Energy-saving control method and device for fresh air system

Abstract

The invention discloses an energy-saving control method and device for a fresh air system. The energy-saving control method comprises the following steps that current environment detection data of a target area is obtained, and the current environment detection data and historical environment detection data are jointly input into a preset LSTM model to predict the personnel density change rate in the future short period, so that a short-period control instruction is output to control the short-period output air volume of a fresh air system; the personnel density change rate in the future short period is combined with current environment detection data to be input into a layered DRL controller, and a long-term control instruction is output to control the long-term output air volume of the fresh air system; and based on preset judgment logic, an execution judgment result of the fresh air system is obtained, an air volume control result is obtained based on the execution judgment result, and therefore the fan rotating speed, the valve opening degree and the bypass valve opening degree of the fresh air system are controlled according to the air volume control result. The energy-saving control method can dynamically balance the contradiction between the fresh air volume and the energy consumption, so that the energy consumption of the fresh air system is reduced, and the overall operation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to an energy-saving control method for a fresh air system and also to a corresponding energy-saving control device, belonging to the technical field of air conditioning. Background Art

[0002] Fresh air systems play a key role in regulating indoor air quality in modern buildings. However, their energy consumption typically accounts for 20% to 40% of a building's total energy consumption, making them a key focus of green building and energy-saving retrofits. With rising energy-saving standards, effectively reducing the energy consumption of fresh air systems has become a hot topic in current research and engineering practice. Traditional control strategies primarily rely on fixed air volume or simple variable air volume control. However, these strategies struggle to optimize energy consumption while maintaining air quality when occupancy density fluctuates dramatically or under extreme climate conditions.

[0003] At present, common fresh air control methods include four typical methods: the first is timed start-stop control, that is, running according to a preset schedule. It is simple to operate but cannot cope with sudden personnel changes; the second is CO2 concentration feedback control, which adjusts the air volume through the PID algorithm. Although it can respond to changes in air quality, it has response lag and is prone to system oscillation; the third is dual air volume switching control, which only sets high and low air volume levels, has limited energy-saving effects, and is difficult to adapt to complex scenarios; the fourth is PM2.5 / VOC-based linkage control. Although it introduces pollutant indicators, it ignores the impact of heat load on system operation.

[0004] The above methods generally have three defects: first, the control logic relies on static threshold settings and lacks the ability to dynamically adapt to environmental changes; second, it fails to effectively coordinate the multi-objective conflicts between energy conservation, air quality and human comfort; third, it lacks the ability to predict future conditions in a forward-looking manner, resulting in a slow response of the system when the population density changes suddenly, and the air volume adjustment is prone to oscillation, making it impossible to achieve real-time coordinated optimization of the building population density and external environmental parameters. Summary of the Invention

[0005] The primary technical problem to be solved by the present invention is to provide an energy-saving control method for a fresh air system.

[0006] Another technical problem to be solved by the present invention is to provide an energy-saving control device for a fresh air system.

[0007] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0008] According to a first aspect of an embodiment of the present invention, there is provided an energy-saving control method for a fresh air system, comprising the following steps:

[0009] Obtain the current environmental detection data of the target area;

[0010] Combining the current environmental monitoring data with the historical environmental monitoring data of the target area and inputting the data into a preset LSTM model to predict the rate of change of population density in the short term in the future; wherein the LSTM model is trained based on the historical environmental monitoring data of the target area;

[0011] Based on the population density change rate in the short term in the future, output a short-term control instruction; wherein the short-term control instruction is used to directly control the short-term output air volume of the fresh air system;

[0012] The population density change rate in the short term in the future is combined with the current environmental detection data and inputted into the hierarchical DRL controller to output a long-term control instruction; wherein the long-term control instruction is used to adjust the energy consumption weight, CO2 excess weight, and comfort weight of the fresh air system to control the long-term output air volume of the fresh air system by weighted average;

[0013] Based on the preset judgment logic, obtaining the execution judgment result of the fresh air system; wherein the execution judgment result is one of a short-term control instruction or a short-term and long-term weighted control instruction;

[0014] Based on the execution judgment result, the air volume control result of the fresh air system is obtained, so as to control the fan speed, valve opening and bypass valve opening of the fresh air system based on the air volume control result.

[0015] Preferably, obtaining the current environmental detection data of the target area specifically includes:

[0016] The CO2 concentration in the target area is obtained by deploying a dual CO2 sensor array on the ceiling of the target area;

[0017] Use infrared detection devices in conjunction with Wi-Fi detection devices to detect the current population density in the target area;

[0018] The enthalpy difference value of the target area is obtained by deploying an enthalpy difference calculation module in the corridor of the target area;

[0019] Obtain outdoor temperature, humidity, and PM2.5 test results in the target area through external environmental sensors;

[0020] Among them, the CO2 concentration, current population density, enthalpy difference, outdoor temperature and humidity, and PM2.5 detection results of the target area together constitute the current environmental detection data of the target area.

[0021] Preferably, the outputting of short-term control instructions based on the population density change rate in the short term in the future specifically includes:

[0022] If the rate of change of the population density is greater than a first threshold, the short-term control instruction is to rapidly increase the air volume and close the bypass valve;

[0023] If the rate of change of the population density is not greater than the first threshold and greater than the second threshold, the short-term control instruction is to increase the air volume in a step-by-step manner by increasing the air volume gradient by one per preset interval;

[0024] If the rate of change of the personnel density is not greater than the second threshold and greater than the third threshold, the short-term control instruction is to maintain the current air volume and fine-tune the air volume by a preset amplitude;

[0025] If the rate of change of the personnel density is not greater than a third threshold, the short-term control instruction is to reduce the air volume and start the night cleaning mode;

[0026] The first threshold value is greater than the second threshold value and greater than the third threshold value. The first threshold value and the second threshold value are both positive percentages, and the third threshold value is a negative percentage.

[0027] Preferably, the long-term control instruction is obtained through the following steps:

[0028] Based on the current environmental detection data, the outdoor temperature Tout is obtained; and the energy consumption weight α is calculated based on the temperature setting value Tset and the temperature sensitivity coefficient k. ;

[0029] Based on the population density change rate and the CO2 concentration in the target area, the CO2 excess weight β is calculated. ; Wherein, σ represents the population density sensitivity coefficient; ΔOcc represents the population density change rate predicted by the LSTM model, ranging from 0 to 1; CO2 represents the CO2 concentration in the target area; λ represents the CO2 compensation coefficient; and 800 represents the target threshold value of CO2 concentration.

[0030] Based on the energy consumption weight α and the CO2 exceeding standard weight β, the comfort weight γ is calculated, γ=1-α-β, and γ≥γ min ; Among them, γ min represents the lower limit of the comfort weight of the target area; if γ≥γ min , then maintain the current weight; if γ<γ min , then let γ=γ min , and proportionally compress the energy consumption weight α and the CO2 exceeding standard weight β;

[0031] Based on the energy consumption weight α, the CO2 exceeding standard weight β and the comfort weight γ, the long-term output air volume of the fresh air system is output through weighted calculation.

[0032] Preferably, the long-term output air volume is weightedly calculated by the following steps:

[0033] Calculate the recommended air volume for energy consumption based on the minimum energy consumption curve;

[0034] Get recommended air volume for air quality based on CO2 change rate;

[0035] According to the comfort level, obtain the recommended air volume required to maintain the human body thermal response PMV∈[-0.5,+0.5];

[0036] Based on the maximum air volume of the fresh air system, the energy consumption recommended air volume, air quality recommended air volume and comfort recommended air volume are normalized; wherein, u energy = Recommended air volume for energy consumption / Maximum air volume for the system 100%,u air = Recommended air volume for air quality / Maximum air volume for the system 100%,u comfort = Recommended air volume for comfort / Maximum air volume of the system 100%;

[0037] Based on the normalized calculation results, the final weighted calculation result u of the fresh air system is obtained final ,in ;

[0038] According to the final weighted calculation result u final , calculate the long-term output air volume Q of the fresh air system final , where Q final =u final Maximum air volume of the system.

[0039] Preferably, the preset judgment logic specifically includes:

[0040] Calculating an output air volume difference based on the short-term output air volume and the long-term output air volume;

[0041] Comparing the output air volume difference with the maximum allowable deviation of air volume control preset for the fresh air system;

[0042] If the output air volume difference is not greater than the maximum allowable deviation of air volume control, a weighted average is performed based on the short-term output air volume and the long-term output air volume according to a preset ratio, so as to execute a weighted control instruction based on the weighted average result;

[0043] If the output air volume difference is greater than the maximum allowable deviation of the air volume control, the arbitration mechanism is triggered to output an arbitration result based on the arbitration mechanism, and thus the corresponding control instruction is executed based on the arbitration result; wherein the arbitration result is to directly execute the short-term control instruction, or to execute a weighted control instruction synthesized after adjusting the weights of the short-term control instruction and the long-term control instruction.

[0044] Preferably, the arbitration mechanism specifically includes:

[0045] performing conflict detection based on the short-term control instruction and the long-term control instruction;

[0046] If there is a conflict, the short-term control instruction shall be executed first;

[0047] If there is no conflict, continue to determine whether CO2 exceeds the standard; if CO2 exceeds the standard, increase the weight of the short-term control instruction by 30%; if CO2 does not exceed the standard, increase the weight of the long-term control instruction by 20% as the weighted control instruction after weight adjustment.

[0048] Preferably, the energy-saving control method further includes:

[0049] For the CO2 sensor array and external environment sensor, Mahalanobis distance is used to detect sensor abnormal values:

[0050] ;

[0051] Among them, D M Represents the Mahalanobis distance value, x represents the observation value vector of the current sensor, μ represents the mean vector of the sensor data, Indicates the deviation of the current data from the normal state, represents the inverse matrix of the covariance matrix;

[0052] Based on the preset threshold and the abnormal value of each sensor, determine whether each sensor is invalid;

[0053] If a single external environment sensor fails, the redundant sensor is switched; if the sensor array fails, the GAN data generator is enabled to replace the sensor array.

[0054] According to a second aspect of an embodiment of the present invention, there is provided an energy-saving control device for a fresh air system, comprising:

[0055] The data collection layer is used to obtain the current environmental detection data of the target area;

[0056] An edge computing layer, having a built-in preset LSTM model and a hierarchical DRL controller, and connected to the data acquisition layer; the edge computing layer is used to predict the rate of change of population density in the short term in the future based on the LSTM model to output short-term control instructions; the edge computing layer is also used to output long-term control instructions based on the hierarchical DRL controller;

[0057] An execution control layer is connected to the edge computing layer and has a preset judgment logic to obtain an execution judgment result of the fresh air system based on the preset judgment logic; and based on the execution judgment result, an air volume control result of the fresh air system is obtained to control the fan speed, valve opening and bypass valve opening of the fresh air system based on the air volume control result.

[0058] According to a third aspect of an embodiment of the present invention, another energy-saving control device for a fresh air system is provided, comprising a processor and a memory, wherein the processor reads a computer program in the memory to implement the above-mentioned energy-saving control method.

[0059] Compared with the prior art, the present invention has the following technical effects:

[0060] (1) By predicting the rate of change of population density in the short term in the future, short-term control instructions are output; and combined with long-term control instructions, joint control is performed to dynamically balance the contradiction between fresh air volume and energy consumption, thereby reducing the energy consumption of the fresh air system and improving the overall operating efficiency.

[0061] (2) By balancing the conflict between CO2 concentration and heat recovery efficiency, the duration of CO2 exceeding the standard is significantly reduced, so that the peak capacity of CO2 can be controlled below 800ppm.

[0062] (3) By detecting abnormalities in the sensor, corresponding remedial measures can be taken according to the abnormal results, so that the fault tolerance of the energy-saving control method is improved and the stability of the fresh air system operation can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is an overall flow chart of an energy-saving control method for a fresh air system provided by the first embodiment of the present invention;

[0064] Figure 2 This is an architecture diagram of the LSTM model in the first embodiment of the present invention;

[0065] Figure 3 This is a structural diagram of a hierarchical DRL controller in the first embodiment of the present invention;

[0066] Figure 4 This is a flow chart of calculating comfort weight in the first embodiment of the present invention;

[0067] Figure 5 This is a flowchart of the preset judgment logic in the first embodiment of the present invention;

[0068] Figure 6 This is a flowchart of the arbitration mechanism in the first embodiment of the present invention;

[0069] Figure 7A structural diagram of an energy-saving control device for a fresh air system provided by a second embodiment of the present invention;

[0070] Figure 8 This is a structural diagram of an energy-saving control device for a fresh air system provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0071] The technical content of the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0072] By applying multimodal perception of the external environment and hierarchical reinforcement learning to energy-saving control of fresh air systems, this embodiment aims to address the inability of traditional control strategies to dynamically balance fresh air volume and energy consumption, particularly when CO2 concentration control conflicts with heat recovery efficiency. Furthermore, it can respond in real time to changes in building occupancy density and the external environment, ensuring the overall ventilation effectiveness of the fresh air system. Furthermore, it can avoid energy waste caused by surges in fresh air loads under extreme climate conditions.

[0073] First embodiment

[0074] like Figure 1 As shown, the first embodiment of the present invention provides an energy-saving control method for a fresh air system, which specifically includes the following steps:

[0075] S10: Obtain current environmental detection data of the target area.

[0076] Specifically, it includes steps S11 to S14:

[0077] S11: Obtain the CO2 concentration in the target area.

[0078] Specifically, the target area in this embodiment can be an office space (e.g., an office building), a classroom, or a conference hall. A dual CO2 sensor array installed on the ceiling of each zone collects CO2 concentration data for each zone. A voting mechanism is used to eliminate outliers, and the CO2 concentration in the target area is ultimately calculated using a weighted average.

[0079] S12: Obtain the current population density of the target area.

[0080] First, the Wi-Fi detection device detects the current number of Wi-Fi connections to roughly estimate the real-time number of people. Then, the infrared detection device uses the principle of infrared thermal imaging to detect human heat signals to fill in Wi-Fi blind spots (for example, where there are no mobile phone users), thereby adjusting the estimated real-time number of people. Finally, the current population density in the target area is obtained based on the adjusted real-time number of people.

[0081] In addition, in this step, the number of people attending the scheduled meeting can be obtained through access control / conference system data, which can be used to assist in predicting changes in population density within the next 15 to 30 minutes.

[0082] S13: Obtaining the enthalpy difference of the target area.

[0083] Specifically, the enthalpy difference calculation module is deployed in the corridor of the target area, so as to obtain the enthalpy difference value of the target area through enthalpy difference calculation. The method of enthalpy difference calculation is common knowledge in the field and will not be elaborated here.

[0084] S14: Obtain outdoor temperature, humidity, and PM2.5 detection results of the target area through external environmental sensors.

[0085] It can be understood that the CO2 concentration, current population density, enthalpy difference, outdoor temperature and humidity, and PM2.5 detection results of the target area together constitute the current environmental detection data of the target area.

[0086] S20: Predict the rate of change of population density in the short term.

[0087] After the current environmental detection data of the target area is collected through the above step S10, it is necessary to combine the current environmental detection data with the historical environmental detection data of the target area (historical detection data within 6 hours in this embodiment) and input them into the preset LSTM model to predict the rate of change of personnel density in the short term in the future.

[0088] In this embodiment, the LSTM model is trained based on the historical environmental detection data of the target area. Figure 2 As shown in the figure, the architecture of the LSTM model includes:

[0089] ① Input layer: used to input 12-dimensional features.

[0090] Specifically, by preprocessing the historical environmental detection data of the target area, 12-dimensional features are obtained, including: outdoor dry-bulb temperature, indoor average temperature, indoor relative humidity, current CO2 concentration, current population density, CO2 change rate, PM2.5 detection results, conference calendar mark, week characteristics and time period characteristics, etc.

[0091] ②Hidden layer: 64 LSTM units, dropout = 0.2.

[0092] ③Attention layer: Bahdanau attention mechanism.

[0093] ④ Output layer: predicted value of the population density change rate in the next 15 to 30 minutes.

[0094] It can be understood that after model training using the historical environmental detection data of the target area as the training set, a preliminary model is formed; and, smooth L1 loss is used as the loss function, and the Adam optimizer is used for model optimization, finally forming a pre-trained LSTM model.

[0095] Therefore, after the current environmental detection data of the target area is collected based on the above step S10, it is combined with the historical data within 6 hours and input into the pre-trained LSTM model to output the population density change rate in the next 15 to 30 minutes.

[0096] S30: Output short-term control instructions.

[0097] Specifically, after obtaining the population density change rate ΔOcc of the target area in the next 15 to 30 minutes based on step S20, the population density change rate ΔOcc is compared with multiple preset thresholds, and different short-term control instructions are output based on the comparison results. The details are as follows:

[0098] (1) If ΔOcc>15% (i.e., the first threshold), the air volume is controlled to increase rapidly.

[0099] (2) If 5% (i.e., the second threshold) < ΔOcc ≤ 15%, the air volume is increased in a step-by-step manner.

[0100] (3) If -10% (i.e., the third threshold) < ΔOcc ≤ 5%, the fan is controlled to maintain the current air volume and fine-tune the air volume.

[0101] (4) If ΔOcc≤-10%, the air volume is reduced and the night cleaning mode is activated.

[0102] For example, in a specific embodiment, the following execution strategies can be adopted for the above-mentioned different types of short-term control instructions: (1) Rapid increase in air volume: Control the air valve opening to increase by 25%, increase the fan speed by 20%, and close the bypass valve; (2) Stepwise increase in air volume: Control the fan speed to increase by 10%, and adjust the air volume gradient by 5% every 30 seconds; (3) Fine-tune the air volume: Fine-tune the air volume by ±2%; (4) Reduce the air volume: Reduce to 30% of the maximum air volume.

[0103] Night wash mode is an energy-saving ventilation strategy that operates at night or when the building is unoccupied. Its main purpose is to use the cool night air to pre-cool the building structure while removing indoor pollutants.

[0104]

[0105] The initial control unit sends a command to the EC fan to reduce its speed to 30% of its maximum airflow. The control unit also adjusts the damper opening to 90° (to increase airflow). The filter's electrostatic dust removal function activates, beginning to remove particulate matter from the air.

[0106] During the periodic self-cleaning phase (performed every 30 minutes), the controller briefly increases the EC fan speed to 80% of its maximum airflow rate and maintains this speed for two minutes. The high-speed EC fan generates high-pressure airflow, backflushing the filter and removing any dust that adheres to it. After backflushing, the filter measures the current pressure differential (indicating the degree of clogging) and transmits this data to the controller for filter status monitoring. This cycle repeats every 30 minutes, ensuring the filter maintains high filtration capacity while preventing dust accumulation caused by prolonged low-speed operation.

[0107] It can be understood that, in this embodiment, the short-term control instruction output based on the rate of change of the population density in the short term in the future is used to directly control the short-term output air volume of the fresh air system.

[0108] S40: Output long-term control instructions.

[0109] Specifically, based on the change rate of the population density in step S20, combined with the current environment detection data in step S10, they are input into the hierarchical DRL controller to output long-term control instructions. In this embodiment, Figure 3 As shown in Figure 1, the hierarchical DRL controller consists of a state space, an action space, and a reward function. The state space is used to obtain the aforementioned occupant density change rate and current environmental monitoring data; the action space is used to assign weights for energy consumption, CO2 excess, and comfort to the fresh air system; and the reward function is used to calculate the long-term output air volume of the fresh air system.

[0110] like Figure 4 As shown, step S40 specifically includes the following steps:

[0111] S41: Calculate the energy consumption weight α.

[0112] Specifically, based on the current environmental detection data, obtain the outdoor temperature T out . And according to the temperature setting value T set And the temperature sensitivity coefficient k, the default k = -0.5℃, together calculate the energy consumption weight α,

[0113] .

[0114] S42: Calculate the CO2 exceeding standard weight β.

[0115] Specifically, based on the population density change rate ΔOcc and the CO2 concentration in the target area, the CO2 excess weight β is calculated.

[0116] ;

[0117] Here, σ represents the sensitivity coefficient of personnel density. Different coefficients are selected according to different target areas. For example, in office scenarios, σ = 0.3 (the impact of personnel density is moderate); in classrooms / conference venues, σ = 0.5 (the impact of personnel density is dominant).

[0118] ΔOcc represents the change rate of population density predicted by the LSTM model, ranging from 0 to 1.

[0119] CO2 indicates the CO2 concentration in the target area.

[0120] λ represents the CO2 compensation coefficient. Generally, the default value is λ = 0.002. It is strongly correlated with the population density and will be adjusted within a certain range as the population density changes.

[0121] S43: Calculate the comfort weight γ.

[0122] After the energy consumption weight α and CO2 exceeding standard weight β are calculated respectively through the above steps S41 and S42, γ = 1 - α - β. In addition, in this embodiment, γ also needs to meet a constraint condition, γ ≥ γ min Among them, γ min It is the preset lower limit of comfort weight (for example, 0.1), which can be configured according to different building types or user needs.

[0123] In this embodiment, γ min Indicates the lower limit of the comfort weight of the target area. If γ≥γ min , then maintain the current weight; if γ<γ min , then let γ=γ min , and proportionally compress the energy consumption weight α and CO2 exceeding standard weight β.

[0124] The above steps S41 to S43 are described below with reference to examples:

[0125] For example: The predicted occupancy density increases by 30% (i.e., ΔOcc=0.3), and the outdoor temperature T out =28℃(T set =24°C), and assuming σ=0.3, λ=0.002, CO2=850ppm.

[0126] (1) Calculate α:

[0127] T out -T set =4℃, α=1 / 1+e-0.5×4=0.88.

[0128] (2) Calculate β:

[0129] β=0.3×0.3+0.002×(850-800)=0.09+0.1=0.19.

[0130] (3) Calculate γ:

[0131] γ=1-0.88-0.19=-0.07.

[0132] Since -0.07 is less than 0.1, the constraint is triggered, γ = 0.1, and α and β are redistributed based on the compression factor;

[0133] Compression factor = (1-γ min ) / (α+β)=0.9 / 1.07≈0.8411;

[0134] New α = original α Compression factor = 0.88 0.8411≈0.740;

[0135] New β = original β Compression factor = 0.19 0.8411≈0.16;

[0136] Final weights: α = 0.74, β = 0.16, γ = 0.1.

[0137] S44: Long-term output air volume of the fresh air system.

[0138] After the energy consumption weight α, CO2 excess weight β, and comfort weight γ are calculated based on the above steps S41 to S43, the long-term output air volume of the fresh air system is output through weighted calculation. Specifically, the following steps are included:

[0139] S441: Calculate the energy consumption recommended air volume based on the minimum energy consumption curve;

[0140] S442: Obtaining air quality recommended air volume based on CO2 change rate;

[0141] S443: Based on the comfort level, obtain the comfort recommended air volume required to maintain the human body thermal response PMV∈[-0.5,+0.5];

[0142] S444: Based on the maximum air volume of the fresh air system, normalize the recommended air volume for energy consumption, air quality, and comfort; where u energy = Recommended air volume for energy consumption / Maximum air volume for the system 100%,u air = Recommended air volume for air quality / Maximum air volume for the system 100%,u comfort = Recommended air volume for comfort / Maximum air volume of the system 100%;

[0143] S445: Based on the normalized calculation result, obtain the final weighted calculation result u of the fresh air system final , ;

[0144] S446: According to the final weighted calculation result u final , calculate the long-term output air volume Q of the fresh air system final , Q final =u final Maximum air volume of the system.

[0145] The specific calculation process of step S44 is described below with an example:

[0146] For ease of calculation, let's assume another scenario where the weights calculated using the hierarchical DRL are: energy consumption weight α = 0.6, CO2 excess weight β = 0.3, comfort weight γ = 0.1, and the system's maximum air volume is 1500 m³ / h. The recommended air volumes calculated independently for each strategy are as follows:

[0147] (1) Each strategy independently calculates the recommended value:

[0148] u energy Energy consumption optimization strategy: Based on the minimum energy consumption curve, the recommended air volume is 800m³ / h (low wind speed saves energy);

[0149] u air Air quality strategy: Dynamically adjust according to the CO2 change rate, with a recommended air volume of 1200m³ / h (rapidly dilute CO2);

[0150] u comfort Comfort strategy: Maintain the required air volume at PMV∈[-0.5,+0.5]. The recommended air volume is 1000 m³ / h (maintain PMV=0).

[0151] (2) Instruction normalization calculation:

[0152]

[0153] (3) Weighted synthesis calculation:

[0154]

[0155] (4) Inverse calculation of physical quantities

[0156] .

[0157] It can be understood that the long-term output air volume is related to the three parameters α, β, and γ. Since the three parameters α, β, and γ are a dynamic adjustment process, the long-term output air volume Q of the system is final It is also a control result that changes dynamically with the changes of the three parameters α, β, and γ.

[0158] S50: Based on the preset judgment logic, obtain the execution judgment result of the fresh air system.

[0159] In this embodiment, Figure 5 As shown, the preset judgment logic includes:

[0160] S51: Calculate the output air volume difference.

[0161] Specifically, the short-term output air volume of the fresh air system is obtained through the short-term control instruction of step S30, and the long-term output air volume of the fresh air system is obtained through the long-term control instruction of step S40. Thus, the output air volume difference is calculated based on the short-term output air volume and the long-term output air volume.

[0162] S52: Compare the output air volume difference with the maximum allowable deviation of air volume control preset for the fresh air system.

[0163] S53: Execute the weighted control instruction.

[0164] If the output air volume difference is no greater than the maximum allowable deviation for air volume control, the air volume control is normal. At this point, a weighted average is taken based on the short-term and long-term output air volumes according to a preset ratio (for example, 70% for short-term output air volume and 30% for long-term output air volume). The weighted control command is then executed based on the weighted average result (i.e., the final weighted system air volume).

[0165] S54: triggering the arbitration mechanism and executing the corresponding control instruction based on the arbitration result.

[0166] If the output air volume difference exceeds the maximum allowable deviation for air volume control, the arbitration mechanism is triggered, which outputs an arbitration result based on the arbitration mechanism, and then executes the corresponding control instruction based on the arbitration result. The arbitration result is the direct execution of the short-term control instruction, or the execution of a weighted control instruction synthesized by adjusting the weights of the short-term control instruction and the long-term control instruction.

[0167] Specifically, such as Figure 6 As shown, the following steps are included:

[0168] S541: Conflict detection is performed based on the short-term control instruction and the long-term control instruction;

[0169] Specifically, in this embodiment, when any of the scenarios listed in Table 1 occurs, short-term control instructions are prioritized. When any of the scenarios listed in Table 2 occurs, long-term control instructions are prioritized. It should be understood that during arbitration conflict detection, if a scenario listed in Table 1 is identified, the corresponding short-term response action is prioritized (overriding the original short-term control instruction); if a scenario listed in Table 2 is identified, the corresponding long-term policy modification is prioritized (overriding or modifying the original long-term control instruction).

[0170] Table 1: Scenarios where short-term control instructions are preferred

[0171]

[0172] Table 2: Scenarios where long-term control instructions are preferred

[0173]

[0174] S542: If there is a conflict, the short-term control instruction is executed first;

[0175] S543: If no conflict exists, the process continues by determining whether CO2 exceeds the standard. If so, the weight of the recommended air volume in the short-term control command is increased by 30 percentage points when synthesizing the control command. If CO2 does not exceed the standard, the weight of the recommended air volume in the long-term control command is increased by 20 percentage points, forming the final execution command. It should be noted that the "percentage points" here are used to specify the absolute increase in air volume weight. For example, the air volume weight increases by 30% or 20% from the original ratio, not the relative increase of 30% or 20%.

[0176] It is understood that in this embodiment, the execution judgment result obtained based on the above judgment logic is one of a short-term control instruction or a weighted control instruction of the short-term and long-term. In addition, in this embodiment, the weighted control instruction has two forms: (1) when the arbitration mechanism is not triggered, the weighted control instruction is an instruction obtained by weighted average of the short-term output air volume and the long-term output air volume according to a preset ratio; (2) when the arbitration mechanism is triggered, the weighted control instruction is a weighted control instruction re-synthesized after adjusting the weights of the short-term control instruction and the long-term control instruction based on the preset ratio.

[0177] S60: Fresh air system control.

[0178] Specifically, based on the execution judgment result determined in step S50, the air volume control result of the fresh air system is obtained. It is understood that if the execution judgment result is a short-term control instruction, the air volume control result is the short-term output air volume; if the execution judgment air volume result is a weighted control instruction of the short-term and long-term, the weighted calculation result of the output air volume is obtained by weighted calculation of the short-term output air volume and the long-term output air volume.

[0179] After determining the required output air volume, the fan speed, valve opening and bypass valve opening of the fresh air system are controlled so that the final air volume of the fresh air system is equal to the required output air volume.

[0180] In addition, in a preferred embodiment of the present invention, the energy-saving control method further includes:

[0181] S70: Exception handling.

[0182] Specifically, first, for the CO2 sensor array and external environment sensor, Mahalanobis distance is used to detect sensor abnormal values:

[0183] ;

[0184] Among them, D M represents the Mahalanobis distance value, which quantifies the degree of deviation of the current observation value x from the normal state defined by μ and Σ; x represents the observation value vector of the current sensor; μ represents the mean vector of the sensor data; Indicates the deviation of current data from the normal state; represents the inverse of the covariance matrix.

[0185] Secondly, based on the preset threshold and the abnormal value of each sensor, it is determined whether each sensor is failed.

[0186] Finally, if a single external environment sensor fails, the redundant sensor is switched; if the sensor array fails, the GAN data generator is enabled to replace the sensor array.

[0187] Therefore, by performing fault detection on a single sensor or a sensor array and taking corresponding remedial measures, the fault tolerance of the energy-saving control method is improved, and the stability of the operation of the fresh air system can be improved.

[0188] Second embodiment

[0189] like Figure 7 As shown, based on the above-mentioned first embodiment, the second embodiment of the present invention provides an energy-saving control device for a fresh air system, including a data acquisition layer 1, an edge computing layer 2 and an execution control layer 3.

[0190] The data acquisition layer 1 includes components such as a CO2 sensor array, an infrared detection device, a Wi-Fi detection device, an enthalpy difference calculation module, and external environmental sensors to obtain the current environmental detection data of the target area.

[0191] Edge computing layer 2 has a built-in LSTM model and a hierarchical DRL controller and is connected to data acquisition layer 1. This layer is used to predict the rate of change of occupant density in the short term based on the LSTM model to output short-term control instructions. Furthermore, this layer is used to output long-term control instructions based on the hierarchical DRL controller.

[0192] The execution control layer 3 is connected to the edge computing layer 2 and has a preset judgment logic to obtain the execution judgment result of the fresh air system based on the preset judgment logic; and based on the execution judgment result, the air volume control result of the fresh air system is obtained to control the fan speed, valve opening and bypass valve opening of the fresh air system based on the air volume control result.

[0193] It can be understood that the functions and connection relationships of the various module units in this embodiment are only a specific implementation method for realizing the energy-saving control method in the above-mentioned first embodiment. In other embodiments, the functions and connection relationships of the various module units can be adaptively adjusted as needed to realize the energy-saving control method in the above-mentioned first embodiment, and no specific limitation is made here.

[0194] Third embodiment

[0195] like Figure 8 As shown, based on the above energy-saving control method for a fresh air system, the third embodiment of the present invention further provides another energy-saving control device for a fresh air system. The energy-saving control device includes one or more processors and a memory. The memory is coupled to the processor and is used to store one or more programs. When the programs are executed by the processor, the processor implements the energy-saving control method of the above embodiment.

[0196] The processor is used to control the overall operation of the energy-saving control device to complete all or part of the steps of the above-mentioned energy-saving control method. The processor can be a central processing unit (CPU), a graphics processing unit (GPU), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a digital signal processing (DSP) chip, etc. The memory is used to store various types of data to support the operation of the energy-saving control device. This data may include, for example, instructions for any application or method operating on the energy-saving control device, as well as application-related data. The memory can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, etc.

[0197] In an exemplary embodiment, the energy-saving control device can be implemented by a computer chip or entity, or by a product with certain functions, to implement the above-mentioned energy-saving control method and achieve the same technical effect as the above-mentioned method. A typical embodiment is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, an in-vehicle human-computer interaction device, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0198] In another exemplary embodiment, the present invention further provides a computer-readable storage medium including program instructions, which, when executed by a processor, implement the steps of the energy-saving control method described in any of the above embodiments. For example, the computer-readable storage medium may be the aforementioned memory including the program instructions, which may be executed by a processor of an energy-saving control device to perform the above energy-saving control method and achieve the same technical effects as the above methods.

[0199] It should be noted that the above embodiments are merely examples, and the technical solutions of the various embodiments may be combined and are all within the scope of protection of the present invention.

[0200] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature identified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0201] The above describes in detail the energy-saving control method and device for a fresh air system provided by the present invention. For those skilled in the art, any obvious modification made thereto without departing from the essence of the present invention would constitute an infringement of the patent rights of the present invention and would result in corresponding legal liability.

Claims

1. An energy-saving control method for a fresh air system, characterized in that The steps include: Obtain the current environmental detection data of the target area; Combining the current environmental monitoring data with the historical environmental monitoring data of the target area and inputting the data into a preset LSTM model to predict the rate of change of population density in the short term in the future; wherein the LSTM model is trained based on the historical environmental monitoring data of the target area; Based on the population density change rate in the short term in the future, output a short-term control instruction; wherein the short-term control instruction is used to directly control the short-term output air volume of the fresh air system; The population density change rate in the short term in the future is combined with the current environmental detection data and inputted into the hierarchical DRL controller to output a long-term control instruction; wherein the long-term control instruction is used to adjust the energy consumption weight, CO2 excess weight, and comfort weight of the fresh air system to control the long-term output air volume of the fresh air system by weighted average; Based on the preset judgment logic, obtaining the execution judgment result of the fresh air system; wherein the execution judgment result is one of a short-term control instruction or a short-term and long-term weighted control instruction; Based on the execution judgment result, the air volume control result of the fresh air system is obtained, so as to control the fan speed, valve opening and bypass valve opening of the fresh air system based on the air volume control result.

2. The energy-saving control method according to claim 1, characterized in that The obtaining of the current environmental detection data of the target area specifically includes: The CO2 concentration in the target area is obtained by deploying a dual CO2 sensor array on the ceiling of the target area; Use infrared detection devices in conjunction with Wi-Fi detection devices to detect the current density of people in the target area; The enthalpy difference value of the target area is obtained by deploying an enthalpy difference calculation module in the corridor of the target area; Obtain outdoor temperature, humidity, and PM2.5 test results in the target area through external environmental sensors; Among them, the CO2 concentration, current population density, enthalpy difference, outdoor temperature and humidity, and PM2.5 detection results of the target area together constitute the current environmental detection data of the target area.

3. The energy-saving control method according to claim 2, characterized in that The outputting of short-term control instructions based on the population density change rate in the short term in the future specifically includes: If the rate of change of the personnel density is greater than a first threshold, the short-term control instruction is to rapidly increase the air volume and close the bypass valve; If the rate of change of the population density is not greater than the first threshold and greater than the second threshold, the short-term control instruction is to increase the air volume in a step-by-step manner by increasing the air volume gradient by one per preset interval; If the rate of change of the personnel density is not greater than the second threshold and greater than the third threshold, the short-term control instruction is to maintain the current air volume and fine-tune the air volume by a preset amplitude; If the rate of change of the personnel density is not greater than a third threshold, the short-term control instruction is to reduce the air volume and start the night cleaning mode; The first threshold value>the second threshold value>the third threshold value, and the first threshold value and the second threshold value are both positive percentages, and the third threshold value is a negative percentage.

4. The energy-saving control method according to claim 2, characterized in that The long-term control instruction is obtained by the following steps: Based on the current environmental detection data, the outdoor temperature Tout is obtained; and the energy consumption weight α is calculated based on the temperature setting value Tset and the temperature sensitivity coefficient k. ; Based on the population density change rate and the CO2 concentration in the target area, the CO2 excess weight β is calculated. ; Wherein, σ represents the population density sensitivity coefficient; ΔOcc represents the population density change rate predicted by the LSTM model, ranging from 0 to 1; CO2 represents the CO2 concentration in the target area; λ represents the CO2 compensation coefficient; and 800 represents the target threshold value of CO2 concentration. Based on the energy consumption weight α and the CO2 exceeding standard weight β, the comfort weight γ is calculated, γ=1-α-β and γ≥γ min ; Among them, γ min represents the lower limit of the comfort weight of the target area; if γ≥γ min , then maintain the current weight; if γ<γ min , then let γ=γ min , and proportionally compress the energy consumption weight α and the CO2 exceeding standard weight β; Based on the energy consumption weight α, the CO2 exceeding standard weight β and the comfort weight γ, the long-term output air volume of the fresh air system is output through weighted calculation.

5. The energy-saving control method according to claim 4, characterized in that The long-term output air volume is weightedly calculated by the following steps: Calculate the recommended air volume for energy consumption based on the minimum energy consumption curve; Get recommended air volume for air quality based on CO2 change rate; According to the comfort level, obtain the recommended air volume required to maintain the human body thermal response PMV∈[-0.5,+0.5]; Based on the maximum air volume of the fresh air system, the energy consumption recommended air volume, air quality recommended air volume and comfort recommended air volume are normalized; wherein, u energy = Recommended air volume for energy consumption / Maximum air volume for the system 100%,u air = Recommended air volume for air quality / Maximum air volume for the system 100%,u comfort = Recommended air volume for comfort / Maximum air volume of the system 100%; Based on the normalized calculation results, the final weighted calculation result u of the fresh air system is obtained final ,in ; According to the final weighted calculation result u final , calculate the long-term output air volume Q of the fresh air system final , where Q final =u final Maximum air volume of the system.

6. The energy-saving control method according to claim 1, characterized in that The preset judgment logic specifically includes: Calculating an output air volume difference based on the short-term output air volume and the long-term output air volume; Comparing the output air volume difference with the maximum allowable deviation of air volume control preset for the fresh air system; If the output air volume difference is not greater than the maximum allowable deviation of air volume control, a weighted average is performed based on the short-term output air volume and the long-term output air volume according to a preset ratio, so as to execute a weighted control instruction based on the weighted average result; If the output air volume difference is greater than the maximum allowable deviation of the air volume control, the arbitration mechanism is triggered to output an arbitration result based on the arbitration mechanism, and thus the corresponding control instruction is executed based on the arbitration result; wherein the arbitration result is to directly execute the short-term control instruction, or to execute a weighted control instruction synthesized after adjusting the weights of the short-term control instruction and the long-term control instruction.

7. The energy-saving control method according to claim 6, characterized in that The arbitration mechanism specifically includes: performing conflict detection based on the short-term control instruction and the long-term control instruction; If there is a conflict, the short-term control instruction shall be executed first; If there is no conflict, continue to determine whether CO2 exceeds the standard; if CO2 exceeds the standard, increase the weight of the short-term control instruction by 30%; if CO2 does not exceed the standard, increase the weight of the long-term control instruction by 20% as the weighted control instruction after weight adjustment.

8. The energy-saving control method according to claim 2, characterized in that Also includes: For the CO2 sensor array and external environment sensor, Mahalanobis distance is used to detect sensor abnormal values: ； Among them, D M Represents the Mahalanobis distance value, x represents the observation value vector of the current sensor, μ represents the mean vector of the sensor data, Indicates the deviation of the current data from the normal state, represents the inverse matrix of the covariance matrix; Based on the preset threshold and the abnormal value of each sensor, determine whether each sensor is invalid; If a single external environment sensor fails, the redundant sensor is switched; if the sensor array fails, the GAN data generator is enabled to replace the sensor array.

9. An energy-saving control device for a fresh air system, used to implement the energy-saving control method according to any one of claims 1 to 8, characterized in that include: The data collection layer is used to obtain the current environmental detection data of the target area; An edge computing layer, having a built-in preset LSTM model and a hierarchical DRL controller, and connected to the data acquisition layer; the edge computing layer is used to predict the rate of change of population density in the short term in the future based on the LSTM model to output short-term control instructions; the edge computing layer is also used to output long-term control instructions based on the hierarchical DRL controller; An execution control layer is connected to the edge computing layer and has a preset judgment logic to obtain an execution judgment result of the fresh air system based on the preset judgment logic; and based on the execution judgment result, an air volume control result of the fresh air system is obtained to control the fan speed, valve opening and bypass valve opening of the fresh air system based on the air volume control result.

10. An energy-saving control device for a fresh air system, characterized in that The system comprises a processor and a memory, wherein the processor reads a computer program in the memory to implement the energy-saving control method according to any one of claims 1 to 8.

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