Human body monitoring method and system based on brain wave analysis

Abstract

The present application relates to the field of smart mattress technology and discloses a human body monitoring method and system based on brainwave analysis. The method comprises: obtaining the left and right brainwave characteristics of a target user and generating difference characteristics between the left and right brainwave characteristics; creating multiple parallel data processing channels when processing the data of each characteristic, wherein, when each data processing channel is initialized, only one priority processing flag in the data processing channel is set to a specified value; for any data processing channel, if the priority processing flag in the data processing channel meets a preset condition, calling a shared computing power module to process the data blocks in the characteristic within the current processing cycle; after completing the processing of the data of each characteristic, outputting the health status of the target user. The beneficial effect is that the computing power of the computing power module can be fully utilized, thereby improving the efficiency of brainwave analysis.

Description

Technical Field

[0001] The present application relates to the technical field of smart mattresses, and in particular to a human body monitoring method and system based on brain wave analysis. Background Art

[0002] As smart mattresses continue to evolve, they can now be equipped with brainwave monitoring components. By wearing these components while sleeping, users can monitor their brainwave activity in real time. The smart mattress's analysis module analyzes the collected brainwaves to assess the user's sleep status and identify potential physical abnormalities, triggering an alert if any are present.

[0003] At present, the computing power modules equipped in smart mattresses usually do not have strong computing power. Therefore, when analyzing brain waves, it is necessary to rationally plan the analysis process to fully utilize the computing power of the computing power module, thereby improving the efficiency of brain wave analysis. Summary of the Invention

[0004] The present application provides a human body monitoring method and system based on brain wave analysis, which can fully utilize the computing power of the computing module and thereby improve the efficiency of brain wave analysis.

[0005] In order to achieve the above objectives, the main technical solutions adopted in this application include:

[0006] In a first aspect, an embodiment of the present application provides a human body monitoring method based on brain wave analysis, wherein the method is applied to a smart mattress equipped with a brain wave monitoring component, and the method comprises:

[0007] Acquire the left brainwave characteristics and the right brainwave characteristics of the target user through the brainwave monitoring component, and generate difference characteristics between the left brainwave characteristics and the right brainwave characteristics;

[0008] Inputting the left brainwave feature, the right brainwave feature, and the difference feature into a human body monitoring model, wherein the human body monitoring model creates multiple parallel data processing channels when processing data of each feature, wherein, when initializing each data processing channel, only a priority processing flag within one data processing channel is set to a specified value;

[0009] For any data processing channel, if the priority processing flag in the data processing channel meets the preset conditions, the shared computing power module is called to process the data blocks in the feature in the current processing cycle. After the processing is completed, the next processing cycle is entered, and an activation instruction is sent to the next data processing channel at the same time. The activation instruction is used to update the data processing capacity in the next data processing channel. If the updated data processing capacity is empty, the next data processing channel sets the data capacity to be processed to the initial value and updates its own priority processing flag to another value different from the current value.

[0010] After completing the processing of the data of each feature, the health status of the target user is outputted through the human body monitoring model.

[0011] In one embodiment, obtaining the left brainwave characteristics and the right brainwave characteristics of the target user through the brainwave monitoring component includes:

[0012] The left brainwave data and right brainwave data of the target user are collected respectively by the brainwave monitoring component. For any brainwave data, the brainwave data is divided into sub-data of different frequency bands, and the CSP features of each sub-data are extracted, and the combination of the extracted CSP features is used as the brainwave features of the brainwave data.

[0013] In one embodiment, generating a difference feature between the left brainwave feature and the right brainwave feature includes:

[0014] A feature residual between the left brain wave feature and the right brain wave feature is calculated, and the feature residual is used as a difference feature between the left brain wave feature and the right brain wave feature.

[0015] In one embodiment, when each of the data processing channels is initialized, the method further includes:

[0016] Assigning a comparison flag in each of the data processing channels to another value different from the specified value;

[0017] Accordingly, the priority processing flag in the data processing channel satisfies the preset conditions including:

[0018] The assignment of the priority processing identifier in the data processing channel is different from the assignment of the comparison identifier.

[0019] In one embodiment, after the data processing channel enters the next processing cycle, the method further includes:

[0020] In the next processing cycle, the data processing channel identifies the type identifier of the next processing cycle. If the type identifier is different from the type identifier of the previous processing cycle, the data processing channel keeps the assignment of the priority processing identifier unchanged.

[0021] In one embodiment, the method further comprises:

[0022] If the type identifier is the same as the type identifier of the previous processing cycle, the data processing channel updates the value assigned to the priority processing identifier to another value different from the current value.

[0023] In one embodiment, if the updated data processing amount is not empty, the method further includes:

[0024] The next data processing channel detects whether the current priority processing identifier assigned to the data processing channel that sends the activation instruction is the specified value. If so, the next data processing channel keeps the value of its own priority processing identifier unchanged; if not, the next data processing channel updates the value of its own priority processing identifier from the current value to another value.

[0025] On the other hand, the present application also provides a human body monitoring system based on brain wave analysis, the system comprising:

[0026] A brainwave monitoring component, configured to obtain the left brainwave characteristics and the right brainwave characteristics of the target user and generate difference characteristics between the left brainwave characteristics and the right brainwave characteristics;

[0027] a model processing component, configured to input the left brainwave feature, the right brainwave feature, and the difference feature into a human body monitoring model, wherein the human body monitoring model creates a plurality of parallel data processing channels when processing data of the respective features, wherein, when initializing each of the data processing channels, only one of the data processing channels has a priority processing flag set to a specified value;

[0028] A channel updating component is configured to, for any data processing channel, if the priority processing flag in the data processing channel satisfies a preset condition, call a shared computing power module to process the data blocks in the feature within the current processing cycle, enter the next processing cycle after the processing is completed, and simultaneously send an activation instruction to the next data processing channel, wherein the activation instruction is used to update the data processing capacity within the next data processing channel. If the updated data processing capacity is empty, the next data processing channel sets the data capacity to be processed to the initial value and updates its own priority processing flag to another value different from the current value;

[0029] The status acquisition component is used to acquire the health status of the target user output by the human body monitoring model after completing the processing of the data of each feature.

[0030] In one embodiment, the brainwave monitoring component is specifically used to collect the left brainwave data and right brainwave data of the target user respectively, divide the brainwave data into sub-data of different frequency bands for any brainwave data, extract the CSP features of each sub-data, and use the combination of the extracted CSP features as the brainwave features of the brainwave data.

[0031] In one embodiment, the brainwave monitoring component is specifically used to calculate the feature residual between the left brainwave feature and the right brainwave feature, and use the feature residual as the difference feature between the left brainwave feature and the right brainwave feature.

[0032] The technical solution provided by the present application can comprehensively obtain the active states of the left and right hemispheres of the user by processing the left and right brain wave characteristics and difference characteristics, and based on the difference characteristics between the left and right brains, it can be determined whether the user has potential physical abnormalities. When analyzing these characteristics, a multi-channel parallel analysis method can be adopted, but the current computing power module is not sufficient to support the data analysis process of multiple channels at the same time. It can only try to make the process of data analysis and calculation of each channel consistent in time sequence, so as to make full use of the computing power of the computing power module as much as possible. Specifically, by setting priority identification and activation instructions, and by updating the amount of data to be processed, it can ensure that the shared computing power module is called in sequence by different channels, so as to give full play to the computing power of the computing power module, thereby improving the efficiency of brain wave analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific implementation methods or the description of the prior art. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0034] Figure 1 A flowchart of a human body monitoring method based on brain wave analysis provided in an embodiment of the present application;

[0035] Figure 2 A schematic diagram of a human body monitoring system based on brain wave analysis provided in an embodiment of the present application;

[0036] Figure 3 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] To make the purpose, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of this application.

[0038] See also Figure 1 The present application provides a human body monitoring method based on brain wave analysis, which is applied to a smart mattress equipped with a brain wave monitoring component. The method includes the following steps.

[0039] S1: Acquire the left brainwave characteristics and the right brainwave characteristics of the target user through the brainwave monitoring component, and generate difference characteristics between the left brainwave characteristics and the right brainwave characteristics.

[0040] Specifically, the left brainwave data and right brainwave data of the target user can be collected separately through the brainwave monitoring component. For any brainwave data, the brainwave data can be divided into sub-data of different frequency bands, and the CSP (Common Spatial Pattern) features of each sub-data can be extracted, and the combination of the extracted CSP features can be used as the brainwave features of the brainwave data.

[0041] Then, a feature residual between the left brain wave feature and the right brain wave feature may be calculated, and the feature residual may be used as a difference feature between the left brain wave feature and the right brain wave feature.

[0042] S2: Input the left brain wave feature, the right brain wave feature and the difference feature into the human body monitoring model. When processing the data of each feature, the human body monitoring model creates multiple parallel data processing channels. When each data processing channel is initialized, only the priority processing flag in one data processing channel is set to a specified value.

[0043] S3: For any data processing channel, if the priority processing flag in the data processing channel meets the preset conditions, the shared computing power module is called to process the data blocks in the feature in the current processing cycle, and enters the next processing cycle after the processing is completed, and at the same time sends an activation instruction to the next data processing channel. The activation instruction is used to update the data processing volume in the next data processing channel. If the updated data processing volume is empty, the next data processing channel sets the data volume to be processed to the initial value, and updates its own priority processing flag to another value different from the current value.

[0044] In this embodiment, the human body monitoring model may be a model constructed based on a conventional neural network, and the input feature data may be processed through multiple parallel data processing channels within the model.

[0045] For each data processing channel, a priority processing flag and a comparison flag can be set. During initialization, only one channel's priority processing flag is set to a specified value, which can be a Boolean value of true, and the priority processing flags of other channels can be set to false. In addition, to ensure the normal rotation of the computing power module, during initialization, the comparison flag in each data processing channel can be assigned a value different from the specified value, that is, the comparison flag in each channel is assigned to false. In this way, when each channel is preparing to perform feature analysis, it can compare the assigned value of the priority processing flag with the assigned value of the comparison flag. When the two are different, it is considered that the priority processing flag in the data processing channel meets the preset conditions. At this time, the channel can call the shared computing power module to process the data blocks in the feature during the current processing cycle, while the other channels are in a paused state. Since the priority processing flag in only one channel is set to true during initialization, only one channel will meet the preset conditions, allowing the channel to exclusively use the computing power module.

[0046] After the current channel completes its current processing cycle, it directly enters the next processing cycle. It should be noted that the computing power module is not necessarily required for each processing cycle. Normally, adjacent processing cycles consist of a feature analysis cycle and a data storage cycle. Therefore, when the current channel enters the next processing cycle, it actually performs data storage and does not require the computing power module. Therefore, the current channel sends an activation command to the next adjacent data processing channel. Upon receiving the activation command, the next data processing channel updates the amount of data to be processed. This is because the previous data processing channel has already analyzed the feature data for the current batch. Therefore, when the next data processing channel is activated, the feature data for the current batch has already been processed, and the amount of data to be processed needs to be updated. If the previous data processing channel completes data processing successfully, the updated data processing amount for the next data processing channel will be empty. In this case, the next data processing channel needs to set the amount of data to be processed to the initial value, which can be the total amount of data to be processed in the batch. Then, its own priority processing flag will be updated to another value different from the current value. During initialization, the priority processing flag of the data processing channel is initialized to false. Then, after resetting the data volume, the priority processing flag will be set to true. Subsequently, the data processing channel will also compare the priority processing flag and the comparison flag. At this time, the two assigned values are inconsistent, so the data processing channel will call the computing power module for feature analysis.

[0047] In one embodiment, in the next processing cycle, the data processing channel identifies the type identifier of the next processing cycle. If the type identifier is different from the type identifier of the previous processing cycle, the data processing channel maintains the assignment of the priority processing identifier unchanged. Specifically, the type identifier can represent different processing requirements. For example, feature analysis and data storage are different type identifiers. After entering the next processing cycle, if the type identifier changes, it indicates that the processing requirements have changed. At this time, the data processing channel can maintain the assignment of the priority processing identifier unchanged, so that the next processing cycle can also execute the processes of other processing requirements normally. In other words, the data processing channel can perform the data storage process, and the next data processing channel that is activated will also enter the feature analysis process because the data volume is reset and the priority processing identifier is reassigned. These two processes only need to call the computing power module during feature analysis, which realizes the full utilization of the computing power module and ensures the parallel processing process.

[0048] In one embodiment, if the type identifier is the same as the type identifier of the previous processing cycle, the data processing channel updates the assigned priority identifier to a different value than the current value. This way, when the current data processing channel enters the next processing cycle, if it still requires invoking the computing power module, the change in the priority identifier will prevent the feature analysis conditions from being met and the computing power module from being invoked. This prevents the data processing channel from competing with the next activated data processing channel for the computing power module, thus ensuring system stability.

[0049] In one embodiment, if the updated data processing volume of the next data processing channel is not empty, the next data processing channel detects whether the current priority processing identifier of the data processing channel that sends the activation instruction is the specified value. If so, the next data processing channel maintains the value of its own priority processing identifier unchanged; if not, the next data processing channel updates the value of its own priority processing identifier from the current value to another value. The purpose of this processing is that the updated data processing volume of the next data processing channel is not empty, indicating that the previous data processing channel has not completed the processing of the current batch. Therefore, the computing power module can remain in the previous data processing channel at this time until the previous data processing channel completes the feature analysis process of the current batch. This can ensure that the data of the same batch is completed in the same data processing channel, avoiding data disorder.

[0050] S4: After completing the processing of the data of each feature, the health status of the target user is output through the human body monitoring model.

[0051] See also Figure 2 The present application also provides a human body monitoring system based on brain wave analysis, the system comprising:

[0052] A brainwave monitoring component, configured to obtain the left brainwave characteristics and the right brainwave characteristics of the target user and generate difference characteristics between the left brainwave characteristics and the right brainwave characteristics;

[0053] a model processing component, configured to input the left brainwave feature, the right brainwave feature, and the difference feature into a human body monitoring model, wherein the human body monitoring model creates a plurality of parallel data processing channels when processing data of the respective features, wherein, when initializing each of the data processing channels, only one of the data processing channels has a priority processing flag set to a specified value;

[0054] A channel updating component is configured to, for any data processing channel, if the priority processing flag in the data processing channel satisfies a preset condition, call a shared computing power module to process the data blocks in the feature within the current processing cycle, enter the next processing cycle after the processing is completed, and simultaneously send an activation instruction to the next data processing channel, wherein the activation instruction is used to update the data processing capacity within the next data processing channel. If the updated data processing capacity is empty, the next data processing channel sets the data capacity to be processed to the initial value and updates its own priority processing flag to another value different from the current value;

[0055] The status acquisition component is used to acquire the health status of the target user output by the human body monitoring model after completing the processing of the data of each feature.

[0056] In one embodiment, the brainwave monitoring component is specifically used to collect the left brainwave data and right brainwave data of the target user respectively, divide the brainwave data into sub-data of different frequency bands for any brainwave data, extract the CSP features of each sub-data, and use the combination of the extracted CSP features as the brainwave features of the brainwave data.

[0057] In one embodiment, the brainwave monitoring component is specifically used to calculate the feature residual between the left brainwave feature and the right brainwave feature, and use the feature residual as the difference feature between the left brainwave feature and the right brainwave feature.

[0058] The technical solution provided by the present application can comprehensively obtain the active states of the left and right hemispheres of the user by processing the left and right brain wave characteristics and difference characteristics, and based on the difference characteristics between the left and right brains, it can be determined whether the user has potential physical abnormalities. When analyzing these characteristics, a multi-channel parallel analysis method can be adopted, but the current computing power module is not sufficient to support the data analysis process of multiple channels at the same time. It can only try to make the process of data analysis and calculation of each channel consistent in time sequence, so as to make full use of the computing power of the computing power module as much as possible. Specifically, by setting priority identification and activation instructions, and by updating the amount of data to be processed, it can ensure that the shared computing power module is called in sequence by different channels, so as to give full play to the computing power of the computing power module, thereby improving the efficiency of brain wave analysis.

[0059] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0060] The unit in this embodiment refers to an ASIC (Application Specific Integrated Circuit), a processor and a memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0061] See also Figure 3 , Figure 3 A schematic diagram of the structure of a computer device provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 3 A processor 10 is taken as an example.

[0062] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0063] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0064] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0065] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0066] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0067] The embodiments of the present application also provide a computer-readable storage medium. The above-mentioned method according to the embodiment of the present application can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0068] The systems, devices, and units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0069] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0070] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods or apparatuses. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0071] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices and apparatuses according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0072] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0073] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0074] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0075] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0076] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

[0077] Although the embodiments of the present application have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations shall fall within the scope defined by the appended claims.

Claims

1. A human body monitoring method based on brain wave analysis, characterized in that: The method is applied to a smart mattress equipped with a brain wave monitoring component, and the method includes: Acquire the left brainwave characteristics and the right brainwave characteristics of the target user through the brainwave monitoring component, and generate difference characteristics between the left brainwave characteristics and the right brainwave characteristics; Inputting the left brainwave feature, the right brainwave feature, and the difference feature into a human body monitoring model, wherein the human body monitoring model creates multiple parallel data processing channels when processing data of each feature, wherein, when each of the data processing channels is initialized, only a priority processing flag in one of the data processing channels is set to a specified value, and a comparison flag in each of the data processing channels is assigned another value different from the specified value; For any data processing channel, if the assignment of the priority processing identifier in the data processing channel is different from the assignment of the comparison identifier, the shared computing power module is called to process the data blocks in the feature in the current processing cycle, and enters the next processing cycle after the processing is completed, and at the same time sends an activation instruction to the next data processing channel, the activation instruction is used to update the data processing capacity in the next data processing channel. If the updated data processing capacity is empty, the next data processing channel sets the data capacity to be processed to the initial value, and updates its own priority processing identifier to another value different from the current value; if the updated data processing capacity is not empty, the next data processing channel detects the current value of the data processing channel that sent the activation instruction. whether the assigned value of the priority processing identifier is the specified value; if so, the next data processing channel keeps the assigned value of its own priority processing identifier unchanged; if not, the next data processing channel updates the assigned value of its own priority processing identifier from the current value to another value; within the next processing cycle, the data processing channel identifies the type identifier of the next processing cycle; if the type identifier is different from the type identifier of the previous processing cycle, the data processing channel keeps the assigned value of the priority processing identifier unchanged; if the type identifier is the same as the type identifier of the previous processing cycle, the data processing channel updates the assigned value of the priority processing identifier to another value different from the current value, the type identifier including feature analysis and data storage; After completing the processing of the data of each feature, the health status of the target user is outputted through the human body monitoring model.

2. The method according to claim 1, characterized in that Acquiring the left brainwave characteristics and the right brainwave characteristics of the target user by the brainwave monitoring component includes: The left brainwave data and right brainwave data of the target user are collected respectively by the brainwave monitoring component. For any brainwave data, the brainwave data is divided into sub-data of different frequency bands, and the CSP features of each sub-data are extracted, and the combination of the extracted CSP features is used as the brainwave features of the brainwave data.

3. The method according to claim 1 or 2, characterized in that Generating the difference feature between the left brain wave feature and the right brain wave feature includes: A feature residual between the left brain wave feature and the right brain wave feature is calculated, and the feature residual is used as a difference feature between the left brain wave feature and the right brain wave feature.

4. A human body monitoring system based on brain wave analysis, characterized in that: The system comprises: A brainwave monitoring component, configured to obtain the left brainwave characteristics and the right brainwave characteristics of the target user and generate difference characteristics between the left brainwave characteristics and the right brainwave characteristics; a model processing component, configured to input the left brainwave feature, the right brainwave feature, and the difference feature into a human body monitoring model, wherein the human body monitoring model creates a plurality of parallel data processing channels when processing data of the respective features, wherein, upon initialization of each of the data processing channels, only a priority processing flag within one of the data processing channels is set to a specified value, and a comparison flag within each of the data processing channels is assigned a value different from the specified value; The channel update component is used for any data processing channel. If the value assigned to the priority processing identifier in the data processing channel is different from the value assigned to the comparison identifier, the shared computing power module is called to process the data blocks in the feature in the current processing cycle, and enters the next processing cycle after the processing is completed, and at the same time sends an activation instruction to the next data processing channel. The activation instruction is used to update the data processing capacity in the next data processing channel. If the updated data processing capacity is empty, the next data processing channel sets the data capacity to be processed to the initial value, and updates its own priority processing identifier to another value different from the current value; if the updated data processing capacity is not empty, the next data processing channel detects and sends the whether the current priority processing identifier assigned to the data processing channel of the activation instruction is the specified value; if so, the next data processing channel maintains the assigned value of its own priority processing identifier unchanged; if not, the next data processing channel updates the assigned value of its own priority processing identifier from the current value to another value; within the next processing cycle, the data processing channel identifies the type identifier of the next processing cycle; if the type identifier is different from the type identifier of the previous processing cycle, the data processing channel maintains the assigned value of the priority processing identifier unchanged; if the type identifier is the same as the type identifier of the previous processing cycle, the data processing channel updates the assigned value of the priority processing identifier to another value different from the current value; The status acquisition component is used to acquire the health status of the target user output by the human body monitoring model after completing the processing of the data of each feature.

5. The system according to claim 4, characterized in that The brainwave monitoring component is specifically used to collect the left brainwave data and right brainwave data of the target user respectively, divide the brainwave data into sub-data of different frequency bands for any brainwave data, extract the CSP features of each sub-data, and use the combination of the extracted CSP features as the brainwave features of the brainwave data.

6. The system according to claim 4 or 5, characterized in that The brainwave monitoring component is specifically used to calculate the feature residual between the left brainwave feature and the right brainwave feature, and use the feature residual as the difference feature between the left brainwave feature and the right brainwave feature.

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