A Multimodal Dynamic Optimal Control Method and System Based on Humidity Threshold Triggering

Abstract

This invention provides a multimodal dynamic optimal control method and system based on humidity threshold triggering, relating to the field of HVAC automation control technology. The method includes collecting outdoor temperature and humidity, return air temperature and humidity at both ends of the station, and station CO2 concentration using temperature and humidity sensors, return air temperature and humidity sensors, and CO2 sensors, respectively, and preprocessing the data to obtain key control parameters. A humidity threshold judgment mechanism is constructed to compare and analyze the key control parameters and dynamically select seasonal judgment logic. Based on the selected seasonal judgment logic, a multi-parameter collaborative judgment mechanism is used to analyze and evaluate the key control parameters, generate an air conditioning operation mode switching command, and automatically switch to the corresponding air conditioning operation mode. This allows for dynamic switching of air conditioning operation modes, reducing the energy consumption of the air conditioning system and ensuring station air quality and passenger comfort, thus meeting the energy-saving operation requirements of urban rail transit networks.

Description

Technical Field

[0001] This invention relates to the field of HVAC automation control technology, and in particular to a multimodal dynamic optimal control method and system based on humidity threshold triggering. Background Technology

[0002] The environmental control system of urban rail transit is a crucial facility for ensuring station environmental comfort and air quality, and its operational efficiency directly affects passenger experience and energy consumption. Currently, urban rail transit environmental control systems primarily employ two control methods: one is a timetable preset mode, relying on manual pre-setting of air conditioning operation modes based on seasonal changes, with fixed instructions issued through the BAS system; the other is using only enthalpy value judgment logic as the sole seasonal judgment logic.

[0003] However, existing technologies have significant technical shortcomings. First, the automation level of air conditioning operation mode switching is low. Preset timetables require manual adjustment weekly, failing to respond in real-time to weather changes and seasonal variations. The preset operating modes are severely out of sync with the actual load demands of the air conditioning system, leading to wasted energy and poor passenger comfort. Second, relying solely on enthalpy-based logic results in insufficient temperature control accuracy, with traditional temperature control deviations exceeding ±1℃. This leads to premature cooling or delayed dehumidification under extreme conditions such as high temperature / low humidity or low temperature / high humidity, failing to accurately respond to changes in indoor and outdoor temperature and humidity, thus wasting energy and reducing passenger comfort. Third, the air conditioning system lacks reliability and data cross-calibration and redundancy design. A malfunction of a single return air temperature and humidity sensor can easily misjudge the air conditioning operation mode. Furthermore, the disconnect between station CO2 concentration and air conditioning operation mode prevents intelligent control of the small fresh air units based on station CO2 concentration, resulting in either excessive operation and wasted energy or delayed response affecting air quality.

[0004] Therefore, it is necessary to provide a multimodal dynamic optimal control method and system based on humidity threshold triggering to solve the above-mentioned technical problems. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a multimodal dynamic optimal control method and system based on humidity threshold triggering, which solves the problems of low automation switching of air conditioning operation modes, insufficient temperature accuracy control, poor adaptability to extreme operating conditions, and high energy consumption of air conditioning systems in the prior art.

[0006] This invention provides a multimodal dynamic optimal control method based on humidity threshold triggering, the method comprising: The outdoor temperature and humidity, the return air temperature and humidity, and the CO2 concentration at the station's main and secondary ends were collected by temperature and humidity sensors, return air temperature and humidity sensors, and CO2 sensors, and the data were preprocessed to obtain key control parameters. A humidity threshold judgment mechanism is constructed to compare and analyze the key control parameters and dynamically select the seasonal judgment logic, wherein the seasonal judgment logic includes temperature judgment logic and enthalpy value judgment logic. Based on the selected seasonal judgment logic, a multi-parameter collaborative judgment mechanism is used to analyze and evaluate the key control parameters, generate an air conditioning operation mode switching command, and automatically switch to the corresponding air conditioning operation mode. The air conditioning operation modes include small fresh air operation mode, 100% fresh air operation mode, and ventilation operation mode.

[0007] Preferably, the step of collecting outdoor temperature and humidity, return air temperature and humidity, and station CO2 concentration using temperature and humidity sensors, return air temperature and humidity sensors, and CO2 sensors respectively, and then performing preprocessing, specifically includes: A dynamic sampling frequency adjustment algorithm is employed to automatically adjust the sampling intervals of the temperature and humidity sensor, the return air temperature and humidity sensor, and the CO2 sensor based on the entropy changes in environmental data. The outdoor temperature and humidity, the return air temperature and humidity at both ends of the station, and the station CO2 concentration are collected by the temperature and humidity sensor, the return air temperature and humidity at both ends of the station, and the CO2 concentration at the station, respectively. The outdoor temperature and humidity includes outdoor temperature. Outdoor humidity The temperature and humidity of the return air at both ends of the station include the return air temperature at the large end of the station. Humidity of return air at the station's main end Station small end return air temperature Humidity of return air at the station's small end ; Regarding the outdoor temperature The outdoor humidity The return air temperature at the large end of the station The humidity of the return air at the large end of the station The return air temperature at the small end of the station The humidity of the return air at the small end of the station The CO2 concentration at the station was then processed by an exponentially weighted moving average algorithm for filtering and noise reduction, followed by data verification and completion operations using a trend prediction algorithm.

[0008] Preferably, based on the outdoor temperature The outdoor humidity The return air temperature at the large end of the station The humidity of the return air at the large end of the station The return air temperature at the small end of the station and the humidity of the return air at the small end of the station The outdoor enthalpy value is calculated using a high-precision enthalpy calculation formula. Large-end return air enthalpy Enthalpy of return air at the small end The corresponding calculation formula is as follows: In the formula, This indicates the specific heat capacity of dry air at constant pressure. This represents the relative humidity percentage conversion factor. This represents the latent heat of vaporization of water at 0℃; This represents the specific heat capacity of water vapor at constant pressure. The return air temperature at the large end of the station was respectively... The return air temperature at the small end of the station The humidity of the return air at the large end of the station With respect to the return air humidity at the small end of the station The large-end return air enthalpy value With the small end return air enthalpy value Perform cross-calibration and weighted processing to generate return air temperature. Return air humidity Enthalpy of return air The corresponding calculation formula is as follows: In the formula, This indicates the weight of the larger end of the station; This indicates the weight of the station's little end; The outdoor temperature The outdoor humidity The return air temperature at the large end of the station The humidity of the return air at the large end of the station The return air temperature at the small end of the station The humidity of the return air at the small end of the station The station's CO2 concentration and the enthalpy of the large-end return air. The enthalpy value of the small-end return air The outdoor enthalpy value The return air temperature The return air humidity and the aforementioned return air enthalpy value The key control parameters are summarized and transmitted to the central control unit.

[0009] Preferably, the historical outdoor temperature and humidity data are retrieved and divided according to seasonal type, and the outdoor humidity change rate corresponding to the historical outdoor temperature and humidity data in each season is extracted. ; The seasonal type, the air conditioning system load rate L, and the outdoor humidity change rate are included. The humidity threshold is inferred from the ANFIS threshold model through its adaptive neural fuzzy inference mechanism. .

[0010] According to claim 4, a multimodal dynamic optimal control method based on humidity threshold triggering is characterized in that the key control parameters are compared and analyzed based on the humidity threshold judgment mechanism, and the seasonal judgment logic is dynamically selected, wherein the seasonal judgment logic includes temperature judgment logic and enthalpy judgment logic, specifically including: The outdoor humidity is set within a preset time range. The inferred humidity threshold For comparison, within the time range of the change, if the outdoor humidity Less than the humidity threshold If so, then the temperature judgment logic is selected; If the outdoor humidity Greater than or equal to the humidity threshold If so, then the enthalpy value judgment logic is selected.

[0011] Preferably, based on the selected seasonal judgment logic, a multi-parameter collaborative judgment mechanism is used to analyze and evaluate the key control parameters, generate an air conditioning operation mode switching command, and automatically switch to the corresponding air conditioning operation mode. The air conditioning operation modes include a small fresh air operation mode, a 100% fresh air operation mode, and a ventilation operation mode, specifically including: The air conditioning operation mode switching instructions include the small fresh air operation mode instruction, the 100% fresh air operation mode instruction, and the ventilation operation mode instruction; When the temperature judgment logic is selected, a preset station ventilation temperature reference value is used. and the station's small fresh air temperature benchmark value The temperature judgment deviation value DT is adaptively adjusted using the double exponential smoothing method, and the corresponding calculation formula is as follows: In the formula, This represents the first-order smoothed temperature judgment deviation value at time t; This represents the deviation value of the second-order smoothed temperature judgment at time t; Indicates the smoothing coefficient; If the outdoor temperature Greater than the station's small fresh air temperature reference value The sum of the temperature judgment deviation value DT and the central control unit generates the small fresh air operation mode command. If the outdoor temperature Less than or equal to the reference value of the station's small fresh air temperature The difference between the temperature judgment deviation value DT and the outdoor temperature Greater than the station ventilation temperature reference value Then the central control unit generates the fresh air operation mode command; If the outdoor temperature Less than or equal to the station ventilation temperature reference value The difference between the temperature judgment deviation value DT and the value of the temperature deviation value DT is used by the central control unit to generate the ventilation operation mode command. When the enthalpy judgment logic is selected, the enthalpy judgment deviation value DI is calculated using a nonlinear function. The corresponding calculation formula is as follows: In the formula, This indicates the critical deviation value for enthalpy value judgment; If the outdoor enthalpy value Greater than the return air enthalpy value The sum of the enthalpy value judgment deviation value DI and the central control unit generates the small fresh air operation mode command; If the outdoor enthalpy value Less than or equal to the return air enthalpy value The difference between the enthalpy value judgment deviation value DI and the outdoor temperature Greater than the station ventilation temperature reference value Then the central control unit generates the fresh air operation mode command; If the outdoor temperature Less than or equal to the station ventilation temperature reference value The difference between the temperature judgment deviation value DT and the value of the temperature deviation value DT is used by the central control unit to generate the ventilation operation mode command. The air conditioning equipment automatically switches to the small fresh air operation mode according to the small fresh air operation mode instruction; the air conditioning equipment automatically switches to the fresh air operation mode according to the fresh air operation mode instruction; the air conditioning equipment automatically switches to the ventilation operation mode according to the ventilation operation mode instruction.

[0012] Preferably, after the air conditioning equipment automatically switches to the small fresh air operation mode according to the small fresh air operation mode command, it also includes classifying the CO2 concentration of the station into concentration levels: Based on the CO2 concentration at each station, a weighted average CO2 concentration was calculated. The corresponding calculation formula is as follows: In the formula, This represents the passenger flow weight where the i-th CO2 sensor is located; This represents the CO2 concentration at the station collected by the i-th CO2 sensor; n represents the total number of CO2 sensors. The preset station CO2 concentration control thresholds include an opening threshold Cs, a closing deviation value DC, and a station CO2 concentration control upper limit Ch, combined with the station's average CO2 concentration. Establish station CO2 concentration classification standards; Based on the station CO2 concentration classification standard, when the average CO2 concentration of the station is... The average CO2 concentration at the station is greater than or equal to the opening threshold Cs. If the CO2 concentration is less than the upper limit Ch for station CO2 concentration control, then the average CO2 concentration of the station is marked. Level 1 concentration; When the average CO2 concentration of the station The difference between the opening threshold Cs and the closing deviation value DC is greater than or equal to the average CO2 concentration at the station. If the concentration is less than the opening threshold Cs, then the average CO2 concentration of the station is marked. It is a secondary concentration; When the average CO2 concentration of the station If the difference between the opening threshold Cs and the closing deviation value DC is less than the average CO2 concentration at the station, then the average CO2 concentration at the station is marked. It is a level three concentration.

[0013] Preferably, after classifying the CO2 concentration at the station into different concentration levels, the method further includes generating a graded frequency conversion control strategy for the CO2 concentration at different levels and intelligently controlling the small fresh air unit. When the average CO2 concentration of the station When the concentration reaches the first level, the small fresh air unit is started and its frequency is adjusted using an integral separation PID algorithm. The corresponding frequency adjustment formula is as follows: In the formula, Indicates the proportionality coefficient; Indicates the integral coefficient; This represents the opening threshold Cs at time t and the average CO2 concentration at the station. The difference; Indicates the integral separation threshold; When the average CO2 concentration of the station When the concentration is at the secondary level, maintain the current state and frequency of the small fresh air unit; When the average CO2 concentration of the station When the concentration reaches level three, the small fresh air fan is turned off.

[0014] A multimodal dynamic optimization control system based on humidity threshold triggering, the system comprising: The data acquisition and preprocessing module is used to collect outdoor temperature and humidity, return air temperature and humidity and station CO2 concentration through temperature and humidity sensors, return air temperature and humidity sensors and CO2 sensors, respectively, and preprocess them to obtain key control parameters. The seasonal judgment logic selection module is used to construct a humidity threshold judgment mechanism to compare and analyze the key control parameters and dynamically select the seasonal judgment logic, wherein the seasonal judgment logic includes temperature judgment logic and enthalpy value judgment logic. The instruction generation and mode switching module is used to analyze and evaluate the key control parameters based on the selected seasonal judgment logic using a multi-parameter collaborative judgment mechanism, generate an air conditioning operation mode switching instruction, and automatically switch to the corresponding air conditioning operation mode. The air conditioning operation modes include a small fresh air operation mode, a 100% fresh air operation mode, and a ventilation operation mode.

[0015] Compared with related technologies, the multimodal dynamic optimal control method and system based on humidity threshold triggering provided by the present invention has the following beneficial effects: This invention collects outdoor temperature and humidity, return air temperature and humidity, and station CO2 concentration data using temperature and humidity sensors, return air temperature and humidity sensors, and CO2 sensors, respectively, and preprocesses the data to obtain key control parameters. A humidity threshold judgment mechanism is constructed to compare and analyze the key control parameters and dynamically select seasonal judgment logic, which includes temperature judgment logic and enthalpy value judgment logic. Based on the selected seasonal judgment logic, a multi-parameter collaborative judgment mechanism is used to analyze and evaluate the key control parameters, generate air conditioning operation mode switching instructions, and automatically switch to the corresponding air conditioning operation mode. The air conditioning operation modes include a small fresh air operation mode, a 100% fresh air operation mode, and a ventilation operation mode. This allows for dynamic switching of air conditioning operation modes, reducing the energy consumption of the air conditioning system and ensuring station air quality and passenger comfort, thus meeting the energy-saving operation requirements of urban rail transit networks.

[0016] This invention overcomes the limitations of traditional single-season judgment logic by employing two seasonal judgment logics triggered by humidity thresholds. It effectively solves the problems of premature cooling or delayed dehumidification under extreme conditions such as high temperature and low humidity and low temperature and high humidity, improving temperature control accuracy to within ±0.5℃ and reducing temperature fluctuations by 40%, significantly enhancing passenger comfort. Furthermore, by dynamically and automatically switching air conditioning operating modes according to the load requirements of the air conditioning system, this invention reduces energy consumption by 15%-20% during transitional seasons, thereby saving energy in the refrigeration system. Compared to existing technologies that rely on manual pre-setting of air conditioning operating modes, this reduces labor costs by 80%, and optimizes the air conditioning operating mode switching response time from 1 hour to 30 minutes, an improvement of 50%. Response efficiency: This invention links the operation of the small fresh air blower with the station's CO2 concentration through a graded frequency conversion control strategy based on CO2 concentration. Combined with an integral separation PID algorithm, it precisely adjusts the frequency of the small fresh air blower, reducing its energy consumption by 25%. This further reduces the energy consumption of the air conditioning system while ensuring air quality in the station. Furthermore, this invention improves data reliability by 60% through redundant design of return air sensors at both the large and small ends of the station, and through data cross-calibration and weighted processing. This effectively solves the problem of misjudgment of air conditioning operation modes caused by single-point sensor failures in existing technologies. This allows the air conditioning system to flexibly adapt to changes in operating conditions across different seasons, fully meeting the high availability and low energy consumption requirements of urban rail transit environmental control systems. Attached Figure Description

[0017] Figure 1 A flowchart of a multimodal dynamic optimal control method based on humidity threshold triggering provided in an embodiment of the present invention; Figure 2 A system block diagram of a multimodal dynamic optimization control system based on humidity threshold triggering provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figure 1 The diagram shown is a flowchart of a multimodal dynamic optimal control method based on humidity threshold triggering provided by an embodiment of the present invention. Figure 1The execution entity of the method shown can be a software and / or hardware device. The execution entity of this application can include, but is not limited to, at least one of the following: user equipment, network equipment, etc. User equipment can include, but is not limited to, computers, smartphones, personal digital assistants (PDAs), and the aforementioned electronic devices. Network equipment can include, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of computers or network servers. Cloud computing is a type of distributed computing, consisting of a super virtual computer composed of a group of loosely coupled computers. This embodiment does not limit this. Steps S1 to S3 are detailed as follows: S1 collects outdoor temperature and humidity, return air temperature and humidity, and CO2 concentration at the station's main and secondary ends through temperature and humidity sensors, return air temperature and humidity sensors, and CO2 concentration at the station, and performs preprocessing to obtain key control parameters. Key control parameters include outdoor temperature. Outdoor humidity Station large end return air temperature Humidity of return air at the station's main end Station small end return air temperature Humidity of return air at the station's small end Station CO2 concentration, large-end return air enthalpy Small-end return air enthalpy Outdoor enthalpy Return air temperature Return air humidity and return air enthalpy The larger end of a station refers to areas within the station with high passenger density, long dwell times, and high air conditioning system load, including platform waiting areas, station hall turnstiles, and areas near escalators. The smaller end of a station refers to areas within the station with low passenger density, short dwell times, and low air conditioning load, including entrance / exit passages and transfer passages.

[0020] In practical applications, temperature and humidity sensors can be installed at outdoor ventilation openings in the station, avoiding direct sunlight and rain immersion, to ensure the accuracy of the collected outdoor temperature and humidity data. Return air temperature and humidity sensors are deployed in return air ducts with high and low air conditioning system loads, close to the return air flow channels, to accurately collect the return air temperature and humidity at both ends of the station. CO2 sensors are deployed in the station concourse and public areas of the station platform, focusing on areas with high passenger flow, such as turnstiles and escalators, to ensure that the collected station CO2 concentration accurately reflects the overall air quality of the station.

[0021] The process involves collecting outdoor temperature and humidity, return air temperature and humidity, and CO2 concentration data from the station's main and secondary return air sources using temperature and humidity sensors, return air temperature and humidity sensors, and CO2 concentration data, and then preprocessing these data. Specifically, this includes: A dynamic sampling frequency adjustment algorithm is employed to automatically adjust the sampling intervals of the temperature and humidity sensor, the return air temperature and humidity sensor, and the CO2 sensor based on the entropy changes in environmental data. The outdoor temperature and humidity, the return air temperature and humidity at both ends of the station, and the station CO2 concentration are collected by the temperature and humidity sensor, the return air temperature and humidity at both ends of the station, and the CO2 concentration at the station, respectively. The outdoor temperature and humidity includes outdoor temperature. Outdoor humidity The temperature and humidity of the return air at both ends of the station include the return air temperature at the large end of the station. Humidity of return air at the station's main end Station small end return air temperature Humidity of return air at the station's small end ; Regarding the outdoor temperature The outdoor humidity The return air temperature at the large end of the station The humidity of the return air at the large end of the station The return air temperature at the small end of the station The humidity of the return air at the small end of the station The CO2 concentration at the station was then processed by an exponentially weighted moving average algorithm for filtering and noise reduction, followed by data verification and completion operations using a trend prediction algorithm.

[0022] Based on the outdoor temperature The outdoor humidity The return air temperature at the large end of the station The humidity of the return air at the large end of the station The return air temperature at the small end of the station and the humidity of the return air at the small end of the station The outdoor enthalpy value is calculated using a high-precision enthalpy calculation formula. Large-end return air enthalpy Enthalpy of return air at the small end The corresponding calculation formula is as follows: In the formula, This indicates the specific heat capacity of dry air at constant pressure. This represents the relative humidity percentage conversion factor. This represents the latent heat of vaporization of water at 0℃; This represents the specific heat capacity of water vapor at constant pressure. The return air temperature at the large end of the station was respectively... The return air temperature at the small end of the station The humidity of the return air at the large end of the station With respect to the return air humidity at the small end of the station The large-end return air enthalpy value With the small end return air enthalpy value Perform cross-calibration and weighted processing to generate return air temperature. Return air humidity Enthalpy of return air The corresponding calculation formula is as follows: In the formula, This indicates the weight of the larger end of the station; This indicates the weight of the station's little end; The outdoor temperature The outdoor humidity The return air temperature at the large end of the station The humidity of the return air at the large end of the station The return air temperature at the small end of the station The humidity of the return air at the small end of the station The station's CO2 concentration and the enthalpy of the large-end return air. The enthalpy value of the small-end return air The outdoor enthalpy value The return air temperature The return air humidity and the aforementioned return air enthalpy value The key control parameters are summarized and transmitted to the central control unit.

[0023] Understandably, the dynamic sampling frequency adjustment algorithm adjusts the sampling interval by calculating the entropy of environmental data changes, thereby dynamically adapting the sampling interval to environmental changes. The higher the entropy of environmental data changes, the more drastic the fluctuations in environmental data. For example, when the outdoor temperature rises sharply during the morning rush hour or sudden rainfall causes large changes in outdoor humidity, the dynamic sampling frequency adjustment algorithm will automatically shorten the sampling interval corresponding to the sensor to promptly capture fluctuations in environmental data. When environmental data tends to stabilize during the nighttime shutdown period, the dynamic sampling frequency adjustment algorithm will automatically extend the sampling interval corresponding to the sensor, effectively reducing the power consumption of the sensor.

[0024] Furthermore, for the outdoor temperature and humidity, return air temperature and humidity, and CO2 concentration collected by the temperature and humidity sensors, return air temperature and humidity at both ends of the station, and station CO2 concentration, respectively, an exponentially weighted moving average algorithm is used to assign weights to the current and historical data. This filters out high-frequency noise caused by sensor errors, airflow interference, and electromagnetic interference from equipment, ensuring the real-time performance and stability of the collected data. A trend prediction algorithm is used to compare the filtered and noise-reduced current data with historical valid data. By setting a deviation threshold, abnormal data in the current data is detected and automatically corrected and supplemented. For example, if the return air temperature and humidity sensor communication is interrupted, causing the loss of return air temperature and humidity at both ends of the station, the trend prediction algorithm is used to automatically calculate and supplement the return air temperature and humidity at both ends of the station based on the changing trend of the historical data collected in the previous 10 minutes, ensuring the continuity of the collected data.

[0025] Among them, outdoor enthalpy refers to the total energy contained in a unit mass of outdoor air, used to reflect the degree of influence of outdoor temperature and humidity on outdoor air energy. Large-end return air enthalpy refers to the total energy contained in a unit mass of return air within the large-end return air duct of the air conditioning system, used to accurately reflect the energy state of the large-end return air. Small-end return air enthalpy refers to the total energy contained in a unit mass of return air within the small-end return air duct of the air conditioning system, used to accurately reflect the energy state of the small-end return air. Return air enthalpy is an indicator used to reflect the energy state of the return air of the overall station air conditioning system after cross-calibration and weighting of the large-end and small-end return air enthalpy values. It retains the energy states of the large and small-end return air of the air conditioning system, improving data reliability.

[0026] Specifically, the mutual calibration and weighted processing involves spatial redundancy comparison of the return air temperature at the large end of the station with that at the small end, the return air humidity at the large end of the station with that at the small end, and the return air enthalpy at the large end with that at the small end. Then, weighted fusion is performed according to the load weights of the large and small ends of the station to generate unified return air temperature, return air humidity, and return air enthalpy, thereby improving the reliability and representativeness of key control parameters. The return air temperature and humidity sensors deployed in the large-end return air duct and the small-end return air duct of the air conditioning system form a spatial redundancy. The data collected by the return air temperature and humidity sensors at both ends have a consistent trend. If there is a normal deviation in the data collected by the return air temperature and humidity sensors at both ends, the data collected by the return air temperature and humidity sensors at both ends are weighted to avoid data distortion caused by the failure of a single return air temperature and humidity sensor. If there is an abnormal deviation in the data collected by the return air temperature and humidity sensors at both ends, it indicates that at least one return air temperature and humidity sensor is faulty or there is a short circuit in the local return airflow. This abnormal deviation is then marked and a maintenance reminder is triggered.

[0027] By using redundant design of return air temperature and humidity sensors at both ends of the station, the accuracy and reliability of key control parameters are effectively ensured, and the problem of misjudging the air conditioning operation mode due to the failure of a single return air temperature and humidity sensor is solved.

[0028] S2, Construct a humidity threshold judgment mechanism to compare and analyze the key control parameters and dynamically select seasonal judgment logic, wherein the seasonal judgment logic includes temperature judgment logic and enthalpy value judgment logic; Among them, the temperature judgment logic refers to using temperature as a decision indicator to determine the air conditioner's operating mode. The enthalpy value judgment logic refers to using enthalpy value as a decision indicator to determine the air conditioner's operating mode.

[0029] It should be noted that in existing technologies, urban rail transit environmental control systems either employ a fixed timetable preset mode, relying on manual adjustments and unable to respond to environmental changes in real time; or they rely solely on a single enthalpy value judgment logic, leading to premature cooling or delayed dehumidification. Therefore, a dynamic optimization mechanism using either temperature judgment logic or enthalpy value judgment logic is needed to ensure that the air conditioning system can accurately control its operating mode and meet cooling and dehumidification requirements under different operating conditions.

[0030] The historical outdoor temperature and humidity data are retrieved and divided according to season type. The outdoor humidity change rate corresponding to the historical outdoor temperature and humidity data in each season is extracted. ; The seasonal type, the air conditioning system load rate L, and the outdoor humidity change rate are included. The humidity threshold is inferred from the ANFIS threshold model through its adaptive neural fuzzy inference mechanism. .

[0031] Understandably, seasonal type reflects the basic pattern of outdoor humidity change, air conditioning system load rate reflects the real-time demand of air conditioning system load, and outdoor humidity change rate reflects the dynamic trend of outdoor humidity change. The three factors are input into the ANFIS threshold model to obtain the humidity threshold, ensuring that the inferred humidity threshold accurately matches the actual environmental conditions and air conditioning system status, thus solving the problem that traditional fixed humidity thresholds cannot adapt to the actual environmental conditions throughout the year.

[0032] The mechanism for comparing and analyzing key control parameters based on the humidity threshold and dynamically selecting seasonal judgment logic includes temperature judgment logic and enthalpy value judgment logic, specifically comprising: The outdoor humidity is set within a preset time range. The inferred humidity threshold For comparison, within the time range of the change, if the outdoor humidity Less than the humidity threshold If so, then the temperature judgment logic is selected; If the outdoor humidity Greater than or equal to the humidity threshold If so, then the enthalpy value judgment logic is selected.

[0033] It should be noted that when the outdoor humidity is less than the humidity threshold, it indicates that the current air is dry and the current environment is in a low humidity condition. At this time, the heat load of the air is mainly determined by the temperature. Therefore, using temperature judgment logic can more directly and accurately match the cooling or ventilation needs of the air conditioning system, and solve the problem of using enthalpy value judgment logic to over-consider humidity and latent heat, which leads to the air conditioning cooling prematurely and wasting the energy consumption of the air conditioning system.

[0034] When the outdoor humidity is greater than or equal to the humidity threshold, it indicates that the air is humid and the current environment is in a high humidity condition. At this time, the heat load of the air includes the sensible heat of dry air and the latent heat of water vapor. If the temperature judgment logic is still used, it will lead to the distortion of energy assessment. The enthalpy value judgment logic can comprehensively consider the sensible heat of dry air and the latent heat of water vapor, ensuring that the air conditioning system takes into account both cooling and dehumidification, thereby improving passenger comfort.

[0035] By using two seasonal judgment logics, the current environmental humidity conditions are accurately determined, effectively ensuring energy saving of the air conditioning system and passenger comfort. This solves the problems of energy waste, passenger comfort defects, and insufficient temperature control accuracy caused by using only enthalpy value judgment logic.

[0036] S3. Based on the selected seasonal judgment logic, a multi-parameter collaborative judgment mechanism is used to analyze and evaluate the key control parameters, generate an air conditioning operation mode switching command, and automatically switch to the corresponding air conditioning operation mode. The air conditioning operation mode includes a small fresh air operation mode, a 100% fresh air operation mode, and a ventilation operation mode.

[0037] Among them, the "small fresh air" operation mode refers to the air conditioning system operating primarily with station return air supplemented by a small amount of outdoor fresh air, used to reduce the energy consumption of the air conditioning system and ensure that the air quality in the station meets standards. The "100% fresh air" operation mode refers to the air conditioning system completely introducing outdoor fresh air and not utilizing station return air, used to enhance ventilation and further reduce the energy consumption of the air conditioning system. The "ventilation" operation mode refers to the air conditioning system operating only with the supply and exhaust fans activated for mechanical ventilation, completely shutting down the air conditioning cooling function, achieving maximum energy savings for the air conditioning system.

[0038] It should be noted that the multi-parameter collaborative judgment mechanism is adopted to adapt to the dynamic changes in environmental conditions, thereby dynamically switching the air conditioning operation mode. This solves the problems of existing technologies that rely on manually preset air conditioning operation modes, resulting in the air conditioning system still operating in cooling mode when the outdoor temperature is suitable, and failing to dynamically switch the air conditioning operation mode when outdoor air is available, leading to wasted air conditioning system energy or insufficient passenger comfort.

[0039] The selected seasonal judgment logic employs a multi-parameter collaborative judgment mechanism to analyze and evaluate the key control parameters, generate an air conditioning operation mode switching command, and automatically switch to the corresponding air conditioning operation mode. The air conditioning operation modes include a small fresh air operation mode, a 100% fresh air operation mode, and a ventilation operation mode, specifically including: The air conditioning operation mode switching instructions include the small fresh air operation mode instruction, the 100% fresh air operation mode instruction, and the ventilation operation mode instruction; When the temperature judgment logic is selected, a preset station ventilation temperature reference value is used. and the station's small fresh air temperature benchmark value The temperature judgment deviation value DT is adaptively adjusted using the double exponential smoothing method, and the corresponding calculation formula is as follows: In the formula, This represents the first-order smoothed temperature judgment deviation value at time t; This represents the deviation value of the second-order smoothed temperature judgment at time t; Indicates the smoothing coefficient; If the outdoor temperature Greater than the station's small fresh air temperature reference value The sum of the temperature judgment deviation value DT and the central control unit generates the small fresh air operation mode command. If the outdoor temperature Less than or equal to the reference value of the station's small fresh air temperature The difference between the temperature judgment deviation value DT and the outdoor temperature Greater than the station ventilation temperature reference value Then the central control unit generates the fresh air operation mode command; If the outdoor temperature Less than or equal to the station ventilation temperature reference value The difference between the temperature judgment deviation value DT and the value of the temperature deviation value DT is used by the central control unit to generate the ventilation operation mode command. When the enthalpy judgment logic is selected, the enthalpy judgment deviation value DI is calculated using a nonlinear function. The corresponding calculation formula is as follows: In the formula, This indicates the critical deviation value for enthalpy value judgment; If the outdoor enthalpy value Greater than the return air enthalpy value The sum of the enthalpy value judgment deviation value DI and the central control unit generates the small fresh air operation mode command; If the outdoor enthalpy value Less than or equal to the return air enthalpy value The difference between the enthalpy value judgment deviation value DI and the outdoor temperature Greater than the station ventilation temperature reference value Then the central control unit generates the fresh air operation mode command; If the outdoor temperature Less than or equal to the station ventilation temperature reference value The difference between the temperature judgment deviation value DT and the value of the temperature deviation value DT is used by the central control unit to generate the ventilation operation mode command. The air conditioning equipment automatically switches to the small fresh air operation mode according to the small fresh air operation mode instruction; the air conditioning equipment automatically switches to the fresh air operation mode according to the fresh air operation mode instruction; the air conditioning equipment automatically switches to the ventilation operation mode according to the ventilation operation mode instruction.

[0040] The station ventilation temperature benchmark value refers to the critical temperature at which outdoor air can be directly used for ventilation and cooling. The station's small fresh air temperature benchmark value refers to the critical temperature used to switch between the 100% fresh air operation mode and the small fresh air operation mode.

[0041] Understandably, based on the selected seasonal judgment logic, the temperature judgment deviation value is calculated using either the double exponential smoothing method or the enthalpy value judgment deviation value is calculated using a nonlinear function. This is then compared with the outdoor temperature or outdoor enthalpy value to generate a corresponding air conditioning operation mode switching command and automatically switch to the corresponding air conditioning operation mode. Through a closed-loop process of selecting seasonal judgment logic, dynamically calculating judgment deviation values, multi-condition branch judgment, and command generation and automatic switching, intelligent decision-making for air conditioning operation modes is achieved. Both the temperature and enthalpy judgment logics have three progressive air conditioning operation modes to ensure that the air conditioning operation mode adapts to dynamic changes in environmental conditions. Simultaneously, the temperature and enthalpy judgment logics use a unified judgment logic to switch to the ventilation operation mode, ensuring consistency of the ventilation operation mode across different seasonal judgment logics. This improves the response speed of air conditioning operation mode switching and enhances the automation level, energy-saving effect, and operational reliability of the urban rail transit environmental control system.

[0042] After the air conditioning equipment automatically switches to the small fresh air operation mode according to the small fresh air operation mode command, it also includes classifying the CO2 concentration of the station into concentration levels: Based on the CO2 concentration at each station, a weighted average CO2 concentration was calculated. The corresponding calculation formula is as follows: In the formula, This represents the passenger flow weight where the i-th CO2 sensor is located; This represents the CO2 concentration at the station collected by the i-th CO2 sensor; n represents the total number of CO2 sensors. The preset station CO2 concentration control thresholds include an opening threshold Cs, a closing deviation value DC, and a station CO2 concentration control upper limit Ch, combined with the station's average CO2 concentration. Establish station CO2 concentration classification standards; Based on the station CO2 concentration classification standard, when the average CO2 concentration of the station is... The average CO2 concentration at the station is greater than or equal to the opening threshold Cs. If the CO2 concentration is less than the upper limit Ch for station CO2 concentration control, then the average CO2 concentration of the station is marked. Level 1 concentration; When the average CO2 concentration of the station The difference between the opening threshold Cs and the closing deviation value DC is greater than or equal to the average CO2 concentration at the station. If the concentration is less than the opening threshold Cs, then the average CO2 concentration of the station is marked. It is a secondary concentration; When the average CO2 concentration of the station If the difference between the opening threshold Cs and the closing deviation value DC is less than the average CO2 concentration at the station, then the average CO2 concentration at the station is marked. It is a level three concentration.

[0043] After classifying the CO2 concentration at the station into different levels, the system further includes generating a graded frequency conversion control strategy for CO2 concentration at different levels and intelligently controlling the small fresh air unit. When the average CO2 concentration of the station When the concentration reaches the first level, the small fresh air unit is started and its frequency is adjusted using an integral separation PID algorithm. The corresponding frequency adjustment formula is as follows: In the formula, Indicates the proportionality coefficient; Indicates the integral coefficient; This represents the opening threshold Cs at time t and the average CO2 concentration at the station. The difference; Indicates the integral separation threshold; When the average CO2 concentration of the station When the concentration is at the secondary level, maintain the current state and frequency of the small fresh air unit; When the average CO2 concentration of the station When the concentration reaches level three, the small fresh air fan is turned off.

[0044] The passenger flow weights for CO2 sensors include dense passenger flow weights and non-dense passenger flow weights, which are used to reflect the passenger flow contribution of different areas and ensure that the station's average CO2 concentration can accurately reflect the station's air quality. Dense passenger flow weights correspond to areas with concentrated passenger flow and long dwell times, such as platform waiting areas and station hall turnstiles; non-dense passenger flow weights correspond to areas with fast passenger flow and short dwell times, such as entrance and exit passages and transfer passages.

[0045] It should be noted that current technologies cannot control the start / stop and adjust the frequency of small fresh air units based on CO2 concentration. This results in insufficient fresh air supply during peak hours when CO2 concentrations exceed the standard, and excessive fresh air supply during off-peak hours when CO2 concentrations are normal. Therefore, by classifying CO2 concentration levels and implementing frequency conversion control, the fresh air volume can be precisely controlled.

[0046] Furthermore, for the average CO2 concentration of primary stations, an integral separation threshold is introduced through an integral separation PID algorithm. This threshold is the difference between the opening threshold at time t and the average CO2 concentration of the station. When the value is large, the frequency of the small fresh air fan is adjusted only by the proportional coefficient to avoid integral saturation and overshoot. The difference between the opening threshold at time t and the average CO2 concentration at the station is considered. When the frequency is relatively low, the integral coefficient is used to eliminate static deviation, thus balancing the speed and stability of the small fresh air unit's frequency adjustment.

[0047] Subsequently, for the average CO2 concentration of the secondary station, the current status and operating frequency of the small fresh air unit are recorded by the status latch of the small fresh air unit. This eliminates the need for adjusting the frequency and judgment logic of the small fresh air unit through integral separation PID algorithm, effectively solving the problem of frequent start-stop and frequency adjustment of the small fresh air unit caused by small oscillations in the average CO2 concentration of the station.

[0048] Understandably, the station CO2 concentration graded frequency conversion control strategy deeply integrates the station CO2 concentration with the energy-saving operation control of the small fresh air unit. By dividing the average CO2 concentration of the three-level station, the small fresh air unit can be controlled in a refined manner, reducing its energy consumption while meeting the station's air quality requirements to the greatest extent.

[0049] like Figure 2 The diagram shows a multimodal dynamic optimization control system based on humidity threshold triggering provided by an embodiment of the present invention. The system includes: The data acquisition and preprocessing module is used to collect outdoor temperature and humidity, return air temperature and humidity and station CO2 concentration through temperature and humidity sensors, return air temperature and humidity sensors and CO2 sensors, respectively, and preprocess them to obtain key control parameters. The seasonal judgment logic selection module is used to construct a humidity threshold judgment mechanism to compare and analyze the key control parameters and dynamically select the seasonal judgment logic, wherein the seasonal judgment logic includes temperature judgment logic and enthalpy value judgment logic. The instruction generation and mode switching module is used to analyze and evaluate the key control parameters based on the selected seasonal judgment logic using a multi-parameter collaborative judgment mechanism, generate an air conditioning operation mode switching instruction, and automatically switch to the corresponding air conditioning operation mode. The air conditioning operation modes include a small fresh air operation mode, a 100% fresh air operation mode, and a ventilation operation mode.

[0050] Figure 2 The apparatus of the illustrated embodiment can be used to perform corresponding actions. Figure 1 The steps in the method embodiments shown are implemented in a similar manner and have similar technical effects, and will not be repeated here.

[0051] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the steps of a multimodal dynamic optimal control method based on a humidity threshold trigger as described in any of the above.

[0052] like Figure 3 The diagram shown is a hardware structure schematic of an electronic device according to an embodiment of the present invention. The electronic device 30 includes: a processor 31, a memory 32, and a computer program; wherein... The memory 32 is used to store the computer program, and the memory may also be flash memory. The computer program is, for example, an application program or functional module that implements the above method.

[0053] Processor 31 is configured to execute the computer program stored in the memory to implement the various steps performed by the device in the above method. For details, please refer to the relevant descriptions in the preceding method embodiments.

[0054] Alternatively, the memory 32 can be either standalone or integrated with the processor 31.

[0055] When the memory 32 is a device independent of the processor 31, the device may further include: Bus 33 is used to connect the memory 32 and the processor 31.

[0056] A readable storage medium storing a computer program, which, when executed by a processor, is used to implement the steps of a humidity threshold-triggered multimodal dynamic optimal control method as described in any of the preceding claims.

[0057] The readable storage medium can be a computer storage medium or a communication medium. A communication medium includes any medium that facilitates the transfer of computer programs from one location to another. A computer storage medium can be any available medium accessible to a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application-Specific Integrated Circuit (ASIC). Alternatively, the ASIC can be located in a user equipment. Of course, the processor and the readable storage medium can also exist as discrete components in a communication device. The readable storage medium can be a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0058] The present invention also provides a program product including executable instructions stored in a readable storage medium. At least one processor of the device can read the executable instructions from the readable storage medium, and the at least one processor executes the executable instructions to cause the device to implement the methods provided in the various embodiments described above.

[0059] In the embodiments of the above-described device, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0060] Through the above embodiments, this invention collects outdoor temperature and humidity, return air temperature and humidity, and station CO2 concentration using temperature and humidity sensors, return air temperature and humidity sensors, and CO2 sensors, respectively, and performs preprocessing to obtain key control parameters. A humidity threshold judgment mechanism is constructed to compare and analyze the key control parameters and dynamically select seasonal judgment logic, which includes temperature judgment logic and enthalpy value judgment logic. Based on the selected seasonal judgment logic, a multi-parameter collaborative judgment mechanism is used to analyze and evaluate the key control parameters, generate air conditioning operation mode switching instructions, and automatically switch to the corresponding air conditioning operation mode. The air conditioning operation modes include a small fresh air operation mode, a 100% fresh air operation mode, and a ventilation operation mode. This allows for dynamic switching of air conditioning operation modes, reducing the energy consumption of the air conditioning system and ensuring station air quality and passenger comfort, thus meeting the energy-saving operation requirements of urban rail transit networks.

[0061] This invention overcomes the limitations of traditional single-season judgment logic by employing two seasonal judgment logics triggered by humidity thresholds. It effectively solves the problems of premature cooling or delayed dehumidification under extreme conditions such as high temperature and low humidity and low temperature and high humidity, improving temperature control accuracy to within ±0.5℃ and reducing temperature fluctuations by 40%, significantly enhancing passenger comfort. Furthermore, by dynamically and automatically switching air conditioning operating modes according to the load requirements of the air conditioning system, this invention reduces energy consumption by 15%-20% during transitional seasons, thereby saving energy in the refrigeration system. Compared to existing technologies that rely on manual pre-setting of air conditioning operating modes, this reduces labor costs by 80%, and optimizes the air conditioning operating mode switching response time from 1 hour to 30 minutes, an improvement of 50%. Response efficiency: This invention links the operation of the small fresh air blower with the station's CO2 concentration through a graded frequency conversion control strategy based on CO2 concentration. Combined with an integral separation PID algorithm, it precisely adjusts the frequency of the small fresh air blower, reducing its energy consumption by 25%. This further reduces the energy consumption of the air conditioning system while ensuring air quality in the station. Furthermore, this invention improves data reliability by 60% through redundant design of return air sensors at both the large and small ends of the station, and through data cross-calibration and weighted processing. This effectively solves the problem of misjudgment of air conditioning operation modes caused by single-point sensor failures in existing technologies. This allows the air conditioning system to flexibly adapt to changes in operating conditions across different seasons, fully meeting the high availability and low energy consumption requirements of urban rail transit environmental control systems.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multimodal dynamic optimal control method based on humidity threshold triggering, characterized in that, The method includes: The outdoor temperature and humidity, the return air temperature and humidity, and the CO2 concentration at the station's main and secondary ends were collected by temperature and humidity sensors, return air temperature and humidity sensors, and CO2 sensors, and the data were preprocessed to obtain key control parameters. A humidity threshold judgment mechanism is constructed to compare and analyze the key control parameters and dynamically select the seasonal judgment logic, wherein the seasonal judgment logic includes temperature judgment logic and enthalpy value judgment logic. Based on the selected seasonal judgment logic, a multi-parameter collaborative judgment mechanism is used to analyze and evaluate the key control parameters, generate an air conditioning operation mode switching command, and automatically switch to the corresponding air conditioning operation mode. The air conditioning operation modes include small fresh air operation mode, 100% fresh air operation mode, and ventilation operation mode.

2. The multimodal dynamic optimal control method based on humidity threshold triggering according to claim 1, characterized in that, The process involves collecting outdoor temperature and humidity, return air temperature and humidity, and CO2 concentration data from the station's main and secondary return air sources using temperature and humidity sensors, return air temperature and humidity sensors, and CO2 concentration data, and then preprocessing these data. Specifically, this includes: A dynamic sampling frequency adjustment algorithm is employed to automatically adjust the sampling intervals of the temperature and humidity sensor, the return air temperature and humidity sensor, and the CO2 sensor based on the entropy changes in environmental data. The outdoor temperature and humidity, the return air temperature and humidity at both ends of the station, and the station CO2 concentration are collected by the temperature and humidity sensor, the return air temperature and humidity at both ends of the station, and the CO2 concentration at the station, respectively. The outdoor temperature and humidity includes outdoor temperature. Outdoor humidity The temperature and humidity of the return air at both the large and small ends of the station include the return air temperature at the large end of the station. Humidity of return air at the station's main end Station small end return air temperature Humidity of return air at the station's small end ; Regarding the outdoor temperature The outdoor humidity The return air temperature at the large end of the station The humidity of the return air at the large end of the station The return air temperature at the small end of the station The humidity of the return air at the small end of the station The CO2 concentration at the station was then processed by an exponentially weighted moving average algorithm for filtering and noise reduction, followed by data verification and completion operations using a trend prediction algorithm.

3. The multimodal dynamic optimal control method based on humidity threshold triggering according to claim 2, characterized in that, Based on the outdoor temperature The outdoor humidity The return air temperature at the large end of the station The humidity of the return air at the large end of the station The return air temperature at the small end of the station and the humidity of the return air at the small end of the station The outdoor enthalpy is calculated using a high-precision enthalpy calculation formula. Large-end return air enthalpy Enthalpy of return air at the small end The corresponding calculation formula is as follows: In the formula, This indicates the specific heat capacity of dry air at constant pressure. This represents the relative humidity percentage conversion factor. This represents the latent heat of vaporization of water at 0℃; This represents the specific heat capacity of water vapor at constant pressure. The return air temperature at the large end of the station was respectively... The return air temperature at the small end of the station The humidity of the return air at the large end of the station With respect to the return air humidity at the small end of the station The large-end return air enthalpy value With the small end return air enthalpy value Perform cross-calibration and weighted processing to generate return air temperature. Return air humidity Enthalpy of return air The corresponding calculation formula is as follows: In the formula, This indicates the weight of the larger end of the station; This indicates the weight of the station's little end; The outdoor temperature The outdoor humidity The return air temperature at the large end of the station The humidity of the return air at the large end of the station The return air temperature at the small end of the station The humidity of the return air at the small end of the station The station's CO2 concentration and the enthalpy of the large-end return air. The enthalpy value of the small-end return air The outdoor enthalpy value The return air temperature The return air humidity and the aforementioned return air enthalpy value The key control parameters are summarized and transmitted to the central control unit.

4. The multimodal dynamic optimal control method based on humidity threshold triggering according to claim 1, characterized in that, The historical outdoor temperature and humidity data are retrieved and divided according to season type. The outdoor humidity change rate corresponding to the historical outdoor temperature and humidity data in each season is extracted. ; The seasonal type, the air conditioning system load rate L, and the outdoor humidity change rate are included. The humidity threshold is inferred from the ANFIS threshold model through its adaptive neural fuzzy inference mechanism. .

5. The multimodal dynamic optimal control method based on humidity threshold triggering according to claim 4, characterized in that, The mechanism for comparing and analyzing key control parameters based on the humidity threshold and dynamically selecting seasonal judgment logic includes temperature judgment logic and enthalpy value judgment logic, specifically comprising: The outdoor humidity is set within a preset time range. The inferred humidity threshold For comparison, within the time range of the change, if the outdoor humidity Less than the humidity threshold If so, then the temperature judgment logic is selected; If the outdoor humidity Greater than or equal to the humidity threshold If so, then the enthalpy value judgment logic is selected.

6. The multimodal dynamic optimal control method based on humidity threshold triggering according to claim 5, characterized in that, The selected seasonal judgment logic employs a multi-parameter collaborative judgment mechanism to analyze and evaluate the key control parameters, generate an air conditioning operation mode switching command, and automatically switch to the corresponding air conditioning operation mode. The air conditioning operation modes include a small fresh air operation mode, a 100% fresh air operation mode, and a ventilation operation mode, specifically including: The air conditioning operation mode switching instructions include the small fresh air operation mode instruction, the 100% fresh air operation mode instruction, and the ventilation operation mode instruction; When the temperature judgment logic is selected, a preset station ventilation temperature reference value is used. and the station's small fresh air temperature benchmark value The temperature judgment deviation value DT is adaptively adjusted using the double exponential smoothing method, and the corresponding calculation formula is as follows: In the formula, This represents the first-order smoothed temperature judgment deviation value at time t; This represents the deviation value of the second-order smoothed temperature judgment at time t; Indicates the smoothing coefficient; If the outdoor temperature Greater than the station's small fresh air temperature reference value The sum of the temperature judgment deviation value DT and the central control unit generates the small fresh air operation mode command. If the outdoor temperature Less than or equal to the reference value of the station's small fresh air temperature The difference between the temperature judgment deviation value DT and the outdoor temperature Greater than the station ventilation temperature reference value Then the central control unit generates the fresh air operation mode command; If the outdoor temperature Less than or equal to the station ventilation temperature reference value The difference between the temperature judgment deviation value DT and the value of the temperature difference is used by the central control unit to generate the ventilation operation mode command. When the enthalpy judgment logic is selected, the enthalpy judgment deviation value DI is calculated using a nonlinear function. The corresponding calculation formula is as follows: In the formula, This indicates the critical deviation value for enthalpy value judgment; If the outdoor enthalpy value Greater than the return air enthalpy value The sum of the enthalpy value judgment deviation value DI and the central control unit generates the small fresh air operation mode command; If the outdoor enthalpy value Less than or equal to the return air enthalpy value The difference between the enthalpy value judgment deviation value DI and the outdoor temperature Greater than the station ventilation temperature reference value Then the central control unit generates the fresh air operation mode command; If the outdoor temperature Less than or equal to the station ventilation temperature reference value The difference between the temperature judgment deviation value DT and the value of the temperature difference is used by the central control unit to generate the ventilation operation mode command. The air conditioning equipment automatically switches to the small fresh air operation mode according to the small fresh air operation mode instruction; the air conditioning equipment automatically switches to the fresh air operation mode according to the fresh air operation mode instruction; the air conditioning equipment automatically switches to the ventilation operation mode according to the ventilation operation mode instruction.

7. The multimodal dynamic optimal control method based on humidity threshold triggering according to claim 6, characterized in that, After the air conditioning equipment automatically switches to the small fresh air operation mode according to the small fresh air operation mode command, it also includes classifying the CO2 concentration of the station into concentration levels: Based on the CO2 concentration at each station, a weighted average CO2 concentration was calculated. The corresponding calculation formula is as follows: In the formula, This represents the passenger flow weight where the i-th CO2 sensor is located; This represents the CO2 concentration at the station collected by the i-th CO2 sensor; n represents the total number of CO2 sensors. The preset station CO2 concentration control thresholds include an opening threshold Cs, a closing deviation value DC, and a station CO2 concentration control upper limit Ch, combined with the station's average CO2 concentration. Establish station CO2 concentration classification standards; Based on the station CO2 concentration classification standard, when the average CO2 concentration of the station is... The average CO2 concentration at the station is greater than or equal to the opening threshold Cs. If the CO2 concentration is less than the upper limit Ch for station CO2 concentration control, then the average CO2 concentration of the station is marked. Level 1 concentration; When the average CO2 concentration of the station The difference between the opening threshold Cs and the closing deviation value DC is greater than or equal to the average CO2 concentration at the station. If the concentration is less than the opening threshold Cs, then the average CO2 concentration of the station is marked. It is a secondary concentration; When the average CO2 concentration of the station If the difference between the opening threshold Cs and the closing deviation value DC is less than the average CO2 concentration at the station, then the average CO2 concentration at the station is marked. It is a level three concentration.

8. The multimodal dynamic optimal control method based on humidity threshold triggering according to claim 7, characterized in that, After classifying the CO2 concentration at the station into different levels, the system further includes generating a graded frequency conversion control strategy for CO2 concentration at different levels and intelligently controlling the small fresh air unit. When the average CO2 concentration of the station When the concentration reaches the first level, the small fresh air unit is started and its frequency is adjusted using an integral separation PID algorithm. The corresponding frequency adjustment formula is as follows: In the formula, Indicates the proportionality coefficient; Indicates the integral coefficient; This represents the opening threshold Cs at time t and the average CO2 concentration at the station. The difference; Indicates the integral separation threshold; When the average CO2 concentration of the station When the concentration is at the secondary level, maintain the current state and frequency of the small fresh air unit; When the average CO2 concentration of the station When the concentration reaches level three, the small fresh air fan is turned off.

9. A multimodal dynamic optimal control system based on humidity threshold triggering, applied to the multimodal dynamic optimal control method based on humidity threshold triggering as described in any one of claims 1-8, characterized in that, The system includes: The data acquisition and preprocessing module is used to collect outdoor temperature and humidity, return air temperature and humidity and station CO2 concentration through temperature and humidity sensors, return air temperature and humidity sensors and CO2 sensors, respectively, and preprocess them to obtain key control parameters. The seasonal judgment logic selection module is used to construct a humidity threshold judgment mechanism to compare and analyze the key control parameters and dynamically select the seasonal judgment logic, wherein the seasonal judgment logic includes temperature judgment logic and enthalpy value judgment logic. The instruction generation and mode switching module is used to analyze and evaluate the key control parameters based on the selected seasonal judgment logic using a multi-parameter collaborative judgment mechanism, generate an air conditioning operation mode switching instruction, and automatically switch to the corresponding air conditioning operation mode. The air conditioning operation modes include a small fresh air operation mode, a 100% fresh air operation mode, and a ventilation operation mode.

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