PMV-PPD model-based temperature control method, system and device and medium

Abstract

The embodiment of the invention provides a temperature control method, system and device based on a PMV-PPD model and a medium, and belongs to the technical field of smart home. The method comprises the following steps: acquiring environmental parameters, user thermal characteristic parameters and a temperature control device preset temperature regulation starting temperature for starting a heating mode or a refrigeration mode; through a PMV-PPD model, the temperature of a heat neutralization point is determined according to the ASHRAE standard, temperature adjustment comfortable temperatures are determined according to the environment parameters, the user heat characteristic parameters and the temperature adjustment starting temperature, and the temperature adjustment comfortable temperatures are different in the heating mode or the refrigerating mode; determining a target temperature difference between the temperature adjustment comfortable temperature and the heat neutralization point temperature, and calculating and determining a target temperature according to the temperature adjustment comfortable temperature and the heat neutralization point temperature based on the target temperature difference; and controlling the temperature control device to adjust the indoor temperature according to the target temperature. The embodiment of the invention aims to improve the accuracy of indoor temperature regulation and control.

Description

Technical Field

[0001] This application relates to the field of smart home technology, and in particular to a temperature control method, system, device and medium based on the PMV-PPD model. Background Technology

[0002] In related technologies, with the development of IoT and smart home technologies, people's requirements for indoor environmental comfort are constantly increasing. Currently, the temperature control strategies for indoor temperature control devices such as air conditioners are often based on fixed temperature settings or monotonous feedback control to achieve indoor temperature regulation. This method of regulation relies too much on human experience and lacks adaptability to actual application scenarios and different user needs, affecting the temperature control effect and user experience of the device.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this application is to propose a temperature control method, system, device, and medium based on the PMV-PPD model, aiming to improve the accuracy of indoor temperature control.

[0005] To achieve the above objectives, one aspect of this application proposes a temperature control method based on the PMV-PPD model, the method comprising: The system acquires environmental parameters, user thermal characteristic parameters, and the temperature control device's start-up temperature. The temperature control device is equipped with at least two working modes: a heating mode and a cooling mode. The temperature control start-up temperature is a preset temperature for starting the heating mode or the cooling mode. The thermal neutralization point temperature is determined using the PMV-PPD model according to the ASHRAE standard, and the temperature adjustment comfort temperature is determined based on the environmental parameters, the user thermal characteristic parameters, and the temperature adjustment start-up temperature. The temperature adjustment comfort temperature is different in the heating mode and the cooling mode. Determine the target temperature difference between the temperature adjustment comfort temperature and the thermal neutralization point temperature, and calculate and determine the target temperature based on the target temperature difference, according to the temperature adjustment comfort temperature and the thermal neutralization point temperature; The temperature control device adjusts the indoor temperature according to the target temperature.

[0006] In some embodiments, the step of calculating and determining the target temperature based on the target temperature difference, according to the temperature-regulating comfort temperature and the thermal neutralization point temperature, includes: In response to the target temperature difference being less than or equal to a first temperature threshold, the target temperature is determined based on the temperature adjustment comfort temperature; In response to the target temperature difference being greater than the first temperature threshold, the target temperature is determined based on a preset coefficient, the temperature adjustment comfort temperature, and the thermal neutralization point temperature.

[0007] In some embodiments, the temperature adjustment comfort temperature includes a heating comfort temperature and a cooling comfort temperature. After determining the target temperature difference between the temperature adjustment comfort temperature and the thermal neutrality point temperature, the method further includes: Acquire radar detection data, and determine the personnel occupancy status based on the radar detection data and the personnel occupancy prediction algorithm; In response to the current personnel occupancy status being that there are personnel indoors, the working mode is determined according to the environmental parameters, and according to the working mode, the target temperature is calculated and determined based on the target temperature difference, the temperature adjustment comfort temperature, and the thermal neutralization point temperature; In response to the current occupancy status being that there are no people in the room, the working mode is switched to leisure mode; in leisure mode, the target temperature is determined according to preset conditions, and the step of controlling the temperature control device to adjust the indoor temperature according to the target temperature is executed until the occupancy status is switched to that there are people in the room; wherein, the preset conditions include the target temperature approaching the outdoor temperature in the environmental parameters and falling within the range formed by the heating comfort temperature and the cooling comfort temperature.

[0008] In some embodiments, the environmental parameters include indoor temperature, and determining the operating mode based on the environmental parameters includes: In response to the indoor temperature being less than a first difference between the heating comfort temperature and the target temperature, the operating mode is switched to the heating mode; In response to the indoor temperature being greater than a first sum of the differences between the cooling comfort temperature and the target temperature, the operating mode is controlled to switch to the cooling mode.

[0009] In some embodiments, determining the personnel occupancy status based on the radar detection data and the personnel occupancy prediction algorithm includes: Each natural day is divided into a preset number of time slices, and the characteristic probability of each time slice is determined based on the radar detection data. The characteristic probability is the probability value of whether a person appears in the corresponding time slice. A habit model is established based on the characteristic probabilities of multiple natural days, and the habit model is iteratively updated as the natural days change, in conjunction with a preset historical decay factor. Continuous feature modeling is performed based on the time slices preceding the current moment and their corresponding feature probabilities to obtain the continuous occupancy factor; The fusion probability is calculated and determined based on the preset weights, the habit model, the continuous occupancy factor, and the feature probability of the current time slice. A daily occupancy status table is generated based on the fusion probability and the habit model, and the occupancy status of the personnel is determined through the daily occupancy status table.

[0010] In some embodiments, generating a daily occupancy status table based on the fusion probability and the habit model includes: The upper and lower boundaries of long-term occupancy rates are determined based on the characteristic probabilities of the target time slices. The target time slices include time slices of the same period in multiple historical natural days. The upper and lower boundaries of long-term occupancy rates represent probability intervals formed based on the average characteristic probabilities of the target time slices. In response to the fusion probability being greater than or equal to the upper boundary of the long-term occupancy rate, the personnel occupancy status of the current time slice is determined to be that there are people indoors; in response to the fusion probability being less than or equal to the lower boundary of the long-term occupancy rate, the personnel occupancy status of the current time slice is determined to be that there are no people indoors; in response to the fusion probability being greater than the lower boundary of the long-term occupancy rate and less than the upper boundary of the long-term occupancy rate, the personnel occupancy status of the current time slice is determined based on the previous time slice. The daily occupancy status table is generated based on the current time slot's occupancy status and the future time slot's occupancy status determined by the habit model.

[0011] In some embodiments, the environmental parameters include indoor temperature, and the method further includes: Based on the 24-hour occupancy status table, the target time period in which people are present indoors is determined as the occupancy status. Based on the target time period, a pre-start-stop strategy is fitted in advance by linear regression according to the difference between the indoor temperature and the target temperature. The pre-start-stop strategy is used to pre-start the temperature control device to adjust the temperature so that the indoor temperature reaches the target temperature when the target time period begins. The temperature control device adjusts the indoor temperature according to the target temperature by executing the pre-start / stop strategy.

[0012] To achieve the above objectives, another aspect of this application proposes a temperature control system based on the PMV-PPD model, the system comprising: The information collection module is used to acquire environmental parameters, user thermal characteristic parameters, and the temperature control device's temperature adjustment start temperature. The temperature control device is equipped with at least two working modes: a heating mode and a cooling mode. The temperature adjustment start temperature is a preset temperature for activating the heating mode or the cooling mode. The model processing module is used to determine the thermal neutral point temperature according to the ASHRAE standard using the PMV-PPD model, and to determine the temperature adjustment comfort temperature according to the environmental parameters, the user thermal characteristic parameters and the temperature adjustment start temperature. The temperature adjustment comfort temperature is different in the heating mode and the cooling mode. The temperature decision module is used to determine the target temperature difference between the temperature adjustment comfort temperature and the thermal neutralization point temperature, and to calculate and determine the target temperature based on the target temperature difference, according to the temperature adjustment comfort temperature and the thermal neutralization point temperature. The temperature control module is used to control the temperature control device to adjust the indoor temperature according to the target temperature.

[0013] To achieve the above objectives, another aspect of this application provides a control device, which includes a memory and a processor. The memory stores a code program, and the processor executes the code program to implement the above-described method.

[0014] To achieve the above objectives, another aspect of the embodiments of this application proposes a readable storage medium storing a code program that, when executed by a processor, implements the above-described method.

[0015] The embodiments of this application include at least the following beneficial effects: This application provides a temperature control method, system, device, and medium based on the PMV-PPD model. This scheme obtains environmental parameters, user thermal characteristic parameters, and the temperature adjustment start-up temperature of the temperature control device. The start-up temperature determines the preset start-up temperature for the heating or cooling mode of the temperature control device. Then, using the PMV-PPD model, the thermal neutralization point temperature is determined according to the ASHRAE standard, and combined with the start-up temperature, the comfortable temperature is determined, confirming the temperature that feels comfortable to the user in either cooling or heating mode. The target temperature difference between the comfortable temperature and the thermal neutralization point temperature is then calculated. Based on this target temperature difference, the final suitable target temperature is calculated according to the comfortable temperature and the thermal neutralization point temperature, allowing for indoor temperature adjustment. Compared to indoor temperature adjustment based on fixed temperature settings or monotonous feedback control, this application's method combines the PMV-PPD model to determine the user's comfortable temperature, and also incorporates the start-up temperature as a user decision factor. Finally, it comprehensively considers multiple dimensions of factors, including user decision-making, environment, user thermal characteristics, and user comfort, to determine the target temperature, thus improving the accuracy of indoor temperature control. Attached Figure Description

[0016] Figure 1 This is a flowchart of a temperature control method based on the PMV-PPD model provided in an embodiment of this application; Figure 2 yes Figure 1A flowchart of another embodiment in step S103; Figure 3 yes Figure 1 Partial flowchart of step S103 in the process; Figure 4 yes Figure 2 Partial flowchart of step S202 in the process; Figure 5 yes Figure 2 A partial flowchart of step S201 in the process; Figure 6 This is a flowchart of yet another embodiment of a temperature control method based on the PMV-PPD model provided in this application; Figure 7 This is a schematic diagram of the structure of a temperature control system based on the PMV-PPD model provided in an embodiment of this application; Figure 8 This is a schematic diagram of the hardware structure of the control device provided in the embodiments of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0018] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0019] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0021] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0022] 1) The PMV-PPD model is a comprehensive evaluation model for human thermal comfort, used to quantitatively assess the impact of the thermal environment on human thermal comfort. It includes two parts: PMV (Predicted Mean Vote) and PPD (Predicted Percentage of Dissatisfied). PMV quantifies the average thermal sensation of the human body to the thermal environment, including various thermal sensations such as slightly cold, comfortable, and slightly warm, and uses voting values ​​to quantify the differences between these sensations. PPD quantifies the proportion of people who feel uncomfortable with the current thermal environment, expressed as a percentage.

[0023] 2) ASHRAE standards are a system of technical specifications for the heating, ventilation, air conditioning and refrigeration fields developed by the American Society of Heating, Refrigeration and Air Conditioning Engineers (ASHRAE).

[0024] In related technologies, with the development of IoT and smart home technologies, people's requirements for indoor environmental comfort are constantly increasing. Currently, the temperature control strategies for indoor temperature control devices such as air conditioners are often based on fixed temperature settings or monotonous feedback control to achieve indoor temperature regulation. This method of regulation relies too much on human experience and lacks adaptability to actual application scenarios and different user needs, affecting the temperature control effect and user experience of the device.

[0025] In view of this, this application provides a temperature control method, system, device, and medium based on the PMV-PPD model. This solution obtains environmental parameters, user thermal characteristic parameters, and the temperature control device's start-up temperature. The start-up temperature determines the preset start-up temperature for the heating or cooling mode of the temperature control device. Then, using the PMV-PPD model, the thermal neutralization point temperature is determined according to the ASHRAE standard, and combined with the start-up temperature, a comfortable temperature is determined, confirming the temperature that feels comfortable to the user in either cooling or heating mode. The target temperature difference between the comfortable temperature and the thermal neutralization point temperature is then calculated. Based on this target temperature difference, the final suitable target temperature is calculated using both the comfortable temperature and the thermal neutralization point temperature, allowing for indoor temperature adjustment. Compared to indoor temperature adjustment based on fixed temperature settings or monotonous feedback control, this method combines the PMV-PPD model to determine the user's comfortable temperature, incorporating the start-up temperature as a user decision factor. Ultimately, it comprehensively considers multiple dimensions of user decision-making, environment, user thermal characteristics, and user comfort to determine the target temperature, improving the accuracy of indoor temperature control.

[0026] The temperature control method based on the PMV-PPD model provided in this application relates to the field of smart home technology. This temperature control method based on the PMV-PPD model can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or in-vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the temperature control method based on the PMV-PPD model, but is not limited to the above forms.

[0027] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0028] Figure 1 This is an optional flowchart of the temperature control method based on the PMV-PPD model provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S104.

[0029] Step S101: Obtain environmental parameters, user thermal characteristic parameters, and the temperature control device's start-up temperature. The temperature control device is equipped with at least two working modes: heating mode and cooling mode. The temperature control start-up temperature is the preset temperature for starting the heating mode or cooling mode.

[0030] The method described in this embodiment is used for intelligent temperature control within a target indoor environment. To perform calculations using the PMV-PPD model, it is necessary to first obtain environmental parameters, user thermal characteristic parameters, and the temperature control activation temperature.

[0031] Environmental parameters are collected by a sensor system installed within the target indoor environment, including data such as indoor temperature and humidity. User thermal characteristic parameters are determined based on user-inputted data, including data such as human metabolic rate (estimated by combining data such as age, height, and weight) and clothing thermal resistance (determined based on clothing type), used in thermal perception calculations within the PMV-PPD model. The temperature control start-up temperature is a setting parameter of the temperature control device in the target indoor environment, accessible via the Internet of Things (IoT) of smart homes; this temperature control device is a smart home product such as an air conditioner, supporting the function of automatically starting cooling or heating modes based on indoor temperature; the temperature control start-up temperature is the temperature value set by this temperature control device to intelligently start the heating mode or the cooling mode.

[0032] Step S102: Using the PMV-PPD model, determine the thermal neutralization point temperature according to the ASHRAE standard, and determine the temperature adjustment comfort temperature according to environmental parameters, user thermal characteristic parameters, and temperature adjustment start-up temperature. The temperature adjustment comfort temperature is different in heating mode and cooling mode.

[0033] The temperature control start-up temperature reflects the user's decision regarding temperature regulation, representing the desired indoor temperature at which cooling or heating is required. Specifically, the PMV-PPD model can be represented as shown in Table 1 below.

[0034] Table 1 PMV-PPD Model

[0035] The temperature difference between different thermal sensations is quantified using PMV=0, i.e., the most comfortable temperature, as the benchmark. This model is calculated by establishing a human body thermal balance equation, which can be abstractly represented as shown in equation (1): (1) in, For air temperature, The mean radiation temperature. air velocity, Relative humidity, For the thermal resistance of clothing, This refers to the human metabolic rate. By inputting the above six data points, the corresponding PMV value and PPD can be calculated; conversely, if the target is predetermined to be a certain PMV value, let... If it is an unknown term, the temperature value corresponding to a certain PMV value can be deduced by combining the Brent algorithm.

[0036] Based on this, according to the environmental parameters, user thermal characteristic parameters, and temperature control start-up temperature obtained in step S101, the temperature control comfort temperature can be calculated using the PMV-PPD model. The temperature control comfort temperature represents the temperature at which the user feels most comfortable thermally after temperature control in heating or cooling mode. Let... To determine the temperature adjustment start-up temperature, the corresponding PMV value is calculated. It can be understood that this PMV value is generally not equal to zero. Therefore, with the goal of correcting it to zero, another temperature value is calculated in reverse, thus obtaining the aforementioned temperature adjustment comfort temperature. Furthermore, the temperature adjustment comfort temperature has different values ​​for both cooling and heating modes, which correspond to the temperature adjustment start-up temperature for each mode. This will not be elaborated further in this embodiment; it can be assumed that the temperature adjustment comfort temperature and temperature adjustment start-up temperature are described based on the same operating mode.

[0037] On the other hand, the thermal neutralization point temperature is the temperature at which the user feels most comfortable in the target indoor environment. Based on the standard settings of several other data in the ASHRAE standard, with PMV=0 as the target, the thermal neutralization point temperature can be calculated by reverse calculation.

[0038] Step S103: Determine the target temperature difference between the temperature adjustment comfort temperature and the thermal neutralization point temperature, and calculate and determine the target temperature based on the target temperature difference, according to the temperature adjustment comfort temperature and the thermal neutralization point temperature.

[0039] The temperature adjustment comfort temperature and the thermal neutralization point temperature obtained in step S102 can both represent the user's most comfortable thermal sensation to a certain extent. Therefore, the difference between the two temperature values ​​can be further determined and defined as the target temperature difference. Then, by combining this target temperature difference, the final target temperature is fitted based on the temperature adjustment comfort temperature and the thermal neutralization point temperature.

[0040] refer to Figure 2 In step S103 of some embodiments, after determining the target temperature difference between the temperature-adjusting comfort temperature and the thermal neutralization point temperature, the method further includes: Step S201: Obtain radar detection data and determine the personnel occupancy status based on the radar detection data and the personnel occupancy prediction algorithm.

[0041] In step S202, in response to the current personnel occupancy status being that there are personnel indoors, the working mode is determined according to environmental parameters, and according to the working mode, the target temperature is calculated and determined based on the target temperature difference, the temperature adjustment comfort temperature, and the thermal neutralization point temperature.

[0042] Step S203: In response to the current occupancy status being that there are no people in the room, the control working mode is switched to leisure mode; in leisure mode, the target temperature is determined according to preset conditions, and the step of controlling the temperature control device to adjust the indoor temperature according to the target temperature is executed until the occupancy status is switched to that there are people in the room.

[0043] Figure 2 Steps S202 and S203 are parallel steps.

[0044] This embodiment requires a prerequisite for temperature control in the target indoor environment: the presence of people within the environment. Controlling the temperature in the absence of people would result in wasted energy. Therefore, the presence of people is detected using infrared sensors or other sensors in the sensor system, and the detected data is defined as radar detection data. Simultaneously, a personnel occupancy prediction algorithm is used to analyze users' habits of spending time in the target indoor environment, improving the accuracy of determining personnel occupancy status.

[0045] The occupancy status includes two states: the presence of people in the room and the absence of people in the room. When it is confirmed that people are in the room, the system determines whether to start the cooling mode or the heating mode based on the environmental parameters, and performs subsequent temperature control based on the determined mode. When it is confirmed that people are not in the room, the system controls the temperature control device to switch to leisure mode.

[0046] In Leisure Mode, the temperature control strategy aims to bring the indoor temperature close to the outdoor temperature. Simultaneously, to achieve a comfortable thermal sensation, limitations are set based on the desired temperature comfort level. Specifically, the desired temperature comfort level is defined as the cooling comfort temperature for cooling mode and the heating comfort temperature for heating mode. The target temperature is set to fall within the temperature range formed by these two comfort levels while remaining as close as possible to the outdoor temperature. This makes it easier to adjust the indoor temperature to the desired comfort level when switching back from Leisure Mode to cooling or heating mode, thus improving the temperature control effect.

[0047] In other embodiments, the analysis can also incorporate lighting conditions. Environmental parameters may also include indoor lighting data collected by a sensor system, as changes in indoor lighting vary over time. Therefore, depending on whether the indoor lighting meets preset lighting conditions, different compensation amounts can be set to adjust the range formed by the aforementioned cooling comfort temperature and heating comfort temperature, enabling the temperature control device to provide a more suitable temperature regulation effect for indoor environments without people in leisure mode.

[0048] refer to Figure 3 In step S103 of some embodiments, the step of calculating and determining the target temperature based on the temperature-adjusting comfort temperature and the thermal neutralization point temperature includes: Step S301: In response to the target temperature difference being less than or equal to the first temperature threshold, the target temperature is determined based on the temperature adjustment comfort temperature.

[0049] Step S302: In response to the target temperature difference being greater than the first temperature threshold, the target temperature is determined based on the preset coefficient, the temperature adjustment comfort temperature, and the thermal neutralization point temperature.

[0050] Figure 3 Steps S301 and S302 are parallel steps.

[0051] Regarding the difference between the temperature adjustment comfort temperature and the thermal neutrality point temperature, the magnitude of the target temperature difference represents the degree of difference between the current situation and the ideal target. Both should be combined, and the temperature adjustment comfort temperature, which represents the current situation, should be appropriately favored when determining the final target temperature.

[0052] Optionally, when the target temperature difference is less than or equal to a first temperature threshold, for example... or When the difference between the two is small, the temperature for comfortable temperature adjustment can be directly selected as the target temperature. However, when the target temperature difference exceeds the first temperature threshold, the difference already exists in the thermal perception difference of the PMV-PPD model. Setting preset coefficients allows for different preset coefficients to be set for the temperature for comfortable temperature adjustment and the thermal neutralization point temperature, reflecting the preference for the temperature for comfortable temperature adjustment. The target temperature is determined by calculating the sum of the products of the two and the preset coefficients, thus achieving a fitting to obtain the target temperature. For cases where the target temperature difference exceeds the first temperature threshold, a second temperature threshold can be further set, which is greater than the first temperature threshold. For example, it could be set as follows: or When the target temperature difference is less than or equal to the second temperature threshold, or greater than the second temperature threshold, different preset coefficients are selected respectively. The larger the target temperature difference, the more inclined it is to determine the target temperature based on the average temperature of the temperature adjustment comfort temperature and the thermal neutralization point temperature.

[0053] Furthermore, if the target temperature calculation results in a floating-point number, it can be rounded up or down depending on the operating mode. For example, in heating mode, the calculated target temperature is rounded down, while in cooling mode, it is rounded up.

[0054] By setting different fitting temperature and thermal neutralization point temperatures based on the magnitude of the target temperature difference, the degree of fit between the target temperature and the user's thermal sensation is improved, thereby enhancing the accuracy of temperature control.

[0055] refer to Figure 4 In step S202 of some embodiments, determining the operating mode based on environmental parameters includes: In step S401, in response to the indoor temperature being less than the first difference between the heating comfort temperature and the target temperature, the control mode is switched to heating mode.

[0056] In step S402, in response to the indoor temperature being greater than the first sum of the differences between the cooling comfort temperature and the target temperature, the control mode is switched to cooling mode.

[0057] Figure 4 Steps S401 and S402 are parallel steps.

[0058] To improve the overall indoor temperature experience, this embodiment proposes a temperature standard for switching the temperature control device to heating or cooling mode. For heating mode, a first difference between the comfortable heating temperature and the target temperature is calculated. When the detected indoor temperature is less than this first difference, the control switches to heating mode. For cooling mode, a first sum of the differences between the comfortable cooling temperature and the target temperature is calculated. When the detected indoor temperature is greater than this first sum, the control switches to cooling mode.

[0059] It should be noted that the temperature standard for switching to heating or cooling mode mentioned above applies to scenarios where the working mode is automatically switched after the temperature control device is started; in other embodiments, it can also replace the temperature adjustment start temperature, and the temperature value for starting cooling or heating mode can be re-determined based on the first difference and the first sum.

[0060] In addition, a standard can be set for switching from cooling mode or heating mode to idle mode. Specifically, for heating mode, a second sum of the target temperature and the difference between the target temperature and the target temperature is calculated. When the detected indoor temperature reaches this second sum, the control switches to idle mode. For cooling mode, a second difference between the target temperature and the target temperature is calculated. When the detected indoor temperature reaches this second difference, the control switches to idle mode.

[0061] By setting conditions for the temperature control device to automatically switch between cooling and heating modes, it can intelligently regulate the temperature of the target indoor environment based on the actual indoor temperature, thereby improving the accuracy of indoor temperature control.

[0062] Step S104: Control the temperature control device to adjust the indoor temperature according to the target temperature.

[0063] Based on the working mode and target temperature determined by the above embodiments and steps, the temperature control device can be controlled to adjust the indoor temperature to the target temperature, so that the indoor temperature approaches the target temperature until the target temperature is reached, and then maintains the indoor temperature at the target temperature.

[0064] In addition, the temperature control device may also include a fan speed setting, which also affects the effectiveness of room temperature control. Specifically, when the temperature control device is in cooling or heating mode, the fan speed should be set to high; when the temperature control device is in idle mode, the fan speed should be set to medium; and when the temperature control device is in energy-saving mode, the fan speed should be set to low. The above only lists the four operating modes of a typical temperature control device. If other operating modes are also provided, the high, medium, or low fan speed settings can be used as a reference for the three scenarios described above.

[0065] Steps S101 to S104 as shown in the embodiments of this application determine the temperature that the user feels comfortable by combining the PMV-PPD model. The temperature adjustment start temperature is included as a user decision factor. Finally, the target temperature is determined by comprehensively considering multiple factors such as user decision, environment, user thermal characteristics and user comfort, thereby improving the accuracy of indoor temperature control.

[0066] refer to Figure 5 In step S201 of some embodiments, determining the personnel occupancy status based on radar detection data and a personnel occupancy prediction algorithm includes: Step S501: Divide each natural day into a preset number of time slices, and determine the characteristic probability of each time slice based on radar detection data. The characteristic probability is the probability value of whether a person appears in the corresponding time slice.

[0067] Step S502: Establish a habit model based on the characteristic probabilities of multiple natural days, and iteratively update the habit model as the natural days change, in conjunction with a preset historical decay factor.

[0068] Step S503: Perform continuous feature modeling based on the time slices before the current time and the corresponding feature probabilities to obtain the continuous occupancy factor.

[0069] Step S504: Calculate and determine the fusion probability based on the preset weights, habit model, continuous occupancy factor, and the feature probability of the current time slice.

[0070] Step S505: Generate a full-day occupancy status table based on the fusion probability and habit model, and determine the occupancy status of personnel through the full-day occupancy status table.

[0071] To improve the accuracy of determining occupancy status, this study combines radar detection data obtained through sensors with occupancy prediction algorithms to analyze the current indoor detection results and users' habits of being in the target indoor environment at different times.

[0072] Regarding the personnel occupancy prediction algorithm, specifically, each natural day is first divided into a preset number of time slices, each with the same duration. Within each time slice, the sensor performs multiple detections according to a set detection cycle, and the feature probability is determined based on the ratio of the number of times personnel are detected to the total number of detections. Each time slice has its corresponding feature probability. Then, a habit model is established based on the natural day and the corresponding feature probability for each natural day. This habit model is used to extrapolate habits from historical radar detection data. Simultaneously, to adapt to changes in user habits, a historical attenuation factor is set; using natural days as separators, the further away the data is from the current natural day, the smaller the historical attenuation factor, thereby reducing the impact of outdated user habits. Furthermore, two sets of habit models can be set for weekdays and holidays respectively. These two models are dynamically updated as the date changes, depending on whether the current natural day is a weekday or a holiday; for example, if the current day is Monday, the habit model from Friday is used for iterative updates; if the current day is Saturday, the habit model from the previous Sunday is used for iterative updates.

[0073] In addition to lightning detection data and habitual models, this embodiment also considers continuity features. Continuity feature modeling is performed based on multiple consecutive time slices prior to the current moment and their corresponding feature probabilities to obtain the continuity occupancy factor, as shown in equations (2) and (3).

[0074] (2) (3) in, The number of time slices to backtrack; For weight parameters, For specific weight values, the values ​​decay exponentially. Representative before The occupancy flag of each time slice; if the characteristic probability of the time slice is greater than zero, its occupancy flag can be determined to be 1, and if the characteristic probability of the time slice is not greater than zero, its occupancy flag can be determined to be 0.

[0075] By setting preset weights for the feature probabilities of the habit model, continuous occupancy factor, and current time slice, and combining these weights to calculate the fusion probability that integrates historical habits, continuous features, and current detection results, a full-day occupancy status table is generated based on the fusion probability and habit model. This table determines the occupancy status of people in each time slice of the current day, improving the accuracy of occupancy status prediction and facilitating advance planning of temperature control schemes, thus enhancing the effectiveness of temperature control.

[0076] In step S505 of some embodiments, generating a full-day occupancy status table based on the fusion probability and habit model includes: The upper and lower boundaries of long-term occupancy rates are determined based on the characteristic probabilities of the target time slices. The target time slices include time slices of the same period in multiple historical natural days. The upper and lower boundaries of long-term occupancy rates represent probability intervals formed based on the average characteristic probabilities of the target time slices.

[0077] In response to a fusion probability greater than or equal to the upper boundary of the long-term occupancy rate, the personnel occupancy status of the current time slice is determined to be that there are people indoors; in response to a fusion probability less than or equal to the lower boundary of the long-term occupancy rate, the personnel occupancy status of the current time slice is determined to be that there are no people indoors; in response to a fusion probability greater than the lower boundary of the long-term occupancy rate and less than the upper boundary of the long-term occupancy rate, the personnel occupancy status of the current time slice is determined based on the previous time slice.

[0078] Based on the current time slot's occupancy status and the future time slot's occupancy status determined by the habit model, generate a full-day occupancy status table.

[0079] In addition to the aforementioned conventional model, long-term occupancy rate boundary conditions can be set separately to avoid misjudgments caused by temporary fluctuations. These long-term occupancy rate boundary conditions include an upper boundary and a lower boundary.

[0080] For any given time slice, a target time slice is defined as a time slice that falls within the same period in historical natural days. The probability interval is formed by calculating the average characteristic probability of these target time slices and combining it with the fluctuation range (e.g., adding or subtracting the standard deviation of the characteristic probability from the average characteristic probability). The lower limit of this probability interval is the lower boundary of the long-term occupancy rate, and the upper limit is the upper boundary of the long-term occupancy rate.

[0081] When the fusion probability is greater than or equal to the upper boundary of the long-term occupancy rate, the occupancy status is determined to be that there are people indoors; when the fusion probability is less than or equal to the lower boundary of the long-term occupancy rate, the occupancy status is determined to be that there are no people indoors; when the fusion probability is greater than the lower boundary of the long-term occupancy rate but less than the upper boundary, if there were people indoors in the previous time slice, it is determined to be that there are people indoors; if there were no people indoors in the previous time slice, it is determined to be that there are no people indoors. The case where the fusion probability is greater than the lower boundary of the long-term occupancy rate but less than the upper boundary is a fuzzy result, and misjudgment can be avoided by using the judgment from the previous time slice.

[0082] It should be noted that the occupancy status table includes the occupancy status of people in every time slot of the current calendar day. However, only the current time slot (and the same applies to time slots that have already passed in the day) can be combined with the detected radar detection data to determine the fusion probability and the occupancy status of people in combination with the long-term occupancy rate boundary conditions. For those future time slots of the day that have not yet been experienced, the occupancy status of people in the future is first predicted based on the habit model in order to plan the room temperature control strategy in advance. When the actual time arrives, the fusion probability is determined to finally confirm the occupancy status of people in the future.

[0083] By setting long-term occupancy boundary conditions, misjudgments caused by temporary fluctuations can be avoided, thereby improving the accuracy of personnel occupancy prediction.

[0084] refer to Figure 6 In some embodiments, the method further includes: Step S601: Determine the target time period in which personnel occupancy status is determined based on the all-day occupancy status table.

[0085] Step S602: Based on the target time period, a pre-start-stop strategy is fitted in advance by linear regression according to the difference between the indoor temperature and the target temperature. The pre-start-stop strategy is used to pre-start the temperature control device to adjust the temperature so that the indoor temperature reaches the target temperature when the target time period begins.

[0086] Step S603: The pre-start-stop strategy is used to control the temperature control device to adjust the indoor temperature according to the target temperature.

[0087] Based on the occupancy status table, the temperature control device can be controlled to execute different temperature regulation strategies. Referring to the above embodiment, the temperature control device only operates in cooling or heating mode when there are people in the room. The set of time slices in which people are present in the room is defined as the target time period. However, the most ideal temperature regulation effect is that when the user returns to the target indoor environment, the environment has already adjusted the indoor temperature to the target temperature, and when the user is about to leave the target indoor environment, the temperature regulation can be stopped in advance to ensure that the indoor temperature remains near the target temperature for the remaining time until the user leaves, without needing to continue consuming energy to maintain the temperature.

[0088] Based on this, this embodiment proposes a pre-start / stop strategy for the temperature control device. According to this pre-start / stop strategy, the target time period is first determined. Before the target time period is reached, the temperature control device is started in advance, and the steps mentioned above, such as confirming the working mode, target temperature difference, and target temperature, are executed, thereby controlling the temperature control device to adjust the temperature in advance. The advance time can be determined in advance by linear regression fitting based on the difference between the indoor temperature and the target temperature in the test scenario. The same applies to the advance shutdown time.

[0089] By employing a pre-start / stop strategy, the temperature control effect can be improved while maintaining energy costs, thereby providing users with a better temperature control experience.

[0090] The following is a detailed description and explanation of the solutions in the embodiments of the present invention, using specific application examples: In this embodiment of the application, a temperature control method based on the PMV-PPD model is provided, which can be applied to the intelligent temperature control of smart home indoor environments.

[0091] The system acquires environmental parameters, user thermal characteristic parameters, and the temperature control device's start-up temperature. The temperature control device has at least two operating modes: heating mode and cooling mode. The start-up temperature is the preset temperature for starting the heating or cooling mode. Environmental parameters include indoor temperature.

[0092] The thermal neutralization point temperature is determined using the PMV-PPD model according to the ASHRAE standard, and the temperature adjustment comfort temperature is determined based on environmental parameters, user thermal characteristic parameters, and temperature adjustment start-up temperature. The temperature adjustment comfort temperature includes heating comfort temperature and cooling comfort temperature.

[0093] The system determines the target temperature difference between the heating comfort temperature or cooling comfort temperature and the thermal neutralization point temperature; acquires radar detection data, and determines the occupancy status based on the radar detection data and a occupancy prediction algorithm; when the current occupancy status indicates the presence of people indoors, it determines the operating mode (heating or cooling) based on environmental parameters; when the current occupancy status indicates the absence of people indoors, it switches the operating mode to leisure mode. In leisure mode, the system determines the target temperature based on preset conditions and controls the temperature control device to adjust the indoor temperature according to this target temperature until the occupancy status changes to the presence of people indoors; the preset conditions include the target temperature approaching the outdoor temperature in the environmental parameters and falling within the range formed by the heating comfort temperature and cooling comfort temperature. In cooling or heating mode, the system compares the target temperature difference with a first temperature threshold; when the target temperature difference is less than or equal to the first temperature threshold, it determines the target temperature based on the temperature adjustment comfort temperature; when the target temperature difference is greater than the first temperature threshold, it determines the target temperature based on a preset coefficient, the temperature adjustment comfort temperature, and the thermal neutralization point temperature.

[0094] On the other hand, the operating mode is related to the indoor temperature, the heating comfort temperature, the cooling comfort temperature, and the target temperature difference. Specifically, when the indoor temperature is less than the first difference between the heating comfort temperature and the target temperature, the operating mode is switched to heating mode; when the indoor temperature is greater than the first sum of the cooling comfort temperature and the target temperature difference, the operating mode is switched to cooling mode.

[0095] The indoor temperature is adjusted by controlling the temperature control device according to the determined target temperature.

[0096] Furthermore, the determination of personnel occupancy status includes: dividing each natural day into a preset number of time slices, and determining the characteristic probability of each time slice based on radar detection data. The characteristic probability is the probability value of whether personnel appear in the corresponding time slice; establishing a habit model based on the characteristic probabilities of multiple natural days, and iteratively updating the habit model as natural days change by combining a preset historical decay factor; performing continuous feature modeling based on the time slices before the current moment and the corresponding characteristic probabilities to obtain a continuous occupancy factor; and calculating and determining the fusion probability based on preset weights, the habit model, the continuous occupancy factor, and the characteristic probability of the current time slice.

[0097] Based on this, time slices that coincide with the current time slice across multiple historical natural days are identified as target time slices. The upper and lower boundaries of long-term occupancy rates are determined based on the average characteristic probability of the target time slices, forming a probability interval. When the fusion probability is greater than or equal to the upper boundary of the long-term occupancy rate, the occupancy status of the current time slice is determined to be "indoors with people present." When the fusion probability is less than or equal to the lower boundary of the long-term occupancy rate, the occupancy status of the current time slice is determined to be "no people present indoors." When the fusion probability is greater than the lower boundary of the long-term occupancy rate but less than the upper boundary, the occupancy status of the current time slice is determined based on the previous time slice. Then, combining the occupancy status of the current time slice with the occupancy status of future time slices determined by the habit model, a full-day occupancy status table is generated.

[0098] Based on the occupancy status table for the whole day, the target time period when there are people in the room can be determined. For the target time period, a pre-start-stop strategy is fitted in advance by linear regression based on the difference between the indoor temperature and the target temperature. The pre-start-stop strategy is used to pre-start the temperature control device to adjust the temperature so that the indoor temperature reaches the target temperature when the target time period begins. The temperature control device is controlled by the pre-start-stop strategy to adjust the indoor temperature according to the target temperature, so as to achieve the control effect of pre-start-stop.

[0099] This application embodiment determines the temperature that users feel comfortable by combining the PMV-PPD model, incorporating the temperature adjustment start temperature as a user decision factor, and finally determining the target temperature by comprehensively considering multiple dimensions of user decision, environment, user thermal characteristics and user comfort, thereby improving the accuracy of indoor temperature control.

[0100] Please see Figure 7 This application also provides a temperature control system based on the PMV-PPD model, which can implement the above method. The system includes: The information collection module is used to acquire environmental parameters, user thermal characteristic parameters, and the temperature control device's temperature adjustment start temperature. The temperature control device is set with at least two working modes: heating mode and cooling mode. The temperature adjustment start temperature is the preset temperature for starting the heating mode or cooling mode. The model processing module is used to determine the thermal neutral point temperature according to the ASHRAE standard using the PMV-PPD model, and to determine the temperature adjustment comfort temperature according to environmental parameters, user thermal characteristic parameters and temperature adjustment start temperature. The temperature adjustment comfort temperature is different in heating mode and cooling mode. The temperature decision module is used to determine the target temperature difference between the temperature adjustment comfort temperature and the thermal neutralization point temperature, and to calculate and determine the target temperature based on the target temperature difference, according to the temperature adjustment comfort temperature and the thermal neutralization point temperature. The temperature control module is used to control the temperature control device to adjust the indoor temperature according to the target temperature.

[0101] It is understood that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0102] This application also provides a control device, which includes a memory and a processor. The memory stores code programs, and the processor executes the code programs to implement the above-described method. This control device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0103] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0104] Please see Figure 8 , Figure 8 The hardware structure of a control device according to another embodiment is illustrated. The control device includes: The processor 801 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 802 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 802 and is called and executed by the processor 801 using the methods described in the embodiments of this application. The 803 input / output interface is used to implement information input and output. The communication interface 804 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 805 transmits information between various components of the device (e.g., processor 801, memory 802, input / output interface 803, and communication interface 804); The processor 801, memory 802, input / output interface 803, and communication interface 804 are connected to each other within the device via bus 805.

[0105] This application also provides a readable storage medium storing code that, when executed by a processor, implements the above-described method.

[0106] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0107] Memory, as a non-transitory readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0108] The temperature control method, system, device, and medium based on the PMV-PPD model provided in this application obtain environmental parameters, user thermal characteristic parameters, and the temperature control device's start-up temperature. The start-up temperature determines the preset start-up temperature for the heating or cooling mode of the temperature control device. Then, using the PMV-PPD model, the thermal neutralization point temperature is determined according to the ASHRAE standard, and combined with the start-up temperature, a comfortable temperature is determined, confirming the temperature at which the user feels comfortable in either cooling or heating mode. The target temperature difference between the comfortable temperature and the thermal neutralization point temperature is then calculated. Based on this target temperature difference, the final suitable target temperature is calculated using both the comfortable temperature and the thermal neutralization point temperature, allowing for indoor temperature adjustment. Compared to indoor temperature adjustment based on fixed temperature settings or monotonous feedback control, this method combines the PMV-PPD model to determine the user's comfortable temperature, incorporating the start-up temperature as a user decision factor. Ultimately, it comprehensively considers multiple dimensions of factors, including user decision-making, environment, user thermal characteristics, and user comfort, to determine the target temperature, thus improving the accuracy of indoor temperature control.

[0109] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0110] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0111] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0112] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0113] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0114] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0115] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0116] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0117] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0118] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0119] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A temperature control method based on the PMV-PPD model, characterized by, The method comprises the following steps: obtaining an environmental parameter, a user thermal characteristic parameter, and a temperature control device temperature control starting temperature, the temperature control device being provided with at least two working modes of a heating mode and a cooling mode, and the temperature control starting temperature being a temperature preset to start the heating mode or the cooling mode; determining a thermal neutral point temperature according to an ASHRAE standard through a PMV-PPD model, and determining a temperature control comfort temperature according to the environmental parameter, the user thermal characteristic parameter, and the temperature control starting temperature, the temperature control comfort temperature being different in the heating mode or the cooling mode; determining a target temperature difference between the temperature control comfort temperature and the thermal neutral point temperature, and calculating a target temperature according to the temperature control comfort temperature and the thermal neutral point temperature based on the target temperature difference; controlling the temperature control device to adjust an indoor temperature according to the target temperature.

2. The method of claim 1, wherein, The calculating a target temperature according to the temperature control comfort temperature and the thermal neutral point temperature based on the target temperature difference comprises: in response to the target temperature difference being less than or equal to a first temperature threshold, determining the target temperature according to the temperature control comfort temperature; in response to the target temperature difference being greater than the first temperature threshold, determining the target temperature according to a preset coefficient, the temperature control comfort temperature, and the thermal neutral point temperature.

3. The method of claim 1, wherein, The temperature control comfort temperature comprises a heating comfort temperature and a cooling comfort temperature, and after the determining a target temperature difference between the temperature control comfort temperature and the thermal neutral point temperature, the method further comprises: obtaining radar detection data, and determining a personnel occupancy state according to the radar detection data and a personnel occupancy prediction algorithm; in response to the current personnel occupancy state being that there is a person in the room, determining the working mode according to the environmental parameter, and performing the calculating a target temperature according to the temperature control comfort temperature and the thermal neutral point temperature based on the target temperature difference according to the working mode; in response to the current personnel occupancy state being that there is no person in the room, controlling the working mode to switch to a leisure mode; in the leisure mode, determining the target temperature according to a preset condition, and performing the controlling the temperature control device to adjust the indoor temperature according to the target temperature until the personnel occupancy state switches to that there is a person in the room; wherein the preset condition comprises that the target temperature approaches an outdoor temperature in the environmental parameter and falls within an interval formed by the heating comfort temperature and the cooling comfort temperature.

4. The method of claim 3, wherein, The environmental parameter comprises an indoor temperature, and the determining the working mode according to the environmental parameter comprises: in response to the indoor temperature being less than a first difference value of the heating comfort temperature and the target temperature difference, controlling the working mode to switch to the heating mode; in response to the indoor temperature being greater than a first sum value of the cooling comfort temperature and the target temperature difference, controlling the working mode to switch to the cooling mode.

5. The method of claim 3, wherein, The determining a personnel occupancy state according to the radar detection data and the personnel occupancy prediction algorithm comprises: dividing each natural day into a preset number of time slices, and determining a feature probability of each of the time slices according to the radar detection data, the feature probability being a probability value of whether a person appears in the corresponding time slice; establishing a habit model according to the feature probabilities of multiple natural days, and iteratively updating the habit model with the change of natural days in combination with a preset historical decay factor; performing continuity feature modeling according to a time slice before a current time and a corresponding feature probability to obtain a continuous occupancy factor; calculating and determining a fusion probability according to a preset weight, the habit model, the continuous occupancy factor, and a feature probability of a time slice at the current time; generating a whole-day occupancy state table according to the fusion probability and the habit model, and determining the occupancy state of the person through the whole-day occupancy state table.

6. The method of claim 5, wherein, The generating of the whole-day occupancy state table according to the fusion probability and the habit model comprises: determining a long-term occupancy rate upper bound and a long-term occupancy rate lower bound according to a feature probability of a target time slice, wherein the target time slice comprises time slices in the same period in multiple historical natural days, and the long-term occupancy rate upper bound and the long-term occupancy rate lower bound represent a probability interval formed according to an average feature probability of the target time slice; in response to the fusion probability being greater than or equal to the long-term occupancy rate upper bound, determining that the occupancy state of the person in the time slice at the current time is indoor presence; in response to the fusion probability being less than or equal to the long-term occupancy rate lower bound, determining that the occupancy state of the person in the time slice at the current time is indoor absence; and in response to the fusion probability being greater than the long-term occupancy rate lower bound and less than the long-term occupancy rate upper bound, determining the occupancy state of the person in the time slice at the current time according to a previous time slice; generating the whole-day occupancy state table according to the occupancy state of the person in the time slice at the current time and the occupancy state of the person in future time slices of the day determined according to the habit model.

7. The method of claim 5, wherein, The environmental parameters comprise indoor temperature, and the method further comprises: determining a target time period in which the occupancy state of the person is indoor presence according to the whole-day occupancy state table; based on the target time period, fitting a pre-start-stop strategy according to a difference between the indoor temperature and the target temperature in advance through linear regression, the pre-start-stop strategy being used to pre-start the temperature control device to adjust the temperature so as to make the indoor temperature reach the target temperature when the start point of the target time period is reached; controlling the temperature control device to adjust the indoor temperature according to the target temperature through the pre-start-stop strategy.

8. A temperature control system based on the PMV-PPD model, characterized in that, The system comprises: an information collection module configured to acquire environmental parameters, user thermal characteristic parameters, and a temperature control device temperature adjustment start temperature, the temperature control device being provided with at least two working modes of a heating mode and a cooling mode, and the temperature adjustment start temperature being a temperature for preset starting of the heating mode or the cooling mode; a model processing module configured to determine a thermal neutral point temperature according to ASHRAE standard and a temperature comfort according to the environmental parameters, the user thermal characteristic parameters and the thermostat start temperature by a PMV-PPD model, the temperature comfort being different in the heating mode or the cooling mode respectively; a temperature decision module configured to determine a target temperature difference between the temperature comfort and the thermal neutral point temperature, and calculate a target temperature according to the temperature comfort and the thermal neutral point temperature based on the target temperature difference; a temperature execution module configured to control the temperature control device to adjust indoor temperature according to the target temperature.

9. A control device characterized by comprising: The control device comprises a memory and a processor, the memory stores a code program, and the processor implements the method in any one of claims 1 to 7 when executing the code program.

10. A readable storage medium, the readable storage medium storing a code program, characterized in that, The code program, when executed by the processor, implements the method in any one of claims 1 to 7.

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