Air conditioner control method, device and equipment based on environment agent quantity quota and medium

Abstract

The invention discloses an air conditioner control method, device and equipment based on environment agent quantity quota and a medium, and the method comprises the steps: collecting multi-source scoring parameters and safety judgment parameters in a target space, and calculating a comprehensive score reflecting a microbial risk level by utilizing a fusion algorithm of rule judgment and a comprehensive scoring model. The problems that the microbial risk cannot be evaluated in real time and the purification action is extensive in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the field of air conditioner control technology, and in particular to an air conditioner control method, device, equipment and medium based on environmental proxy quotas. Background Technology

[0002] With rising living standards and heightened public health awareness, the impact of indoor air quality on human health has become a major social concern. Modern residential and commercial air conditioning equipment is no longer limited to traditional temperature control; it increasingly integrates various air purification technologies, such as HEPA high-efficiency filtration, electrostatic dust removal, negative ion generation, and even UV-C ultraviolet sterilization, aiming to provide users with a healthier and safer breathing environment. However, current intelligent air conditioning control methods on the market have revealed a series of deep-seated technical bottlenecks when addressing complex indoor air health challenges.

[0003] The core problem lies in the fact that existing technologies heavily rely on the direct monitoring of single or a few pollutants. For example, systems commonly use PM2.5 sensors to determine air cleanliness and activate or deactivate purification functions accordingly. While inhalable particulate matter is a significant air pollutant, it is only one aspect of air health issues. The real threat to human health often comes from the microbial load attached to particulate matter or transmitted through the air, including bacteria, viruses, and mold spores. These microorganisms are key culprits in the spread of respiratory diseases and allergic reactions. However, directly, online, and in real-time detection of microbial concentrations is technically extremely complex, costly, and time-consuming, making it completely unsuitable for consumer-grade air conditioning products. This puts existing systems in a dilemma: either ignore the unseen microbial risks or over-purify indiscriminately "just in case," resulting in significant resource waste.

[0004] Accompanying this blind spot in risk assessment is the problem of inefficient management of high-efficiency purification resources. While UV-C ultraviolet light has a highly effective ability to inactivate microorganisms, it poses potential hazards to human skin and eyes, requiring safe use only in unoccupied environments. Similarly, high-airflow, high-power operation modes activated for rapid purification, while quickly reducing pollutant concentrations, significantly increase energy consumption and operating noise, impacting user comfort. Currently, these energy-efficient, high-risk purification actions lack effective scheduling mechanisms. Systems typically rely on preset schedules or simple threshold triggers, failing to dynamically respond to real-time changes in risk. For example, even if there is a sharply increased risk of microorganisms due to large gatherings indoors, the system may fail to activate UV-C due to "outside the set time period"; conversely, when the risk is low, the system may frequently activate high-power modes due to misjudgment. This one-size-fits-all or mechanical control approach completely severs the dynamic connection between risk assessment and resource scheduling.

[0005] Therefore, existing air conditioning control methods face a fundamental contradiction: they cannot achieve dynamic assessment of microbial risks and intelligently and precisely schedule efficient purification actions based on this assessment, while ensuring personnel safety and system energy consumption constraints. They lack the ability to integrate multi-source environmental information into risk perception, and even more so, they lack an intelligent decision-making system that can optimize the allocation of limited, high-cost purification capacity as a resource quota. This not only leads to energy waste and shortened equipment lifespan but also fails to effectively protect users' respiratory health. To solve these problems, an innovative control method is urgently needed to address the technical challenge of existing air conditioning control methods, as described above, failing to achieve dynamic assessment of microbial risks and intelligent scheduling of efficient purification actions based on this assessment, while ensuring personnel safety and energy consumption constraints. Summary of the Invention

[0006] The embodiments of the present invention provide an air conditioning control method, device, equipment and medium based on environmental proxy quota, which aims to solve the technical problem that the existing air conditioning control methods cannot achieve dynamic assessment of microbial risk and intelligent scheduling of efficient purification actions based on it while ensuring personnel safety and energy consumption constraints.

[0007] In a first aspect, embodiments of the present invention provide an air conditioning control method based on environmental proxy quotas. The method includes: collecting multi-source scoring parameters and safety judgment parameters within a target space; generating a comprehensive score reflecting the level of microbial risk based on the multi-source scoring parameters by fusion rule judgment and comprehensive scoring model calculation; and dynamically scheduling the air conditioning purification execution mode according to the comprehensive score, combined with preset purification action quotas and safety constraints.

[0008] Secondly, embodiments of the present invention also provide an air conditioning control device based on environmental proxy quotas, for executing the air conditioning control method based on environmental proxy quotas as described above.

[0009] Thirdly, embodiments of the present invention also provide a computer device, the computer device including a memory and a processor connected to the memory; the memory is used to store a computer program; the processor is used to run the computer program stored in the memory to perform the steps of the above-described air conditioning control method based on environmental proxy quotas.

[0010] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, can implement the steps of the above-described air conditioning control method based on environmental proxy quotas.

[0011] Compared with the prior art, the beneficial effects of the present invention are: In the technical solution of this invention, the air conditioning control method based on environmental proxy quotas is applied to an air conditioning attitude monitoring system. This method collects multi-source scoring parameters and safety judgment parameters within the target space, and uses a fusion algorithm of rule-based judgment and a comprehensive scoring model to calculate a comprehensive score reflecting the level of microbial risk. This solves the problems of existing technologies being unable to assess microbial risk in real time and having coarse purification actions. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart of the air conditioning control method based on environmental proxy quota provided by the present invention; Figure 2 The first sub-flowchart of the air conditioning control method based on environmental proxy quota provided by the present invention; Figure 3 This is a sub-flowchart of the second sub-flowchart of the air conditioning control method based on environmental proxy quota provided by the present invention; Figure 4 The third sub-flowchart of the air conditioning control method based on environmental proxy quota provided by the present invention; Figure 5 The fourth sub-flowchart of the air conditioning control method based on environmental proxy quota provided by the present invention; Figure 6 The fifth sub-flowchart of the air conditioning control method based on environmental proxy quota provided by the present invention; Figure 7 The sixth sub-flowchart of the air conditioning control method based on environmental proxy quota provided by the present invention; Figure 8 The seventh sub-flowchart of the air conditioning control method based on environmental proxy quota provided by the present invention; Figure 9 The eighth sub-flowchart of the air conditioning control method based on environmental proxy quota provided by the present invention; Figure 10 A schematic block diagram of a unit of an air conditioning control device based on environmental proxy quota provided by the present invention; Figure 11 A schematic block diagram of a computer device provided for an embodiment of the present invention. Detailed Implementation

[0014] 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, not all, of the embodiments of the present invention. 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.

[0015] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0016] It should also be understood that the terminology used in this specification is for the purpose of describing embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0017] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0018] In order to solve the technical problem that existing air conditioning control methods cannot achieve dynamic assessment of microbial risk and intelligent scheduling of efficient purification actions while ensuring personnel safety and energy consumption constraints, this invention discloses an air conditioning control method based on environmental proxy quota, which is applied to an air conditioning attitude monitoring system.

[0019] Reference Figures 1 to 9 The air conditioning control method based on environmental proxy quota includes the following steps: S110. Collect multi-source scoring parameters and safety judgment parameters within the target space; S120. Based on the multi-source scoring parameters, a comprehensive score reflecting the level of microbial risk is generated by combining rule judgment and comprehensive scoring model calculation. S130. Based on the comprehensive score, and combined with the preset purification action quota and safety constraints, dynamically schedule the purification execution mode of the air conditioner.

[0020] The air conditioning control method based on environmental proxy quotas is implemented through the collaborative operation of a sensing system, edge computing unit, and actuator integrated within the air conditioning equipment. First, the air conditioning's environmental sensors are activated, collecting multi-source scoring parameters within the target space. These parameters include PM1, PM2.5, and PM10 concentration data acquired by a particulate matter sensor; carbon dioxide concentration acquired by a non-dispersive infrared sensor; relative humidity acquired by a temperature and humidity sensor; total volatile organic compound concentration acquired by a metal oxide sensor; and the pressure difference across the air conditioning filter acquired by a differential pressure sensor.

[0021] At the same time, the system collects safety judgment parameters, including the presence status of people in the target space obtained by infrared motion sensors or cameras, the on / off status of air conditioning ducts obtained by microswitches or position sensors in the ducts, and the ozone concentration in the target space obtained by electrochemical sensors.

[0022] Subsequently, the edge computing unit of the air conditioner runs a preset fusion algorithm based on the collected multi-source scoring parameters. This algorithm combines predefined rule judgment logic with a lightweight comprehensive scoring model deployed at the edge to perform feature extraction and weighted analysis on the multi-source scoring parameters, and calculates and generates a normalized comprehensive score that reflects the current level of microbial risk. This score comprehensively reflects the risk of microbial growth and spread caused by human activities and changes in environmental conditions.

[0023] Next, the air conditioner's control module dynamically schedules the air conditioner's purification execution mode based on the real-time generated comprehensive score, the preset purification action quota, and the real-time safety judgment conditions. For example, when the comprehensive score is high, the purification action quota is sufficient, and the safety judgment conditions are met, the control module schedules the air conditioner to enter a high-intensity purification execution mode; when the comprehensive score is in the middle range, it schedules the air conditioner to enter a medium-intensity or low-intensity purification execution mode.

[0024] This implementation method enables intelligent and dynamic assessment of microbial risks that cannot be directly measured, and ensures the accurate and safe delivery of efficient purification resources through the coordinated management of quotas and safety constraints.

[0025] Furthermore, the air conditioner's control module can record the user's manual adjustment behavior of the purification execution mode under different comprehensive score ranges, and dynamically fine-tune the mapping relationship between the comprehensive score and the purification execution mode accordingly, so that the control logic gradually adapts to the user's personalized needs.

[0026] In one embodiment, the air conditioning control method based on environmental proxy quotas further includes: S140. During the purification process, the cumulative load of the filter is continuously calibrated and calculated based on the multi-source scoring parameters and the safety judgment parameters, and maintenance reminders or filter self-cleaning actions are triggered according to the preset alarm rules based on the cumulative load of the filter.

[0027] During the purification process, the system continuously performs dynamic calibration and maintenance management of the filter's cumulative load. Specifically, the air conditioning control module calculates the filter's cumulative load in real time based on the aforementioned multi-source scoring parameters, particularly particulate matter concentration and pressure difference across the filter, as well as fan operating parameters. The calculation process first estimates the real-time airflow volumetric flow rate through the filter by combining fan operating parameters with the fan characteristic curve.

[0028] Then, using the particulate matter mass concentration on the inlet side measured by the particulate matter sensor and the estimated airflow volumetric flow rate, the incremental mass of particulate matter captured by the filter per unit time is calculated. By integrating this mass increment over time, the total mass of particulate matter captured by the filter since its activation is obtained. To improve estimation accuracy, the system further uses the pressure difference across the filter measured by the differential pressure sensor to perform online calibration of the cumulative load model. Based on the factory-calibrated pressure difference-mass empirical relationship model, i.e., ΔP = a·m + b, the control module compares the real-time pressure difference value with the cumulative mass calculated based on the mass integration method, dynamically correcting the model coefficients a and b to obtain a more accurate filter cumulative load. When the calibrated filter cumulative load reaches the threshold of the preset alarm rule, the air conditioner control module triggers a maintenance reminder, which notifies the user via the user terminal APP or the device display screen that the filter needs to be replaced or professionally cleaned. If the air conditioning equipment is equipped with a back-blowing self-cleaning mechanism, the control module can automatically schedule the fan to perform short-term back-blowing or pulse cleaning when the system is running at a low load and the safety judgment parameters allow, so as to partially restore the permeability of the filter. After performing the self-cleaning action, the estimated value of the cumulative load of the filter is updated accordingly based on the cleaning effect model.

[0029] This implementation method ensures the accuracy and timeliness of filter maintenance, effectively preventing energy efficiency degradation and secondary pollution risks caused by filter clogging.

[0030] In one embodiment, the step of collecting multi-source scoring parameters in the target space in S110 includes: S111. Collect particulate matter concentration, carbon dioxide concentration, relative humidity, and total volatile organic compound concentration in the target space using environmental sensors; S112. The differential pressure difference across the air conditioner's filter is collected using a differential pressure sensor; S113. Obtain the air conditioner fan operating parameters through the air conditioner fan drive module; S114. The particulate matter concentration, carbon dioxide concentration, relative humidity, total volatile organic compound concentration, pressure difference before and after the filter, and fan operating parameters are used as multi-source scoring parameters.

[0031] The environmental sensor array is responsible for collecting environmental parameters of the target space: a high-precision laser scattering particulate sensor with a sampling period of 10 seconds monitors the particulate matter concentration in the target space in real time, especially the values ​​of PM2.5 and PM1.0; a non-dispersive infrared (NDIR) carbon dioxide sensor with a sampling period of 30 seconds obtains the carbon dioxide concentration in the target space, serving as an important proxy indicator reflecting the intensity of human activity and respiratory sources; a capacitive temperature and humidity sensor simultaneously collects the relative humidity and temperature in the target space, providing a basis for assessing the survival environment of microorganisms; and a metal oxide semiconductor gas sensor is used to detect the total volatile organic compound concentration in the target space to capture aerosol fluctuations caused by human activities such as cooking and cleaning.

[0032] Meanwhile, differential pressure sensors are installed at both ends of the filter assembly in the air conditioning duct to measure in real time the change in airflow resistance caused by dust accumulation on the filter, i.e., the pressure difference across the filter. This data directly reflects the degree of physical blockage of the filter.

[0033] In addition, the system interacts with the internal communication bus of the air conditioner fan drive module to obtain the fan's operating parameters in real time. These parameters include the fan's current speed, operating level, and cumulative operating time. The air conditioner's control module integrates the particulate matter concentration, carbon dioxide concentration, relative humidity, and total volatile organic compound concentration collected by environmental sensors, the pressure difference across the filter collected by differential pressure sensors, and the fan operating parameters obtained by the air conditioner fan drive module into a unified multi-source scoring parameter set. This set serves as the basis for subsequent calculations of the comprehensive score and the cumulative filter load.

[0034] To further improve data reliability, a reasonableness verification mechanism is first performed when receiving data from each sensor. For example, if the particulate matter concentration changes drastically within a few seconds, exceeding physical possibilities, the data point is determined to be abnormal, and valid data from the previous moment is used or compensation is made through interpolation algorithms, thereby ensuring the accuracy and stability of the multi-source scoring parameters input into the decision model.

[0035] In one embodiment, the step of collecting security determination parameters in S110 includes: S115. Obtain the air duct switch status through the air duct valve of the air conditioner; S116. Obtain the presence status of personnel in the target space through the personnel detection module; S117. Obtain the ozone concentration in the target space using an ozone sensor; S118. The presence status of the personnel, the operating parameters of the air conditioner fan, and the ozone concentration are used as safety determination parameters.

[0036] The air conditioning duct system is equipped with duct valves for controlling internal and external circulation. These duct valves are equipped with position sensors or limit switches. The air conditioning control module reads the electrical signal from the sensor to obtain the duct opening and closing status in real time. This status clearly indicates whether the duct is currently in "internal circulation closed" mode or "external circulation open" mode.

[0037] Simultaneously, the system acquires the presence status of people within the target space through a personnel detection module. This module can employ various technologies, such as passive infrared (PIR) motion sensors to detect heat source movement within the space, or millimeter-wave radar sensors to accurately detect subtle human movements through lightweight obstructions. In some air conditioning or air purifier devices equipped with cameras, the system can also combine the camera installed on the device with edge computing-based human contour recognition algorithms to confirm the presence of people. All detection results are received by the control module as binary signals of "personnel present" or "no personnel present."

[0038] In addition, to monitor potential byproducts during the purification process, the system is equipped with a high-precision electrochemical ozone sensor. This sensor continuously monitors the ozone concentration in the target space and transmits the real-time data to the control module. The air conditioning control module uses the presence status of personnel obtained from the personnel detection module, the duct opening / closing status obtained from the duct valve position sensor, and the ozone concentration obtained from the ozone sensor as safety judgment parameters.

[0039] In addition, although the fan operating parameters are provided by the fan drive module, they are also included in the scope of safety judgment parameters because they directly affect the airflow organization and potential ozone diffusion rate. For example, when assessing the safety of the ionization purification module, the system will make a judgment by combining the fan operating parameters with the trend of ozone concentration changes.

[0040] To enhance the robustness of the system's safety assessment, a multi-source personnel detection fusion strategy is applied during personnel detection. The control module does not rely on the judgment of a single sensor, but instead integrates the identification results from PIR, millimeter-wave radar, and cameras. It then uses majority voting or weighted decision algorithms to derive the final personnel presence status, effectively reducing the safety risks caused by false alarms from a single sensor and ensuring high reliability of safety assessments.

[0041] In one embodiment, step S120 includes: S121. The multi-source scoring parameters are binarized according to the preset fusion rules to generate multiple rule trigger signals; S122. The rule trigger signals are weighted and summed to obtain the rule layer score; S123. Input the multi-source scoring parameters into the comprehensive scoring model and output the model probability score; S124. The rule layer score and the model probability score are dynamically weighted and fused to generate the comprehensive score.

[0042] The air conditioner's control module analyzes the multi-source scoring parameters acquired in real time according to preset fusion rules, and performs binarization logic judgment to generate multiple independent rule trigger signals.

[0043] Specifically, when the carbon dioxide concentration in the multi-source scoring parameters exceeds a preset first concentration threshold, the control module generates and activates a first rule signal, which clearly indicates that there is a high risk of respiratory exposure in the space due to the gathering of people or poor ventilation.

[0044] When the rate of increase of particulate matter concentration exceeds a preset second rate of increase threshold, the control module generates and activates a second rule signal, which is used to capture sudden increases in pollutants caused by cooking, cleaning, or external pollution intrusion.

[0045] When the relative humidity exceeds the preset third humidity threshold, the control module generates and activates a third rule signal, which reflects that the current high humidity environment is conducive to the growth and survival of microorganisms such as bacteria and mold.

[0046] When the rate of increase of the pressure difference across the filter exceeds the preset fourth growth rate threshold, the control module generates and activates the fourth rule signal. This signal not only indicates that the filter has captured a large amount of particulate matter, but also indirectly indicates that there is a continuous source of particulate matter pollution in the environment, which may be accompanied by an increase in microbial carriers.

[0047] Subsequently, the system performs a weighted summation of the four rule-triggered signals to calculate a rule-layer score. Each rule signal corresponds to a preset weight coefficient; for example, the first rule signal has a weight of 0.3, the second rule signal has a weight of 0.3, the third rule signal has a weight of 0.2, and the fourth rule signal has a weight of 0.2, with a total of 1.0. The control module multiplies the value of each activation signal by its corresponding weight and then sums the results to obtain a rule-layer score between 0 and 1. This score quantifies the baseline of microbial risk directly contributed by these explicit events.

[0048] Simultaneously, the system inputs a complete set of multi-source scoring parameters, including but not limited to particulate matter concentration, carbon dioxide concentration, relative humidity, total volatile organic compound concentration, pressure difference before and after the filter, and their historical variation sequences, into a lightweight comprehensive scoring model deployed on an edge computing unit. This model employs a lightweight gradient boosting tree algorithm trained on massive amounts of environmental data, enabling it to learn the nonlinear coupling relationships and complex patterns between various parameters. Its output is a model probability score between 0 and 1, representing the microbial risk probability inferred by the model based on the overall data pattern.

[0049] Finally, the air conditioner's control module dynamically weights and fuses the rule-layer score and the model probability score to generate a final comprehensive score. The fusion process uses a weighted average formula: Overall score = α × rule layer score + (1-α) × model probability score The dynamic weight α ranges from 0.4 to 0.7, and the system dynamically adjusts α based on real-time data quality and confidence levels. When multiple rule trigger signals are activated simultaneously, indicating a clear high-risk event, the system increases α to 0.7 to emphasize the high reliability of the rule layer. When all rule signals are not triggered, but environmental parameters exhibit complex fluctuations, the system decreases α to 0.4, relying more on the deep patterns mined from the data by the model. This dynamic weighting mechanism ensures that the system responds quickly and decisively to sudden events, and makes accurate and smooth decisions under normal circumstances. When users provide feedback on the system's risk assessment results through the app, this feedback data is anonymized and used for incremental fine-tuning of the comprehensive scoring model, allowing the model's judgment results to better align with actual scenarios and user perception.

[0050] In one embodiment, S121 includes: S1211. When the carbon dioxide concentration exceeds the first concentration threshold, the first rule signal is triggered; S1212. When the rate of increase of the particulate matter concentration exceeds the second rate of increase threshold, the second rule signal is triggered. S1213. When the relative humidity exceeds the third humidity threshold, the third rule signal is triggered; S1214. When the filter pressure difference growth rate exceeds the fourth growth rate threshold, the fourth rule signal is triggered.

[0051] The control module compares the currently acquired carbon dioxide concentration with a preset first concentration threshold. This first concentration threshold is set based on indoor air quality standards. For example, when the carbon dioxide concentration is detected to continuously exceed the threshold of 1000 ppm, the control module determines that the metabolic activity of people in the space is vigorous, the concentration of exhaled bioaerosols in the enclosed space is significantly increased, and there is a high risk of microbial transmission. Therefore, it immediately triggers and sets the first rule signal.

[0052] The control module performs real-time differential calculations on the particulate matter concentration data stream, calculates its rate of increase within a short time window, and compares this rate value with a preset second rate of increase threshold. When the rate of increase exceeds the threshold, it indicates that a "pollutant outbreak" has occurred in the space (such as someone starting to cook, lighting candles, or outdoor smog), and the concentration of particulate matter suspended in the air that can act as a carrier of microorganisms increases sharply. At this time, the control module triggers and sets the second rule signal.

[0053] The control module compares the real-time relative humidity value with a preset third humidity threshold, which is set at 60%RH. This is because numerous studies have shown that when the ambient humidity is consistently higher than this level, it is conducive to the reproduction and activity of microorganisms such as dust mites and mold. Therefore, once the humidity exceeds this threshold, the control module triggers and sets the third rule signal.

[0054] The control module performs trend analysis on the pressure difference data before and after the filter, calculates its growth rate per unit time, and compares it with the preset fourth growth rate threshold. When the pressure difference growth rate exceeds the threshold, it indicates that the filter is rapidly accumulating a large amount of particulate matter. This not only means that the purification load is increasing, but also indirectly reflects that the particulate matter pollution source in the environment is active, which may be accompanied by an increase in organic pollutants such as dander and hair, thereby increasing the substrate for microbial growth. Therefore, the control module triggers and sets the fourth rule signal.

[0055] All four rule trigger signals are Boolean variables, with clear generation logic and rapid response, providing a solid and reliable data foundation for subsequent rule-level scoring calculations.

[0056] In one embodiment, step S130 includes the following steps: S131. Obtain the current comprehensive score, the remaining amount of the purification action quota, the security constraints, and the user-input preference conditions; S132. When the comprehensive score is not lower than the first score threshold, the remaining amount of the purification action quota is not lower than the preset minimum execution time, and the safety constraints allow for high-intensity purification, a high-intensity purification action is triggered. S133. When the comprehensive score is not lower than the first score threshold, but the remaining amount of the purification action quota is lower than the preset minimum execution time, or the safety constraint condition does not meet the high-intensity purification condition, a medium-intensity purification action is triggered, and a compensation request is uploaded to the cloud to automatically schedule high-intensity purification tasks during low-occupancy periods. S134. When the overall score is lower than the first score threshold but not lower than the second score threshold, a medium-intensity purification action or a low-intensity purification action is triggered according to the user's input preference conditions, wherein the first score threshold is higher than the second score threshold. S135. When the comprehensive score is lower than the second score threshold, a low-intensity purification action is triggered, or the purification function is turned off and only the basic ventilation mode of the air conditioner is maintained.

[0057] The control module first polls and obtains the current comprehensive score, the remaining time of the daily purification action quota, the security constraints evaluated in real time based on security judgment parameters, and the user's input preferences stored locally or synchronized from the cloud. Then, it makes a decision based on the above information. In the embodiments of the present invention, there are four decision branches as follows.

[0058] The first decision branch: When the overall score is not lower than the preset first score threshold, it indicates that the system determines that there is a high risk of microorganisms in the current environment, requiring immediate response. Under this high-risk premise, the control module further checks two key resources and safety conditions. First, it checks resource conditions: determining whether the remaining amount of the current purification action quota is not less than the preset minimum execution time. This is to ensure that there is sufficient time to produce actual effects after starting high-intensity purification, avoiding ineffective short-term operation. Second, it checks safety conditions, assessing whether safety constraints allow for high-intensity purification, i.e., confirming that no personnel are present and the ozone concentration is below the safety threshold, or that the air duct is closed. Only when both resource and safety conditions are met will the control module issue a high-intensity purification action command to the execution module. This command includes a series of parallel actions: adjusting the fan drive signal to the highest level to maximize airflow speed; commanding the HEPA filter unit to operate at full power to ensure the highest particulate matter capture efficiency; and simultaneously activating the drive circuit of the UV-C ultraviolet sterilization module to start working. During high-intensity purification operations, the control module starts a countdown timer to precisely deduct from the total daily quota based on the actual number of seconds of operation, ensuring the accuracy of quota management.

[0059] The second decision branch: If the overall score is not lower than the first score threshold, but resource conditions or safety conditions are not met, the system cannot immediately execute high-intensity purification. In this case, to control the spread of risk, the control module triggers a medium-intensity purification action as a degradation strategy. This action includes adjusting the fan speed to a medium-high range and maintaining high-efficiency HEPA filter operation to provide strong purification capabilities, but without activating high-risk modules such as UV-C. Crucially, while executing medium-intensity purification, the control module sends a structured compensation request to the cloud server via Wi-Fi or 4G. This request includes the current overall score, the trigger time, and the suggested compensation execution duration. Upon receiving the request, the cloud server stores it in a task queue and subsequently analyzes historical data on occupancy throughout the house to automatically select a predicted low-occupancy and safe time period, such as late at night or when users typically leave home, and sends a scheduling command to the air conditioning unit to automatically execute a complete high-intensity purification task, thereby achieving risk compensation.

[0060] The third decision branch: When the overall score is lower than the first scoring threshold but not lower than the second scoring threshold, the system enters the normal maintenance mode. At this time, the decision-making power is partially returned to the user. The control module reads the user's preset preference conditions: if the preference condition is "health priority", the system ignores energy-saving considerations and directly triggers a medium-intensity purification action to proactively reduce potential risks; if the preference condition is "energy-saving priority" or "quiet priority", the system prioritizes low power consumption and low noise, triggering a low-intensity purification action, that is, the fan runs smoothly at a low speed to maintain basic air circulation and filtration.

[0061] Fourth decision branch: When the overall score is below the second score threshold, it indicates that the risk of environmental microorganisms is extremely low. At this time, the control module triggers a low-intensity purification action, or, according to the user's more advanced settings in the APP, completely shuts down all active purification functions, using only the basic temperature control circulation of the air conditioner for weak air agitation, minimizing energy consumption and noise.

[0062] Furthermore, to enhance system flexibility and user control, this embodiment introduces a dual mechanism of "emergency coverage" and "execution confirmation." Users can generate a high-priority local command via the app interface or a mechanical button to forcibly trigger a high-intensity purification action. The system will consume the corresponding quota and record this event. Before each high-intensity purification attempt, the control module checks the safety conditions. If the conditions are not fully met, a confirmation pop-up will be sent to the user via the app, requiring explicit user authorization before execution. This establishes a reliable "human-machine co-governance" defense between automation and security, ensuring the robustness and user-friendliness of the entire control logic.

[0063] In one embodiment, the safety constraints in S130 include: S136a. Ultraviolet disinfection is permitted during the high-intensity purification process only when the personnel presence status is no one present, or when the air duct switch status is air duct closed. S136b. Ionization purification is permitted during the high-intensity purification process only when the ozone concentration does not exceed a preset safety threshold.

[0064] In the embodiments of the present invention, there are two hard control gate logics for safety constraints. The first safety logic is about the activation condition of ultraviolet disinfection (UV-C), and the second safety logic is about the activation condition of ionization purification (such as electrostatic dust removal and negative ion generation).

[0065] For ultraviolet disinfection, the control module must perform an "OR" logic judgment on the presence of personnel and the duct switch status before activating the UV-C sterilization module. Specifically, the control module receives signals from the personnel detection module in real time. If the signal explicitly indicates "no one is present," the safety condition is met. Simultaneously, the control module also monitors the position sensor feedback from the duct valves in real time. If the feedback indicates the duct switch status is "duct closed," meaning all internal and external circulation dampers are closed, forming a completely sealed internal circulation loop, ensuring that the ultraviolet light is completely contained within the duct and cannot escape into the indoor space, the safety condition is also met. Only when either of these two conditions is true—"no one is present" or "duct closed"—will the control module allow sending an activation command to the UV-C light source's drive circuit. During UV-C module operation, the system continuously monitors these two states. Once either condition is detected as broken, such as personnel entering the room or the duct being accidentally opened, the control module will immediately and without delay cut off the UV-C power supply and record the safety event.

[0066] For ionization purification, ozone may be produced as a byproduct during operation. Before activating the ionization purification function, the control module must strictly check the real-time ozone concentration. Specifically, the control module reads the latest data from the ozone sensor and compares it with a preset safety threshold. The control module only allows the ionization purification function to be activated when the ozone concentration is below this safety threshold. During ionization purification operation, the system continuously monitors the ozone concentration. Once an increase in ozone concentration is detected and exceeds the safety threshold, the control module immediately executes a safety fallback logic: first, it shuts off the power to the ionization purification module, stopping ozone production; second, it instructs the fan to switch to its highest setting and activate the maximum fresh air mode (if the external air quality permits) to accelerate the dilution and removal of the generated ozone; finally, it pushes a safety alarm to the user's app stating "Ozone exceeds the standard, ionization function has been automatically shut down."

[0067] In one embodiment, the step of continuously calibrating and calculating the cumulative filter load based on the multi-source scoring parameters in S140 includes: S141. The air conditioning airflow volumetric flow rate is calculated using the fan operating parameters. S142. Calculate the increase in the mass of captured particles per unit time based on the particulate matter concentration and the air conditioning airflow volumetric flow rate. S143. The particle mass increment is accumulated and summed to obtain the total captured particle mass; S144. The cumulative load of the filter is continuously calibrated and calculated using a preset empirical relationship model between the filter pressure difference and the total mass of captured particles.

[0068] The air conditioner's control module obtains the fan's current operating parameters, primarily the fan's real-time rotational speed (RPM), through real-time communication with the fan drive module. The control module utilizes a pre-stored fan characteristic curve, obtained through factory calibration, which describes the relationship between rotational speed and airflow. The current rotational speed is converted into the real-time airflow volumetric flow rate (Q) through the filter using the fan characteristic curve, in cubic meters per hour (m³ / h). 3 / h). Secondly, the control module obtains the particulate matter concentration (C) on the air intake side of the target space from the environmental sensor, here expressed as the mass concentration of PM2.5 (μg / m³). 3 For example, according to the principle of mass conservation, the increase in the mass of particulate matter captured by the filter per unit time (e.g., per minute) (Δm) can be calculated using the following formula: Δm=C×Q×η×Δt Where η is the nominal PM2.5 capture efficiency of the HEPA filter, for example, 99.97%, and Δt is the calculation time step. The control module continuously calculates this mass increment at short time intervals, such as 1 minute, and accumulates it into a dynamic variable to obtain the total mass of particulate matter (m) captured since the filter was installed, i.e., the total captured particulate matter mass.

[0069] However, since the actual capture efficiency η varies dynamically due to factors such as partial filter blockage and uneven airflow, simple mass integration will result in cumulative errors. Therefore, a differential pressure sensor is introduced for online calibration in the third step. The control module simultaneously reads the real-time value of the pressure difference (ΔP) before and after the filter. The system has a built-in preset empirical relationship model, which is expressed as: ΔP=a×m+b Where a and b are model coefficients, b represents the initial differential pressure baseline when the filter is new, and a represents the rate of increase in differential pressure as particulate matter accumulates. a and b are initially calibrated through standard dust tests at the time of equipment shipment.

[0070] In actual operation, the control module uses the currently calculated total captured particle mass (m) and the real-time measured pressure difference (ΔP) to continuously and dynamically correct the model coefficients a and b using online regression algorithms such as least squares or recursive least squares (RLS). Through this closed loop of "prediction-measurement-correction," the final calculated cumulative load of the filter can closely approximate the actual physical state of the filter. This accurate cumulative load value provides a reliable basis for subsequent filter life prediction and self-cleaning scheduling.

[0071] To cope with extreme situations, when the filter pressure differential increases abnormally rapidly, the system will temporarily ignore the mass integral result and directly use the pressure differential exceeding the limit as an emergency signal that the filter needs immediate maintenance, thus ensuring the system reliability in sudden high pollution scenarios.

[0072] In one embodiment, the air conditioning control method based on environmental proxy quotas further includes: S150. Upload the local operation log of the air conditioner to the cloud, and use the cloud server to aggregate and optimize the strategy of the comprehensive scoring model and the purification action quota based on the local operation log, and receive the update information based on the optimization results from the cloud server.

[0073] During local operation, the air conditioner's control module periodically generates an anonymized local operation log. This log is not a simple copy of the raw sensor data, but rather an anonymized data packet that has been pre-processed and aggregated locally, containing no user-identified information or precise geographic location. Its core content includes: a comprehensive score calculated under specific environmental conditions, the corresponding purification execution mode and its duration, the usage and remaining status of purification action quotas, calculation and calibration data of cumulative filter load, and contextual snapshots of the system at key decision-making points. This local operation log is securely uploaded to a cloud server via the device's wireless communication module.

[0074] After receiving logs from a massive number of similar air conditioning devices deployed in the cloud server, the aggregation and optimization process is initiated. First, for optimizing the comprehensive scoring model, the cloud utilizes a federated learning framework, using the local logs from each device to train a more powerful global model on the central server. This global model learns complex environmental patterns across different regions, apartment types, and user habits, thus more accurately mapping multi-source parameters to real-world microbial risks. Second, regarding the purification action quota strategy, the cloud uses big data analysis to identify typical temporal patterns of high-risk events and the preference distribution of different user groups, and optimizes the initial quota allocation scheme and the scheduling algorithm for compensation tasks accordingly.

[0075] After optimization, the cloud server packages the optimization results into update information and sends it to each air conditioning unit via secure over-the-air (OTA) download. This update information may include: a new, optimized set of comprehensive scoring model parameters to replace or fine-tune the old model on the device; or an updated quota management strategy configuration file, including adjusted first / second scoring thresholds, minimum execution time, and other parameters. Upon receiving the update information, the air conditioning unit will automatically complete the update during periods of low system load and may initiate an A / B test, running the old and new strategies in parallel for a period of time to compare their effects. The test results will then be fed back to the cloud, forming a closed-loop continuous optimization cycle.

[0076] Furthermore, to protect user privacy, controllable random noise is added to the uploaded local operation logs to ensure that even if the logs are accessed without authorization, it is impossible to infer any individual user's private behavior or indoor environment details. This ensures that while achieving global optimization, the bottom line of user data security is upheld.

[0077] The air conditioning control method based on environmental proxy quota of the present invention can be widely applied to various intelligent environmental conditioning devices with high requirements for air health and energy efficiency management. The following uses three typical scenarios as examples: residential central air conditioning, medical clean air conditioning and commercial fresh air system.

[0078] In residential central air conditioning scenarios, this method provides families with 24 / 7, personalized health protection. For example, during the evening when family members gradually return home and the increased population density leads to a simultaneous rise in CO2 and PM2.5 levels, the system calculates a high comprehensive score by integrating agent volume and confirms sufficient purification quotas for the night. If it detects that family members are asleep and their presence is static, the system immediately dispatches the fan for a short, high-intensity pulse purification to quickly remove pollutants, and automatically switches to a low-noise mode after purification, all without user intervention. Simultaneously, the system accurately predicts replacement cycles based on the accumulated filter load from daily activities such as cooking and intelligently orders replacements via the app, greatly improving convenience.

[0079] In medical cleanroom air conditioning scenarios, this method can be applied to hospital wards or laboratories to proactively control microbial risks. For example, when the system detects a sudden increase in personnel density and TVOC concentration in a ward due to visitation, causing the overall score to exceed the high-risk threshold, and if it determines that the ward is currently unoccupied and it is during cleaning and disinfection time, it will immediately and automatically activate a high-intensity purification mode, combined with UV-C for deep disinfection, effectively reducing the risk of cross-infection. Through quota management, the system ensures that daily high-intensity purification is concentrated during the highest-risk periods, guaranteeing environmental cleanliness while avoiding equipment overload, extending the lifespan of expensive filters and UV lamps, and meeting the stringent safety and reliability requirements of medical equipment.

[0080] In commercial fresh air systems, this method is suitable for public spaces with high personnel flow, such as office buildings and conference rooms. For example, after a long meeting, the system determines that the overall score is high, but UV-C cannot be activated due to the high number of people during the day. In this case, the system will first perform medium-intensity purification and upload a high-intensity purification compensation task to the cloud. After the nighttime security system confirms that the building has been cleared, the cloud instructs the fresh air system to start a deep purification task, utilizing the low electricity cost period at night to complete the cyclical disinfection of the entire space. This mode achieves intelligent coordination between purification actions and the building's operational cycle, significantly reducing overall operating costs while ensuring public health.

[0081] As can be seen from the above applications, the air conditioning control method based on environmental agent quantity quota of the present invention, through the innovative combination of agent quantity fusion assessment of risk and quota-based intelligent scheduling of resources, can achieve safe, energy-saving and efficient air health management in various air conditioning and air purification devices such as home, medical and commercial facilities, demonstrating strong adaptability and broad market application prospects.

[0082] Figure 10 This is a schematic block diagram of an air conditioning control device 600 based on environmental proxy quotas provided in an embodiment of the present invention. Figure 10 As shown, corresponding to the above-described air conditioning control method based on environmental proxy quotas, the present invention also provides an air conditioning control device 600 based on environmental proxy quotas. This air conditioning control device 600 includes a unit for executing the above-described air conditioning control method based on environmental proxy quotas, and the device can be configured in a desktop computer, tablet computer, smartphone, or other terminal.

[0083] Specifically, please refer to Figure 10 The air conditioning control device 600 based on environmental agency quotas includes: Data acquisition unit 610 is used to collect multi-source scoring parameters and safety judgment parameters within the target space; Risk assessment unit 620 is used to generate a comprehensive score reflecting the level of microbial risk based on the multi-source scoring parameters by using fusion rule judgment and comprehensive scoring model calculation. The purification scheduling unit 630 is used to dynamically schedule the purification execution mode of the air conditioner based on the comprehensive score and in combination with the preset purification action quota and safety constraints.

[0084] In one embodiment, the air conditioning control device 600 based on environmental proxy quotas further includes: The filter management unit is used to continuously calibrate and calculate the cumulative load of the filter based on the multi-source scoring parameters during the purification process, and trigger maintenance reminders or filter self-cleaning actions according to preset alarm rules based on the cumulative load of the filter.

[0085] In one embodiment, the data acquisition unit 610 includes: The environmental parameter acquisition unit is used to collect particulate matter concentration, carbon dioxide concentration, relative humidity, and total volatile organic compound concentration in the target space through environmental sensors; The differential pressure acquisition unit is used to acquire the pressure difference across the air conditioner's filter via a differential pressure sensor. The fan parameter acquisition unit is used to acquire the air conditioner fan operating parameters through the air conditioner fan drive module; The parameter collection unit is used to collect the particulate matter concentration, carbon dioxide concentration, relative humidity, total volatile organic compound concentration, pressure difference before and after the filter, and fan operating parameters as multi-source scoring parameters.

[0086] In one embodiment, the data acquisition unit 610 further includes: The air duct status acquisition unit is used to acquire the air duct switch status through the air duct valves of the air conditioner; The personnel status detection unit is used to obtain the presence status of personnel in the target space through the personnel detection module. An ozone concentration detection unit is used to obtain the ozone concentration in the target space through an ozone sensor. The safety parameter collection unit is used to collect the personnel presence status, the air conditioner fan operating parameters, and the ozone concentration as safety judgment parameters.

[0087] In one embodiment, the risk assessment unit 620 includes: The rule triggering unit is used to perform binarization judgment on the multi-source scoring parameters according to the preset fusion rules and generate multiple rule triggering signals; The rule scoring unit is used to perform a weighted summation of the rule triggering signals to obtain a rule layer score; The model scoring unit is used to input the multi-source scoring parameters into the comprehensive scoring model and output the model probability score. The scoring fusion unit is used to dynamically weight and fuse the rule-layer score and the model probability score to generate the comprehensive score.

[0088] In one embodiment, the rule triggering unit includes: A threshold determination unit is used to trigger a first rule signal when the carbon dioxide concentration exceeds a first concentration threshold. The mutation detection unit is used to trigger a second rule signal when the rate of increase of the particulate matter concentration exceeds a second rate of increase threshold. A humidity determination unit is used to trigger a third rule signal when the relative humidity exceeds a third humidity threshold. The differential pressure monitoring unit is used to trigger a fourth rule signal when the differential pressure growth rate of the filter exceeds the fourth growth rate threshold.

[0089] In one embodiment, the purification scheduling unit 630 includes: The decision input acquisition unit is used to acquire the current comprehensive score, the remaining amount of the purification action quota, the security constraints, and the user-input preference conditions. A high-intensity triggering unit is used to trigger a high-intensity purification action when the comprehensive score is not lower than the first score threshold, the remaining amount of the purification action quota is not lower than the preset minimum execution time, and the safety constraints allow for high-intensity purification. The compensation scheduling unit is used to trigger a medium-intensity purification action and upload a compensation request to the cloud when the comprehensive score is not lower than the first score threshold, but the remaining amount of the purification action quota is lower than the preset minimum execution time, or the safety constraint conditions do not meet the high-intensity purification conditions, so as to automatically schedule high-intensity purification tasks during low-occupancy periods. The normal mode scheduling unit is used to trigger a medium-intensity purification action or a low-intensity purification action based on the user's input preference conditions when the comprehensive score is lower than the first score threshold but not lower than the second score threshold, wherein the first score threshold is higher than the second score threshold. The energy-saving mode scheduling unit is used to trigger a low-intensity purification action or turn off the purification function and only maintain the basic ventilation mode of the air conditioner when the comprehensive score is lower than the second score threshold.

[0090] In one embodiment, the purification scheduling unit 630 further includes: The ultraviolet safety determination unit is used to allow ultraviolet disinfection to be performed during the high-intensity purification process only when the personnel are not present or the air duct is closed. An ionization safety determination unit is used to allow ionization purification to be performed during the high-intensity purification process only when the ozone concentration does not exceed a preset safety threshold.

[0091] In one embodiment, the filter management unit includes: An airflow calculation unit is used to calculate the air conditioning airflow volumetric flow rate based on the fan operating parameters. The mass increment calculation unit is used to calculate the mass increment of captured particles per unit time based on the particulate matter concentration and the air conditioning airflow volume flow rate. A mass accumulation unit is used to accumulate and sum the particle mass increments to obtain the total captured particle mass; The load calibration unit is used to continuously calibrate and calculate the cumulative load of the filter screen using a preset empirical relationship model between the filter screen pressure difference and the total mass of captured particles.

[0092] In one embodiment, the air conditioning control device 600 based on environmental proxy quotas further includes: The cloud-edge collaboration unit is used to upload the local operation logs of the air conditioner to the cloud, and to optimize the comprehensive scoring model and the purification action quota strategy based on the local operation logs through the cloud server, and to receive the update information based on the optimization results issued by the cloud server.

[0093] The aforementioned air conditioning control device 600 based on environmental proxy quotas can be implemented as a computer program, which can, for example... Figure 11 It runs on the computer device shown.

[0094] Please see Figure 11 , Figure 11 This is a schematic block diagram of a computer device 500 provided in an embodiment of this application. The computer device 500 can be a terminal or a server. The terminal can be an electronic device with communication functions, such as an air conditioner, air purifier, desktop computer, tablet computer, or smartphone. The server can be a standalone server or a server cluster composed of multiple servers.

[0095] See Figure 11 The computer device 500 includes a processor 502, a memory, and a network interface 505 connected via a system bus 501. The memory may include a non-volatile storage medium 503 and internal memory 504.

[0096] The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions that, when executed, cause the processor 502 to perform an air conditioning control method based on environmental proxy quotas.

[0097] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0098] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute an air conditioning control method based on environmental proxy quotas.

[0099] This network interface 505 is used for network communication with other devices. Those skilled in the art will understand that... Figure 11 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device 500 to which the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0100] The processor 502 is used to run a computer program 5032 stored in a memory to implement the steps of the above method.

[0101] It should be understood that in the embodiments of this application, the processor 502 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0102] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0103] Therefore, the present invention also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program includes program instructions. When executed by a processor, the program instructions cause the processor to perform the steps of the above-described method.

[0104] The storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0105] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0106] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0107] The steps in the method of this invention can be adjusted, merged, or reduced in order according to actual needs. The units in the device of this invention can be merged, divided, or reduced according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0108] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0109] It should be noted that any AI models, software tools, or components not belonging to this company appearing in the embodiments of this application are merely illustrative examples and do not represent actual use. All user personal information involved in the embodiments of this application has been authorized (with the knowledge and consent) by the relevant parties or has been fully authorized by all parties, and the executing entity may obtain it through various legal and compliant means. The collection, storage, use, processing, transmission, provision, and disclosure of the information, data, and signals involved all comply with relevant laws and regulations and do not violate public order and good morals.

[0110] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An air conditioning control method based on environmental proxy quotas, characterized in that, The method includes: Collect multi-source scoring parameters and safety assessment parameters within the target space; Based on the multi-source scoring parameters, a comprehensive score reflecting the level of microbial risk is generated through fusion rule judgment and comprehensive scoring model calculation. Based on the comprehensive score, and combined with the preset purification action quota and safety constraints, the purification execution mode of the air conditioner is dynamically scheduled.

2. The air conditioning control method based on environmental proxy quota according to claim 1, characterized in that, The method further includes: During the purification process, the cumulative load of the filter is continuously calibrated and calculated based on the multi-source scoring parameters, and maintenance reminders or filter self-cleaning actions are triggered according to preset alarm rules based on the cumulative load of the filter.

3. The air conditioning control method based on environmental proxy quota according to claim 2, characterized in that, The steps for collecting multi-source scoring parameters within the target space include: Environmental sensors are used to collect data on particulate matter concentration, carbon dioxide concentration, relative humidity, and total volatile organic compound concentration within the target space. The differential pressure sensor collects the pressure difference across the air conditioner's filter. The air conditioner's fan operating parameters are obtained through the air conditioner fan drive module; The particulate matter concentration, carbon dioxide concentration, relative humidity, total volatile organic compound concentration, pressure difference across the filter, and fan operating parameters are used as multi-source scoring parameters.

4. The air conditioning control method based on environmental proxy quota according to claim 2, characterized in that, The steps for collecting security determination parameters include: The air duct switch status is obtained through the air duct valves of the air conditioner; The presence status of people in the target space is obtained through the personnel detection module; Ozone concentration in the target space is obtained using an ozone sensor; The presence status of the personnel, the operating parameters of the air conditioner fan, and the ozone concentration are used as safety assessment parameters.

5. The air conditioning control method based on environmental proxy quota according to claim 3, characterized in that, The step of generating a comprehensive score reflecting the microbial risk level based on the multi-source scoring parameters, through fusion rule judgment and comprehensive scoring model calculation, includes: The multi-source scoring parameters are binarized according to the preset fusion rules to generate multiple rule trigger signals; The rule-triggered signals are weighted and summed to obtain the rule-layer score; The multi-source scoring parameters are input into the comprehensive scoring model, and the model probability score is output. The rule-based score and the model probability score are dynamically weighted and fused to generate the comprehensive score.

6. The air conditioning control method based on environmental proxy quota according to claim 5, characterized in that, The step of binarizing the multi-source scoring parameters according to the preset fusion rules and generating multiple rule trigger signals includes: When the carbon dioxide concentration exceeds a first concentration threshold, a first rule signal is triggered; When the rate of increase of the particulate matter concentration exceeds the second rate of increase threshold, the second rule signal is triggered; When the relative humidity exceeds the third humidity threshold, the third rule signal is triggered; When the filter pressure differential growth rate exceeds the fourth growth rate threshold, the fourth rule signal is triggered.

7. The air conditioning control method based on environmental proxy quota according to claim 4, characterized in that, The step of dynamically scheduling the air conditioner's purification execution mode based on the comprehensive score, combined with preset purification action quotas and safety constraints, includes: Obtain the current comprehensive score, the remaining amount of the purification action quota, the security constraints, and the user-inputted preference conditions; A high-intensity purification action is triggered when the overall score is not lower than the first score threshold, the remaining amount of the purification action quota is not lower than the preset minimum execution time, and the safety constraints allow for high-intensity purification. When the overall score is not lower than the first score threshold, but the remaining amount of the purification action quota is lower than the preset minimum execution time, or the security constraint does not meet the high-intensity purification conditions, a medium-intensity purification action is triggered, and a compensation request is uploaded to the cloud to automatically schedule high-intensity purification tasks during low-occupancy periods. When the overall score is lower than the first score threshold but not lower than the second score threshold, a medium-intensity purification action or a low-intensity purification action is triggered based on the user's input preference conditions, wherein the first score threshold is higher than the second score threshold. When the overall score is lower than the second score threshold, a low-intensity purification action is triggered, or the purification function is turned off and only the basic ventilation mode of the air conditioner is maintained.

8. The air conditioning control method based on environmental proxy quota according to claim 7, characterized in that, The security constraints are as follows: Ultraviolet disinfection is permitted during the high-intensity purification process only when the personnel are not present or when the air duct is closed. Ionization purification is permitted during the high-intensity purification process only when the ozone concentration does not exceed a preset safety threshold.

9. The air conditioning control method based on environmental proxy quota according to claim 3, characterized in that, The steps for continuously calibrating and calculating the cumulative load of the filter based on the multi-source scoring parameters include: The air conditioning airflow volumetric flow rate is calculated using the aforementioned fan operating parameters. The increase in the mass of captured particles per unit time is calculated based on the particulate matter concentration and the air conditioning airflow volumetric flow rate. The total captured particle mass is obtained by summing the cumulative particle mass increments. The cumulative load of the filter is continuously calibrated and calculated using a preset empirical relationship model between the filter pressure difference and the total mass of captured particles.

10. The air conditioning control method based on environmental proxy quota according to claim 1, characterized in that, The method further includes: The local operation logs of the air conditioner are uploaded to the cloud. The cloud server then aggregates and optimizes the comprehensive scoring model and the purification action quota strategy based on the local operation logs, and receives update information based on the optimization results from the cloud server.

11. An air conditioning control device based on environmental proxy quotas, characterized in that, Used to perform the air conditioning control method based on environmental agent quantity quota as described in any one of claims 1 to 10.

12. A computer device, characterized in that, The computer device includes a memory and a processor connected to the memory; the memory is used to store a computer program; the processor is used to run the computer program stored in the memory to perform the steps of the method as described in any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which includes program instructions that, when executed by a processor, can implement the steps of the method as described in any one of claims 1 to 10.

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