CN116400601A - Scene self-adaptive control method, system and storage medium for environment change equipment - Google Patents
Scene self-adaptive control method, system and storage medium for environment change equipment Download PDFInfo
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The invention discloses a scene self-adaptive control method, a system and a readable storage medium for environment change equipment, wherein the method comprises the following steps: acquiring current scene information, acquiring scene trigger configuration condition information, transmitting the current scene information and the scene trigger configuration condition information to a preset equipment scene self-adaptive control neural network model to obtain one or more intelligent furniture control signals, and transmitting the one or more intelligent furniture control signals to corresponding equipment ends. The invention integrates environment and equipment scenes, so that the household becomes more intelligent and comfortable, and simultaneously solves the problems of manual control of equipment and incapability of timely adjusting living environment when the environment changes, provides more comfortable environment for residents and improves the quality of life.
Description
Technical Field
The present application relates to the field of data processing and data transmission, and more particularly, to a method, a system, and a readable storage medium for adaptive control of an environment-changing device scene.
Background
Along with the increasing of the living standard of people, more and more families put forward higher requirements on living environments, along with the development of science and technology, people have higher requirements on home intellectualization, and the development of the self-adaptive intelligent home control system can be further combined with an automation system and a computer network system, so that more convenient and high-quality living experience is brought to people. Along with the development of social and economic productivity and scientific technology, particularly the rapid improvement of the computer technology and the control technology level, the development progress of modernization of household life is promoted, more comfortable and safe intelligent household environment is provided for residents, the intelligent household control system is well applied and popularized in the current household control, ideal application effect and value are truly shown, particularly in the aspect of environment self-adaptive intelligent household control, the life of people is greatly facilitated, the development idea of novel intelligent household is provided, and better service is provided for residents.
Disclosure of Invention
In view of the foregoing, it is an object of the present invention to provide an environment-changing device scene adaptive control method, system, and readable storage medium.
The first aspect of the present invention provides a scene adaptive control method for an environmental change device, including:
acquiring current scene information;
acquiring scene trigger configuration condition information;
transmitting the current scene information and the scene trigger configuration condition information to a preset equipment scene self-adaptive control neural network model;
obtaining one or more intelligent furniture control signals;
and sending the one or more intelligent furniture control signals to corresponding equipment ends.
In this scheme, before obtaining scene trigger configuration condition information, still include:
acquiring current environmental parameter information;
judging whether the current environmental parameter information is within a preset environmental threshold range, if not, recording the current environmental parameter information for one scene triggering;
acquiring current scene triggering frequency information, judging whether the current scene triggering frequency information is larger than a preset threshold value, and if so, sending the scene triggering configuration condition information to a preset terminal.
In this solution, the sending the current scene information and the scene trigger configuration condition information to a preset neural network model further includes:
acquiring historical scene information and historical scene trigger configuration condition information, and screening according to preset rules according to the historical scene information and the historical scene trigger configuration condition information;
obtaining sample set information;
training the sample set information according to a preset rule;
and obtaining the preset equipment scene self-adaptive control neural network model.
In this scheme, before obtaining current scene information, still include:
acquiring current face information;
the current face information is sent to a preset face recognition model, and whether the current face information is matched with the preset face information or not is judged;
if yes, acquiring the current scene information.
In this scheme, sending the current scene information and the scene trigger configuration condition information to a preset device scene adaptive control neural network model includes:
the current sound information is acquired and,
sending the sound information to a preset equipment scene self-adaptive control neural network model to obtain one or more intelligent furniture control signals;
and sending the one or more intelligent furniture control signals to corresponding equipment ends for adjustment.
In this scheme, after obtaining current sound information, still include:
acquiring voiceprint information in the sound information;
judging whether the voiceprint information is preset voiceprint information or not according to the voiceprint information in the voiceprint information;
if yes, the sound information is sent to a preset equipment scene self-adaptive control neural network model, and if not, warning information is sent to a user side.
The second aspect of the present invention provides an environment-changing device scene adaptive control system, including a memory and a processor, where the memory includes an environment-changing device scene adaptive control method program, and when the environment-changing device scene adaptive control method program is executed by the processor, the following steps are implemented:
acquiring current scene information;
acquiring scene trigger configuration condition information;
transmitting the current scene information and the scene trigger configuration condition information to a preset equipment scene self-adaptive control neural network model;
obtaining one or more intelligent furniture control signals;
and sending the one or more intelligent furniture control signals to corresponding equipment ends.
In this scheme, before obtaining scene trigger configuration condition information, still include:
acquiring current environmental parameter information;
judging whether the current environmental parameter information is within a preset environmental threshold range, if not, recording the current environmental parameter information for one scene triggering;
acquiring current scene triggering frequency information, judging whether the current scene triggering frequency information is larger than a preset threshold value, and if so, sending the scene triggering configuration condition information to a preset terminal.
In this solution, the sending the current scene information and the scene trigger configuration condition information to a preset neural network model further includes:
acquiring historical scene information and historical scene trigger configuration condition information, and screening according to preset rules according to the historical scene information and the historical scene trigger configuration condition information;
obtaining sample set information;
training the sample set information according to a preset rule;
and obtaining the preset equipment scene self-adaptive control neural network model.
In this scheme, before obtaining current scene information, still include:
acquiring current face information;
the current face information is sent to a preset face recognition model, and whether the current face information is matched with the preset face information or not is judged;
if yes, acquiring the current scene information.
In this scheme, sending the current scene information and the scene trigger configuration condition information to a preset device scene adaptive control neural network model includes:
the current sound information is acquired and,
sending the sound information to a preset equipment scene self-adaptive control neural network model to obtain one or more intelligent furniture control signals;
and sending the one or more intelligent furniture control signals to corresponding equipment ends for adjustment.
In this scheme, after obtaining current sound information, still include:
acquiring voiceprint information in the sound information;
judging whether the voiceprint information is preset voiceprint information or not according to the voiceprint information in the voiceprint information;
if yes, the sound information is sent to a preset equipment scene self-adaptive control neural network model, and if not, warning information is sent to a user side.
A third aspect of the present invention provides a computer-readable storage medium having embodied therein an environment-changing device scene adaptive control method program which, when executed by a processor, implements the steps of an environment-changing device scene adaptive control method as described in any one of the above.
The invention discloses a scene self-adaptive control method, a system and a readable storage medium for environment change equipment, wherein the method comprises the following steps: acquiring current scene information, acquiring scene trigger configuration condition information, transmitting the current scene information and the scene trigger configuration condition information to a preset equipment scene self-adaptive control neural network model to obtain one or more intelligent furniture control signals, and transmitting the one or more intelligent furniture control signals to corresponding equipment ends. The invention integrates environment and equipment scenes, so that the household becomes more intelligent and comfortable, and simultaneously solves the problems of manual control of equipment and incapability of timely adjusting living environment when the environment changes, provides more comfortable environment for residents and improves the quality of life.
Drawings
FIG. 1 is a flow chart of a scene adaptive control method for an environment changing device of the present invention;
FIG. 2 shows a flow chart of a data preprocessing method of the present invention;
FIG. 3 shows a flow chart of a model training method of the present invention;
fig. 4 shows a block diagram of an environment-changing device scene adaptive control system of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
Fig. 1 shows a flow chart of a scene adaptive control method of an environment changing device according to the present invention.
As shown in fig. 1, the invention discloses a scene self-adaptive control method for environment change equipment, which comprises the following steps:
s102, acquiring current scene information;
s104, acquiring scene trigger configuration condition information;
s106, the current scene information and the scene trigger configuration condition information are sent to a preset equipment scene self-adaptive control neural network model;
s108, obtaining one or more intelligent furniture control signals;
s110, sending the one or more intelligent furniture control signals to corresponding equipment ends.
According to the embodiment of the invention, firstly, the current environmental parameter information is acquired, wherein the current environmental parameter information comprises temperature environmental parameter information, humidity environmental parameter information, noise environmental parameter information, ray environmental parameter information, weather environmental parameter information and the like, and the scene triggering configuration condition information is acquired, and then comprises home returning scene parameter information, night starting scene parameter information, departure scene parameter information, bath scene parameter information, rainy day scene parameter information, entertainment scene parameter information, rest scene parameter information, comfort scene parameter information, sports scene parameter information and sleep scene parameter information. And sending the current scene information and the scene trigger configuration condition information to a preset equipment scene self-adaptive control neural network model. The preset equipment scene self-adaptive control neural network model automatically generates one or more intelligent furniture control signals according to the input current scene information and the scene trigger configuration condition information, so that intelligent adjustment of corresponding equipment, such as an air conditioner, a lamp, a television, a refrigerator, a curtain, a fan and the like, is realized.
According to the embodiment of the invention, before acquiring the scene trigger configuration condition information, the method further comprises the following steps:
s202, acquiring current environmental parameter information;
s204, judging whether the current environmental parameter information is within a preset environmental threshold range, if not, recording the current environmental parameter information for one scene triggering;
s206, acquiring current scene trigger frequency information, judging whether the current scene trigger frequency information is larger than a preset threshold value, and if so, sending the scene trigger configuration condition information to a preset terminal.
It should be noted that, the present invention firstly obtains the current environmental parameter information, where the current environmental parameter information includes temperature environmental parameter information, humidity environmental parameter information, noise environmental parameter information, light environmental parameter information, weather environmental parameter information, etc., and because the above-mentioned obtaining the current environmental parameter information all adopts corresponding sensors, the corresponding sensors are all arranged in the house and are easy to be interfered by some special conditions, taking the sensors for obtaining the light environmental parameter information as an example, the walking of the child in the daytime room is easy to be blocked to the sensors for obtaining the light environmental parameter information, so that the obtained light environmental parameter information is lower. The above situation is easy for the system to misjudge the blackish scene. Based on the problems, the method comprises the steps of firstly judging whether the current environmental parameter information is in a preset environmental threshold range, if not, recording the current environmental parameter information for one time for scene triggering, finally obtaining the current scene triggering times information, judging whether the current scene triggering times information is larger than a preset threshold value, and if so, sending the scene triggering configuration condition information to a preset terminal. The preset threshold value is set in a self-defined mode according to the corresponding current environment parameter information, and the problems are solved.
According to an embodiment of the present invention, the current scene information and the scene trigger configuration condition information are sent to a preset neural network model, and the method further includes:
s302, acquiring historical scene information and historical scene trigger configuration condition information, and screening according to preset rules according to the historical scene information and the historical scene trigger configuration condition information;
s304, sample set information is obtained;
s306, training the sample set information according to a preset rule;
and S308, obtaining the preset equipment scene self-adaptive control neural network model.
Firstly, acquiring historical scene information and historical scene trigger configuration condition information, matching the historical scene information with the historical scene trigger configuration condition information to manufacture a sample set, and dividing the sample set into a training set and a test set. The method is used for training a preset equipment scene self-adaptive control neural network model.
According to the embodiment of the invention, before acquiring the current scene information, the method further comprises the following steps:
acquiring current face information;
the current face information is sent to a preset face recognition model, and whether the current face information is matched with the preset face information or not is judged;
if yes, acquiring the current scene information.
Before acquiring the current scene information, in order to confirm the user using the system, the invention firstly acquires the current face information, sends the current face information to a preset face recognition model, judges whether the current face information is matched with the preset face information,
if yes, acquiring the current scene information.
According to an embodiment of the present invention, the sending the current scene information and the scene trigger configuration condition information to a preset device scene adaptive control neural network model includes:
the current sound information is acquired and,
sending the sound information to a preset equipment scene self-adaptive control neural network model to obtain one or more intelligent furniture control signals;
and sending the one or more intelligent furniture control signals to corresponding equipment ends for adjustment.
It should be noted that, the system may directly acquire the current sound information and send the current sound information to a preset device scene adaptive control neural network model, generate one or more intelligent furniture control signals, send the one or more intelligent furniture control signals to a corresponding device end, for example, please adjust the temperature of the warm air conditioner to 26 ℃, and the system automatically generates an adjusting signal of the air conditioner according to the current sound information of the user to perform corresponding adjustment.
According to an embodiment of the present invention, after obtaining the current sound information, the method further includes:
acquiring voiceprint information in the sound information;
judging whether the voiceprint information is preset voiceprint information or not according to the voiceprint information in the voiceprint information;
if yes, the sound information is sent to a preset equipment scene self-adaptive control neural network model, and if not, warning information is sent to a user side.
It should be noted that, each piece of voiceprint information is unique, so that the voiceprint information can be compared with the preset voiceprint information to determine whether the voiceprint information is the same, if so, the voiceprint information is sent to the preset equipment scene adaptive control neural network model, and if not, the caution information is sent to the user side. The preset voiceprint information can be one or a plurality of voiceprint information.
According to an embodiment of the present invention, further includes:
acquiring gesture information of a user;
the user gesture information is sent to a preset user gesture recognition model, and one or more intelligent furniture control signals are obtained;
and sending the one or more intelligent furniture control signals to corresponding equipment ends for adjustment.
It should be noted that, the invention can directly obtain user gesture information, and perform information conversion through the preset user gesture recognition model to generate one or more intelligent furniture control signals, thereby directly controlling the corresponding equipment end to realize intelligent control of furniture.
According to an embodiment of the present invention, further includes:
acquiring current user priority information;
judging whether the current user priority information is the current highest priority or not;
if yes, acquiring scene trigger configuration conditions according to the current user;
if not, acquiring the highest-level user information, and acquiring scene trigger configuration conditions according to the highest-level user.
It should be noted that, because two or more persons may exist in the same room during the use of the environment-changing device scene adaptive control method, the present invention determines whether the current user priority information is the current highest priority according to the current user priority information, if so, acquires scene trigger configuration conditions according to the current user. According to the current scene information and the scene triggering configuration condition information, one or more intelligent furniture control signals are generated through a preset equipment scene self-adaptive control neural network model, so that equipment is intelligently adjusted. If not, acquiring the highest-level user information, acquiring the scene trigger configuration conditions according to the highest-level user, and acquiring the scene trigger configuration conditions according to the highest-level user. Thereby intelligently adjusting the device.
According to an embodiment of the present invention, further comprising:
acquiring body parameter information of a current user;
judging whether the body parameter information of the current user is in a preset corresponding threshold range or not;
if not, the current user priority is updated.
It should be noted that, firstly, the wearable device is adopted to acquire the body parameter information of the current user, the wearable device can be electronic devices such as an electronic wristband and an electronic watch which can acquire the body parameter information of the user, and judge whether the body parameter information of the current user is within a preset corresponding threshold range, if not, update the priority of the current user. For example, when the preset temperature threshold is 36-37 degrees and the electronic equipment detects that the body temperature of the current user is not within 36-37 degrees, the priority of the current user is processed by adding one, and the intelligent equipment can meet the requirements of the patient on the environment as far as possible.
Fig. 4 shows a block diagram of an environment-changing device scene adaptive control system of the present invention.
As shown in fig. 4, a second aspect of the present invention provides an environment changing device scene adaptive control system 4, including a memory 41 and a processor 42, where the memory includes an environment changing device scene adaptive control method program, and when the environment changing device scene adaptive control method program is executed by the processor, the following steps are implemented:
acquiring current scene information;
acquiring scene trigger configuration condition information;
transmitting the current scene information and the scene trigger configuration condition information to a preset equipment scene self-adaptive control neural network model;
obtaining one or more intelligent furniture control signals;
and sending the one or more intelligent furniture control signals to corresponding equipment ends.
According to the embodiment of the invention, firstly, the current environmental parameter information is acquired, wherein the current environmental parameter information comprises temperature environmental parameter information, humidity environmental parameter information, noise environmental parameter information, ray environmental parameter information, weather environmental parameter information and the like, and the scene triggering configuration condition information is acquired, and then comprises home returning scene parameter information, night starting scene parameter information, departure scene parameter information, bath scene parameter information, rainy day scene parameter information, entertainment scene parameter information, rest scene parameter information, comfort scene parameter information, sports scene parameter information and sleep scene parameter information. And sending the current scene information and the scene trigger configuration condition information to a preset equipment scene self-adaptive control neural network model. The preset equipment scene self-adaptive control neural network model automatically generates one or more intelligent furniture control signals according to the input current scene information and the scene trigger configuration condition information, so that intelligent adjustment of corresponding equipment, such as an air conditioner, a lamp, a television, a refrigerator, a curtain, a fan and the like, is realized.
According to the embodiment of the invention, before acquiring the scene trigger configuration condition information, the method further comprises the following steps:
acquiring current environmental parameter information;
judging whether the current environmental parameter information is within a preset environmental threshold range, if not, recording the current environmental parameter information for one scene triggering;
acquiring current scene triggering frequency information, judging whether the current scene triggering frequency information is larger than a preset threshold value, and if so, sending the scene triggering configuration condition information to a preset terminal.
It should be noted that, the present invention firstly obtains the current environmental parameter information, where the current environmental parameter information includes temperature environmental parameter information, humidity environmental parameter information, noise environmental parameter information, light environmental parameter information, weather environmental parameter information, etc., and because the above-mentioned obtaining the current environmental parameter information all adopts corresponding sensors, the corresponding sensors are all arranged in the house and are easy to be interfered by some special conditions, taking the sensors for obtaining the light environmental parameter information as an example, the walking of the child in the daytime room is easy to be blocked to the sensors for obtaining the light environmental parameter information, so that the obtained light environmental parameter information is lower. The above situation is easy for the system to misjudge the blackish scene. Based on the problems, the method comprises the steps of firstly judging whether the current environmental parameter information is in a preset environmental threshold range, if not, recording the current environmental parameter information for one time for scene triggering, finally obtaining the current scene triggering times information, judging whether the current scene triggering times information is larger than a preset threshold value, and if so, sending the scene triggering configuration condition information to a preset terminal. The preset threshold value is set in a self-defined mode according to the corresponding current environment parameter information, and the problems are solved.
According to an embodiment of the present invention, the current scene information and the scene trigger configuration condition information are sent to a preset neural network model, and the method further includes:
acquiring historical scene information and historical scene trigger configuration condition information, and screening according to preset rules according to the historical scene information and the historical scene trigger configuration condition information;
obtaining sample set information;
training the sample set information according to a preset rule;
and obtaining the preset equipment scene self-adaptive control neural network model.
Firstly, acquiring historical scene information and historical scene trigger configuration condition information, matching the historical scene information with the historical scene trigger configuration condition information to manufacture a sample set, and dividing the sample set into a training set and a test set. The method is used for training a preset equipment scene self-adaptive control neural network model.
According to the embodiment of the invention, before acquiring the current scene information, the method further comprises the following steps:
acquiring current face information;
the current face information is sent to a preset face recognition model, and whether the current face information is matched with the preset face information or not is judged;
if yes, acquiring the current scene information.
Before acquiring the current scene information, in order to confirm the user using the system, the invention firstly acquires the current face information, sends the current face information to a preset face recognition model, judges whether the current face information is matched with the preset face information,
if yes, acquiring the current scene information.
According to an embodiment of the present invention, the sending the current scene information and the scene trigger configuration condition information to a preset device scene adaptive control neural network model includes:
the current sound information is acquired and,
sending the sound information to a preset equipment scene self-adaptive control neural network model to obtain one or more intelligent furniture control signals;
and sending the one or more intelligent furniture control signals to corresponding equipment ends for adjustment.
It should be noted that, the system may directly acquire the current sound information and send the current sound information to a preset device scene adaptive control neural network model, generate one or more intelligent furniture control signals, send the one or more intelligent furniture control signals to a corresponding device end, for example, please adjust the temperature of the warm air conditioner to 26 ℃, and the system automatically generates an adjusting signal of the air conditioner according to the current sound information of the user to perform corresponding adjustment.
According to an embodiment of the present invention, after obtaining the current sound information, the method further includes:
acquiring voiceprint information in the sound information;
judging whether the voiceprint information is preset voiceprint information or not according to the voiceprint information in the voiceprint information;
if yes, the sound information is sent to a preset equipment scene self-adaptive control neural network model, and if not, warning information is sent to a user side.
It should be noted that, each piece of voiceprint information is unique, so that the voiceprint information can be compared with the preset voiceprint information to determine whether the voiceprint information is the same, if so, the voiceprint information is sent to the preset equipment scene adaptive control neural network model, and if not, the caution information is sent to the user side. The preset voiceprint information can be one or a plurality of voiceprint information.
According to an embodiment of the present invention, further includes:
acquiring gesture information of a user;
the user gesture information is sent to a preset user gesture recognition model, and one or more intelligent furniture control signals are obtained;
and sending the one or more intelligent furniture control signals to corresponding equipment ends for adjustment.
It should be noted that, the invention can directly obtain user gesture information, and perform information conversion through the preset user gesture recognition model to generate one or more intelligent furniture control signals, thereby directly controlling the corresponding equipment end to realize intelligent control of furniture.
According to an embodiment of the present invention, further includes:
acquiring current user priority information;
judging whether the current user priority information is the current highest priority or not;
if yes, acquiring scene trigger configuration conditions according to the current user;
if not, acquiring the highest-level user information, and acquiring scene trigger configuration conditions according to the highest-level user.
It should be noted that, because two or more persons may exist in the same room during the use of the environment-changing device scene adaptive control method, the present invention determines whether the current user priority information is the current highest priority according to the current user priority information, if so, acquires scene trigger configuration conditions according to the current user. According to the current scene information and the scene triggering configuration condition information, one or more intelligent furniture control signals are generated through a preset equipment scene self-adaptive control neural network model, so that equipment is intelligently adjusted. If not, acquiring the highest-level user information, acquiring the scene trigger configuration conditions according to the highest-level user, and acquiring the scene trigger configuration conditions according to the highest-level user. Thereby intelligently adjusting the device.
According to an embodiment of the present invention, further comprising:
acquiring body parameter information of a current user;
judging whether the body parameter information of the current user is in a preset corresponding threshold range or not;
if not, the current user priority is updated.
It should be noted that, firstly, the wearable device is adopted to acquire the body parameter information of the current user, the wearable device can be electronic devices such as an electronic wristband and an electronic watch which can acquire the body parameter information of the user, and judge whether the body parameter information of the current user is within a preset corresponding threshold range, if not, update the priority of the current user. For example, when the preset temperature threshold is 36-37 degrees and the electronic equipment detects that the body temperature of the current user is not within 36-37 degrees, the priority of the current user is processed by adding one, and the intelligent equipment can meet the requirements of the patient on the environment as far as possible.
A third aspect of the present invention provides a computer-readable storage medium having embodied therein an environment-changing device scene adaptive control method program which, when executed by a processor, implements the steps of an environment-changing device scene adaptive control method as described in any one of the above.
The invention discloses a scene self-adaptive control method, a system and a readable storage medium for environment change equipment, wherein the method comprises the following steps: acquiring current scene information, acquiring scene trigger configuration condition information, transmitting the current scene information and the scene trigger configuration condition information to a preset equipment scene self-adaptive control neural network model to obtain one or more intelligent furniture control signals, and transmitting the one or more intelligent furniture control signals to corresponding equipment ends. The invention integrates environment and equipment scenes, so that the household becomes more intelligent and comfortable, and simultaneously solves the problems of manual control of equipment and incapability of timely adjusting living environment when the environment changes, provides more comfortable environment for residents and improves the quality of life.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
Claims (10)
1. The scene self-adaptive control method for the environment change equipment is characterized by comprising the following steps of:
acquiring current scene information;
acquiring scene trigger configuration condition information;
transmitting the current scene information and the scene trigger configuration condition information to a preset equipment scene self-adaptive control neural network model;
obtaining one or more intelligent furniture control signals;
and sending the one or more intelligent furniture control signals to corresponding equipment ends.
2. The method for adaptively controlling a scene of an environment-changing device according to claim 1, further comprising, before acquiring the scene trigger configuration condition information:
acquiring current environmental parameter information;
judging whether the current environmental parameter information is within a preset environmental threshold range, if not, recording the current environmental parameter information for one scene triggering;
acquiring current scene triggering frequency information, judging whether the current scene triggering frequency information is larger than a preset threshold value, and if so, sending the scene triggering configuration condition information to a preset terminal.
3. The method for adaptively controlling a scene of an environmental change device according to claim 1, wherein the current scene information and the scene trigger configuration condition information are transmitted to a predetermined neural network model, further comprising:
acquiring historical scene information and historical scene trigger configuration condition information, and screening according to preset rules according to the historical scene information and the historical scene trigger configuration condition information;
obtaining sample set information;
training the sample set information according to a preset rule;
and obtaining the preset equipment scene self-adaptive control neural network model.
4. The method for adaptively controlling a scene of an environment-changing device according to claim 1, further comprising, before acquiring the current scene information:
acquiring current face information;
the current face information is sent to a preset face recognition model, and whether the current face information is matched with the preset face information or not is judged;
if yes, acquiring the current scene information.
5. The method for adaptively controlling an environment-changing device scene according to claim 1, wherein transmitting the current scene information and the scene trigger configuration condition information to a preset device scene adaptive control neural network model comprises:
the current sound information is acquired and,
sending the sound information to a preset equipment scene self-adaptive control neural network model to obtain one or more intelligent furniture control signals;
and sending the one or more intelligent furniture control signals to corresponding equipment ends for adjustment.
6. The method for adaptively controlling a scene of an environment-changing device according to claim 5, further comprising, after acquiring the current sound information:
acquiring voiceprint information in the sound information;
judging whether the voiceprint information is preset voiceprint information or not according to the voiceprint information in the voiceprint information;
if yes, the sound information is sent to a preset equipment scene self-adaptive control neural network model, and if not, warning information is sent to a user side.
7. The environment change equipment scene self-adaptive control system is characterized by comprising a memory and a processor, wherein the memory comprises an environment change equipment scene self-adaptive control method program, and the environment change equipment scene self-adaptive control method program realizes the following steps when being executed by the processor:
acquiring current scene information;
acquiring scene trigger configuration condition information;
transmitting the current scene information and the scene trigger configuration condition information to a preset equipment scene self-adaptive control neural network model;
obtaining one or more intelligent furniture control signals;
and sending the one or more intelligent furniture control signals to corresponding equipment ends.
8. The context adaptive control system of claim 7, further comprising, prior to obtaining context trigger configuration condition information:
acquiring current environmental parameter information;
judging whether the current environmental parameter information is within a preset environmental threshold range, if not, recording the current environmental parameter information for one scene triggering;
acquiring current scene triggering frequency information, judging whether the current scene triggering frequency information is larger than a preset threshold value, and if so, sending the scene triggering configuration condition information to a preset terminal.
9. The system of claim 7, wherein the sending the current scene information and the scene trigger configuration condition information to a predetermined neural network model further comprises:
acquiring historical scene information and historical scene trigger configuration condition information, and screening according to preset rules according to the historical scene information and the historical scene trigger configuration condition information;
obtaining sample set information;
training the sample set information according to a preset rule;
and obtaining the preset equipment scene self-adaptive control neural network model.
10. A computer-readable storage medium, characterized in that it includes therein an environment-changing device scene adaptive control method program, which when executed by a processor, implements the steps of an environment-changing device scene adaptive control method according to any one of claims 1 to 6.
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