CN115628554B - Water heater hot water method and device, electronic equipment and storage medium - Google Patents
Water heater hot water method and device, electronic equipment and storage medium Download PDFInfo
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 260
- 238000000034 method Methods 0.000 title claims abstract description 68
- 238000010438 heat treatment Methods 0.000 claims abstract description 101
- 238000012545 processing Methods 0.000 claims abstract description 19
- 239000008236 heating water Substances 0.000 claims abstract description 5
- 230000009471 action Effects 0.000 claims description 66
- 230000006399 behavior Effects 0.000 claims description 52
- 238000010606 normalization Methods 0.000 claims description 27
- 238000012549 training Methods 0.000 claims description 21
- 238000004891 communication Methods 0.000 claims description 15
- 230000015654 memory Effects 0.000 claims description 15
- 238000004590 computer program Methods 0.000 claims description 5
- 238000005516 engineering process Methods 0.000 abstract description 3
- 230000000875 corresponding effect Effects 0.000 description 39
- 230000008569 process Effects 0.000 description 10
- 238000001514 detection method Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 4
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- 238000007781 pre-processing Methods 0.000 description 2
- 230000006403 short-term memory Effects 0.000 description 2
- 230000003542 behavioural effect Effects 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000000802 evaporation-induced self-assembly Methods 0.000 description 1
- 230000005021 gait Effects 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 230000007787 long-term memory Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24H—FLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT-GENERATING MEANS, e.g. HEAT PUMPS, IN GENERAL
- F24H15/00—Control of fluid heaters
- F24H15/20—Control of fluid heaters characterised by control inputs
- F24H15/269—Time, e.g. hour or date
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24H—FLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT-GENERATING MEANS, e.g. HEAT PUMPS, IN GENERAL
- F24H15/00—Control of fluid heaters
- F24H15/10—Control of fluid heaters characterised by the purpose of the control
- F24H15/172—Scheduling based on user demand, e.g. determining starting point of heating
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24H—FLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT-GENERATING MEANS, e.g. HEAT PUMPS, IN GENERAL
- F24H15/00—Control of fluid heaters
- F24H15/40—Control of fluid heaters characterised by the type of controllers
- F24H15/486—Control of fluid heaters characterised by the type of controllers using timers
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24H—FLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT-GENERATING MEANS, e.g. HEAT PUMPS, IN GENERAL
- F24H9/00—Details
- F24H9/18—Arrangement or mounting of grates or heating means
- F24H9/1809—Arrangement or mounting of grates or heating means for water heaters
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24H—FLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT-GENERATING MEANS, e.g. HEAT PUMPS, IN GENERAL
- F24H9/00—Details
- F24H9/20—Arrangement or mounting of control or safety devices
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/762—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
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- G—PHYSICS
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
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- G06V40/20—Movements or behaviour, e.g. gesture recognition
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Abstract
The application relates to a water heater hot water method and device, electronic equipment and storage medium. The method comprises the following steps: acquiring behavior sequence information obtained by detecting a target object; processing the behavior sequence information to obtain expected hot water use time, wherein the expected hot water use time is used for indicating the expected hot water use time of the target object; and determining the heating time of the water heater according to the expected hot water use time, wherein the heating time of the water heater is the time for indicating the water heater to start heating water. The method provided by the application can effectively solve the technical problems that the heating time of the water heater cannot be accurately predicted in the related technology, and the efficiency of the hot water is low.
Description
Technical Field
The application relates to the technical field of intelligent household equipment, in particular to a water heater hot water method and device, electronic equipment and a storage medium.
Background
Currently, more and more intelligent household equipment enters a common household, and meanwhile, the life quality of people can be effectively improved through the intelligent household equipment.
In the aspect of the domestic water heater, no substantial intelligent application function exists at present, the existing technical scheme only models and judges the water consumption time of a user through the parameters of the water heater, and the water consumption time of the user cannot be accurately predicted and obtained to determine the heating time of the water heater due to the fact that the used data contains less information.
Aiming at the technical problem that the water heater in the related technology cannot accurately predict and obtain the heating time of the water heater, no effective solution is provided at present.
Disclosure of Invention
In order to solve the technical problem that the water heater cannot accurately predict the heating time of the water heater, the application provides a water heating method and device of the water heater, electronic equipment and a storage medium.
In a first aspect, an embodiment of the present application provides a water heating method of a water heater, including:
Acquiring behavior sequence information obtained by detecting a target object;
processing the behavior sequence information to obtain expected hot water use time, wherein the expected hot water use time is used for indicating the expected hot water use time of the target object;
and determining the heating time of the water heater according to the predicted hot water use time, wherein the heating time of the water heater is the time for indicating the water heater to start heating water.
Optionally, in the foregoing water heater water heating method, the obtaining behavior sequence information obtained by detecting the target object includes
Detecting a target environment through a radar sensor to obtain a radar signal;
determining the target object in the target environment according to the radar signal;
Detecting the target object to obtain action sequence data and position sequence data of the target object, wherein the action sequence information comprises the action sequence data and the position sequence data, the action sequence data is used for indicating the sequence of different actions executed by the target object, and the position sequence data is used for indicating the sequence of the stop of the target object between different positions.
Optionally, in the foregoing water heater water heating method, the processing the behavior sequence information to obtain the predicted water usage time includes:
performing first normalization operation on the position sequence data to obtain first normalization data; performing a second normalization operation on the action sequence data to obtain second normalization data;
Inputting the first normalization data and the second normalization data into a preset target network, and predicting to obtain the predicted hot water use time.
Optionally, in the foregoing water heater water heating method, the determining the target object located in the target environment according to the radar signal includes:
obtaining point cloud data of the target environment according to the radar signals;
determining a moving point of the movement in the point cloud data;
And clustering the motion points to obtain the target object.
Optionally, in the foregoing water heater water heating method, the tracking the target object to obtain the action sequence data of the target object includes:
Determining the maximum Doppler speed, the minimum Doppler speed and the average Doppler speed corresponding to each position according to all the motion points corresponding to the target object in each position;
based on the maximum Doppler speed, the minimum Doppler speed and the average Doppler speed corresponding to each position, obtaining a characteristic sequence corresponding to each position;
And obtaining the action sequence data according to the feature sequence corresponding to each position and the sequence of the target object in which the target object stays among different positions.
Optionally, in the foregoing water heater water heating method, the determining the water heater heating time according to the predicted water heating time includes:
determining the heating time of the water heater as the current time under the condition that the time difference value of the current time of the predicted hot water use time interval is smaller than or equal to the preset heating time length;
And under the condition that the time difference between the predicted hot water use time and the current time is larger than the preset heating time, determining the heating time of the water heater as the difference between the predicted hot water use time and the preset heating time.
Optionally, in the water heater water heating method, before the processing the behavior sequence information to obtain the predicted water usage time, the method further includes:
Acquiring, by a radar sensor, historical behavior sequence information generated by an action of the target object in a target environment in each of a plurality of historical time periods, and historical hot water use time corresponding to each historical time period, wherein each historical behavior sequence information comprises historical position sequence data and historical action sequence data, the historical position sequence data and the historical action sequence data corresponding to each historical time period are used for indicating a residence precedence relationship of the target object between different positions in the historical time period, and the historical action sequence data is used for indicating a precedence relationship of the target object executing different actions in the historical time period;
Obtaining training data according to the historical position sequence data, the historical action sequence data and the historical hot water use time corresponding to the same historical time period;
training the network to be trained through the training data until the network to be trained meets the preset precision requirement, and obtaining the target network.
In a second aspect, an embodiment of the present application provides a water heater water heating apparatus, including:
The acquisition module is used for acquiring behavior sequence information obtained by detecting the target object;
The processing module is used for processing the behavior sequence information to obtain expected hot water use time, wherein the expected hot water use time is used for indicating the expected hot water use time of the target object;
and the determining module is used for determining the heating time of the water heater according to the predicted hot water using time, wherein the heating time of the water heater is the time for indicating the water heater to start heating.
In a third aspect, an embodiment of the present application provides an electronic device, including: the device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
The memory is used for storing a computer program;
The processor is configured to implement a method as claimed in any one of the preceding claims when executing the computer program.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, the storage medium comprising a stored program, wherein the program when run performs a method according to any one of the preceding claims.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages:
According to the method provided by the embodiment of the application, the behavior sequence information of the target object is determined, and the predicted water consumption time of the target object is determined based on the behavior sequence information, so that the water heater heating time when the water heater starts to perform hot water can be determined more accurately, the water heater heating time can be more in line with the actual hot water using requirement of the target object, and meanwhile, the technical problem that the water heater heating time cannot be accurately predicted in the related technology, and the hot water efficiency is low can be effectively solved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic flow chart of a water heating method of a water heater according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a water heating method of a water heater according to another embodiment of the present application;
FIG. 3 is a schematic flow chart of a water heating method of a water heater according to another embodiment of the present application;
fig. 4 is a schematic layout diagram of a radar sensor according to an embodiment of the present application;
FIG. 5 is a block diagram of a water heater unit according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
According to one aspect of an embodiment of the present application, a water heater water heating method is provided. Alternatively, in the present embodiment, the water heater heating method described above may be applied to a hardware environment constituted by a terminal and a server. The server is connected with the terminal through a network, and can be used for providing services (such as data processing services, application services and the like) for the terminal or a client installed on the terminal, and a database can be arranged on the server or independent of the server and used for providing data storage services for the server.
The network may include, but is not limited to, at least one of: wired network, wireless network. The wired network may include, but is not limited to, at least one of: a wide area network, a metropolitan area network, a local area network, and the wireless network may include, but is not limited to, at least one of: WIFI (WIRELESS FIDELITY ), bluetooth. The terminal may not be limited to a PC, a mobile phone, a tablet computer, or the like.
The water heating method of the water heater can be executed by the server, the terminal and the server together. The terminal performs the water heating method of the water heater according to the embodiment of the application, and the method can also be performed by a client installed on the terminal.
Taking a server for executing the water heater water heating method in the embodiment as an example, fig. 1 is a schematic diagram of a water heater water heating method according to an embodiment of the present application, including the following steps:
step S101, obtaining behavior sequence information obtained by detecting a target object;
The water heating method of the water heater in the embodiment can be applied to a scene in which the time for starting the water heating of the water heater needs to be controlled.
In order to determine the time for controlling the water heater to start heating water, and the hot water is used by a person, the position of the person can be detected to determine the time for the person to use the hot water.
The target object may be a person using the water heater.
Alternatively, the person in the target environment (for example, a home environment, a work environment, or the like) may be detected by an image detection device or a radar sensor or the like to obtain the behavior sequence information corresponding to each person.
The behavior sequence information of the target object may be a sequencing order for instructing the target object to perform the respective behaviors. For example, the order in which the target object resides at various locations in the target environment, the order in which the target object performs different actions, and so forth.
Since the life work and rest of a person is generally regular, for example, a face is washed, a bath is required after getting up (i.e., hot water is required), and a bath is required after exercise is performed (i.e., hot water is required). Accordingly, the time when the target object is to use hot water can be determined based on the behavior sequence information of the target object at a later stage.
And step S102, processing the behavior sequence information to obtain the expected hot water use time, wherein the expected hot water use time is used for indicating the expected hot water use time of the target object.
After the behavior sequence information of the target object is acquired, the behavior sequence information can be processed through a preset target network.
The target network may be a model that is trained in advance and can predict the expected hot water usage time, and the data input to the target network for prediction may be behavior sequence information, or information obtained by preprocessing (e.g., normalizing) the behavior sequence information.
And after the behavior sequence information is processed through a preset target network, the expected hot water use time can be obtained. The predicted hot water usage time may be a time predicted by the target network when the target object uses hot water.
Step S103, determining the heating time of the water heater according to the expected hot water use time, wherein the heating time of the water heater is the time for indicating the water heater to start heating.
Since the water heater needs a certain process, it is also necessary to determine the water heater heating time based on the estimated water usage time after the estimated water usage time is obtained.
Alternatively, the water heater heating time may be determined based on the efficiency of the water heater, and in general, the higher the efficiency of the water heater, the shorter the time interval between the water heater heating time and the expected hot water usage time, the lower the efficiency of the water heater, and the longer the time interval between the water heater heating time and the expected hot water usage time.
After the water heater heating time is obtained, the water heater can be controlled to start to perform hot water at the water heater heating time.
According to the method, the behavior sequence information of the target object is determined, and the predicted water consumption time of the target object is determined based on the behavior sequence information, so that the water heater heating time when the water heater starts to perform water heating can be determined more accurately, the water heater heating starting time can be more in line with the actual hot water using requirement of the target object, and meanwhile, the technical problem that the water heater heating time cannot be accurately predicted in the related art, and the hot water efficiency is low is effectively solved.
As an alternative embodiment, in the foregoing water heater water heating method, the step S101 of obtaining the behavior sequence information obtained by detecting the target object includes the following steps:
in step S201, the radar sensor detects the target environment to obtain a radar signal.
That is, in this embodiment, the radar sensor detects the target environment to obtain the radar signal in the target environment.
The radar sensor can be arranged in one or more target environments, and the detection range of the radar sensor and the size of the target environments can be selected so that detection signals emitted by the one or more radar sensors can cover the target environments.
For example, in a home environment as shown in fig. 4, one radar sensor may be provided in each relatively independent area (i.e., bedroom, bathroom, kitchen, and living room), respectively.
Step S202, determining a target object in a target environment according to the radar signal.
After the radar signal is acquired, a target object located in the target environment may be determined based on the radar signal.
Alternatively, an object moving in the monitored area can be obtained through radar signals, and the object is considered as a moving target; and the number of moving targets in the monitoring area can be 0 or one or more at the same time.
Because different people have certain difference in the habit of using hot water, in order to solve the problem, the identity of the moving object can be identified according to the radar signal, and when the identity of the moving object is the same as the identity of the target object in the database, the moving object in the target environment is determined to be the target object. Alternatively, the identification may be achieved by adding an identification algorithm for identifying different persons, for example, by a camera-based human-like appearance identification, or a gait identification method using a millimeter wave radar.
And, a candidate network can be trained for each different person respectively, so that each candidate network can be more accurately adapted to the habit of using hot water for each person.
Step S203, detecting the target object to obtain action sequence data and position sequence data of the target object, where the action sequence information includes action sequence data and position sequence data, the action sequence data is used to indicate a sequence of different actions performed by the target object, and the position sequence data is used to indicate a sequence of stay of the target object between different positions.
After the target object is determined, a mode of tracking and detecting the target object can be adopted to obtain action sequence data and position sequence data of the target object.
Alternatively, the target environment may be divided into a plurality of different positions according to the usage scenario in advance, as shown in fig. 4, for example: windows, living rooms, sofas, dining tables, beds, desks, toilets, stoves, etc., in addition, for ranges with areas greater than a preset threshold, the division may be further performed, resulting in the positions as shown: 3.5, 6, 7, 14, so that position sequence data can be determined more accurately.
Furthermore, position sequence data indicating the order in which the target object stays between different positions may be determined based on all the detected radar information. For example, get up (position 1) -go to toilet (position 2) -kitchen (position 3) -dining table (position 4) -go out (position 5), start time (time-start), end time (time-end) and duration (time-duration) at each position are recorded. The sample sequence is: { position 1, time-start 1, time-end 1, time-duration 1}, { position 2, time-start 2, time-end 2, time-duration 2}, { position 3, time-start 3, time-end 3, time-duration 3}, … }.
The action sequence data may be data for indicating a sequence in which the target object performs different actions, for example: getting up-going to toilet-going to kitchen-eating-going out; and, optionally, each location may have a corresponding action.
By the method in the embodiment, the action sequence data and the position sequence data of the target object are obtained by detecting the target object, and then the behavior sequence information of the target object can be determined through multiple dimensions, so that the behavior of the target object can be determined more accurately.
As an alternative embodiment, the step S102 processes the behavior sequence information to obtain the predicted hot water usage time, which includes the following steps:
step S301, performing a first normalization operation on the position sequence data to obtain first normalization data; performing second normalization operation on the motion sequence data to obtain second normalization data;
Step S302, inputting the first normalization data and the second normalization data into a preset target network, and predicting to obtain the expected hot water use time.
That is, the data input into the target network is actually the first normalization operation on the position sequence data, so as to obtain first normalization data; and performing a second normalization operation on the motion sequence data to obtain second normalization data.
Preferably, the first normalization operation and the second normalization operation may be normalized to a normalization operation between [0,1], by which the effect of the scalar size of the input data may be eliminated, and for any feature, the maximum (max) and minimum (min) are set first, and if the current value is x, then x= (x-min)/(max-min) is performed for x normalization.
After the first normalized data and the second normalized data are obtained, the first normalized data and the second normalized data can be input into a target network, and the predicted hot water use time is predicted.
As an alternative embodiment, the method for heating a water heater, in the foregoing step S202, determines a target object located in a target environment according to a radar signal, including the following steps:
step S401, obtaining point cloud data of a target environment according to the radar signals.
Specifically, the radar signal is an echo signal of the radar, and the monitoring area is scanned according to the radar detection signal to obtain point cloud data with geometric position information; the point cloud is a massive point set of the characteristics of the surface of the object; each point in the point cloud data has a corresponding three-dimensional coordinate.
In step S402, a moving point of the motion is determined in the point cloud data.
Because the radar detection device can obtain the point cloud data corresponding to one frame every time the radar detection device transmits radar signals, stationary points and moving points (namely, moving points) in the point cloud data can be obtained by comparing the point cloud data of different frames.
Step S403, clustering the motion points to obtain a target object.
Specifically, a moving object (e.g., a person) typically acquires a plurality of moving points to represent the moving object, so that the moving points of the same moving object need to be clustered together by a clustering algorithm.
Optionally, the clustering method of the point cloud data is based on a distance threshold value and a point threshold value, and comprises the following steps:
1) Acquiring points which are not clustered at present, randomly selecting one point, taking the point as a central point, sequentially selecting one point from the rest points to calculate the distance between the two selected points, 2) discarding the point if the distance is greater than a distance threshold, and sequentially selecting the next point from the rest points to calculate the distance between the two selected points; if the distance is smaller than the distance threshold, the selected point is stored, the average value is calculated with the point stored before, then the average value is used, the next point is selected in the rest points in sequence to calculate the distance from the average value, and 3) the process is repeated. Until the point cloud is traversed one pass. Each cluster is a new target.
After the moving object is obtained through clustering, the moving object can be identified, and when the identity of the moving object is identical to that of the target object, the target object is obtained through clustering.
The tracking of the target object can use an extended Kalman filtering tracking algorithm to track the existing target at the previous moment.
As shown in fig. 2, as an alternative embodiment, a water heating method of a water heater, for example, in the foregoing step S203, the tracking of the target object to obtain the action sequence data of the target object includes the following steps:
in step S501, according to all the motion points corresponding to the target object in each position, the maximum doppler velocity, the minimum doppler velocity and the average doppler velocity corresponding to each position are determined.
In each position, the corresponding motion points of the target objects are different, for example, in a kitchen, the target objects may be mainly the upper body and move, so the motion amplitude of the motion points of the upper body is larger than that of the motion points of the lower body; when the sofa is on, the movement of each part of the target object is very little or basically motionless, so that the movement amplitude of each movement point is very small.
After all the motion points corresponding to the target object in each position are obtained, since the point cloud data information includes the distance (r), the azimuth pitch angle (θ) and the doppler velocity, the maximum doppler velocity with the maximum doppler velocity and the minimum doppler velocity with the minimum doppler velocity can be calculated in all the motion points, and the average doppler velocity obtained by calculating the average value of the doppler velocities of all the motion points.
Step S502, obtaining a characteristic sequence corresponding to each position based on the maximum Doppler speed, the minimum Doppler speed and the average Doppler speed corresponding to each position;
And obtaining the maximum Doppler speed, the minimum Doppler speed and the average Doppler speed corresponding to each position, wherein the maximum Doppler speed, the minimum Doppler speed and the average Doppler speed can be used as the characteristic sequences corresponding to each position.
For example, when the maximum doppler velocity is D max, the minimum doppler velocity D min, and the average doppler velocity D aver are used in the kitchen, the feature sequence in the kitchen is [ D max,Dmin,Daver ].
Step S503, according to the feature sequence corresponding to each position and the sequence of the target object staying between different positions, the action sequence data is obtained.
After determining the feature sequence corresponding to each position, all the feature sequences can be connected based on the sequence of the target object staying among different positions, so that action sequence data can be obtained.
For example, different positions have different sequences of motion characteristics: [ [ maximum Doppler velocity 1, minimum Doppler velocity 1, average Doppler velocity 1], [ maximum Doppler velocity 2, minimum Doppler velocity 2, average Doppler velocity 2], [ maximum Doppler velocity 3, minimum Doppler velocity 3, average Doppler velocity 3], … ].
As an alternative embodiment, the step S103 of determining the heating time of the water heater according to the predicted hot water usage time according to the foregoing water heating method of the water heater includes the following steps:
Step S601, determining the heating time of the water heater as the current time under the condition that the time difference value of the current time of the predicted hot water use time interval is smaller than or equal to the preset heating time length;
In step S602, when the time difference between the predicted hot water usage time and the current time is greater than the preset heating time, the heating time of the water heater is determined to be the difference between the predicted hot water usage time and the preset heating time.
After the expected hot water usage time is obtained, that is, the time when the target object is expected to use the hot water is estimated, because the hot water is not heated all the time for energy saving, the time when the water heater starts to heat the hot water needs to be determined.
The preset heating time length can be determined in advance, and the preset heating time length can be the time length required by the water heater to heat the water in the water heater, and further, when the time difference between the preset time intervals of the predicted hot water use is smaller than or equal to the preset heating time length, the heating time of the water heater is determined to be the current time, namely, the water heater is heated immediately; and under the condition that the time difference between the predicted hot water use time and the current time is larger than the preset heating time, determining that the heating time of the water heater is the difference between the predicted hot water use time and the preset heating time, namely heating when the predicted hot water use time is advanced by the preset heating time.
For example, when the preset heating period is 1 hour:
1) Expected hot water usage time-current time < = 1 hour;
Illustratively, the predicted expected hot water usage time, which is to be used within 1 hour, immediately turns on the water heater hot water mode:
2) The expected hot water usage time-current time >1 hour;
the hot water use time is predicted to be used after 1 hour, and then the heating time of the water heater is predicted:
water heater heating time = hot water usage time is expected to be-1 hour.
As shown in fig. 3, as an alternative embodiment, the water heater water heating method, before the step S102 processes the behavior sequence information to obtain the predicted water usage time, the method further includes the following steps:
In step S701, historical behavior sequence information generated by a target object moving in a target environment in each of a plurality of historical time periods and historical hot water usage time corresponding to each of the historical time periods are acquired through a radar sensor, wherein each of the historical behavior sequence information includes historical position sequence data and historical action sequence data, the historical position sequence data and the historical action sequence data corresponding to each of the historical time periods are used for indicating a precedence relationship of stay of the target object between different positions in the historical time periods, and the historical action sequence data is used for indicating a precedence relationship of execution of different actions by the target object in the historical time periods.
In order to train a target network meeting the preset precision requirement, multiple sets of historical data of a target object need to be collected in advance to train the network to be trained.
Alternatively, historical behavioral sequence information generated by the target object's actions within the target environment during each of a plurality of historical time periods may be obtained by radar.
Similarly, the historical behavior sequence information may include: historical position sequence data for indicating a precedence relationship of stay of the target object between different positions in the historical time period; and historical position sequence data for indicating a precedence relationship of stay of the target object between different positions in the historical period.
The duration of each history period may be a preset duration, for example, half an hour, and further, history behavior sequence information of each history period and history hot water use time corresponding to each history period may be acquired. In general, the duration of acquiring the behavior sequence information is the same as the duration of each historical time period.
For example, the target object may be continuously tracked and detected by the radar sensor to obtain total information of a historical behavior sequence in a total historical time period, then the total historical time period is divided according to a preset duration of each historical time period to obtain a plurality of historical time periods, and the historical behavior sequence information corresponding to each historical time period is obtained according to a time corresponding to each historical behavior (i.e., a historical position and a historical action) in the total information of the historical behavior sequence. And the historical hot water use time corresponding to the historical total time period may be taken as the historical hot water use time corresponding to each of the historical total time periods.
Step S702, obtaining training data according to the historical position sequence data, the historical action sequence data and the historical hot water using time corresponding to the same historical time period.
After the historical position sequence data, the historical action sequence data and the historical hot water use time corresponding to each historical time period are obtained, training data can be obtained according to the historical position sequence data, the historical action sequence data and the historical hot water use time corresponding to the same historical time period.
Optionally, when the prediction is performed, the normalized data corresponding to the position sequence data and the normalized data corresponding to the action sequence data are used as input data, the first historical normalized data may be obtained based on the historical position sequence data, the second historical normalized data may be obtained based on the historical action sequence data, and the training data composed of the first historical normalized data, the second historical normalized data and the historical hot water use time may be obtained. Wherein, the historical hot water use time is the label data.
Step S703, training the network to be trained through a plurality of training data until the network to be trained meets the preset precision requirement, and obtaining the target network.
After the training data are obtained, the network to be trained can be trained through a plurality of training data in all the training data until the network to be trained meets the preset precision requirement, and the target network is obtained.
Further, the network to be trained may be a long and short memory network (LSTM).
By the method in the embodiment, the target network corresponding to the target object can be obtained through training, so that the target network can be more accurately suitable for the target object to predict the heating time of the water heater, and the user experience can be effectively improved.
As described below, an application example to which any of the foregoing embodiments is applied is provided:
p11, setting sensor equipment;
1. Radar sensor distribution setting:
a plurality of radar sensors are distributed as shown in fig. 4.
2. Water sensor for water heater
When a user uses the water heater to heat water, the water consumption behavior of the user can be detected, and the water consumption moment is recorded for training of a subsequent model lstm to be trained.
P12: data acquired by radar sensor
The radar can finally extract point cloud data, namely data corresponding to a target object, wherein the information contained in each point comprises distance, azimuth angle, elevation angle, doppler speed, signal-to-noise ratio and the like through signal processing.
P13: radar-based target cluster tracking
The information of the moving points is obtained after the signal processing, and the specific position of each moving point in the space coordinate system can be calculated according to the information of the moving points. A moving object (e.g., a person) typically radar will acquire multiple points of motion to represent the moving object, so that the moving points of the same moving object need to be clustered together by a clustering algorithm. The invention provides a clustering method for processing point cloud data, which is based on a distance threshold value and a point threshold value and comprises the following steps:
1) Acquiring points which are not clustered at present, randomly selecting one point, taking the point as a central point, sequentially selecting one point from the rest points to calculate the distance between the two selected points, 2) discarding the point if the distance is greater than a distance threshold, and sequentially selecting the next point from the rest points to calculate the distance between the two selected points; if the distance is smaller than the distance threshold, the selected point is stored, the average value is calculated with the point stored before, then the average value is used, the next point is selected in the rest points in sequence to calculate the distance from the average value, and 3) the process is repeated. Until the point cloud is traversed one pass. Each cluster is a new target.
The tracking of the target object can use an extended Kalman filtering tracking algorithm to track the existing target at the previous moment.
P14: position sequence data
First a scene area in the radar detection area is established as shown in fig. 4. The required devices may include: distributed radars, network devices, databases, and information processing systems. Different scenes correspond to different behaviors, such as eating by a person at a table. Different areas of the home are divided into different states, as the numbers in fig. 4 represent different areas in the home (i.e., the target environment). The corresponding start and end times and durations of the target object under different regions may be acquired using the table shown below.
| Location area | Start time | Expiration time | Duration of time |
| 1 | |||
| 2 | |||
| 3 | |||
| …… |
And collecting the duration time of different areas in a period of time, and taking the collected position sequence data as a training sample.
A sequence of positions such as: get up (position 1) -go to toilet (position 2) -kitchen (position 3) -dining table (position 4) -go out (position 5), record the start time (time-start), end time (time-end) and duration (time-duration) at each position. The sample sequence is:
{ position 1, time-start, time-end, time-duration }, { position 2, time-start, time-end, time-duration }, { position 3, time-start, time-end, time-duration }, … }
P15: extraction of motion sequence data
Feature extraction, namely calculating continuous action features in the same position in the step S14 according to the point cloud data under each target, and extracting micro Doppler features: maximum doppler velocity, minimum doppler velocity, average doppler velocity. The three features are calculated by using Doppler speeds in information contained in a point cloud of the target; since there is more than one point cloud data per target person, the maximum Doppler velocity, the minimum Doppler velocity, and the average Doppler velocity can be obtained by calculating the maximum, minimum, and average values.
The same position finally obtains a characteristic sequence: maximum doppler velocity, minimum doppler velocity, average doppler velocity ].
The feature sequence corresponding to each position in the position sequence data constitutes an action feature sequence corresponding to the position sequence data: [ [ maximum Doppler velocity 1, minimum Doppler velocity 1, average Doppler velocity 1], [ maximum Doppler velocity 2, minimum Doppler velocity 2, average Doppler velocity 2], … ].
P16: a long short term memory network (LSTM) predicts an estimated hot water usage time;
The predictive model uses a long and short term memory network (LSTM) to train and predict two types of time series data (i.e., motion series data and position series data) that are collected.
1. Data preparation and preprocessing:
two types of data: position sequence data and motion sequence data;
data amount: extracting the action sequence every half hour;
normalization: the position sequence data and the action sequence data are normalized to be between 0 and 1.
2. Training of LSTM model to be trained (i.e., network to be trained):
training data: position sequence data and motion sequence data (i.e., historical behavior sequence information) in each half hour;
And (3) tag: the user's heating time (i.e., historical hot water usage time) detected by the water heater water sensor.
And training the LSTM model to be trained through the data to obtain a trained LSTM model (i.e. a target network).
Therefore, the user can independently train and learn at home, and the method has better suitability.
3. Prediction by post-training LSTM model:
And extracting position sequence data and action sequence data within the current half hour, inputting the position sequence data and the action sequence data into an LSTM model, and obtaining predicted expected hot water use time.
P17: predicting the heating time of the water heater:
1) Expected hot water usage time-current time < = 1 hour;
Illustratively, the predicted expected hot water usage time, which is to be used within 1 hour, immediately turns on the water heater hot water mode:
2) The expected hot water usage time-current time >1 hour;
the hot water use time is predicted to be used after 1 hour, and then the heating time of the water heater is predicted:
water heater heating time = hot water usage time is expected to be-1 hour.
As shown in fig. 5, according to another aspect of the present application, there is provided a water heater water heating apparatus including:
the acquisition module 1 is used for acquiring behavior sequence information obtained by detecting a target object;
A processing module 2, configured to process the behavior sequence information to obtain an estimated hot water usage time, where the estimated hot water usage time is used to indicate a time when the target object is expected to use hot water;
and the determining module 3 is used for determining the heating time of the water heater according to the expected hot water use time, wherein the heating time of the water heater is the time for indicating the water heater to start heating.
In particular, the specific process of implementing the functions of each module in the apparatus of the embodiment of the present invention may be referred to the related description in the method embodiment, which is not repeated herein.
According to another embodiment of the present application, there is also provided an electronic apparatus including: as shown in fig. 6, the electronic device may include: the device comprises a processor 1501, a communication interface 1502, a memory 1503 and a communication bus 1504, wherein the processor 1501, the communication interface 1502 and the memory 1503 are in communication with each other through the communication bus 1504.
A memory 1503 for storing a computer program;
the processor 1501 is configured to execute the program stored in the memory 1503, thereby implementing the steps of the method embodiment described above.
The bus mentioned above for the electronic device may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, etc. The bus may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
The communication interface is used for communication between the electronic device and other devices.
The Memory may include random access Memory (Random Access Memory, RAM) or may include Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor.
The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but may also be a digital signal Processor (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components.
The embodiment of the application also provides a computer readable storage medium, wherein the storage medium comprises a stored program, and the program executes the method steps of the method embodiment.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. A water heater heating method, comprising:
Acquiring behavior sequence information obtained by detecting a target object;
processing the behavior sequence information to obtain expected hot water use time, wherein the expected hot water use time is used for indicating the expected hot water use time of the target object;
Determining the heating time of the water heater according to the predicted hot water use time, wherein the heating time of the water heater is the time for indicating the water heater to start heating water;
Wherein the obtaining of the behavior sequence information obtained by detecting the target object comprises
Detecting a target environment through a radar sensor to obtain a radar signal;
determining the target object in the target environment according to the radar signal;
Detecting the target object to obtain action sequence data and position sequence data of the target object, wherein the action sequence information comprises the action sequence data and the position sequence data, the action sequence data is used for indicating the sequence of different actions executed by the target object, and the position sequence data is used for indicating the sequence of the stop of the target object between different positions.
2. The water heater heating method according to claim 1, wherein processing the behavior sequence information to obtain an estimated hot water usage time comprises:
performing first normalization operation on the position sequence data to obtain first normalization data; performing a second normalization operation on the action sequence data to obtain second normalization data;
Inputting the first normalization data and the second normalization data into a preset target network, and predicting to obtain the predicted hot water use time.
3. The method of claim 1, wherein said determining the target object located in the target environment from the radar signal comprises:
obtaining point cloud data of the target environment according to the radar signals;
determining a moving point of the movement in the point cloud data;
And clustering the motion points to obtain the target object.
4. A water heater heating method according to claim 3, wherein said tracking of said target object results in action sequence data of said target object, comprising:
Determining the maximum Doppler speed, the minimum Doppler speed and the average Doppler speed corresponding to each position according to all the motion points corresponding to the target object in each position;
based on the maximum Doppler speed, the minimum Doppler speed and the average Doppler speed corresponding to each position, obtaining a characteristic sequence corresponding to each position;
And obtaining the action sequence data according to the feature sequence corresponding to each position and the sequence of the target object in which the target object stays among different positions.
5. The method of claim 1, wherein determining a water heater heating time based on the projected water use time comprises:
determining the heating time of the water heater as the current time under the condition that the time difference value of the current time of the predicted hot water use time interval is smaller than or equal to the preset heating time length;
And under the condition that the time difference between the predicted hot water use time and the current time is larger than the preset heating time, determining the heating time of the water heater as the difference between the predicted hot water use time and the preset heating time.
6. The water heater heating method according to claim 2, wherein before said processing of the behavior sequence information to obtain an estimated hot water usage time, the method further comprises:
Acquiring, by a radar sensor, historical behavior sequence information generated by an action of the target object in a target environment in each of a plurality of historical time periods, and historical hot water use time corresponding to each historical time period, wherein each historical behavior sequence information comprises historical position sequence data and historical action sequence data, the historical position sequence data and the historical action sequence data corresponding to each historical time period are used for indicating a residence precedence relationship of the target object between different positions in the historical time period, and the historical action sequence data is used for indicating a precedence relationship of the target object executing different actions in the historical time period;
Obtaining training data according to the historical position sequence data, the historical action sequence data and the historical hot water use time corresponding to the same historical time period;
training the network to be trained through the training data until the network to be trained meets the preset precision requirement, and obtaining the target network.
7. A water heater water heating apparatus, comprising:
The acquisition module is used for acquiring behavior sequence information obtained by detecting the target object;
The processing module is used for processing the behavior sequence information to obtain expected hot water use time, wherein the expected hot water use time is used for indicating the expected hot water use time of the target object;
The determining module is used for determining the heating time of the water heater according to the predicted hot water use time, wherein the heating time of the water heater is the time for indicating the water heater to start heating water;
the acquisition module is specifically configured to:
detecting a target environment through a radar sensor to obtain a radar signal;
determining the target object in the target environment according to the radar signal;
Detecting the target object to obtain action sequence data and position sequence data of the target object, wherein the action sequence information comprises the action sequence data and the position sequence data, the action sequence data is used for indicating the sequence of different actions executed by the target object, and the position sequence data is used for indicating the sequence of the stop of the target object between different positions.
8. An electronic device, comprising: the device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
The memory is used for storing a computer program;
the processor being adapted to implement the method of any of claims 1 to 6 when executing the computer program.
9. A computer readable storage medium, characterized in that the storage medium comprises a stored program, wherein the program when run performs the method of any of the preceding claims 1 to 6.
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| US9390381B2 (en) * | 2014-03-31 | 2016-07-12 | Shahram Davari | Intelligent water heater controller |
| JP7261548B2 (en) * | 2018-08-22 | 2023-04-20 | 株式会社長府製作所 | Storage hot water heater |
| JP7160656B2 (en) * | 2018-12-04 | 2022-10-25 | 株式会社コロナ | Storage hot water heater |
| JP7253363B2 (en) * | 2018-12-05 | 2023-04-06 | リンナイ株式会社 | hot water system |
| CN114963494B (en) * | 2021-06-09 | 2023-12-12 | 青岛经济技术开发区海尔热水器有限公司 | Intelligent water heater control method and device, electronic equipment and storage medium |
| CN113936424A (en) * | 2021-09-14 | 2022-01-14 | 青岛经济技术开发区海尔热水器有限公司 | Method for monitoring human body behaviors and actively giving alarm, water heater and server |
| CN113819658A (en) * | 2021-10-09 | 2021-12-21 | 华帝股份有限公司 | Control method and system for intelligent bathing, intelligent water heater and intelligent wheelchair |
| CN115013980B (en) * | 2022-06-14 | 2024-04-05 | 珠海格力电器股份有限公司 | Prediction method and device for water consumption time of user, water heater and storage medium |
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