Heating control method, device, electronic equipment and system

Abstract

The invention relates to a heating control method, device, electronic equipment and system, and the method comprises the steps: determining target parameters corresponding to all sensors according to parameters collected by a carbon dioxide sensor, a human body infrared sensor and a sonic sensor, so as to generate a to-be-detected target parameter sample; centroids of a plurality of target parameter sample sets are obtained, each target parameter sample set is marked with a room occupation state, and each target parameter sample set is composed of a plurality of historical target parameter samples with the same room occupation state; respectively determining the distance between the to-be-detected target parameter sample and the centroid of each target parameter sample set; according to the distance between the target parameter sample to be detected and the centroid of each target parameter sample set, obtaining the room occupation state marked by the target parameter sample set corresponding to the minimum distance, and taking the room occupation state as the room occupation state corresponding to the target parameter sample to be detected; and according to the room occupation state corresponding to the to-be-measured target parameter, a heat supply system is controlled to adjust heat supply output to the room.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of heating technology, and more specifically, to a heating control method, device, electronic equipment and system. Background Art

[0002] Nearly 40% of the world's energy is consumed in buildings. In my country, the energy consumed by central heating accounts for an increasing proportion of building energy consumption. Although the central heating system has greatly improved people's living comfort, it has also brought huge energy pressure. Therefore, it is particularly important to optimize building heating management and reduce unnecessary energy waste. Among them, the impact of user behavior on building heating demand is an important aspect that cannot be ignored. For example, the user's work and rest habits, indoor temperature setting habits, and the degree of awareness of energy conservation will directly affect the operating time and energy consumption level of the heating equipment.

[0003] Therefore, there is a need to provide a new technical solution for heating control, which can accurately guide the configuration and operation strategy of the heating system by considering user behavior. Summary of the invention

[0004] An object of the present disclosure is to provide a new technical solution for a heating control method.

[0005] According to a first aspect of the present disclosure, there is provided a heating control method, comprising: Acquire parameters collected by sensors arranged in the room, wherein the sensors include a carbon dioxide sensor, a human infrared sensor, and an acoustic wave sensor; According to the parameters collected by each sensor, the target parameters corresponding to each sensor are determined to generate a sample of the target parameters to be measured; Obtaining the centroids of a plurality of target parameter sample sets, wherein each target parameter sample set is annotated with a room occupancy state, and each target parameter sample set is composed of a plurality of historical target parameter samples having the same room occupancy state; Determining the distances between the target parameter samples to be measured and the centroids of each target parameter sample set respectively; According to the distance between the target parameter sample to be measured and the centroid of each target parameter sample set, the room occupancy state marked by the target parameter sample set corresponding to the minimum distance is obtained as the room occupancy state corresponding to the target parameter sample to be measured; According to the room occupancy status corresponding to the target parameter to be measured, the heating system is controlled to adjust the heating output to the room.

[0006] Optionally, the target parameters corresponding to the carbon dioxide sensor are the average carbon dioxide concentration value in a single time window, the standard deviation of the carbon dioxide concentration in a single time window, and the rate of change of the carbon dioxide concentration in a single time window.

[0007] Optionally, the target parameters corresponding to the human infrared sensor are the number of triggering of the human infrared sensor in a single time window, the number of continuous triggering of the human infrared sensor in a single time window, and the time interval between the time corresponding to the most recent triggering of the human infrared sensor in a single time window and the current time.

[0008] Optionally, the target parameters corresponding to the acoustic wave sensor are an acoustic wave energy value within a single time window, a zero-crossing rate value within a single time window, and a maximum amplitude value of the acoustic wave within a single time window.

[0009] Optionally, the method further includes: Obtain multiple historical target parameter samples; Selecting a preset number of historical target parameter samples from the plurality of historical target parameter samples as first centroids; Based on each historical target parameter sample, respectively determine the distance to each first centroid; Determine the first target parameter sample set corresponding to each first centroid according to the distance between each historical target parameter sample and each first centroid; Determine a second centroid corresponding to each first target parameter sample set based on the historical target parameter samples in each first target parameter sample set; Based on each historical target parameter sample, respectively determine the distance to each second mass center; Determine the second target parameter sample set corresponding to each second mass center according to the distance between each historical target parameter sample and each second mass center; Iterate and update according to the above steps until the distance between each centroid determined in this iteration and the corresponding centroid determined in the previous iteration is less than the preset distance, or the number of iterative updates reaches the preset number, so as to obtain multiple target parameter sample sets and the centroid of each target parameter sample set.

[0010] Optionally, selecting a preset number of historical target parameter samples from the plurality of historical target parameter samples as the first centroid includes: Selecting a historical target parameter sample from the plurality of historical target parameter samples as a first initial centroid; Determine the distance between each historical target parameter sample and the first initial centroid; Determine the probability value of each historical target parameter sample as the second initial centroid according to the distance between each historical target parameter sample and the first initial centroid; According to the probability value of each historical target parameter sample as the second initial centroid, the historical target parameter sample corresponding to the maximum probability value is obtained as the second initial centroid; Determine the distance between each historical target parameter sample and the second initial centroid; Determine the probability value of each historical target parameter sample as the third initial mass center according to the distance between each historical target parameter sample and the second initial mass center; According to the probability value of each historical target parameter sample as the third initial centroid, the historical target parameter sample corresponding to the maximum probability value is obtained as the third initial centroid, until a preset number of initial centroids are obtained as the first centroid.

[0011] Optionally, the target parameter sample set marked with room occupied is at least one data set, the target parameter sample set marked with room unoccupied is at least one data set, and the room occupancy status marked in each target parameter sample set is manually marked.

[0012] According to a second aspect of the present disclosure, there is provided a heating control device, comprising: A sensor parameter acquisition module, used to acquire parameters collected by sensors installed in the room, wherein the sensors include a carbon dioxide sensor, a human infrared sensor, and an acoustic wave sensor; A target parameter determination module is used to determine the target parameters corresponding to each sensor according to the parameters collected by each sensor, so as to generate a target parameter sample to be measured; A sample set centroid acquisition module is used to obtain the centroids of multiple target parameter sample sets, wherein each target parameter sample set is marked with a room occupancy status, and each target parameter sample set is composed of multiple historical target parameter samples with the same room occupancy status; A distance determination module, used to respectively determine the distance between the target parameter sample to be measured and the centroid of each target parameter sample set; A room occupancy state determination module, used to obtain the room occupancy state marked by the target parameter sample set corresponding to the minimum distance according to the distance between the target parameter sample to be measured and the centroid of each target parameter sample set, as the room occupancy state corresponding to the target parameter sample to be measured; The control module is used to control the heating system to adjust the heating output to the room according to the room occupancy status corresponding to the target parameter to be measured.

[0013] According to a third aspect of the present disclosure, an electronic device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is used to control the processor to operate so as to execute the method according to any one of the first aspects.

[0014] According to a fourth aspect of the present disclosure, a heating control system is provided, comprising: the electronic device as described in the third aspect, and a carbon dioxide sensor, a human infrared sensor, and a sound wave sensor arranged in a room.

[0015] The heating control method provided by the present invention monitors the occupancy status of the rooms in real time, for example, accurately perceives the actual usage of each room through parameters collected by sensors, and then adjusts the output power of the heating system according to the actual usage of each room, so that the heating supply is reduced when the room is unoccupied or during low usage periods, and a heating service that meets the user's heating needs is provided when the room is occupied or during high usage periods, thereby achieving refined regulation of the heating output, overcoming the problems of overheating and inefficiency of traditional heating systems, and greatly reducing energy consumption. It can greatly improve the energy utilization efficiency of buildings without affecting indoor comfort, and effectively promote the development of green buildings and the realization of energy conservation and emission reduction goals.

[0016] Features and advantages of the embodiments of the present specification will become apparent from the following detailed description of exemplary embodiments of the present specification with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the specification and, together with the description, serve to explain the principles of the embodiments of the specification.

[0018] Figure 1 is a processing flow chart of a heating control method according to an embodiment of the present invention.

[0019] Figure 2 4 is a functional block diagram of a heating control device according to an embodiment of the present invention.

[0020] Figure 3 is a hardware structure block diagram of an electronic device according to an embodiment of the present invention.

[0021] Figure 4 4 is a block diagram of the equipment composition of a heating control system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] Various exemplary embodiments of the present specification will now be described in detail with reference to the accompanying drawings.

[0023] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the embodiments of the present specification and its application or uses.

[0024] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0025] To solve the above technical problems, the heating control method provided by the present invention monitors the occupancy status of the building rooms in real time, for example, accurately perceives the actual usage of each room through the parameters collected by the sensor, and then adjusts the output power of the heating system according to the actual usage of each room, so that the heating supply is reduced when the room is unoccupied or during low usage periods, and heating services that meet the user's heating needs are provided when the room is occupied or during high usage periods, thereby achieving fine-grained adjustment of the heating output, overcoming the problems of overheating and inefficiency of traditional heating systems, and greatly reducing energy consumption. It can greatly improve the energy utilization efficiency of buildings without affecting indoor comfort, and effectively promote the development of green buildings and the realization of energy conservation and emission reduction goals.

[0026] One embodiment of the present invention provides a heating control method. Figure 1 As shown, the heating control method may include the following steps S110 to S140.

[0027] Step S110, acquiring parameters collected by sensors installed in the room, wherein the sensors include a carbon dioxide sensor, a human infrared sensor, and an acoustic wave sensor.

[0028] The carbon dioxide sensor is used to detect the concentration of carbon dioxide in the air in the room. The concentration of carbon dioxide in the air in the room can indirectly reflect the activities of people in the room. Human breathing produces carbon dioxide. When a large number of people gather in a room or people stay in the room for a long time, the concentration of carbon dioxide in the room will gradually increase. The carbon dioxide sensor will continuously measure and record the real-time concentration value of carbon dioxide in the room. By analyzing the continuously measured and recorded real-time concentration value of carbon dioxide, it can be determined whether there are people in the room. For example, within a time window, if the carbon dioxide concentration rises rapidly, it can indirectly reflect that someone has entered the room or the activities are frequent. If the carbon dioxide concentration remains at a low level, it can indirectly reflect that there are no people in the room and it is vacant.

[0029] The human infrared sensor is used to capture signs of human activity using infrared detection. The human infrared sensor is a PIR (Passive Infrared Sensor). The PIR sensor senses infrared radiation emitted by objects in the environment (especially the human body) based on passive infrared detection technology. When a human body moves within the field of view of the PIR sensor, due to the temperature difference between the human body and the background environment, its surface will release infrared rays of a specific wavelength, which will be captured by the PIR sensor and converted into an electrical signal. The PIR sensor outputs a binary signal of "someone" or "no one" based on the electrical signal. When the PIR sensor is triggered once, it means that signs of human activity are detected. When the PIR sensor is not triggered, it means that no signs of human activity are found.

[0030] The acoustic sensor is used to capture sound signals in the environment and use audio processing technology to analyze the sound signals to evaluate the sound activity level in the room to determine whether the room is occupied. The parameters collected by the acoustic sensor include the frequency and amplitude of the sound wave. The sounds collected by the acoustic sensor include human voices, footsteps, and noise. Sudden high-volume events may represent the occurrence of human conversations or other temporary activities. Continuous low-frequency white noise may be noise unrelated to human activities.

[0031] In this embodiment, a space occupancy information collection sensor system is constructed by combining three types of sensors, namely, a carbon dioxide sensor, a human infrared sensor, and an acoustic wave sensor, to provide an accurate and comprehensive data basis for the subsequent determination of space occupancy information.

[0032] Step S120: determining target parameters corresponding to each sensor in a single time window according to the parameters collected by each sensor.

[0033] The target parameters corresponding to the carbon dioxide sensor in a single time window are the average carbon dioxide concentration value in a single time window, the standard deviation of the carbon dioxide concentration in a single time window, and the rate of change of the carbon dioxide concentration in a single time window.

[0034] Based on the parameters collected by the carbon dioxide sensor, the average carbon dioxide concentration value M in a single time window is calculated according to the following calculation formula: CO2 ,

[0035] Where N is the number of samples of carbon dioxide concentration collected by the carbon dioxide sensor in a single time window, C CO2，i is the carbon dioxide concentration collected by the carbon dioxide sensor for the i-th time in a single time window.

[0036] Based on the parameters collected by the carbon dioxide sensor, the standard value S of the carbon dioxide concentration in a single time window is calculated according to the following calculation formula: CO2 ,

[0037] Where N is the number of samples of carbon dioxide concentration collected by the carbon dioxide sensor in a single time window, C CO2，i is the carbon dioxide concentration collected by the carbon dioxide sensor for the i-th time in a single time window, M CO2 is the average carbon dioxide concentration in a single time window.

[0038] Based on the parameters collected by the carbon dioxide sensor, the rate of change R of carbon dioxide concentration collected twice in a single time window is calculated according to the following formula: CO2 ,

[0039] Among them, C CO2，i is the carbon dioxide concentration collected by the carbon dioxide sensor for the i-th time in a single time window, C CO2，i-1 is the carbon dioxide concentration collected by the carbon dioxide sensor for the i-1th time in a single time window, and Δt is the time interval between two adjacent carbon dioxide concentrations collected. Based on a time window, the change rate R of N-1 carbon dioxide concentrations can be obtained according to this formula CO2 , calculate the change rate R of N-1 carbon dioxide concentrations CO2 The mean of the carbon dioxide concentration in a single time window is taken as the rate of change of carbon dioxide concentration in a single time window.

[0040] The target parameters corresponding to the human infrared sensor in a single time window are the number of triggering of the human infrared sensor in a single time window, the number of continuous triggering of the human infrared sensor in a single time window, and the time interval between the time corresponding to the most recent triggering of the human infrared sensor in a single time window and the current time.

[0041] If the human infrared sensor is triggered once, it means that human activity is detected. If the human infrared sensor is not triggered, it means that no human activity is detected.

[0042] The target parameters corresponding to the acoustic wave sensor in a single time window are the acoustic wave energy value in a single time window, the zero-crossing rate value in a single time window, and the maximum amplitude value of the acoustic wave in a single time window.

[0043] Based on the parameters collected by the acoustic wave sensor, the acoustic wave energy value E in a single time window is calculated according to the following formula: sound ,

[0044] Where N is the number of samples of the sound signal collected by the acoustic sensor in a single time window, s i It is the amplitude of the sound signal collected by the acoustic wave sensor for the i-th time in a single time window.

[0045] Based on the parameters collected by the acoustic wave sensor, the zero-crossing rate value Z in a single time window is calculated according to the following formula: sound ,

[0046] Where N is the number of samples of the sound signal collected by the acoustic sensor in a single time window, and f s is the sampling frequency, is the number of times the signal crosses zero.

[0047] The zero-crossing rate refers to the number of times a signal crosses the zero point of the time axis per unit time, reflecting the frequency characteristics of the signal. Assuming there are N sample points in total, for each sample point n=1,2,...,N−1, determine whether the signs (i.e., positive or negative) of the current sample s[n] and the next sample s[n+1] are different. When s[n]*s[n+1]<0, that is, the product of two adjacent samples is negative, it means that the signal has crossed the zero point between these two points.

[0048] Based on the parameters collected by the acoustic wave sensor, the maximum amplitude value P of the acoustic wave in a single time window is calculated according to the following formula: sound ,

[0049] Where N is the number of samples of the sound signal collected by the acoustic sensor in a single time window, s i It is the amplitude of the sound signal collected by the acoustic wave sensor for the i-th time in a single time window.

[0050] Step S130, obtaining the centroids of a plurality of target parameter sample sets, wherein each target parameter sample set is marked with a room occupancy status, and each target parameter sample set is composed of a plurality of historical target parameter samples having the same room occupancy status.

[0051] The centroid of the target parameter sample set is the center point of the target parameter sample set. The centroid of the target parameter sample set is a vector, and each element in the vector is the average value of the same target parameter in each historical target parameter sample.

[0052] The historical target parameter samples are determined based on the historical parameters collected by each sensor. Each historical target parameter sample includes 9 target parameters, namely, the average carbon dioxide concentration value in a single time window, the standard deviation of the carbon dioxide concentration in a single time window, the rate of change of the carbon dioxide concentration in a single time window, the number of triggers of the human infrared sensor in a single time window, the number of continuous triggers of the human infrared sensor in a single time window, the time interval between the time corresponding to the last trigger of the human infrared sensor in a single time window and the current time, the sound wave energy value in a single time window, the zero crossing rate value in a single time window, and the maximum amplitude value of the sound wave in a single time window.

[0053] The room occupancy state is one of occupied and unoccupied. The target parameter sample set corresponding to the occupied room occupancy state is at least one data set, and the target parameter sample set corresponding to the unoccupied room occupancy state is at least one data set.

[0054] The room occupancy status annotated in each target parameter sample set is manually annotated.

[0055] Step S140, respectively determining the distance between the target parameter sample to be measured and the centroid of each target parameter sample set.

[0056] In some embodiments, determining the centroid of each target parameter sample set includes the following steps S201 to S208.

[0057] Step S201, obtaining multiple historical target parameter samples.

[0058] The multiple historical target parameter samples include multiple historical target parameter samples corresponding to a room occupancy status of occupied and multiple historical target parameter samples corresponding to a room occupancy status of unoccupied.

[0059] Step S202: Select a preset number of historical target parameter samples from a plurality of historical target parameter samples as the first centroid.

[0060] The specific value of the preset quantity can be set according to needs.

[0061] In some embodiments, step S202 specifically includes the following steps S2021 to S2027.

[0062] Step S2021, selecting a historical target parameter sample from multiple historical target parameter samples as the first initial centroid.

[0063] The first initial centroid is any sample among a plurality of historical target parameter samples.

[0064] Step S2022, respectively determine the distance between each historical target parameter sample and the first initial centroid.

[0065] Each historical target parameter sample is a vector, and each element in the vector is the above 9 target parameters. The first initial centroid is a vector, and each element in the vector is each target parameter in the selected historical target parameter sample.

[0066] The distance calculation formula between vectors is used to determine the distance between each historical target parameter sample and each first centroid.

[0067] Step S2023, determining the probability value of each historical target parameter sample as the second initial centroid according to the distance between each historical target parameter sample and the first initial centroid.

[0068] According to the following calculation formula, the probability value of each historical target parameter sample as the second initial centroid is calculated:

[0069] Among them, x i is the i-th historical target parameter sample, is the distance between the i-th historical target parameter sample and the first initial centroid.

[0070] Step S2024, based on the probability value of each historical target parameter sample as the second initial centroid, obtain the historical target parameter sample corresponding to the maximum probability value as the second initial centroid.

[0071] Based on the calculation formula corresponding to the above probability value, the probability value of each historical target parameter sample as the second initial centroid can be obtained. From the probability values ​​of each historical target parameter sample as the second initial centroid, the historical target parameter sample corresponding to the maximum probability value is selected as the second initial centroid.

[0072] Step S2025, respectively determine the distance between each historical target parameter sample and the second initial centroid.

[0073] Each historical target parameter sample is a vector, and each element in the vector is the above 9 target parameters. The second initial centroid is a vector, and each element in the vector is each target parameter in the historical target parameter sample corresponding to the above maximum probability value.

[0074] The distance calculation formula between vectors is used to determine the distance between each historical target parameter sample and each second centroid.

[0075] Step S2026, determining the probability value of each historical target parameter sample as the third initial centroid according to the distance between each historical target parameter sample and the second initial centroid.

[0076] The probability values ​​of each historical target parameter sample being used as the third initial centroid can refer to the probability values ​​of each historical target parameter sample being used as the second initial centroid, which will not be elaborated here.

[0077] Step S2027, based on the probability value of each historical target parameter sample as the third initial centroid, obtain the historical target parameter sample corresponding to the maximum probability value as the third initial centroid, until a preset number of initial centroids are obtained as the first centroid.

[0078] After obtaining the probability value of each historical target parameter sample as the third initial centroid, the historical target parameter sample corresponding to the maximum probability value is selected from the probability values ​​of each historical target parameter sample as the third initial centroid as the third initial centroid.

[0079] The method for determining the initial centroid provided in an embodiment of the present invention is based on the principle of selecting the historical target parameter samples corresponding to the maximum probability value as the initial centroid, so that the obtained initial centroids are relatively dispersed. When the initial centroids are dispersed, it helps to quickly complete subsequent centroid iterations.

[0080] Step S203: Based on each historical target parameter sample, determine the distance to each first mass center respectively.

[0081] Each historical target parameter sample is a vector, and each element in the vector is the above 9 target parameters. Each first centroid is a vector, and each element in the vector is each target parameter in the corresponding selected historical target parameter sample.

[0082] The distance calculation formula between vectors is used to determine the distance between each historical target parameter sample and each first centroid.

[0083] Step S204: determining the first target parameter sample set corresponding to each first centroid according to the distance between each historical target parameter sample and each first centroid.

[0084] Based on each historical target parameter sample, the first centroid corresponding to the minimum distance is obtained from the distances between each first centroid. Each historical target parameter sample is assigned to the first target parameter sample set represented by the first centroid corresponding to the minimum distance, so that the first target parameter sample set corresponding to each first centroid can be generated.

[0085] Step S205 , determining the second centroid corresponding to each first target parameter sample set based on the historical target parameter samples in each first target parameter sample set.

[0086] Based on each first target parameter sample set, the average value of the same target parameter in each historical target parameter sample in the set is determined, and the average values ​​corresponding to each target parameter are combined into the second centroid of the corresponding first target parameter sample set.

[0087] Step S206: Based on each historical target parameter sample, determine the distance to each second centroid respectively.

[0088] Each historical target parameter sample is a vector, and each element in the vector is the above-mentioned 9 target parameters. Each second centroid is a vector, and each element in the vector is the average value of the same target parameter in each historical target parameter sample in the corresponding first target parameter sample set. The distance calculation formula between vectors is used to determine the distance between each historical target parameter sample and each second centroid.

[0089] Step S207, determining the second target parameter sample set corresponding to each second centroid according to the distance between each historical target parameter sample and each second centroid.

[0090] Based on each historical target parameter sample, the second centroid corresponding to the minimum distance is obtained from the distances between each second centroid. Each historical target parameter sample is assigned to the first target parameter sample set represented by the second centroid corresponding to the minimum distance, so that the second target parameter sample set corresponding to each second centroid can be generated.

[0091] Step S208, iteratively update according to the above steps until the distance between each centroid determined in this iteration and the corresponding centroid determined in the previous iteration is less than a preset distance, or the number of iterative updates reaches a preset number, to obtain multiple target parameter sample sets and the centroid of each target parameter sample set.

[0092] When the distance between each centroid determined in this iteration and the corresponding centroid determined in the previous iteration is less than a preset distance, it can be determined that the position of each centroid is relatively stable and will not fluctuate beyond a preset range.

[0093] The method for determining the centroid of each target parameter sample set provided by the embodiment of the present invention improves the accuracy of the clustering allocation of each historical target parameter, makes the classification results of each target parameter sample set obtained more accurate, and provides an accurate data basis for the classification of subsequent target parameter samples to be tested.

[0094] Step S150, according to the distance between the target parameter sample to be measured and the centroid of each target parameter sample set, obtain the room occupancy state marked by the target parameter sample set corresponding to the minimum distance as the room occupancy state corresponding to the target parameter sample to be measured.

[0095] Step S160, controlling the heating system to adjust the heating output to the room according to the room occupancy status corresponding to the target parameter to be measured.

[0096] When the room occupancy state corresponding to the target parameter to be measured is occupied, the heating system is controlled to increase the heating output to the room. When the room occupancy state corresponding to the target parameter to be measured is unoccupied, the heating system is controlled to reduce the heating output to the room.

[0097] The heating control method of the present invention is described in detail below with reference to a specific embodiment.

[0098] In this embodiment, a residential unit is selected as the observation object. Four people live in the residential unit, including two office workers who follow a fixed travel pattern and two retired elderly people who have a relatively free pace of life and irregular activity patterns.

[0099] A carbon dioxide sensor, a PIR sensor, and an acoustic wave sensor are provided indoors in the residence provided in this embodiment. The data collected by the carbon dioxide sensor, the PIR sensor, and the acoustic wave sensor are uploaded to an electronic device so that the electronic device executes the heating control method provided in any of the above embodiments. Table 1 below shows the model information of the carbon dioxide sensor, the PIR sensor, and the acoustic wave sensor, and the related information for communicating with the electronic device.

[0100] Table 1

[0101] Table 2 shows target parameters corresponding to carbon dioxide sensors in ten consecutive time windows according to an embodiment of the present invention.

[0102] Table 2

[0103] As shown in Table 1, each time window represents a continuous observation period. In these ten consecutive time windows, M CO2 Showing a fluctuating trend, M CO2 The range from a minimum of 614ppm to a maximum of 693ppm indicates that human factors have an impact on the carbon dioxide concentration in residential areas. CO2 It reflects the fluctuation range of carbon dioxide concentration in each time window, ranging from 8.59 to 13.17 ppm. CO2 It reflects the trend of carbon dioxide concentration over time, and its value ranges from 0.82 to 1.25. Specifically, from time window 1 to 2, M CO2 It dropped by about 21 ppm and then rebounded during time windows 3 to 4, reflecting the complex interaction of environmental factors. CO2 The S value of time window 9 is 13.17, indicating that the fluctuation of carbon dioxide concentration in this time window is quite significant. CO2 The value is low, indicating that the carbon dioxide concentration is relatively stable in this time window. CO2 Higher, but R CO2 is relatively low, indicating that the carbon dioxide concentration is close to equilibrium in this time window. CO2 Both are high, indicating that an increase in carbon dioxide concentration may have occurred in time windows 2 and 4.

[0104] Table 3 shows the target parameters corresponding to the PIR sensor in ten consecutive time windows according to an embodiment of the present invention. As shown in Table 3, the number of triggers of the PIR sensor ranges from 50 triggers in time window 1 to zero triggers in time window 8, indicating a significant difference in human activity within the monitoring area of ​​the PIR sensor. The number of consecutive triggers of the PIR sensor in time window 1 is equal to the number of triggers of the PIR sensor, indicating that human activity is basically uninterrupted in this time window. The number of consecutive triggers of the PIR sensor in time window 3 is 12 times, and the number of triggers of the PIR sensor is 38 times, indicating that human activity is relatively scattered and intermittent in this time window. All target parameters in time window 8 are zero, indicating that the human body is in a silent state in this time window. The time interval between the time corresponding to the most recent trigger in time window 3 and the current time is long, indicating that human activity is interrupted or at a low activity level.

[0105] Table 3

[0106] Table 4 shows target parameters corresponding to ten consecutive time window acoustic wave sensors according to an embodiment of the present invention.

[0107] Table 4

[0108] As shown in Table 4, the mean value of the sound wave energy fluctuated significantly from 30 (time window 1) to 81 (time window 5). The highest value appeared in time window 5, which may be due to the change in the intensity or type of activities in the residence during this period, resulting in a significant increase in the energy output of the sound source. The mean value of the sound wave energy then decreased, indicating that the intensity of the activity decreased or changed to an activity that produced lower sound energy.

[0109] The mean amplitude of the sound waves ranged from 2.8 to 5.4, reflecting the fluctuation of the sound intensity. The trend of the amplitude and the mean energy of the sound waves was roughly parallel.

[0110] The zero-crossing rate varies from 7 to 28 times / minute, indicating that the frequency of sound events fluctuates greatly over time. In particular, the zero-crossing rate reaches the highest in time window 3, which coincides with the higher mean energy and amplitude of the sound wave during this period, suggesting that there may be fast and frequent sound events.

[0111] According to the method provided in any of the above embodiments, the target parameters corresponding to each time window are processed to obtain the occupancy status of the residence corresponding to each time window, and then according to the occupancy status of the residence corresponding to each time window, the heating system is controlled to adjust the heat output to the room.

[0112] This embodiment provides a heating control device for implementing any of the above method embodiments. Figure 2 FIG. 2 shows a structural block diagram of a heating control device according to an embodiment of the present invention. Figure 2 As shown, the heating control device includes a sensor parameter acquisition module 210, a target parameter determination module 220, a sample set centroid acquisition module 230, a distance determination module 240, a room occupancy status determination module 250 and a control module 260.

[0113] The sensor parameter acquisition module 210 is used to acquire parameters collected by sensors installed in the room, and the sensors include carbon dioxide sensors, human infrared sensors and sound wave sensors.

[0114] The target parameter determination module 220 is used to determine the target parameter corresponding to each sensor according to the parameters collected by each sensor, so as to generate a target parameter sample to be measured.

[0115] The sample set centroid acquisition module 230 is used to obtain the centroids of multiple target parameter sample sets, wherein each target parameter sample set is marked with a room occupancy status, and each target parameter sample set is composed of multiple historical target parameter samples with the same room occupancy status.

[0116] The distance determination module 240 is used to respectively determine the distance between the target parameter sample to be measured and the centroid of each target parameter sample set.

[0117] The room occupancy status determination module 250 is used to obtain the room occupancy status marked by the target parameter sample set corresponding to the minimum distance according to the distance between the target parameter sample to be tested and the centroid of each target parameter sample set, as the room occupancy status corresponding to the target parameter sample to be tested.

[0118] The control module 260 is used to control the heating system to adjust the heating output to the room according to the room occupancy status corresponding to the target parameter to be measured.

[0119] In some embodiments, the device further includes a sample set centroid determination module. The sample set centroid determination module is used to obtain multiple historical target parameter samples; select a preset number of historical target parameter samples from the multiple historical target parameter samples as the first centroid; determine the distance between each historical target parameter sample and each first centroid respectively; determine the first target parameter sample set corresponding to each first centroid according to the distance between each historical target parameter sample and each first centroid; determine the second centroid corresponding to each first target parameter sample set based on the historical target parameter samples in each first target parameter sample set; determine the distance between each historical target parameter sample and each second centroid respectively; determine the second target parameter sample set corresponding to each second centroid according to the distance between each historical target parameter sample and each second centroid; iterate and update according to the above steps until the distance between each centroid determined in this iteration and the corresponding centroid determined in the previous iteration is less than the preset distance, or the number of iterative updates reaches the preset number, so as to obtain multiple target parameter sample sets and the centroid of each target parameter sample set.

[0120] In some embodiments, the sample set centroid determination module is also used to select a historical target parameter sample from multiple historical target parameter samples as the first initial centroid; determine the distance between each historical target parameter sample and the first initial centroid; determine the probability value of each historical target parameter sample as the second initial centroid based on the distance between each historical target parameter sample and the first initial centroid; obtain the historical target parameter sample corresponding to the maximum probability value as the second initial centroid based on the probability value of each historical target parameter sample as the second initial centroid; determine the distance between each historical target parameter sample and the second initial centroid; determine the probability value of each historical target parameter sample as the third initial centroid based on the distance between each historical target parameter sample and the second initial centroid; obtain the historical target parameter sample corresponding to the maximum probability value as the third initial centroid based on the probability value of each historical target parameter sample as the third initial centroid, until a preset number of initial centroids are obtained as the first centroid.

[0121] The present invention also provides an electronic device for implementing any of the above method embodiments. Figure 3 FIG. 4 shows a hardware structure block diagram of an electronic device 300 according to an embodiment of the present invention. Figure 3 As shown, the electronic device 300 includes a processor 310 and a memory 320 for storing instructions executable by the processor 310. The processor 310 is configured to implement the method according to any embodiment of the present disclosure when executing the instructions stored in the memory 320.

[0122] The processor 310 is used to execute computer instructions, which can be written in an instruction set of architectures such as x86, Arm, RISC, MIPS, SSE, etc. The memory 320 includes, for example, ROM (read-only memory), RAM (random access memory), non-volatile memory such as a hard disk, etc., which are not limited here.

[0123] The electronic device 300 can be a PC, a workstation, a notebook computer, a server, etc., which is not limited here.

[0124] The present invention also provides a heating control system for implementing any of the above method embodiments. Figure 4 A schematic diagram of the equipment composition of a heating control system according to an embodiment of the present invention is shown.

[0125] according to Figure 4 As shown, the system includes the electronic device provided in the above embodiment, a carbon dioxide sensor, a human infrared sensor and an acoustic wave sensor arranged in the room. The electronic device is used to collect parameters from the carbon dioxide sensor, the human infrared sensor and the acoustic wave sensor arranged in the room.

[0126] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. For the device embodiment, its related parts can be referred to the partial description of the method embodiment.

[0127] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0128] The embodiments of the present specification may be systems, methods and / or computer program products. The computer program product may include a computer-readable storage medium carrying computer instructions for causing a processor to implement various aspects of the embodiments of the present specification.

[0129] A computer-readable storage medium may be a tangible device that can hold and store computer instructions used by a computer instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which computer instructions are stored, and any suitable combination of the above. The computer-readable storage medium used herein is not to be interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0130] The computer instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device through a network layer, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network layer may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network layer adapter card or network layer interface in each computing / processing device receives the computer instructions from the network layer and forwards the computer instructions for storage in a computer-readable storage medium in each computing / processing device.

[0131] The flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to the multiple embodiments of this specification. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a computer instruction, and a part of a module, a program segment or a computer instruction contains one or more executable computer instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of the boxes in the block diagram and / or the flowchart can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that it is equivalent to implement it by hardware, implement it by software, and implement it by combining software and hardware.

[0132] The embodiments of the present specification have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A heating control method, characterized in that: include: Acquire parameters collected by sensors arranged in the room, wherein the sensors include a carbon dioxide sensor, a human infrared sensor, and an acoustic wave sensor; According to the parameters collected by each sensor, the target parameters corresponding to each sensor are determined to generate a sample of the target parameters to be measured; Obtaining the centroids of a plurality of target parameter sample sets, wherein each target parameter sample set is annotated with a room occupancy state, and each target parameter sample set is composed of a plurality of historical target parameter samples having the same room occupancy state; Determining the distances between the target parameter samples to be measured and the centroids of each target parameter sample set respectively; According to the distance between the target parameter sample to be measured and the centroid of each target parameter sample set, the room occupancy state marked by the target parameter sample set corresponding to the minimum distance is obtained as the room occupancy state corresponding to the target parameter sample to be measured; According to the room occupancy status corresponding to the target parameter to be measured, the heating system is controlled to adjust the heating output to the room.

2. The method according to claim 1, characterized in that The target parameters corresponding to the carbon dioxide sensor are the average carbon dioxide concentration value in a single time window, the standard deviation of the carbon dioxide concentration in a single time window, and the rate of change of the carbon dioxide concentration in a single time window.

3. The method according to claim 1, characterized in that The target parameters corresponding to the human infrared sensor are the number of triggering of the human infrared sensor in a single time window, the number of continuous triggering of the human infrared sensor in a single time window, and the time interval between the time corresponding to the most recent triggering of the human infrared sensor in a single time window and the current time.

4. The method according to claim 1, characterized in that: The target parameters corresponding to the acoustic wave sensor are the acoustic wave energy value within a single time window, the zero-crossing rate value within a single time window, and the maximum amplitude value of the acoustic wave within a single time window.

5. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: Obtain multiple historical target parameter samples; Selecting a preset number of historical target parameter samples from the plurality of historical target parameter samples as first centroids; Based on each historical target parameter sample, respectively determine the distance to each first centroid; Determine the first target parameter sample set corresponding to each first centroid according to the distance between each historical target parameter sample and each first centroid; Determine a second centroid corresponding to each first target parameter sample set based on the historical target parameter samples in each first target parameter sample set; Based on each historical target parameter sample, respectively determine the distance to each second mass center; Determine the second target parameter sample set corresponding to each second mass center according to the distance between each historical target parameter sample and each second mass center; Iterate and update according to the above steps until the distance between each centroid determined in this iteration and the corresponding centroid determined in the previous iteration is less than the preset distance, or the number of iterative updates reaches the preset number, so as to obtain multiple target parameter sample sets and the centroid of each target parameter sample set.

6. The method according to claim 5, characterized in that The step of selecting a preset number of historical target parameter samples from the plurality of historical target parameter samples as the first centroid includes: Selecting a historical target parameter sample from the plurality of historical target parameter samples as a first initial centroid; Determine the distance between each historical target parameter sample and the first initial centroid; Determine the probability value of each historical target parameter sample as the second initial centroid according to the distance between each historical target parameter sample and the first initial centroid; According to the probability value of each historical target parameter sample as the second initial centroid, the historical target parameter sample corresponding to the maximum probability value is obtained as the second initial centroid; Determine the distance between each historical target parameter sample and the second initial centroid; Determine the probability value of each historical target parameter sample as the third initial mass center according to the distance between each historical target parameter sample and the second initial mass center; According to the probability value of each historical target parameter sample as the third initial centroid, the historical target parameter sample corresponding to the maximum probability value is obtained as the third initial centroid, until a preset number of initial centroids are obtained as the first centroid.

7. The method according to claim 1, characterized in that There is at least one target parameter sample set marked with occupied rooms, and at least one target parameter sample set marked with unoccupied rooms. The room occupancy status marked in each target parameter sample set is manually marked.

8. A heating control device, characterized in that: include: A sensor parameter acquisition module, used to acquire parameters collected by sensors installed in the room, wherein the sensors include a carbon dioxide sensor, a human infrared sensor, and an acoustic wave sensor; A target parameter determination module is used to determine the target parameters corresponding to each sensor according to the parameters collected by each sensor, so as to generate a target parameter sample to be measured; A sample set centroid acquisition module is used to obtain the centroids of multiple target parameter sample sets, wherein each target parameter sample set is marked with a room occupancy status, and each target parameter sample set is composed of multiple historical target parameter samples with the same room occupancy status; A distance determination module, used to respectively determine the distance between the target parameter sample to be measured and the centroid of each target parameter sample set; A room occupancy state determination module, used to obtain the room occupancy state marked by the target parameter sample set corresponding to the minimum distance according to the distance between the target parameter sample to be measured and the centroid of each target parameter sample set, as the room occupancy state corresponding to the target parameter sample to be measured; The control module is used to control the heating system to adjust the heating output to the room according to the room occupancy status corresponding to the target parameter to be measured.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the computer program is used to control the processor to operate so as to execute the method according to any one of claims 1 to 7.

10. A heating control system, characterized in that: include: The electronic device as claimed in claim 9, and a carbon dioxide sensor, a human infrared sensor and a sound wave sensor arranged in a room.

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