Air conditioner control method, system, device and medium fusing multi-dimensional confidence

Abstract

The embodiment of the application provides a kind of air conditioner control method, system, device and medium of fusion multidimensional confidence, belong to the technical field of smart home.The method comprises: obtaining temperature data, illumination data, personnel occupancy data and preset control schedule;According to the temperature data, determine the working mode and target temperature of air conditioner;According to the temperature data, the illumination data, the personnel occupancy data and the control schedule, respectively determine corresponding confidence vector, the element in the confidence vector is the confidence of each working state of air conditioner;Each confidence vector is weighted and fused, to determine the working state of air conditioner;According to the working state, determine the control strategy of the air conditioner in the working mode, and control the air conditioner to adjust target indoor environment according to the control strategy and the target temperature.The embodiment of the application aims to improve the flexibility and control effect of air conditioner on indoor environment regulation.

Description

Technical Field

[0001] This application relates to the field of smart home technology, and in particular to an air conditioning control method, system, device and medium that integrates multi-dimensional confidence. Background Technology

[0002] In related technologies, with the development of smart home technology, air conditioners, as the main products for regulating the indoor environment, are also equipped with various temperature control modes such as cooling and heating. However, although current air conditioner temperature control strategies determine whether the air conditioner switches between cooling, heating, or other operating modes based on environmental factors, the specific operating mode switched to still follows a fixed strategy for heating or cooling, lacking flexibility and affecting the effectiveness of indoor temperature control.

[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 an air conditioning control method, system, device, and medium that integrates multi-dimensional confidence, aiming to improve the flexibility and effectiveness of air conditioning in regulating the indoor environment.

[0005] To achieve the above objectives, one aspect of this application proposes an air conditioning control method that integrates multi-dimensional confidence levels, the method comprising: Acquire temperature data, light intensity data, personnel occupancy data, and preset control plans; The operating mode and target temperature of the air conditioner are determined based on the temperature data. Based on the temperature data, the illumination data, the personnel occupancy data, and the control plan table, a corresponding confidence vector is determined, and the elements in the confidence vector are the confidence levels of each working state of the air conditioner. The operating state of the air conditioner is determined by weighted fusion of each confidence vector. The control strategy of the air conditioner in the working mode is determined according to the working state, and the air conditioner is controlled to adjust the target indoor environment according to the control strategy and the target temperature.

[0006] In some embodiments, the temperature data includes the current indoor temperature and the air conditioner's temperature adjustment start temperature, and determining the air conditioner's operating mode and target temperature based on the temperature data includes: The thermal neutralization point temperature is determined using the PMV-PPD model according to the ASHRAE standard, and the temperature adjustment start-up temperature is combined with the temperature adjustment start-up temperature to determine the temperature adjustment comfort temperature. A first temperature difference is determined between the temperature adjustment comfort temperature and the thermal neutralization point temperature. Based on the first temperature difference, a target temperature is determined according to the temperature adjustment comfort temperature and the thermal neutralization point temperature. The operating mode is determined according to the temperature adjustment comfort temperature, the first temperature difference, and the current indoor temperature. The operating mode includes at least a cooling mode and a heating mode.

[0007] In some embodiments, determining the corresponding confidence vector based on the temperature data includes: The PMV-PPD model is updated using the target temperature as the most comfortable temperature for the model. Based on the updated PMV-PPD model, a first confidence vector is determined according to the second temperature difference between the target temperature and the current indoor temperature, and the confidence vector corresponding to the temperature data is defined as the first confidence vector.

[0008] In some embodiments, the personnel occupancy data includes the personnel occupancy status of the current period and the personnel occupancy status of the previous period. A second temperature difference is defined as the difference between the target temperature and the current indoor temperature in the temperature data. A corresponding confidence vector is determined based on the personnel occupancy data, including: Based on the current period's personnel occupancy status, the change in the current period's personnel occupancy status relative to the previous period's personnel occupancy status, and the comparison result between the second temperature difference and the temperature activity threshold of the PMV-PPD model, a second confidence vector is determined, and the confidence vector corresponding to the personnel occupancy data is defined as the second confidence vector.

[0009] In some embodiments, the illumination data corresponds to a time slice, where the time slice is a time segment that divides a natural day according to a preset duration. Determining the corresponding confidence vector based on the illumination data includes: Update the illumination data for the current time slice using a moving average; Extract low light threshold, stable low light period, high light threshold, stable high light period, extreme light threshold, and extreme light period from historical light data; In response to the current time slice falling into the low light stable period, the high light stable period, or the extreme light period, a third confidence vector is determined based on the corresponding low light stable period, high light stable period, or extreme light period, and the confidence vector corresponding to the light data is defined as the third confidence vector. In response to the current time slice not falling into the low light stable period, the high light stable period, or the extreme light period, the third confidence vector is determined based on the light data of the current time slice, the low light threshold, the high light threshold, and the extreme light threshold.

[0010] In some embodiments, determining the corresponding confidence vector based on the control plan table includes: Get the current time; Based on the control plan table, a fourth confidence vector is determined according to the current time, mapping the current day and the current time period. The confidence vector corresponding to the control plan table is defined as the fourth confidence vector.

[0011] In some embodiments, the difference between the target temperature and the current indoor temperature in the temperature data is defined as a second temperature difference. The step of determining the control strategy of the air conditioner in the operating mode based on the operating state, and controlling the air conditioner to adjust the target indoor environment based on the control strategy and the target temperature, includes: Determine the dead zone compensation temperature, fan speed setting, power setting, and minimum cycle time based on the described operating status; Using the target temperature as a reference, and the second temperature difference and the dead zone compensation temperature as the change amount, the temperature adjustment range is determined according to the reference and the change amount. The air conditioner is controlled to adjust the target indoor environment according to the target temperature, the fan speed level and the power level, and to keep the current indoor temperature within the temperature adjustment range. After switching to the current working state, the duration of the state is recorded; the current working state is maintained until the duration of the state reaches the minimum cycle period.

[0012] To achieve the above objectives, another aspect of this application proposes an air conditioning control system that integrates multi-dimensional confidence levels, the system comprising: The sensing module is used to acquire temperature data, light intensity data, personnel occupancy data, and preset control plans. The first analysis module is used to determine the air conditioner's operating mode and target temperature based on the temperature data. The second analysis module is used to determine the corresponding confidence vectors based on the temperature data, the illumination data, the personnel occupancy data, and the control plan table, respectively. The elements in the confidence vectors are the confidence levels of each working state of the air conditioner. The third analysis module is used to perform weighted fusion processing on each of the confidence vectors to determine the working state of the air conditioner; The control execution module is used to determine the control strategy of the air conditioner in the working mode according to the working state, and to control the air conditioner to adjust the target indoor environment according to the control strategy and 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 an air conditioning control method, system, device, and medium that integrates multi-dimensional confidence levels. This solution acquires temperature data, illumination data, occupancy data, and a preset control plan. First, it determines the air conditioning's operating mode and target temperature based on the temperature data. Then, it determines the corresponding confidence vectors based on the temperature data, illumination data, occupancy data, and control plan, thereby determining the confidence levels related to each operating state of the air conditioning. Finally, it performs weighted fusion processing on each confidence vector to determine the air conditioning's operating state. This allows for the specific determination of the air conditioning's control strategy under that operating mode, thereby controlling the air conditioning to regulate the target indoor environment based on the control strategy and the target temperature. Compared to heating or cooling according to a fixed strategy under a specific operating mode, this application's solution can further adjust the control strategy under the operating mode based on the operating state. Furthermore, this operating state can combine four dimensions—temperature, illumination, occupancy, and the control plan—to determine the operating state suitable for the current target indoor environment through confidence level fusion, thereby improving the flexibility and effectiveness of the air conditioning in regulating the indoor environment. Attached Figure Description

[0016] Figure 1 This is a flowchart of an air conditioning control method that integrates multi-dimensional confidence levels, provided in an embodiment of this application; Figure 2 yes Figure 1 Flowchart of step S102; Figure 3 yes Figure 1 Flowchart of the first embodiment of step S103; Figure 4 yes Figure 1 Flowchart of the second embodiment of step S103; Figure 5 yes Figure 1 Flowchart of the third embodiment of step S103; Figure 6 yes Figure 1 Flowchart of the fourth embodiment of step S103; Figure 7 yes Figure 1 Flowchart of step S105; Figure 8 This is a schematic diagram of the structure of the air conditioning control system that integrates multi-dimensional confidence levels provided in the embodiments of this application; Figure 9 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 smart home technology, air conditioners, as the main products for regulating the indoor environment, are also equipped with various temperature control modes such as cooling and heating. However, although current air conditioner temperature control strategies determine whether the air conditioner switches between cooling, heating, or other operating modes based on environmental factors, the specific operating mode switched to still follows a fixed strategy for heating or cooling, lacking flexibility and affecting the effectiveness of indoor temperature control.

[0025] In view of this, this application provides an air conditioning control method, system, device, and medium that integrates multi-dimensional confidence levels. This solution acquires temperature data, illumination data, occupancy data, and a preset control schedule. First, it determines the air conditioner's operating mode and target temperature based on the temperature data. Then, it determines the corresponding confidence vectors based on the temperature data, illumination data, occupancy data, and control schedule, thereby determining the confidence levels related to each operating state of the air conditioner. Finally, it performs weighted fusion processing on each confidence vector to determine the air conditioner's operating state. Based on the operating state, it determines the control strategy for the air conditioner in that operating mode, thereby controlling the air conditioner to regulate the target indoor environment according to the control strategy and the target temperature. Compared to heating or cooling according to a fixed strategy in an operating mode, this application's solution can further adjust the control strategy in the operating mode based on the operating state. Furthermore, this operating state can combine four dimensions—temperature, illumination, occupancy, and schedule—to determine the operating state suitable for the current target indoor environment through confidence level fusion, thereby improving the flexibility and effectiveness of the air conditioner in regulating the indoor environment.

[0026] The air conditioning control method integrating multi-dimensional confidence levels provided in this application relates to the field of smart home technology. This method 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 air conditioning control method integrating multi-dimensional confidence levels, 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 air conditioning control method that integrates multi-dimensional confidence levels provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S105.

[0029] Step S101: Obtain temperature data, light intensity data, personnel occupancy data, and a preset control plan. This embodiment's method is used for intelligent control of a target indoor environment, including at least indoor temperature. To fully understand the current state of the target indoor environment and how to control it, it is necessary to first acquire temperature data, light data, occupancy data, and a preset control plan.

[0030] Temperature data, illumination data, and personnel occupancy data include data collected through sensor systems and preset data in the control system. The control plan is a pre-set schedule for the working status in subsequent steps. The preset data can be set by staff or users. Specifically, temperature data represents data related to the temperature of the target indoor or outdoor environment, illumination data represents data related to the illumination conditions of the target indoor or outdoor environment, personnel occupancy data represents data related to the presence of personnel in the target indoor environment, and the control plan is a table of data that sets the working status according to time periods.

[0031] Step S102: Determine the air conditioner's operating mode and target temperature based on the temperature data; First, the operating mode and target temperature of the air conditioner are determined based on temperature data. In this embodiment, the operating mode of the air conditioner is defined as such as cooling mode and heating mode. In addition to cooling mode and heating mode, the operating mode in other embodiments may also include modes such as idle mode. The target temperature is equivalent to the ideal temperature of the target indoor environment determined by the control system after analysis, and the target indoor environment will be controlled with the target temperature as the target.

[0032] refer to Figure 2 In some embodiments, step S102 includes: Step S201: Using the PMV-PPD model, determine the thermal neutralization point temperature according to the ASHRAE standard, and combine it with the temperature control start-up temperature to determine the temperature control comfort temperature. Step S202: Determine the first temperature difference between the temperature adjustment comfort temperature and the thermal neutralization point temperature. Based on the first temperature difference, determine the target temperature according to the temperature adjustment comfort temperature and the thermal neutralization point temperature. Determine the working mode according to the temperature adjustment comfort temperature, the first temperature difference and the current indoor temperature. The working mode includes at least a cooling mode and a heating mode.

[0033] This embodiment uses the PMV-PPD model to determine the operating mode and target temperature. Specifically, to integrate with the PMV-PPD model for analysis, the temperature data includes at least the current indoor temperature and the air conditioner's temperature control activation temperature, which is the critical temperature at which the air conditioner automatically activates heating or cooling mode. In summary, the PMV-PPD model requires analysis of environmental parameters and user thermal characteristics. Environmental parameters are collected by a sensor system installed in the target indoor environment; the current indoor temperature is one such environmental parameter, and other parameters include humidity. User thermal characteristics are determined based on user-input data, including data such as metabolic rate (estimated by combining data such as age, height, and weight) and clothing thermal resistance (determined based on clothing type), which are used in the thermal perception calculations within the PMV-PPD model.

[0034] 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.

[0035] Table 1 PMV-PPD Model

[0036] 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 the value is unknown, the temperature value corresponding to a certain PMV value can be deduced by combining the Brent algorithm. The Brent algorithm is a one-dimensional function zero-point search algorithm. This algorithm adaptively combines the bisection method, secant method, and inverse quadratic interpolation method. When the function performs well, the secant method or inverse quadratic interpolation method is used to speed up convergence. When the iteration may fail, it reverts to the bisection method to ensure convergence.

[0037] Based on this, according to environmental parameters, user thermal characteristic parameters, and temperature control start-up temperature, the comfortable temperature can be calculated using the PMV-PPD model. The comfortable 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 temperature, the corresponding PMV value is calculated. It's understood that this PMV value is generally not zero, hence the user needs to initiate temperature adjustment at the start temperature. The goal is to correct this value to zero, and then calculate another temperature value to obtain the desired comfortable temperature. Furthermore, the comfortable temperature has different values ​​for both cooling and heating modes, which correspond to the start temperature for each mode. This will not be elaborated further in this embodiment; it can be assumed that the description of the comfortable temperature and the start temperature is based on the same operating mode.

[0038] 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.

[0039] Furthermore, the difference between the temperature adjustment comfort temperature and the thermal neutrality point temperature, i.e., the first temperature difference representing the difference between the current situation and the ideal target, should be considered in conjunction with both, with an appropriate bias towards the temperature adjustment comfort temperature representing the current situation when determining the final target temperature. This embodiment sets a first temperature threshold and a second temperature threshold. When the first temperature difference is less than or equal to the first temperature threshold, for example... or When the difference between the two is small, the temperature adjustment comfort temperature can be directly selected as the target temperature. However, when the first temperature difference is greater than the first temperature threshold, the difference between the two already exists in the thermal sensation difference of the PMV-PPD model. At this time, preset coefficients can be introduced to set different preset coefficients for the temperature adjustment comfort temperature and the thermal neutralization point temperature, with the preset coefficient for the temperature adjustment comfort temperature being greater than that for the thermal neutralization point temperature, to reflect the preference for the temperature adjustment comfort temperature. The target temperature is determined by calculating the sum of the products of the two and the preset coefficients, thus achieving the fitting to obtain the target temperature. Furthermore, for the case where the first temperature difference is greater than the first temperature threshold, a second temperature threshold can be further set, which is greater than the first temperature threshold, for example, set as follows: or When the first 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 first temperature difference, the closer the preset coefficients of the temperature adjustment comfort temperature and the thermal neutralization point temperature are to the same half, thus tending to determine the target temperature based on the average temperature of the temperature adjustment comfort temperature and the thermal neutralization point temperature.

[0040] Regarding the operating mode, the temperature adjustment comfort temperature has two settings for different operating modes: heating comfort temperature and cooling comfort temperature. The system is set to operate in heating mode when the current indoor temperature is less than the difference between the heating comfort temperature and a first temperature difference; and in cooling mode when the current indoor temperature is greater than the sum of the cooling comfort temperature and the first temperature difference. If neither of these conditions is met, the air conditioner will not start cooling or heating and will operate in other modes such as standby mode.

[0041] By combining the PMV-PPD model for analysis, and taking the user's thermal sensation as a reference factor, the target temperature and working mode suitable for the user are determined, thereby improving the accuracy of intelligent control of the target indoor environment.

[0042] Step S103: Determine the corresponding confidence vectors based on temperature data, illumination data, personnel occupancy data, and control plan table. The elements in the confidence vectors are the confidence levels of each working state of the air conditioner. After determining the target temperature and operating mode through the above step S102, it is necessary to further determine the operating status. The operating status can be set to various states such as sleep state, energy-saving state, comfort state, and powerful state. The operating status is used to fine-tune the control strategy for different operating modes, mainly cooling mode and heating mode. Different operating states are also equivalent to driving the cooling mode or heating mode with different intensities. Taking the above four operating states as an example, their driving intensity is set as powerful state > comfort state > energy-saving state > sleep state.

[0043] In this application, by introducing confidence level fusion, the target indoor environment is further analyzed based on factors such as temperature, light intensity, occupancy, and schedule to determine the working state that meets the actual needs of users, thereby driving the working mode. The four dimensions of temperature, light intensity, occupancy, and schedule reflect the tendency towards different working states, which are expressed as confidence levels for different working states. For example, the confidence levels for sleep state, energy-saving state, comfort state, and high-power state are determined based on temperature data, and these confidence levels are then used as vector elements to form the confidence level vector corresponding to the temperature data.

[0044] refer to Figure 3 In step S103 of some embodiments, determining the corresponding confidence vector based on the temperature data includes: Step S301: Update the PMV-PPD model using the target temperature as the most comfortable temperature for the PMV-PPD model. Step S302: Based on the updated PMV-PPD model, determine the first confidence vector of the mapping according to the second temperature difference between the target temperature and the current indoor temperature.

[0045] Define the confidence vector corresponding to the temperature data as the first confidence vector.

[0046] Optionally, the first confidence vector can be analyzed around the target temperature determined in step S102. The PMV-PPD model is updated by resetting the target temperature to the most comfortable temperature of the PMV-PPD model (i.e., corresponding to PMV=0); after the PMV-PPD model is updated, the second temperature difference between the target temperature and the current indoor temperature can be calculated, and this second temperature difference can be compared with the model (see Table 1). A comparison was made to confirm the difference in PPD; furthermore, different levels were set for the second temperature difference, for example, a temperature difference less than One level, greater than or equal to And less than For another level, each level is mapped to a preset first confidence vector, and the number of levels is at least the same as the number of working states, thus dividing the system into different first confidence vectors that favor a particular working state. For example, taking the four working states mentioned above as examples, the preset first confidence vectors can be set to include [0.7,0.1,0.1,0.1], [0.1,0.7,0.1,0.1], [0.1,0.1,0.7,0.1], and [0.1,0.1,0.1,0.7], where the four elements in the vector correspond to sleep state, energy-saving state, comfort state, and powerful state in order.

[0047] By combining the PMV-PPD model to determine the first confidence vector corresponding to the temperature data, the tendency of temperature to affect the working state can be determined. This supports the analysis and determination of the air conditioner's working state through confidence fusion, enabling fine-tuning of the control strategy for the working mode and improving the accuracy of intelligent control.

[0048] refer to Figure 4 In step S103 of some embodiments, determining the corresponding confidence vector based on personnel occupancy data includes: Step S401: Determine the current period's personnel occupancy status and the previous period's personnel occupancy status based on the personnel occupancy data; Step S402: Based on the current period's personnel occupancy status, the change in the current period's personnel occupancy status relative to the previous period's personnel occupancy status, and the comparison result between the second temperature difference and the temperature activity threshold of the PMV-PPD model, determine the second confidence vector of the mapping.

[0049] Define the confidence vector corresponding to the personnel occupancy data as the second confidence vector.

[0050] Regarding personnel occupancy data, it can be obtained by extracting the detection signal of the target indoor environment through the infrared sensor in the sensor system; the personnel occupancy status is represented by "0" or "1", which are used to represent two states: no personnel in the room and personnel in the room.

[0051] Personnel occupancy data includes the current period's personnel occupancy status and the previous period's personnel occupancy status. Besides determining the presence of personnel in the target indoor environment based on the current period's personnel occupancy status, the stability of personnel within the target indoor environment also affects the assessment of work status. For example, whether personnel have just entered the room or have been staying indoors for an extended period. Therefore, the personnel occupancy status from the previous period can also be incorporated, and the changes in personnel occupancy status between the two periods can be compared. Furthermore, a second temperature difference can be introduced, and this second temperature difference can be compared with the temperature activity threshold of the PMV-PPD model (…). You can select an appropriate level based on your actual needs. This is compared to further analyze the combined temperature judgment, such as whether the indoor temperature of the target indoor environment has been adequately regulated due to people staying indoors for a long time.

[0052] For example, the current period's personnel occupancy status is defined as 'occupation', and an arrow is used to indicate the change in the current period's personnel occupancy status relative to the previous period's personnel occupancy status. This indicates the second temperature difference. The schematic temperature activity threshold and the second confidence vector corresponding to different personnel occupancy situations can be referred to in the following formula (2): (2) The second confidence vector in the examples above can be modified according to the needs of staff or users, and optional cases can be added or deleted as needed. The four elements in the vector correspond to sleep state, energy-saving state, comfort state, and powerful state in order.

[0053] By analyzing the second confidence vector based on personnel occupancy data, the tendency of different personnel occupancy situations to affect the working state can be determined. This supports the analysis and determination of the air conditioner's working state through confidence fusion, enabling fine-tuning of the control strategy for the working mode and improving the accuracy of intelligent control.

[0054] refer to Figure 5 In step S103 of some embodiments, determining the corresponding confidence vector based on the illumination data includes: Step S501: Update the illumination data of the current time slice by moving average; Step S502: Extract low light threshold, low light stable time period, high light threshold, high light stable time period, extreme light threshold and extreme light time period based on historical light data; Step S503: In response to the current time slice falling into a low light stable period, a high light stable period, or an extreme light period, determine the mapped third confidence vector based on the corresponding low light stable period, high light stable period, or extreme light period, and define the confidence vector corresponding to the light data as the third confidence vector. Step S504: In response to the current time slice not falling into a low-light stable period, a high-light stable period, or an extreme-light period, determine a third confidence vector based on the current time slice's illumination data, low-light threshold, high-light threshold, and extreme-light threshold.

[0055] Figure 5 Steps S503 and S504 are parallel steps.

[0056] Define the confidence vector corresponding to the illumination data as the third confidence vector.

[0057] In this embodiment, each natural day is divided into a first preset number of time slices of equal size according to a preset duration, which are defined as the time slices, and the first preset number is defined as N. Each time slice continues... Minutes; on the other hand, the illumination data corresponds to the time slice, representing the illumination conditions of the target indoor environment within the time period corresponding to that time slice.

[0058] For each time slice, especially the illumination data of the current time slice, the illumination value is updated by moving average, referring to the following formulas (3) and (4): (3) (4) in, To determine the sampling frequency of the light sensor The number of samples is determined by the duration of the current time slice. ; This is the median or final value of the current time slice after being updated by the moving average; To count the number of samples in the current time slice; This is the new sample value.

[0059] On the other hand, historical, i.e., previous natural day records of illumination data are analyzed. Illumination data of each time slice throughout the day are bubble sorted to form a sampling sequence. Then, illumination intensity quantiles are selected in the sampling sequence according to illumination intensity, such as 25% and 75%, or 30% and 80%, etc., which are defined as low illumination quantiles and high illumination quantiles, respectively.

[0060] Specifically, linear interpolation is used to calculate quantiles in the sampled sequence after bubble sorting. Linear interpolation avoids the ladder effect, reduces estimation bias, and makes the interpolated sequence more consistent with the true brightness distribution trend. In addition, there is the following equation (5): (5) in, This represents the degree of dispersion of the illumination in the sampling sequence. For high illumination quantile sites, For low-light quantile sites, This represents the minimum illumination difference, used to avoid a value of 0.

[0061] Then, the sampled sequence is scanned in chronological order according to the stable segment detection conditions. When several consecutive time slices meet the stability conditions, they are identified as stable time segments. The identification conditions include preliminary screening based on low illumination thresholds and high illumination thresholds, and then judging the relative amplitude of consecutive time slices, satisfying the following formula (6): (6) Where S represents a time slice. This represents the relative stability tolerance.

[0062] And the relative coefficient of variation satisfies the following equation (7): (7) in, That is, the relative coefficient of variation. This represents the standard deviation for that time period. To prevent small constants from being divided by zero, The coefficient of variation is an indicator, and it can generally be set to a value such as 0.15.

[0063] Based on whether the median value of the stable time period is significantly lower or higher than the baseline, stable time periods under low illumination and stable time periods under high illumination are distinguished. The baseline can be the median value of the sampling sequence. In addition, in order to eliminate the influence of noise such as short-term occlusion and transient strong light, the length of the stable time period must be greater than or equal to the second preset number of time slices.

[0064] On the other hand, to identify individual time slices with extremely high illumination performance, refer to the following formula (8): (8) in, If the value is a stable outlier in this time slice, it can be identified as an extreme illumination threshold. This is an adjustment factor. When the lighting performance within a time slice exceeds the extreme lighting threshold, it can be identified as an extreme lighting period.

[0065] Then, quantiles and stable periods are fused. The low-light threshold is determined by fusing the illumination data from the identified stable low-light periods with the low-light quantiles, and the high-light threshold is determined by fusing the illumination data from the identified stable high-light periods with the high-light quantiles. It should be noted that extreme light periods and stable high-light periods can be distinguished based on their length. For example, an extreme light period can be defined as consisting of only one time slice; if multiple time slices show consistently high illumination intensity, it is then classified as a stable high-light period.

[0066] By comprehensively analyzing the illumination data of different natural days, we extracted low illumination thresholds, stable low illumination periods, high illumination thresholds, stable high illumination periods, extreme illumination thresholds, and extreme illumination periods. It can be understood that the illumination performance during stable low illumination periods meets the low illumination threshold, the illumination performance during stable high illumination periods meets the high illumination threshold, and the illumination performance during extreme illumination periods meets the extreme illumination threshold. Considering general life scenarios, stable low illumination periods should correspond to users' sleep periods, i.e., when the work state is sleep, nighttime sleep is characterized by no light or low light. Stable high illumination periods should correspond to users' evening activity periods, i.e., when the work state is comfortable, and nighttime activities are illuminated by artificial light. Other time periods may exhibit sudden increases or decreases due to changes in the angle of sunlight, and extreme illumination periods correspond to the midday period of direct sunlight.

[0067] Optionally, a third confidence vector is set for each of the low-light-stable, high-light-stable, and extreme-light-stable periods. For other time periods, a third confidence vector is set for different lighting conditions, such as morning or afternoon, based on the specific light intensity and time period. Therefore, when the current time slice falls within a low-light-stable, high-light-stable, or extreme-light-stable period, the third confidence vector is determined according to the corresponding low-light-stable, high-light-stable, or extreme-light-stable period. When the current time slice does not fall within a low-light-stable, high-light-stable, or extreme-light-stable period, the lighting data of the current time slice is compared with the low-light threshold, high-light threshold, and extreme-light threshold, and the third confidence vector is determined based on the aforementioned mapping relationship.

[0068] By analyzing the third confidence vector based on illumination data, the tendency of different times and different illumination conditions on the working state can be determined. This supports the analysis and determination of the air conditioner's working state through confidence fusion, enabling fine-tuning of the control strategy for the working mode and improving the accuracy of intelligent control.

[0069] refer to Figure 6 In step S103 of some embodiments, determining the corresponding confidence vector based on the control plan table includes: Step S601: Obtain the current time; Step S602: Based on the control plan table, determine the fourth confidence vector mapped to the current day and the current time period according to the current time.

[0070] Define the confidence vector corresponding to the control plan table as the fourth confidence vector.

[0071] Optionally, the control schedule is used by staff or users to specifically set the preferences of different work states for different days of the week and different time periods of each day; the days of the week can be divided according to weekdays and rest days, or specific days of the week; the start and end points of each time period in the day can be customized by the user. For example, taking a rest day as an example, the time period from 7:00 am to 12:00 pm is set as [0,1,0,0], the time period from 12:00 pm to 6:00 pm is set as [0,0,0,1], the time period from 6:00 pm to 11:00 pm is set as [0,0,1,0], and the time period from 11:00 pm to 7:00 am is set as [1,0,0,0]. The four elements in the vector correspond to sleep state, energy-saving state, comfort state, and powerful state in order.

[0072] Based on the control plan, by obtaining the current time and comparing it with the control plan, the fourth confidence vector mapped to the current day and the current time period can be determined according to the current time.

[0073] By analyzing the fourth confidence vector based on the control plan, the tendency of the working state in different time periods can be determined. This supports the analysis and determination of the air conditioner's working state through confidence fusion, allowing for fine-tuning of the control strategy for the working mode and improving the accuracy of intelligent control.

[0074] Step S104: Perform weighted fusion processing on each confidence vector to determine the working status of the air conditioner; Based on confidence vectors determined from temperature data, occupancy data, illumination data, and control plan tables, and combined with confidence weights corresponding to the four data items, a weighted fusion calculation is performed. Specifically, a target vector is calculated by weighting the first confidence vector and first weight, the second confidence vector and second weight, the third confidence vector and third weight, and the fourth confidence vector and fourth weight. This target vector includes four elements corresponding to the four operating states mentioned above, each element representing the confidence probability of the corresponding operating state. The operating state of the air conditioner is determined based on the element with the highest confidence probability.

[0075] Furthermore, the confidence weights can be set to vary according to time periods to further improve the flexibility of work status and control strategies. Specifically, the confidence weights can be configured as a set of vectors corresponding to four confidence vectors, such as [0.4, 0.3, 0.2, 0.1], with an element sum of 1. For different time periods of each natural day, reference priorities can be set for the four work statuses, and their corresponding weight values ​​can be set according to the reference priorities. For example, if the reference priority for the time period from 0:00 to 6:00 is set as Control Plan Table > Light Data > Personnel Occupancy Data > Temperature Data, then the confidence weights are configured as [0.1, 0.2, 0.3, 0.4], with the element order corresponding to the first weight, second weight, third weight, and fourth weight, respectively.

[0076] By using confidence-weighted fusion, the tendency of four dimensions—temperature, occupancy, lighting, and schedule—to influence the working state is determined. This identifies the working state used to fine-tune the control strategy for the working mode, improves the matching degree between the working state and the actual needs of users, and thus enhances the control effect on the indoor environment.

[0077] Step S105: Determine the control strategy of the air conditioner in the working mode according to the working status, and control the air conditioner to adjust the target indoor environment according to the control strategy and the target temperature.

[0078] For each operating mode, the air conditioner has a preset control strategy. This strategy includes the target temperature, fan speed, circulation time, and power level, instructing the air conditioner on how to specifically regulate the target indoor environment. The control strategy is adjusted based on the operating state determined in the above steps to further optimize its regulatory effect.

[0079] refer to Figure 7 In some embodiments, step S105 includes: Step S701: Determine the dead zone compensation temperature, fan speed setting, power setting, and minimum cycle time based on the working status; Step S702: Using the target temperature as a reference and the second temperature difference and dead zone compensation temperature as the change amount, determine the temperature adjustment range according to the reference and the change amount, and control the air conditioner to adjust the target indoor environment according to the target temperature, fan speed and power level, and keep the current indoor temperature within the temperature adjustment range. Step S703: After switching to the current working state, record the duration of the state; maintain the current working state until the duration of the state reaches the minimum cycle period.

[0080] Figure 7 Steps S702 and S703 are parallel steps, both being subsequent steps of step S701.

[0081] If there is no operating status, the air conditioner will adjust the temperature according to the preset control strategy, that is, the default fan speed and power settings of the operating mode. In this embodiment, the fan speed and power settings adapted to the current needs are determined according to the operating status determined in the above steps, as well as the dead zone compensation temperature and minimum cycle period, so as to adjust the control strategy.

[0082] Specifically, the air conditioner will activate the corresponding operating mode based on the temperature adjustment start temperature. After the operating mode is activated, it will adjust the temperature according to the corresponding fan speed and power settings. The fan speed and power settings can be set to two or three levels depending on the air conditioner model. Simultaneously, a temperature adjustment range is determined based on the target temperature, the second temperature difference, and the dead zone compensation temperature, ensuring the current indoor temperature remains within this range. The left boundary of this temperature adjustment range is the difference between the target temperature, the second temperature difference, and the dead zone compensation temperature, while the right boundary is the sum of these three values. To avoid frequent switching between operating modes, which could affect system stability and temperature adjustment effectiveness, a minimum cycle period is set for each operating mode. After switching to the current operating mode, the air conditioner must maintain the current operating mode for at least the minimum cycle period before switching to another operating mode. This is achieved by recording the duration of each mode and comparing it with the minimum cycle period. For example, the minimum cycle period for comfort and powerful modes can be set to 180 seconds, the minimum cycle period for sleep mode to be 240 seconds, and the minimum cycle period for energy-saving mode to be 300 seconds.

[0083] By adjusting the control strategy under the working mode according to the specific working conditions, the fan speed level, power level, temperature range and minimum cycle period under the fixed working mode can be flexibly changed according to the working conditions, thereby improving the flexibility of the temperature control scheme for the target indoor environment.

[0084] This application embodiment further adjusts the control strategy under the working mode according to the working status, and the working status can be determined by combining four dimensions: temperature, light, occupancy and schedule, through confidence fusion, thereby improving the flexibility and control effect of air conditioning in controlling the indoor environment.

[0085] 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, an air conditioning control method integrating multi-dimensional confidence is provided, which is applied to air conditioning equipment for regulating indoor environment.

[0086] Acquire temperature data, light intensity data, personnel occupancy data, and preset control plans.

[0087] Based on the current indoor temperature and the air conditioner's temperature adjustment start-up temperature from the temperature data, the thermal neutralization point temperature is determined according to the ASHRAE standard using the PMV-PPD model, and the temperature adjustment comfort temperature is determined in combination with the temperature adjustment start-up temperature. The first temperature difference between the temperature adjustment comfort temperature and the thermal neutralization point temperature is determined. Based on the first temperature difference, the target temperature is determined according to the temperature adjustment comfort temperature and the thermal neutralization point temperature. The operating mode is determined according to the temperature adjustment comfort temperature, the first temperature difference, and the current indoor temperature. The operating mode includes at least a cooling mode and a heating mode.

[0088] The PMV-PPD model is updated by taking the target temperature as the most comfortable temperature. Based on the updated PMV-PPD model, the first confidence vector is determined according to the second temperature difference between the target temperature and the current indoor temperature. The confidence vector corresponding to the temperature data is defined as the first confidence vector.

[0089] Based on the current period's personnel occupancy status and the previous period's personnel occupancy status in the personnel occupancy data, and according to the current period's personnel occupancy status, the change of the current period's personnel occupancy status relative to the previous period's personnel occupancy status, and the comparison results of the second temperature difference and the temperature activity threshold of the PMV-PPD model, the second confidence vector of the mapping is determined, and the confidence vector corresponding to the personnel occupancy data is defined as the second confidence vector.

[0090] The natural day is divided into multiple time slices based on a preset duration, and these slices are defined as time slices. Illumination data is then correlated with these time slices. Illumination data for the current time slice is updated using a moving average. Low illumination thresholds, stable low illumination periods, high illumination thresholds, and stable high illumination periods are extracted from historical illumination data. If the current time slice falls within a stable low illumination period or a stable high illumination period, a third confidence vector is determined based on that period, and the confidence vector corresponding to the illumination data is defined as the third confidence vector. If the current time slice does not fall within a stable low illumination period or a stable high illumination period, the third confidence vector is determined based on the illumination data of the current time slice, the low illumination threshold, and the high illumination threshold.

[0091] Obtain the current time; based on the control plan table, determine the fourth confidence vector mapped to the current day and the current time period according to the current time, and define the confidence vector corresponding to the control plan table as the fourth confidence vector.

[0092] The first confidence vector, the second confidence vector, the third confidence vector, and the fourth confidence vector are weighted and fused to determine the working status of the air conditioner.

[0093] The dead zone compensation temperature, fan speed, power level, and minimum cycle period are determined based on the operating status. Using the target temperature as a reference and the second temperature difference and dead zone compensation temperature as variables, the temperature adjustment range is determined based on the reference and variables. The air conditioner is controlled to adjust the target indoor environment according to the target temperature, fan speed, and power level, and the current indoor temperature is kept within the temperature adjustment range. After switching to the current operating status, the duration of the status is recorded. The current operating status is maintained until the duration of the status reaches the minimum cycle period.

[0094] This application embodiment further adjusts the control strategy under the working mode according to the working status, and the working status can be determined by combining four dimensions: temperature, light, occupancy and schedule, through confidence fusion, thereby improving the flexibility and control effect of air conditioning in controlling the indoor environment.

[0095] Please see Figure 8 This application also provides an air conditioning control system that integrates multi-dimensional confidence levels, which can implement the above-mentioned method. The system includes: The sensing module is used to acquire temperature data, light intensity data, personnel occupancy data, and preset control plans. The first analysis module is used to determine the air conditioner's operating mode and target temperature based on temperature data; The second analysis module is used to determine the corresponding confidence vectors based on temperature data, light data, personnel occupancy data, and control plan. The elements in the confidence vectors are the confidence levels of each working state of the air conditioner. The third analysis module is used to perform weighted fusion processing on each confidence vector to determine the working status of the air conditioner; The control execution module is used to determine the control strategy of the air conditioner in the working mode according to the working status, and to control the air conditioner to adjust the target indoor environment according to the control strategy and the target temperature.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] Please see Figure 9 , Figure 9 The hardware structure of a control device according to another embodiment is illustrated. The control device includes: The processor 901 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 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 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 902 and is called and executed by the processor 901 using the methods described in the embodiments of this application. The input / output interface 903 is used to implement information input and output; The communication interface 904 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 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904); The processor 901, memory 902, input / output interface 903, and communication interface 904 are interconnected within the device via bus 905.

[0100] This application also provides a readable storage medium storing code that, when executed by a processor, implements the above-described method.

[0101] 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.

[0102] 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.

[0103] The air conditioning control method, system, device, and medium fused with multi-dimensional confidence levels provided in this application embodiment acquire temperature data, illumination data, occupancy data, and a preset control plan. First, it determines the air conditioning's operating mode and target temperature based on the temperature data. Then, it determines the corresponding confidence vectors based on the temperature data, illumination data, occupancy data, and control plan, thereby determining the confidence levels related to each operating state of the air conditioning. Finally, it performs weighted fusion processing on each confidence vector to determine the air conditioning's operating state. Based on the operating state, it determines the specific control strategy for the air conditioning in that operating mode, thereby controlling the air conditioning to regulate the target indoor environment according to the control strategy and target temperature. Compared to heating or cooling according to a fixed strategy in an operating mode, this application's solution can further adjust the control strategy in the operating mode based on the operating state. Furthermore, this operating state can combine four dimensions—temperature, illumination, occupancy, and the plan—to determine the operating state suitable for the current target indoor environment through confidence fusion, thereby improving the flexibility and effectiveness of the air conditioning in regulating the indoor environment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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. An air conditioning control method integrating multi-dimensional confidence, characterized in that, The method includes the following steps: Acquire temperature data, light intensity data, personnel occupancy data, and preset control plans; The operating mode and target temperature of the air conditioner are determined based on the temperature data. Based on the temperature data, the illumination data, the personnel occupancy data, and the control plan table, a corresponding confidence vector is determined, and the elements in the confidence vector are the confidence levels of each working state of the air conditioner. The operating state of the air conditioner is determined by weighted fusion of each confidence vector. The control strategy of the air conditioner in the working mode is determined according to the working state, and the air conditioner is controlled to adjust the target indoor environment according to the control strategy and the target temperature.

2. The method according to claim 1, characterized in that, The temperature data includes the current indoor temperature and the air conditioner's temperature adjustment start temperature. Determining the air conditioner's operating mode and target temperature based on the temperature data includes: The thermal neutralization point temperature is determined using the PMV-PPD model according to the ASHRAE standard, and the temperature adjustment start-up temperature is combined with the temperature adjustment start-up temperature to determine the temperature adjustment comfort temperature. A first temperature difference is determined between the temperature adjustment comfort temperature and the thermal neutralization point temperature. Based on the first temperature difference, a target temperature is determined according to the temperature adjustment comfort temperature and the thermal neutralization point temperature. The operating mode is determined according to the temperature adjustment comfort temperature, the first temperature difference, and the current indoor temperature. The operating mode includes at least a cooling mode and a heating mode.

3. The method according to claim 2, characterized in that, Determine the corresponding confidence vector based on the temperature data, including: The PMV-PPD model is updated using the target temperature as the most comfortable temperature for the model. Based on the updated PMV-PPD model, a first confidence vector is determined according to the second temperature difference between the target temperature and the current indoor temperature, and the confidence vector corresponding to the temperature data is defined as the first confidence vector.

4. The method according to claim 1, characterized in that, The personnel occupancy data includes the current period's personnel occupancy status and the previous period's personnel occupancy status. The difference between the target temperature and the current indoor temperature in the temperature data is defined as the second temperature difference. A corresponding confidence vector is determined based on the personnel occupancy data, including: Based on the current period's personnel occupancy status, the change in the current period's personnel occupancy status relative to the previous period's personnel occupancy status, and the comparison result between the second temperature difference and the temperature activity threshold of the PMV-PPD model, a second confidence vector is determined, and the confidence vector corresponding to the personnel occupancy data is defined as the second confidence vector.

5. The method according to claim 1, characterized in that, The illumination data corresponds to a time slice, which is a time slice that divides a natural day into segments according to a preset duration. A corresponding confidence vector is determined based on the illumination data, including: Update the illumination data for the current time slice using a moving average; Extract low light threshold, stable low light period, high light threshold, stable high light period, extreme light threshold, and extreme light period from historical light data; In response to the current time slice falling into the low light stable period, the high light stable period, or the extreme light period, a third confidence vector is determined based on the corresponding low light stable period, high light stable period, or extreme light period, and the confidence vector corresponding to the light data is defined as the third confidence vector. In response to the current time slice not falling into the low light stable period, the high light stable period, or the extreme light period, the third confidence vector is determined based on the light data of the current time slice, the low light threshold, the high light threshold, and the extreme light threshold.

6. The method according to claim 1, characterized in that, The corresponding confidence vector is determined based on the control plan table, including: Get the current time; Based on the control plan table, a fourth confidence vector is determined according to the current time, mapping the current day and the current time period. The confidence vector corresponding to the control plan table is defined as the fourth confidence vector.

7. The method according to any one of claims 1 to 6, characterized in that, The difference between the target temperature and the current indoor temperature in the temperature data is defined as the second temperature difference. The step of determining the control strategy of the air conditioner in the operating mode based on the operating state, and controlling the air conditioner to adjust the target indoor environment based on the control strategy and the target temperature, includes: Determine the dead zone compensation temperature, fan speed setting, power setting, and minimum cycle time based on the described operating status; Using the target temperature as a reference, and the second temperature difference and the dead zone compensation temperature as the change amount, the temperature adjustment range is determined according to the reference and the change amount. The air conditioner is controlled to adjust the target indoor environment according to the target temperature, the fan speed level and the power level, and to keep the current indoor temperature within the temperature adjustment range. After switching to the current working state, the duration of the state is recorded; the current working state is maintained until the duration of the state reaches the minimum cycle period.

8. An air conditioning control system integrating multi-dimensional confidence, characterized in that, The system includes: The sensing module is used to acquire temperature data, light intensity data, personnel occupancy data, and preset control plans. The first analysis module is used to determine the air conditioner's operating mode and target temperature based on the temperature data. The second analysis module is used to determine the corresponding confidence vectors based on the temperature data, the illumination data, the personnel occupancy data, and the control plan table, respectively. The elements in the confidence vectors are the confidence levels of each working state of the air conditioner. The third analysis module is used to perform weighted fusion processing on each of the confidence vectors to determine the working state of the air conditioner; The control execution module is used to determine the control strategy of the air conditioner in the working mode according to the working state, and to control the air conditioner to adjust the target indoor environment according to the control strategy and the target temperature.

9. A control device, characterized in that, The control device includes a memory and a processor, the memory storing code programs, and the processor executing the code programs to implement the method according to any one of claims 1 to 7.

10. A readable storage medium storing code programs, characterized in that, When the code program is executed by the processor, it implements the method of any one of claims 1 to 7.

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