ,in, and Respectively and The mean of and Respectively and The standard deviation of , k represents the empirical coefficient; S2.4: According to the characteristics of the parameters and the energy-saving control requirements, the domain of each parameter is divided into N fuzzy subsets, and the membership function is defined for the fuzzy subsets; S2.5: For and For each parameter in , the membership function corresponding to the parameter is used to calculate the membership under N fuzzy subsets; S2.6: Combine the membership values ​​under the N fuzzy subsets to form a fuzzy variable set of the parameter; S2.7: Traversal and All parameters in are obtained to obtain the fuzzy variable set corresponding to all parameters.

[0009] Specifically, the specific steps of step S4 include: S4.1: Obtain the input fuzzy variables after fuzzification processing, and load the pre-established fuzzy rule base into the memory; S4.2: For each rule in the loaded fuzzy rule base, the fuzzified input fuzzy variable is matched with the rule condition part in the fuzzy rule base; If the fuzzy subset of each input fuzzy variable is consistent with the corresponding fuzzy subset in the rule condition, it means that the match is successful; S4.3: For each successfully matched rule, record the corresponding output fuzzy variable part and the entire rule information; S4.4: For each successfully matched rule, calculate its excitation intensity ,in, represents the bth input fuzzy variable The corresponding membership value, b represents the number of input fuzzy variables, represents the bth input fuzzy variable of the Ath matching rule, represents the minimum function, represents the weight corresponding to the bth input fuzzy variable of the Ath rule in the fuzzy rule base, E represents the environmental factor adjustment parameter, and , S represents the system operation status correction factor, and .

[0010] Specifically, the specific steps of step S4 also include: S4.5: The calculated excitation intensity of each matching rule , the membership function of the output fuzzy variable corresponding to the rule Perform truncation operation; like , then keep constant; like , then let ; S4.6: Take the maximum value of all truncated membership functions at the same output variable value, and obtain a comprehensive output fuzzy variable membership function through aggregation operation , generate the fuzzy set of the control quantity of the fresh air unit, where represents the membership function of the aggregated output fuzzy variable, represents the output fuzzy variable membership function after truncation of the Ath rule, represents the maximum value function, c represents the total number of matching rules, represents the importance coefficient corresponding to the Ath rule, and F represents the feedback adjustment parameter; S4.7: Convert the fuzzy set of the control quantity of the fresh air unit into an accurate numerical output as the control quantity of the fresh air unit.

[0011] Specifically, the specific steps of step S5 include: S5.1: Obtain the fuzzy control quantity and the corresponding membership function obtained by fuzzy reasoning, and at the same time, obtain the communication interface configuration information of the fresh air unit controller; S5.2: According to the pre-set discretization accuracy , discretize the domain of the fuzzy control quantity and calculate the number of domain points after discretization ,in, and Respectively represent the maximum and minimum values ​​of the fuzzy control quantity domain; S5.3: Generate a discretized set of domain points based on the number of discretized domain points Q ; S5.4: For each domain point in the discretized domain point set , get Membership value in the membership function of fuzzy control quantity ,according to and , calculate the product of the membership value of each point and the value of the point ; S5.5: Sum the products of all discretized domain points At the same time, the membership values ​​of all discretized domain points are summed up ; S5.6: Divide the above two sums to obtain the precise control value , and the calculated precise control value According to the pre-acquired fresh air unit controller communication interface configuration information, it is sent to the controller of the fresh air unit through the communication interface.

[0012] Specifically, the indoor and outdoor environmental parameter information in step S1 includes indoor temperature, indoor humidity, outdoor temperature, outdoor humidity, and indoor carbon dioxide concentration; the operating status parameter information of the fresh air unit includes fan speed, air supply volume, cooling capacity, heating capacity, and power consumption.

[0013] Specifically, the predetermined time interval in step S6 is set to 3-10 minutes.

[0014] The energy-saving optimization control system of the air-conditioning fresh air unit based on fuzzy control includes: data acquisition module, fuzzy processing module, fuzzy rule base module, execution and feedback module; The data acquisition module is used to collect indoor and outdoor environmental parameter information and operating status parameter information of the fresh air unit in real time through sensors; The fuzzy processing module is used to perform fuzzy processing on the real-time collected indoor and outdoor environmental parameter information and the operating status parameter information of the fresh air unit; The fuzzy rule base module is used to establish a fuzzy control rule base according to the operating characteristics and energy-saving requirements of the air-conditioning system in combination with actual test data, and to obtain a fuzzy set of control quantities of the fresh air unit using a fuzzy reasoning method; The execution and feedback module is used to convert the fuzzy control quantity obtained by fuzzy reasoning into an accurate control value through a clarification method, send it to the controller of the fresh air unit to execute the control operation, and monitor the indoor and outdoor environmental parameters and the operating status of the fresh air unit.

[0015] Specifically, the fuzzy rule base module includes: a rule formulation unit and a fuzzy reasoning unit; The rule formulation unit is used to establish a fuzzy control rule base according to the operating characteristics and energy-saving requirements of the air-conditioning system in combination with actual test data; The fuzzy reasoning unit is used to calculate the fuzzy output variables using a fuzzy reasoning method according to the fuzzy variables and the established fuzzy rule base, so as to obtain the fuzzy set of the control quantity of the fresh air unit.

[0016] Specifically, the execution and feedback module includes: a clarification unit and a control execution unit; The clarification unit is used to convert the fuzzy control quantity obtained by fuzzy reasoning into an accurate control value by using a clarification method; The control execution unit is used to send the clarified precise control value to the controller of the fresh air unit, so that the fresh air unit adjusts the operating state according to the control instruction.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention proposes an energy-saving optimization control method for an air-conditioning fresh air unit based on fuzzy control. By real-time collection of indoor and outdoor environmental parameter information and operating status parameter information of the fresh air unit, the actual operating status of the system can be accurately grasped; the collected data is fuzzified, a fuzzy control rule base is established, and the fuzzy reasoning method is used to calculate the control quantity. The application of this fuzzy logic enables the system to simulate the human way of thinking and process complex and uncertain environmental and operating status information, thereby formulating a control strategy that is more in line with actual needs, effectively improving the intelligence and adaptability of the control.

[0018] The present invention proposes an energy-saving optimization control method for an air-conditioning fresh air unit based on fuzzy control, which clarifies the fuzzy control quantity and sends it to a controller to execute the operation, and forms a closed-loop feedback control system through monitoring. The above steps are repeated at predetermined time intervals, so that the system can adjust the control strategy in real time according to changes in actual conditions and always maintain an energy-saving and optimized operating state. It not only helps to reduce the energy consumption of the air-conditioning fresh air unit and achieve the goal of energy conservation and emission reduction, but also can improve the operating efficiency of the equipment, reduce unnecessary energy waste and extend the service life of the equipment under the premise of ensuring indoor air quality and comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the energy-saving optimization control method of the air-conditioning fresh air unit based on fuzzy control of the present invention; Figure 2 This is a working flow chart of the energy-saving optimization control method of the air-conditioning fresh air unit based on fuzzy control of the present invention; Figure 3This is a structural diagram of the energy-saving optimization control system of the air-conditioning fresh air unit based on fuzzy control of the present invention. DETAILED DESCRIPTION

[0020] Example 1 See also Figure 1 and Figure 2 The present invention provides an embodiment: an energy-saving optimization control method for an air-conditioning fresh air unit based on fuzzy control, comprising the following steps: Step S1: Collect indoor and outdoor environmental parameter information and operating status parameter information of the fresh air unit in real time through sensors arranged indoors and outdoors; In step S1, the indoor and outdoor environmental parameter information includes indoor temperature, indoor humidity, outdoor temperature, outdoor humidity, and indoor carbon dioxide concentration; the operating status parameter information of the fresh air unit includes fan speed, air supply volume, cooling capacity, heating capacity, and power consumption.

[0021] Furthermore, the specific steps of step S1 include: S1.1: Real-time information on indoor and outdoor environmental parameters is obtained through sensors placed indoors and outdoors, such as temperature sensors, humidity sensors, and carbon dioxide sensors. These parameters reflect the actual indoor and outdoor environmental conditions and are an important basis for subsequent control decisions. For example, if the indoor temperature is too high, the cooling capacity may need to be increased, and if the carbon dioxide concentration exceeds the standard, the fresh air volume needs to be increased to ensure air quality. S1.2: Collect the current operating status parameters of the fresh air unit, including fan speed, air supply, cooling capacity, heating capacity, power consumption, etc. These parameters help to understand the working status of the fresh air unit so that reasonable adjustments can be made according to the actual operating status to achieve energy-saving optimization. For example, if the current fan speed is high but the indoor temperature has not reached the set value, it may be necessary to check whether the refrigeration system is working properly or consider whether the speed or cooling capacity needs to be further increased; S1.3: During system operation, sensors collect indoor and outdoor environmental parameters and fresh air unit operating status parameters in real time, and transmit these data to the central controller. The central controller filters and preprocesses the collected data to remove abnormal data and noise interference to ensure the accuracy and reliability of the data.

[0022] Step S2: fuzzy process the collected indoor and outdoor environmental parameter information and fresh air unit operating status parameter information and convert them into corresponding fuzzy variable sets; Among them, the fuzzy subset of the indoor temperature includes low, medium and high, the fuzzy subset of the indoor humidity includes low, medium and high, the fuzzy subset of the outdoor temperature includes low, moderate and high, the fuzzy subset of the outdoor humidity includes low, medium and high, the fuzzy subset of the indoor carbon dioxide concentration includes low, medium and high, the fuzzy subset of the fan speed includes slow, medium and fast, and the fuzzy subsets of the cooling capacity and heating capacity include low, medium and high.

[0023] Step S3: Establish a fuzzy control rule base based on the operating characteristics and energy-saving requirements of the air-conditioning system and in combination with actual test data; Among them, the output fuzzy variable control rule refers to the rule that specifies the corresponding output fuzzy variable according to different combinations of input fuzzy variables in the fuzzy control system. The fuzzy control rules include: (1) Conditions Input fuzzy variable combination: This is the conditional part of the rule, which consists of multiple input fuzzy variables and their corresponding fuzzy subsets. Taking the air conditioning unit as an example, the input fuzzy variables may include indoor temperature, indoor humidity, and outdoor temperature. The condition of the rule describes the specific combination of these variables. For example, if the indoor temperature is high, the indoor humidity is humid, and the outdoor temperature is warm; Logical relations: There may be logical relations between multiple input fuzzy variables, such as and, or. For example, if the indoor temperature is high and the indoor humidity is humid or the outdoor temperature is hot, this logical relation enriches the situations that the rules can describe, making it more flexible to deal with complex system states; (2) Conclusion Output fuzzy variable adjustment strategy: This is the conclusion part of the rule, which stipulates what kind of adjustment should be made to the output fuzzy variables when the condition is partially met. For the control of the air-conditioning fresh air unit, the output fuzzy variables may be the cooling capacity, such as large, medium, and small; the fan speed, such as fast, medium, and slow; the fresh air valve opening, such as fully open, half open, and closed; for example, under the above conditions, if the indoor temperature is high, the indoor humidity is humid, and the outdoor temperature is warm, the conclusion may be: the cooling capacity is adjusted to large, the fan speed is adjusted to fast, and the fresh air valve opening remains half open. The adjustment strategies of these output fuzzy variables guide how the air-conditioning fresh air unit changes its operating status to adapt to changes in indoor and outdoor environments and energy-saving requirements.

[0024] Step S4: according to the fuzzy variables and the established fuzzy rule base, the fuzzy output variables are calculated by using the fuzzy reasoning method to obtain the fuzzy set of the control quantity of the fresh air unit; Step S5: converting the fuzzy control quantity obtained by fuzzy reasoning into an accurate control value through a clarification method, and sending the clarified control value to the controller of the fresh air unit to perform a control operation; Step S6: Monitor indoor and outdoor environmental parameters and the operating status of the fresh air unit, and repeat the above steps at predetermined time intervals to form a closed-loop feedback control system.

[0025] In step S6, the predetermined time interval is set to 3-10 minutes.

[0026] Exemplarily, an air-conditioning fresh air unit is installed in the office area of ​​a commercial building, and temperature sensors, humidity sensors, carbon dioxide sensors, power sensors, etc. are reasonably arranged indoors and outdoors to ensure that the required environmental parameters and fresh air unit operating status parameters can be accurately collected. The sensors are connected to a central controller, which has functions such as data collection, processing, fuzzy control algorithm operation, and control signal output. Taking the indoor temperature as an example, assuming that its measured value is 28°C, according to the pre-set indoor temperature membership function, its membership in the two fuzzy subsets of medium and high is determined to be 0.4 and 0.6 respectively. Similarly, other environmental parameters and fresh air unit operating status parameters are fuzzy processed. Assuming that the current indoor temperature is high, the indoor humidity is medium, the indoor carbon dioxide concentration is high, and the outdoor temperature is moderate, the corresponding fuzzy control rules are obtained according to the fuzzy rule base. If the indoor temperature is high, the indoor humidity is medium, the indoor carbon dioxide concentration is high, and the outdoor temperature is moderate, the fan speed should be fast and the cooling capacity should be high. Then, the Mamdani fuzzy reasoning method is used in combination with the above fuzzy rules. and the fuzzy subset membership of each input variable, perform fuzzy reasoning operation, and obtain the fuzzy output set of fan speed and cooling capacity; secondly, clarify the fuzzy output set through the centroid method to obtain the precise adjustment values ​​of fan speed and cooling capacity, for example, the fan speed is adjusted to 1500rpm, and the cooling capacity is adjusted to 8kW; finally, the central controller sends the clarified control value to the inverter and cooling and heating controller of the fresh air unit to adjust the fan speed and cooling and heating power. After a period of operation, the indoor and outdoor environmental parameters and the operating status parameters of the fresh air unit are collected again to evaluate whether the indoor air quality meets the requirements, such as the indoor temperature is controlled at 24-26°C, the indoor carbon dioxide concentration is lower than 1000ppm, and the energy consumption is calculated at the same time, and compared with the energy consumption under the traditional control method. After actual testing, under the energy-saving optimization control method based on fuzzy control of the present invention, the energy consumption of the air-conditioning fresh air unit in the office area is reduced by about 25% compared with the traditional control method while ensuring that the indoor air quality meets the standard, and a significant energy-saving effect is achieved.

[0027] The specific steps of step S2 include: S2.1: Obtain indoor and outdoor environmental parameter information and fresh air unit operating status parameter information , and preprocessing is performed, where represents the nth indoor and outdoor environmental parameter, represents the mth fresh air unit operating status parameter, n represents the number of indoor and outdoor environmental parameters, and m represents the number of fresh air unit operating status parameters; S2.2: Collect and organize the energy-saving optimization control targets and actual operation characteristic data of the air conditioning fresh air unit, and and Conduct analysis; Furthermore, the specific steps of S2.2 include: (1) Start the fuzzy parameter candidate determination process and clarify the data types and sources to be collected; (2) According to the data collection steps, comprehensively collect the energy-saving optimization control target data and actual operation characteristic data of the air-conditioning fresh air unit to ensure the accuracy and completeness of the data; Among them, the energy-saving optimization control target data include: determining indoor air quality standards and collecting energy consumption indicators; determining indoor air quality standards, such as the allowable upper limit of indoor carbon dioxide concentration, such as 1000ppm, the appropriate range of indoor relative humidity, such as 40%-60%, etc. These standards will be used as part of the control objectives to measure the comfort of the indoor environment and whether the air quality meets the standards; collecting energy consumption indicators, such as the expected energy consumption limit per unit area, for example, the daily power consumption per square meter does not exceed a certain value. These target values ​​will guide the optimization control of operating parameters such as cooling capacity, heating capacity, fan power, etc., and help screen related fuzzy parameters.

[0028] Actual operating characteristic data: mainly obtain historical operating data from the control system records of the fresh air unit, including indoor and outdoor environmental parameters at different time periods and operating status parameters of the fresh air unit and corresponding energy consumption data.

[0029] (3) Organize and preprocess the collected data to clarify the priority and constraints of each indicator. For example, if in a specific scenario, the priority of indoor air quality is higher than that of energy consumption control, then when screening fuzzy parameters, more emphasis will be placed on parameters closely related to air quality, such as carbon dioxide concentration, air pollutant concentration, etc. At the same time, energy consumption factors should also be considered to find a balance between the two.

[0030] S2.3: The final fuzzy parameters are selected from the fuzzy parameter candidate list based on the correlation analysis. At the same time, for each selected fuzzy parameter, the domain range of the corresponding fuzzy parameter is determined in combination with the statistical characteristics of the actual operation data. and ,in, and Respectively and The mean of and Respectively and The standard deviation of , k represents the empirical coefficient; Furthermore, the specific steps of S2.3 include: (1) Calculating the Pearson correlation coefficient between the indoor and outdoor environmental parameters and the operating status parameters of the fresh air unit, wherein the Pearson correlation coefficient calculation formula is the prior art content in this field, is not the inventive solution of the present application, and is not described in detail here; (2) All indoor and outdoor environmental parameters and fresh air unit operating status parameters are used as the rows and columns of the matrix to construct a parameter relationship matrix. The corresponding correlation coefficient is filled in each element position of the matrix to intuitively observe the strength of the correlation between the parameters. Parameters with high correlation with multiple key parameters are selected as the focus objects and included in the fuzzy parameter candidate list; (3) Through sensitivity analysis, the sensitivity of each parameter is quantitatively evaluated to determine the parameters that have a greater impact on energy consumption and indoor environmental comfort. Sensitivity analysis is a prior art in this field and is not an inventive solution of this application, so it will not be elaborated here. (4) Organize experts to conduct empirical judgment and evaluation, optimize and supplement the screening results based on expert opinions, and form a candidate list of fuzzy parameters; (5) According to the complexity of the actual system and the control accuracy requirements, the parameters in the fuzzy parameter candidate list are sorted, and the first M parameters are selected from the sorted parameter list as the final fuzzy parameters; (6) For each selected final fuzzy parameter, calculate the statistical characteristics of the actual operating data of the selected fuzzy parameter, including the minimum value, maximum value, mean value, and standard deviation. According to the calculated statistical characteristics, determine the domain range of the fuzzy parameter. Among them, the commonly used method is based on the principle of mean value ± k × standard deviation, where k is a coefficient selected based on experience, usually between 2 and 3, which is used to consider the normal fluctuation range of the data.

[0031] S2.4: According to the characteristics of the parameters and the energy-saving control requirements, the domain of each parameter is divided into N fuzzy subsets, and the membership function is defined for the fuzzy subsets; Furthermore, the specific steps of S2.4 include: (1) For each selected fuzzy parameter, conduct an in-depth study of its physical meaning and its role in the energy-saving control of the air conditioning fresh air unit. For example, for the indoor temperature parameter, understand the human body's comfort level in different temperature ranges and the energy consumption variation of cooling or heating at different temperatures; for the fan speed parameter, understand its relationship with air supply volume and energy consumption; (2) According to the energy-saving control requirements, clarify the impact of each parameter on the control strategy within different value ranges. For example, in energy-saving mode, when the indoor temperature is close to the set comfortable temperature range, the power of the cooling or heating equipment should be appropriately reduced; when the fan speed is low, the energy consumption is relatively low, but the air supply volume is also small. The relationship between the two needs to be balanced to meet the air quality requirements; (3) Determine the number of fuzzy subsets based on the characteristics of the parameters and the complexity of the control. Taking indoor temperature as an example, if the control requirements are relatively simple, it may be divided into three fuzzy subsets: low, medium, and high. If more sophisticated control is required, it can be divided into five fuzzy subsets: very low, low, moderate, high, and very high. (4) Based on the domain range of the parameter and the number of selected fuzzy subsets, the boundaries of the fuzzy subsets are evenly divided. For example, if the domain of indoor temperature is [18℃, 28℃], it is divided into three fuzzy subsets, and the boundaries can be 18℃-22℃, 22℃-26℃, and 26℃-28℃. (5) According to the parameter characteristics and the distribution of the fuzzy subsets, a triangle membership function is selected and its parameters are determined. The triangle membership function is a prior art in the art and is not an inventive solution of the present application, and is not described in detail here.

[0032] S2.5: For and For each parameter in, the membership function corresponding to the parameter is used to calculate the membership under N fuzzy subsets, wherein the membership function calculation formula is the prior art content in this field, is not the inventive solution of the present application, and is not described in detail here; S2.6: Combine the membership values ​​under the N fuzzy subsets to form a fuzzy variable set of the parameter; S2.7: Traversal and All parameters in are obtained to obtain the fuzzy variable set corresponding to all parameters.

[0033] The specific steps of step S4 include: S4.1: Obtain the input fuzzy variables after fuzzification processing, and load the pre-established fuzzy rule base into the memory; S4.2: For each rule in the loaded fuzzy rule base, the fuzzified input fuzzy variable is matched with the rule condition part in the fuzzy rule base; If the fuzzy subset of each input fuzzy variable is consistent with the corresponding fuzzy subset in the rule condition, it means that the match is successful; Furthermore, the matching process judgment process includes: (1) Ensure that the fuzzy rule base has been loaded into the memory and that both the condition part and the conclusion part of the rule can be accessed. The condition part of the rule usually contains a fuzzy subset combination of multiple input fuzzy variables, for example, the indoor temperature is high, the indoor humidity is high, and the outdoor temperature is moderate; (2) Obtain the input fuzzy variables after fuzzification, including the fuzzy subset name and membership value corresponding to each variable. For example, the indoor temperature after fuzzification is high 0.7, which means that the indoor temperature belongs to the fuzzy subset high, with a membership value of 0.7; (3) For each rule in the fuzzy rule base, start checking the input fuzzy variables in its condition part and compare the fuzzy subset name of each input fuzzy variable with the corresponding part in the rule condition. For example, for the indoor temperature in the rule condition, check whether the input indoor temperature fuzzy subset matches it. (4) If the fuzzy subset name matches, record the position of the variable in the rule and the corresponding membership value. For example, if the indoor temperature is at the front in a rule and the membership of the input indoor temperature is 0.7, record this position and membership value. (5) After checking all the input fuzzy variables in the rule condition part, determine whether each variable is successfully matched. If all variables are matched, then this rule completely matches the input fuzzy variables; if any variable does not match, this rule does not meet the current input situation.

[0034] S4.3: For each successfully matched rule, record the corresponding output fuzzy variable part and the entire rule information; S4.4: For each successfully matched rule, calculate its excitation intensity ,in, represents the bth input fuzzy variable The corresponding membership value, b represents the number of input fuzzy variables, represents the bth input fuzzy variable of the Ath matching rule, represents the minimum function, represents the weight corresponding to the bth input fuzzy variable of the Ath rule in the fuzzy rule base, E represents the environmental factor adjustment parameter, and , S represents the system operation status correction factor, and ; S4.5: The calculated excitation intensity of each matching rule , the membership function of the output fuzzy variable corresponding to the rule Perform truncation operation; like , then keep constant; like , then let ; S4.6: Take the maximum value of all truncated membership functions at the same output variable value, and obtain a comprehensive output fuzzy variable membership function through aggregation operation , generate the fuzzy set of the control quantity of the fresh air unit, where represents the membership function of the aggregated output fuzzy variable, represents the output fuzzy variable membership function after truncation of the Ath rule, represents the maximum value function, c represents the total number of matching rules, represents the importance coefficient corresponding to the Ath rule, and F represents the feedback adjustment parameter; Among them, taking the maximum value of all truncated membership functions at the same output variable value means: at each discretized output variable value point, compare the membership values ​​corresponding to all membership functions, take the maximum value as the membership of the value point in the comprehensive output fuzzy variable membership function, and then combine each discretized output variable value point and its corresponding maximum membership value to construct a comprehensive output fuzzy variable membership function.

[0035] S4.7: Convert the fuzzy set of the control quantity of the fresh air unit into an accurate numerical output as the control quantity of the fresh air unit.

[0036] The specific steps of step S5 include: S5.1: Obtain the fuzzy control quantity and the corresponding membership function obtained by fuzzy reasoning, and at the same time, obtain the communication interface configuration information of the fresh air unit controller; S5.2: According to the pre-set discretization accuracy , discretize the domain of the fuzzy control quantity and calculate the number of domain points after discretization ,in, and Respectively represent the maximum and minimum values ​​of the fuzzy control quantity domain; S5.3: Generate a discretized set of domain points based on the number of discretized domain points Q ; S5.4: For each domain point in the discretized domain point set , get Membership value in the membership function of fuzzy control quantity ,according to and , calculate the product of the membership value of each point and the value of the point ; S5.5: Sum the products of all discretized domain points At the same time, the membership values ​​of all discretized domain points are summed up ; S5.6: Divide the above two sums to obtain the precise control value , and the calculated precise control value According to the pre-acquired fresh air unit controller communication interface configuration information, it is sent to the controller of the fresh air unit through the communication interface.

[0037] Example 2 See also Figure 3 Another embodiment provided by the present invention is an energy-saving optimization control system for an air-conditioning fresh air unit based on fuzzy control, comprising: Data acquisition module, fuzzy processing module, fuzzy rule base module, execution and feedback module; A data acquisition module is used to collect indoor and outdoor environmental parameter information and operating status parameter information of the fresh air unit in real time through sensors, wherein the sensors include temperature sensors, humidity sensors, and carbon dioxide sensors; The fuzzy processing module is used to perform fuzzy processing on the real-time collected indoor and outdoor environmental parameter information and the operating status parameter information of the fresh air unit to provide input for subsequent fuzzy reasoning; The fuzzy rule base module is used to establish a fuzzy control rule base based on the operating characteristics and energy-saving requirements of the air-conditioning system and in combination with actual test data, and to obtain the fuzzy set of control quantities of the fresh air unit using the fuzzy reasoning method; The execution and feedback module is used to convert the fuzzy control quantity obtained by fuzzy reasoning into an accurate control value through a clarification method, send it to the controller of the fresh air unit to execute the control operation, and monitor the indoor and outdoor environmental parameters and the operating status of the fresh air unit.

[0038] The fuzzy rule base module includes: a rule formulation unit and a fuzzy reasoning unit; A rule-making unit is used to establish a fuzzy control rule base based on the operating characteristics and energy-saving requirements of the air-conditioning system in combination with actual test data. These rules cover the corresponding relationship between possible indoor and outdoor environmental parameter combinations and the optimal operating state adjustment strategy that the fresh air unit should adopt. For example, when the indoor temperature is high and the humidity is high, and the outdoor temperature is moderate, the rules may stipulate that the cooling capacity should be increased and the fan speed should be appropriately increased to speed up the heat and moisture exchange of the indoor air, so as to achieve a comfortable and energy-saving effect; The fuzzy reasoning unit is used to calculate the fuzzy output variables based on the fuzzy variables and the established fuzzy rule base, and adopt the fuzzy reasoning method to obtain the fuzzy set of the control quantity of the fresh air unit. This process simulates the thinking process of human beings making reasoning decisions based on fuzzy knowledge and experience. By matching and calculating various rules, a comprehensive fuzzy description of the control quantity is obtained, which provides a basis for subsequent clarification processing.

[0039] The execution and feedback module includes: a clarification unit, a control execution unit, and a feedback monitoring unit; The clarification unit is used to convert the fuzzy control quantity obtained by fuzzy reasoning into an accurate control value by using a clarification method. This step converts the fuzzy control information into an accurate instruction that can be directly executed by the fresh air unit, for example, converting the fuzzy adjustment quantity of the fan speed into a specific speed value, so as to accurately control the operation of the fresh air unit; The control execution unit is used to send the clarified precise control value to the controller of the fresh air unit, so that the fresh air unit can adjust the operating state according to the control instruction, such as changing the fan speed, cooling capacity or heating capacity; The feedback monitoring unit is used to continuously monitor indoor and outdoor environmental parameters and the operating status of the fresh air unit. It triggers the entire system to re-execute the above steps at regular time intervals to form a closed-loop feedback control system. Through continuous monitoring and feedback, the system can dynamically adjust the control strategy in real time according to environmental changes and the actual operating conditions of the fresh air unit to ensure that while meeting indoor air quality requirements, it always maintains an energy-saving and optimized operating state.

[0040] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0041] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0042] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in the field can also change, modify, replace and modify the above-mentioned embodiments without departing from the purpose and scope of protection of the present invention, which are all within the protection of the present invention.

Claims

1. An energy-saving optimization control method for an air-conditioning fresh air unit based on fuzzy control, characterized in that: include: Step S1: Collect indoor and outdoor environmental parameter information and operating status parameter information of the fresh air unit in real time through sensors arranged indoors and outdoors; Step S2: fuzzy process the collected indoor and outdoor environmental parameter information and fresh air unit operating status parameter information and convert them into corresponding fuzzy variable sets; Step S3: Establish a fuzzy control rule base based on the operating characteristics and energy-saving requirements of the air-conditioning system and in combination with actual test data; Step S4: according to the fuzzy variables and the established fuzzy rule base, the fuzzy output variables are calculated by using the fuzzy reasoning method to obtain the fuzzy set of the control quantity of the fresh air unit; Step S5: converting the fuzzy control quantity obtained by fuzzy reasoning into an accurate control value through a clarification method, and sending the clarified control value to the controller of the fresh air unit to perform a control operation; Step S6: Monitor indoor and outdoor environmental parameters and the operating status of the fresh air unit, and repeat the above steps at predetermined time intervals to form a closed-loop feedback control system.

2. The energy-saving optimization control method for the air-conditioning fresh air unit based on fuzzy control according to claim 1, characterized in that: The specific steps of step S2 include: S2.1: Obtain indoor and outdoor environmental parameter information and fresh air unit operating status parameter information , and preprocessing is performed, where represents the nth indoor and outdoor environmental parameter, represents the mth fresh air unit operating status parameter, n represents the number of indoor and outdoor environmental parameters, and m represents the number of fresh air unit operating status parameters; S2.2: Collect and organize the energy-saving optimization control targets and actual operation characteristic data of the air conditioning fresh air unit, and and Conduct analysis, combine energy-saving optimization control objectives and actual operation characteristic data, and preliminarily determine the candidate list of fuzzy parameters; S2.3: The final fuzzy parameters are selected from the fuzzy parameter candidate list based on the correlation analysis. At the same time, for each selected fuzzy parameter, combined with the statistical characteristics of the actual operation data, the domain range of the corresponding fuzzy parameter is determined as and ,in, and Respectively and The mean of and Respectively and The standard deviation of , k represents the empirical coefficient; S2.4: According to the characteristics of the parameters and the energy-saving control requirements, the domain of each parameter is divided into N fuzzy subsets, and the membership function is defined for the fuzzy subsets; S2.5: For and For each parameter in , the membership function corresponding to the parameter is used to calculate the membership under N fuzzy subsets; S2.6: Combine the membership values ​​under the N fuzzy subsets to form a fuzzy variable set of the parameter; S2.7: Traversal and All parameters in are obtained to obtain the fuzzy variable set corresponding to all parameters.

3. The energy-saving optimization control method for the air-conditioning fresh air unit based on fuzzy control according to claim 2 is characterized in that: The specific steps of step S4 include: S4.1: Obtain the input fuzzy variables after fuzzification processing, and load the pre-established fuzzy rule base into the memory; S4.2: For each rule in the loaded fuzzy rule base, the fuzzified input fuzzy variable is matched with the rule condition part in the fuzzy rule base; If the fuzzy subset of each input fuzzy variable is consistent with the corresponding fuzzy subset in the rule condition, it means that the match is successful; S4.3: For each successfully matched rule, record the corresponding output fuzzy variable part and the entire rule information; S4.4: For each successfully matched rule, calculate its excitation intensity ,in, represents the bth input fuzzy variable The corresponding membership value, b represents the number of input fuzzy variables, represents the bth input fuzzy variable of the Ath matching rule, represents the minimum function, represents the weight corresponding to the bth input fuzzy variable of the Ath rule in the fuzzy rule base, E represents the environmental factor adjustment parameter, and , S represents the system operation status correction factor, and .

4. The energy-saving optimization control method for the air-conditioning fresh air unit based on fuzzy control according to claim 3 is characterized in that: The specific steps of step S4 also include: S4.5: The calculated excitation intensity of each matching rule , the membership function of the output fuzzy variable corresponding to the rule Perform truncation operation; like , then keep constant; like , then let ; S4.6: Take the maximum value of all truncated membership functions at the same output variable value, and obtain a comprehensive output fuzzy variable membership function through aggregation operation , generate the fuzzy set of the control quantity of the fresh air unit, where represents the membership function of the aggregated output fuzzy variable, represents the output fuzzy variable membership function after truncation of the Ath rule, represents the maximum value function, c represents the total number of matching rules, represents the importance coefficient corresponding to the Ath rule, and F represents the feedback adjustment parameter; S4.7: Convert the fuzzy set of the control quantity of the fresh air unit into an accurate numerical output as the control quantity of the fresh air unit.

5. The energy-saving optimization control method for the air-conditioning fresh air unit based on fuzzy control according to claim 4 is characterized in that: The specific steps of step S5 include: S5.1: Obtain the fuzzy control quantity and the corresponding membership function obtained by fuzzy reasoning, and at the same time, obtain the communication interface configuration information of the fresh air unit controller; S5.2: According to the pre-set discretization accuracy , discretize the domain of the fuzzy control quantity and calculate the number of domain points after discretization ,in, and Respectively represent the maximum and minimum values ​​of the fuzzy control quantity domain; S5.3: Generate a discretized set of domain points based on the number of discretized domain points Q ; S5.4: For each domain point in the discretized domain point set , get Membership value in the membership function of fuzzy control quantity ,according to and , calculate the product of the membership value of each point and the value of the point ; S5.5: Sum the products of all discretized domain points At the same time, the membership values ​​of all discretized domain points are summed up ; S5.6: Divide the above two sums to obtain the precise control value , and the calculated precise control value According to the pre-acquired fresh air unit controller communication interface configuration information, it is sent to the controller of the fresh air unit through the communication interface.

6. The energy-saving optimization control method for the air-conditioning fresh air unit based on fuzzy control according to claim 5 is characterized in that: In step S1, the indoor and outdoor environmental parameter information includes indoor temperature, indoor humidity, outdoor temperature, outdoor humidity, and indoor carbon dioxide concentration; the operating status parameter information of the fresh air unit includes fan speed, air supply volume, cooling capacity, heating capacity, and power consumption.

7. The energy-saving optimization control method for the air-conditioning fresh air unit based on fuzzy control according to claim 6, characterized in that: The predetermined time interval in step S6 is set to 3-10 minutes.

8. An energy-saving optimization control system for an air-conditioning fresh air unit based on fuzzy control, which is used to implement the energy-saving optimization control method for an air-conditioning fresh air unit based on fuzzy control according to any one of claims 1 to 7, characterized in that: include: Data acquisition module, fuzzy processing module, fuzzy rule base module, execution and feedback module; The data acquisition module is used to collect indoor and outdoor environmental parameter information and operating status parameter information of the fresh air unit in real time through sensors; The fuzzy processing module is used to perform fuzzy processing on the real-time collected indoor and outdoor environmental parameter information and the operating status parameter information of the fresh air unit; The fuzzy rule base module is used to establish a fuzzy control rule base according to the operating characteristics and energy-saving requirements of the air-conditioning system in combination with actual test data, and to obtain a fuzzy set of control quantities of the fresh air unit using a fuzzy reasoning method; The execution and feedback module is used to convert the fuzzy control quantity obtained by fuzzy reasoning into an accurate control value through a clarification method, send it to the controller of the fresh air unit to execute the control operation, and monitor the indoor and outdoor environmental parameters and the operating status of the fresh air unit.

9. The energy-saving optimization control system for the air-conditioning fresh air unit based on fuzzy control as claimed in claim 8, characterized in that: The fuzzy rule base module includes: a rule formulation unit and a fuzzy reasoning unit; The rule formulation unit is used to establish a fuzzy control rule base according to the operating characteristics and energy-saving requirements of the air-conditioning system in combination with actual test data; The fuzzy reasoning unit is used to calculate the fuzzy output variables using a fuzzy reasoning method according to the fuzzy variables and the established fuzzy rule base, so as to obtain the fuzzy set of the control quantity of the fresh air unit.

10. The energy-saving optimization control system for the air-conditioning fresh air unit based on fuzzy control according to claim 9, characterized in that: The execution and feedback module includes: a clarification unit and a control execution unit; The clarification unit is used to convert the fuzzy control quantity obtained by fuzzy reasoning into an accurate control value by using a clarification method; The control execution unit is used to send the clarified precise control value to the controller of the fresh air unit, so that the fresh air unit adjusts the operating state according to the control instruction.

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