CN113611332A - Intelligent control switching power supply method and device based on neural network - Google Patents
Intelligent control switching power supply method and device based on neural network Download PDFInfo
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Abstract
The invention relates to an artificial intelligence technology, and discloses an intelligent control switch power supply method based on a neural network, which comprises the following steps: receiving control switch voice sent by portable electronic equipment, converting the control switch voice into a switch control text, extracting control keywords of the switch control text, identifying a switch model corresponding to the control keywords by using a switch control neural network which is trained in advance, leading out a switch to be controlled according to the switch model, sending a switch control request to a pre-constructed multimedia communication system, receiving a communication construction message sent by the multimedia communication system in response to the switch control request, constructing a control channel according to the communication construction message, and executing switch control on the switch to be controlled by using the control channel. The invention also discloses an intelligent control switch power supply device and electronic equipment based on the neural network, and the problems of low intelligence and limited distance of the control switch can be solved.
Description
Technical Field
The invention relates to an artificial intelligence technology, in particular to a method and a device for intelligently controlling a switching power supply based on a neural network and electronic equipment.
Background
Along with the development of artificial intelligence technology, the development of all walks of life has been promoted, and similarly, the intelligent house field is also flourishing, also includes intelligent control switching power supply wherein.
At present, a commonly used intelligent control switching power supply is mainly based on voiceprint recognition, for example, if a user sends out voice for turning off a hall lamp, a voice catcher where a living room is located captures the voice, and then converts the voice into an electric signal for turning off the hall lamp.
Although the method can improve the intelligence of controlling the switching power supply, the method is limited by distance, a user can only be near the switching power supply, and then the phenomenon of misoperation is caused because the user can not send out voice control by accident, such as voice related to turning off the hall lamp when the user talks with people, but the phenomenon that the user does not intend to turn off the hall lamp but directly causes the hall lamp to turn off is caused.
Disclosure of Invention
The invention provides a method and a device for intelligently controlling a switching power supply based on a neural network and electronic equipment, and mainly aims to solve the problems of low intelligence and limited distance of the switching power supply.
In order to achieve the above object, the present invention provides a method for intelligently controlling a switching power supply based on a neural network, comprising:
receiving control switch voice sent by the portable electronic equipment, and extracting voice characteristics from the control switch voice;
detecting whether the voice features are the voice of the false control switch or not by using a pre-trained voice emotion detection model;
when the voice feature is detected not to be the false control switch voice, converting the voice feature into a switch control text, and extracting a control keyword of the switch control text;
identifying a switch model corresponding to the control keyword by using a switch control neural network which is trained in advance;
a switch to be controlled is led out according to the switch model, and a switch control request is sent to a pre-constructed multimedia communication system;
and receiving a communication construction message sent by the multimedia communication system in response to the switch control request, establishing a control channel between the switch to be controlled and the portable electronic equipment according to the communication construction message, and executing switch control on the switch to be controlled by utilizing the control channel.
Optionally, the pre-trained switch-controlled neural network comprises:
extracting network layers in a preset sequence from a MobileNet V2 neural network to obtain a MobileNet network layer;
acquiring a full convolution neural network, and connecting the MobileNet network layer with the full convolution neural network to obtain a convolution neural network layer;
constructing a switch model prediction function, and connecting the activation function with the convolutional neural network layer to obtain the switch control neural network to be trained;
acquiring a keyword training set and a corresponding real label set, and inputting the keyword training set into the switch control neural network for training to obtain a prediction label set;
and adjusting the switch control neural network to be trained according to the real label set and the prediction label set to obtain the trained switch control neural network.
Optionally, the obtaining a keyword training set and a corresponding real label set, and inputting the keyword training set into the on-off control neural network for training to obtain a predicted label set includes:
performing convolution feature extraction on the keyword training set by utilizing the convolution neural network layer to obtain a keyword feature set;
and taking the keyword feature set as an input parameter of the switch model prediction function, and calculating to obtain the prediction tag set.
Optionally, the adjusting the switch control neural network to be trained according to the real tag set and the predicted tag set to obtain the trained switch control neural network includes:
calculating error values of the real label set and the prediction label set;
judging the magnitude relation between the error value and a specified error threshold value;
if the error value is larger than the specified error threshold value, adjusting internal parameters of the switch control neural network, and returning the keyword training set to be input into the switch control neural network;
and if the error value is less than or equal to the specified error threshold value, obtaining the trained switch control neural network.
Optionally, the receiving a communication setup message sent by the multimedia communication system in response to the switch control request further includes:
receiving a serial number sent by the portable electronic equipment, and generating virtual number information according to the serial number;
sending the virtual number information to the portable electronic equipment, and converting a registration request into a multimedia communication protocol verification request when receiving the registration request returned by the portable electronic equipment in response to the virtual number information;
sending an authentication request to the portable electronic equipment according to the multimedia communication protocol registration request;
and calling a pre-constructed registration server to authenticate the portable electronic equipment according to the authentication request, and receiving a communication building message sent by the multimedia communication system responding to the switch control request when the authentication is passed.
Optionally, the establishing a control channel between the switch to be controlled and the portable electronic device according to the communication building message includes:
converting the communication building message into a building protocol conforming to the communication protocol of the portable electronic equipment according to a pre-built signaling gateway;
and sending the setting protocol to the portable electronic equipment to complete setting of the control channel of the switch to be controlled and the portable electronic equipment.
Optionally, the generating the virtual number information according to the serial number includes:
receiving the serial number by using the multimedia communication system, and acquiring account opening information of the portable electronic equipment according to the serial number;
and generating the corresponding virtual number information by using the account opening information.
In order to solve the above problems, the present invention also provides an intelligent control switching power supply device based on a neural network, the device comprising:
the voice feature extraction module is used for receiving control switch voice sent by the portable electronic equipment and extracting voice features from the control switch voice;
the control keyword extraction module is used for detecting whether the voice feature is the false control switch voice or not by using a pre-trained voice emotion detection model, converting the voice feature into a switch control text when the voice feature is detected not to be the false control switch voice, and extracting a control keyword of the switch control text;
the switch model identification module is used for identifying a switch model corresponding to the control keyword by utilizing a switch control neural network which is trained in advance;
and the switch control module is used for leading out a switch to be controlled according to the switch model, sending a switch control request to a pre-constructed multimedia communication system, receiving a communication construction message sent by the multimedia communication system in response to the switch control request, establishing a control channel between the switch to be controlled and the portable electronic equipment according to the communication construction message, and executing switch control on the switch to be controlled by utilizing the control channel.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to implement the neural network based intelligent control switching power supply method described above.
In the embodiment of the invention, compared with the problems that the intelligence of the control switch power supply is lower and the distance of the control switch power supply is limited by using voiceprint recognition to intelligently control the switch power supply in the background technology, on one hand, the control switch voice sent by the portable electronic equipment is received, the voice feature is extracted from the control switch voice, and then whether the voice feature is the error control switch voice is detected by using the pre-trained voice emotion detection model, and when the voice feature is the error control switch voice, the control operation of the switch power supply is not executed, so that the intelligence of the control switch power supply is improved, the problem of the error operation in the background technology is solved, and on the other hand, in order to solve the problem of the distance limitation of the control switch power supply, the embodiment of the invention uses a multimedia communication system as a transmission medium to receive the communication construction message sent by the multimedia communication system in response to the switch control request, and establishing a control channel between the switch to be controlled and the portable electronic equipment according to the communication building message, thereby realizing remote control of the power supply. Therefore, the method, the device and the electronic equipment for intelligently controlling the switching power supply based on the neural network can solve the problems of low intelligence of the control switching power supply and limited distance of the control switching power supply.
Drawings
Fig. 1 is a schematic flowchart of a method for intelligently controlling a switching power supply based on a neural network according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of S1 in the method for intelligently controlling a switching power supply based on a neural network according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of S6 in the method for intelligently controlling a switching power supply based on a neural network according to an embodiment of the present invention;
fig. 4 is a block diagram of an intelligent control switching power supply device based on a neural network according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an internal structure of an electronic device implementing a neural network-based intelligent control method for a switching power supply according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides an intelligent control switch power supply method based on a neural network. The execution subject of the intelligent control switching power supply method based on the neural network includes, but is not limited to, at least one of electronic devices, such as a server, a terminal, and the like, which can be configured to execute the method provided by the embodiments of the present application. In other words, the neural network-based intelligent control switching power supply method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a block chain platform. The server includes but is not limited to: the cloud server can be an independent server, or can be a cloud server providing basic cloud computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, Network service, cloud communication, middleware service, domain name service, security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Fig. 1 is a schematic flow chart of a method for intelligently controlling a switching power supply based on a neural network according to an embodiment of the present invention. In an embodiment of the present invention, the method for intelligently controlling a switching power supply based on a neural network includes:
and S1, receiving control switch voice sent by the portable electronic equipment, and extracting voice characteristics from the control switch voice.
It should be appreciated that the portable electronic device may include a cell phone, tablet, computer, etc.
For example, when the user goes on a business trip in the daytime, feels that the light in the living room at home is not turned off, and wants to turn off the light in the living room at home by using the mobile phone, the mobile phone is used for sending out the voice of the control switch: please turn off the light in the living room at home thanks.
It should be explained that there may be a case where the conversation content of the user talking with others is regarded as the control switch voice, and therefore, the embodiment of the present invention needs to detect whether the control switch voice is the voice of the user actually controlling the switch. In detail, referring to fig. 2, the extracting of the voice feature from the control switch voice includes:
s11, converting the control switch voice into a voice sequence which can be processed by a computer;
s12, performing framing and windowing on the voice sequence to obtain a plurality of voice frames;
it should be explained that a speech sequence is a continuous signal, and since the continuous signal is not beneficial to subsequent feature extraction, the speech sequence needs to be segmented into shorter speech frames through framing processing, in the embodiment of the present invention, the frame length is 10 to 30ms, that is, the frame number of each speech frame is 33 to 100 frames.
In addition, windowing is performed on each voice frame to prevent leakage in the frequency domain, so that a window function is constructed, wherein common window functions comprise a rectangular window, a Hamming window and a Hanning window, and different window functions can be selected according to different situations.
In one embodiment of the invention, the speech sequence can be subjected to framing and windowing in a Hamming window mode to obtain a plurality of speech frames, so that the speech features can be effectively extracted.
And S13, calculating the frame energy of each voice frame, and extracting the voice features from the plurality of voice frames according to the frame energy.
Illustratively, each of the frame energies may be calculated using the following energy algorithm:
wherein the content of the first and second substances,
is as follows
The frame energy of each frame of speech,
is the first
The total duration of a frame of speech,
is the first
One speech frame is
The magnitude of the time of day.
In the embodiment of the invention, the voice frames with the frame energy larger than the specified frame energy threshold are extracted and summarized to obtain the voice characteristics.
And S2, detecting whether the voice features are the voice of the false control switch or not by using the pre-trained voice emotion detection model.
In one embodiment of the present invention, the disclosed SS-LSTM model (sentment and Semantic LSTM) is used to detect whether the speech feature is a false control switching speech.
And S3, when the voice feature is detected not to be the false control switch voice, converting the voice feature into a switch control text, and extracting a control keyword of the switch control text.
It should be explained that, when it is detected that the voice feature is the false control switch voice, it indicates that the user intends not to control the switch, and therefore ignores the current control switch voice and receives a new control switch voice again.
In the embodiment of the invention, the speech features are converted into the switch control text by using a deep speech algorithm based on TensorFlow.
And S4, recognizing the switch model corresponding to the control keyword by using the switch control neural network which is trained in advance.
In an embodiment of the present invention, the switch control neural network that is pre-trained includes:
step A: extracting network layers in a preset sequence from a MobileNet V2 neural network to obtain a MobileNet network layer;
it should be noted that the MobileNetV2 neural network is a lightweight convolutional neural network, and in the embodiment of the present invention, the last three-layer network structure in the network is extracted to obtain the MobileNet network layer.
And B: acquiring a full convolution neural network, and connecting the MobileNet network layer with the full convolution neural network to obtain a convolution neural network layer;
it is to be appreciated that the full convolutional neural network may employ a VGG16 network, UNet neural network, or the like. Further, the MobileNet network layer is placed in the front, the full convolution neural network layer is placed in the rear, and the convolution neural network layer is obtained through connection.
And C: constructing a switch model prediction function, and connecting the activation function with the convolutional neural network layer to obtain the switch control neural network to be trained;
in detail, the switch model prediction function includes, but is not limited to, a Softmax function, a random forest, a decision tree, and the like. In general, the switch model prediction function is placed after the convolutional neural network layer to obtain the switch control neural network.
Step D: acquiring a keyword training set and a corresponding real label set, and inputting the keyword training set into the switch control neural network for training to obtain a prediction label set;
illustratively, the keyword training set comprises a hall lamp, a kitchen lamp, a main lying lamp, a secondary lying television, a washing machine and the like. The real label set and the keyword training set have corresponding relations, for example, the real label of a hall lamp is an M18 photoelectric switch, the real label of a washing machine is xqb40-33, and the like.
In detail, the inputting the keyword training set into the switch control neural network for training to obtain a prediction label set includes:
performing convolution feature extraction on the keyword training set by utilizing the convolution neural network layer to obtain a keyword feature set;
and taking the keyword feature set as an input parameter of the switch model prediction function, and calculating to obtain the prediction tag set.
Step E: and adjusting the switch control neural network to be trained according to the real label set and the prediction label set to obtain the trained switch control neural network.
In detail, the adjusting the switch control neural network to be trained according to the real tag set and the predicted tag set to obtain the trained switch control neural network includes:
calculating error values of the real label set and the prediction label set;
judging the magnitude relation between the error value and a specified error threshold value;
if the error value is larger than the specified error threshold value, adjusting internal parameters of the switch control neural network, and returning the keyword training set to be input into the switch control neural network;
and if the error value is less than or equal to the specified error threshold value, obtaining the trained switch control neural network.
In the embodiment of the invention, the error values of the real label set and the predicted label set are calculated by adopting an MSE function.
And S5, the switch to be controlled is led out according to the switch model, and a switch control request is sent to the pre-constructed multimedia communication system.
For example, a user wants to turn off a light in a living room at home using a mobile phone, and therefore sends a control switch voice to the mobile phone: "please turn off the light in the living room in the home thanks", and the corresponding switch model number is M18 photoelectric switch, BK-200, etc. can be found according to the light in the living room in the home.
The multimedia communication system is a general network architecture for providing multimedia services on an IP-based network, and mainly aims to provide a remote control switch to be controlled.
S6, receiving a communication construction message sent by the multimedia communication system responding to the switch control request, establishing a control channel between the switch to be controlled and the portable electronic equipment according to the communication construction message, and executing switch control on the switch to be controlled by using the control channel.
It should be explained that, before the receiving of the communication setup message sent by the multimedia communication system in response to the switch control request, the method further includes: and executing authentication operation on the portable electronic equipment.
In detail, referring to fig. 3, the performing of the authentication operation on the portable electronic device includes:
s61, receiving the serial number sent by the portable electronic equipment, and generating virtual number information according to the serial number;
in the embodiment of the present invention, the portable electronic device sends a serial number to the multimedia communication system through an HTTP request, and further authenticates the portable electronic device using the multimedia communication system.
The HTTP request refers to a request message from a client to a server, such as a request message from a client represented by a portable electronic device to a server represented by a multimedia communication system in the embodiment of the present invention. The Serial Number (SN) is a unique identification code of the portable electronic device, and is used to identify the identity of the portable electronic device.
In detail, the generating of the virtual number information according to the serial number includes: and receiving the serial number by using the multimedia communication system, acquiring account opening information of the portable electronic equipment according to the serial number, and generating corresponding virtual number information according to the account opening information.
And the account opening information is registered and bound with the multimedia communication system by the user in advance.
S62, sending the virtual number information to the portable electronic device, and converting the registration request into a multimedia communication protocol verification request when receiving the registration request returned by the portable electronic device in response to the virtual number information;
in the preferred embodiment of the present invention, the registration request is sent in the form of private PROTOBUF's TCP protocol. The PROTOBUF is a serialization framework of Google open source, is similar to XML, json and the like, has the greatest characteristic that communication data is based on binary and is much more simplified than the communication data in the traditional XML form, and in addition, the multimedia communication protocol verification request is constructed on the basis of the multimedia communication system and is one of key protocols of the Next Generation Network (NGN).
S63, according to the multimedia communication protocol register request, sending an authentication request to the portable electronic device.
It should be appreciated that the primary purpose of the authentication request is to allow the portable electronic device to receive and respond to the authentication information to verify that information interaction between the multimedia communication system and the portable electronic device is possible.
And S64, calling a pre-constructed registration server to authenticate the portable electronic equipment according to the authentication request.
It is to be explained that, among others, the registration server is a server corresponding to the portable electronic device, and mainly serves a registration function of the portable electronic device.
Further, when the authentication is passed, receiving a communication setup message sent by the multimedia communication system in response to the switch control request, and establishing a control channel between the switch to be controlled and the portable electronic device according to the communication setup message, including: and converting the communication building message into a building protocol which accords with the communication protocol of the portable electronic equipment according to a pre-built signaling gateway, and sending the building protocol to the portable electronic equipment to complete building of the switch to be controlled and the control channel of the portable electronic equipment.
It should be explained that the signaling gateway is a control program for communication transmission, and includes functions of authority control, protocol conversion, etc. before message transmission. Further, after the signaling gateway is used for completing a control channel, the control channel is used for transmitting an instruction for turning on the switching power supply or turning off the switching power supply to the switch to be controlled. Illustratively, hall lantern M18 photoelectric switch, BK-200, etc. are all turned off in accordance with the command of "turn off the hall lantern at home".
In the embodiment of the invention, compared with the problems that the intelligence of the control switch power supply is lower and the distance of the control switch power supply is limited by using voiceprint recognition to intelligently control the switch power supply in the background technology, on one hand, the control switch voice sent by the portable electronic equipment is received, the voice feature is extracted from the control switch voice, and then whether the voice feature is the error control switch voice is detected by using the pre-trained voice emotion detection model, and when the voice feature is the error control switch voice, the control operation of the switch power supply is not executed, so that the intelligence of the control switch power supply is improved, the problem of the error operation in the background technology is solved, and on the other hand, in order to solve the problem of the distance limitation of the control switch power supply, the embodiment of the invention uses a multimedia communication system as a transmission medium to receive the communication construction message sent by the multimedia communication system in response to the switch control request, and establishing a control channel between the switch to be controlled and the portable electronic equipment according to the communication building message, thereby realizing remote control of the power supply. Therefore, the method, the device and the electronic equipment for intelligently controlling the switching power supply based on the neural network can solve the problems of low intelligence of the control switching power supply and limited distance of the control switching power supply.
Fig. 4 is a functional block diagram of the intelligent control switching power supply device based on the neural network according to the present invention.
The neural network-based intelligent control switching power supply device 100 of the present invention may be installed in an electronic apparatus. According to the realized functions, the intelligent control switch power supply device based on the neural network can comprise a voice feature extraction module 101, a control keyword extraction module 102, a switch model identification module 103 and a switch control module 104. A module according to the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the voice feature extraction module 101 is configured to receive a control switch voice sent by the portable electronic device, and extract a voice feature from the control switch voice;
the control keyword extraction module 102 is configured to detect whether the voice feature is a false control switch voice by using a pre-trained voice emotion detection model, convert the voice feature into a switch control text when it is detected that the voice feature is not the false control switch voice, and extract a control keyword of the switch control text;
the switch model identification module 103 is configured to identify a switch model corresponding to the control keyword by using a switch control neural network that is pre-trained;
the switch control module 104 is configured to retrieve a switch to be controlled according to the switch model, send a switch control request to a pre-constructed multimedia communication system, receive a communication setup message sent by the multimedia communication system in response to the switch control request, establish a control channel between the switch to be controlled and the portable electronic device according to the communication setup message, and perform switch control on the switch to be controlled by using the control channel.
In detail, when the modules in the intelligent control switching power supply device 100 based on the neural network according to the embodiment of the present invention are used, the same technical means as the above-mentioned intelligent control switching power supply method based on the neural network shown in fig. 1 is adopted, and the same technical effects can be produced, which is not described herein again.
Fig. 5 is a schematic structural diagram of an electronic device 1 for implementing the intelligent control method of the switching power supply based on the neural network according to the present invention.
The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may further include a computer program, such as a neural network-based intelligent control switching power supply program, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device 1, connects various components of the electronic device 1 by using various interfaces and lines, and executes various functions and processes data of the electronic device 1 by running or executing programs or modules (for example, executing an intelligent Control switching power supply program based on a neural network, and the like) stored in the memory 11 and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only to store application software installed in the electronic device 1 and various types of data, such as codes of a neural network-based intelligent control switching power supply program, but also to temporarily store data that has been output or will be output.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device 1 and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are generally used for establishing a communication connection between the electronic device 1 and other electronic devices 1. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.
Fig. 5 shows only the electronic device 1 with components, and it will be understood by those skilled in the art that the structure shown in fig. 5 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The intelligent neural network-based control switching power supply program stored in the memory 11 of the electronic device 1 is a combination of a plurality of computer programs, and when running in the processor 10, can realize:
receiving control switch voice sent by the portable electronic equipment, and extracting voice characteristics from the control switch voice;
detecting whether the voice features are the voice of the false control switch or not by using a pre-trained voice emotion detection model;
when the voice feature is detected not to be the false control switch voice, converting the voice feature into a switch control text, and extracting a control keyword of the switch control text;
identifying a switch model corresponding to the control keyword by using a switch control neural network which is trained in advance;
a switch to be controlled is led out according to the switch model, and a switch control request is sent to a pre-constructed multimedia communication system;
and receiving a communication construction message sent by the multimedia communication system in response to the switch control request, establishing a control channel between the switch to be controlled and the portable electronic equipment according to the communication construction message, and executing switch control on the switch to be controlled by utilizing the control channel.
Specifically, the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the computer program, which is not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (9)
1. An intelligent control switching power supply method based on a neural network is characterized by comprising the following steps:
receiving control switch voice sent by the portable electronic equipment, and extracting voice characteristics from the control switch voice;
detecting whether the voice features are the voice of the false control switch or not by using a pre-trained voice emotion detection model;
when the voice feature is detected not to be the false control switch voice, converting the voice feature into a switch control text, and extracting a control keyword of the switch control text;
identifying a switch model corresponding to the control keyword by using a switch control neural network which is trained in advance;
a switch to be controlled is led out according to the switch model, and a switch control request is sent to a pre-constructed multimedia communication system;
and receiving a communication construction message sent by the multimedia communication system in response to the switch control request, establishing a control channel between the switch to be controlled and the portable electronic equipment according to the communication construction message, and executing switch control on the switch to be controlled by utilizing the control channel.
2. The neural network-based intelligent control switching power supply method of claim 1, wherein the pre-trained switching control neural network comprises:
extracting network layers in a preset sequence from a MobileNet V2 neural network to obtain a MobileNet network layer;
acquiring a full convolution neural network, and connecting the MobileNet network layer with the full convolution neural network to obtain a convolution neural network layer;
constructing a switch model prediction function, and connecting the activation function with the convolutional neural network layer to obtain the switch control neural network to be trained;
acquiring a keyword training set and a corresponding real label set, and inputting the keyword training set into the switch control neural network for training to obtain a prediction label set;
and adjusting the switch control neural network to be trained according to the real label set and the prediction label set to obtain the trained switch control neural network.
3. The method of claim 2, wherein the obtaining a keyword training set and a corresponding real label set, inputting the keyword training set into the neural network for training, and obtaining a predicted label set comprises:
performing convolution feature extraction on the keyword training set by utilizing the convolution neural network layer to obtain a keyword feature set;
and taking the keyword feature set as an input parameter of the switch model prediction function, and calculating to obtain the prediction tag set.
4. The method according to claim 2, wherein the adjusting the switch control neural network to be trained according to the real label set and the predicted label set to obtain the trained switch control neural network comprises:
calculating error values of the real label set and the prediction label set;
judging the magnitude relation between the error value and a specified error threshold value;
if the error value is larger than the specified error threshold value, adjusting internal parameters of the switch control neural network, and returning the keyword training set to be input into the switch control neural network;
and if the error value is less than or equal to the specified error threshold value, obtaining the trained switch control neural network.
5. The method according to claim 1, wherein the receiving of the communication setup message sent by the multimedia communication system in response to the switch control request further comprises:
receiving a serial number sent by the portable electronic equipment, and generating virtual number information according to the serial number;
sending the virtual number information to the portable electronic equipment, and converting a registration request into a multimedia communication protocol verification request when receiving the registration request returned by the portable electronic equipment in response to the virtual number information;
sending an authentication request to the portable electronic equipment according to the multimedia communication protocol registration request;
and calling a pre-constructed registration server to authenticate the portable electronic equipment according to the authentication request, and receiving a communication building message sent by the multimedia communication system responding to the switch control request when the authentication is passed.
6. The neural network-based intelligent control switching power supply method of claim 5, wherein said establishing a control channel between said switch to be controlled and said portable electronic device according to said communication setup message comprises:
converting the communication building message into a building protocol conforming to the communication protocol of the portable electronic equipment according to a pre-built signaling gateway;
and sending the setting protocol to the portable electronic equipment to complete setting of the control channel of the switch to be controlled and the portable electronic equipment.
7. The intelligent control switching power supply method based on the neural network as claimed in claim 5, wherein the generating of the virtual number information according to the serial number comprises:
receiving the serial number by using the multimedia communication system, and acquiring account opening information of the portable electronic equipment according to the serial number;
and generating the corresponding virtual number information by using the account opening information.
8. An intelligent control switching power supply apparatus based on a neural network, the apparatus comprising:
the voice feature extraction module is used for receiving control switch voice sent by the portable electronic equipment and extracting voice features from the control switch voice;
the control keyword extraction module is used for detecting whether the voice feature is the false control switch voice or not by using a pre-trained voice emotion detection model, converting the voice feature into a switch control text when the voice feature is detected not to be the false control switch voice, and extracting a control keyword of the switch control text;
the switch model identification module is used for identifying a switch model corresponding to the control keyword by utilizing a switch control neural network which is trained in advance;
and the switch control module is used for leading out a switch to be controlled according to the switch model, sending a switch control request to a pre-constructed multimedia communication system, receiving a communication construction message sent by the multimedia communication system in response to the switch control request, establishing a control channel between the switch to be controlled and the portable electronic equipment according to the communication construction message, and executing switch control on the switch to be controlled by utilizing the control channel.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the neural network based intelligent control switching power supply method of any one of claims 1 to 7.
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Effective date of registration: 20240511 Address after: No. 2 Chenxi Road, Mushanhou Village, Liushi Town, Leqing City, Wenzhou City, Zhejiang Province, 325000 Patentee after: Guqiao Power Technology Co.,Ltd. Country or region after: China Address before: 252000 No. C4, C7 and C8, phase I, Huawei science and Technology Park, 500m southeast corner of the intersection of South Outer Ring Road and Huashan Road, Jiuzhou street, high tech Zone, Liaocheng City, Shandong Province Patentee before: Liaocheng zhongsai Electronic Technology Co.,Ltd. Country or region before: China |