Control method and system for whole house air quality comfort adjustment

Abstract

The invention provides a control method and system for whole house air quality comfort adjustment, and relates to the technical field of intelligent control, and the method comprises the steps: encoding a control parameter set into a gene string of a genetic algorithm, and defining a fitness function for evaluating the comfort degree of indoor air quality under the current control parameter set; according to the value of the fitness function, corresponding individuals are selected from the current population, the selected individuals are randomly paired, and new individuals are generated through crossover operation; performing random variation on the newly generated individuals, and repeating selection, crossover and variation operations until a termination condition is met, so as to obtain an optimized control parameter set; and the tail end wire controller receives the optimized control parameter set and adjusts the air quality adjusting equipment of each area according to the optimized control parameter set. The comfort of the whole living environment can be improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to a control method and system for regulating the comfort of whole-house air quality. Background Art

[0002] As people's requirements for indoor environmental quality continue to increase, the whole-house air quality comfort adjustment system has become an important part of modern homes. This type of system aims to achieve precise adjustment of key indicators such as indoor temperature, humidity, air flow rate and pollutant concentration through intelligent control, thereby ensuring a comfortable and healthy living environment.

[0003] In traditional air quality control systems, the setting of control parameters sometimes relies on manual experience or simple automation rules, which are difficult to adapt to complex and changing indoor environments. In addition, due to the lack of a global optimization mechanism, air quality control between different areas is often unbalanced and uncoordinated, affecting the improvement of overall comfort. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a control method and system for regulating the comfort of the air quality in the whole house, which can improve the comfort of the overall living environment.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] In a first aspect, a control method for adjusting the comfort of whole-house air quality is provided, the method comprising:

[0007] Set the comfort index of indoor air quality, including temperature, humidity, air flow rate and pollutant concentration; configure the initial control parameter set for the main control wire controller and the sub-control wire controller;

[0008] The main control wire controller is used to calculate the global control instructions based on the current indoor air quality data and the set comfort index; the sub-control wire controller receives the global control instructions of the main control wire controller and performs local control adjustments based on the real-time data of the area.

[0009] The control parameter set is encoded into a genetic algorithm gene string, and a fitness function is defined to evaluate the comfort level of indoor air quality under the current control parameter set;

[0010] According to the value of the fitness function, the corresponding individuals are selected from the current population, the selected individuals are randomly paired, and new individuals are generated through crossover operations;

[0011] Perform random mutation on the newly generated individuals, and repeat the selection, crossover and mutation operations until the termination condition is met to obtain the optimized control parameter set;

[0012] The terminal wire controller receives the optimized control parameter set and adjusts the air quality control equipment in the respective area according to the optimized control parameter set.

[0013] Furthermore, according to the current indoor air quality data and the set comfort index, the global control instructions are calculated, including:

[0014] According to the indoor air quality data and the set comfort index, the key fuzzy variables are determined, including temperature, humidity, air flow rate and pollutant concentration;

[0015] Determine the corresponding membership function for each fuzzy variable, which is used to describe the degree to which the variable value belongs to the corresponding fuzzy set;

[0016] Determine the goal of global control, that is, to achieve the set comfort index; based on historical data, determine the fuzzy logic rules, which describe the scheme of adjusting control parameters under different air quality conditions to achieve the comfort index;

[0017] The actual value of the current indoor air quality data is converted into the membership value of the fuzzy variable through the membership function;

[0018] According to the rules in the fuzzy logic rule base, the current fuzzy variables are inferred to obtain the corresponding adjustment fuzzy conclusions of each control parameter;

[0019] The adjustment fuzzy conclusion is converted into a specific control parameter adjustment value, and the global control instruction is generated according to the defuzzified control parameter adjustment value.

[0020] Furthermore, the global control instructions include the adjustment of temperature set points, the change of humidity control targets, the setting of air flow rates, and the operating parameters of pollutant treatment equipment.

[0021] Furthermore, the calculation formula of the fitness function is:

[0022]

[0023] Where, T is the current temperature; T set is the temperature setting value; σ T is the standard deviation of the temperature range; H is the current humidity; H set is the humidity set point; σ H is the standard deviation of the humidity range; V is the current air velocity; V set is the air velocity setting value; σ V is the standard deviation of the suitable range of air velocity; C is the current pollutant concentration; C max is the maximum permissible value of pollutant concentration; w 1 、w 2 、w 3and w 4 represents the weight coefficient; e represents the base of the natural logarithm.

[0024] Furthermore, the newly generated individuals are randomly mutated, and the selection, crossover and mutation operations are repeated until the termination condition is met to obtain the optimized control parameter set, including:

[0025] According to the fitness value, individuals are selected through roulette selection to enter the next generation, two individuals are randomly selected as parents, new offspring individuals are generated through multi-point crossover operation, the newly generated offspring individuals are randomly mutated, and the corresponding individuals in the original population are replaced with the newly generated offspring individuals to form a new population. If the termination condition is met, stop and output the current corresponding individual. The current corresponding individual represents the optimized control parameter set.

[0026] In a second aspect, a control system for adjusting the air quality comfort of a whole house includes:

[0027] The setting module is used to set the comfort index of indoor air quality, including temperature, humidity, air flow rate and pollutant concentration; configure the initial control parameter set for the main control wire controller and the sub-control wire controller; the main control wire controller is used to calculate the global control instructions according to the current indoor air quality data and the set comfort index; the sub-control wire controller receives the global control instructions of the main control wire controller, and performs local control adjustments in combination with the real-time data of the area.

[0028] A processing module is used to encode the control parameter set into a gene string of a genetic algorithm and define a fitness function for evaluating the comfort level of indoor air quality under the current control parameter set;

[0029] The selection module is used to select corresponding individuals from the current population according to the value of the fitness function, randomly pair the selected individuals, and generate new individuals through crossover operations; randomly mutate the newly generated individuals, and repeat the selection, crossover and mutation operations until the termination condition is met to obtain the optimized control parameter set;

[0030] The control module is used for the terminal wire controller to receive the optimized control parameter set and adjust the air quality control equipment in the respective area according to the optimized control parameter set.

[0031] According to a third aspect, a computing device includes:

[0032] one or more processors;

[0033] The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0034] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0035] The above solution of the present invention includes at least the following beneficial effects:

[0036] The present invention first defines the comfort index of indoor air quality, including temperature, humidity, air flow rate and pollutant concentration. In order to achieve global optimization of the control parameter set, the present invention introduces a genetic algorithm. The genetic algorithm is an optimization algorithm that simulates natural selection and genetic mechanism, and has a strong global search capability and adaptability. By encoding the control parameter set as a gene string and defining a fitness function to evaluate its quality, the genetic algorithm can find the optimal or near-optimal solution in a complex solution space. The fitness function is a key element in the genetic algorithm, which is used to evaluate the comfort level of indoor air quality under the current control parameter set. Through the selection, crossover and mutation operations of the genetic algorithm, the present invention can continuously iterate and optimize the control parameter set. In each iteration, excellent individuals are selected according to the value of the fitness function for pairing and crossover operations to generate a new control parameter set; at the same time, the newly generated individuals are randomly mutated to increase the diversity of the population and the search range. By repeating the above operations until the termination condition is met, the optimized control parameter set is finally obtained.

[0037] The optimized control parameter set is transmitted to the terminal wire controller, which adjusts the air quality control equipment in each area according to the received instructions. This distributed control method can ensure that the air quality adjustment between each area is more balanced and coordinated, thereby improving the comfort of the overall living environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of a control method for adjusting the comfort of whole-house air quality provided by an embodiment of the present invention.

[0039] Figure 2 It is a schematic diagram of a control system for whole-house air quality comfort adjustment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0041] like Figure 1As shown, an embodiment of the present invention provides a control method for adjusting the comfort of whole-house air quality, and the method comprises the following steps:

[0042] Step 11, setting the comfort index of indoor air quality, including temperature, humidity, air flow rate and pollutant concentration; configuring the initial control parameter set for the main control wire controller and the sub-control wire controller;

[0043] Step 12, the master wire controller is used to calculate the global control command according to the current indoor air quality data and the set comfort index; the slave wire controller receives the global control command of the master wire controller, and performs local control adjustment in combination with the real-time data of the area.

[0044] Step 13, encoding the control parameter set into a gene string of the genetic algorithm, and defining a fitness function for evaluating the comfort level of the indoor air quality under the current control parameter set;

[0045] Step 14, according to the value of the fitness function, select corresponding individuals from the current population, randomly pair the selected individuals, and generate new individuals through crossover operation;

[0046] Step 15, randomly mutate the newly generated individuals, and repeat the selection, crossover and mutation operations until the termination condition is met to obtain the optimized control parameter set;

[0047] Step 16: The terminal wire controller receives the optimized control parameter set and adjusts the air quality control equipment in the respective area according to the optimized control parameter set.

[0048] In the embodiment of the present invention, the specific indicators of comfort are clearly defined, providing clear goals and directions for the subsequent control system. This helps to ensure that the indoor environment can meet people's comfort needs, including suitable temperature, humidity, good air circulation and low pollutant concentration. By pre-configuring the initial control parameters, the system can enter the working state faster and reduce the initial debugging time. At the same time, these parameters provide a starting point for the subsequent optimization process, ensuring the consistency and stability of the system operation. This hierarchical control method not only ensures global unity, but also takes into account local flexibility. The main control wire controller can formulate strategies according to the overall situation, while the sub-control wire controller can be fine-tuned according to the actual situation of each area, thereby achieving more accurate environmental control. Through the encoding method of the genetic algorithm, complex control problems are converted into mathematical problems, which facilitates efficient optimization. The definition of the fitness function provides a quantitative evaluation criterion, making the optimization process more clear and targeted. By simulating natural selection and genetic processes, the optimal or near-optimal solution can be found among many possible control parameter combinations. This not only improves the performance of the system, but also enhances the adaptability of the system to different environmental conditions. As an actuator, the terminal wire controller can directly adjust the device status according to the optimized parameters, thereby quickly responding to environmental changes. This instant feedback mechanism ensures that the system can continue to provide a high-quality indoor environment and improve the user's comfort experience.

[0049] The specific steps of setting the comfort index of indoor air quality and configuring the initial control parameter set for the main control wire controller and the sub-control wire controller are as follows:

[0050] Setting comfort levels for indoor air quality:

[0051] temperature:

[0052] In summer, the air-conditioned room temperature should be set at 22-28℃, and the optimal range is 24-26℃.

[0053] In winter, the room temperature for heating should be set at 18-24℃, and the optimal range is 20-22℃.

[0054] humidity:

[0055] In summer, the humidity in air-conditioned rooms should be controlled at 40%-80%, and the optimal range is 50%-60%.

[0056] In winter, the humidity in the heated room should be controlled at 30%-60%, and the optimal range is 45%-55%.

[0057] Air flow rate:

[0058] During cooling in summer, the indoor air flow rate is controlled at 0.3m / s.

[0059] When heating in winter, the indoor air flow rate is controlled at 0.2m / s.

[0060] Pollutant concentration:

[0061] Ensure that indoor air quality meets relevant national or regional standards, especially the concentrations of harmful substances such as formaldehyde, benzene, and ammonia should be lower than the prescribed limits.

[0062] The concentration of fine particulate matter such as PM2.5 should also be maintained at a low level.

[0063] Configure the initial control parameter set for the master and slave wired controllers:

[0064] Main control wire controller parameter settings:

[0065] According to the above comfort indicators, the initial temperature, humidity, air flow rate and pollutant concentration control parameters are set for the main control wire controller.

[0066] These parameters will serve as the basis for global control and are used to guide local adjustments of the sub-control wire controller.

[0067] Parameter settings of the sub-control wire controller:

[0068] According to the specific conditions and needs of each area, local parameters of the sub-control wire controller are set.

[0069] These parameters will ensure that each area can achieve more precise local environmental adjustment under the global control of the main control wire controller.

[0070] Through the above steps, it can be ensured that the indoor air quality comfort adjustment system can be accurately controlled according to the set comfort indicators in actual operation, thereby providing users with a more comfortable and healthy indoor environment.

[0071] In the embodiment of the present invention, the above step 12, calculating the global control instruction according to the current indoor air quality data and the set comfort index, may include:

[0072] Step 121, based on the indoor air quality data and the set comfort index, determine the key fuzzy variables, including temperature, humidity, air flow rate and pollutant concentration, specifically including: collecting indoor air quality related data, including real-time measurement values ​​such as temperature, humidity, air flow rate and pollutant concentration; analyzing the collected data to understand the variation range and distribution of each parameter; based on the analysis results, determine temperature, humidity, air flow rate and pollutant concentration as key fuzzy variables. These variables will be used as input parameters in the subsequent fuzzy logic control.

[0073] Step 122, determining a corresponding membership function for each fuzzy variable, which is used to describe the degree to which the variable value belongs to the corresponding fuzzy set, specifically includes:

[0074] For each fuzzy variable (temperature, humidity, air velocity, pollutant concentration), analyze its characteristics and its impact in the control system; select the appropriate membership function type according to the characteristics of the variable and the control requirements. For example, for continuously changing variables such as temperature and humidity, a triangular membership function is used. Collect and analyze historical data to understand the distribution and change patterns of variable values; determine the parameters of the membership function, such as the center point (indicating the point with the highest membership) and the width (indicating the range of membership changes). Ensure that the parameter settings can reflect the degree to which the variable belongs to different fuzzy sets, such as "low temperature", "moderate temperature" and "high temperature". Encode the selected membership function and its parameters into the software or hardware of the control system. Verify whether the implemented membership function works correctly, that is, whether it can output reasonable membership values ​​when different variable values ​​are input.

[0075] Taking temperature as an example, considering that temperature changes continuously and it is hoped that the temperature belongs to the "moderate" set within a certain range, and gradually belongs to the "low temperature" or "high temperature" set outside the range, the triangular membership function is selected.

[0076] Determine the membership function parameters:

[0077] Assume that based on historical data and expert experience, the range of moderate temperatures is determined to be 22°C to 26°C.

[0078] The center point of the triangular membership function is set to 24°C and the width is set to 4°C (i.e., the range of 22°C to 26°C). For temperatures below 22°C, its membership to the "low temperature" set will gradually increase; for temperatures above 26°C, its membership to the "high temperature" set will gradually increase.

[0079] The above triangle membership function is implemented by programming in the control system.

[0080] When the control system receives real-time temperature input (such as 23°C), the membership function calculates the degree of membership of the temperature to the "moderate temperature" set (such as 0.75). At the same time, the degree of membership of the temperature to the "low temperature" and "high temperature" sets (such as 0.25 and 0, respectively) can also be calculated to facilitate subsequent reasoning and decision-making based on fuzzy logic rules.

[0081] According to the above-mentioned temperature membership function (taking the triangle membership function as an example), corresponding membership functions can be generated for humidity, air velocity and pollutant concentration, and a specific implementation process is given.

[0082] The purpose of humidity membership function is to describe the degree to which humidity values ​​belong to fuzzy sets such as "low humidity", "moderate humidity" and "high humidity".

[0083] Parameter determination:

[0084] Center point: The center point of moderate humidity, such as 50% relative humidity (RH).

[0085] Width: The width of each fuzzy set, such as "moderate humidity" can be set to 40% to 60% RH.

[0086] The real-time humidity value is received as input, and the membership degree of the humidity value to each fuzzy set is calculated according to the triangular membership function and preset parameters, and the membership degree of each fuzzy set corresponding to the humidity value is output.

[0087] Air velocity membership function:

[0088] Selection and Definition:

[0089] Type: Trapezoidal membership function

[0090] Purpose: To describe the extent to which air velocity values ​​belong to the fuzzy sets of "low velocity", "moderate velocity" and "high velocity".

[0091] Center point and boundary: For example, "moderate flow rate" can be set to 0.2 to 0.5 meters per second (m / s).

[0092] Trapezoidal width and slope: set according to comfort and actual needs.

[0093] The real-time air flow rate value is received as input, the membership degree of the flow rate value to each fuzzy set is calculated using the trapezoidal membership function and parameters, and the membership degree of each fuzzy set corresponding to the flow rate value is output.

[0094] Pollutant concentration membership function:

[0095] Selection and Definition:

[0096] Type: descending semi-trapezoidal membership function;

[0097] Purpose: To describe the degree to which pollutant concentration values ​​belong to fuzzy sets such as "low concentration", "moderate concentration" and "high concentration", but usually more attention is paid to the membership of "low concentration".

[0098] Parameter determination:

[0099] Threshold: For example, "low concentration" can be set below a certain standard value, such as 35 micrograms per cubic meter (μg / m 3 ).

[0100] Width and slope: Define how quickly the membership decreases as concentration increases.

[0101] Receive the real-time pollutant concentration value as input, and calculate the membership of the concentration value to each fuzzy set, especially the membership of "low concentration" according to the descending semi-trapezoidal membership function and parameters. Output the membership of each fuzzy set corresponding to the pollutant concentration value, especially the membership of "low concentration" for the control system to make decisions. Through these specific implementation processes, the control system can calculate the membership of different fuzzy sets based on real-time humidity, air flow rate and pollutant concentration data, thereby providing a decision-making basis for realizing intelligent control of indoor air quality.

[0102] Step 123, determine the goal of global control, that is, to achieve the set comfort index; determine fuzzy logic rules based on historical data, the fuzzy logic rules describe the scheme of adjusting control parameters under different air quality conditions to achieve the comfort index, specifically including: determine the goal of global control, that is, to ensure that the indoor air quality reaches the set comfort index; adjust and analyze the historical indoor air quality data and control parameters to understand how to adjust the control parameters under different conditions to achieve the comfort index; based on historical data analysis and expert experience, formulate a set of fuzzy logic rules, these rules describe how to adjust each control parameter under different air quality conditions, verify and optimize the formulated fuzzy logic rules through simulation or actual testing to ensure their effectiveness and reliability.

[0103] Step 124, converting the actual value of the current indoor air quality data into the membership value of the fuzzy variable through the membership function, specifically including: obtaining the actual measurement values ​​of the current temperature, humidity, air flow rate and pollutant concentration from the indoor air quality monitoring system, using the obtained actual measurement values ​​as input, applying the corresponding membership function to calculate, and calculating the membership value of each fuzzy variable under the current actual measurement value;

[0104] Step 125, according to the rules in the fuzzy logic rule base, the current fuzzy variable is inferred to obtain the adjustment fuzzy conclusion corresponding to each control parameter, which specifically includes: determining each rule in the fuzzy logic rule base, the rule form is usually "if (the fuzzy variable is in a certain state), then (the control parameter should be adjusted in a certain way)"; the defined rules are stored in the control system in an appropriate data structure for fast search and matching. The real-time values ​​of each fuzzy variable (such as temperature, humidity, air flow rate, pollutant concentration) at the current moment are read from the control system. According to the previously determined membership function, the membership values ​​of these real-time values ​​corresponding to each fuzzy set (such as "low temperature", "high temperature", "low humidity", etc.) are calculated. The rules that match or are most relevant to the membership value of the current fuzzy variable are searched in the fuzzy logic rule base. According to the size of the membership value and the preset threshold, it is determined which rules are "matched" or "relevant". For each matched rule, according to the conditions and conclusions defined in the rule, it is inferred how the control parameter should be adjusted. If multiple rules are matched at the same time, a comprehensive strategy (such as weighted average, maximum value selection, etc.) is needed to combine the influence of these rules and draw a comprehensive adjustment conclusion. The adjustment conclusion is usually expressed in the form of fuzzy sets, such as "increase wind speed", "reduce humidity", etc., or a specific adjustment amount (such as increasing wind speed by 2%). The adjustment conclusion is passed to the actuator of the control system to achieve real-time adjustment of environmental parameters.

[0105] Step 126, converting the adjustment fuzzy conclusion into a specific control parameter adjustment value, and generating a global control instruction according to the defuzzified control parameter adjustment value; the global control instruction includes the adjustment of the temperature setting value, the change of the humidity control target, the setting of the air flow rate, and the operating parameters of the pollutant treatment equipment, and specifically includes: converting the obtained fuzzy adjustment conclusion into a specific control parameter adjustment value through a defuzzification method (such as the maximum membership method, the weighted average method, etc.), and generating a global control instruction according to the defuzzified control parameter adjustment value. These instructions include the adjustment of the temperature setting value, the change of the humidity control target, the setting of the air flow rate, and the operating parameters of the pollutant treatment equipment, and the generated global control instruction is sent to the corresponding actuator or equipment to achieve the regulation and control of the indoor air quality.

[0106] In an embodiment of the present invention, by determining key fuzzy variables (temperature, humidity, air flow rate and pollutant concentration), the system can more accurately identify the environmental parameters that need to be adjusted. This precise control helps to ensure that the indoor environment is always within the set comfort index range. The application of the membership function enables the system to flexibly process various air quality data and convert them into membership values ​​of fuzzy variables. This processing method enhances the adaptability of the system to different environmental conditions and makes the control instructions more in line with actual needs. The fuzzy logic rules determined based on historical data provide intelligent decision support for the system. These rules describe how to adjust the control parameters under different air quality conditions to achieve comfort indicators, thereby ensuring the scientificity and effectiveness of the control instructions. By converting the adjustment fuzzy conclusions into specific control parameter adjustment values, the system can quickly generate global control instructions. This efficient response mechanism ensures that the indoor environment can reach the set comfort standard in the shortest time, improving the user experience. The global control instructions cover multiple aspects such as temperature, humidity, air flow rate and pollutant treatment, and achieve comprehensive optimization of the indoor environment.

[0107] In the embodiment of the present invention, the calculation formula of the fitness function is:

[0108]

[0109] Where, T is the current temperature; T set is the temperature setting value; σ T is the standard deviation of the temperature range; H is the current humidity; H set is the humidity set point; σ H is the standard deviation of the humidity range; V is the current air velocity; V set is the air velocity setting value; σ V is the standard deviation of the suitable range of air velocity; C is the current pollutant concentration; C max is the maximum permissible value of pollutant concentration; w 1 、w 2 、w 3 and w 4 represents the weight coefficient; e represents the base of the natural logarithm.

[0110] In an embodiment of the present invention, the function can comprehensively evaluate multiple aspects of the indoor environment, including temperature, humidity, air flow rate and pollutant concentration, to ensure the comprehensiveness and balance of environmental control. For example, in certain occasions, it may be necessary to pay more attention to air quality (pollutant concentration), while in other occasions, more attention may be paid to comfort (temperature, humidity and air flow rate). By using a Gaussian function (normal distribution function) to describe the deviation between temperature, humidity and air flow rate and the set value, the function can accurately reflect the degree of proximity between these environmental factors and the ideal state. This accuracy helps to achieve more sophisticated environmental control. By introducing the maximum allowable value of pollutant concentration, the function can give a negative evaluation when the pollutant concentration exceeds the safety threshold, thereby ensuring the safety of the indoor environment. The structure of the function allows environmental factors to be easily added or modified in the future to adapt to different application scenarios or new environmental parameter requirements.

[0111] Furthermore, the newly generated individuals are randomly mutated, and the selection, crossover and mutation operations are repeated until the termination condition is met to obtain the optimized control parameter set, including:

[0112] According to the fitness value, individuals are selected through roulette selection to enter the next generation, two individuals are randomly selected as parents, new offspring individuals are generated through multi-point crossover operation, the newly generated offspring individuals are randomly mutated, and the corresponding individuals in the original population are replaced with the newly generated offspring individuals to form a new population. If the termination condition is met, stop and output the current corresponding individual. The current corresponding individual represents the optimized control parameter set.

[0113] In an embodiment of the present invention, through random mutation and multi-point crossover operations, the method can maintain the diversity of the population during the search process. This diversity helps the algorithm explore a wider solution space and avoid falling into a local optimal solution too early. Due to the use of a roulette selection mechanism, the method can search for the optimal solution in a global scope. The roulette selection is selected according to the fitness value of the individual, which not only ensures that excellent individuals have more opportunities to be selected, but also gives individuals with lower fitness a certain chance of survival, thereby enhancing the global search ability of the algorithm. In traditional genetic algorithms, premature convergence is a common problem, that is, the algorithm stops searching before finding the global optimal solution. However, this method can effectively avoid premature convergence and increase the probability of finding the global optimal solution through continuous random mutation and crossover operations. The random mutation and multi-point crossover operations in the method have certain adaptability. During the search process, these operations can dynamically adjust the search strategy according to the current state of the population, so as to better adapt to different problem scenarios.

[0114] like Figure 2 As shown, an embodiment of the present invention also provides a control system for adjusting the air quality comfort of the whole house, including:

[0115] The setting module is used to set the comfort index of indoor air quality, including temperature, humidity, air flow rate and pollutant concentration; configure the initial control parameter set for the main control wire controller and the sub-control wire controller; the main control wire controller is used to calculate the global control instructions according to the current indoor air quality data and the set comfort index; the sub-control wire controller receives the global control instructions of the main control wire controller, and performs local control adjustments in combination with the real-time data of the area.

[0116] A processing module is used to encode the control parameter set into a gene string of a genetic algorithm and define a fitness function for evaluating the comfort level of indoor air quality under the current control parameter set;

[0117] The selection module is used to select corresponding individuals from the current population according to the value of the fitness function, randomly pair the selected individuals, and generate new individuals through crossover operations; randomly mutate the newly generated individuals, and repeat the selection, crossover and mutation operations until the termination condition is met to obtain the optimized control parameter set;

[0118] The control module is used for the terminal wire controller to receive the optimized control parameter set and adjust the air quality control equipment in the respective area according to the optimized control parameter set.

[0119] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0120] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0121] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0122] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A control method for adjusting the comfort of air quality in a whole house, characterized in that: The method comprises: Set the comfort index of indoor air quality, including temperature, humidity, air flow rate and pollutant concentration; configure the initial control parameter set for the main control wire controller and the sub-control wire controller; The master wired controller is used to calculate the global control instructions based on the current indoor air quality data and the set comfort index; the sub-control wired controller receives the global control instructions from the master wired controller and makes local control adjustments based on the real-time data of the area; The control parameter set is encoded into a genetic algorithm gene string, and a fitness function is defined to evaluate the comfort level of indoor air quality under the current control parameter set; According to the value of the fitness function, the corresponding individuals are selected from the current population, the selected individuals are randomly paired, and new individuals are generated through crossover operations; Perform random mutation on the newly generated individuals, and repeat the selection, crossover and mutation operations until the termination condition is met to obtain the optimized control parameter set; The terminal wire controller receives the optimized control parameter set and adjusts the air quality control equipment in the respective area according to the optimized control parameter set.

2. The control method for adjusting the comfort of whole-house air quality according to claim 1, characterized in that: Based on the current indoor air quality data and the set comfort index, the global control instructions are calculated, including: According to the indoor air quality data and the set comfort index, the key fuzzy variables are determined, including temperature, humidity, air flow rate and pollutant concentration; Determine the corresponding membership function for each fuzzy variable, which is used to describe the degree to which the variable value belongs to the corresponding fuzzy set; Determine the goal of global control, that is, to achieve the set comfort index; based on historical data, determine the fuzzy logic rules, which describe the scheme of adjusting control parameters under different air quality conditions to achieve the comfort index; The actual value of the current indoor air quality data is converted into the membership value of the fuzzy variable through the membership function; According to the rules in the fuzzy logic rule base, the current fuzzy variables are inferred to obtain the corresponding adjustment fuzzy conclusions of each control parameter; The adjustment fuzzy conclusion is converted into a specific control parameter adjustment value, and the global control instruction is generated according to the defuzzified control parameter adjustment value.

3. The control method for adjusting the comfort of the whole-house air quality according to claim 2, characterized in that: Global control instructions include adjustments to temperature set points, changes to humidity control targets, settings for air flow rates, and operating parameters for pollutant handling equipment.

4. The control method for adjusting the comfort of the whole-house air quality according to claim 3 is characterized in that: The calculation formula of the fitness function is: Where, T is the current temperature; T set is the temperature setting value; σ T is the standard deviation of the temperature range; H is the current humidity; H set is the humidity set point; σ H is the standard deviation of the humidity range; V is the current air velocity; V set is the air velocity setting value; σ V is the standard deviation of the suitable range of air velocity; C is the current pollutant concentration; C max is the maximum allowable value of pollutant concentration; w1, w2, w3 and w4 represent weight coefficients; e represents the base of natural logarithm.

5. The control method for adjusting the comfort of the whole-house air quality according to claim 4, characterized in that: The newly generated individuals are randomly mutated, and the selection, crossover and mutation operations are repeated until the termination condition is met to obtain the optimized control parameter set, including: According to the fitness value, individuals are selected through roulette selection to enter the next generation, two individuals are randomly selected as parents, new offspring individuals are generated through multi-point crossover operation, the newly generated offspring individuals are randomly mutated, and the corresponding individuals in the original population are replaced with the newly generated offspring individuals to form a new population. If the termination condition is met, stop and output the current corresponding individual. The current corresponding individual represents the optimized control parameter set.

6. A control system for adjusting the air quality comfort of the whole house, characterized in that: Applied to the method according to any one of claims 1 to 5, comprising: The setting module is used to set the comfort index of indoor air quality, including temperature, humidity, air flow rate and pollutant concentration; configure the initial control parameter set for the main control wire controller and the sub-control wire controller; the main control wire controller is used to calculate the global control command according to the current indoor air quality data and the set comfort index; the sub-control wire controller receives the global control command of the main control wire controller, and makes local control adjustments in combination with the real-time data of the area; A processing module is used to encode the control parameter set into a gene string of a genetic algorithm and define a fitness function for evaluating the comfort level of indoor air quality under the current control parameter set; The selection module is used to select corresponding individuals from the current population according to the value of the fitness function, randomly pair the selected individuals, and generate new individuals through crossover operations; randomly mutate the newly generated individuals, and repeat the selection, crossover and mutation operations until the termination condition is met to obtain the optimized control parameter set; The control module is used for the terminal wire controller to receive the optimized control parameter set and adjust the air quality control equipment in the respective area according to the optimized control parameter set.

7. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which implements the method according to any one of claims 1 to 5 when executed by a processor.

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