Air conditioner control method and device, electronic equipment and computer program product

Abstract

The invention is suitable for the technical field of air conditioner control, and provides an air conditioner control method and device, electronic equipment and a computer program product.The method is applied to a multi-split air conditioner system and comprises the steps that a physiological monitoring result of a user is obtained according to physiological data of the user in the operating environment of the multi-split air conditioner system, obtaining an environment monitoring result of the environment where the multi-split air conditioning system is located according to the environment data of the operation environment of the multi-split air conditioning system, and performing anomaly detection on the operation environment of the multi-split air conditioning system according to the physiological monitoring result and the environment monitoring result to obtain an anomaly detection result; and under the condition that the abnormal detection result comprises an abnormal flag bit, an adjusting instruction value is calculated according to the abnormal flag bit, the abnormal flag bit is used for reflecting the abnormal degree of the physiological activity and / or environmental activity of the user, and the multi-split air conditioning system is controlled according to a control strategy corresponding to the adjusting instruction value. The accuracy of air conditioner control can be improved.

Description

Technical Field

[0001] This application belongs to the field of air conditioning control technology, and in particular relates to an air conditioning control method, device, electronic equipment and computer program product. Background Technology

[0002] With the popularization and development of air conditioning systems, existing air conditioning systems have gradually evolved into intelligent air conditioners that can be connected to the network, and have gradually added the function of detecting gases in the environment, thereby dynamically adjusting the ambient air based on the gas detection results.

[0003] However, the current data sources for smart air conditioners are limited to environmental sensors, and they do not take into account the physiological data of users in the environment when adjusting the environment, resulting in inaccurate air conditioning control. Summary of the Invention

[0004] This application provides an air conditioning control method, device, electronic equipment, and computer program product, which can improve the accuracy of control of multi-split air conditioning systems.

[0005] In a first aspect, embodiments of this application provide an air conditioning control method applied to a multi-split air conditioning system, comprising: Acquire physiological data of users in the operating environment of the multi-split air conditioning system, as well as environmental data of the operating environment of the multi-split air conditioning system; The physiological monitoring results of the user are obtained based on the physiological data, and the environmental monitoring results of the environment in which the multi-split air conditioning system is located are obtained based on the environmental data. Based on the physiological monitoring results and the environmental monitoring results, anomaly detection is performed on the operating environment of the multi-split air conditioning system to obtain anomaly detection results; If the abnormality detection result includes an abnormality flag, the adjustment command value is calculated based on the abnormality flag; the abnormality flag is used to reflect the degree of abnormality of the user's physiological activities and / or environmental activities. The multi-split air conditioning system is controlled according to the control strategy corresponding to the adjustment command value.

[0006] The beneficial effects of the embodiments in this application compared with the prior art are: In this embodiment, physiological monitoring results of users are obtained through user physiological data, and environmental monitoring results are obtained based on environmental data. Anomaly detection of the operating environment of the multi-split air conditioning system is then performed based on these physiological and environmental monitoring results. This means that when detecting environmental anomalies in the operating environment of the multi-split air conditioning system, not only environmental data but also the user's physiological data within the environment are considered, thereby improving the accuracy of environmental anomaly detection. Furthermore, when the anomaly detection results include anomaly flags, adjustment command values ​​are calculated based on the anomaly flags, and the multi-split air conditioning system is controlled according to the control strategy corresponding to the adjustment command values. Since the anomaly flags reflect the degree of anomalies in user physiological activities and environmental activities, a comprehensive and accurate analysis of the control of the multi-split air conditioning system can be performed, improving the accuracy of the multi-split air conditioning system control.

[0007] Secondly, embodiments of this application provide an air conditioning control device applied to a multi-split air conditioning system, comprising: The data acquisition module is used to acquire the physiological data of users in the operating environment of the multi-split air conditioning system, as well as the environmental data of the operating environment of the multi-split air conditioning system. The monitoring module is used to obtain the physiological monitoring results of the user based on the physiological data, and to obtain the environmental monitoring results of the environment in which the multi-split air conditioning system is located based on the environmental data. An anomaly detection module is used to perform anomaly detection on the operating environment of the multi-split air conditioning system based on the physiological monitoring results and the environmental monitoring results, and obtain anomaly detection results. The adjustment instruction calculation module is used to calculate the adjustment instruction value based on the abnormal flag bit when the abnormality detection result includes an abnormal flag bit; the abnormal flag bit is used to reflect the degree of abnormality of the user's physiological activities and / or environmental activities. The control module is used to control the multi-split air conditioning system according to the control strategy corresponding to the adjustment command value.

[0008] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the air conditioning control method described in the first aspect.

[0009] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the air conditioning control method described in the first aspect.

[0010] Fifthly, embodiments of this application provide a computer program product that, when run on an electronic device, causes the electronic device to execute the steps of the air conditioning control method described in the first aspect. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic diagram illustrating the operational scenario between a multi-split air conditioning system and its control equipment, provided in an embodiment of this application. Figure 2 This is a hardware configuration block diagram of a control device provided in an embodiment of this application; Figure 3 This is a schematic flowchart of an air conditioning control method provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of the air conditioning control device provided in the embodiments of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0013] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0014] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0015] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0016] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0017] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0018] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0019] Figure 1 This is a schematic diagram illustrating the operational scenarios between a multi-split air conditioning system and its control equipment, as provided in some embodiments of this application. Figure 1 As shown, users can operate the multi-split air conditioning system 200 via touch operation, mobile terminal 300, and control device 100. For example, control device 100 can be a remote control, stylus, or handle.

[0020] In some embodiments, the control device 100 can be a remote control or a smart home controller, etc. For example, the control device is a remote control, and the communication method between the remote control and the multi-split air conditioning system 200 includes, but is not limited to, infrared protocol communication, Bluetooth protocol communication, or other short-range communication methods, controlling the multi-split air conditioning system 200 wirelessly or via wired means. Users can input user commands through buttons on the remote control, voice input, or control panel input to control the multi-split air conditioning system 200.

[0021] It should be understood that the multi-split air conditioning system 200 includes multi-split air conditioning systems, air-to-ground water heat pump systems, etc.

[0022] In some embodiments, a mobile terminal 300 (such as a tablet computer, computer, or mobile phone) can also be used to control the multi-split air conditioning system 200. For example, an application running on the mobile terminal 300 can be used to control the multi-split air conditioning system 200.

[0023] In some embodiments, the multi-split air conditioning system may receive commands not through the aforementioned mobile terminal 300 or control device 100, but through buttons or other means provided on the multi-split air conditioning system.

[0024] Figure 2 Provided for some embodiments of this application Figure 1 Hardware configuration block diagram of the central control device. (Example) Figure 2 As shown, the control device 100 may include: a controller 110, a communication interface 130, a user input / output interface, a memory, and a power supply.

[0025] The control device 100 is configured to control the multi-split air conditioning system 200, and to receive user input operation commands and convert the operation commands into commands that the multi-split air conditioning system 200 can recognize and respond to, thus acting as an intermediary for interaction between the user and the multi-split air conditioning system 200.

[0026] In some embodiments, the control device 100 may be an intelligent device. For example, the control device 100 may be equipped with various applications for controlling the multi-split air conditioning system 200 according to user needs.

[0027] In some embodiments, such as Figure 1 As shown, a mobile terminal 300 or other intelligent electronic device can perform similar functions to control device 100 after an application for controlling the multi-split air conditioning system 200 is installed.

[0028] The controller 110 includes a processor 112, RAM 113, ROM 114, a communication interface 130, and a communication bus. The controller 110 is used to control the operation of the control device 100, as well as the communication and cooperation between internal components and the external and internal data processing functions.

[0029] Under the control of the controller 110, the communication interface 130 enables communication of control signals and data signals with the multi-split air conditioning system 200. The communication interface 130 may include at least one of other near-field communication modules such as WiFi chip 131, Bluetooth module 132, and NFC module 133.

[0030] User input / output interface 140, wherein the input interface includes at least one of other input interfaces such as microphone 141, touchpad 142, sensor 143, and button 144.

[0031] In some embodiments, the control device 100 includes at least one of a communication interface 130 and an input / output interface 140. The control device 100 is configured with the communication interface 130, such as a WiFi, Bluetooth, or NFC module, which can encode user input commands via WiFi, Bluetooth, or NFC protocols and send them to the multi-split air conditioning system 200.

[0032] The memory 190 is used to store various operating programs, data, and applications for driving and controlling the control device 100 under the control of the controller. The memory 190 can also store various control signal instructions input by the user.

[0033] The power supply 180 is used to provide operating power support for the various components of the control device 100 under the control of the controller.

[0034] With the continuous development of multi-split air conditioning systems, they are no longer simple cooling and heating tools. By combining sensors, intelligent algorithms and interconnection technologies, they are gradually becoming intelligent devices capable of dynamic environmental adjustment.

[0035] In some related technologies, smart air conditioners can be networked to add environmental monitoring functions and perform static air conditioning based on the monitoring results. For example, a smart air conditioner can detect volatile organic compounds (VOCs) and carbon dioxide (CO2) in the environment, and then use a control module to generate negative ions or disinfect the environment. However, this method only considers the environmental data of the air conditioner's operating environment and does not take into account the physiological experience of the user in that environment when controlling the air conditioner, resulting in inaccurate air conditioning control and difficulty in meeting user needs.

[0036] To improve the accuracy of air conditioning control in multi-split air conditioning systems, this application provides an air conditioning control method. In this method, physiological data of users and environmental data of the operating environment of the multi-split air conditioning system are acquired. Based on the physiological data, physiological monitoring results of the users are obtained, and based on the environmental data, environmental monitoring results of the environment in which the multi-split air conditioning system is located are obtained. Anomaly detection of the operating environment of the multi-split air conditioning system is performed using the physiological and environmental monitoring results to obtain anomaly detection results. If the anomaly detection results include an anomaly flag, an adjustment command value is calculated based on the anomaly flag. Finally, the multi-split air conditioning system is controlled according to the control strategy corresponding to the adjustment command value.

[0037] Figure 3 A flowchart illustrating an air conditioning control method according to an embodiment of this application is shown, applied to a multi-split air conditioning system, and is described in detail below: S31. Obtain the physiological data of the user in the operating environment of the multi-split air conditioning system, as well as the environmental data of the operating environment of the multi-split air conditioning system.

[0038] It should be understood that the aforementioned physiological data refers to data reflecting the user's physiological activity status in the operating environment, which may include physiological parameters such as body temperature, respiratory rate, blood oxygen saturation, and heart rate. The aforementioned environmental data refers to data reflecting the state of the environment inside and around a specific space, which may include temperature, humidity, weather, geographical location, etc. Of course, it may also include data reflecting the activity status of people in the operating environment, including but not limited to data collected by sensors, audio data collected by audio and video equipment, and video data.

[0039] Specifically, wearable devices can be deployed to collect users' physiological data in the operating environment of a multi-split air conditioning system in real time at a preset sampling rate. Simultaneously, different types of sensors can be deployed in the operating environment of the multi-split air conditioning system to collect environmental data on the activity status of people and the environment within the space.

[0040] For example, for physiological data, wearable devices such as smart bracelets and chest patches can collect users' blood oxygen saturation, heart rate, respiratory rate, body temperature and other physiological parameters in real time through a sampling rate of no less than 1 Hz; for environmental data, millimeter-wave radar can collect the range and amplitude of human activity in the operating environment, and environmental sensor arrays (such as temperature and humidity composite sensors, infrared CO2 sensors, laser scattering PM2.5 sensors, etc.) can collect environmental parameters such as CO2 concentration and PM2.5 concentration in the operating environment.

[0041] In this embodiment, physiological and environmental data are collected using wearable devices and different types of sensors, which can diversify the data collection of the environment in which the multi-split air conditioning system is located, thereby improving the accuracy of data collection.

[0042] S32. Obtain the physiological monitoring results of the user based on the above physiological data, and obtain the environmental monitoring results of the environment where the multi-split air conditioning system is located based on the above environmental data.

[0043] It should be understood that the above physiological monitoring results include the results of continuous monitoring of different physiological parameters over a certain period of time, and the above environmental parameters include the results of continuous monitoring of different environmental parameters over a certain period of time.

[0044] Specifically, the average and extreme values ​​of physiological and environmental parameters over a certain period can be statistically analyzed to continuously monitor physiological and environmental data. Simultaneously, to improve the accuracy of data monitoring, preprocessing methods can be used to preprocess the physiological and environmental data, and then the average and extreme values ​​of the preprocessed physiological and environmental data over a certain period can be statistically analyzed. This data preprocessing can include one or more of the following: data filtering, data standardization, and data validation.

[0045] For example, physiological data such as body temperature, blood oxygen saturation, and heart rate can be processed by sliding window averaging (the window width can be 10 sampling points), then Z-score normalization can be used to eliminate dimensional differences, and finally, CRC-16 checksum method can be used to ensure the integrity of physiological data. For audio and video data in environmental data, audio analysis and video analysis can be performed to determine the user's cough frequency, etc. Other environmental parameters are similar and will not be elaborated here.

[0046] In this embodiment of the application, the accuracy of physiological and environmental data can be further improved by preprocessing and continuously monitoring the physiological and environmental data.

[0047] S33. Based on the above physiological monitoring results and the above environmental monitoring results, anomaly detection is performed on the operating environment of the above multi-split air conditioning system to obtain anomaly detection results.

[0048] Specifically, after obtaining the physiological and environmental monitoring results, anomaly detection can be performed on the operating environment of the multi-split air conditioning system based on the degree of change in the physiological and / or environmental monitoring results, the magnitude of the difference between the physiological and / or environmental monitoring results and the preset baseline value, or the length of time the physiological and / or environmental monitoring results exceed the preset baseline value. The greater the degree of change in the physiological and / or environmental monitoring results, the greater the difference between the physiological and / or environmental monitoring results and the preset baseline value, or the longer the physiological and / or environmental monitoring results exceed the preset baseline value, the greater the degree of anomaly in the operating environment.

[0049] For example, when the rate of change in blood oxygen saturation exceeds 5%, an individual's physiological activity can be identified as abnormal; when the CO2 concentration remains above 1000 ppm for more than 5 minutes, an environmental activity abnormality can be identified.

[0050] In this embodiment of the application, anomaly detection of the operating environment of the multi-split air conditioning system is performed by combining physiological monitoring results and environmental monitoring results. This approach comprehensively considers both the user's physiological activities and the environmental activities of the user's environment, thereby improving the accuracy of environmental monitoring.

[0051] S34. If the above abnormality detection result includes an abnormality flag, calculate the adjustment command value based on the above abnormality flag; the above abnormality flag is used to reflect the degree of abnormality of the user's physiological activities and / or environmental activities.

[0052] It should be understood that the aforementioned abnormal flags are automatically triggered based on physiological and / or environmental monitoring results. For example, a significant change in the physiological and / or environmental monitoring results, a large difference between the physiological and / or environmental monitoring results and a preset baseline value, or a prolonged period exceeding the preset baseline value will all trigger the corresponding abnormal flag. Furthermore, these abnormal flags can also correspond to different levels. For instance, the levels of the abnormal flags can include "crisis" and "high risk," with the "high risk" level indicating a greater degree of abnormality than the "crisis" level.

[0053] Specifically, corresponding instruction values ​​can be pre-set for different anomaly flags, and then the instruction values ​​corresponding to all anomaly flags can be added together to obtain the aforementioned adjustment instruction value. Alternatively, an evaluation model can be pre-trained based on different combinations of anomaly flags, and then, if the anomaly detection result includes anomaly flags, the anomaly flags can be input into the aforementioned evaluation model to obtain the adjustment instruction value. The aforementioned evaluation model can be a machine learning model (e.g., a decision tree model, a support vector machine model, etc.), a neural network model (e.g., a convolutional neural network CNN), etc.

[0054] In this embodiment of the application, the abnormality level of physiological data and the abnormality level of environmental data can be uniformly quantified through the above-mentioned abnormality flag bit, so as to better analyze the user's physiological activities and the environmental activities of the environment, and improve the accuracy of the calculation of the regulation command value.

[0055] S35. Control the multi-split air conditioning system according to the control strategy corresponding to the above adjustment command value.

[0056] It should be understood that the above control strategy may include adjustments to different air conditioning parameters, including but not limited to fan speed, air supply parameters, and air conditioning valve opening.

[0057] Specifically, a mapping table of adjustment command values ​​and control strategies can be constructed in advance. Then, the corresponding control strategies can be matched from the mapping table based on the obtained adjustment command values. Finally, the corresponding air conditioning parameters can be adjusted according to the matched control strategies, thereby realizing the control of the multi-split air conditioning system.

[0058] In this embodiment, physiological monitoring results of users are obtained through user physiological data, and environmental monitoring results are obtained based on environmental data. Anomaly detection of the operating environment of the multi-split air conditioning system is then performed based on these physiological and environmental monitoring results. This means that when detecting environmental anomalies in the operating environment of the multi-split air conditioning system, not only environmental data but also the user's physiological data within the environment are considered, thereby improving the accuracy of environmental anomaly detection. Furthermore, when the anomaly detection results include anomaly flags, adjustment command values ​​are calculated based on the anomaly flags, and the multi-split air conditioning system is controlled according to the control strategy corresponding to the adjustment command values. Since the anomaly flags reflect the degree of anomalies in user physiological activities and environmental activities, a comprehensive and accurate analysis of the control of the multi-split air conditioning system can be performed, improving the accuracy of the multi-split air conditioning system control.

[0059] In some embodiments, the above-mentioned abnormality detection results include physiological abnormality detection results and environmental abnormality detection results. The above-mentioned physiological abnormality detection results may include the degree of physiological abnormality corresponding to different physiological parameters, and the above-mentioned environmental abnormality detection results may include the degree of environmental abnormality corresponding to different environmental parameters.

[0060] Correspondingly, based on the aforementioned physiological monitoring results and environmental monitoring results, anomaly detection was performed on the operating environment of the aforementioned multi-split air conditioning system, and the anomaly detection results were obtained, including: The deviation between the above physiological monitoring results and the preset physiological baseline value is calculated to obtain the above physiological abnormality detection results; The deviation between the above environmental monitoring results and the preset environmental baseline value is calculated to obtain the above environmental anomaly detection results.

[0061] It should be understood that different preset physiological baselines can be set for different physiological parameters in the physiological monitoring results, and different preset environmental parameters can also be set for different environmental parameters in the environmental monitoring results. For example, the preset physiological baseline values ​​may include baseline values ​​for body temperature, blood oxygen saturation, and heart rate; the preset environmental parameter baseline values ​​may include baseline values ​​for CO2 concentration, PM2.5 concentration, and cough frequency.

[0062] Specifically, taking physiological abnormality detection results as an example, the corresponding physiological abnormality detection result can be calculated using a preset formula for calculating the degree of physiological abnormality. This preset formula can be: Physiological abnormality degree = (a certain physiological monitoring result – a certain preset physiological baseline value) / preset threshold range. It should be noted that, in order to normalize the deviation of different physiological monitoring results, each physiological monitoring result can correspond to a different threshold range. Environmental abnormality detection results are similar and will not be elaborated upon here.

[0063] In the implementation of this application, by calculating the degree of deviation between physiological monitoring results and preset physiological baseline values, and by calculating the degree of deviation between environmental monitoring results and preset environmental baseline values, abnormalities in the user's physiology and environment can be accurately quantified.

[0064] In some embodiments, the physiological abnormality detection results include individual physiological abnormality detection results and global physiological abnormality detection results. The calculation of the deviation between the physiological monitoring results and a preset physiological baseline value to obtain the physiological abnormality detection results includes: For any user in the above-mentioned multi-split air conditioning system operating environment, the above-mentioned individual physiological abnormality detection results are obtained based on the deviation of the user's corresponding physiological monitoring results and the preset physiological baseline value; The above-mentioned individual physiological abnormality detection results of all users in the above-mentioned multi-split air conditioning system operating environment are weighted and averaged to obtain the above-mentioned global physiological abnormality detection results.

[0065] Specifically, for any user in the operating environment of a multi-split air conditioning system, the degree of physiological abnormality corresponding to different physiological parameters of that user can first be calculated using a preset formula for calculating the degree of physiological abnormality, thus obtaining the individual physiological abnormality detection result. Then, all the physiological abnormalities in the individual physiological abnormality detection result of that user are comprehensively evaluated to obtain an individual comprehensive score. Finally, the weighted average of the individual comprehensive scores of each user is calculated to obtain the aforementioned global physiological abnormality detection result. It should be noted that different weights can be set for different users to better comprehensively consider the needs of different users. For example, a greater weight can be set for the elderly and children.

[0066] For example, suppose a multi-split air conditioning system includes three users, A, B, and C. Each user has three individual physiological abnormality detection results: heart rate abnormality, body temperature abnormality, and respiratory rate abnormality. Then, the individual comprehensive score is calculated using the following formula: Individual Comprehensive Score = x * Heart Rate Abnormality + y * Body Temperature Abnormality + z * Respiratory Rate Abnormality, where x + y + z = 1. Finally, the global physiological abnormality detection result is calculated using the following weighted average formula: Global Physiological Abnormality Detection Result = a * User A's Individual Comprehensive Score + b * User B's Individual Comprehensive Score + c * User C's Individual Comprehensive Score, where a, b, and c are the corresponding weights.

[0067] In this embodiment, since the operating environment of a multi-split air conditioning system may include multiple users, in addition to calculating the individual physiological abnormality detection results of a single user, the global physiological abnormality detection results of all users are also calculated. This can comprehensively consider all users in the operating environment of the multi-split air conditioning system and improve the accuracy of determining the needs corresponding to user physiological abnormalities.

[0068] In some embodiments, the aforementioned abnormal flags include one or more of individual physiological abnormality flags, global environmental abnormality flags, and environmental quality abnormality flags. The individual physiological abnormality flag is triggered when the individual physiological abnormality detection result is greater than or equal to a preset individual physiological threshold. The global environmental abnormality flag is triggered when the global physiological abnormality detection result is greater than or equal to a preset global physiological threshold. The environmental quality abnormality flag is triggered when the environmental abnormality detection result is greater than or equal to a preset environmental quality threshold.

[0069] It should be understood that when one or more physiological abnormalities in an individual's physiological abnormality test results are greater than or equal to a preset individual physiological threshold, an individual physiological abnormality marker will be automatically triggered. Furthermore, the more physiological abnormalities that exceed the preset individual physiological threshold, the higher the level of the individual physiological abnormality marker. For example, when the heart rate abnormality exceeds a preset psychological threshold, a "critical" level individual physiological abnormality marker will be triggered; while when both the heart rate and respiratory rate abnormalities exceed the preset individual physiological thresholds, a "high-risk" level individual physiological abnormality marker will be triggered. Environmental quality abnormality markers are similar and will not be elaborated upon here.

[0070] Correspondingly, the calculation of the adjustment command value based on the aforementioned abnormal flag bit includes: The respiratory health risk value is calculated based on one or more of the above-mentioned individual physiological abnormality markers, global environmental abnormality markers, and environmental quality abnormality markers. The comprehensive environmental anomaly value is obtained by weighting the different degrees of environmental anomaly in the above environmental anomaly detection results. The above-mentioned adjustment instruction values ​​are calculated based on the above-mentioned respiratory health risk values ​​and the above-mentioned comprehensive environmental anomaly values.

[0071] Specifically, one or more of the following can be used as inputs to a pre-trained respiratory health assessment model: individual physiological abnormality markers, global environmental abnormality markers, and environmental quality abnormality markers. The model then outputs a respiratory health risk value, which reflects the degree of impact of the current operating environment on the user's respiratory health. The different degrees of environmental abnormality in the environmental abnormality detection results are then weighted according to preset weights to obtain a comprehensive environmental abnormality value. Finally, the respiratory health risk value and the comprehensive environmental abnormality value are weighted to obtain an adjustment instruction value.

[0072] It should be noted that the training process of the pre-trained respiratory health assessment model can be as follows: First, respiratory health training data is acquired, which may include different combinations of abnormal markers, physiological data of different users, environmental data under different environments, etc.; then, the respiratory health training data is input into the pre-built neural network model, and the prediction results are output; the loss value is calculated based on the prediction results, and when the loss value is greater than or equal to a preset loss threshold, the model parameters of the neural network model are adjusted until the loss value is less than the preset loss threshold, thus obtaining the trained respiratory health assessment model.

[0073] In this embodiment, respiratory health risk values ​​are calculated using anomaly flags. Since these flags comprehensively consider both user physiological activities and environmental activities, the accuracy of respiratory health risk analysis can be improved. Furthermore, combining the respiratory health risk value with comprehensive environmental anomalies to calculate the final adjustment command value further enhances the accuracy of the calculation from both user and environmental perspectives.

[0074] In some optional embodiments, before calculating the adjustment command value based on the above-mentioned abnormal flag bit, the method further includes: Based on the above physiological data and environmental data, calculate the user activity status information in the operating environment of the above multi-split air conditioning system.

[0075] It should be understood that different user activity states will affect the user's adjustment needs for air conditioning. Therefore, the user's specific activity state can be determined before calculating the adjustment command value.

[0076] Specifically, user activity status information can be determined using user physiological data (such as respiratory rate and body temperature) and environmental data (such as user limb movements determined by millimeter-wave radar). For example, the aforementioned user activity status information can include a static state, an active state, and an unoccupied state, with corresponding values ​​of 1, 0.5, and 0, respectively.

[0077] Correspondingly, the calculation of the adjustment command value based on the aforementioned abnormal flag bit includes: The respiratory health risk value is calculated based on the above-mentioned abnormal markers, physiological data, environmental data, and personnel activity status information. The comprehensive environmental anomaly value is obtained by weighting the different environmental parameters in the above environmental anomaly detection results. The adjustment instruction value is calculated based on the above respiratory health risk value, the above personnel activity status information, and the above comprehensive environmental anomaly value.

[0078] Specifically, abnormal markers, physiological data, environmental data, and personnel activity status information can be used as inputs to a pre-trained respiratory health assessment model. The final respiratory health risk value is then output. Different degrees of environmental abnormality in the environmental anomaly detection results are weighted according to preset weights to obtain a comprehensive environmental anomaly value. Finally, the respiratory health risk value, personnel activity status information, and comprehensive environmental anomaly value are weighted to obtain a regulation instruction value.

[0079] Optionally, the above adjustment instruction value can be calculated using the following formula: Adjustment instruction value = K1 * Respiratory health risk value + K2 * Personnel activity status information + K3 * Comprehensive environmental anomaly value, where K1, K2 and K3 are preset weights, and K1 + K2 + K3 = 1.

[0080] In this embodiment, by comprehensively considering the user's respiratory health risk, the person's activity status, and the comprehensive abnormal values ​​of the environment, the user's air conditioning control needs can be assessed from different dimensions, thereby improving the accuracy of the adjustment command value calculation.

[0081] In some embodiments, controlling the multi-split air conditioning system according to the control strategy corresponding to the adjustment command value includes: The control strategy is obtained based on the threshold range into which the above adjustment command value falls; the above control strategy includes one or more of the following: fresh air control strategy, negative ion control strategy, and humidity control strategy; Based on the above physiological and environmental data, the operating environment of the above multi-split air conditioning system is divided into multiple operating zones; Adjust the air conditioning parameters of each of the above-mentioned operating areas in the multi-split air conditioning system according to the above control strategy.

[0082] It should be understood that the aforementioned fresh air control strategy, negative ion control strategy, and humidity control strategy can be implemented to control the multi-split air conditioning system by adjusting air conditioning parameters. Specifically, the fresh air control strategy can adjust the airflow by setting different motor and valve parameters; the negative ion control strategy can generate different concentrations of negative oxygen ions using needle electrodes; and the humidity control strategy can adjust the equipment parameters of the ultrasonic humidifier through control algorithms (such as PID closed-loop control algorithms) to maintain stable humidity. It should also be noted that the fresh air control strategy can automatically limit the maximum fan speed (e.g., ≤2m / s) in a preset night mode (e.g., 22:00-6:00) or when sleep mode is detected, thereby reducing noise levels. The negative ion control strategy can continue to operate until the concentration drops to a safe threshold (e.g., PM2.5 ≤ 25μg / m³ and CO2 ≤ 800ppm) or reaches a preset maximum operating time, at which point it automatically shuts down; the humidity control strategy can automatically switch to energy-saving mode (e.g., power reduced to 20% of rated power) when millimeter-wave radar continuously detects that an area is unoccupied for more than a set time (e.g., 10 minutes).

[0083] Specifically, different command thresholds can be pre-set to define different threshold ranges. Each threshold range can correspond to different strategy combinations, which may include one or more of the following: fresh air control strategy, negative ion control strategy, and humidity control strategy. Then, the corresponding strategy combination is matched based on the threshold range into which the actual adjustment command value falls, i.e., the aforementioned control strategy. Simultaneously, the operating environment of the multi-split air conditioning system (e.g., the room where the air conditioner is located) can be divided into a 1m × 1m grid. Then, based on physiological and environmental data, the operating environment of the multi-split air conditioning system is divided into multiple operating zones. These operating zones may include areas where people are present and areas where people are not present. Different air conditioning parameters are then adjusted for areas where people are present and areas where people are not present.

[0084] For example, when a fresh air control strategy is matched, the air supply angle can be adjusted to avoid blowing directly on people in the area where they are located, thereby improving the user experience.

[0085] In this embodiment, by combining strategy matching and operating area division, spatial distribution and user experience can be comprehensively considered, thereby improving the accuracy of multi-split air conditioning system control.

[0086] In some embodiments, the above method further includes: The system stores the aforementioned physiological data and environmental data, and sends the aforementioned abnormality detection results, the aforementioned adjustment command values, and the aforementioned control strategy to the cloud server associated with the aforementioned multi-split air conditioning system.

[0087] Specifically, to improve data security, physiological and environmental data can be encrypted and stored locally. Simultaneously, a data analysis platform is set up on the cloud server associated with the multi-split air conditioning system. By sending anomaly detection results, the aforementioned adjustment command values, and the aforementioned control strategies to the cloud server, the cloud analysis platform can perform big data learning based on the uploaded data to study user activity habits, environmental activity conditions, and other characteristics of the environment in which the multi-split air conditioning system operates. It then optimizes control model parameters (such as updating the parameters of the pre-trained respiratory health assessment model, updating PID parameters, adjusting weights (such as K1, K2, K3, etc.), and adjusting command thresholds), and distributes the updated model parameters to local edge nodes, thereby achieving collaboration between the cloud and local systems. Furthermore, to improve the efficiency of data transmission and cloud-local collaboration, key event summaries (e.g., "User A experienced a heart rate abnormality alarm at 14:05, triggering a physiological abnormality flag, high-risk level") can be extracted from the anomaly detection results, the aforementioned adjustment command values, and the aforementioned control strategies, and the final key event summary is uploaded to the cloud server.

[0088] In this embodiment of the application, through the collaboration between the cloud and local edge nodes, the local control capabilities can be continuously optimized based on the cloud's data analysis capabilities, thereby improving the accuracy of control over the multi-split air conditioning system.

[0089] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0090] Corresponding to the air conditioning control method described in the above embodiments, Figure 4 A schematic diagram of the structure of an air conditioning control device provided in an embodiment of this application is shown. For ease of explanation, only the parts related to the embodiment of this application are shown.

[0091] Reference Figure 4 The device can be an air conditioning control device 41, which can be applied to a multi-split air conditioning system, including: The data acquisition module 411 is used to acquire the physiological data of users in the operating environment of the multi-split air conditioning system, as well as the environmental data of the operating environment of the multi-split air conditioning system. The monitoring module 412 is used to obtain the physiological monitoring results of the user based on the physiological data, and to obtain the environmental monitoring results of the environment in which the multi-split air conditioning system is located based on the environmental data. Anomaly detection module 413 is used to perform anomaly detection on the operating environment of the multi-split air conditioning system based on the physiological monitoring results and the environmental monitoring results, and obtain anomaly detection results; The adjustment instruction calculation module 414 is used to calculate an adjustment instruction value based on the abnormal flag bit when the abnormal detection result includes an abnormal flag bit; the abnormal flag bit is used to reflect the degree of abnormality of the user's physiological activities and / or environmental activities. The control module 415 is used to control the multi-split air conditioning system according to the control strategy corresponding to the adjustment command value.

[0092] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 As shown, the electronic device 5 of this embodiment includes: at least one processor 50 ( Figure 5 Only one is shown in the diagram), memory 51, and computer program 52 stored in said memory 51 and executable on said at least one processor 50. When the processor 50 executes said computer program 52, it implements the steps in any of the various method embodiments.

[0093] The electronic device 5 can be a desktop computer, laptop, handheld computer, or cloud server, or a microcomputer used in industrial equipment, smart homes, and multi-split air conditioners. The electronic device may include, but is not limited to, a processor 50 and a memory 51. Those skilled in the art will understand that... Figure 5 This is merely an example of electronic device 5 and does not constitute a limitation on electronic device 5. It may include more or fewer components than shown, or combine certain components, or different components. For example, the electronic device may also include input transmitting devices, network access devices, buses, etc.

[0094] The processor 50 may be a central processing unit (CPU), or 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. A general-purpose processor may be a microprocessor or any conventional processor.

[0095] In some embodiments, the memory 51 may be an internal storage unit of the electronic device 5, such as a hard disk or memory of the electronic device 5. The memory 51 may also be an external storage device of the electronic device 5, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD) card, flash card, etc., equipped on the electronic device 5. Furthermore, the memory 51 may include both internal and external storage units of the electronic device 5. The memory 51 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 51 can also be used to temporarily store data that has been sent or will be sent.

[0096] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the division of the functional units and modules is only described as an example. In practical applications, the functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0097] This application also provides a network device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the various method embodiments.

[0098] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments.

[0099] This application provides a computer program product that, when run on an electronic device, enables the electronic device to perform the steps described in the various method embodiments.

[0100] 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 computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the embodiments described in this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographic device / electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0101] In the embodiments described, each embodiment has its own emphasis. For parts not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0102] 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, or a combination of computer software and electronic hardware. 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 implementation should not be considered beyond the scope of this application.

[0103] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0104] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0105] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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 of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An air conditioner control method characterized by comprising: The method is applied to a multi-connected air conditioning system, and the method comprises the following steps: Obtaining physiological data of a user in an operating environment of the multi-connected air conditioning system and environmental data of the operating environment of the multi-connected air conditioning system; Obtaining a physiological monitoring result of the user according to the physiological data and obtaining an environmental monitoring result of an environment in which the multi-connected air conditioning system is located according to the environmental data; Performing abnormality detection on the operating environment of the multi-connected air conditioning system according to the physiological monitoring result and the environmental monitoring result to obtain an abnormality detection result; In a case where the abnormality detection result comprises an abnormality flag, calculating an adjustment instruction value according to the abnormality flag, wherein the abnormality flag is used to reflect an abnormality degree of a user's physiological activity and / or environmental activity; Controlling the multi-connected air conditioning system according to a control strategy corresponding to the adjustment instruction value.

2. The air conditioner control method of claim 1, wherein, The abnormality detection result comprises a physiological abnormality detection result and an environmental abnormality detection result, and the step of performing abnormality detection on the operating environment of the multi-connected air conditioning system according to the physiological monitoring result and the environmental monitoring result to obtain an abnormality detection result comprises the following steps: Calculating a deviation degree of the physiological monitoring result from a preset physiological baseline value to obtain the physiological abnormality detection result; Calculating a deviation degree of the environmental monitoring result from a preset environmental baseline value to obtain the environmental abnormality detection result.

3. The air conditioner control method according to claim 2, wherein The physiological abnormality detection result comprises an individual physiological abnormality detection result and a global physiological abnormality detection result, and the step of calculating a deviation degree of the physiological monitoring result from a preset physiological baseline value to obtain the physiological abnormality detection result comprises the following steps: For any user in the operating environment of the multi-connected air conditioning system, obtaining the individual physiological abnormality detection result according to a deviation degree of a user's corresponding physiological monitoring result from a preset physiological baseline value; Performing weighted average on the individual physiological abnormality detection results of all users in the operating environment of the multi-connected air conditioning system to obtain the global physiological abnormality detection result.

4. The air conditioner control method according to claim 3, wherein The abnormality flag comprises one or more of an individual physiological abnormality flag, a global environmental abnormality flag and an environmental quality abnormality flag, wherein the individual physiological abnormality flag is a flag triggered when the individual physiological abnormality detection result is greater than or equal to a preset individual physiological threshold value, the global environmental abnormality flag is a flag triggered when the global physiological abnormality detection result is greater than or equal to a preset global physiological threshold value, and the environmental quality abnormality flag is a flag triggered when the environmental abnormality detection result is greater than or equal to a preset environmental quality threshold value; The step of calculating an adjustment instruction value according to the abnormality flag comprises the following steps: Calculating a respiratory health risk value according to one or more of the individual physiological abnormality flag, the global environmental abnormality flag and the environmental quality abnormality flag; Performing weighted calculation on different environmental abnormality degrees in the environmental abnormality detection result to obtain an environmental comprehensive abnormality value; Calculating the adjustment instruction value according to the respiratory health risk value and the environmental comprehensive abnormality value.

5. The air conditioner control method according to claim 3, wherein Before the step of calculating an adjustment instruction value according to the abnormality flag, the method further comprises the following steps: According to the physiological data and the environmental data, personnel activity state information of a user in an operation environment of the multi-connected air conditioning system is calculated; The adjustment instruction value is calculated according to the abnormal flag bit, the physiological data, the environmental data, and the personnel activity state information. The abnormal flag bit is used to reflect an abnormal degree of physiological activity and / or environmental activity of the user. The abnormal flag bit is used to reflect an abnormal degree of physiological activity and / or environmental activity of the user. The abnormal flag bit is used to reflect an abnormal degree of physiological activity and / or environmental activity of the user.

6. The air conditioner control method according to any one of claims 1-5, wherein, The abnormal flag bit is used to reflect an abnormal degree of physiological activity and / or environmental activity of the user. The abnormal flag bit is used to reflect an abnormal degree of physiological activity and / or environmental activity of the user. The abnormal flag bit is used to reflect an abnormal degree of physiological activity and / or environmental activity of the user. The abnormal flag bit is used to reflect an abnormal degree of physiological activity and / or environmental activity of the user.

7. The air conditioner control method according to any one of claims 1-5, wherein, The abnormal flag bit is used to reflect an abnormal degree of physiological activity and / or environmental activity of the user. The abnormal flag bit is used to reflect an abnormal degree of physiological activity and / or environmental activity of the user.

8. An air conditioner control device characterized by comprising: The abnormal flag bit is used to reflect an abnormal degree of physiological activity and / or environmental activity of the user. The abnormal flag bit is used to reflect an abnormal degree of physiological activity and / or environmental activity of the user. The abnormal flag bit is used to reflect an abnormal degree of physiological activity and / or environmental activity of the user. The abnormal flag bit is used to reflect an abnormal degree of physiological activity and / or environmental activity of the user. The abnormal flag bit is used to reflect an abnormal degree of physiological activity and / or environmental activity of the user. The abnormal flag bit is used to reflect an abnormal degree of physiological activity and / or environmental activity of the user.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The abnormal flag bit is used to reflect an abnormal degree of physiological activity and / or environmental activity of the user.

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