Code development method and system based on brain-computer interface and server

Abstract

The invention discloses a code development method based on a brain-computer interface. The code development method comprises the following steps that 1, brain waves of developers are collected and preprocessed; 2, extracting features from the preprocessed brain waves, and classifying and identifying the features to understand the intention of developers; 3, judging required code logic according to the intention of the issuing personnel, and generating and displaying corresponding codes; and 4, a developer carries out modification and feedback according to the displayed code, and code development is completed. According to the method, the brain signals are directly read and interpreted, so that a user can control a computer with thoughts, and efficient code writing and management are realized in combination with natural language processing and code generation technologies, so that the software development efficiency and the user experience are improved.

Description

Technical Field

[0001] This application belongs to the field of software development, and particularly relates to a code development method, system and server based on a brain-computer interface. Background Art

[0002] With the rapid development of information technology, the complexity and scale of software development have been increasing continuously, and developers are facing huge workloads and challenges. Although the traditional software development method of manually writing code (i.e., "typing code by hand") is an inevitable part of the development process, there are also some drawbacks, such as: 1. Low efficiency and repetitive labor: Manually writing similar or repetitive code segments wastes a lot of time, especially when repetitive labor can be reduced through automatic generation or code templates; slow speed: Compared with automated tools or scripts, manually writing code is slower, especially when dealing with a large amount of code.

[0003] 2. Prone to errors and human errors: Manually entering code is prone to spelling mistakes, syntax errors, logical errors, etc., and these errors may cause the program to crash or the function to malfunction; consistency issues: When manually writing the same or similar code, the code written by different developers or the same developer at different times may not be consistent, resulting in difficulties in maintenance.

[0004] 3. Difficult to maintain and code redundancy: Manually written code may contain a large amount of redundant code, which will increase the maintenance cost; poor readability: When manually writing code, if a unified coding specification is not followed, the readability of the code may become poor, increasing the difficulty of subsequent maintenance.

[0005] 4. Lack of standardization and inconsistent styles: The coding styles of different developers may be different, resulting in a lack of consistency in the code library and increasing the difficulty of code review and maintenance; lack of specifications: When manually writing code, if there is no strict coding specification, the code quality may vary, affecting the overall quality of the project.

[0006] 5. Difficult to expand and poor scalability: Manually written code may lack good design and structure, resulting in difficulties in subsequent function expansion; insufficient modularity: When manually writing code, developers may ignore the modular design of the code, making the code difficult to reuse and expand.

[0007] 6. Lack of automated support and lack of automated testing: When manually writing code, if automated testing tools are not combined, the test coverage and test efficiency of the code may be low; lack of continuous integration: When manually writing code, if continuous integration tools are not combined, the integration and deployment process of the code may be cumbersome and inefficient.

[0008] Therefore, a technical solution for code development is needed to solve the above problems. Summary of the Invention

[0009] To address the deficiencies of the aforementioned prior art, the present application provides a code development method, system, and server based on a brain-computer interface. By directly reading and interpreting brain signals, users can control a computer with their thoughts. Combining natural language processing and code generation technologies, efficient code writing and management are achieved, thereby improving software development efficiency and user experience.

[0010] The technical effects to be achieved by the present application are realized through the following solutions: According to a first aspect of the present application, a code development method based on a brain-computer interface is provided, including the following steps: Step 1: Collect the brainwaves of the developer and perform preprocessing; Step 2: Extract features from the preprocessed brainwaves, classify and identify the features, and understand the developer's intention; Step 3: Determine the required code logic based on the developer's intention, generate the corresponding code, and display it; Step 4: The developer modifies and provides feedback on the displayed code to complete code development.

[0011] Preferably, in Step 1, a brainwave sensor is used to collect brainwaves. The specific method of preprocessing is as follows: A filter is used to remove low-frequency and high-frequency noise, and independent component analysis is used to remove eye movement artifacts and other physiological interferences, retaining the effective signal frequency band.

[0012] Preferably, in Step 2, the extracted features of the brainwaves include frequency domain features and / or time-frequency features, where: Fourier transform is used to extract frequency domain features, and wavelet transform is used to extract time-frequency features.

[0013] Preferably, the features are input into a trained brainwave recognition model for brainwave recognition, and the developer's intention is judged based on the results of the brainwave recognition.

[0014] Preferably, determining the developer's intention based on the recognition results includes selection, movement, copying, deletion, pasting, code writing, and text editing, which are used to guide subsequent programming and interaction processes.

[0015] Preferably, in Step 3, generating the corresponding code based on the intention recognized from the developer's brainwaves is specifically as follows: Based on the intention analyzed from the brainwaves, an AI large model is used for code programming to output the required code.

[0016] Preferably, according to the intention, the AI large model selects the corresponding template in the template library, fills in the corresponding parameters in the template, and outputs the required code.

[0017] Preferably, in step 4, the specific method of feedback is: providing a feedback form or directly recording the editing history, and improving the signal processing, feature extraction, and code generation methods.

[0018] According to a second aspect of the present application, there is provided a code development system adopting the above-mentioned code development method based on a brain-computer interface, including: An electroencephalogram acquisition module, configured to acquire the electroencephalogram of a developer and perform preprocessing; An electroencephalogram signal analysis module, configured to extract features from the preprocessed electroencephalogram, classify and identify the features, and understand the intention of the developer; A code generation module, configured to generate corresponding code according to the intention of the developer; A user interface module, configured to display the generated code and be able to modify and provide feedback.

[0019] According to a third aspect of the present application, there is provided a server, including: a memory and at least one processor; The memory stores a computer program, and the at least one processor executes the computer program stored in the memory to implement the above-mentioned code development method based on a brain-computer interface.

[0020] According to an embodiment of the present application, the beneficial effects of adopting the code development method based on a brain-computer interface of the present application are as follows: Improve development efficiency: Through brain-computer interface technology and natural language processing technology, the workload of manual coding is reduced, and development efficiency is improved; Reduce error rate: Automatically generated code reduces the possibility of human errors and improves code quality; Enhance user experience: A friendly user interface and a real-time feedback system enhance the user's usage experience; Flexibility and scalability: The system can continuously learn and optimize, adapt to different programming languages and development environments, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the existing technical solutions, the following will briefly introduce the drawings required for use in the description of the embodiments or the existing technical solutions. Obviously, the drawings in the following description are only some embodiments recorded in the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0022] Figure 1Flow chart of a code development method based on a brain-computer interface in an embodiment of the present application; Figure 2 Schematic structural diagram of a server in an embodiment of the present application. Detailed implementation manners

[0023] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Apparently, the described embodiments are only a part rather than all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0024] As Figure 1 shown, the code development method based on a brain-computer interface in an embodiment of the present application includes the following steps: Step 1: Collect the brain waves of the developer and perform preprocessing; In this step, the developer wears a brain wave sensor to ensure that the sensor fits the scalp correctly; the position of the sensor needs to be adjusted according to the requirements of brain wave collection to ensure the accuracy and stability of the signal; Turn on the brain wave collection software and select the correct sensor configuration. The software usually provides a real-time signal monitoring function to help users confirm the clarity and stability of the signal. When necessary, perform sensor calibration to ensure the accuracy of the data; Start collecting brain wave signals. The software displays the signal waveform in real time. Users can monitor the signal quality and adjust the collection parameters (such as sampling rate and gain) to optimize the data quality. Ensure that the collection environment is quiet to reduce external interference; The collected signals usually contain noise and artifacts and need to be preprocessed. Use a filter (such as a band-pass filter) to remove low-frequency and high-frequency noise (such as electromyogram and power interference), and apply independent component analysis (ICA) to remove eye movement artifacts and other physiological interferences to ensure the purity of the signal, which is applicable to the analysis of biological signals such as EEG / MEG.

[0025] Step 2: Extract features from the preprocessed brain waves, classify and identify the features, and understand the developer's intention; In this step, the preprocessed signal is input into the feature extraction module. The extracted features include frequency domain features and / or time-frequency features, where: Fourier transform is used to extract frequency domain features, and wavelet transform is used to extract time-frequency features. These features are used to reflect different brain wave states, such as relaxation, concentration or specific intentions.

[0026] The specific process of using Fourier transform to extract frequency domain features is as follows: Perform Fourier transform on each electroencephalogram segment to convert the time-domain signal into a frequency-domain signal and obtain the spectrum: ; where x(t) is the time-domain signal and X(f) is the frequency-domain signal; Divide the spectrum according to the electroencephalogram frequency bands (such as δ, θ, α, β, γ), calculate the energy or power of each frequency band, and extract features from the spectrum, such as the average power, peak frequency, spectrum entropy, etc. of each frequency band. After selecting the features most useful for the task, use methods such as PCA to reduce the feature dimension and lower the computational complexity.

[0027] The specific method for extracting time-frequency features using wavelet transform is as follows: Select a suitable wavelet basis function according to the characteristics of the electroencephalogram signal so that the wavelet basis function should match the local characteristics of the EEG signal; For non-stationary signals, the continuous wavelet transform method can provide high-resolution time-frequency information. The following formula is used: ; where x(t) is the electroencephalogram signal, ψ(t) is the wavelet basis function, a is the scale parameter (controlling the frequency), b is the translation parameter (controlling the time), and the output is the time-frequency matrix, representing the energy distribution of the signal at different times and frequencies; When extracting features, calculate the energy of each time point and frequency point in the time-frequency matrix, extract the time-frequency energy corresponding to the electroencephalogram frequency bands (such as δ, θ, α, β, γ), and find the frequency corresponding to the maximum energy at each time point. Select the time-frequency features most useful for the task and use methods such as PCA and LDA to reduce the feature dimension and lower the computational complexity.

[0028] For the multi-resolution analysis of signals, the discrete wavelet transform is used, which has high computational efficiency. The signal is decomposed into sub-signals (approximation coefficients and detail coefficients) of different frequency bands through low-pass and high-pass filters. The following formula is used: ; where j is the decomposition level and k is the translation parameter, and the output is the approximation coefficients and detail coefficients of the multi-level decomposition.

[0029] When extracting features, calculate the coefficient energy of each decomposition layer (corresponding to different frequency bands), calculate statistical quantities such as the mean, variance, and entropy of each layer of coefficients, and analyze the frequency band energy change in a specific time period. Select the time-frequency features most useful for the task and use methods such as PCA and LDA to reduce the feature dimension and lower the computational complexity.

[0030] Input the frequency-domain features and / or the time-frequency features into a trained electroencephalogram (EEG) recognition model for EEG pattern recognition. The EEG recognition model is a support vector machine or a neural network model, which can recognize common EEG patterns after training. By continuously updating and optimizing the model, the accuracy and reliability of recognition can be improved.

[0031] For example, pre-collect the EEGs of developers when they have various intentions, such as when they have intentions like selecting, moving, copying, deleting, pasting, etc. Extract different features of the EEGs under different intentions, such as frequency-domain features, time-frequency domain features, non-linear dynamics features, and spatial features (multi-channel data), etc. Input each feature into the EEG recognition model for EEG recognition training, and use each intention as the output, and perform iterative training until the accuracy of the output intention reaches more than 90%, then the training is completed.

[0032] According to the results of pattern recognition, the system determines the intentions of the developers (such as selecting, moving, copying, deleting, and pasting, etc.). These intentions are used to guide the subsequent programming and interaction processes. The system can provide real-time feedback to help developers adjust their thinking states to improve the recognition effect.

[0033] Step 3: Generate corresponding code according to the intentions of the developers and display it; In this step, pass the parsed programming intentions to an AI large model such as a code generation engine. This large model is responsible for converting the abstract intentions into specific code implementations. The system can be optimized according to historical records and context information to provide code that better meets the user's needs; For example, during the writing process of a time program for a timer class, when parameters such as numbers appear, they will be defined as time parameters in combination with the context to improve the efficiency during the program editing process.

[0034] Select a suitable code template according to the intention. For example, by analyzing the intention of the developer, it is known that a time utility class is needed. By thinking about the EEG intention of the tool class design, the time class program template can be automatically selected from the existing AI code library, and all the code for the time utility class can be automatically generated in this time class program template.

[0035] Use a template engine (such as FreeMarker) to fill in the template variables to generate code segments. The code generation engine can be optimized according to the context and historical records to ensure that the generated code is efficient and compliant; Display the generated code in the user interface module. The interface provides an editor function, supporting syntax highlighting, auto-completion, and error prompts to help developers review and modify the code. Users can perform interactive debugging in the interface to ensure the correctness and functionality of the code.

[0036] During the process of writing code and text editing, most editing tasks can be completed using commands such as selection, movement, copying, deletion, and pasting. To complete more complex text editing tasks, such as filling in and modifying parameters, re-editing code, etc., the following steps can be taken: Collect the voice commands of developers, and determine the developers' intentions after semantic analysis; In this step, the developer inputs voice commands through a microphone, and a speech recognition software (such as Google Speech-to-Text) converts the speech into text to ensure accurate capture of the developer's instructions. Voice input needs to be carried out in a quiet environment to reduce the interference of background noise; Use natural language processing tools (such as NLTK, spaCy) to analyze the text. The system extracts key commands and parameters, understands the developer's programming intentions, and through context analysis, the system can more accurately understand complex instructions. For example, controlling the position of the cursor, filling in specific parameters, voice inputting program content, etc.

[0037] Step 4: The developer modifies and gives feedback based on the displayed code to complete code development.

[0038] In this step, the developer views the generated code through the user interface, and uses the built-in editor to make necessary modifications and optimizations. The interface supports version control and code comparison functions to help developers track the change history.

[0039] The user interface module records the developer's modifications and feedback. The system can provide a feedback form or directly record the editing history for subsequent analysis and improvement. By collecting user feedback, the system can continuously optimize the user experience and code generation quality.

[0040] Analyze the feedback to improve the signal processing, feature extraction, and code generation modules. Update the machine learning model and code template library to improve system performance and user satisfaction. Regularly conduct system evaluations and iterations to ensure continuous improvement. The system can further enhance the recognition and generation capabilities by introducing new algorithms and technologies.

[0041] In an embodiment of the present application, a code development system adopting the above code development method based on a brain-computer interface includes: An electroencephalogram acquisition module, which is used to acquire the electroencephalogram of the developer and perform preprocessing, including an electroencephalogram sensor and a signal processing unit. The electroencephalogram sensor is used to acquire the electroencephalogram signal of the developer; the signal processing power supply is used to perform preprocessing on the acquired electroencephalogram signal, such as filtering, denoising, etc.

[0042] The brain wave signal analysis module is used to extract features from the preprocessed brain waves, classify and identify the features, and understand the developer's intention. It includes a feature extraction unit and a pattern recognition unit, where: The feature extraction unit: extracts features from the preprocessed brain wave signals, such as frequency domain features, time domain features, etc.; The pattern recognition unit: uses machine learning algorithms to classify and identify the extracted features to determine the developer's intention; The natural language processing module is used to collect the developer's voice instructions, perform semantic analysis, and determine the developer's intention. It includes a speech recognition unit and an intention understanding unit, where: The speech recognition unit: converts the developer's voice instructions into text; The intention understanding unit: performs semantic analysis on the converted text to understand the developer's programming intention; The code generation module is used to generate corresponding code according to the developer's intention. It includes a code template library and a code generation engine, where: The code template library: stores common code templates and fragments; The code generation engine: selects code templates and fragments from the code template library according to the developer's intention, automatically generates the corresponding code and fills it; The user interface module is used to display the generated code and can be modified and feedback, including a visual interface and a feedback system, where: The visual interface: provides a friendly user interface, displays the generated code, and allows the user to edit and modify it; The feedback system: receives the user's feedback and further optimizes the code generation process.

[0043] As Figure 2 shown, a server in an embodiment of the present application includes: a memory 201 and at least one processor 202; The memory 201 stores a computer program, and the at least one processor 202 executes the computer program stored in the memory 201 to implement the above-mentioned code development method based on the brain-computer interface.

[0044] According to an embodiment of the present application, the beneficial effects of adopting the code development method based on the brain-computer interface of the present application are as follows: Improve development efficiency: Through brain-computer interface technology and natural language processing technology, the workload of manual coding is reduced, and the development efficiency is improved; Reduce the error rate: Automatically generating code reduces the possibility of human errors and improves the code quality; Enhance the user experience: A friendly user interface and a real-time feedback system enhance the user's usage experience; Flexibility and scalability: The system can continuously learn and optimize, adapt to different programming languages and development environments, and has broad application prospects.

[0045] It should be noted that the above detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs.

[0046] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should also be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0047] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above drawings are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that such terms can be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.

[0048] In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0049] For ease of description, spatial relative terms such as "above", "on top of", "on the upper surface", "above", etc. can be used herein to describe the spatial positional relationship of one device or feature to other devices or features as shown in the figures. It should be understood that spatial relative terms are intended to include different orientations in use or operation in addition to the orientation of the device described in the figures. For example, if the device in the figure is inverted, the device described as "above other devices or structures" or "on top of other devices or structures" will then be positioned "below other devices or structures" or "beneath other devices or structures". Thus, the exemplary term "above" can include both the orientation of "above" and "below". The device can also be positioned in other different ways, such as rotated 90 degrees or in other orientations, and corresponding interpretations of the spatial relative descriptions used herein will be made.

[0050] In the foregoing detailed description, reference has been made to the accompanying drawings, which form a part hereof. In the drawings, like numerals typically identify like components, unless the context indicates otherwise. The illustrated embodiments described in the detailed description, the drawings, and the claims are not meant to be limiting. Other embodiments may be used and other changes may be made without departing from the spirit or scope of the subject matter presented herein.

[0051] The foregoing are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A code development method based on a brain-computer interface, characterized in that, It includes the following steps: Step 1: Collect the brainwaves of developers and perform preprocessing; Step 2: Extract features from the preprocessed brainwaves, classify and identify the features, and understand the intentions of developers; Step 3: Judge the required code logic according to the intentions of developers, generate corresponding code and display it; Step 4: Developers modify and give feedback according to the displayed code to complete code development.

2. The code development method based on a brain-computer interface according to claim 1, wherein In Step 1, a brainwave sensor is used to collect brainwaves. The specific method of preprocessing is as follows: A filter is used to remove low-frequency and high-frequency noises, and independent component analysis is used to remove eye movement artifacts and other physiological interferences, and the effective signal frequency band is retained.

3. The code development method based on a brain-computer interface according to claim 1, wherein In Step 2, the extracted features of brainwaves include frequency-domain features and / or time-frequency features, where: Fourier transform is used to extract frequency-domain features, and wavelet transform is used to extract time-frequency features.

4. The method for code development based on a brain-computer interface according to claim 3, wherein The features are input into a trained brainwave recognition model for brainwave recognition, and the intentions of developers are judged according to the results of brainwave recognition.

5. The method for code development based on a brain-computer interface according to claim 4, wherein, Determining the intentions of developers according to the recognition results includes selection, movement, copying, deletion, pasting, code writing, and text editing, which are used to guide the subsequent programming and interaction processes.

6. The code development method based on a brain-computer interface according to claim 1, characterized in that In Step 3, generating corresponding code according to the intentions recognized from the brainwaves of developers is specifically as follows: According to the intentions analyzed from the brainwaves, an AI large model is used for code programming to output the required code.

7. The method for code development based on a brain-computer interface according to claim 6, characterized in that The AI large model selects corresponding templates in the template library according to the intentions, and fills in corresponding parameters in the templates to output the required code.

8. The method for code development based on a brain-computer interface according to claim 4, wherein In Step 4, the specific method of feedback is as follows: Provide a feedback form or directly record the editing history to improve signal processing, feature extraction, and code generation methods.

9. A code development system adopting the code development method based on a brain-computer interface according to any one of claims 1 to 8, characterized in that, It includes: A brainwave acquisition module, which is used to collect the brainwaves of developers and perform preprocessing; A brainwave signal analysis module, which is used to extract features from the preprocessed brainwaves, classify and identify the features, and understand the intentions of developers; A code generation module, which is used to generate corresponding code according to the intentions of developers; A user interface module, which is used to display the generated code and can perform modification and feedback.

10. A server, characterized in that, It includes: A memory and at least one processor; The memory stores a computer program, and the at least one processor executes the computer program stored in the memory to implement the code development method based on a brain-computer interface according to any one of claims 1 to 8.

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