Human body monitoring method and system based on brain wave analysis

Abstract

The invention relates to the technical field of intelligent mattresses, and discloses a human body monitoring method and system based on brain wave analysis, and the method comprises the steps: obtaining a left brain wave feature and a right brain wave feature of a target user, and generating a difference feature between the left brain wave feature and the right brain wave feature; when the data of each feature is processed, a plurality of parallel data processing channels are created, and when each data processing channel is initialized, the priority processing identifier in only one data processing channel is set as a specified value; for any data processing channel, if the priority processing identifier in the data processing channel meets a preset condition, calling a shared computing power module to process the data blocks in the features in the current processing period; and after the data of each feature is processed, outputting the health state of the target user. The method has the beneficial effects that the computing power of the computing power module can be fully utilized, so that the brain wave analysis efficiency is improved.

Description

Technical Field

[0001] This application relates to the technical field of intelligent mattresses, and particularly to a human body monitoring method and system based on electroencephalogram analysis. Background Art

[0002] With the continuous development of the functions of intelligent mattresses, electroencephalogram monitoring components can be configured on the intelligent mattresses. When a user is sleeping, by wearing the electroencephalogram monitoring component, the active state of the user's own electroencephalogram can be monitored in real time. The analysis module of the intelligent mattress analyzes the collected electroencephalogram, can count the user's sleep state and judge whether there are potential physical abnormalities. If so, an alarm will be issued.

[0003] Currently, the computing power modules equipped in intelligent mattresses usually do not have strong computing power. Therefore, when analyzing electroencephalograms, it is necessary to reasonably plan the analysis process to make full use of the computing power of the computing power module, thereby improving the efficiency of electroencephalogram analysis. Summary of the Invention

[0004] This application provides a human body monitoring method and system based on electroencephalogram analysis, which can make full use of the computing power of the computing power module, thereby improving the efficiency of electroencephalogram analysis.

[0005] To achieve the above object, the main technical solutions adopted in this application include:

[0006] In a first aspect, an embodiment of this application provides a human body monitoring method based on electroencephalogram analysis. The method is applied to an intelligent mattress, and the intelligent mattress is configured with an electroencephalogram monitoring component. The method includes:

[0007] Obtain the left electroencephalogram feature and the right electroencephalogram feature of a target user through the electroencephalogram monitoring component, and generate a difference feature between the left electroencephalogram feature and the right electroencephalogram feature;

[0008] Input the left electroencephalogram feature, the right electroencephalogram feature, and the difference feature into a human body monitoring model. When the human body monitoring model processes the data of each feature, multiple parallel data processing channels are created. Among them, when each data processing channel is initialized, only the priority processing flag in one data processing channel is set to a specified value;

[0009] For any data processing channel, if the priority processing identifier in the data processing channel meets a preset condition, a shared computing power module is called to process the data chunks in the feature during the current processing cycle. After the processing is completed, it enters the next processing cycle, and at the same time, an activation instruction is sent to the next data processing channel. The activation instruction is used to update the data processing amount in the next data processing channel. If the updated data processing amount is empty, the next data processing channel sets the data amount to be processed to the initial value, and at the same time updates its priority processing identifier to another value different from the current value;

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

[0011] In one embodiment, obtaining the left brain wave feature and the right brain wave feature of the target user by the brain wave monitoring component includes:

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

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

[0014] Calculate the feature residual between the left brain wave feature and the right brain wave feature, and use the feature residual as the difference feature between the left brain wave feature and the right brain wave feature.

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

[0016] Assign a comparison identifier in each data processing channel to another value different from the specified value;

[0017] Correspondingly, the priority processing identifier in the data processing channel meeting the preset condition includes:

[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] During 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 includes:

[0022] If the type identifier is the same as the type identifier of the previous processing cycle, the data processing channel updates the assignment of 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 assignment of the priority processing identifier of the data processing channel that sent the activation instruction is the specified value. If so, the next data processing channel keeps the assignment of its own priority processing identifier unchanged; if not, the next data processing channel updates the assignment 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 electroencephalogram analysis. The system includes:

[0026] An electroencephalogram monitoring component for acquiring the left electroencephalogram characteristics and right electroencephalogram characteristics of a target user and generating the difference characteristics between the left electroencephalogram characteristics and the right electroencephalogram characteristics;

[0027] A model processing component for inputting the left electroencephalogram characteristics, the right electroencephalogram characteristics, and the difference characteristics into a human body monitoring model. When the human body monitoring model processes the data of each characteristic, a plurality of parallel data processing channels are created. Among them, when each data processing channel is initialized, only the priority processing identifier in one data processing channel is set to the specified value;

[0028] A channel update component for any data processing channel. If the priority processing identifier in the data processing channel meets the preset conditions, it calls the shared computing power module to process the data blocks in the characteristic during the current processing cycle, enters the next processing cycle after completion, and simultaneously sends an activation instruction to the next data processing channel. The activation instruction is used to update the data processing amount in the next data processing channel. If the updated data processing amount is empty, the next data processing channel sets the data amount to be processed to the initial value and simultaneously updates its own priority processing identifier to another value different from the current value;

[0029] A status acquisition component, configured to acquire the health status of the target user output by the human body monitoring model after processing the data of each feature.

[0030] In one embodiment, the electroencephalogram monitoring component is specifically configured to respectively collect the left electroencephalogram data and the right electroencephalogram data of the target user. For any electroencephalogram data, the electroencephalogram data is divided into sub-data of different frequency bands, the CSP features of each sub-data are extracted, and the combination of the extracted CSP features is used as the electroencephalogram feature of the electroencephalogram data.

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

[0032] The technical solution provided by this application can comprehensively obtain the active states of the left and right hemispheres of the user by processing the left and right electroencephalogram features and the difference features. And based on the difference features between the left and right brains, it can be determined whether the user has potential physical abnormalities. When analyzing these features, a multi-channel parallel analysis method can be adopted. However, the current computing power module is not sufficient to support the data analysis process of multiple channels simultaneously, and only try to make the data analysis and calculation process involved in each channel be coherent in time sequence, so as to make the best use of the computing power of the computing power module as much as possible. Specifically, by setting a priority flag and an activation instruction, and updating the data volume to be processed, it can be ensured that the shared computing power module is called by different channels in turn, so as to give full play to the computing power of the computing power module, and then improve the efficiency of electroencephalogram analysis. Description of the Drawings

[0033] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0034] Figure 1 It is a flowchart of a human body monitoring method based on electroencephalogram analysis provided by an embodiment of the present application;

[0035] Figure 2 It is a schematic diagram of a human body monitoring system based on electroencephalogram analysis provided by an embodiment of the present application;

[0036] Figure 3 It is a schematic diagram of the structure of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without making creative efforts shall fall within the protection scope of the present application.

[0038] Please refer to Figure 1 , the present application provides a human body monitoring method based on electroencephalogram analysis. The method is applied to an intelligent mattress, and the intelligent mattress is configured with an electroencephalogram monitoring component. The method includes the following steps.

[0039] S1: Obtain the left electroencephalogram feature and the right electroencephalogram feature of a target user through the electroencephalogram monitoring component, and generate a difference feature between the left electroencephalogram feature and the right electroencephalogram feature.

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

[0041] Then, the feature residual between the left electroencephalogram feature and the right electroencephalogram feature can be calculated, and the feature residual is used as the difference feature between the left electroencephalogram feature and the right electroencephalogram feature.

[0042] S2: Input the left electroencephalogram feature, the right electroencephalogram feature, and the difference feature into a human body monitoring model. When processing the data of each feature, the human body monitoring model creates a plurality of parallel data processing channels. Among them, 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 condition, call the shared computing power module to process the data blocks in the feature within the current processing cycle. After the processing is completed, enter the next processing cycle, and at the same time send 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 as the initial value, and at the same time updates its own priority processing flag to another value different from the current value.

[0044] In this embodiment, the human body monitoring model can be a model constructed based on a conventional neural network. Multiple parallel data processing channels can be used in the model to process the input feature data.

[0045] For each data processing channel, a priority processing flag and a comparison flag can be set. Among them, during initialization, only the priority processing flag of one channel is set to a specified value, and the specified value can be the boolean value true. 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 flags in each data processing channel can be assigned another value different from the specified value, that is, the comparison flags in each channel are assigned false. In this way, when each channel is ready to perform feature analysis, it can compare the assignment of the priority processing flag with the assignment 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 condition. At this time, the channel can call the shared computing power module to process the data blocks in the feature within the current processing cycle, while other channels are in a suspended state. Since only the priority processing flag in one channel is set to true during initialization, only one channel will meet the preset condition, so that the channel can exclusively use the computing power module.

[0046] After the current processing cycle of the current channel is completed, it will directly enter the next processing cycle. It should be noted that it is not necessarily required to call the computing power module in different processing cycles. Normally, adjacent processing cycles generally include a feature analysis cycle and a data storage cycle. Therefore, after the current channel enters the next processing cycle, it actually performs data storage, and at this time, there is no need to utilize the computing power module. Therefore, the current channel will send an activation instruction to the next adjacent data processing channel. After receiving the activation instruction, the next data processing channel will update the amount of data to be processed. The reason is that the previous data processing channel has already analyzed the feature data of the current batch. Therefore, when activating the next data processing channel, the feature data of the current batch has been processed, and the amount of data to be processed needs to be updated. After the previous data processing channel normally completes data processing, it will cause the updated data processing amount of the next data processing channel to be empty. Then, the next data processing channel needs to set the amount of data to be processed to an initial value, which can be the total amount of data to be processed within a batch. Then, its priority processing flag will be updated to another value different from the current value. During initialization, the priority processing flag of this data processing channel is initialized to false. Then, after resetting the data volume, the priority processing flag will be set to true. Subsequently, this data processing channel will also compare the priority processing flag with the comparison flag. At this time, the two assignments are inconsistent. Therefore, this data processing channel will call the computing power module for feature analysis.

[0047] In one embodiment, during 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 flag 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 keep the assignment of the priority processing flag unchanged, so that the next processing cycle can also normally execute the process of other processing requirements. That is to say, this data processing channel can perform the process of data storage, while the next activated data processing channel, due to the data volume being reset and the priority processing flag being re-assigned, will also enter the process of feature analysis. And these two processes only need to call the computing power module during feature analysis, realizing the full utilization of the computing power module and ensuring the parallel processing process.

[0048] In one embodiment, if the type identifier is the same as that of the previous processing cycle, the data processing channel updates the assignment of the priority processing identifier to another value different from the current value. In this way, when the current data processing channel enters the next processing cycle, if it is still a cycle that requires the call of the computing power module, since the priority processing identifier has changed at this time, the condition for feature analysis is not met, and the computing power module cannot be called. In this way, this data processing channel will not compete with the next activated data processing channel for the computing power module, ensuring the stability of the system.

[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 assignment of 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 keeps the assignment of its own priority processing identifier unchanged; if not, the next data processing channel updates the assignment of its own priority processing identifier from the current value to another value. The purpose of such processing is that since the updated data processing volume of the next data processing channel is not empty, it indicates that the previous data processing channel has not completed the current batch of processing. Therefore, at this time, the computing power module can still stay in the previous data processing channel until the previous data processing channel completes the current batch of feature analysis process. This can ensure that the data of the same batch is completed within the same data processing channel, avoiding data disorder.

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

[0051] Please refer to Figure 2 , this application also provides a human body monitoring system based on electroencephalogram analysis. The system includes:

[0052] An electroencephalogram monitoring component, configured to obtain the left electroencephalogram feature and the right electroencephalogram feature of the target user, and generate a difference feature between the left electroencephalogram feature and the right electroencephalogram feature;

[0053] A model processing component, configured to input the left electroencephalogram feature, the right electroencephalogram feature, and the difference feature into a human body monitoring model. When the human body monitoring model processes the data of each feature, multiple parallel data processing channels are created. Among them, when each data processing channel is initialized, only the priority processing identifier in one data processing channel is set to the specified value;

[0054] A channel update component, for any data processing channel, if the priority processing identifier in the data processing channel meets a preset condition, call a shared computing power module to process the data chunks in the feature within the current processing cycle. After the processing is completed, enter the next processing cycle, and at the same time send 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 at the same time updates its own priority processing identifier to another value different from the current value;

[0055] A status acquisition component, for acquiring the health status of the target user output by the human body monitoring model after the data of each feature is processed.

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

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

[0058] The technical solution provided by this application can comprehensively obtain the active states of the left and right hemispheres of the user by processing the left and right electroencephalogram features and the difference features, and based on the difference features between the left and right brains, it can be determined whether the user has potential physical abnormalities. When analyzing these features, a multi-channel parallel analysis method can be adopted. However, the current computing power module is not sufficient to support the data analysis process of multiple channels simultaneously. It can only try to make the data analysis and calculation process involved in each channel be coherent in time sequence, so as to make the best use of the computing power of the computing power module as much as possible. Specifically, by setting the priority identifier and the activation instruction, and by updating the data volume to be processed, it can be ensured that the shared computing power module is called by different channels in turn, so as to give full play to the computing power of the computing power module, and then improve the efficiency of electroencephalogram analysis.

[0059] The further function descriptions of the above-mentioned various modules and units are the same as those in the corresponding above-mentioned embodiments, and will not be repeated here.

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

[0061] Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of a computer device provided by an embodiment of the present application. As Figure 3 shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system). Figure 3 In

[0062] which, one processor 10 is taken as an example.

[0063] The memory 20 stores instructions executable 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. 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 according to 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-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely disposed relative to the processor 10, and these remote memories may be connected to the computer device through a network. Examples of the above networks 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 further include a combination of the above types of memories.

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

[0067] The embodiments of the present application further provide a computer-readable storage medium. The methods according to the embodiments of the present application may be implemented in hardware, firmware, or may be implemented as computer code that can be recorded on a storage medium, or may be implemented as computer code originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and to be stored in a local storage medium, so that the methods described herein may be stored in such software processes on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium may further include a combination of the above types of memories. 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, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0068] The systems, devices, and units illustrated in the above embodiments may be specifically implemented by a computer chip or an entity, or by a product with a certain function. 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 smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0069] For convenience of description, when describing the above device, it is divided into various units according to functions and described separately. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0070] Those skilled in the art should understand that the embodiments of the present application can be provided as methods and devices. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to 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 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 the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0072] These computer program instructions can 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 generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0073] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0074] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising said element.

[0075] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the description of the method embodiment.

[0076] The above are only the embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

[0077] Although the embodiments of the present application are described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations 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, wherein the smart mattress is equipped with a brain wave monitoring component, and the method comprises: Acquire the left brain wave characteristics and the right brain wave characteristics of the target user through the brain wave monitoring component, and generate difference characteristics between the left brain wave characteristics and the right brain wave characteristics; Inputting the left brain wave feature, the right brain wave 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 each feature, wherein when each of the data processing channels is initialized, only a priority processing flag in one 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 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, an activation instruction is sent to the next data processing channel, and the activation instruction is used to update the data processing amount in the next data processing channel. If the updated data processing amount is empty, the next data processing channel sets the amount of data to be processed as the initial value, and updates its own priority processing flag to another value different from the current value; 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 brain wave characteristics and the right brain wave characteristics of the target user by the brain wave monitoring component includes: The left brain wave data and the right brain wave data of the target user are collected respectively by the brain wave monitoring component. For any brain wave data, the brain wave 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 brain wave feature of the brain wave 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: The feature residual between the left brain wave feature and the right brain wave feature is calculated, and the feature residual is used as the difference feature between the left brain wave feature and the right brain wave feature.

4. The method according to claim 1, characterized in that: When each of the data processing channels is initialized, the method further includes: Assigning a comparison flag in each of the data processing channels to another value different from the specified value; Accordingly, the priority processing flag in the data processing channel meets the preset conditions including: The assignment of the priority processing identifier in the data processing channel is different from the assignment of the comparison identifier.

5. The method according to claim 1 or 4, characterized in that: After the data processing channel enters the next processing cycle, the method further includes: 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.

6. The method according to claim 5, characterized in that The method further comprises: 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.

7. The method according to claim 1, characterized in that If the updated data processing amount is not empty, the method further includes: 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.

8. A human monitoring system based on brain wave analysis, characterized in that: The system comprises: A brain wave monitoring component, used to obtain the left brain wave characteristics and the right brain wave characteristics of the target user, and generate difference characteristics between the left brain wave characteristics and the right brain wave characteristics; A model processing component, used for inputting the left brain wave feature, the right brain wave 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 each feature, wherein when each of the data processing channels is initialized, only a priority processing flag in one data processing channel is set to a specified value; A channel updating component is used for calling a shared computing power module to process the data blocks in the feature in the current processing cycle for any data processing channel if the priority processing flag in the data processing channel meets the preset conditions, entering the next processing cycle after the processing is completed, and sending an activation instruction to the next data processing channel at the same time, wherein the activation instruction is used to update the data processing amount in the next data processing channel, and if the updated data processing amount is empty, the next data processing channel sets the amount of data to be processed as the initial value, and updates its own priority processing flag to another value different from the current value; The state acquisition component is used to acquire the health state of the target user output by the human body monitoring model after completing the processing of the data of each feature.

9. The system according to claim 8, characterized in that The brain wave monitoring component is specifically used to collect the left brain wave data and the right brain wave data of the target user respectively, divide the brain wave data into sub-data of different frequency bands for any brain wave data, extract the CSP features of each sub-data, and use the combination of the extracted CSP features as the brain wave features of the brain wave data.

10. The system according to claim 8 or 9, characterized in that The brain wave monitoring component is specifically used to calculate the feature residual between the left brain wave feature and the right brain wave feature, and use the feature residual as the difference feature between the left brain wave feature and the right brain wave feature.

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