Construction method of man-machine environment data processing agent and edge computing equipment
By constructing an intelligent agent for human-machine environment data processing and utilizing edge computing devices to automate the design and verification process, the problems of complex operation and poor scalability of existing systems have been solved. This has enabled the generation of flexible and accurate data processing processes, improving user experience and system efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- KINGFAR INTERNATIONAL INC
- Filing Date
- 2025-12-25
- Publication Date
- 2026-05-08
AI Technical Summary
Existing multi-source data acquisition and analysis systems are complex to operate, require professional technical backgrounds, have poor scalability, and are difficult to adapt to the needs of different application scenarios, causing users to spend a lot of time and energy on process design and adjustment.
A method for constructing a human-machine environment data processing intelligent agent is provided. By acquiring demand information, matching data processing modules and their configuration parameters are determined, and the modules are connected according to the time-series processing logic relationship to generate an executable intelligent agent. It supports configuration of natural language and graphical user interface, and realizes automated design and verification by combining edge computing devices.
It reduces the time and effort users spend on process design and adjustments, improves user experience, enhances system flexibility and scalability, and ensures the accuracy and consistency of data processing.
Smart Images
Figure CN121997968A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the fields of electronic digital data processing, bioinformatics, human factors intelligence, and human factors and ergonomics, and particularly to a method for constructing a human-machine environment data processing intelligent agent and an edge computing device. Background Technology
[0002] In existing technologies, multi-source data acquisition and analysis systems often require users to have a high level of technical background knowledge to configure processes, making them complex to operate and inconvenient for non-professionals. Furthermore, existing multi-source data acquisition and analysis systems have poor scalability, making it difficult to adapt to the needs of different application scenarios, resulting in users spending a significant amount of time and effort on process design and adjustments. Summary of the Invention
[0003] This application provides a method for constructing a human-machine environment data processing intelligent agent and an edge computing device. This application also provides a computer-readable storage medium to enable the generation and adjustment of human-machine environment data processing flow by configuring the intelligent agent, thereby reducing the time and effort spent by users in process design and adjustment and improving user experience.
[0004] In a first aspect, embodiments of this application provide a method for constructing a human-machine environment data processing intelligent agent, comprising: acquiring human-machine environment data processing requirement information; determining multiple different human-machine environment data processing modules that match the aforementioned requirement information, and configuration parameters of each human-machine environment data processing module; determining the temporal processing logic relationship between each human-machine environment data processing module, and connecting each human-machine environment data processing module according to the aforementioned temporal processing logic relationship; and generating an executable intelligent agent based on the established connection of each human-machine environment data processing module.
[0005] The above-mentioned method for constructing intelligent agents for human-machine environment data processing can generate and adjust human-machine environment data processing flows by configuring intelligent agents, thereby reducing the time and effort users spend on flow design and adjustment and improving user experience.
[0006] One possible implementation involves obtaining the human-machine environment data processing requirements information by: obtaining the natural language input by the user; parsing the natural language to obtain the human-machine environment data processing requirements information.
[0007] In one possible implementation, the timing processing logic relationship between each human-machine environment data processing module can be determined as follows: based on the aforementioned human-machine environment data processing requirements information, the timing processing logic relationship between each human-machine environment data processing module is determined.
[0008] In the above implementation method, through the conversational large model, users can describe their human-machine environment data processing needs in natural language, and the edge computing device automatically generates a synchronous acquisition and intelligent analysis agent, thereby realizing the automated design of the data processing process.
[0009] In one possible implementation, determining multiple different human-machine environment data processing modules that match the requirement information, and the configuration parameters of each human-machine environment data processing module, can be achieved by: obtaining multiple different human-machine environment data processing modules selected by the user through a graphical user interface; and obtaining the configuration parameters of each human-machine environment data processing module configured by the user through the graphical user interface.
[0010] In one possible implementation, determining the timing processing logic relationship between each human-machine environment data processing module can be achieved by: obtaining the timing processing logic relationship between each human-machine environment data processing module set by the user through the above graphical user interface.
[0011] The above implementation allows users to easily configure human-machine environment data processing flow through a graphical user interface.
[0012] In one possible implementation, determining the multiple different human-machine environment data processing modules that match the aforementioned requirement information, and the configuration parameters of each human-machine environment data processing module, can be achieved by: obtaining a template selected by the user through a graphical user interface, wherein the template includes multiple different human-machine environment data processing modules that match the aforementioned requirement information, and the configuration parameters of each human-machine environment data processing module; thus, determining the timing processing logic relationship between each human-machine environment data processing module can be achieved by: obtaining the timing processing logic relationship between each human-machine environment data processing module from the aforementioned template.
[0013] In one possible implementation, after generating an executable intelligent agent based on the established human-machine environment data processing modules, the edge computing device can also test and run the intelligent agent to verify the output results of the human-machine environment data processing flow generated by the intelligent agent; based on the verification results, adjust the configuration parameters of each human-machine environment data processing module and / or the timing processing logic relationship between each human-machine environment data processing module; and regenerate the executable intelligent agent based on the adjustment results.
[0014] The above implementation provides automated process verification and adjustment tools, ensuring the accuracy and consistency of data processing.
[0015] In one possible implementation, the aforementioned human-machine environment data includes one or a combination of the following: human-related data acquisition devices, machine-related data acquisition devices, human-computer interaction-related data acquisition devices, and environment-related data acquisition devices; the aforementioned human-related data includes one or a combination of the following: skin conductance and temperature data, pulse data, blood pressure data, blood oxygen data, electrocardiogram data, electromyography data, muscle oxygenation data, respiratory data, biomechanical data, near-infrared brain imaging data, electroencephalogram data, transcranial stimulation data, heart rate variability data, heart rate data, image / video data, sound data, eye-tracking data, and gesture or movement data; the aforementioned machine-related data packets... Includes one or a combination of the following: machine operation data, fault alarm data, machine control data, machine model data, machine communication data, and machine positioning data; the above-mentioned human-computer interaction related data includes one or a combination of the following: human-computer voice interaction data, human-computer text interaction data, human-computer touch interaction data, human-computer gesture or action interaction data, human-computer EEG interaction data, human-computer eye-tracking interaction data, and human-computer facial expression interaction data; the above-mentioned environmental related data includes one or a combination of the following: location data, humidity data, temperature data, color data, brightness data, weather data, road condition data, traffic data, stimulus signal data, and event or signal tagging data; The aforementioned various human-machine environment data processing modules include: a data acquisition module, a data processing module, a data analysis module, and a report generation module; When the aforementioned human-machine environment data is EEG data, the configuration parameters of the aforementioned data acquisition module include: connected device model, sampling rate, number of channels, real-time filtering parameters and / or lead selection; the configuration parameters of the aforementioned data processing module include: bad lead removal / interpolation, filtering, rereference, artifact removal and / or data segmentation; the configuration parameters of the aforementioned data analysis module include: time domain analysis, frequency domain analysis, spectrum analysis and / or AI-based human state coding analysis; the configuration parameters of the aforementioned report generation module include: report title, report information, rich text editing and / or chart selection.
[0016] Secondly, embodiments of this application also provide an edge computing device, including: one or more processors; a memory; multiple applications; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when executed by the edge computing device, cause the edge computing device to perform the method provided in the first aspect.
[0017] Thirdly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when run on a computer, causes the computer to perform the method provided in the first aspect.
[0018] Fourthly, embodiments of this application provide a computer program that, when executed by a computer, performs the method provided in the first aspect.
[0019] In one possible design, the program in the fourth aspect can be stored wholly or partially on a storage medium packaged with the processor, or it can be stored wholly or partially on a memory not packaged with the processor. Attached Figure Description
[0020] Figure 1 This is a schematic diagram of the structure of an edge computing device provided in one embodiment of this application; Figure 2 A flowchart illustrating a method for constructing a human-machine environment data processing intelligent agent according to an embodiment of this application; Figure 3 A schematic diagram illustrating a method for constructing a human-machine environment data processing intelligent agent according to an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a human-machine environment data processing intelligent agent construction device provided in one embodiment of this application. Detailed Implementation
[0021] The terminology used in the implementation section of this application is for the purpose of explaining specific embodiments of this application only, and is not intended to limit this application.
[0022] In existing related technologies, the automation level of multi-source data acquisition and analysis systems is low, which requires users to spend a lot of time and effort on process design and adjustment.
[0023] Based on the above problems, this application provides a method for constructing a human-machine environment data processing intelligent agent, which can solve the problems of insufficient modularity, process-orientation and automation in the existing multi-source data acquisition and analysis system, and can automatically generate and adjust the data processing process.
[0024] The method for constructing a human-machine environment data processing intelligent agent provided in this application embodiment can be applied to edge computing devices. The edge computing devices can be personal computers (PCs), smartphones, tablets, wearable devices, in-vehicle devices, augmented reality (AR) / virtual reality (VR) devices, laptops, or ultra-mobile personal computers (UMPCs), etc. This application embodiment does not impose any restrictions on the specific type of edge computing device.
[0025] For example, Figure 1This is a schematic diagram of the structure of an edge computing device provided in one embodiment of this application, as shown below. Figure 1 As shown, the edge computing device 100 may include one or more processors 110; a memory 130; multiple applications; and one or more computer programs, wherein the one or more computer programs are stored in the memory 130, and the one or more computer programs include instructions that, when executed by the edge computing device 100, may cause the edge computing device 100 to execute the human-machine environment data processing intelligent agent construction method provided in the embodiments of this application.
[0026] In addition, the aforementioned edge computing device 100 may also include a communication interface 120. The processor 110, the communication interface 120, and the memory 130 can communicate with each other via internal connection paths to transmit control and / or data signals.
[0027] The processor 110 and memory 130 can be combined into a single processing device, but more commonly they are independent components. The processor 110 is used to execute the program code stored in the memory 130. In specific implementations, the memory 130 can be integrated into the processor 110, or it can be independent of the processor 110.
[0028] In addition, to further enhance the functionality of the edge computing device 100, the edge computing device 100 may also include one or more of an input unit 160 and a display unit 170.
[0029] Optionally, the edge computing device 100 may further include a power supply 150 for providing power to various devices or circuits in the edge computing device 100.
[0030] It should be understood that Figure 1 The processor 110 in the edge computing device 100 shown can be a system-on-a-chip (SoC). The processor 110 may include a central processing unit (CPU) and may further include other types of processors, such as a graphics processing unit (GPU).
[0031] The following is combined Figure 1 This application introduces a method for constructing a human-machine environment data processing intelligent agent according to embodiments of the present application.
[0032] The method for constructing a human-machine environment data processing intelligent agent provided in this application adopts a decoupled platform functional modular design, decomposing the complex data processing flow into a series of independent, loosely coupled functional modules, each responsible for handling a specific task. This method not only improves the maintainability and scalability of the system but also provides users with greater flexibility and customization capabilities. By defining clear application programming interfaces (APIs) and data protocols, low coupling between functional modules is ensured, allowing each module to be developed, deployed, and maintained independently.
[0033] For example, the above functional modules may include: a data acquisition module, a data processing module, a data analysis module, and a report generation module; The data acquisition module supports access to multiple data sources, including data from EEG, near-infrared spectroscopy, eye-tracking, physiological data, motion capture, and / or cameras. Furthermore, it provides data synchronization technology to ensure temporal consistency across multiple data sources. Real-time tagging is also supported for easier subsequent data processing and analysis.
[0034] The data processing module provides preprocessing functions such as filtering and / or artifact removal to improve data quality, and also supports data format conversion to facilitate data exchange between different modules.
[0035] The data analysis module offers a variety of data analysis algorithms, allowing users to select analysis methods according to their needs. It also supports custom analysis models, improving the system's flexibility. For example, the data analysis module can perform electroencephalogram (EEG) analysis, physiological analysis, and / or near-infrared spectroscopy analysis.
[0036] Report generation module: Supports the selection of visual charts and rich text modules, making it easy for users to customize report content, and provides automated report generation functions to reduce the workload of manual editing.
[0037] The embodiments of this application adopt a modular design, and each of the above functional modules can be developed, tested and replaced independently, which improves the flexibility and maintainability of process design.
[0038] In this embodiment, the client of the edge computing device 100 provides a user interface and process configuration tools, allowing users to manually configure data processing flows and generate intelligent agents through a graphical interface; or supporting users to interact with a conversational large model using natural language to automatically generate intelligent agents. The server of the edge computing device 100 carries the aforementioned independent functional modules, such as a data acquisition module, a data processing module, a data analysis module, and a report generation module. These modules run as independent services. Each module can clearly define its input, output, and configuration parameters through a protocol. Modules can be connected manually or automatically, and the connection condition is that the inter-module communication protocol is met. For example, the output of the previous module can be used as the input of the next module.
[0039] The following is combined Figure 2 This application introduces a method for constructing a human-machine environment data processing intelligent agent according to embodiments of the present application.
[0040] Figure 2 This is a flowchart of a method for constructing a human-machine environment data processing intelligent agent according to an embodiment of this application. The method for constructing the human-machine environment data processing intelligent agent can be applied to an edge computing device 100.
[0041] like Figure 2 As shown, the method for constructing the above-mentioned human-machine environment data processing intelligent agent may include: Step 201: Obtain human-machine environment data processing requirements information.
[0042] Step 202: Determine multiple different human-machine environment data processing modules that match the above-mentioned requirements information, as well as the configuration parameters of each human-machine environment data processing module.
[0043] Step 203: Determine the timing processing logic relationship between each human-machine environment data processing module, and connect each human-machine environment data processing module according to the above timing processing logic relationship.
[0044] Step 204: Generate an executable intelligent agent based on the human-machine environment data processing modules after the connection is established.
[0045] In one implementation of this embodiment, step 201 may be: obtaining the natural language input by the user, parsing the natural language, and obtaining the human-machine environment data processing requirement information.
[0046] Thus, the timing processing logic relationship between each human-machine environment data processing module can be determined as follows: Based on the above human-machine environment data processing requirements information, determine the timing processing logic relationship between each human-machine environment data processing module.
[0047] In this implementation, the server of the edge computing device 100 integrates a large conversational model. Users can describe their needs using natural language. After the client of the edge computing device 100 obtains the user's input natural language, it can send the user's input natural language to the server of the edge computing device 100. The server automatically parses the natural language and uses an intelligent agent generation algorithm to generate a corresponding intelligent agent. This intelligent agent generation algorithm combines user needs with the server's built-in process knowledge base, automatically selecting appropriate human-machine environment data processing modules and configuring parameters to construct a complete data processing flow. The edge computing device 100 can provide real-time feedback and interaction to the user through the client, thereby ensuring that the generated intelligent agent meets the user's needs. In this implementation, the intelligent agent generation process may include: Step 1, Input of Requirements: Users describe their human-machine environment data processing requirements in natural language, such as: "Connect to EEG device, collect EEG data, process and analyze the attention level in the EEG data, and generate an attention change trend analysis report."
[0048] Step 2, Requirement Analysis: After the client of the edge computing device 100 obtains the natural language input by the user, it sends the natural language input by the user to the server of the edge computing device 100. The server uses the integrated conversational big model to parse the natural language and extract key information from it, such as data type and analysis target. Then, it can obtain the user's human-machine environment data processing requirement information based on the key information.
[0049] Step 3, Agent Generation: After the server parses and obtains the user's human-machine environment data processing requirements, it can, based on these requirements and a pre-built knowledge base, acquire the necessary human-machine environment data processing modules, such as: EEG acquisition module, EEG data preprocessing module, EEG analysis module, and trend analysis report module. Next, the server can configure the configuration parameters for each module. For example, when configuring the EEG analysis module, the server can configure multiple EEG analysis methods and multiple configuration parameters. In this implementation, the server can configure the EEG analysis method as frequency domain analysis and attention algorithm, and the output indicator as attention. Furthermore, the server can determine the temporal processing logic relationships between the human-machine environment data processing modules based on the aforementioned requirements, and then connect each module according to these relationships. Thus, based on the established connections between the human-machine environment data processing modules, the server can generate an executable agent.
[0050] Step 4, Real-time Feedback and Interaction: The edge computing device 100 can provide real-time feedback through the client and interact with the user to clarify the user's human-machine environment data processing needs, thereby ensuring that the generated intelligent agent accurately reflects the user's intent.
[0051] Step 5, Process Verification: Users can test and run the generated intelligent agent to verify the correctness and effectiveness of the human-machine environment data processing process generated based on the above intelligent agent.
[0052] Step 6, Adjustment and Optimization: Based on the verification results, users can adjust the intelligent agent, such as modifying the parameters of each human-machine environment data processing module or the connection relationship between each human-machine environment data processing module.
[0053] Step 7, Save and Execute: The user saves the adjusted agent. The server can then convert the agent into an executable human-machine environment data processing flow. After that, the user can start the human-machine environment data processing flow to process the data.
[0054] In this implementation, through a conversational large model, users can describe their needs in natural language, and the edge computing device 100 automatically generates a synchronous acquisition and intelligent analysis agent, thereby enabling the automated design of the data processing flow.
[0055] In another implementation of this embodiment, step 202 may be: obtaining multiple different human-machine environment data processing modules selected by the user through the graphical user interface, and obtaining the configuration parameters of each human-machine environment data processing module configured by the user through the graphical user interface.
[0056] Thus, determining the timing processing logic relationship between each human-machine environment data processing module can be achieved by obtaining the timing processing logic relationship between each human-machine environment data processing module set by the user through the above graphical user interface.
[0057] In this implementation, users can manually configure the human-machine environment data processing flow through a graphical user interface, including selecting the required human-machine environment data processing modules, setting the configuration parameters of each module, and defining the timing processing logic relationships between the modules. In this implementation, the agent generation process can include: Step 1, Interface Guidance: Users access the process and select the human-machine environment data processing module through the graphical user interface. The graphical user interface provides intuitive drag-and-drop functionality, allowing users to select and arrange different human-machine environment data processing modules.
[0058] Step 2, Module Selection and Configuration: Users can select the required human-machine environment data processing modules from the module library, such as data acquisition, data processing, data analysis, and report generation modules, and set the corresponding configuration parameters.
[0059] Step 3, Connection Definition: Users can set the timing processing logic relationship between the various human-machine environment data processing modules through the above graphical user interface, specify the human-machine environment data flow direction and processing order. The edge computing device 100 can obtain the timing processing logic relationship between the various human-machine environment data processing modules set by the user through the above graphical user interface in real time, and verify the legality of the connection.
[0060] Step 4, Process Preview and Adjustment: Users can preview the entire human-machine environment data processing process and make necessary adjustments, such as adjusting the configuration parameters of the human-machine environment data processing module or modifying the timing processing logic relationship between each human-machine environment data processing module.
[0061] Step 5, Save and Execute: The user saves the configured human-machine environment data processing flow, and the edge computing device 100 can convert it into an executable intelligent agent and allow the user to start the above human-machine environment data processing flow to process the data.
[0062] In another implementation of this embodiment, the edge computing device 100 provides a process template library, and users can quickly start process configuration by selecting a suitable template through a graphical interface.
[0063] In this implementation, step 202 can be: obtaining the template selected by the user through the graphical user interface, wherein the template includes multiple different human-machine environment data processing modules that match the above-mentioned requirement information, as well as the configuration parameters of each human-machine environment data processing module.
[0064] Thus, the timing processing logic relationship between each human-machine environment data processing module can be determined by obtaining the timing processing logic relationship between each human-machine environment data processing module from the above template.
[0065] In another implementation of this embodiment, after step 204, the edge computing device 100 can further test and run the aforementioned intelligent agent to verify the output results of the human-machine environment data processing flow generated by the aforementioned intelligent agent. Then, based on the verification results, the configuration parameters of each human-machine environment data processing module and / or the timing processing logic relationship between each human-machine environment data processing module are adjusted, and an executable intelligent agent can be regenerated based on the adjustment results.
[0066] In this implementation, users can use the verification tools provided by the edge computing device 100 to test and run the intelligent agent, checking whether the output results meet expectations. The edge computing device 100 also provides a verification function to check the integrity and consistency of the intelligent agent, ensuring the correctness of the human-machine environment data processing flow. Users can adjust the intelligent agent based on the verification and validation results; the edge computing device 100 provides an intuitive adjustment interface.
[0067] In addition, in this embodiment, the edge computing device 100 can also store the created intelligent agents in a repository for easy management and reuse. In this embodiment, the server of the edge computing device 100 provides an intelligent agent copying function, allowing users to copy the intelligent agents stored in the repository to quickly build similar processes and improve work efficiency.
[0068] In the above-described method for constructing an intelligent agent for human-machine environment data processing, the edge computing device 100 acquires human-machine environment data processing requirement information, determines multiple different human-machine environment data processing modules that match the requirement information, and the configuration parameters of each human-machine environment data processing module. Then, the edge computing device 100 determines the temporal processing logic relationship between each human-machine environment data processing module and connects them according to this relationship. Next, based on the established connections between the human-machine environment data processing modules, the edge computing device 100 generates an executable intelligent agent. Furthermore, the edge computing device 100 can generate the aforementioned data processing flow based on this intelligent agent. This allows for the generation and adjustment of the human-machine environment data processing flow through the configuration of the intelligent agent, reducing the time and effort users spend on process design and adjustment, and improving user experience.
[0069] The method for constructing a human-machine environment data processing intelligent agent provided in this application significantly improves the flexibility, scalability, and user interaction experience of the data processing system through a decoupled platform functional modular design based on a client / server (C / S) architecture. This method allows users to conveniently configure the human-machine environment data processing workflow through a graphical user interface or natural language processing technology, while providing automated workflow verification and adjustment tools to ensure the accuracy and consistency of human-machine environment data processing. Furthermore, the modular design promotes efficient resource utilization, supports remote operation and access, and simplifies system maintenance and upgrades, thereby enhancing system stability and reliability. These technical effects not only optimize the process of multi-source data collaborative acquisition and intelligent analysis but also provide users with a highly flexible and easily customizable human-machine environment data processing platform, laying a solid foundation for future technological innovation and research.
[0070] In summary, the method for constructing a human-machine environment data processing intelligent agent provided in this application improves the efficiency and accuracy of human-machine environment data processing through automated and intelligent process configuration, while lowering the user's barrier to entry. The modular and decoupled design of the above-mentioned method for constructing a human-machine environment data processing intelligent agent allows each functional module to be developed, tested, and deployed independently, thereby quickly adapting to constantly changing human-machine environment data processing needs and technological advancements, demonstrating significant technological innovation and broad application value.
[0071] This application Figure 2 In the illustrated embodiment, the aforementioned human-machine environment data includes one or a combination of the following: human body-related data collector, machine-related data collector, human-computer interaction-related data collector, and environment-related data collector; The aforementioned human body-related data includes one or a combination of the following: skin conductance and temperature data, pulse data, blood pressure data, blood oxygen data, electrocardiogram data, electromyography data, muscle oxygenation data, respiratory data, biomechanical data, near-infrared brain imaging data, electroencephalogram data, transcranial stimulation data, heart rate variability data, heart rate data, image / video data, sound data, eye movement data, gesture or movement data; The aforementioned machine-related data includes one or a combination of the following: machine operation data, fault alarm data, machine control data, machine model data, machine communication data, and machine positioning data; The aforementioned human-computer interaction related data includes one or a combination of the following: human-computer voice interaction data, human-computer text interaction data, human-computer touch interaction data, human-computer gesture or action interaction data, human-computer EEG interaction data, human-computer eye-tracking interaction data, and human-computer facial expression interaction data; The aforementioned environmental data includes one or a combination of the following: location data, humidity data, temperature data, colorimetric data, brightness data, weather data, road condition data, traffic data, stimulus signal data, and event or signal tagging data.
[0072] The following uses EEG data as an example to illustrate the method for constructing a human-machine environment data processing intelligent agent provided in the embodiments of this application.
[0073] Figure 3 This is a schematic diagram illustrating a method for constructing a human-machine environment data processing intelligent agent according to an embodiment of this application, as shown below. Figure 3 As shown, the EEG data processing flow can include an EEG acquisition module, an EEG processing module, an EEG analysis module, and a visualization report module.
[0074] The EEG acquisition module has no input. The configurable parameters of this module may include: connected device model, sampling rate, number of channels, real-time filtering parameters, and lead selection, and the output can be raw EEG data.
[0075] The input to the EEG processing module can be raw EEG data. The configurable parameters of this module can include: bad lead removal / interpolation, filtering, rereference, artifact removal, and data segmentation, etc. The output can be processed EEG data.
[0076] The input to the EEG analysis module can be the processed EEG data described above. The configurable parameters of this module can include: time domain analysis, frequency domain analysis, spectrum analysis, and artificial intelligence (AI) human state coding analysis, etc. The output can be analysis result data, analysis visualization charts, and statistical indicators.
[0077] The input to the visualization report module can be data sources, dynamic fields, images, visualization charts, and rich text. The configurable parameters of this module can include: report title, report information, rich text editing, and chart selection. The output can be a visualization analysis report.
[0078] The module selection, parameter configuration, and module connection in the above EEG data processing flow can adopt... Figure 2 The three implementation methods provided in the illustrated embodiments will not be described in detail here.
[0079] It is understood that some or all of the steps or operations in the above embodiments are merely examples, and other operations or variations thereof can be performed in the embodiments of this application. Furthermore, the steps may be performed in different orders as presented in the above embodiments, and it is not necessary to perform all the operations in the above embodiments.
[0080] It is understood that, in order to achieve the above-mentioned functions, edge computing devices include hardware and / or software modules that perform the respective functions. Based on the algorithm steps of the examples described in conjunction with the embodiments disclosed in this application, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application in conjunction with the embodiments, but such implementation should not be considered beyond the scope of this application.
[0081] This embodiment can divide the edge computing device into functional modules according to the above method embodiment. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into one module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.
[0082] Figure 4This is a schematic diagram of the structure of a human-machine environment data processing intelligent agent construction device provided in one embodiment of this application. The human-machine environment data processing intelligent agent construction device in this embodiment can be set in an edge computing device.
[0083] When dividing each function into modules according to its corresponding function. Figure 4 A schematic diagram of a possible composition of the human-machine environment data processing intelligent agent construction device 400 involved in the above embodiments is shown.
[0084] like Figure 4 As shown, the construction device 400 for human-machine environment data processing intelligent agent may include: an acquisition module 401, a determination module 402, a connection module 403, and a generation module 404; Among them, the acquisition module 401 is used to acquire human-machine environment data processing requirement information; The determination module 402 is used to determine multiple different human-machine environment data processing modules that match the above-mentioned requirement information, as well as the configuration parameters of each human-machine environment data processing module; The connection module 403 is used to determine the timing processing logic relationship between each human-machine environment data processing module, and connect each human-machine environment data processing module according to the timing processing logic relationship. The generation module 404 is used to generate an executable intelligent agent based on the human-machine environment data processing modules after the connection is established.
[0085] In one implementation of this embodiment, the acquisition module 401 is specifically used to acquire the natural language input by the user, parse the natural language, and acquire the human-machine environment data processing requirement information.
[0086] Thus, the connection module 403 is specifically used to determine the timing processing logic relationship between each human-machine environment data processing module based on the above-mentioned human-machine environment data processing requirements information.
[0087] In another implementation of this embodiment, the determining module 402 is specifically used to obtain multiple different human-machine environment data processing modules selected by the user through the graphical user interface, and to obtain the configuration parameters of each human-machine environment data processing module configured by the user through the graphical user interface.
[0088] Thus, the connection module 403 is specifically used to obtain the timing processing logic relationship between the various human-machine environment data processing modules set by the user through the graphical user interface.
[0089] In another implementation of this embodiment, the determining module 402 is specifically used to obtain the template selected by the user through the graphical user interface. The template includes multiple different human-machine environment data processing modules that match the above-mentioned requirement information, as well as the configuration parameters of each human-machine environment data processing module.
[0090] Thus, the connection module 403 is specifically used to obtain the timing processing logic relationship between each human-machine environment data processing module from the above template.
[0091] In another implementation of this embodiment, the above-mentioned human-machine environment data processing intelligent agent construction device 400 may further include: a verification module 405 and an adjustment module 406; The verification module 405 is used to test and run the executable agent after the generation module 404 generates the agent, and to verify the output results of the human-machine environment data processing flow generated by the agent. The adjustment module 406 is used to adjust the configuration parameters of each human-machine environment data processing module and / or the timing processing logic relationship between each human-machine environment data processing module according to the verification results. The generation module 404 is also used to regenerate an executable agent based on the adjustment results.
[0092] This application Figures 2-3 All relevant content of each step involved in the method embodiment shown can be referenced from the functional description of the corresponding functional module, and will not be repeated here.
[0093] The human-machine environment data processing intelligent agent construction device 400 provided in this embodiment is used to execute this application. Figures 2-3 The method for constructing a human-machine environment data processing intelligent agent provided in the illustrated embodiment can achieve the same effect as the method described above.
[0094] It should be understood that the construction device 400 for human-machine environment data processing intelligent agents can correspond to Figure 1 The edge computing device 100 shown. The functions of the acquisition module 401, determination module 402, connection module 403, generation module 404, verification module 405, and adjustment module 406 can be derived by… Figure 1 The processor 110 in the edge computing device 100 shown is implemented.
[0095] When using integrated units, the construction apparatus 400 for human-machine environment data processing intelligent agents may include a processing module, a storage module, and a communication module.
[0096] The processing module can be used to control and manage the actions of the human-machine environment data processing intelligent agent construction device 400. For example, it can be used to support the human-machine environment data processing intelligent agent construction device 400 in executing the steps executed by the aforementioned modules. The storage module can be used to support the human-machine environment data processing intelligent agent construction device 400 in storing program code and data. The communication module can be used to support communication between the human-machine environment data processing intelligent agent construction device 400 and other devices.
[0097] The processing module can be a processor or controller, which can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination of computing functions, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc. The storage module can be a memory. The communication module can specifically be a device that interacts with other edge computing devices, such as radio frequency circuitry, a Bluetooth chip, and / or a Wi-Fi chip.
[0098] In one embodiment, when the processing module is a processor and the storage module is a memory, the construction device 400 for the human-machine environment data processing intelligent agent involved in this embodiment can be a device having... Figure 1 The device with the structure shown.
[0099] This application also provides a computer-readable storage medium storing a computer program that, when run on a computer, causes the computer to execute this application. Figures 2-3 The method provided in the illustrated embodiment.
[0100] This application also provides a computer program product, which includes a computer program that, when run on a computer, causes the computer to execute this application. Figures 2-3 The method provided in the illustrated embodiment.
[0101] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.
[0102] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0103] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0104] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0105] The above description is merely a specific embodiment of this application. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application. The protection scope of this application should be determined by the protection scope of the claims.
Claims
1. A method for constructing an intelligent agent for human-machine environment data processing, characterized in that, include: Obtain information on human-machine environment data processing requirements; Identify multiple different human-machine environment data processing modules that match the required information, as well as the configuration parameters of each human-machine environment data processing module; Determine the timing processing logic relationship between each of the human-machine environment data processing modules, and connect each of the human-machine environment data processing modules according to the timing processing logic relationship; An executable intelligent agent is generated based on the human-machine environment data processing modules after the connection is established.
2. The method according to claim 1, characterized in that, The information required for acquiring human-machine environment data processing includes: Obtain natural language input from the user; The natural language is parsed to obtain the human-machine environment data processing requirements.
3. The method according to claim 2, characterized in that, Determining the timing processing logic relationship between each of the human-machine environment data processing modules includes: Based on the human-machine environment data processing requirements, the timing processing logic relationship between each of the human-machine environment data processing modules is determined.
4. The method according to claim 1, characterized in that, The determination of multiple different human-machine environment data processing modules that match the demand information, and the configuration parameters of each human-machine environment data processing module, include: Acquire multiple different human-machine environment data processing modules selected by the user through the graphical user interface; Obtain the configuration parameters of each of the human-machine environment data processing modules configured by the user through the graphical user interface.
5. The method according to claim 4, characterized in that, Determining the timing processing logic relationship between each of the human-machine environment data processing modules includes: Obtain the timing processing logic relationship between the various human-machine environment data processing modules set by the user through the graphical user interface.
6. The method according to claim 1, characterized in that, The determination of multiple different human-machine environment data processing modules that match the demand information, and the configuration parameters of each human-machine environment data processing module, include: Obtain the template selected by the user through the graphical user interface, wherein the template includes multiple different human-machine environment data processing modules that match the requirement information, and configuration parameters of each human-machine environment data processing module; Determining the timing processing logic relationship between each of the human-machine environment data processing modules includes: Obtain the timing processing logic relationship between each of the human-machine environment data processing modules from the template.
7. The method according to any one of claims 1-6, characterized in that, After generating an executable intelligent agent based on the established human-machine environment data processing modules, the process further includes: The intelligent agent is tested and run, and the output results of the human-machine environment data processing flow generated by the intelligent agent are verified. Based on the verification results, the configuration parameters of each human-machine environment data processing module and / or the timing processing logic relationship between each human-machine environment data processing module are adjusted. An executable agent is regenerated based on the adjustment results.
8. The method according to any one of claims 1-6, characterized in that, The human-machine environment data includes one or a combination of the following: human body related data collectors, machine related data collectors, human-computer interaction related data collectors, and environment related data collectors; The human body-related data includes one or a combination of the following: skin conductance and temperature data, pulse data, blood pressure data, blood oxygen data, electrocardiogram data, electromyography data, muscle oxygen data, respiratory data, biomechanical data, near-infrared brain imaging data, electroencephalogram data, transcranial stimulation data, heart rate variability data, heart rate data, image / video data, sound data, eye movement data, gesture or movement data. The machine-related data includes one or a combination of the following: machine operation data, fault alarm data, machine control data, machine model data, machine communication data, and machine positioning data. The human-computer interaction related data includes one or a combination of the following: human-computer voice interaction data, human-computer text interaction data, human-computer touch interaction data, human-computer gesture or action interaction data, human-computer EEG interaction data, human-computer eye-tracking interaction data, and human-computer facial expression interaction data; The environmental data includes one or a combination of the following: location data, humidity data, temperature data, colorimetric data, brightness data, weather data, road condition data, traffic data, stimulus signal data, and event or signal tagging data; The various human-machine environment data processing modules include: a data acquisition module, a data processing module, a data analysis module, and a report generation module; When the human-machine environment data is EEG data, the configuration parameters of the data acquisition module include: connected device model, sampling rate, number of channels, real-time filtering parameters and / or lead selection; the configuration parameters of the data processing module include: bad lead removal / interpolation, filtering, rereference, artifact removal and / or data segmentation; the configuration parameters of the data analysis module include: time domain analysis, frequency domain analysis, spectrum analysis and / or AI-based human state coding analysis; the configuration parameters of the report generation module include: report title, report information, rich text editing and / or chart selection.
9. An edge computing device, characterized in that, include: One or more processors; Memory; Multiple applications; And one or more computer programs, wherein the one or more computer programs are stored in the memory, the one or more computer programs including instructions that, when executed by the edge computing device, cause the edge computing device to perform the method as described in any one of claims 1-8.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when run on a computer, causes the computer to perform the method as described in any one of claims 1-8.