CN110275455B - Control method based on electroencephalogram signals, central control equipment, cloud server and system - Google Patents
Control method based on electroencephalogram signals, central control equipment, cloud server and system Download PDFInfo
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Abstract
The invention discloses a control method based on electroencephalogram signals, which comprises the following steps: acquiring electroencephalogram physiological data detected by detection equipment, and converting the electroencephalogram physiological data into image data; sending the image data to a cloud server; and receiving a control instruction for the peripheral equipment, which is generated based on the image data and sent by the cloud server, and sending the control instruction to the corresponding peripheral equipment. The invention also discloses a central control device, a cloud server and a system.
Description
Technical Field
The invention relates to household appliances and information processing technology, in particular to a control method based on electroencephalogram signals, a central control device, a cloud server and a system.
Background
At present, artificial intelligence includes a very wide range of scientific technologies, which are composed of different fields, such as machine learning, computer vision, biological science, neural network science, energy technology, genetic engineering, etc., and the main purpose of artificial intelligence research is to allow machines to perform complex work that can be completed by human intelligence. At present, the intelligent products on the market are strictly defined or cannot become artificial intelligence in theory, and the real artificial intelligence products can perform tasks from the human idea level. But the products produced by the current household appliance industry are still in the stage of multifunctional single products. Users have different requirements, so that the machine needs to read the thought of the human, thereby completing the things that the human wants to do, and the brain wave control can realize the function. However, the current brain wave intelligent control system is only used as a tool for assisting human-computer interaction, and the intelligence, convenience and practicability of the brain wave intelligent control system are not sufficiently developed and exploited.
Disclosure of Invention
In view of this, the embodiments of the present invention are expected to provide a control method based on electroencephalogram signals, a central control device, a cloud server, and a system, which can construct a new experience path of human-computer interaction through data acquisition, image recognition, and other processing, implement interconnection of home appliances, and enable a user to control home appliances through brain waves.
In order to achieve the above object, an embodiment of the present invention provides a control method based on an electroencephalogram signal, where the method includes:
acquiring electroencephalogram physiological data detected by detection equipment, and converting the electroencephalogram physiological data into image data;
sending the image data to a cloud server;
and receiving a control instruction for the peripheral equipment, which is generated based on the image data and sent by the cloud server, and sending the control instruction to the corresponding peripheral equipment.
Wherein, the electroencephalogram physiological data comprise electroencephalogram amplitude data;
correspondingly, the converting the electroencephalogram physiological data into image data comprises:
filtering data with amplitude exceeding a preset amplitude data range in the brain wave data to obtain brain wave amplitude data to be converted;
and converting the brain wave amplitude data to be converted into image data, wherein the color attribute information of the display composition unit in the image corresponds to the brain wave amplitude in the brain wave amplitude data to be converted.
Before the sending the image data to the cloud server, the method further includes:
adjusting the brightness, color distribution and contrast parameters of the image data according to a preset adjustment rule to obtain adjusted image data;
filtering the adjusted image data, and filtering out fuzzy image data in the adjusted image data to obtain clear image data;
and coding and compressing the clear image data, reducing the redundant data amount in the clear image data, and compressing the data size of the clear image data to obtain the image data which can be transmitted to a cloud server.
Wherein, the sending of the control instruction to the corresponding peripheral device comprises:
and sending the control instruction to a single chip microcomputer controller of corresponding peripheral equipment, and driving the peripheral equipment by the single chip microcomputer controller of the peripheral equipment according to the control instruction.
The embodiment of the invention also provides a control method based on the electroencephalogram signals, which comprises the following steps:
after receiving image data, the cloud server identifies the image data and determines characteristic data of the image data;
matching the feature data of the image data with the feature data of a control instruction in a preset control instruction database to determine the control instruction of the peripheral equipment to which the image data aims;
and sending the control instruction to a central control device.
The identifying the image data and determining the feature data of the image data includes:
carrying out digital description processing on the pixel points of the image data, and representing the color and brightness attributes of each pixel point in the image data by color data and brightness data respectively;
classifying the pixels of the image data after the digital description processing based on the color data and the brightness data, and determining the color data and the brightness data of the pixels in the classified pixel set as the characteristic data of the image data.
The determining the classified data in the pixel point set as the feature data of the image data includes:
determining the shape of the electroencephalogram signal in the image data according to the color data and the brightness data of the pixel points of the image data after the digital description processing;
and determining the color data and the brightness data of the pixel points in the shape range of the electroencephalogram signals in the image data as the characteristic data of the image data.
The matching of the feature data of the image data and the feature data of the control instruction in a preset control instruction database to determine the control instruction of the peripheral device to which the image data is directed includes:
matching the feature data of the image data with the feature data of a control instruction in a preset control instruction database, when the matching similarity exceeds a preset similarity threshold, determining the control instruction of which the matching similarity exceeds the preset similarity threshold in the control instruction database, determining peripheral equipment corresponding to the control instruction, and generating the control instruction of the peripheral equipment aiming at the image data by using the control instruction and the corresponding peripheral equipment.
An embodiment of the present invention provides a central control device, where the device includes:
the receiving module is used for acquiring electroencephalogram physiological data detected by the detection equipment; the control instruction for the peripheral equipment generated based on the image data and sent by the cloud server is also received
The conversion module is used for converting the electroencephalogram physiological data into image data;
and the sending module is used for sending the control instruction to corresponding peripheral equipment.
Wherein, the conversion module is used for:
the electroencephalogram physiological data comprise electroencephalogram amplitude data, and data with amplitude exceeding a preset amplitude data range in the electroencephalogram data are filtered out to obtain electroencephalogram amplitude data to be converted;
and converting the brain wave amplitude data to be converted into image data, wherein the color attribute information of the display composition unit in the image corresponds to the brain wave amplitude in the brain wave amplitude data to be converted.
Wherein, above-mentioned equipment still includes:
the adjusting module is used for adjusting the brightness, color distribution and contrast parameters of the image data according to a preset adjusting rule to obtain adjusted image data;
the filtering module is used for filtering the adjusted image data, filtering fuzzy image data in the adjusted image data and obtaining clear image data;
and the image processing module is used for coding and compressing the clear image data, reducing the redundant data amount in the clear image data, and compressing the data size of the clear image data to obtain the image data which can be transmitted to the cloud server.
The sending module is used for sending the control instruction to a single chip microcomputer controller of corresponding peripheral equipment, and the single chip microcomputer controller of the peripheral equipment drives the peripheral equipment according to the control instruction.
An embodiment of the present invention provides a cloud server, where the cloud server includes:
the identification module is used for identifying the image data after the cloud server receives the image data and determining the characteristic data of the image data;
the matching module is used for matching the feature data of the image data with the feature data of the control instruction in a preset control instruction database to determine the control instruction of the peripheral equipment to which the image data aims;
and the sending module is used for sending the control instruction to the central control equipment.
The identification module is used for carrying out digital description processing on the pixel points of the image data, and representing the color and brightness attributes of each pixel point in the image data by color data and brightness data respectively;
classifying the pixels of the image data after the digital description processing based on the color data and the brightness data, and determining the color data and the brightness data of the pixels in the classified pixel set as the characteristic data of the image data.
The identification module is specifically configured to determine the shape of the electroencephalogram signal in the image data according to the color data and the luminance data of the pixel point of the image data after the digital description processing;
and determining the color data and the brightness data of the pixel points in the shape range of the electroencephalogram signals in the image data as the characteristic data of the image data.
The matching module is configured to match feature data of the image data with feature data of a control instruction in a preset control instruction database, determine, when matching similarity exceeds a preset similarity threshold, a control instruction of which the matching similarity exceeds the preset similarity threshold in the control instruction database, determine a peripheral device corresponding to the control instruction, and generate, with the control instruction and the corresponding peripheral device, a control instruction of the peripheral device to which the image data is directed.
The embodiment of the invention provides a control system based on electroencephalogram information, which comprises:
the central control equipment is used for acquiring the electroencephalogram physiological data detected by the detection equipment and converting the electroencephalogram physiological data into image data; sending the image data to a cloud server; receiving a control instruction for peripheral equipment, which is generated based on the image data and sent by the cloud server, and sending the control instruction to the corresponding peripheral equipment;
the cloud server is used for identifying the image data after receiving the image data and determining the characteristic data of the image data; matching the feature data of the image data with the feature data of a control instruction in a preset control instruction database to determine the control instruction of the peripheral equipment to which the image data aims; and sending the control instruction to a central control device.
The electroencephalogram signal detection equipment is used for acquiring electroencephalogram physiological data through the detection equipment arranged on the wearable equipment; and sending the acquired electroencephalogram physiological data to a central control device according to preset network configuration.
An embodiment of the present invention provides a central control device, including: a processor and a memory for storing a computer program capable of running on the processor,
wherein the processor is configured to execute, when running the computer program, to implement:
acquiring electroencephalogram physiological data detected by detection equipment, and converting the electroencephalogram physiological data into image data; sending the image data to a cloud server; and receiving a control instruction for the peripheral equipment, which is generated based on the image data and sent by the cloud server, and sending the control instruction to the corresponding peripheral equipment.
An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements:
acquiring electroencephalogram physiological data detected by detection equipment, and converting the electroencephalogram physiological data into image data; sending the image data to a cloud server; and receiving a control instruction for the peripheral equipment, which is generated based on the image data and sent by the cloud server, and sending the control instruction to the corresponding peripheral equipment.
An embodiment of the present invention provides a cloud server, including: a processor and a memory for storing a computer program capable of running on the processor,
wherein the processor is configured to execute, when running the computer program:
after receiving image data, identifying the image data and determining the characteristic data of the image data; matching the feature data of the image data with the feature data of a control instruction in a preset control instruction database to determine the control instruction of the peripheral equipment to which the image data aims; and sending the control instruction to a central control device.
An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement:
after receiving image data, identifying the image data and determining the characteristic data of the image data; matching the feature data of the image data with the feature data of a control instruction in a preset control instruction database to determine the control instruction of the peripheral equipment to which the image data aims; and sending the control instruction to a central control device.
According to the control method based on the electroencephalogram signal, the central control device, the cloud server and the system, electroencephalogram physiological data detected by the detection device are obtained, and the electroencephalogram physiological data are converted into image data; sending the image data to a cloud server; receiving a control instruction for peripheral equipment, which is generated based on the image data and sent by the cloud server, and sending the control instruction to the corresponding peripheral equipment; thus, the household appliance can be controlled by brain waves.
Drawings
FIG. 1 is a flow chart of a control method of a central control device based on electroencephalogram signals in an embodiment of the invention;
fig. 2 is a schematic flow chart of a control method of a cloud server side based on brain electrical signals according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of an electroencephalogram signal acquisition process by an electroencephalogram signal detection device according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a central control apparatus according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a cloud server according to an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of a control system based on electroencephalogram signals according to an embodiment of the present invention;
FIG. 7 is a schematic flow chart of a brain wave controlled appliance according to an embodiment of the present invention;
FIG. 8 is a schematic structural diagram of an electroencephalogram physiological data acquisition device according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of an intelligent control system based on electroencephalogram signals according to an embodiment of the present invention.
Detailed Description
The invention is further described in detail below with reference to the drawings and the specific embodiments.
Example one
Fig. 1 is a schematic flow chart of a control method based on electroencephalogram signals of a central control device in an embodiment of the present invention, and as shown in fig. 1, the flow chart of the control method based on electroencephalogram signals in an embodiment of the present invention includes the following steps:
step 101: acquiring electroencephalogram physiological data detected by detection equipment, and converting the electroencephalogram physiological data into image data.
In practical application, electroencephalogram physiological data detected by detection equipment cannot be directly identified, and a control instruction expressed by the electroencephalogram physiological data is determined, so that the acquired electroencephalogram physiological data are converted into image data, the control instruction is identified based on the image data, and when the electroencephalogram physiological data comprise electroencephalogram amplitude data, data with amplitude exceeding a preset amplitude data range in the electroencephalogram data can be filtered out, and electroencephalogram amplitude data to be converted are obtained; converting the brain wave amplitude data to be converted into image data, wherein the color attribute information of a display composition unit in the image corresponds to the brain wave amplitude in the brain wave amplitude data to be converted, or matching the brain wave amplitude data with a preset AM amplitude value, imaging the brain wave amplitude data according to the preset AM amplitude value after matching is successful, and re-matching the brain wave amplitude data after matching is failed; by the method, the electroencephalogram physiological data can be converted into image data with higher identification reliability, some interference data in the electroencephalogram physiological data can be filtered out at the same time, and the identification accuracy is improved.
Step 102: and sending the image data to a cloud server.
Before sending the image data to the cloud server, in order to adapt the image data to a transmission process, the image data needs to be processed, and the processing includes:
adjusting the brightness, color distribution and contrast parameters of the image data according to a preset adjustment rule to obtain adjusted image data;
filtering the adjusted image data, and filtering out fuzzy image data in the adjusted image data to obtain clear image data;
and coding and compressing the clear image data, reducing the redundant data amount in the clear image data, and compressing the data size of the clear image data to obtain the image data which can be transmitted to a cloud server.
Wherein the image data can be sent to a cloud server through the internet.
Step 103: and receiving a control instruction for the peripheral equipment, which is generated based on the image data and sent by the cloud server, and sending the control instruction to the corresponding peripheral equipment.
And sending the control instruction to a single chip microcomputer controller of corresponding peripheral equipment, and driving the peripheral equipment by the single chip microcomputer controller of the peripheral equipment according to the control instruction.
In practical application, after the single chip microcomputer obtains a control instruction, the driving circuit can be started according to the control instruction, and therefore the purpose of controlling the household appliance is achieved.
In practical application, the central control device may send the control information to the home devices joining the Wireless network through a Wireless fidelity (WiFi) module of the central control device and a home router.
Example two
Fig. 2 is a schematic flow chart of a method for controlling a cloud server side based on a brain electrical signal according to an embodiment of the present invention, and as shown in fig. 2, the method for controlling a cloud server side based on a brain electrical signal according to an embodiment of the present invention includes the following steps:
step 201: after receiving the image data, the cloud server identifies the image data and determines the characteristic data of the image data.
In practical application, the cloud server needs to perform digital processing on the received image data, so that the image data can be identified in the next step, and the method specifically includes the following steps:
carrying out digital description processing on the pixel points of the image data, and representing the color and brightness attributes of each pixel point in the image data by color data and brightness data respectively;
classifying the pixels of the image data after the digital description processing based on the color data and the brightness data, and determining the color data and the brightness data of the pixels in the classified pixel set as the feature data of the image data, wherein the determining the data in the classified pixel set as the feature data of the image data comprises:
determining the shape of the electroencephalogram signal in the image data according to the color data and the brightness data of the pixel points of the image data after the digital description processing;
and determining the color data and the brightness data of the pixel points in the shape range of the electroencephalogram signals in the image data as the characteristic data of the image data.
Step 202: and matching the characteristic data of the image data with the characteristic data of the control instruction in a preset control instruction database, and determining the control instruction of the peripheral equipment aiming at the image data.
According to the feature data of the image data determined in step 201, matching the feature data with a preset control instruction database to determine a control instruction represented by the image data, specifically,
matching the feature data of the image data with the feature data of a control instruction in a preset control instruction database, when the matching similarity exceeds a preset similarity threshold, determining the control instruction of which the matching similarity exceeds the preset similarity threshold in the control instruction database, determining peripheral equipment corresponding to the control instruction, and generating the control instruction of the peripheral equipment aiming at the image data by using the control instruction and the corresponding peripheral equipment.
In practical application, the recognition processing method for determining the image data may also apply a statistical method (or a decision theory method), a syntax (or structure) method, a neural network method, a template matching method and a geometric transformation method, and the specific implementation methods all need to establish a model through which the image data is recognized, so that the model establishment method plays a crucial role in recognition processing, wherein,
(1) statistical method: the method is used for carrying out a large amount of statistical analysis on the researched images, finding out the rules in the images and extracting the characteristics reflecting the essential characteristics of the images to carry out image recognition. It is a method with minimum classification error by establishing a statistical recognition model based on a mathematical decision theory. Commonly used image statistical models are bayesian (Bayes) and markov (Markow) random field (MRF) models.
(2) The neural network method comprises the following steps: the method is a method for identifying images by using a neural network algorithm. A neural network system is a complex network system formed by a large number of, and at the same time very simple, processing units (called neurons) that are connected to each other in a wide range of ways, and although the structure and function of each neuron is very simple, the behavior of a network system formed by a large number of neurons is very colorful and complex. It reflects many basic features of human brain function, and is a simplification, abstraction and simulation of human brain neural network system. The syntactic method focuses on simulating the logical thinking of a human, while the neural network focuses on the sensory perception process, the visual thinking, the distributed memory and the self-learning self-organizing process in the process of simulating and realizing the cognition of the human, and is a complementary relation with the symbol processing. The neural network has the advantages of nonlinear mapping approximation, large-scale parallel distributed storage and comprehensive optimization processing, strong fault tolerance, unique associative memory and self-organization, self-adaptation and self-learning capabilities.
(3) Template matching method: this is one of the most basic image recognition methods. The template is an array designed for detecting the characteristics of some regions of the image to be recognized, and can be a digital quantity, a symbol string and the like, so that the template can be regarded as a special case of statistics or syntax. The template matching method is to compare a template of a known object with all unknown objects in an image, and if an unknown object matches the template, the object is detected and considered as the same object as the template. In addition, due to the existence of noise in the image and uncertainty in the shape and structure of the detected object, the template matching method often does not achieve an ideal effect under complex conditions, and is difficult to be absolutely accurate. The classical image matching method uses cross-correlation to calculate a matching measure or uses the sum of squares of absolute differences as a mismatch measure, but these two methods often suffer from mismatch, and therefore, the matching method using geometric transformation contributes to improvement of robustness.
Step 203: and sending the control instruction to a central control device.
The cloud server can send the control instruction to the central control device through the internet.
EXAMPLE III
Fig. 3 is a schematic diagram of a flow of acquiring an electroencephalogram signal by an electroencephalogram signal detection device according to an embodiment of the present invention, and as shown in fig. 3, the flow of acquiring an electroencephalogram signal by an electroencephalogram signal detection device according to an embodiment of the present invention includes the following steps:
step 301: the electroencephalogram physiological data are obtained through the detection equipment arranged on the wearable equipment.
In practical application, the detection equipment can be an electroencephalogram physiological data acquisition device with a Think Gear AM chip as a core.
Step 302: and sending the acquired electroencephalogram physiological data to a central control device according to preset network configuration.
The electroencephalogram signal detection device is provided with an automatic distribution network module which is used for being connected to other household appliances and central control equipment, and the automatic distribution network module automatically stores configuration parameters after being paired with the other household appliances or the central control equipment for the first time, so that the user experience is improved without being paired again when the electroencephalogram signal detection device is used again.
Example four
In order to implement the foregoing method, an embodiment of the present invention provides a central control device, which is disposed in a home appliance or a home gateway, and as shown in fig. 4, the central control device includes: a receiving module 401, a converting module 402 and a sending module 403; wherein the content of the first and second substances,
the receiving module 401 is configured to acquire electroencephalogram physiological data detected by the detection device; the control instruction for the peripheral equipment generated based on the image data and sent by the cloud server is also received
A conversion module 402, configured to convert the electroencephalogram physiological data into image data;
a sending module 403, configured to send the control instruction to a corresponding peripheral device.
Wherein the conversion module 402 is configured to:
the electroencephalogram physiological data comprise electroencephalogram amplitude data, and data with amplitude exceeding a preset amplitude data range in the electroencephalogram data are filtered out to obtain electroencephalogram amplitude data to be converted;
and converting the brain wave amplitude data to be converted into image data, wherein the color attribute information of the display composition unit in the image corresponds to the brain wave amplitude in the brain wave amplitude data to be converted.
Wherein the apparatus further comprises:
an adjusting module 404, configured to adjust brightness, color distribution, and contrast parameters of the image data according to a preset adjusting rule, to obtain adjusted image data;
a filtering module 405, configured to filter the adjusted image data, filter out blurred image data in the adjusted image data, and obtain clear image data;
the image processing module 406 is configured to encode and compress the clear image data, reduce the amount of redundant data in the clear image data, and compress the data size of the clear image data to obtain image data that can be transmitted to a cloud server.
The sending module 403 is configured to send the control instruction to a single chip microcomputer controller of a corresponding peripheral device, where the single chip microcomputer controller of the peripheral device drives the peripheral device according to the control instruction.
EXAMPLE five
Fig. 5 is a schematic structural diagram of a cloud server according to an embodiment of the present invention, and as shown in fig. 5, the cloud server according to the embodiment of the present invention includes: an identification module 501, a matching module 502 and a sending module 503; wherein the content of the first and second substances,
the identification module 501 is configured to identify image data after the cloud server receives the image data, and determine feature data of the image data;
a matching module 502, configured to match feature data of the image data with feature data of a control instruction in a preset control instruction database, and determine a control instruction of a peripheral device to which the image data is directed;
a sending module 503, configured to send the control instruction to a central control device.
The identification module 501 is configured to perform digital description processing on the pixels of the image data, and represent the color and brightness attributes of each pixel in the image data by color data and brightness data, respectively;
classifying the pixels of the image data after the digital description processing based on the color data and the brightness data, and determining the color data and the brightness data of the pixels in the classified pixel set as the characteristic data of the image data.
The identification module 501 is specifically configured to determine the shape of the electroencephalogram signal in the image data according to the color data and the luminance data of the pixel point of the image data after the digital description processing;
and determining the color data and the brightness data of the pixel points in the shape range of the electroencephalogram signals in the image data as the characteristic data of the image data.
The matching module 502 is configured to match feature data of the image data with feature data of a control instruction in a preset control instruction database, determine, when matching similarity exceeds a preset similarity threshold, a control instruction of which the matching similarity exceeds the preset similarity threshold in the control instruction database, determine a peripheral device corresponding to the control instruction, and generate, by using the control instruction and the corresponding peripheral device, a control instruction of the peripheral device to which the image data is directed.
EXAMPLE six
Fig. 6 is a schematic structural diagram of a control system based on electroencephalogram signals according to an embodiment of the present invention, and as shown in fig. 6, the control system based on electroencephalogram signals according to an embodiment of the present invention includes:
the central control device 601 is used for acquiring electroencephalogram physiological data detected by the detection device and converting the electroencephalogram physiological data into image data; sending the image data to a cloud server; receiving a control instruction for peripheral equipment, which is generated based on the image data and sent by the cloud server, and sending the control instruction to the corresponding peripheral equipment;
the cloud server 602 is configured to, after receiving image data, identify the image data and determine feature data of the image data; matching the feature data of the image data with the feature data of a control instruction in a preset control instruction database to determine the control instruction of the peripheral equipment to which the image data aims; and sending the control instruction to a central control device.
The electroencephalogram signal detection device 603 is used for acquiring electroencephalogram physiological data through the detection device arranged on the wearable device; and sending the acquired electroencephalogram physiological data to a central control device according to preset network configuration.
It should be noted that the central control device 601 may refer to the description of the central control device in the third embodiment for understanding, and the cloud server 602 may refer to the description of the cloud server in the fourth embodiment for understanding, which is not described herein again.
An embodiment of the present invention provides a central control device, including: a processor and a memory for storing a computer program capable of running on the processor,
wherein the processor is configured to execute, when running the computer program, to implement:
acquiring electroencephalogram physiological data detected by detection equipment, and converting the electroencephalogram physiological data into image data; sending the image data to a cloud server; and receiving a control instruction for the peripheral equipment, which is generated based on the image data and sent by the cloud server, and sending the control instruction to the corresponding peripheral equipment.
An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements:
acquiring electroencephalogram physiological data detected by detection equipment, and converting the electroencephalogram physiological data into image data; sending the image data to a cloud server; and receiving a control instruction for the peripheral equipment, which is generated based on the image data and sent by the cloud server, and sending the control instruction to the corresponding peripheral equipment.
An embodiment of the present invention provides a cloud server, including: a processor and a memory for storing a computer program capable of running on the processor,
wherein the processor is configured to execute, when running the computer program:
after receiving image data, identifying the image data and determining the characteristic data of the image data; matching the feature data of the image data with the feature data of a control instruction in a preset control instruction database to determine the control instruction of the peripheral equipment to which the image data aims; and sending the control instruction to a central control device.
An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement:
after receiving image data, identifying the image data and determining the characteristic data of the image data; matching the feature data of the image data with the feature data of a control instruction in a preset control instruction database to determine the control instruction of the peripheral equipment to which the image data aims; and sending the control instruction to a central control device.
The modules in the central control device, the cloud server and the electroencephalogram signal detection device can be arranged in a mobile terminal, a server, a wearable device and other entity devices, and can be implemented by any type of volatile or non-volatile storage device, or a combination of the volatile or non-volatile storage devices. The nonvolatile Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a magnetic Random Access Memory (FRAM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical Disc, or a Compact Disc Read-Only Memory (CD-ROM); the magnetic surface storage may be disk storage or tape storage. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Enhanced Synchronous Dynamic Random Access Memory (Enhanced DRAM), Synchronous Dynamic Random Access Memory (SLDRAM), Direct Memory (DRmb Access), and Random Access Memory (DRAM). The memory in the central control apparatus, the cloud server, and the brain electrical signal detection device described in the embodiments of the present invention are intended to include, without being limited to, these and any other suitable types of memory.
In an exemplary embodiment, each module in the central control apparatus, the cloud server, and the electroencephalogram signal detection Device may be implemented by one or more Application Specific Integrated Circuits (ASICs), DSPs, Programmable Logic Devices (PLDs), Complex Programmable Logic Devices (CPLDs), Field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, Micro Controllers (MCUs), microprocessors (microprocessors), or other electronic components, for performing the foregoing methods.
EXAMPLE seven
Fig. 7 is a schematic flow chart of a brain wave controlled household appliance according to an embodiment of the present invention, and as shown in fig. 7, the brain wave controlled household appliance according to the embodiment of the present invention includes the following steps:
step 701: acquiring electroencephalogram amplitude data and sending the electroencephalogram amplitude data to central control equipment;
the electroencephalogram data can be acquired through wearable equipment provided with detection equipment, the wearable equipment is portable equipment and can be used as a carrier of an electroencephalogram data detection module, and the wearable equipment is further expanded with virtual-reality equipment; the brain communication good detection equipment provided by the embodiment of the invention at least comprises a microcontroller, a serial communication module, a data acquisition module and a filter circuit, and preferably, the brain communication good detection equipment can also be provided with the data acquisition module and a data transceiver module; the module brain wave data processing is integrated in a chip, and only brain wave physiological data is output externally, so that the module is highly integrated, small in size, light in weight and convenient to install and transplant to portable wearable equipment.
Step 702-: the central control equipment matches the electroencephalogram amplitude data; judging whether the matching is successful; after the matching is successful, executing step 704; after the matching fails, executing step 701;
the electroencephalogram amplitude data analysis program reads brain wave amplitude data from a serial port buffer area and matches the brain wave amplitude data with a preset amplitude value;
after the matching is successful, executing step 704; if the matching is not successful, the step 701 is returned to continue to receive the electroencephalogram amplitude data.
Wherein the central control apparatus includes at least: the system comprises an automatic network distribution module, an embedded control system, a brain wave data analysis and image display program, a single chip microcomputer controller and the like.
Step 704: generating image data;
filtering data of which the amplitude exceeds a preset amplitude data range in the brain wave data to obtain brain wave amplitude data to be converted;
and converting the brain wave amplitude data to be converted into image data, wherein the color attribute information of the display composition unit in the image corresponds to the brain wave amplitude in the brain wave amplitude data to be converted.
In practical applications, image processing utilizes a computer to analyze the image to achieve a desired result.
Image processing can be divided into analog image processing and digital image processing, and image processing generally refers to digital image processing.
Most of this processing relies on software implementation. The method aims to remove interference and noise, and programs an original image into a form suitable for computer to extract features, and mainly comprises image sampling, image enhancement, image restoration, image coding and compression and image segmentation.
(1) Image acquisition
Image acquisition is the primary means of digital image data extraction. The digital image is obtained by sampling and digitizing with the aid of digital cameras, scanners, digital cameras, and other devices, and also includes some dynamic images, and can be converted into a digital image. Image extraction is the first step in transforming an image into a form suitable for computer processing.
(2) Image enhancement
The quality of the image in the processes of imaging, collecting, transmitting, copying and the like can be degraded to a certain extent, and the visual effect of the digitized image is not very satisfactory. In order to highlight the interesting parts of the image and make the main structure of the image more definite, the image must be improved, i.e. enhanced. Through image enhancement, the noise of the image in the image is reduced, and the parameters of brightness, color distribution, contrast and the like of the original image are changed. The image enhancement improves the definition and the quality of the image, so that the outline of an object in the image is clearer and the details are more obvious. The image enhancement does not consider the reason of image degradation, and the enhanced image is more pleasant and lays a foundation for later image analysis and image understanding.
(3) Image restoration
Image restoration is also called image restoration, the image is blurred due to the influence of environmental noise, image blurring caused by movement, light intensity and other reasons when the image is acquired, the image needs to be restored in order to extract a clearer image, and the image restoration mainly adopts a filtering method to restore an original image from a degraded image. Another particular technique for image reconstruction is image reconstruction, which builds an image from a set of projection data of a cross-section of an object.
(4) Image coding and compression
Digital images are characterized by large data size and need to occupy considerable storage space. However, the processing, storage and transmission of data images cannot be performed based on the network bandwidth and mass storage of the computer. In order to transmit an image or video in a network environment quickly and conveniently, the image must be encoded and compressed. At present, image compression and encoding forms an international standard, such as the relatively well-known still image compression standard JPEG, which mainly aims at the resolution, color image and gray image of an image and is suitable for aspects of digital photos, color photos and the like transmitted through a network. Since video can be viewed as a time series of still images that are different but closely related, single frame image compression of motion video can apply the compression standard for still images. The image coding compression technology can reduce the redundant data amount and the memory capacity of the image, improve the image transmission speed and shorten the processing time.
(5) Image segmentation technique
Image segmentation is to divide an image into sub-regions that do not overlap each other and have respective characteristics, each region being a continuum of pixels, where the characteristics may be color, shape, grayscale, texture, etc. of the image. Image segmentation represents an image as a collection of physically meaningful connected regions based on a priori knowledge of the target and the background. Namely, the target and the background in the image are marked and positioned, and then the target is separated from the background. The image segmentation method mainly includes a segmentation method based on region features, a segmentation method based on correlation matching, and a segmentation method based on boundary features. The image segmentation is difficult because the acquired image is affected by various conditions, such as blurring and noise interference of the image. In the actual image, an appropriate image segmentation method needs to be selected according to different scene conditions. Image segmentation lays the foundation for further image recognition, analysis and understanding.
Step 705: sending the image data to a cloud server;
wherein, the image data is transmitted to a cloud server through a network, and the network can be the internet;
before sending the image data to the cloud server, the image data should be further processed:
adjusting the brightness, color distribution and contrast parameters of the image data according to a preset adjustment rule to obtain adjusted image data;
filtering the adjusted image data, and filtering out fuzzy image data in the adjusted image data to obtain clear image data;
and coding and compressing the clear image data, reducing the redundant data amount in the clear image data, and compressing the data size of the clear image data to obtain the image data which can be transmitted to a cloud server.
Step 706-707: the cloud server filters the image; matching the filtered image data with a deep learning model to determine a control instruction;
step 706 and step 707 are executed by a program running on the cloud server and implement the relevant matching operation.
Performing digital description processing on pixel points of the image data, and representing the color and brightness attributes of each pixel point in the image data by color data and brightness data respectively; specifically, the shape of the electroencephalogram signal in the image data is determined according to the color data and the brightness data of the pixel points of the image data after the digital description processing;
determining color data and brightness data of pixel points in the shape range of the electroencephalogram signals in the image data as characteristic data of the image data;
classifying the pixels of the image data after the digital description processing based on the color data and the brightness data, and determining the color data and the brightness data of the pixels in the classified pixel set as the characteristic data of the image data;
matching the feature data of the image data with the feature data of a control instruction in a preset control instruction database, when the matching similarity exceeds a preset similarity threshold, determining the control instruction of which the matching similarity exceeds the preset similarity threshold in the control instruction database, determining peripheral equipment corresponding to the control instruction, and generating the control instruction of the peripheral equipment aiming at the image data by using the control instruction and the corresponding peripheral equipment.
The data in the deep learning model is image data corresponding to each control instruction determined through early-stage data collection and model training; and determining the control instruction corresponding to the image data in a later stage in a matching mode.
Step 708: sending a control instruction to the central control equipment;
the cloud server can send the control instruction to the central control device through the network.
Step 709: the central control equipment sends a control instruction to a single chip microcomputer of the household appliance;
the central control equipment can send the control instruction to the single chip microcomputer of the household appliance through a serial port or a wireless network.
Step 710: and the household appliance responds to the control instruction and executes the control instruction.
The single chip of the household electrical appliance actually controls other parts of the household electrical appliance except the electronic controller, for example: a motor, a heating plate and the like.
Example eight
Fig. 8 is a schematic structural diagram of an electroencephalogram physiological data acquisition device according to an embodiment of the present invention, and as shown in fig. 8, the structure of the electroencephalogram physiological data acquisition device according to the embodiment of the present invention includes:
a multi-channel analog-to-Digital converter (ADC) module 801, configured to receive analog signals through multiple channels and convert the analog signals into Digital data;
a Complex Programmable Logic Device (CPLD) module 802, which includes a Programmable Logic macro unit and is formed by a digital integrated circuit, and generates a specific circuit structure according to the requirement;
an Acorn RISC Machine processor 803 including a one-chip core component for implementing a program function;
a Universal Asynchronous Receiver/Transmitter (UART) module 804, which is a serial communication interface, converts data to be transmitted between serial communication and parallel communication.
Example nine
Fig. 9 is a schematic structural diagram of an intelligent control system based on electroencephalogram signals according to an embodiment of the present invention, and as shown in fig. 9, the structure of the intelligent control system based on electroencephalogram signals according to an embodiment of the present invention includes:
the wearable device 901 is a portable device, and can be used as a carrier for mounting the brain wave control module.
The electroencephalogram data acquisition equipment 902 has a microcontroller and a serial port communication circuit, has the functions of electroencephalogram data acquisition and analysis and data transmission, is an independent module, is small in size, only outputs electroencephalogram sampling data externally, can be connected to different wearable equipment, and has the advantages of independence, flexibility, easiness in maintenance and the like.
And the automatic network distribution module 903 is used for automatically storing the parameters after the first configuration, does not need repeated configuration, and has the function of associating the mobile equipment with the household appliance to form a portable and movable human-computer interaction system.
The central control device 905 comprises an automatic distribution network module, an embedded control system, a brain wave data analysis and image display program and a single chip microcomputer controller. The brain wave data analysis program reads brain wave amplitude data from the serial port buffer area, the image processing program converts the brain wave amplitude data into an image, the image is transmitted to the cloud server through the network, a user control instruction is finally identified through image filtering, identification and classification, the user control instruction is returned to the embedded control system through the network, and the control instruction is transmitted to the single chip microcomputer controller through the serial port, so that the brain wave remote control is realized.
The big data processing service 906 is used for receiving image data fed back by massive users by the server, and carrying out classification processing, image screening and attribute marking on the data by the big data; the method has the function of optimizing and filtering data, so that the materials of computer vision training are reliable, and the trained model is accurate and reliable.
The deep learning image recognition system 907 obtains a mathematical model of each control action through data collection and model training in the early stage by a program running on a cloud server; and obtaining an operation code of the brain wave control action in a data matching mode at the later stage, and feeding back the operation code to the central control system.
In summary, the electroencephalogram-based control method, the device and the system provided in the embodiments of the present invention can be used as an intelligent single product development scheme, and can also be used as an Internet of Things (IoT) architecture design architecture, and are connected to peripheral devices through a wearable device and a networking module (preferably, a wireless networking module), and are used as a central controller to control the peripheral devices to implement different scenarios. Particularly, the intelligent household appliance in the embodiment of the invention preferably comprises a driving circuit module, an Internet and serial port communication module, can control peripheral equipment through brain waves, and not only has the functions of currently popular App control and IOT interactive mode of the intelligent remote mobile phone, but also has the functions of big data and image recognition. The control method based on the electroencephalogram signals provided by the embodiment of the invention can be used in intelligent equipment such as electric cookers, refrigerators, air conditioners and the like in the household appliance industry, and the developed intelligent system can be used for kitchen novice training and can also be used for disabled people and old people with language expression disorder.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
Claims (21)
1. A control method based on electroencephalogram signals, characterized in that the method comprises:
acquiring electroencephalogram physiological data detected by detection equipment, and filtering data with amplitude exceeding a preset amplitude data range in electroencephalogram amplitude data in the electroencephalogram physiological data to obtain electroencephalogram amplitude data to be converted;
converting the brain wave amplitude data to be converted into image data, wherein the color attribute information of a display composition unit in the image corresponds to the brain wave amplitude in the brain wave amplitude data to be converted;
sending the image data to a cloud server; the cloud server is used for identifying the image data and determining the characteristic data of the image data; matching the feature data of the image data with the feature data of a control instruction in a preset control instruction database to determine the control instruction of the peripheral equipment to which the image data aims;
and receiving a control instruction for the peripheral equipment, which is generated based on the image data and sent by the cloud server, and sending the control instruction to the corresponding peripheral equipment.
2. The method of claim 1, wherein prior to sending the image data to a cloud server, the method further comprises:
adjusting the brightness, color distribution and contrast parameters of the image data according to a preset adjustment rule to obtain adjusted image data;
filtering the adjusted image data, and filtering out fuzzy image data in the adjusted image data to obtain clear image data;
and coding and compressing the clear image data, reducing the redundant data amount in the clear image data, and compressing the data size of the clear image data to obtain the image data which can be transmitted to a cloud server.
3. The method of claim 1, wherein sending the control instruction to the corresponding peripheral device comprises:
and sending the control instruction to a single chip microcomputer controller of corresponding peripheral equipment, and driving the peripheral equipment by the single chip microcomputer controller of the peripheral equipment according to the control instruction.
4. A control method based on electroencephalogram signals, characterized in that the method comprises:
after receiving image data, the cloud server identifies the image data and determines characteristic data of the image data; the image data is obtained by filtering data of which the amplitude exceeds a preset amplitude data range in brain wave amplitude data in the electroencephalogram physiological data to obtain brain wave amplitude data to be converted; then converting the brain wave amplitude data to be converted; wherein the color attribute information of the display constituting unit in the image corresponds to the brain wave amplitude in the brain wave amplitude data to be converted;
matching the feature data of the image data with the feature data of a control instruction in a preset control instruction database to determine the control instruction of the peripheral equipment to which the image data aims;
and sending the control instruction to a central control device.
5. The method of claim 4, wherein the identifying the image data and determining the feature data of the image data comprises:
carrying out digital description processing on the pixel points of the image data, and representing the color and brightness attributes of each pixel point in the image data by color data and brightness data respectively;
classifying the pixels of the image data after the digital description processing based on the color data and the brightness data, and determining the color data and the brightness data of the pixels in the classified pixel set as the characteristic data of the image data.
6. The method of claim 5, wherein determining the data in the classified set of pixels as the feature data of the image data comprises:
determining the shape of the electroencephalogram signal in the image data according to the color data and the brightness data of the pixel points of the image data after the digital description processing;
and determining the color data and the brightness data of the pixel points in the shape range of the electroencephalogram signals in the image data as the characteristic data of the image data.
7. The method according to claim 4, wherein the matching the feature data of the image data with the feature data of the control command in a preset control command database to determine the control command of the peripheral device to which the image data is directed comprises:
matching the feature data of the image data with the feature data of a control instruction in a preset control instruction database, when the matching similarity exceeds a preset similarity threshold, determining the control instruction of which the matching similarity exceeds the preset similarity threshold in the control instruction database, determining peripheral equipment corresponding to the control instruction, and generating the control instruction of the peripheral equipment aiming at the image data by using the control instruction and the corresponding peripheral equipment.
8. A central control apparatus, characterized in that the apparatus comprises:
the receiving module is used for acquiring electroencephalogram physiological data detected by the detection equipment; the control device is also used for receiving a control instruction which is sent by the cloud server and generated based on the image data and aims at the peripheral equipment; the cloud server is used for identifying the image data and determining the characteristic data of the image data; matching the feature data of the image data with the feature data of a control instruction in a preset control instruction database to determine the control instruction of the peripheral equipment to which the image data aims;
the conversion module is used for filtering data of which the amplitude exceeds a preset amplitude data range in brain wave amplitude data in the electroencephalogram physiological data to obtain brain wave amplitude data to be converted; converting the brain wave amplitude data to be converted into image data, wherein the color attribute information of a display composition unit in the image corresponds to the brain wave amplitude in the brain wave amplitude data to be converted;
and the sending module is used for sending the control instruction to corresponding peripheral equipment.
9. The apparatus of claim 8, further comprising:
the adjusting module is used for adjusting the brightness, color distribution and contrast parameters of the image data according to a preset adjusting rule to obtain adjusted image data;
the filtering module is used for filtering the adjusted image data, filtering fuzzy image data in the adjusted image data and obtaining clear image data;
and the image processing module is used for coding and compressing the clear image data, reducing the redundant data amount in the clear image data, and compressing the data size of the clear image data to obtain the image data which can be transmitted to the cloud server.
10. The device according to claim 8, wherein the sending module is configured to send the control instruction to a single chip microcomputer controller of a corresponding peripheral device, and the single chip microcomputer controller of the peripheral device drives the peripheral device according to the control instruction.
11. A cloud server, the cloud server comprising:
the identification module is used for identifying the image data after the cloud server receives the image data and determining the characteristic data of the image data; the image data is obtained by filtering data of which the amplitude exceeds a preset amplitude data range in brain wave amplitude data in the electroencephalogram physiological data to obtain brain wave amplitude data to be converted; then converting the brain wave amplitude data to be converted; wherein the color attribute information of the display constituting unit in the image corresponds to the brain wave amplitude in the brain wave amplitude data to be converted;
the matching module is used for matching the feature data of the image data with the feature data of the control instruction in a preset control instruction database to determine the control instruction of the peripheral equipment to which the image data aims;
and the sending module is used for sending the control instruction to the central control equipment.
12. The cloud server of claim 11, wherein the identification module is configured to perform digital description processing on the pixels of the image data, and characterize color and brightness attributes of each pixel in the image data by color data and brightness data, respectively;
classifying the pixels of the image data after the digital description processing based on the color data and the brightness data, and determining the color data and the brightness data of the pixels in the classified pixel set as the characteristic data of the image data.
13. The cloud server of claim 12, wherein the identification module is specifically configured to determine a shape of a brain electrical signal in the image data according to color data and luminance data of a pixel of the image data after the digital description processing;
and determining the color data and the brightness data of the pixel points in the shape range of the electroencephalogram signals in the image data as the characteristic data of the image data.
14. The cloud server according to claim 12, wherein the matching module is configured to match feature data of the image data with feature data of a control instruction in a preset control instruction database, determine, when matching similarity exceeds a preset similarity threshold, a control instruction in the control instruction database, of which the matching similarity exceeds the preset similarity threshold, determine, at the same time, a peripheral device corresponding to the control instruction, and generate, by the control instruction and the corresponding peripheral device, a control instruction of the peripheral device to which the image data is directed.
15. A control system based on electroencephalogram signals, the system comprising:
the central control equipment is used for acquiring electroencephalogram physiological data detected by the detection equipment, and filtering data with amplitude exceeding a preset amplitude data range in electroencephalogram amplitude data in the electroencephalogram physiological data to obtain electroencephalogram amplitude data to be converted; converting the brain wave amplitude data to be converted into image data; sending the image data to a cloud server; receiving a control instruction for peripheral equipment, which is generated based on the image data and sent by the cloud server, and sending the control instruction to the corresponding peripheral equipment; the color attribute information of the display composition unit in the image corresponds to the brain wave amplitude in the brain wave amplitude data to be converted;
the cloud server is used for identifying the image data after receiving the image data and determining the characteristic data of the image data; matching the feature data of the image data with the feature data of a control instruction in a preset control instruction database to determine the control instruction of the peripheral equipment to which the image data aims; sending the control instruction to a central control device;
the electroencephalogram signal detection equipment is used for acquiring electroencephalogram physiological data through the detection equipment arranged on the wearable equipment; and sending the acquired electroencephalogram physiological data to a central control device according to preset network configuration.
16. A system according to claim 15, wherein the central control apparatus implements the method of any one of claims 1 to 3.
17. The system of claim 15, wherein the cloud server implements the method of any of claims 4 to 7.
18. A central control apparatus, characterized by comprising: a processor and a memory for storing a computer program capable of running on the processor,
wherein the processor is adapted to perform the steps of the method of any one of claims 1 to 3 when running the computer program.
19. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 4.
20. A cloud server, comprising: a processor and a memory for storing a computer program capable of running on the processor,
wherein the processor is adapted to perform the steps of the method of any one of claims 4 to 7 when running the computer program.
21. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any one of claims 4 to 7.
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