A device control system, method, device and storage medium

Abstract

The embodiments of this specification provide a device control system, method, apparatus and storage medium, the system including an environment monitoring unit, a human detection unit, and a processor; the environment monitoring unit is configured to detect environment monitoring data inside and / or outside the space; the human detection unit is configured to detect user distribution characteristics of users in the space; the processor is configured to: match user adjustment data and environment monitoring data based on adjustment time to obtain at least one single adjustment feature; determine the user's continuous adjustment feature based on the adjustment difference between the single adjustment features; determine user preference data based on user adjustment data, environment monitoring data, continuous adjustment features, user distribution features, perception delay, and action delay; determine corresponding control parameters based on the user preference data to control the environment adjustment components.

Description

[0001] Description of the case

[0002] This application is a divisional application filed for the Chinese application with the application date of February 19, 2024, application number CN202410182610.1, and invention name “A HVAC equipment control system, control method, control device and storage medium”. Technical Field

[0003] The present invention relates to the technical field of HVAC equipment, and in particular to an equipment control system, method, device and storage medium. Background Art

[0004] HVAC equipment refers to the system or related equipment responsible for heating, ventilation and air conditioning in the room or car. It is an important part of the building automatic control system and is used to achieve comprehensive control of the temperature, humidity, ventilation and heating of the building's internal environment. The currently commonly used artificial setting value or PID control method often cannot make indoor people feel comfortable, and also makes the HVAC equipment always in a high energy consumption state, resulting in a great waste of energy.

[0005] In response to the above problems, CN107120782B proposes a HVAC system control method based on multi-user thermal comfort data. Through the comfort probability curves of different users in different indoor temperature environments, the different comfort probability curves of multiple people are integrated to obtain the total comfort probability curve, so as to solve the contradictions of temperature and other aspects when multiple people are in the same space, and determine the optimal temperature setting value inside the space, so as to save energy and meet the comfort needs of most users. However, this method ignores the situation that the position of the HVAC system outlet makes the temperature inside the space uneven, and only considers the user's feeling in terms of temperature, which easily ignores the impact of humidity, ventilation and other aspects on user comfort.

[0006] Therefore, an equipment control system, method, device and storage medium are provided to help improve the overall comfort of users when using HVAC equipment. Summary of the invention

[0007] One or more embodiments of the present specification provide a device control system, the system comprising an environment monitoring unit, a human body detection unit, and a processor; the environment monitoring unit comprises a temperature sensor, a humidity sensor, and an oxygen content sensor deployed at multiple first preset points in the space, and the environment monitoring unit is configured to detect environment monitoring data inside and / or outside the space; the human body detection unit comprises a human body sensor deployed at multiple second preset points in the space, and the human body detection unit is configured to detect user distribution characteristics of users in the space; the processor is configured to: match the user adjustment data and the environment monitoring data based on the adjustment time to obtain at least one single adjustment characteristic; determine the user's continuous adjustment characteristics based on the adjustment difference between the single adjustment characteristics; determine user preference data based on the user adjustment data, the environment monitoring data, the continuous adjustment characteristics, the user distribution characteristics, perception delay, and action delay; determine corresponding control parameters based on the user preference data to control the environment adjustment component.

[0008] One or more embodiments of the present specification provide a device control method, which is implemented by a processor of the device control system, and the method includes: matching user adjustment data and environmental monitoring data based on adjustment time to obtain at least one single adjustment feature; determining the user's continuous adjustment feature based on the adjustment difference between the single adjustment features; determining user preference data based on the user adjustment data, the environmental monitoring data, the continuous adjustment feature, user distribution characteristics, perception delay, and action delay; and determining corresponding control parameters based on the user preference data to control the environmental adjustment components.

[0009] One or more embodiments of the present specification provide a device control apparatus, the apparatus comprising at least one processor and at least one memory, the at least one memory being used to store computer instructions; the at least one processor being used to execute at least part of the computer instructions to implement the device control method.

[0010] One or more embodiments of the present specification provide a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the device control method. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0012] Figure 1is a schematic diagram of the structure of a HVAC equipment control system according to some embodiments of this specification;

[0013] Figure 2 is an exemplary flow chart of a control method of a HVAC equipment control system according to some embodiments of this specification;

[0014] Figure 3 is an exemplary schematic diagram of a preference model according to some embodiments of this specification;

[0015] Figure 4 It is an exemplary schematic diagram of determining control parameters according to some embodiments of this specification. DETAILED DESCRIPTION

[0016] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0017] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0018] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprises" and "includes" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0019] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0020] The HVAC system is a general term for equipment and piping systems used to adjust indoor temperature, humidity, fresh air volume, air cleanliness and other parameters. It controls the temperature and humidity of the indoor air to keep the indoor environment comfortable and improve the quality of people's production and living environment. However, the HVAC system still has problems such as low control accuracy and energy waste. Although CN107120782B can solve the contradictions in temperature and other aspects when multiple people are in the same space, due to the limitations of the location of each air outlet in the HVAC system, the temperature near the air outlet is higher, while the temperature far away from the air outlet is lower, resulting in uneven indoor temperature; and users in multiple positions in the space cannot feel comfortable at the same time only by setting the temperature value.

[0021] In view of this, some embodiments of this specification provide a HVAC equipment control system that can determine control parameters that meet user habits and accurately and comprehensively control the HVAC equipment in a timely manner, thereby improving energy utilization efficiency and reducing overall costs while fully meeting user needs.

[0022] In some embodiments, the HVAC equipment control system can be used in various fields that require indoor environment regulation. For example, in the construction industry, the indoor temperature, humidity and air quality can be adjusted based on the HVAC equipment control system to provide a comfortable living and working environment; for example, in industrial production, the HVAC equipment control system can be used to control the temperature and humidity of workshops, warehouses and other places to ensure product quality and production efficiency; for example, in commercial places, the HVAC equipment control system can automatically adjust the equipment operation status according to factors such as the number of people in the room and the ambient temperature to provide a suitable indoor environment.

[0023] Figure 1 It is a structural schematic diagram of a HVAC equipment control system according to some embodiments of this specification.

[0024] like Figure 1 As shown, the HVAC equipment control system 100 may include an environment monitoring unit 110 , a human body detection unit 120 , a wind direction control device 130 , a temperature control unit 140 , a humidity control unit 150 , a ventilation unit 160 , and a processor 170 .

[0025] The environmental monitoring unit 110 is a related device for real-time monitoring of various environmental data of the space. The space refers to a place where HVAC equipment is installed. For example, the space may include but is not limited to a residence, a restaurant, a parking lot, etc. In some embodiments, the environmental monitoring unit 110 can monitor various environmental data inside and / or outside the space in real time to obtain environmental monitoring data. Environmental data is a physical quantity that describes the relevant environmental conditions inside / outside the space, such as temperature, humidity, pressure, oxygen content, etc.

[0026] In some embodiments, the environment monitoring unit 110 includes a temperature sensor 111, a humidity sensor 112, an oxygen content sensor 113, etc. deployed at a plurality of first preset points in the space. The temperature sensor 111 can be used to detect the temperature inside the space and / or outside the space. The humidity sensor 112 can be used to detect the humidity inside the space and / or outside the space. The oxygen content sensor 113 can be used to detect the oxygen content in the gas inside the space and / or outside the space.

[0027] The first preset point may refer to a preset installation position of the environment monitoring unit 110. For example, the first preset point may be set at any position inside / outside the space. Exemplarily, the first preset point may be a position in the middle / above the middle / above the space.

[0028] The human body detection unit 120 is a related device for detecting user distribution characteristics in a space. User distribution characteristics are data that characterize the population distribution of each area in the space. The area may be the result of dividing the space. The processor 170 may divide the space based on a grid or function to obtain at least one area. For example, the area may include a work area, a rest area, a passage area, etc.

[0029] In some embodiments, the human body detection unit includes human body sensors 121 deployed at a plurality of second preset points in the space.

[0030] The human body sensor 121 is a device for real-time monitoring and analysis of human flow. For example, the human body sensor 121 may include one or any combination of a camera, a sensor, etc. Exemplarily, the sensor may include but is not limited to an infrared sensor, a temperature sensor, etc.

[0031] The second preset point may refer to a preset installation position of the human body sensor 121. For example, the second preset point may be set at any position inside / outside the space. Exemplarily, the second preset point may be an entrance, exit, passage, etc. of the space.

[0032] In some embodiments, the human body sensor 121 can be deployed in the space in a variety of ways. In some embodiments, the human body sensor 121 can be deployed on each air outlet in the space. In some embodiments, the human body sensor 121 can be deployed on some air outlets in the space.

[0033] The wind direction control device 130 is a device for controlling or changing the direction or speed of the fresh air sent out from the air outlet. For example, the wind direction control device 130 may include a wind guide plate, a fan blade, and the like.

[0034] In some embodiments, the wind direction control device includes a rotatable blade 131 installed at the air outlet.

[0035] The rotatable blades 131 are components of the wind direction control device 130, and are devices for adjusting the direction or speed of wind flow by controlling the angle and rotation speed of the blades. For example, the rotatable blades 131 are composed of a series of rotatable blades, and the blades can be rotated by manual or electric means to adjust the ventilation in the space. When the blades are rotated to different angles, the direction of air flow can be controlled, thereby achieving indoor air conditioning in the space.

[0036] In some embodiments, the processor 170 can be communicated with the wind direction control device 130 to send wind direction control parameters to the wind direction control device 130 for adjusting the rotation angle, rotation speed, etc. of the rotatable blades 131 to change the direction or speed of the fresh air delivered from the air outlet.

[0037] The wind direction control parameter is a parameter used by the wind direction control device 130 when adjusting the direction of the wind flow. In some embodiments, the wind direction control parameter includes a wind sweep range and a wind sweep speed. For more information about the wind sweep range and wind sweep speed, please refer to Figure 2 Related description.

[0038] The temperature control unit 140 refers to a device for regulating the ambient temperature inside the space. For example, the temperature control unit 140 can control the heating or cooling of the temperature control unit 140 according to the control instruction of the processor 170 to achieve the purpose of regulating the indoor temperature. Accordingly, the temperature control unit 140 may include: a heater and a refrigerator. The heater can achieve heating inside the space through electric current, and the refrigerator can achieve cooling inside the space through the circulation of refrigerant.

[0039] In some embodiments, the processor 170 can communicate with the temperature control unit 140 and send a temperature control instruction to the temperature control unit 140 to control the temperature control unit 140 to adjust the direction and amplitude of the temperature inside the space. For more information about the temperature control instruction, please refer to Figure 2 Related description.

[0040] The humidity control unit 150 is a device for regulating the humidity of the environment inside the space. For example, the humidity control unit 150 can control the dehumidification or humidification of the humidity control unit 150 according to the control instruction of the processor 170 to achieve the purpose of regulating the indoor humidity.

[0041] In some embodiments, the processor 170 can communicate with the humidity control unit 150 and send a humidity control instruction to the humidity control unit 150 to control the opening / closing of the humidity control unit 150, the adjustment direction and adjustment range of the humidity inside the space, etc. For more information about the humidity control instruction, please refer to Figure 2 Related description.

[0042] In some embodiments, the humidity control unit 150 includes a humidifying device 151 and a dehumidifying device 152 .

[0043] The humidifying device 151 is a device for increasing the humidity of the internal environment of the space. For example, the humidifying device 151 may include one of an electric heating humidifier, an ultrasonic humidifier, and the like.

[0044] The dehumidification device 152 is a device for reducing the humidity of the internal environment of the space. For example, the dehumidification device 152 may include one of an adsorption dehumidifier, a duct dehumidifier, and the like.

[0045] In some embodiments, the humidity control unit 150 may include multiple units, each of which is disposed in each effective area. For more information about the effective area, see Figure 4 Related description.

[0046] The ventilation unit 160 is a device for introducing fresh air from outside into the space and exhausting dirty air from inside the space to outside. For example, the ventilation unit 160 may include a ventilator, a fresh air system, and the like.

[0047] In the space where HVAC equipment is operating, the environment is relatively closed and the air circulation is poor. Users are likely to feel stuffy and uncomfortable after a period of time. Ventilation devices can bring fresh air from the outside into the space and expel dirty air from the room, thereby improving user comfort.

[0048] In some embodiments, the processor 170 can be connected to the ventilation unit 160 for communication and send ventilation control instructions to the ventilation unit 160 to control the opening time and opening duration of the ventilation unit 160. For more information about ventilation control instructions, please refer to Figure 2 Related description.

[0049] The processor 170 refers to a system with computing capabilities, such as a computer, an industrial computer, a computing cloud platform, etc. In some embodiments, the processor 170 may include one or more sub-processors, such as a central processing unit (CPU), a graphics processing unit (GPU), etc., or any combination thereof.

[0050] In some embodiments, the processor 170 may obtain data and / or information from the environment monitoring unit 110, the human body detection unit 120, the wind direction control device 130, the temperature control unit 140, the humidity control unit 150, the ventilation unit 160, etc. in the HVAC equipment control system 100. The processor 170 may issue program instructions based on these data, information, and / or processing results to execute one or more functions described in the embodiments of this specification.

[0051] In some embodiments, the processor 170 can be communicatively connected with the environment monitoring unit 110, the human body detection unit 120, the wind direction control device 130, the temperature control unit 140, the humidity control unit 150, the ventilation unit 160, etc. The processor 170 can be used to control the operation of the HVAC equipment control system 100: based on user distribution characteristics, determine the user distribution range and user density distribution; determine the sweeping range based on the edge of the user distribution range; determine the sweeping speed based on the user density distribution; determine user preference data based on environmental monitoring data and user adjustment data; determine control parameters based on the user preference data, and determine at least one set of control instructions based on the control parameters to control the operation of at least one of the wind direction control device 130, the temperature control unit 140, the humidity control unit 150, and the ventilation unit 160.

[0052] In some embodiments, the HVAC equipment control system 100 may include a network and / or other components that connect the system to external resources. The processor 170 may obtain data and / or information related to the HVAC equipment control system 100 through the network.

[0053] In some embodiments, the HVAC equipment control system 100 may further include a user terminal. The user terminal may refer to one or more terminal devices used by a user, and the processor 170 may be integrated in the user terminal. The user may refer to a manager or operator of the HVAC equipment control system 100, etc.

[0054] In some embodiments, the HVAC equipment control system 100 may further include an interaction module, which is used to interact with the user. In some embodiments, the interaction module may be used to display environmental monitoring data to the user and obtain user operation feedback. In some embodiments, the interaction module may include input components and output components such as buttons, touch sensors, joysticks, keypads, microphones, and display screens.

[0055] In some embodiments, the interactive module can be used to display information related to the environment inside the space or the operating status of the HVAC equipment control system 100. In some embodiments, the interactive module can be used to display the actual environmental conditions in the space, such as the actual temperature, humidity, oxygen content, etc. In some embodiments, the display interface of the display screen can be used to display the on or off status of one or more components in the HVAC equipment control system 100, set parameters, etc. The set parameters may refer to the parameters set by the user by operating the interactive module, which are the target parameter values ​​(such as a certain temperature value, a certain humidity value) that the user hopes to achieve inside the space. For example, the set parameters may include setting the temperature, setting the humidity, etc.

[0056] In some embodiments, the HVAC equipment control system 100 may further include a storage module (not shown in the figure), which may be used to store data, instructions and / or any other information. For example, the storage module may store environmental monitoring data, user adjustment data, user distribution characteristics, etc. In some embodiments, the storage module may include a random access memory (RAM), a read-only memory (ROM), a large-capacity memory, a removable memory, a volatile read-write memory, etc., or any combination thereof. In some embodiments, the storage module may be integrated or included in one or more other components (e.g., a processor 170, a user terminal, etc.) of the HVAC equipment control system 100.

[0057] In some embodiments of the present specification, by adjusting the working status of each component in the HVAC equipment through the processor, the indoor heating, ventilation, and air conditioning effects can be optimized, and the user's comfort can be further improved.

[0058] Figure 2 is an exemplary flow chart of a control method for a HVAC equipment control system according to some embodiments of the present specification.

[0059] In some embodiments, the process 200 can be implemented based on the processor 170 of the HVAC equipment control system. Figure 2 As shown, process 200 includes the following steps:

[0060] Step 210: Determine the user distribution range and user density distribution based on the user distribution characteristics.

[0061] In some embodiments, the processor may obtain user distribution features through human body sensors installed at each second preset point in the space. For example, in some embodiments, the user distribution features may be collected by the human body sensors and uploaded to the processor via the network periodically or in real time. In some embodiments, the user distribution features may be collected by the human body sensors and uploaded to the storage module in real time, and the processor may read the user distribution features from the storage module periodically or in real time.

[0062] In some embodiments, the user distribution characteristics may include the number of people in each area of ​​the space and the location of each person. For more information about user distribution characteristics, please refer to Figure 1 Related description.

[0063] In some embodiments, the processor can detect the situation of people in the space based on the human body detection unit in a variety of ways. For example, the situation of people in the space can be detected by periodic continuous acquisition through devices such as sensors and / or cameras. Among them, periodic continuous acquisition refers to continuous acquisition every certain period of time. For another example, the situation of people in the space can be detected by devices such as sensors and / or cameras using interval acquisition. For example, at night or when there are no people in the space, the time for the sensor and / or camera to collect data can be intervals. The interval can be determined by the system or manually preset.

[0064] The user distribution range refers to the area range where users exist in space. For example, the user distribution range may be an area range described by a series of data points. The data points may be the location information of the edge of the user distribution range in space. In some embodiments, the processor may identify the position of the user at the edge of the user distribution feature, and sequentially connect the positions of all the users at the edge into a closed area to determine the user distribution range.

[0065] User density distribution refers to the distribution of population in each area of ​​the user distribution range. For example, user density distribution may include population density at each location in space. Population density refers to the number of people per unit area or unit volume.

[0066] In some embodiments, the user density distribution may include an image recording the heat or temperature radiated by people in each area. The heat or temperature radiated by locations where people are concentrated in the area is higher, and the heat or temperature radiated by locations where people are sparse is lower. The user density distribution may mark the heat or temperature radiated by people at different locations in the target area with different colors. The corresponding relationship between color and temperature may indicate the distribution of people in different areas (e.g., the number of people at a certain location, etc.).

[0067] In some embodiments, the processor can determine the user distribution range and user density distribution based on the user distribution characteristics in a variety of ways. For example, the processor can perform statistical analysis on historical data (e.g., the past week / month, etc.), determine the distribution pattern of personnel in each area, and determine the user density distribution based on the number of users in each area. For another example, the processor can divide the space based on a grid to obtain multiple grid-shaped areas, count the number of users in different areas based on the user distribution characteristics, and determine the user density distribution.

[0068] Step 220: determine the sweep range based on the edge of the user distribution range.

[0069] The wind sweeping range refers to the angle range within which the rotatable blades can rotate (eg, swing left and right, swing up and down), and the wind sweeping range determines the air supply angle and coverage area of ​​the wind direction control device.

[0070] For more information on rotatable blades, see Figure 1 Related description.

[0071] In some embodiments, the processor may determine the sweep range based on the edge of the user distribution range in a variety of ways. For example, the processor may use the user distribution range as the sweep range. For another example, the processor may use the user distribution range and a preset area outside the user distribution range as the sweep range.

[0072] The preset area refers to a part of the area outside the edge of the user distribution range. For example, the edge can be a dividing line corresponding to the user distribution range. The edge can be represented by different line segments, such as a straight line, a curve, etc.

[0073] In some embodiments, the preset area may be an area at a preset distance from the edge of the user distribution range. In some embodiments, points located outside the boundary of the user distribution range and at a preset distance from the boundary of the user distribution range may be connected to obtain the preset area outside the user distribution range. The preset distance may be a system default value, an experience value, a manually preset value, or any combination thereof, and may be set according to actual needs, and this specification does not limit this.

[0074] Step 230, determining the sweep speed based on the user density distribution.

[0075] The swept air speed refers to the speed at which the rotatable blades rotate. For example, the swept air speed may be the angle through which the rotatable blades rotate per unit time.

[0076] In some embodiments, the processor may determine the wind sweeping speed in a variety of ways based on the user density distribution. For example, different user density distributions may correspond to different wind sweeping speeds. In some embodiments, the correspondence between different user density distributions and different wind sweeping speeds may be preset by the processor. For example, within the wind sweeping range, the larger the user density distribution area, the slower the wind sweeping speed; the smaller the user density distribution area, the faster the wind sweeping speed.

[0077] In some embodiments, the processor may determine the appropriate sweeping range and sweeping speed according to specific usage scenarios and requirements. For example, if the space is large or rapid cooling / heating is required, a larger sweeping range and a higher sweeping speed may be selected. If the space is small or gentle air supply is required, a smaller sweeping range and a lower sweeping speed may be selected.

[0078] In some embodiments, the processor can determine the wind direction control parameters based on the wind sweeping speed and wind sweeping range, generate a wind direction control instruction, and send it to the wind direction control device to control the wind direction control device to adjust the direction or speed of the airflow at the air outlet.

[0079] Step 240, determining user preference data based on the environment monitoring data and the user adjustment data.

[0080] Environmental monitoring data is data used to characterize various environmental conditions inside / outside a space. For example, environmental monitoring data may include temperature data inside / outside a space, humidity data, oxygen content data in the air, and the like.

[0081] In some embodiments, the environmental monitoring data can be collected by the environmental monitoring unit and uploaded to the processor regularly or in real time. In some embodiments, the environmental monitoring data can be collected by the environmental monitoring unit and uploaded to the storage module in real time for storage, and the processor can read the environmental monitoring data from the storage module regularly or in real time.

[0082] User adjustment data refers to data that users actively operate the HVAC equipment control system to adjust various indoor environments. The object of adjustment can be a set parameter or an operating parameter of each component determined by the processor.

[0083] For example, the user adjustment data may include data on user adjustment of temperature, data on turning on a humidifier / dehumidifier, data on turning on a ventilation unit, data on adjusting wind direction, adjustment time, etc. Among them, the data on user adjustment of temperature refers to data related to the adjustment of temperature. For example, the data on user adjustment of temperature may include the adjustment direction of temperature, the adjustment amplitude of temperature, etc. The adjustment amplitude of temperature refers to the amount of change in temperature compared to before adjustment. The adjustment direction may include increasing or decreasing, etc. The data on adjusting wind direction refers to data related to the adjustment of the sweeping range and the sweeping speed. For example, the data on adjusting wind direction may include the adjustment amplitude of the sweeping range, the adjustment amplitude of the sweeping speed, etc.

[0084] In some embodiments, the processor may be in communication with a wind direction control device, a temperature control unit, a humidity control unit, a ventilation unit, etc. The processor may obtain device logs of the wind direction control device, the temperature control unit, the humidity control unit, the ventilation unit, etc. under certain circumstances (e.g., in real time, at intervals, or triggered under certain circumstances) to determine user adjustment data. The device log is used to record the operating status of the device. For example, the device log includes the start time and stop time of the device, and the user's operation record of the device. The device may be one or more components of the wind direction control device, the temperature control unit, the humidity control unit, the ventilation unit, etc.

[0085] User preference data refers to data related to the user's preferences, preferences and needs for various environmental data. For example, user preference data can be represented by a vector as (A1, A2, A3, ...), where A1, A2, A3 represent the user's preferred parameters such as temperature, humidity, oxygen content, etc.

[0086] It should be noted that in a certain temperature and humidity environment, the user may feel comfortable in a short period of time, but may feel uncomfortable for a long time, so the user preference data changes over time.

[0087] In some embodiments, the user preference data may include the user's preference data for the temperature, humidity, and ventilation cycle inside the space in different time periods. For example, the user preference data may be a sequence of user preferred environmental data in different time periods. For another example, the user preference data may also be represented as a specific curve (e.g., a temperature curve, a humidity curve, an oxygen content curve, etc.). The specific curve may be used to represent the user's preferred environmental data in different time periods.

[0088] In some embodiments, the processor may determine the user preference data in a variety of ways based on the environmental monitoring data and the user adjustment data. For example, the processor may perform statistical analysis on historical environmental monitoring data (e.g., the past week / month, etc.), and determine environmental monitoring data with a duration greater than a time threshold as user preference data.

[0089] For another example, the processor can perform statistical analysis on the device logs of the wind direction control device, temperature control unit, humidity control unit, ventilation unit, etc. within a historical period of time (for example, the past week / month, etc.), and determine the user adjustment data that appears more than the number threshold as the user preference data. The time threshold and the number threshold can be system preset values, system default values, etc. The duration refers to the length of time from the beginning to the change of a certain environmental monitoring data.

[0090] For example, when the environmental monitoring data is: temperature 24°C, humidity 55%, and oxygen content higher than 20%, and the user adjusts the set parameters in the space two hours later, indicating that the user's comfort level is higher under the environmental monitoring data, the processor can determine the environmental monitoring data as user preference data.

[0091] In some embodiments, the user preference data may also include adjustment conditions and their corresponding user adjustment data. The adjustment conditions are judgment conditions for measuring whether the user actively adjusts the setting parameters or operating parameters. For example, the adjustment conditions may include that the temperature exceeds the temperature threshold and the duration is greater than the time threshold, the humidity is greater than the humidity threshold, etc.

[0092] For example, when the actual indoor temperature is 26°C and the ventilation unit is not turned on, one hour later the user can adjust the set parameters (such as the set temperature) by operating the air conditioner remote control or display interface: lower the set temperature by 3°C and turn on the ventilation unit. The processor can determine that the user's adjustment data is: lower the set temperature; lower the set temperature by 3°C and turn on the ventilation unit. The adjustment conditions are: the actual temperature exceeds 26°C and lasts for more than one hour.

[0093] In some embodiments, the processor may collect statistics on user adjustment data within a period of time (e.g., the past month, the past six months, etc.), and when the number of occurrences of a certain user adjustment data is greater than a corresponding threshold, the processor may determine the user adjustment data as user preference data. The threshold may be determined based on experience or experiments.

[0094] It should be noted that when the windows or doors in the space are not well sealed, or the indoor insulation is poor, or there are a large number of people in the room, or the equipment generates a large amount of heat, the set temperature in the room is usually inconsistent with the actual temperature in the space, so the temperature / humidity adjusted by the user is the set temperature / set humidity.

[0095] Step 250, determining control parameters according to the user preference data, and determining at least one set of control instructions based on the control parameters to control the operation of at least one of the temperature control unit, the humidity control unit, and the ventilation unit.

[0096] Control parameters refer to the data used to adjust and set the parameters of the components in the HVAC equipment control system during operation. By determining the control parameters, the operating status and results of one or more components in the HVAC equipment control system can be changed. For example, control parameters may include temperature control parameters, humidity control parameters, ventilation control parameters, etc. Among them, the temperature control parameters are used to control the temperature control unit's temperature adjustment direction, temperature adjustment range, etc.; the humidity control parameters are used to control the opening time / closing time of the humidification device and dehumidification device; the ventilation control parameters are used to control the opening time and opening duration of the ventilation unit. The adjustment range is used to adjust the temperature.

[0097] In some embodiments, the processor may determine whether the adjustment conditions are met based on the environmental monitoring data at the current time; if so, the corresponding user preference data is used as the adjustment parameter.

[0098] Control instructions are instructions used to control the operation of one or more components in the HVAC equipment control system.

[0099] In some embodiments, the control instruction may refer to an instruction for controlling the operation of a temperature control unit, a humidity control unit, a ventilation unit, etc. In some embodiments, the control instruction may include one or more instructions. For example, a temperature control instruction corresponding to a temperature control parameter; a humidity control instruction corresponding to a humidity control parameter; a ventilation control instruction corresponding to a ventilation control parameter, etc.

[0100] For more information about temperature control units, humidity control units, and ventilation units, see Figure 1 Related description.

[0101] In some embodiments, the processor may send control instructions to corresponding devices (such as temperature control units, humidity control units, ventilation units, etc.) to control the operation of the corresponding devices. For example, the processor may send temperature control instructions to the temperature control unit to control the temperature control unit to adjust the temperature in the space (such as heating or cooling). For another example, the processor may send ventilation control instructions to the ventilation unit to control the opening / closing of the ventilation unit.

[0102] In some embodiments, the processor may determine the historical control instructions of the same historical period as the current control instructions.

[0103] In some embodiments, the processor may determine a preset table of different user preference data and corresponding control parameters based on historical production experience, and determine the current control parameters by looking up the table. The processor may generate control instructions based on the control parameters to control the operation of at least one of the temperature control unit, the humidity control unit, the ventilation unit, etc., and adjust the environmental data in the space.

[0104] In some embodiments, the control method of the HVAC equipment control system also includes: determining user preference data and corresponding index tags based on environmental monitoring data, user adjustment data, and user distribution characteristics within a preset time period; and establishing a preference database based on the user preference data and corresponding index tags.

[0105] The preset time period refers to a preset historical time range. For example, the preset time period may be the past month, the past six months, etc.

[0106] The preference database is a database used to store, index and query vectors. The preference database can store multiple reference vectors and user preference data corresponding to each reference vector.

[0107] In some embodiments, the processor may determine the index tag as a reference vector.

[0108] Index tags are specific information used for indexing in the preference database.

[0109] In some embodiments, the database can be constructed based on a large amount of historical data. For example, in the historical data, historical environmental monitoring data with a duration greater than a time threshold and historical user adjustment data with a number of occurrences greater than a number threshold are determined as user preference data, and historical user distribution features corresponding to the timestamp of the user preference data are determined as reference vectors.

[0110] In some embodiments, the timestamp may be the time when the user preference data is generated. For example, when the user preference data is environmental monitoring data, the timestamp is the start time of the environmental monitoring data; when the user preference data is user adjustment data, the timestamp is the time when the user adjusts the environmental data.

[0111] In some embodiments, the processor may store the preference database in a storage module.

[0112] In some embodiments, the processor may use the user distribution feature as an index tag and create a single column index based on the index tag of the user distribution feature. For example, the processor sorts the user distribution features according to a preset rule (e.g., based on the time when the user distribution features were collected), and stores the sorted user distribution features and their corresponding storage addresses in correspondence. The storage address refers to the address where the user preference data is stored in the preference database.

[0113] In some embodiments of the present specification, by establishing a preference database and reasonably establishing index tags, the performance and query efficiency of the database can be improved.

[0114] In some embodiments, the index tag of the user preference data may further include outdoor climate data. The processor may obtain the outdoor climate data corresponding to the user preference data based on the environment monitoring unit; and add the outdoor climate data to the index tag.

[0115] Outdoor climate data refers to environmental data outside a space. For example, outdoor climate data includes data such as temperature, humidity, and wind speed outside the space.

[0116] It should be noted that the user's preference for the environment data inside the space may be affected by the environment outside the space. For example, when a user enters a room at 27°C from an outdoor temperature of 32°C, they will feel cool, but when a user enters a room at 27°C from an outdoor temperature of 28°C, they may not feel cool enough, so the temperature inside the space needs to be adjusted to 25°C.

[0117] In some embodiments, the index tag may include at least two levels. Each level may include at least one index tag. Each higher-level index tag may have a higher search priority than a lower-level index tag. For example, a first-level index tag in a group of index tags may be "user distribution characteristics", and a second-level index tag may be "outdoor climate data".

[0118] In some embodiments, the processor may use the outdoor climate data as an index tag and create a single column index based on the index tag of the outdoor climate data. In some embodiments, the processor may also use user distribution features and outdoor climate data as index tags to create a composite index.

[0119] In some embodiments of the present specification, by adding outdoor climate data as an index tag, the preference database can quickly locate specific user preference data in the preference database according to a combination of multiple columns, thereby accelerating retrieval operations and improving query performance.

[0120] In some embodiments of the present specification, by analyzing user preference data and determining control parameters, more personalized services can be provided, and the temperature and humidity in the space can be automatically adjusted according to the user's habits to improve the user's comfort; by intelligently adjusting the environmental data in the space, energy waste can be effectively reduced and energy can be saved.

[0121] It should be noted that the above description of the relevant process is only for example and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the process under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0122] Figure 3 is an exemplary schematic diagram of a preference model according to some embodiments of the present specification.

[0123] In some embodiments, Figure 3 As shown, the processor can determine the user's continuous adjustment characteristics 313 based on the user adjustment data 311 and the environmental monitoring data 312; based on the user adjustment data 311, the environmental monitoring data 312, the continuous adjustment characteristics 313, and the user distribution characteristics 314, determine the user preference data 330 of at least one time period through the preference model 320.

[0124] The user's continuous adjustment feature is data reflecting the characteristics of the user's continuous multiple adjustments. For example, the user's continuous adjustment feature may include the adjustment direction, adjustment amplitude, adjustment time interval, environmental monitoring data before and after the adjustment time, etc. Among them, the adjustment time interval refers to the time interval compared to the previous adjustment.

[0125] In some embodiments, the user's continuous adjustment feature can be represented in various forms such as a sequence or a matrix. For example, the user's continuous adjustment feature can be represented in the form of a sequence {A, B, C, ...}, where the A, B, and C elements in the sequence represent continuous adjustment features at different adjustment times, respectively. In some embodiments, each element in the sequence can include multiple sub-elements. As an example only, the continuous adjustment feature at time A can be represented as (A1, A2, ...), where A1 represents the adjustment direction when adjusting time A, and A2 represents the adjustment amplitude when adjusting time A.

[0126] It should be noted that after each adjustment by the user, if the comfort level still does not meet the requirements, it will be adjusted again until the comfort level requirements are met. The continuous adjustment feature can filter out data that continues to be adjusted due to an inadequate adjustment, avoid interference in judging user preference data, and filter out data that users adjust due to the long duration of certain environmental data.

[0127] In some embodiments, the processor may determine whether the time interval between two adjacent adjustments meets a preset condition based on each user adjustment data, and in response, determine that it is a continuous adjustment; and determine the continuous adjustment feature based on multiple continuous adjustment user adjustment data. For example, the processor may combine multiple continuous adjustment user adjustment data into a continuous adjustment feature. The continuously adjusted user adjustment data may include user adjustment data of multiple continuous adjustments by the user within a period of time.

[0128] The preset condition is a judgment condition for evaluating whether two adjacent adjustments are continuous adjustments. For example, the preset condition may include that the adjustment time interval between two adjacent adjustments is less than a preset time. The preset time may be a system preset value, a system default value, etc.

[0129] Exemplarily, the user adjusts the environmental data at 10:00, 10:15, 10:25, 10:34, and 10:50, respectively, and the preset time is 15 minutes. Then the adjustment between 10:00 and 10:15 is continuous adjustment; the adjustment between 10:15 and 10:25 is continuous adjustment; the adjustment between 10:25 and 10:34 is continuous adjustment; the user adjustment data of 10:00, 10:15, 10:25, and 10:34 is taken as user adjustment data of continuous adjustment and determined as a continuous adjustment feature.

[0130] In some embodiments, the processor may match the user adjustment data and the environmental monitoring data based on the adjustment time to obtain at least one single adjustment feature; and determine the continuous adjustment feature based on the adjustment difference between the single adjustment features.

[0131] The single adjustment feature is data reflecting the characteristics of a certain adjustment made by the user. For example, the single adjustment feature may include at least one of the adjustment time, adjustment amplitude, and adjustment direction of a certain adjustment made by the user.

[0132] Matching refers to the process of associating user-adjusted data with environmental monitoring data. For example, the matched user-adjusted data and environmental monitoring data have a causal relationship in time.

[0133] In some embodiments, the processor can search the environmental monitoring data based on the adjustment time of each adjustment made by the user, obtain the environmental monitoring data before and after the adjustment time, and use the adjustment time, environmental monitoring data before the adjustment time, adjustment direction, and adjustment amplitude as the characteristics of a single adjustment.

[0134] The environmental monitoring data before and after the adjustment time refers to the environmental monitoring data of a period of time before and after the adjustment time of a certain adjustment. For example, the period of time can be a system preset value, a system default value, etc. For example, the period of time can be 1 minute before and after, 2 minutes before and after, etc.

[0135] The adjustment difference refers to the difference between the single adjustment features of two adjacent adjustments. For example, the adjustment difference may include the adjustment time interval between two adjacent adjustments, the adjustment direction of each adjustment, the adjustment amplitude of each adjustment, and the environmental monitoring data before and after the adjustment time of each adjustment.

[0136] In some embodiments, based on each adjustment made by the user, the processor can determine whether the environmental monitoring data before the adjustment time of the current adjustment is stable environmental data; if not, the current adjustment will be treated as a continuous adjustment of the previous adjustment; the adjustment difference between two adjacent adjustments in the continuous adjustment will be used as a continuous adjustment feature.

[0137] Stable environmental data is used to measure whether the changes in environmental monitoring data are stable. Stable environmental data can be used to indicate that the operating parameters in the HVAC equipment control system do not change, or that the environmental data in the space (such as temperature and humidity) do not change or change slightly.

[0138] In some embodiments, the processor can determine whether the environmental monitoring data is stable environmental data in a variety of ways. For example, the processor can count the duration of the environmental monitoring data; in response to the duration exceeding a preset duration, the environmental monitoring data is regarded as stable environmental data. The preset duration can be determined based on experiments or experience.

[0139] In some embodiments, the preset duration may be related to the perception delay and the action delay. For example, the preset duration is positively correlated with the perception delay and the action delay. The longer the perception delay and the action delay are, the longer the preset duration is. For another example, the preset duration is the sum of the perception delay and the action delay.

[0140] In some embodiments of the present specification, if the user makes adjustments before the environmental monitoring data has stabilized, it means that the current environmental monitoring data cannot make the user feel comfortable and further adjustments are needed. This adjustment can be regarded as a continuous adjustment of the previous adjustment, which can avoid misjudgment of user preferences (after all, such adjustments do not meet the user's needs) and make the prediction of the preference model more accurate.

[0141] The preference model is a model for determining user preference data for at least one time period. In some embodiments, the preference model may be a machine learning model. For example, the preference model may be a machine learning model of a custom structure described below. The preference model may also be a machine learning model of other structures, for example, any one or combination of a recurrent neural network (RNN) model and a deep neural network (DNN) model.

[0142] In some embodiments, the input of the preference model may include user adjustment data, environmental monitoring data, continuous adjustment characteristics, and user distribution characteristics, and the output may include user preference data for at least one time period.

[0143] In some embodiments, Figure 3 As shown, the input of the preference model also includes perception delay 315 and action delay 316.

[0144] Perception delay refers to the length of time it takes for the environment in a space to make the user feel uncomfortable.

[0145] It should be noted that the user's discomfort with the environment is gradually generated. When the uncomfortable feeling accumulates to a certain level, it will prompt the user to actively adjust the various parameters of the system.

[0146] Action delay refers to the time it takes for a user to perceive discomfort and actually make adjustments.

[0147] It should be noted that action delay means that when this uncomfortable feeling occurs, the user may not adjust it immediately, but wait for a while before adjusting it.

[0148] In some embodiments, the processor can determine the perception delay and action delay in a variety of ways. For example, the processor can send a questionnaire to the user, asking the user about the time point when the user feels uncomfortable and the corresponding environmental monitoring data, and obtain the most recent stable environmental data before the above time point; calculate the perception delay based on the difference between the start time of the most recent stable environmental data and the time point when the user feels uncomfortable; obtain the most recent user adjustment data after the above time point, and calculate the action delay based on the difference between the time point when the user feels uncomfortable and the adjustment time of the most recent user adjustment data.

[0149] In some embodiments of the present specification, perception delay and action delay are used to avoid using the time point of user adjustment as the time limit for judging the user's preference data in a time period, while ignoring the user's discomfort before the actual adjustment, resulting in inaccurate determined preference data, and avoiding determining environmental monitoring data that makes the user uncomfortable as user preference data.

[0150] In some embodiments, the preference model can be trained based on a large number of first training samples with a first label through various feasible methods. For example, parameter updating can be performed based on the gradient descent method. An exemplary training process includes: inputting a plurality of first training samples with a first label into the initial preference model, constructing a loss function through the results of the first label and the initial preference model, and iteratively updating the parameters of the initial preference model based on the loss function through gradient descent or other methods. When the preset conditions are met, the model training is completed and a trained preference model is obtained. Among them, the preset conditions may be that the loss function converges, the number of iterations reaches a threshold, etc.

[0151] In some embodiments, the first training sample may include multiple groups of training samples, each group of training samples includes at least sample user adjustment data, sample environment monitoring data, sample continuous adjustment features, and sample user distribution features. The first training sample may be obtained based on historical data.

[0152] In some embodiments, the first label may include actual user preference data corresponding to the first training sample, and the first label may be obtained through a processor or manual annotation. For example, the user preference data may be determined through feedback from a large number of users or testers. For example, for a user / tester in a space where a HVAC equipment control system is installed, when the user / tester operates the system to adjust environmental data, the processor sends a questionnaire or inquiry to the user through a display screen or a speaker to obtain a first label. For example, the processor may obtain the user's adjustment reasons through a questionnaire and determine the first label. The adjustment reasons may include the inability to achieve the most comfortable environmental data, or feeling comfortable before, but feeling uncomfortable after a period of time, etc.

[0153] Exemplarily, when a user adjusts environmental data, the reasons for the adjustment are obtained through a questionnaire. The processor can extract keywords from the reasons for the adjustment. When the keyword is "felt comfortable before, felt uncomfortable after", the environmental monitoring data in a time period before the adjustment is used as the user preference data in the time period. By analogy, each time the user adjusts, the user preference data of each time period is obtained through the above-mentioned similar inquiry method, and the user preference data of each time period is determined as the first label. The time period refers to the duration of the environmental monitoring data.

[0154] In some embodiments, when the input of the preference model further includes perception delay and action delay, the first training sample may further include sample perception delay and sample action delay.

[0155] In some embodiments, the processor can obtain a pre-trained preference model based on the initial training data through the above method; during actual use by the user, the processor can obtain a pre-trained preference model based on the initial training data. Figure 2 In a similar manner, user preference data is obtained as a label for a reinforcement sample, and user adjustment data, environmental monitoring data, continuous adjustment features, and user distribution features corresponding to the user preference data are used as reinforcement samples. The preference model is retrained based on a large number of reinforcement samples to optimize the parameters of the preference model. For another example, the processor can add multiple reinforcement samples to the first training sample, and retrain the preference model based on the reinforcement samples and the first training sample. Reinforcement training refers to training the model through high-quality reinforcement training samples.

[0156] Each user has different preferences for temperature and humidity in actual use. By adding user personalized information to the sample (such as the user's actual preferred temperature, preferred humidity, etc.), the personalized prediction ability of the preference model can be improved.

[0157] In some embodiments of the present specification, through the preference model, appropriate user preference data can be obtained efficiently and accurately, which improves the accuracy of determining the control parameters, and is beneficial to controlling the accuracy of the temperature control unit, humidity control unit, and ventilation unit, so that the environment inside the space can achieve a comfortable effect for the user.

[0158] Figure 4 It is an exemplary schematic diagram of determining control parameters according to some embodiments of this specification.

[0159] In some embodiments, the processor obtains outdoor climate data; based on the outdoor climate data and user distribution characteristics, searches in a preference database to determine user preference data; and determines control parameters based on the user preference data.

[0160] For more information about outdoor climate data, see Figure 2Related description.

[0161] In some embodiments, the processor generates a retrieval vector based on at least one of outdoor climate data and user distribution characteristics, searches the preference database based on the retrieval vector, and obtains user preference data corresponding to a reference vector that meets the matching condition.

[0162] The matching condition may refer to a judgment condition for determining a target vector. The matching condition may include that the vector distance with the search vector is less than a distance threshold, the vector distance is minimum, etc. There are many methods for calculating vector distance, such as Euclidean distance, cosine distance, etc.

[0163] In some embodiments, the processor determines the control parameters in a variety of ways based on the user preference data. For example, the processor can determine a preset table of different user preference data and corresponding control parameters based on historical data, and determine the current control parameters by looking up the table. For another example, the processor can determine the current control parameters based on the user preference data and the corresponding control parameters based on historical data. Figure 2 Determine the control parameters in a similar way.

[0164] In some embodiments, Figure 4 As shown, the processor obtains the air outlet position 411; divides the space according to the air outlet position to obtain the effective area 412 corresponding to each air outlet; determines the control parameters 460 of each air outlet based on the user distribution characteristics 413 of the effective area, the outdoor climate data 414 and the preference database 415.

[0165] The air outlet position refers to the location of each air outlet in the space.

[0166] In some embodiments, the processor may obtain a three-dimensional model of the space from the storage module, and obtain the position of each air outlet based on the three-dimensional model of the space. The three-dimensional model is a three-dimensional model used to represent the scene inside the space. In some embodiments, the three-dimensional model of the space may be used to represent the internal spatial structure of the space and one or more objects located inside the space. Exemplary one or more objects may include air outlets, users, etc.

[0167] The effective area refers to the indoor area that the airflow from the vent can reach.

[0168] In some embodiments, the processor can divide the space in a variety of ways to obtain the effective area corresponding to each air outlet. For example, the processor can obtain the radiation radius of each air outlet, and determine the effective area corresponding to each air outlet based on the radiation radius of each air outlet. Exemplarily, the effective area corresponding to each air outlet is a semicircle with the air outlet as the center and the radiation radius as the radius. The radiation radius refers to the distance that the wind flow from the air outlet spreads to the surrounding area. The radiation radius can be obtained from the manufacturer of the HVAC equipment or through actual measurement. The effective area corresponding to the air outlet can also be other shapes, such as a square with the radiation radius as the side length, and the air outlet can be located on the side of the square.

[0169] In some embodiments, Figure 4 As shown, the processor can establish a search vector based on the user distribution characteristics 413 and outdoor climate data 414 of the effective area, search in the preference database 415, obtain the user preference data corresponding to the reference vector that meets the matching condition, and determine it as the user preference data 416 of the effective area. For more information about the search, please refer to Figure 4 Related description above. In some embodiments, the processor may determine the user preference data 416 of each effective area as the control parameter 460 of each air outlet.

[0170] It should be noted that for the same space, the HVAC equipment will distribute the generated fresh air to multiple outlets, and the airflow temperature of multiple outlets is roughly the same. The opening of different outlets can be adjusted to adjust the size of the air volume and control the temperature of the effective area corresponding to different outlets. The distribution can be average distribution or distribution based on specified rules.

[0171] In some embodiments, an air outlet valve is provided at each air outlet, and the processor can adjust the opening degree of the air outlet valve to control the size of the air flow at the air outlet to meet different ventilation requirements. Correspondingly, the air outlet control parameters include the opening control parameters of the air outlet, and the opening control parameters are used to control the opening angle of the air outlet valve or the air flow passing through. In some embodiments, the opening control parameter can be expressed as a percentage, for example, the opening control parameter is 20%.

[0172] In some embodiments, the processor can determine the temperature of the airflow in a variety of ways. For example, the processor can obtain user preference data for multiple valid areas, determine the temperature control parameter in the control parameters based on the mean, mode, or maximum value of the temperatures in the multiple valid areas, generate a temperature control instruction based on the temperature control parameter and send it to the temperature control unit to control the operation of the temperature control unit so that the fresh air generated by the HVAC equipment reaches the temperature of the airflow.

[0173] In some embodiments, the processor can determine the opening of different air outlet valves in a variety of ways. For example, the processor can determine a preset table of different user preference data and corresponding air outlet valve openings based on historical data, and determine the opening of the air outlet valve corresponding to each effective area by looking up the table.

[0174] In some embodiments, the processor is communicatively connected with the air outlet valve. The processor can determine the opening control parameters of each air outlet based on the opening of the air outlet corresponding to each effective area, and determine the valve control instruction based on the opening control parameters and send it to the corresponding air outlet valve.

[0175] In some embodiments of the present specification, more detailed control parameters are determined for different air outlets according to the user distribution characteristics of the corresponding areas, which can meet the needs of users in multiple locations in the space more quickly. At the same time, by adjusting the opening of different air outlets, areas with more traffic can heat up faster and cool down slower (heat generated by breathing, etc.). Separate control of different effective areas is conducive to energy saving.

[0176] In some embodiments, Figure 4 As shown, the processor determines at least one group of candidate control parameter groups 417 based on the effective area 412 of each air outlet, the user distribution characteristics 413 of the effective area, the outdoor climate data 414, and the preference database 415; constructs an analysis map 420 based on the effective area 412 of each air outlet, the environmental monitoring data 312, and the candidate control parameter groups 417; inputs the analysis map 420 into the HVAC analysis model 430 to determine the indoor environmental data 440 corresponding to the candidate control parameter groups; and determines the target control parameter group 450 based on the degree of conformity between the indoor environmental data 440 and the user preference data 416 of the effective area.

[0177] The candidate control parameter group refers to a combination of candidate control parameters to be determined as the target control parameter group. Each air outlet corresponds to a candidate control parameter. The candidate control parameter group may be a sequence consisting of candidate control parameter groups of at least one air outlet. For example, the candidate control parameter group may include candidate control parameters corresponding to each air outlet.

[0178] In some embodiments, the processor may determine multiple candidate control parameter groups in a variety of ways. For example, the control system may determine multiple candidate control parameter groups by randomly generating. For another example, the processor may determine the candidate control parameters based on historical data corresponding to the air outlet. For example, the processor may determine the average value of the historical control parameters corresponding to the air outlet as the candidate control parameter.

[0179] In some embodiments, for each valid area, the processor may be based on user distribution characteristics of the valid area, outdoor climate data, and Figure 4In the above, the preference database is retrieved in a similar manner to determine the user preference data of each valid area; based on the user preference data of the valid area, according to the Figure 2 In a similar way to judging whether the preference data condition is met, if the environmental monitoring data corresponding to the effective area meets the adjustment condition, the user preference data of the effective area is determined as the first candidate adjustment parameter of the effective area; the first candidate adjustment parameter is adjusted according to the user distribution characteristics of the effective area to obtain the second candidate adjustment parameter of the effective area. For more information about the adjustment condition, please refer to Figure 2 Related description.

[0180] In some embodiments, for each effective area, the processor can adjust the first candidate control parameter according to the user distribution characteristics of the effective area through a preset rule. Exemplarily, the preset rule is: the adjustment range of humidity is negatively correlated with the population density of the effective area, and the larger the population density of the effective area, the smaller the adjustment range; the opening of the air outlet valve is positively correlated with the population density of the effective area, and the larger the population density of the effective area, the larger the opening of the air outlet valve.

[0181] In some embodiments, the processor may use the first candidate control parameters corresponding to each air outlet as one set of candidate control parameter groups, and use the second candidate control parameters corresponding to each air outlet as another set of candidate control parameter groups.

[0182] The analysis graph represents a graph of effective areas of the air outlet and the relationship between them. In some embodiments, the analysis graph is a data structure composed of nodes and edges, where the edges connect the nodes, and the nodes and edges may have their own characteristics. Figure 4 As shown, the nodes of the analysis graph may include node 1, node 2, node 3, node 4 and node 5.

[0183] The node corresponds to the effective area corresponding to the air outlet. The node characteristics can reflect information related to the air outlet. For example, the node characteristics may include the position of the air outlet in the effective area, the candidate control parameters of the air outlet, the user distribution characteristics in the effective area corresponding to the air outlet, the environmental monitoring data corresponding to the effective area, etc.

[0184] The environmental monitoring data corresponding to the effective area refers to the environmental data detected by the temperature sensor, humidity sensor, oxygen content sensor, etc. at the first preset point corresponding to the effective area. The temperature sensors, humidity sensors, and oxygen content sensors at different first preset points can be used to monitor the temperature, humidity, oxygen content, etc. in different effective areas. A sensor at a first preset point can be used to monitor the temperature, humidity, and oxygen content of one or more effective areas.

[0185] The edge may correspond to the relationship between the effective areas of the air outlet. For example, there is an edge between two adjacent effective areas. In some embodiments, the edge may be a directed edge, and the direction of heat transfer or moisture flow between two adjacent effective areas is the direction of the edge. Indoors, if there is a temperature difference and a humidity difference, heat and moisture will automatically flow from the effective area of ​​high temperature and high humidity to the effective area of ​​low temperature and low humidity. The processor may determine the temperature or humidity of each effective area, and in two adjacent effective areas, determine the effective area of ​​high temperature and high humidity as the starting point of the edge, and determine the effective area of ​​low temperature and low humidity as the end point of the edge. The characteristics of the edge reflect information related to the location of the air outlet. For example, the characteristics of the edge may include the distance between adjacent air outlets.

[0186] The characteristics of nodes and edges can be determined by various methods based on input data. The method can be the method described in the above embodiment, or other methods. The input data can include the effective area of ​​each air outlet, the user distribution characteristics of the effective area, environmental monitoring data, preference database, etc., and can also include the historical effective area of ​​each air outlet, the user distribution characteristics of the historical effective area, historical environmental monitoring data, and historical preference database.

[0187] The HVAC analysis model is a model for determining indoor environment data corresponding to the candidate control parameter group. In some embodiments, the HVAC analysis model may be a machine learning model. For example, the HVAC analysis model may be a neural network model. For another example, the HVAC analysis model may be a graph neural network model (GNN).

[0188] In some embodiments, the input of the HVAC analysis model may include an analysis graph, and the output may include indoor environment data corresponding to each node.

[0189] Indoor environmental data refers to environmental data of a valid area. For example, indoor environmental data includes data such as temperature, humidity, and wind speed of each valid area.

[0190] In some embodiments, the HVAC analysis model can be obtained by training based on training data. The training data includes a second training sample and a second label. For example, the second training sample may include a sample analysis map, and the second label may be indoor environment data in a valid area corresponding to the node. The nodes and their features, edges and their features of the sample analysis map are similar to the above description. The second training sample can be determined based on historical data, and the second label can be determined by a processor or manual annotation.

[0191] In some embodiments, for each group of candidate control parameter groups, the processor can determine the corresponding analysis map, execute the HVAC analysis model, and output the indoor environment data corresponding to the group of candidate control parameter groups.

[0192] The degree of conformity refers to the similarity or degree of similarity between the indoor environment data and the user preference data for the same effective area.

[0193] In some embodiments, for each valid region, the processor may determine the degree of conformity in a variety of ways, such as by calculating a similarity coefficient, Euclidean distance, Manhattan distance, cosine similarity, and the like.

[0194] In some embodiments, for each valid area, the processor determines whether the degree of conformity between the indoor environment data and the user preference data meets the preset requirements: if so, the corresponding candidate control parameter is used as the target control parameter; if not, the size relationship between the indoor environment data and the user preference data is determined. If it is greater than, the candidate control parameter is adjusted down, and the candidate control parameter after the adjustment is determined as the target control parameter of the valid area; if it is less than, the candidate control parameter is adjusted up, and the candidate control parameter after the adjustment is determined as the target control parameter of the valid area.

[0195] The preset requirement is a judgment condition for evaluating how to determine the target control parameter. For example, the preset requirement may include that the degree of compliance is greater than a similarity threshold, etc. The similarity threshold may be determined based on experiments or experience.

[0196] The target control parameters of the effective area refer to the control parameters related to the effective area. The environmental data (such as humidity, air outlet valve opening, etc.) in the effective area can be adjusted by the target control parameters of the effective area. For example, the target control parameters may include the adjustment range of humidity, the adjustment direction of humidity, the adjustment range of the opening of the air outlet valve, etc.

[0197] In some embodiments, the down-regulation process includes: executing multiple rounds of iterations, obtaining candidate control parameters after the down-regulation process based on the iteration results, wherein at least one round of iterations includes: down-regulating the candidate control parameters of the current round of iterations, re-establishing the analysis map based on the down-regulated candidate control parameters, inputting the re-established analysis map into the HVAC analysis model, and obtaining indoor environment data corresponding to each node; in response to the first iteration condition being met, ending the iteration, and using the down-regulated candidate control parameters as the iteration result; in response to the first iteration condition not being met, continuing the next round of iteration based on the down-regulated candidate control parameters as the candidate control parameters for the next round of iteration.

[0198] Adjusting down refers to the process of adjusting down the values ​​in the candidate control parameters (such as humidity adjustment amplitude, valve opening, etc.). In some embodiments, the processor can adjust down in a variety of ways. For example, the processor can subtract the adjustment amount from each value in the candidate control parameter to determine the candidate control parameter after adjustment. The adjustment amount can be a randomly generated value or a system preset value, etc. In some embodiments, different types of parameters correspond to different adjustment amounts. Different types of parameters refer to parameters that represent different physical quantities in the candidate control parameters. For example, different types of parameters may include humidity adjustment amplitude, valve opening, etc.

[0199] The first iteration condition is a judgment condition for evaluating whether the iteration of the adjustment process is stopped. In some embodiments, the first iteration condition may include that the number of iterative updates has reached a first number threshold, that the indoor environment data corresponding to the candidate control parameters after the adjustment process and the user preference data meet the preset requirements, etc. The first number threshold may be a system default value, a system preset value, etc.

[0200] In some embodiments, the increase processing includes: performing multiple rounds of iterations, obtaining candidate control parameters after the increase processing based on the iteration results, wherein at least one round of iterations includes: increasing the candidate control parameters of the current round of iterations, re-establishing the analysis spectrum based on the increased candidate control parameters, inputting the re-established analysis spectrum into the HVAC analysis model, and obtaining indoor environment data corresponding to each node; in response to the second iteration condition being met, ending the iteration, and using the increased candidate control parameters as the iteration result; in response to the second iteration condition not being met, continuing the next round of iteration based on the increased candidate control parameters as the candidate control parameters for the next round of iteration.

[0201] Adjusting up refers to the process of increasing the numerical value in the candidate control parameter (such as the humidity adjustment amplitude, valve opening, etc.). In some embodiments, the processor can adjust up in a variety of ways. For example, the processor can subtract the adjustment amount from each numerical value in the candidate control parameter to determine the increased candidate control parameter. The adjustment amount can be a randomly generated value or a system preset value, etc. In some embodiments, different types of parameters correspond to different adjustment amounts. Different types of parameters refer to parameters that represent different physical quantities in the candidate control parameters. For example, different types of parameters may include humidity adjustment amplitude, valve opening, etc.

[0202] The second iteration condition is a judgment condition for evaluating whether the iteration of the adjustment process is stopped. In some embodiments, the second iteration condition may include that the number of iterative updates has reached a second number threshold, that the indoor environment data corresponding to the candidate control parameter after the adjustment process and the user preference data meet the preset requirements, etc. The second number threshold may be a system default value, a system preset value, etc.

[0203] In the first round of iterations, the candidate control parameters may be candidate control parameters that have not been adjusted down or up.

[0204] In some embodiments, Figure 4 As shown, the processor can determine the target control parameters of each effective area based on the above method, form a target control parameter group 450 based on the target control parameters of each effective area, and determine the target control parameter group 450 as the control parameters 460 of each air outlet.

[0205] In some embodiments of the present specification, environmental data within multiple valid areas will change with the flow of air, heat transfer, and the flow of users. The changes in environmental data within the space caused by the air flow and heat transfer in the space are comprehensively considered through the graph structure, so that the final predicted indoor environmental data is more accurate.

[0206] In some embodiments of the present specification, user preference data can be effectively and accurately obtained by searching in a preference database, which is conducive to setting environmental data in the space according to the user's habits, improving the user's comfort while improving the accuracy of regulation.

[0207] One or more embodiments of the present specification also provide a mobile terminal control device, including a processing device, wherein the processing device is used to execute a control method of a HVAC equipment control system as described in any of the above embodiments.

[0208] One or more embodiments of the present specification also provide a computer-readable storage medium, wherein the storage medium stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer runs a control method for a HVAC equipment control system as described in any of the above embodiments.

[0209] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0210] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0211] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0212] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0213] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0214] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0215] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A device control system, characterized in that: The system includes an environment monitoring unit, a human body detection unit, and a processor; The environmental monitoring unit includes a temperature sensor, a humidity sensor, and an oxygen content sensor deployed at a plurality of first preset points in the space, and the environmental monitoring unit is configured to detect environmental monitoring data inside and / or outside the space; The human body detection unit includes human body sensors deployed at a plurality of second preset points in the space, and the human body detection unit is configured to detect user distribution characteristics of users in the space; The processor is configured to: The user adjustment data and the environmental monitoring data are matched based on the adjustment time to obtain at least one single adjustment feature; wherein the processor searches the environmental monitoring data according to the adjustment time of each adjustment by the user, obtains the environmental monitoring data before and after the adjustment time, and uses the adjustment time, the environmental monitoring data before the adjustment time, the adjustment direction, and the adjustment amplitude as the single adjustment feature; Based on the adjustment difference between the single adjustment features, the continuous adjustment feature of the user is determined; wherein, based on each adjustment of the user, the processor determines whether the environmental monitoring data before the adjustment time of the adjustment is stable environmental data; if not, the adjustment is regarded as a continuous adjustment of the previous adjustment; the adjustment difference between two adjacent adjustments in the continuous adjustment is regarded as a continuous adjustment feature; Based on the user adjustment data, the environment monitoring data, the continuous adjustment characteristics, the user distribution characteristics, the perception delay, and the action delay, the user preference data of at least one time period is determined through a preference model, wherein the preference model is a machine learning model; the perception delay refers to the time required for the environment in the space to make the user feel uncomfortable; the action delay refers to the time required for the user to perceive the discomfort and the user to actually make adjustments; Corresponding control parameters are determined based on the user preference data to control the environment control components.

2. The system according to claim 1, characterized in that The processor is further configured to: Determining the user preference data and corresponding index tags based on the user adjustment data, the environment monitoring data, the continuous adjustment characteristics, the user distribution characteristics, the perception delay, and the action delay; A preference database is established based on the user preference data and the corresponding index tags.

3. The system according to claim 2, characterized in that The processor is further configured to: acquiring outdoor climate data corresponding to the user preference data based on the environment monitoring unit; The outdoor climate data is added to the index tag.

4. The system according to claim 3, characterized in that The processor is further configured to: Get outdoor climate data; Get the air outlet position; Dividing the space based on the positions of the air outlets to obtain an effective area corresponding to each air outlet; Based on the user distribution characteristics of the effective area and the outdoor climate data, searching the preference database to determine the user preference data of the effective area; Based on the user preference data of the effective area, the control parameters of each air outlet are determined.

5. The system according to claim 4, characterized in that The processor is further configured to: Determine at least one set of candidate control parameter groups based on the effective area corresponding to each air outlet, the user distribution characteristics of the effective area, the outdoor climate data, and the preference database; Constructing an analysis graph based on the effective area corresponding to each air outlet, the environmental monitoring data, and the candidate control parameter group; Based on the analysis graph, determining indoor environment data corresponding to the candidate control parameter group; A target control parameter group is determined based on the degree of conformity between the indoor environment data and the user preference data of the effective area.

6. The system according to claim 1, characterized in that The environmental conditioning component includes a wind direction control device, a temperature control unit, a humidity control unit, and a ventilation unit that are communicatively connected to the processor: The wind direction control device includes a rotatable blade installed at the air outlet, and the wind direction control device is configured to control the ventilation in the space based on wind direction control parameters, and the wind direction control parameters include a sweeping range and a sweeping speed; The temperature control unit is configured to adjust the temperature within the space; The humidity control unit includes a humidifying device and a dehumidifying device, and the humidity control unit is configured to adjust the humidity in the space; The ventilation unit is configured to pass outdoor fresh air into the space and discharge dirty air in the space to the outside; The processor is further configured to: Based on the user distribution characteristics, determine the user distribution range and user density distribution; Determine the sweep range based on the edge of the user distribution range; The sweep speed is determined based on the user density distribution.

7. A device control method, characterized in that: The method is implemented by a processor of the device control system according to claim 1, and the method comprises: Matching the user adjustment data and the environmental monitoring data based on the adjustment time to obtain at least one single adjustment feature; wherein the processor searches the environmental monitoring data according to the adjustment time of each adjustment by the user, obtains the environmental monitoring data before and after the adjustment time, and uses the adjustment time, the environmental monitoring data before the adjustment time, the adjustment direction, and the adjustment amplitude as the single adjustment feature; Based on the adjustment difference between the single adjustment features, the continuous adjustment feature of the user is determined; wherein, based on each adjustment of the user, the processor determines whether the environmental monitoring data before the adjustment time of the adjustment is stable environmental data; if not, the adjustment is regarded as a continuous adjustment of the previous adjustment; the adjustment difference between two adjacent adjustments in the continuous adjustment is regarded as a continuous adjustment feature; Based on the user adjustment data, the environment monitoring data, the continuous adjustment characteristics, the user distribution characteristics, the perception delay, and the action delay, the user preference data of at least one time period is determined through a preference model, wherein the preference model is a machine learning model; the perception delay refers to the time required for the environment in the space to make the user feel uncomfortable; the action delay refers to the time required for the user to perceive the discomfort and the user to actually make adjustments; Corresponding control parameters are determined based on the user preference data to control the environment control components.

8. A device control device, characterized in that: The apparatus comprises at least one processor and at least one memory; The at least one memory is used to store computer instructions; The at least one processor is used to execute at least part of the computer instructions to implement the device control method as described in claim 7.

9. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer executes the device control method as claimed in claim 7.

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