Electric appliance collaborative management method, system and device and storage medium

Abstract

The embodiment of the invention provides an electric appliance collaborative management method, system and device and a storage medium, and relates to the technical field of solar heating, the method is applied to an electric appliance collaborative management Internet of Things system, and the method comprises the steps that environment sensing data and space sensing data are acquired; on the basis of the environment sensing data and the space sensing data, supply difference data is determined, and the supply difference data represents the difference between the heat demand data of the user and the heat production data of the heat collection device; based on the supply difference data, operating parameters of the auxiliary heating device are determined. By means of the method, intelligent linkage control over the solar heat collector and the auxiliary heating device can be achieved easily.

Description

Technical Field

[0001] The present invention relates to the field of solar heating technology, and in particular to a method, system, device and storage medium for coordinated management of electrical appliances. Background Art

[0002] Solar thermal collectors can convert sunlight into heat energy for heating buildings such as residential and commercial buildings. However, solar thermal collectors are affected by many factors such as light intensity, angle, weather conditions, etc., resulting in unstable heating capacity. For example, in the absence of sufficient light or extreme weather conditions, the thermal collection efficiency of solar thermal collectors drops significantly and may not be able to meet the heating needs of the building. Users have strict requirements for heating comfort, including stable heating temperature and fast heating rate. When the solar energy supply is insufficient, reliable auxiliary heating devices are needed to fill the gap to ensure the overall efficiency of the solar heating system and user comfort.

[0003] In order to solve the above problems, CN102798224B proposed a solar energy and air source heat pump linkage hot water system, which realizes the heating control of solar energy and air source heat pump by comprehensive control of solar system circulation pump, system water supply solenoid valve, air source heat pump and user circulation booster pump. However, the system has a low degree of intelligence and cannot automatically adjust the parameters related to auxiliary heating according to weather conditions, user needs and real-time energy supply and demand to ensure stable and efficient heating of the system.

[0004] Therefore, it is necessary to provide an electrical appliance collaborative management method, system, device and storage medium to help achieve intelligent linkage control of solar collectors and auxiliary heating devices. Summary of the invention

[0005] One or more embodiments of the present specification provide a method for collaborative management of electrical appliances, which is applied to an electrical appliance collaborative management Internet of Things system, and the method includes: acquiring environmental sensor data and spatial sensor data; determining supply difference data based on the environmental sensor data and the spatial sensor data, wherein the supply difference data represents the difference between the user's heat demand data and the heat production data of the thermal collector; and determining operating parameters of an auxiliary heating device based on the supply difference data.

[0006] One or more embodiments of the present specification provide an Internet of Things system for collaborative management of electrical appliances, the Internet of Things system comprising a heat collecting device, an auxiliary heating device, a data acquisition module, a processor and an interaction module; the heat collecting device is configured to collect thermal energy; the auxiliary heating device is configured to perform auxiliary heating when the heat production data of the heat collecting device meets preset requirements; the data acquisition module comprises an environmental sensor and a space sensor, the data acquisition module is configured to monitor environmental sensor data and space sensor data; the interaction module is configured to obtain information input by a user: the processor is communicatively connected with the heat collecting device, the auxiliary heating device, the data acquisition module and the interaction module, the processor is configured to: obtain environmental sensor data and space sensor data; based on the environmental sensor data and the space sensor data, determine supply difference data, the supply difference data representing the difference between the user's heat demand data and the heat production data of the heat collecting device; based on the supply difference data, determine the operating parameters of the auxiliary heating device.

[0007] One or more embodiments of the present specification provide an appliance collaborative management device, the device comprising at least one processor and at least one memory; the at least one memory is used to store computer instructions; the at least one processor is used to execute at least part of the computer instructions to implement the appliance collaborative management method.

[0008] One or more embodiments of the present specification provide a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the method for collaborative management of electrical appliances.

[0009] Some embodiments of the present specification include at least the following beneficial effects: (1) by monitoring changes in environmental and spatial conditions, the operating parameters of the corresponding time period can be flexibly determined, which helps the system adapt to seasonal changes, sudden weather changes, or changes in user behavior patterns (such as users' exercise habits, etc.); (2) effective coordination between heat collection devices (such as solar collectors) and auxiliary heating devices is achieved to make full use of renewable energy while compensating for the instability of renewable energy supply; (3) by determining the operating parameters of future time periods, it can be ensured that the auxiliary heating device can respond to the user's hot water or heating needs in a timely manner, minimize delays, and improve user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0011] Figure 1is a schematic diagram of the structure of an electrical appliance collaborative management Internet of Things system according to some embodiments of this specification;

[0012] Figure 2 is an exemplary flow chart of an electrical appliance collaborative management method according to some embodiments of this specification;

[0013] Figure 3 is an exemplary schematic diagram of an effect prediction model according to some embodiments of this specification;

[0014] Figure 4 It is an exemplary schematic diagram of a perception determination model according to some embodiments of the present specification. DETAILED DESCRIPTION

[0015] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0016] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0017] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprises" and "includes" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0018] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0019] The efficiency of solar collectors is significantly affected by weather conditions, seasonal changes, and geographic location, resulting in unstable heating capacity. For example, on cloudy days or in winter, the heat production capacity of solar collectors will be greatly reduced and may not meet the heating needs of buildings. Auxiliary heating devices can be started when solar energy is insufficient to ensure stable heating. However, the heat required by people in different spaces, different seasons, and different time periods is different, resulting in different operating parameters of the auxiliary heating device. In addition, the user's mood, activity level, and user's perception of temperature fluctuations may also affect the operating parameters of the auxiliary heating device, which requires dynamic adjustment to improve the effect of coordinated control. At present, the auxiliary heating device uses unified operating parameters when it is turned on, such as unified power, heating time, etc., resulting in excessive auxiliary heating when solar energy is sufficient, resulting in energy waste; and, waiting until the heat is insufficient to start auxiliary heating may cause unstable room temperature.

[0020] In view of this, some embodiments of the present specification provide a method, system, device and storage medium for collaborative management of electrical appliances, which take into account indoor / outdoor environmental data, determine the differences in heat production data of solar collectors and user heat demand data in different time periods, and then control the operation of the auxiliary heating device; at the same time, take into account the user's tolerance for the heating rate or temperature fluctuation range, further control the operation of the auxiliary heating device, and improve energy utilization efficiency and user comfort.

[0021] Figure 1 It is a structural diagram of an electrical appliance collaborative management Internet of Things system according to some embodiments of this specification.

[0022] like Figure 1 As shown, the electrical appliance collaborative management system 100 may include a heat collection device 110 , an auxiliary heating device 120 , a data acquisition module 130 , a processor 150 and an interaction module 140 .

[0023] The appliance collaborative management system provided in some embodiments of this specification can be applied to various scenarios. For example, the appliance collaborative management system 100 can be applied to various places that require heating or heating, such as residences, commercial buildings, offices, swimming pools, hospitals, etc., by automatically adjusting the operating parameters of the auxiliary heating device in the space scene to improve the energy efficiency and comfort of the heating.

[0024] The heat collecting device 110 is a device for collecting and converting natural energy (such as solar energy) into thermal energy. For example, the heat collecting device 110 may include a solar collector (such as a flat plate collector, a vacuum tube collector, etc.).

[0025] In some embodiments, the thermal collector 110 can collect thermal energy.

[0026] In some embodiments, the heat collecting device 110 may be installed on the roof or exterior wall of a building. The building may include but is not limited to a residence, a restaurant, a shopping mall, and the like.

[0027] In some embodiments, the heat collection device 110 may be connected to the processor 150 for periodically or in real time sending information related to the heat collection device 110 to the processor 150, such as the operating status of the heat collection device 110, heat generation data, etc. For more information about heat generation data, see Figure 2 Related description.

[0028] The auxiliary heating device 120 refers to a device for auxiliary heating of a target object. For example, the auxiliary heating device 120 may include an electric heating rod, an electric boiler, an electric heater, etc. The target object may include indoor air, water, or other entities or media that need to be heated.

[0029] In some embodiments, the auxiliary heating device 120 can perform auxiliary heating when the heat generation data meets preset requirements.

[0030] The preset requirement is the judgment condition for evaluating whether to turn on the auxiliary heating device. For example, the preset condition may include that the heat production data in the same time period is lower than the heat demand data. The time period is a preset time range, which can be a system preset value or a system default value. The heat demand data refers to the heat energy required by the user. For more information, see Figure 2 Related description.

[0031] In some embodiments, the auxiliary heating device 120 may be connected to the processor 150 for communication so as to receive a control instruction sent by the processor 150; the auxiliary heating device 120 may perform heating based on the control instruction.

[0032] The data acquisition module 130 can be used to monitor various environmental parameters of the space. Environmental parameters are physical quantities that describe relevant environmental conditions inside / outside the space, such as temperature, humidity, light intensity, etc.

[0033] In some embodiments, the data acquisition module 130 includes environmental sensors and space sensors. Environmental sensors can be deployed outside the space (e.g., the outer wall or roof of the space) to collect environmental sensing data. Environmental sensors can include temperature sensors, light sensors, etc. Environmental sensing data can include the temperature and light intensity outside the space. Space sensors can be deployed inside the space to collect space sensing data. Space sensors can include temperature sensors, humidity sensors, etc. Space sensing data can include the temperature and humidity inside the space.

[0034] In some embodiments, the data acquisition module 130 may also include an image acquisition device (not shown in the figure). The image acquisition device is an optical or electronic imaging device used to capture and record relevant information indoors / outdoors. For example, the image acquisition device may include a camera, an infrared thermal imager, etc. The relevant information includes, but is not limited to, relevant information of indoor personnel (e.g., the number of people indoors, movement status, etc.), relevant information of the outdoor heat collection device 110 (e.g., the shielding condition of the heat collection device 110), etc.

[0035] In some embodiments, the image acquisition device can be installed at any location inside or outside the space. For example, the outdoor image acquisition device can be installed near the heat collection device 110, and the indoor image acquisition device can be installed at a location such as the indoor ceiling.

[0036] In some embodiments, the data acquisition module 130 may also include a voice acquisition device (not shown in the figure). The voice acquisition device is a device for capturing and converting sound wave signals into electronic signals. For example, the voice acquisition device can be used to collect voice data of people in the space. In some embodiments, the voice acquisition device may include a sound card, a voice recorder, an array microphone or other environmental monitoring equipment. The voice acquisition device can be installed at a preset position in the space, for example, in a conference room, classroom, recording studio, live studio and other places, the voice acquisition device can be installed on the ceiling, wall or desktop to capture the sound in the room. In some embodiments, the voice acquisition device can be an independent device or a part of other devices. For example, the voice acquisition device is a built-in microphone on a user terminal or a wearable device.

[0037] In some embodiments, the data acquisition module 130 may be in communication with the processor 150 to periodically or in real time send environmental sensing data, spatial sensing data, image data, voice data, etc. to the processor 150. For more information about the above data, please refer to Figure 2-4 Related description.

[0038] In some embodiments, the appliance collaborative management system 100 may further include an interaction module 140, which is used to interact with the user. In some embodiments, the interaction module 140 may be used to display environmental information such as environmental sensing data and spatial sensing data, system operation status, and obtain user input information. In some embodiments, the interaction module 140 may include input components and output components such as buttons, touch sensors, joysticks, keypads, microphones, and display screens.

[0039] In some embodiments, the interaction module 140 may be an independent device, such as a user terminal, or may be a part of other devices. For example, the interaction module 140 includes a touch screen on the user terminal.

[0040] In some embodiments, the interaction module 140 may be connected in communication with the processor 150 to periodically or in real time send user input information to the processor 150 or obtain environmental information, system operating status, etc. sent by the processor 150.

[0041] The processor 150 refers to a system with computing capabilities, such as a computer, an industrial computer, a computing cloud platform, etc. In some embodiments, the processor 150 may include one or more sub-processors, such as a central processing unit (CPU), a graphics processing unit (GPU), etc., or any combination thereof.

[0042] In some embodiments, the processor 150 can be connected to the heat collection device 110, the auxiliary heating device 120, the data acquisition module 130, the interaction module 140, etc. in the appliance collaborative management system 100 to obtain data and / or information therefrom. The processor 150 can execute program instructions based on these data, information and / or processing results to perform one or more functions described in the embodiments of this specification.

[0043] In some embodiments, the processor 150 may obtain environmental sensing data and spatial sensing data; determine supply difference data based on the environmental sensing data and spatial sensing data; and determine operating parameters of the auxiliary heating device 120 based on the supply difference data. For more information, please refer to Figure 2-Figure 4 Related description.

[0044] In some embodiments, the electrical appliance collaborative management system 100 may include a network and / or other components for connecting the system with external resources. The processor 150 may obtain data and / or information related to the electrical appliance collaborative management system 100 through the network.

[0045] In some embodiments, the appliance collaborative management system 100 may further include a heat storage device ( Figure 1 (not shown). A heat storage device refers to a device capable of storing thermal energy. For example, a heat storage device may include a hot water storage tank, a phase change material heat storage device, etc.

[0046] In some embodiments, when the heat generation data (ie, the heat energy collected by the heat collection device) exceeds the demand data for the corresponding time period (ie, the estimated demand for heat energy), the excess heat is absorbed and stored by the heat storage device.

[0047] In some embodiments, the heat storage device may be in communication with the processor 150, and the heat storage device may send energy storage data in the heat storage device to the processor 150 in real time or periodically. The energy storage data refers to the total amount of heat stored in the energy storage device. For example, the energy storage data may include the amount of energy stored in the energy storage device at each time.

[0048] In some embodiments, the appliance collaborative management system 100 may further include a storage module (not shown in the figure), which may be used to store data, instructions and / or any other information. For example, the storage module may store environmental sensing data, spatial sensing data, and operating parameters, etc. In some embodiments, the storage module may include a random access memory (RAM), a read-only memory (ROM), a large-capacity memory, a removable memory, a volatile read-write memory, etc., or any combination thereof. In some embodiments, the storage module may be integrated or included in one or more other components of the appliance collaborative management system 100 (e.g., a processor 150, a data acquisition module, etc.).

[0049] Figure 2 It is an exemplary flow chart of the method for collaborative management of electrical appliances according to some embodiments of this specification.

[0050] In some embodiments, process 200 may be implemented based on a processor of an electrical appliance collaborative management system. Figure 2 As shown, process 200 includes the following steps:

[0051] Step 210: Acquire environmental sensing data and spatial sensing data.

[0052] Environmental sensing data is data that characterizes environmental conditions outside a space. For example, the environmental sensing data may include temperature, light intensity, etc. outside the space. The space may be a space inside a building.

[0053] Spatial sensor data is data that characterizes the environmental conditions inside a space. For example, spatial sensor data may include the temperature inside the space, indoor humidity, etc.

[0054] In some embodiments, the environmental sensing data and the spatial sensing data may be data of a current time period or data of a historical time period.

[0055] In some embodiments, the environmental sensing data and the spatial sensing data can be collected by the data acquisition module and uploaded to the processor periodically or in real time. In some embodiments, the environmental sensing data and the spatial sensing data can be collected by the data acquisition module and uploaded to the storage module for storage, and the processor can read the environmental monitoring data and the spatial sensing data from the storage module. For more information about the data acquisition module, please refer to Figure 1 Related description.

[0056] Step 220, determining supply difference data based on the environmental sensing data and the spatial sensing data.

[0057] The supply difference data is used to characterize the difference between the user's heat demand data and the heat production data of the thermal collector.

[0058] Heat generation data refers to the total amount of heat energy that the heat collection device can provide at a certain time point / time period. In some embodiments, the heat generation data can be the total amount of heat that the heat collection device can provide in the current time period. The current time period can be a period of time before and after the current moment.

[0059] Heat demand data refers to the total amount of heat energy required by the user at a certain point in time / period of time.

[0060] It should be noted that when determining the supply difference data, the heat demand data and the heat production data correspond one to one. For example, the heat demand data and the heat production data are calculated based on data at the same time point or the same time period. The supply difference data is the difference between the heat production data and the heat demand data at the same time point or the same time period.

[0061] In some embodiments, the processor can determine the heat generation data of the heat collection device in the current time period in a variety of ways. For example, the processor can determine the heat generation data of the current time period based on the product of the instantaneous heat generation at the current moment and the length of the current time period.

[0062] Among them, instantaneous heat generation refers to the heat generated by the solar collector at a certain moment.

[0063] In some embodiments, the processor can determine the instantaneous heat production in a variety of ways based on environmental sensor data. For example, the processor can preset the correspondence between different light intensities and different instantaneous heat production based on historical data; based on the light intensity at the current moment, the processor can determine the instantaneous heat production at the current moment by looking up a table.

[0064] In some embodiments, the processor can estimate the heat demand data for the current time period in a variety of ways based on the environmental sensor data and the space sensor data. For example, the processor can perform statistical analysis based on historical data to determine the unit heat demand under historical environmental conditions that are the same or similar to the current environmental conditions; calculate the difference between the indoor temperature at the current moment and the target temperature; and multiply the difference by the unit heat demand to determine the heat demand data for the current time period.

[0065] The unit heat requirement refers to the amount of heat required to increase the indoor temperature by 1° C. It can be determined by manual analysis or theoretical calculation. For example, the processor can calculate it based on the volume, air density, and specific heat capacity of the air in the space.

[0066] The target temperature refers to the indoor temperature that the user expects to reach within a certain period of time. The target temperature can be determined in a variety of ways. For example, it can be determined by user input. For another example, the historical target temperature set by the user can be used as the current target temperature.

[0067] Environmental conditions may include outdoor temperature, indoor temperature, and target temperature, among others.

[0068] In some embodiments, the processor may calculate the sum of the heat production data of the current time period and the current energy storage capacity; and determine the difference between the sum and the heat demand data of the current time period as the supply difference data of the current time period.

[0069] It should be noted that at a certain moment or time period, the heat stored in the energy storage device changes dynamically, and the energy storage is affected by heat input (for example, increasing the energy storage through heating, solar energy conversion, etc.) and output (for example, reducing the energy storage through heat release, heat loss, etc.). Exemplarily, the current energy storage = the initial energy storage in the previous time period + the heat input in the previous time period - the heat output in the previous time period.

[0070] In some embodiments, the processor may be in communication with the energy storage device to obtain the current amount of stored energy.

[0071] Step 230 , determining operating parameters of the auxiliary heating device based on the supply difference data.

[0072] The operating parameters are parameters related to the operation of the auxiliary heating device.

[0073] In some embodiments, the operating parameters may include heating time point, heating power, and heating duration.

[0074] The heating time point refers to the time point when the auxiliary heating device starts to perform auxiliary heating. The heating power refers to the operating power of the auxiliary heating device. The heating duration refers to the continuous working time of the auxiliary heating device. In some embodiments, the operating parameters are used to control the operation of the auxiliary heating device to achieve the coordinated operation of the auxiliary heating device and the heat collection device. For example, the processor can generate corresponding control instructions based on the operating parameters of the current time period and send them to the auxiliary heating device to control the auxiliary heating device to start at the heating time point, shut down after the heating duration, operate at the heating power, etc.

[0075] In some embodiments, the processor may determine the operating parameters of the auxiliary heating device in the current time period in a variety of ways based on the supply difference data in the current time period. For example, in response to the sum of the heat production data in the current time period and the energy storage in the current time period being less than the corresponding heat demand data (i.e., the supply difference data is a negative number), the processor may take the current moment as the heating time point and immediately control the auxiliary heating device to start heating.

[0076] In some embodiments, the processor may determine the heating time based on a preset time length. For example, the heating time length is a value less than or equal to the preset time length. The preset time length may be a system default value, an experience value, a manually preset value, or any combination thereof, and may be set according to actual needs.

[0077] In some embodiments, the processor can determine the corresponding preset heating power based on the supply difference data of the current time period, and divide the absolute value of the supply difference data by the preset heating power to determine the heating duration. Different supply difference data correspond to different preset heating powers, and the corresponding relationship can be determined based on experiments or experience.

[0078] In some embodiments, the processor may determine the heating power for the current time period based on a ratio of an absolute value of the supply difference data to the heating duration.

[0079] In some embodiments, the processor can determine the electric heat conversion ratio based on the historical heating power consumption and the historical electric heating amount during the historical preset period; and determine the operating parameters based on the supply difference data, the electric heat conversion ratio, and the electricity price data. In this embodiment, the determined operating parameters include the heating time and the heating power, but do not include the heating time point. The heating time point can be based on Figure 2 Determined in the manner described above.

[0080] The historical preset period refers to a period of time before the current moment. For example, the historical preset period is the past week, the past month, etc. The historical preset period can also be any historical time range dynamically defined according to actual needs.

[0081] Historical heating power consumption refers to the total power consumption of the auxiliary heating device during a historical preset period of time.

[0082] In some embodiments, the processor may determine the historical heating power consumption of the auxiliary heating device in a historical preset time period based on an electric energy meter or a smart electric meter.

[0083] The historical electric heating amount refers to the total amount of heat energy actually generated by the auxiliary heating device within a historical preset time period.

[0084] In some embodiments, the processor may determine the total amount of heat generated by the auxiliary heating system during a historical preset period based on a heat meter or a calorimeter.

[0085] The electric heat conversion ratio refers to the efficiency of converting electrical energy into thermal energy. The electric heat conversion ratio can be used to reflect the ability of auxiliary heating equipment to convert consumed electrical energy into actual usable thermal energy.

[0086] In some embodiments, the processor may determine the ratio of the historical electric heating amount to the historical heating power consumption as the electric-to-heat conversion ratio.

[0087] In some embodiments, the processor can determine the operating parameters in a variety of ways based on the supply difference data, the electric heat conversion ratio, and the electricity price data. For example, when the electric heat conversion ratio is low and the electricity price is high, the processor can extend the heating time, reduce the heating power, etc.

[0088] In some embodiments, the processor can construct a first retrieval vector based on the supply difference data, electric-to-heat conversion ratio, and electricity price data of the current time period; search the first database based on the first retrieval vector to determine a target vector that meets the matching conditions, and use the reference operating parameters corresponding to the target vector as the heating time and heating power of the auxiliary heating device.

[0089] The electricity price data refers to the electricity charges collected at different times and under different conditions. For example, the electricity price data may include basic electricity price, tiered electricity price, electricity price in different time periods, etc.

[0090] In some embodiments, the processor may obtain electricity price data in any feasible manner, such as based on manual input, a storage module, or through a third-party website (eg, an electricity supplier or other regulatory agency, etc.).

[0091] The first database is a database for storing, indexing and querying vectors. The first database can store multiple first reference vectors and reference operating parameters corresponding to each first reference vector. The first reference vector is constructed by historical supply difference data, historical electric-to-heat conversion ratio, and historical electricity price data.

[0092] The historical supply difference data, historical electric-to-heat conversion ratio, and historical electricity price data constituting the first reference vector may be data calculated or acquired at the same time point or time period.

[0093] In some embodiments, the processor may construct a first database based on historical data. For example, the processor may determine the historical supply difference data, the historical electric-to-heat conversion ratio, and the historical electricity price data of each historical time period as the first reference vector based on the historical data, and use the historical heating duration and the historical heating power of the historical time period as the reference operating parameters corresponding to the first reference vector. Further, the processor may construct the first database based on multiple first reference vectors and their corresponding reference operating parameters.

[0094] The matching condition may refer to a judgment condition for determining the target vector. The matching condition may include that the vector distance from the search vector is less than a first threshold, etc. There are many methods for calculating the vector distance, such as Euclidean distance, cosine distance, etc. The first distance may be a system preset value, a system default value, etc.

[0095] In some embodiments of this specification, in combination with supply difference data, electric heat conversion ratio, and electricity price data, the system can flexibly adjust the heating power and heating time to adapt to changes in actual demand. For example, when the electric heat conversion ratio is low and the electricity price is high, extending the heating time and reducing the heating power can reduce the power loss per unit time, thereby reducing electricity expenses. This is because the electric heat conversion ratio is low, and reducing the power can reduce the loss in the energy conversion process.

[0096] In some embodiments, the operating parameters are also related to the user motion parameters. The processor may determine the operating parameters based on the supply difference data, the electric-to-heat conversion ratio, the electricity price data, and the user motion parameters.

[0097] The user motion parameters are used to reflect the user's activity level in the current time period.

[0098] In some embodiments, the user motion parameter may include the user's motion level. The higher the user's motion level, the smaller the required heat and the smaller the required heat data.

[0099] In some embodiments, the processor can determine the user's motion level in a variety of ways. For example, the processor can determine the movement trajectory of the same user in continuous image frames based on the indoor image data in the current time period through a target tracking algorithm. The indoor image data includes multiple continuous image frames. Further, the processor can determine the user's moving distance and moving time interval between two adjacent image frames based on the user's moving trajectory in multiple continuous image frames; determine the user's moving speed based on the moving distance and moving time interval corresponding to the adjacent image frames; and determine the user's motion level based on the user's moving speed. The user's motion level is positively correlated with the user's moving speed. The faster the user's moving speed, the higher the user's motion level. When the space includes multiple users, the processor can determine the moving speed of each user separately, and determine the corresponding motion levels of the multiple users based on the average of the moving speeds of the multiple users.

[0100] The target tracking algorithm refers to the method of locating and tracking a specific target in an image sequence. The target tracking algorithm includes but is not limited to optical flow, Kalman filtering, MeanShift, CamShift or deep learning tracking algorithms (such as DeepSORT, FairMOT), etc.

[0101] In some embodiments, the processor can determine the operating parameters in a variety of ways based on the supply difference data, the electric heat conversion ratio, the electricity price data and the user motion parameters. For example, the processor can obtain an initial operating parameter based on the supply difference data, the electric heat conversion ratio and the electricity price data; determine the adjustment coefficient based on the user motion parameter, and adjust the initial operating parameter based on the adjustment coefficient. The adjustment coefficient is negatively correlated with the user's motion level. When the user's motion level is greater than the motion threshold, the adjustment coefficient is a value less than 1. The greater the user's motion level, the greater the heat generated by the user himself, and the smaller the adjustment coefficient. The motion threshold is a system preset value or a system default value, etc.

[0102] In some embodiments, the processor can construct a retrieval vector based on the supply difference data, the electric heat conversion ratio, the electricity price data and the user motion parameters, and perform vector matching in the first database based on the retrieval vector. For more information about vector matching, please refer to the relevant description above. In this embodiment, the first reference vector in the first database is constructed based on the historical supply difference data, the historical electric heat conversion ratio, the historical electricity price data, and the historical user motion parameters. The construction method is similar to the above and will not be repeated here.

[0103] When exercising, the human body generates extra heat, thereby changing the user's perception of environmental parameters. In some embodiments of this specification, the operating parameters can be adjusted more intelligently through the user's motion parameters to meet the user's personalized needs. For example, when the user's motion level is low, it indicates that the user may need more heat, and the heating power can be appropriately increased to improve the user's comfort to a certain extent; when the user's motion level is high, it indicates that the user may be engaged in more active activities. At this time, the user's temperature requirements for the indoor environment are relatively low. By increasing the heating power, preheating in advance and other measures, it can not only ensure that the user has a comfortable environmental experience, but also effectively manage and save energy.

[0104] Since it takes a certain amount of time from starting the heating to the actual rise in indoor temperature, if the auxiliary heating device is controlled only based on the data of the current time period, the actual heating effect may deviate from the expected one, especially when the external ambient temperature changes greatly or the user demand increases suddenly. That is to say, in some embodiments, the processor can also predetermine the operating parameters of the auxiliary heating device in the future time period. In some embodiments, the operating parameters also include a preheating time point, and the processor can predetermine the heating time point, heating power, heating duration, and preheating time point of the auxiliary heating device in the future time period.

[0105] In some embodiments, the processor may determine operating parameters for a future time period based on future supply difference data, future electricity price data, and an electric-to-heat conversion ratio.

[0106] Future supply difference data refers to supply difference data for future time periods. Future time periods refer to time periods after the current time period.

[0107] In some embodiments, the processor may determine the sum of the future heat production data for the future time period and the energy storage capacity for the future time period; and determine the difference between the sum and the future heat demand data for the future time period as the supply difference data for the future time period. For more information about future heat production data and future heat demand data, please refer to Figure 3 Related description.

[0108] In some embodiments, the processor may analyze and process the energy storage capacity of each historical time period based on modeling or various data analysis algorithms, such as regression analysis, and predict the energy storage capacity of future time periods.

[0109] Future electricity price data refers to electricity price data for a future period of time.

[0110] In some embodiments, the processor may obtain future electricity price data in any feasible manner such as manual input, a storage module, or through a third-party website.

[0111] In some embodiments, the processor can determine the operating parameters of the future time period in a variety of ways based on the future supply difference data, the future electricity price data, and the electric-to-heat conversion ratio. The operating parameters of the future time period are determined in a similar manner to the operating parameters of the current time period. For example, the processor can generate a first search vector based on the future supply difference data, the future electricity price data, and the electric-to-heat conversion ratio, and search the first database based on the first search vector to determine the heating power and heating duration of the future time period. For more information, see the relevant description above.

[0112] In some embodiments of this specification, through prediction and planning, the system can reduce the risks caused by insufficient energy supply or overload and improve operational reliability; by predicting the user's thermal energy demand and the thermal energy output of the collector and making adjustments in advance, the user's usage experience can be ensured.

[0113] In some embodiments, the processor can determine the heating time point, heating power and heating duration of the future time period based on the future heat demand data and the future heat production data; and determine the preheating time point of the future time period based on the heating time point and the heating power of the future time period. For more information about this embodiment, please refer to Figure 3 Related description.

[0114] In some embodiments of the present specification, by real-time monitoring of environmental conditions (such as outdoor temperature and humidity) and spatial conditions (such as indoor temperature and indoor humidity), supply difference data can be accurately calculated to ensure that when solar energy is insufficient, the amount of auxiliary heating that conforms to the actual situation is determined, thereby improving overall energy efficiency; by monitoring changes in environmental and spatial conditions, the operating parameters of the corresponding time period can be flexibly determined, which helps the system adapt to seasonal changes, sudden weather changes, or changes in user behavior patterns (such as users' exercise habits, etc.); at the same time, effective coordination between the heat collection device (such as a solar collector) and the auxiliary heating device is achieved, making full use of renewable energy while compensating for the instability of renewable energy supply.

[0115] It should be noted that the above description of the relevant process is only for example and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the process under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0116] Figure 3 It is an exemplary schematic diagram of the effect prediction model shown in some embodiments of this specification.

[0117] In some embodiments, Figure 3 As shown, the processor can predict future heat production data 320 for a future time period based on future weather data 310-4 for the future time period; determine future heat demand data 360 for a future time period based on spatial sensor data 310-1, environmental sensor data 310-2, tolerance parameters, and at least one set of candidate heating data 310-3; determine a heating time point 371, a heating power 372, and a heating duration 373 based on the future heat demand data 360 and the future heat production data 320; and, based on the heating time point 371 and the heating power 372, determine a preheating time point 374.

[0118] Future weather data refers to the weather conditions in the future time period. For example, future weather data may include temperature, humidity, precipitation, light intensity, etc. in the future time period.

[0119] In some embodiments, the processor may obtain future weather data for a future time period through a network (eg, a website of a weather service agency or a website of an online weather service).

[0120] Future heat production data refers to the total amount of heat energy that the collector is expected to generate in the future.

[0121] In some embodiments, the processor may predict the future heat generation data of the future time period in a variety of ways based on the future weather data of the future time period. For example, the processor may process the historical light intensity, historical temperature, and corresponding actual historical heat generation data of each historical time period through a fitting algorithm to determine a first heat generation formula; based on the future weather data and the first heat generation formula, determine the future heat generation data of the corresponding future time period. For example, the light intensity and temperature contained in the future weather data may be input into the first heat generation formula to obtain the future heat generation data of the future time period. The fitting algorithm may include least squares method, Levenberg-Marquardt algorithm, genetic algorithm, Hough transform, etc.

[0122] The historical light intensity of the historical time period may be a statistical value of the light intensity at each historical moment in the historical time period (such as the mean value, median value, etc. of the light intensity). The historical temperature of the historical time period may be a statistical value of the temperature at each historical moment in the historical time period (such as the mean value, variance, etc. of the temperature).

[0123] The heat generation formula can be used to describe the quantitative relationship between heat generation data and various factors that affect the heat generation data.

[0124] Exemplarily, the first heat generation formula is formula (1):

[0125] Q=a*x1+b*x2; (1)

[0126] Wherein, a and b are the first coefficient and the second coefficient respectively; Q is the future heat generation data of the solar collector; x1 is the light intensity, and x2 is the temperature. The first coefficient and the second coefficient can be determined by a fitting algorithm based on historical data.

[0127] It should be noted that the light intensity input into the heat generation formula corresponds to the temperature one by one. For example, the light intensity and the temperature are collected at the same time point or the same time period.

[0128] In some embodiments, the processor obtains surface coverage based on the surface image data; and predicts future heat generation data for a future time period based on the surface coverage and future weather data.

[0129] The surface image data refers to image data containing surface information of the heat collector. The surface image data can reflect the surface features of the heat collector, for example, whether the surface of the heat collector is covered by an obstruction.

[0130] In some embodiments, the processor may photograph the surface of the thermal collector by using an image acquisition device to obtain surface image data.

[0131] The surface coverage is used to reflect the degree to which the surface of the collector is blocked by obstructions (such as dust, snow, ice, leaves, etc.). The surface coverage can be in various forms such as numerical values ​​and grades.

[0132] In some embodiments, the processor may determine the surface coverage based on the percentage of the area of ​​the coverage area to the surface area of ​​the heat collector. The processor may determine the area of ​​the coverage area based on an edge detection algorithm (such as Canny edge detection), a threshold segmentation method, or a machine learning method.

[0133] Covered area refers to the portion of the panel of a heat collecting device (such as a solar photovoltaic panel or a thermal collector) that is occupied by obstructions (such as dust, snow, ice, leaves, etc.).

[0134] In some embodiments, the processor can predict the future heat production data of the future time period in a variety of ways based on the surface coverage and future weather data. For example, based on the heat production formula determined in the previous text, the influence of the surface coverage on the future heat production data of the future time period is considered. For example, the processor can process the historical light intensity, historical temperature, historical surface coverage, and corresponding actual historical heat production data of each historical time period through a fitting algorithm to determine a second heat production formula; based on the future weather data, future surface coverage, and the second heat production formula, determine the future heat production data of the corresponding future time period. For example, the light intensity and temperature contained in the future weather data, as well as the future surface coverage, can be input into the second heat production formula to obtain the future heat production data of the future time period. Exemplarily, the second heat production formula can be formula (2):

[0135] Q=a*x1+b*x2+c*x3; (2)

[0136] Wherein, x3 refers to the future surface coverage, and c is the third coefficient. The third coefficient can be determined by a fitting algorithm based on historical data. For descriptions of other parameters, see the description of formula (1).

[0137] The future surface coverage refers to the surface coverage in a future time period. The processor can determine the future surface coverage in a variety of ways. For example, the processor can use the surface coverage in the current time period as the future surface coverage.

[0138] In some embodiments of the present specification, surface coverage and future weather data are taken into consideration to more accurately predict future heat production data, which helps to subsequently determine the operating parameters for future time periods, and thus helps the system to reasonably arrange the use of auxiliary heating devices in each future time period, thereby reducing costs and ensuring stable and reliable operation of the system.

[0139] The tolerance parameter represents the user's acceptance of indoor environmental parameters and / or operating parameters. For example, the tolerance parameter may include one or a combination of temperature fluctuation tolerance, heating rate tolerance, energy efficiency tolerance, etc.

[0140] Temperature fluctuation tolerance refers to the range of indoor temperature changes that users can accept. The indoor temperature change range refers to the fluctuation range of the indoor temperature in a certain period of time relative to the reference temperature. The reference temperature can be the target temperature or the indoor temperature in the previous period of time.

[0141] In some embodiments, the temperature fluctuation tolerance may include temperature increase tolerance and temperature decrease tolerance.

[0142] Temperature rise tolerance refers to the maximum value of the indoor temperature rise relative to the reference temperature that the user can accept.

[0143] The temperature drop tolerance refers to the maximum value of the indoor temperature drop relative to the reference temperature that the user can accept.

[0144] Heating rate tolerance refers to the range of indoor temperature rise rate that users can accept.

[0145] Energy efficiency tolerance refers to the range of system energy efficiency values ​​that is acceptable to users.

[0146] In some embodiments, the space may include multiple users, each of which corresponds to a tolerance parameter. In this case, the processor may determine future heat demand data based on the tolerance parameters of the multiple users.

[0147] When there are multiple users in the scene, the tolerance parameters may be satisfied only for the owner of the scene (eg, the owner of the building) or for all people in the scene.

[0148] In some embodiments, the user's tolerance parameter may be dynamically changing or fixed. For example, the user's tolerance parameter may be fixed to a basic tolerance parameter. When the user's tolerance parameter changes dynamically, the user's future tolerance parameter in a future time period may be determined by prediction. For more information about future tolerance parameters, see Figure 4 and its related description.

[0149] The basic tolerance parameters refer to initially determined tolerance parameters. In some embodiments, the processor may determine the basic tolerance parameters based on user input.

[0150] In some embodiments, the candidate heating supply data may include future heating supply data for multiple future time periods. The future heating supply data refers to the total amount of heat energy that may be provided to the user in a certain future time period.

[0151] In some embodiments, the processor may generate at least one set of candidate heating data based on a random generation method. For example, the processor may randomly adjust one or more values ​​based on historical heating data of the same period to generate one or more sets of candidate heating data.

[0152] In some embodiments, the processor may determine at least one set of candidate heating data based on basic tolerance parameters, spatial sensing data, spatial structure data, and an electric-to-heat conversion ratio.

[0153] Spatial structure data is data that describes the internal layout and characteristics of a space. For example, spatial structure data may include any one or a combination of the room's shape, size, ceiling height, wall material, etc.

[0154] In some embodiments, the processor may obtain the spatial structure data based on user input or a storage module.

[0155] In some embodiments, the processor may determine at least one set of candidate heating data in a variety of ways based on the basic tolerance parameters, the spatial sensing data, the spatial structure data, and the electric-to-heat conversion ratio. For example, the processor may construct a second search vector based on the basic tolerance parameters, the spatial sensing data, the spatial structure data, and the electric-to-heat conversion ratio, and search in the second database based on the second search vector to determine at least one set of candidate heating data.

[0156] The second database is a database for storing, indexing and querying vectors. The second database can store multiple second reference vectors and reference heating data corresponding to each second reference vector. The second reference vector is constructed by historical basic tolerance parameters, historical spatial sensor data, historical spatial structure data, and historical electric-heat conversion ratio.

[0157] In some embodiments, the processor may construct a second database based on historical data. For example, the processor may construct a second reference vector based on historical data, based on historical basic tolerance parameters, historical spatial sensor data, historical spatial structure data, and historical electric-heat conversion ratios for each historical time period, and use the historical heating data corresponding to the second reference vector as reference heating data.

[0158] In some embodiments, the method of searching the second database based on the second search vector is the same as the method of searching the first database based on the first search vector. For more information, see Figure 2 Related description.

[0159] In some embodiments of the present specification, a series of total amounts of heat energy that may be provided to the user are screened through user-specific spatial structure, tolerance parameters and spatial sensor data, and customized and personalized heating data are provided; at the same time, the electric-to-heat conversion ratio is taken into consideration, which is conducive to selecting the most cost-effective heating mode, especially in the case of fluctuating electricity prices. By determining the corresponding heating data, operating costs can be reduced while ensuring comfort.

[0160] Future heat demand data refers to the total amount of heat energy that users are expected to require in the future period of time.

[0161] In some embodiments, the processor may determine future heat demand data for a future time period in a variety of ways based on space sensor data, environmental sensor data, basic tolerance parameters, and at least one set of candidate heating supply data.

[0162] In some embodiments, Figure 3As shown, the processor can predict the predicted spatial data sequence 350 corresponding to each group of candidate heating data 310-3 through the effect prediction model 340 based on the spatial sensor data 310-1, the environmental sensor data 310-2, and at least one group of candidate heating data 310-3; and determine the future heat demand data 360 for the future time period based on the predicted spatial data sequence 350 corresponding to each group of candidate heating data 310-3 and the user's basic tolerance parameter 330.

[0163] In some embodiments, the effect prediction model is a machine learning model. For example, the effect prediction model may include any one or combination of a convolutional neural network (CNN) model, a neural network (NN) model, or other custom model structures.

[0164] In some embodiments, Figure 3 As shown, the input of the effect prediction model 340 includes candidate heating data 310 - 3 , environmental sensor data 310 - 2 , and spatial sensor data 310 - 1 , and the output may include a predicted spatial data sequence 350 corresponding to the candidate heating data.

[0165] In some embodiments, Figure 3 As shown, the input of the effect prediction model 340 also includes future weather data 310 - 4 and spatial structure data 310 - 5 .

[0166] The predicted spatial data sequence may be a sequence composed of spatial sensing data of multiple future time periods. For example, the predicted spatial data sequence may include indoor temperature, indoor humidity, etc. of multiple future time periods.

[0167] In some embodiments, the predicted spatial data sequence is represented in various forms such as a sequence or a matrix. For example, the predicted spatial data sequence can be represented in the form of a sequence {(A1, B1), (A2, B2), ...}, where A1 represents the indoor temperature of the first future time period, A2 represents the indoor temperature of the second future time period at time B, etc., B1 represents the indoor humidity of the first future time period, and B2 represents the indoor humidity of the second future time period.

[0168] In some embodiments, the effect prediction model can be trained based on a large number of first training samples with a first label through various feasible methods. For example, parameter updating can be performed based on the gradient descent method. An exemplary training process includes: inputting a plurality of first training samples with a first label into the initial effect prediction model, constructing a loss function through the label and the result of the initial effect prediction model, and iteratively updating the parameters of the initial effect prediction model based on the loss function through gradient descent or other methods. When the preset conditions are met, the model training is completed and a trained effect prediction model is obtained. Among them, the preset conditions may be that the loss function converges, the number of iterations reaches a threshold, etc.

[0169] In some embodiments, the first training sample includes at least sample heating data, sample environmental sensor data at a first time, and sample spatial sensor data at a first time. In some embodiments, when the input of the effect prediction model includes future weather data and spatial structure data, the first training sample may also include sample weather data and sample spatial structure data at a second time. The first training sample may be obtained based on historical data.

[0170] In some embodiments, the first label may include an actual spatial data sequence at the second time corresponding to the sample heating data. The first label may be obtained by a processor or manual annotation. For example, under the environmental conditions corresponding to the first training sample (e.g., sample heating data, sample environmental sensor data at the first time, sample spatial sensor data at the first time, sample spatial structure data, etc.), the sample heating data is used to heat the interior of the space, and the spatial sensor data of the interior of the space at each time period at the second time is recorded throughout the process, and determined as the actual spatial data sequence at the second time as the first label.

[0171] The first time and the second time are historical time ranges, and the first time is before the second time.

[0172] Through the effect prediction model, the prediction spatial data sequence corresponding to the candidate heating data can be predicted efficiently and accurately, which can obtain better results than direct experience-based determination, and is conducive to the subsequent determination of the most appropriate future heat demand data.

[0173] In some embodiments, the processor may determine the future heat demand data of the future time period in a variety of ways based on the basic tolerance parameters and the predicted spatial data sequence corresponding to each group of candidate heating parameters in at least one group of candidate heating data. For example, for each group of candidate heating data, the processor may determine the stage data of each future time period based on the predicted spatial data sequence corresponding to the group of candidate heating data, and the stage data includes but is not limited to temperature fluctuation data (e.g., maximum temperature difference, etc.), heating rate, etc. The stage data of each future time period is compared with the basic tolerance parameters: when the stage data of each future time period meets the basic tolerance parameters of each corresponding indoor user, and the heat gap of multiple future time periods in the candidate heating data is less than a preset threshold, then the group of candidate heating data is used as the future heat demand data of the corresponding multiple future time periods. The preset threshold may be a system preset value or a system default value, etc. Exemplarily, for the stage data of each future time period, when the temperature fluctuation data of the future time period simultaneously meets the temperature fluctuation tolerance of each indoor user, the heating rate of the future time period simultaneously meets the heating rate tolerance of each indoor user, etc., and the heat gap of multiple future time periods in the candidate heating data is less than a preset threshold, it is determined that each stage data meets the corresponding tolerance parameters.

[0174] The processor can determine the heat gap in multiple future time periods based on the total amount of future heat supply data in multiple future time periods minus the total amount of future heat production data in multiple future time periods and the total energy storage capacity of the energy storage device in multiple future time periods.

[0175] The stage temperature fluctuation data refers to the range of indoor temperature changes in a certain period of time. The stage heating rate refers to the rate of increase of indoor temperature in a certain period of time.

[0176] In some embodiments, the processor may determine a future tolerance parameter sequence based on the predicted perception sequence; and determine future heat demand data based on the future tolerance parameter sequence. Figure 4 Related description.

[0177] In some embodiments, the processor can determine the heating time point, heating power and heating duration of the future time period in a variety of ways based on future heat demand data of the future time period and future heat production data of the future time period.

[0178] For example, if the future supply difference data of a certain future time period is less than zero, it is determined that the auxiliary heating device needs to be started in the future time period, and the starting time point of the future time period is used as the heating time point of the future time period.

[0179] The heating duration for the future time period may adopt a preset value.

[0180] In some embodiments, when the future supply difference data of a future time period is greater than zero, no heating time point is set in the future time period, or the heating time point of the future time period is set to 0, indicating that no additional heating is performed.

[0181] In some embodiments, the processor can judge each future time period through the above method to obtain multiple heating time points.

[0182] For another example, the processor may determine the heating power of the future time period based on the heating time point and heating duration of the future time period. For example, the processor may determine the corresponding relationship between the combination of different supply difference data and different heating durations and different heating powers based on historical data, and determine the heating power of the future time period by looking up a table.

[0183] In some embodiments, the preheating time is proportional to the heating power. The greater the heating power, the shorter the preheating time. The preheating time refers to the length of time required to start heating in advance.

[0184] The processor may determine the preheating time point based on the difference between the heating time point in the future time period and the preheating duration.

[0185] In some embodiments of this specification, by accurately predicting future heat demand data and future heat production data, the system can intelligently adjust the heating operation parameters to avoid overheating or underheating, thereby significantly reducing energy consumption and improving energy efficiency; to ensure that the auxiliary heating device can respond to the user's hot water or heating needs in a timely manner and minimize delays, it is necessary to consider determining the heating operation parameters in advance. This is because it takes a certain amount of time for the auxiliary heating device to reach the target temperature from startup. If it is not adjusted in advance, delays may occur, resulting in the indoor temperature or hot water temperature not being able to be achieved immediately when the user needs it. By determining the preheating time point in the future time period, it can be ensured that the indoor temperature or hot water can reach the target temperature as soon as possible without waiting for the heating process to be completed, thereby improving the user experience.

[0186] Figure 4 It is an exemplary schematic diagram of a perception determination model according to some embodiments of the present specification.

[0187] In some embodiments, Figure 4 As shown, the processor can obtain user status information 421-1 and user basic information 410-4 based on indoor image data 410-1 and indoor voice data 410-2; determine the predicted perception sequence 430 based on user status information 421-1, user basic information 410-4, current tolerance parameter 410-5, environmental sensor data 410-6, and spatial sensor data 410-7; and determine the future tolerance parameter sequence 440 based on the predicted perception sequence 430.

[0188] For more information about indoor image data, see Figure 2 Related description.

[0189] Indoor voice data refers to indoor sound information. For example, indoor voice data may include user's voice, background noise, etc.

[0190] In some embodiments, the processor may acquire indoor image data and indoor voice data through an image acquisition device and a voice acquisition device in various feasible ways, including but not limited to continuous acquisition, timed acquisition, etc.

[0191] User status information is used to reflect the user's psychological condition. For example, the user status information may include the user's emotional state, stress level, etc.

[0192] Categories of the user's emotional state may include happy, sad, angry, depressed, stable, etc.

[0193] The stress level is used to quantify the degree of stress felt by the user. In some embodiments, the stress level can be a value of 0-1.

[0194] In some embodiments, the processor can determine the user status information in a variety of ways. For example, the processor can determine the category of the user's emotional state in the current time period based on the indoor image data in the current time period through an image recognition algorithm. For another example, the processor can determine the user's face recognition information based on the indoor image data in the current time period through an image recognition algorithm, perform similarity analysis on the user's face recognition information with multiple preset image data respectively, and determine the reference pressure level corresponding to the most similar preset image data as the pressure level of the user in the current time period. Each image data corresponds to a reference pressure level. For another example, the processor can determine the category of the user's emotional state in the current time period through voice analysis based on indoor voice data. Voice analysis includes but is not limited to voice recognition technology, natural language processing technology, etc. Facial recognition information refers to data about the user's facial features, and facial recognition information can be used to uniquely identify the user's identity, expression, etc.

[0195] User basic information refers to information related to the user himself, for example, user basic information includes the user's gender, age, disease type, etc.

[0196] In some embodiments, the processor may obtain basic user information in a variety of ways. For example, the processor may determine the user's identity information based on indoor image data through face recognition technology, and determine the user's basic information by querying the personnel identity database based on the user's identity information. The user's identity information may be pre-entered or stored in the personnel identity database. The personnel identity database may be used to store the user's identity information and its corresponding basic user information. Each user's identity information may include identity information such as a unique corresponding face recognition information user number.

[0197] The current tolerance parameter refers to the user's tolerance parameter in the current time period.

[0198] In some embodiments, the current tolerance parameter may be a basic tolerance parameter of the user, or an updated tolerance parameter of the user.

[0199] The update tolerance parameter is a tolerance parameter of a user's dynamic changes. In some embodiments, the processor may adjust the basic tolerance parameter and determine the update tolerance parameter based on factors such as time and seasonal changes. Exemplarily, different combinations of time and season correspond to different adjustment amounts, and the corresponding relationship may be determined based on prior knowledge or historical data.

[0200] The predicted perception sequence is a predicted user's perception of each environmental parameter or its change data. For example, the predicted perception sequence is a sequence composed of the user's perception of different environmental parameters.

[0201] In some embodiments, when there is only one user in the space, the predicted perception sequence may include the user's perception of different environmental parameters. In some embodiments, when there are multiple users in the space, the predicted perception sequence may include the perceptions of the multiple users to different environmental parameters.

[0202] In some embodiments, the perception may include temperature perception, which is used to indicate the user's perception of temperature or its change data. The temperature perception may include temperature rise perception, temperature drop perception, etc.

[0203] In some embodiments, when the space includes multiple users, the predicted perception sequence can be determined based on the user status information of each of the multiple users, the user basic information of each of the multiple users, the current tolerance parameters of each of the multiple users, the environmental sensor data, and the space sensor data.

[0204] In some embodiments, Figure 4 As shown, the processor may determine a predicted perception sequence 430 based on the perception determination model 420 .

[0205] The perceptual determination model is a model used to determine the predicted perceptual sequence.

[0206] In some embodiments, the perception determination model is a machine learning model. For example, the perception determination model may include any one or combination of a convolutional neural network (CNN) model, a neural network (NN) model, or other customized model structures.

[0207] In some embodiments, the processor may determine a predicted perception sequence through a perception determination model based on user state information, user basic information, current tolerance parameters, environmental sensing data, and spatial sensing data.

[0208] In some embodiments, the space may include one or more users, and the input of the perception determination model may also include the current number of users.

[0209] In some embodiments, when the space includes multiple users, the input of the perception determination model may include the current number of users, basic user information corresponding to each of the multiple users, user status information corresponding to each of the multiple users, current tolerance parameters corresponding to each of the multiple users, environmental sensor data, and spatial sensor data, and the output is a predicted perception sequence corresponding to the multiple users.

[0210] The current number of users refers to the number of users in the space during the current period of time.

[0211] It should be noted that the indoor image data, indoor voice data, current number of users, basic user information, current tolerance parameters, environmental sensor data, and spatial sensor data input to the perception determination model are data collected or calculated in the same time period. For example, the data input to the perception determination model are data collected or calculated in the current time period.

[0212] In some embodiments, the perception determination model can be trained in various feasible ways based on a large number of second training samples with second labels. The second training samples include at least the number of sample users, basic information of sample users corresponding to each of the multiple sample users, state information of sample users corresponding to each of the multiple sample users, sample tolerance parameters corresponding to each of the multiple sample users, sample environment sensor data, and sample space sensor data. The second training samples can be obtained based on historical data. The second label can include a predicted perception sequence corresponding to multiple sample users. The second label can be obtained by a processor or manual annotation. The training of the perception determination model is similar to the training of the effect prediction model. For details, please refer to Figure 3 Related description.

[0213] In some embodiments, the processor may also determine the predicted perception sequence through a perception determination model based on indoor image data, indoor voice data, user basic information, current tolerance parameters, environmental sensor data, and spatial sensor data. In this embodiment, the perception determination model may be composed of multiple processing layers.

[0214] In some embodiments, Figure 4 As shown, the perception determination model 420 may include a state determination layer 421 and a perception prediction layer 422 .

[0215] The state determination layer is a model used to determine the characteristics of environmental impacts.

[0216] In some embodiments, the state determination layer may be a machine learning model, for example, the state determination layer may be, for example, a convolutional neural network (CNN), etc.

[0217] For the purpose of illustration and not limitation, the following description is made using an example where there are multiple users in a space.

[0218] In some embodiments, Figure 4 As shown, the input of the state determination layer 421 may include indoor image data 410 - 1 and indoor voice data 410 - 2 ; and the output may include user state information 421 - 1 corresponding to each of the multiple users.

[0219] In some embodiments, the state determination layer can be trained based on a large number of third training samples with third labels through various feasible methods. The training of the state determination layer is similar to the training of the effect prediction model. For more information about the training, please refer to Figure 3 Related description.

[0220] In some embodiments, the third training sample may include sample indoor image data and sample indoor voice data. The third training sample may be obtained based on historical data.

[0221] In some embodiments, the third label may include user status information corresponding to each of the plurality of sample users corresponding to the third training sample. The second label may be obtained by a processor or by manual annotation. For example, the actual historical user status information may be determined by manual analysis and evaluation based on historical indoor image data and historical indoor voice data as the third label.

[0222] The perceptual prediction layer is a model used to determine the estimated adjustment effects corresponding to candidate adjustment parameters.

[0223] In some embodiments, the perception prediction layer may be a machine learning model, for example, the perception prediction layer may be, for example, a convolutional neural network (CNN), etc.

[0224] In some embodiments, Figure 4 As shown, the input of the perception prediction layer 422 includes the current number of users 410-3, basic user information 410-4 corresponding to each of the multiple users, user status information 421-1, current tolerance parameter 410-5, environmental sensor data 410-6, and spatial sensor data 410-7, and the output includes a predicted perception sequence 430 corresponding to the multiple users.

[0225] In some embodiments, the perception prediction layer can be trained based on a large number of fourth training samples with fourth labels through various feasible methods. The training of the perception prediction layer is similar to the training of the effect prediction model. For more information about the training, please refer to Figure 3 Related description.

[0226] In some embodiments, the fourth training sample may include the number of sample users, basic information of sample users corresponding to each of the plurality of sample users, tolerance parameters of sample users, state information of sample users, sample environmental sensing data, and sample spatial sensing data. The fourth training sample may be obtained based on historical data.

[0227] In some embodiments, the fourth label may include the perception of different environmental parameters of multiple sample users in the room corresponding to the fourth training sample. The fourth label can be obtained by a processor or manual annotation. For example, under experimental conditions corresponding to different third training samples (such as, including the number of sample users, basic information of sample users, tolerance parameters of sample users, sample environmental sensor data, sample space sensor data, and sample user status information), a heating or cooling experiment can be conducted indoors, and each experimenter can feedback whether they feel the heating and cooling. Based on the comparison of the temperature at the time of user feedback and the initial temperature of the experiment, the heating perception or cooling perception of each experimenter is determined as the second label corresponding to the sample condition. For example, under a certain sample condition, when the indoor temperature is gradually raised from the initial temperature value of 26°C to 29°C, each experimenter feedbacks that they feel the heating at different time points, and then the heating perception of each experimenter is recorded. For example, when a certain experimenter is heated to 28°C, the feedback feels the heating, and the heating perception of the experimenter is recorded as 2°C.

[0228] In some embodiments, the processor can monitor the changes in the number of indoor users in real time. When the current number of users indoors changes, the processor can re-execute the perception determination model. The perception determination model can process the indoor image data, indoor voice data, the current number of users, basic user information corresponding to multiple users, current tolerance parameters, user status information, environmental sensor data, and spatial sensor data in the current time period. The processed model output is a predicted perception sequence corresponding to the indoor users in the current time period.

[0229] In some embodiments, the processor may determine user motion parameters based on the indoor image data; and adjust the predicted perception sequence based on the user motion parameters.

[0230] For more information about user motion parameters, see Figure 2 Related description.

[0231] In some embodiments, the higher the user's exercise level, the weaker the user's perception ability is compared to the user with a lower exercise level. When a user's exercise level is higher than a level threshold, the user's temperature rise perception can be increased accordingly. For example, the processor can increase the user's temperature rise perception in the predicted perception sequence based on a preset value. The level threshold and the preset value can be determined based on experiments or experience.

[0232] In some embodiments of the present specification, the corresponding perception is adjusted according to the actual motion state of the user to achieve personalized thermal comfort control and improve user comfort.

[0233] The future tolerance parameter sequence refers to a sequence of future tolerance parameters of multiple future time periods. The future tolerance parameter may be an updated tolerance parameter after dynamic changes.

[0234] In some embodiments, the processor may predict the future tolerance parameter sequence in a variety of ways based on the predicted perception sequence. For example, the processor may adjust the basic tolerance parameters of each indoor user based on the predicted perception sequence of the current time period and the adjustment parameters of each future time period, determine the future tolerance parameters of each user in each future time period, and form a future tolerance parameter sequence based on the future tolerance parameters of each user in each future time period.

[0235] The adjustment parameters refer to parameters for adjusting various parameters in the basic tolerance parameters.

[0236] In some embodiments, the future tolerance may include future temperature increase tolerance and future temperature decrease tolerance.

[0237] Exemplarily, the adjustment parameters may include a first coefficient and a second coefficient. For a certain user's perception of temperature rise indoors, the processor may determine the user's future temperature rise tolerance in each future time period by weighted summation based on the temperature rise perception and the first coefficient, as well as the user's basic temperature rise tolerance and the second coefficient.

[0238] In some embodiments, different future time periods correspond to different first coefficients and second coefficients. The first coefficient and the second coefficient can be determined based on the time interval between the future time period and the current moment. For example, the first coefficient is negatively correlated with the time interval, and the second coefficient is positively correlated with the time interval. The larger the time interval, the smaller the first coefficient and the larger the second coefficient.

[0239] Exemplarily, the adjustment parameters may also include a third coefficient and a fourth coefficient. For a certain user's perception of cooling indoors, the processor may determine the user's future cooling tolerance in each future time period by weighted summation based on the cooling perception and the third coefficient, as well as the user's basic cooling tolerance and the fourth coefficient.

[0240] In some embodiments, different future time periods correspond to different third coefficients and fourth coefficients. The third coefficient and the fourth coefficient can be determined based on the time interval between the future time period and the current moment. For example, the third coefficient is negatively correlated with the time interval, and the fourth coefficient is positively correlated with the time interval. The larger the time interval, the smaller the third coefficient and the larger the fourth coefficient.

[0241] In some embodiments, the processor may compare the temperature rise perception with the basic temperature rise tolerance, and when the temperature rise perception is greater than the basic temperature rise tolerance, increase the basic temperature rise rate tolerance based on the increase amplitude. The increase amplitude is positively correlated with the temperature rise perception, and the greater the temperature rise perception, the greater the increase amplitude.

[0242] In some embodiments, the processor can determine whether the spatial sensor data of each future time period in the predicted spatial data sequence of each group of candidate heating data can meet the future tolerance parameters of each user in the future tolerance parameter sequence in the corresponding future time period, and then determine the future heat demand data. For example, for each group of candidate heating data, the processor can determine the stage data of each future time period based on the predicted spatial data sequence corresponding to the group of candidate heating data; for each future time period, the stage data of the future time period is compared with the future tolerance parameters of each corresponding indoor user: when the stage data of each future time period meets the future tolerance parameters of each corresponding indoor user, and the heat gap of multiple future time periods in the candidate heating data is less than a preset threshold, then the group of candidate heating data is used as the future heat demand data for the corresponding multiple future time periods. The preset threshold can be a system preset value or a system default value, etc. For more information about the heat gap, see Figure 3 and its related description.

[0243] In some embodiments of the present specification, by predicting the perception sequence and the future tolerance parameter sequence, it is helpful to accurately determine the future heat demand data, and it is helpful for the system to plan and adjust parameters in advance to adapt to the user's expected behavior patterns and external environmental changes; by predicting the future heat demand data, the system can optimize energy distribution, reduce unnecessary heating or cooling, and thus save energy.

[0244] One or more embodiments of the present specification also provide an electrical appliance collaborative management apparatus, including a processing device, wherein the processing device is used to execute an electrical appliance collaborative management method as described in any of the above embodiments.

[0245] One or more embodiments of the present specification also provide a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer runs a method for collaborative management of electrical appliances as described in any of the above embodiments.

[0246] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0247] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0248] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0249] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0250] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0251] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0252] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A method for collaborative management of electrical appliances, It is characterized in that The method is applied to an electrical appliance collaborative management Internet of Things system, and the method includes: Acquire environmental sensing data and spatial sensing data; Determining supply difference data based on the environmental sensing data and the spatial sensing data, wherein the supply difference data represents a difference between the heat demand data of the user and the heat production data of the heat collecting device; Based on the supply difference data, operating parameters of the auxiliary heating device are determined.

2. The method according to claim 1, It is characterized in that The operating parameters include heating time point, heating power, heating duration and preheating time point, and the method further includes: Predicting future heat generation data for the future time period based on future weather data for the future time period; Determining the future heat demand data for the future time period based on the space sensing data, the environment sensing data, a tolerance parameter, and at least one set of candidate heating data; wherein the tolerance parameter represents the user's acceptance of indoor environment parameters and / or operation parameters; Determining the heating time point, heating power and heating duration based on the future heat demand data and the future heat production data; and, The preheating time point is determined based on the heating time point and the heating power.

3. The method according to claim 2, It is characterized in that The tolerance parameter includes a future tolerance parameter sequence, and the future tolerance parameter sequence is determined by: Based on indoor image data and indoor voice data, obtain user status information and basic user information; Determine a predicted perception sequence based on the user state information, the user basic information, the current tolerance parameter, the environmental sensor data, and the spatial sensor data; wherein the predicted perception sequence is a sequence composed of the user's perception of different environmental parameters; Based on the predicted perceptual sequence, the future tolerance parameter sequence is determined.

4. The method according to claim 1, It is characterized in that The determining of operating parameters based on the supply difference data includes: Determine the electric-heat conversion ratio based on the historical heating power consumption and the historical electric heating amount during the historical preset period; The operating parameters are determined based on the supply difference data, the electric-to-heat conversion ratio, and electricity price data.

5. An electrical appliance collaborative management Internet of Things system, It is characterized in that The Internet of Things system includes a heat collection device, an auxiliary heating device, a data acquisition module, a processor and an interaction module; The heat collection device is configured to collect thermal energy; The auxiliary heating device is configured to perform auxiliary heating when the heat generation data of the heat collecting device meets the preset requirements; The data acquisition module includes an environmental sensor and a spatial sensor, and the data acquisition module is configured to monitor environmental sensing data and spatial sensing data; The interaction module is configured to obtain information input by the user: The processor is in communication with the heat collection device, the auxiliary heating device, the data acquisition module, and the interaction module, and the processor is configured as follows: Acquire environmental sensing data and spatial sensing data; Determining supply difference data based on the environmental sensing data and the spatial sensing data, wherein the supply difference data represents a difference between the heat demand data of the user and the heat production data of the heat collecting device; Based on the supply difference data, operating parameters of the auxiliary heating device are determined.

6. The Internet of Things system as claimed in claim 5, It is characterized in that The operating parameters include heating time point, heating power, heating duration and preheating time point, and the processor is further configured to: Predicting future heat generation data for the future time period based on future weather data for the future time period; Determining the future heat demand data for the future time period based on the space sensing data, the environment sensing data, a tolerance parameter, and at least one set of candidate heating data; wherein the tolerance parameter represents the user's acceptance of indoor environment parameters and / or operation parameters; Determining the heating time point, heating power and heating duration based on the future heat demand data and the future heat production data; as well as, The preheating time point is determined based on the heating time point and the heating power.

7. The Internet of Things system as claimed in claim 6, It is characterized in that The tolerance parameter includes a future tolerance parameter sequence, and the processor is further configured to: Based on indoor image data and indoor voice data, obtain user status information and basic user information; Determine a predicted perception sequence based on the user state information, the user basic information, the current tolerance parameter, the environmental sensor data, and the spatial sensor data; wherein the predicted perception sequence is a sequence composed of the user's perception of different environmental parameters; Based on the predicted perceptual sequence, the future tolerance parameter sequence is determined.

8. The Internet of Things system as claimed in claim 5, It is characterized in that The processor is further configured to: Determine the electric-heat conversion ratio based on the historical heating power consumption and the historical electric heating amount during the historical preset period; The operating parameters are determined based on the supply difference data, the electric-to-heat conversion ratio, and electricity price data.

9. An electrical appliance collaborative management device, It is characterized in that The apparatus comprises at least one processor and at least one memory; The at least one memory is used to store computer instructions; The at least one processor is used to execute at least part of the computer instructions to implement the electrical appliance collaborative management method according to any one of claims 1 to 4.

10. A computer-readable storage medium, It is characterized in that The storage medium stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer executes the electrical appliance collaborative management method according to any one of claims 1 to 4.

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