An adaptive forest scene weather regulation method, system, device and medium

Abstract

This application discloses an adaptive forest scene weather control method, system, device, and medium, relating to the fields of brain-computer interfaces and virtual reality technologies. It inputs the parameters of a target characteristic wave into a preset correlation model, outputting a quantitative index of the current cognitive state, thus converting physiological signals into cognitive states. Based on the quantitative index of the current cognitive state, it matches the corresponding current forest weather mode and parameters, generating standardized control instructions. This achieves the conversion of cognitive state data into control instructions, and adjusts VR devices and multi-screen devices according to the control instructions to adapt to changes in the user's cognitive state and improve the personalized adaptability of the scene. The current forest weather mode is automatically selected and parameters are adjusted based on the quantitative index of the current cognitive state, allowing the scene to adaptively change with the cognitive state. Standardized control instructions are simultaneously sent to VR devices and multi-screen devices, enabling medical personnel to observe the virtual environment in which the user is located in real time without wearing VR devices, achieving immersive experience and collaborative monitoring.

Description

Technical Field

[0001] This application relates to the fields of brain-computer interface and virtual reality technology, and in particular to an adaptive forest scene weather control method, system, device and medium. Background Technology

[0002] Currently, there are three main technical solutions for weather mode control in virtual reality (VR) forest therapy scenarios. The first is a purely manual control solution, where weather changes are triggered by staff operating a personal computer (PC) or the user controlling a VR controller. This relies entirely on human judgment and cannot adapt to changes in the user's cognitive state. The second is a fixed-rule control solution, which switches weather modes at preset time intervals or according to scene flow, such as cycling between sunny and cloudy modes every 5 minutes. This lacks personalized adaptability. The third is a single brain-computer interface control solution. Some technologies use non-invasive brain-computer interfaces to control simple actions in the VR scene (such as viewpoint movement and scene transitions), but this does not address the correlation between cognitive state and weather mode parameters. It cannot convert cognitive state data into scene control commands and does not achieve multi-screen collaboration and synchronization between the VR device and the PC. This only meets the basic immersion needs of a single user and cannot be adapted to professional scenarios requiring collaborative monitoring by medical personnel.

[0003] Therefore, how to transform cognitive state data into scene control instructions to adapt to changes in the user's cognitive state and improve the personalization of VR healing scenes, and how to achieve collaborative monitoring by medical staff are problems that need to be solved by those skilled in the art. Summary of the Invention

[0004] The purpose of this application is to provide an adaptive forest scene weather control method, system, device, and medium to solve the problem that existing weather pattern control schemes cannot convert cognitive state data into scene control instructions to adapt to changes in the cognitive state of the user and improve the personalization of VR healing scenes, as well as the problem of not being able to adapt to collaborative monitoring by medical staff.

[0005] To address the aforementioned technical problems, this application provides an adaptive weather control method for forest scenes, comprising:

[0006] Acquire the current electroencephalogram (EEG) signals of the user collected by the brain-computer interface device;

[0007] Extract the target feature wave of the current EEG signal and input the parameters of the target feature wave into a preset association model to output a quantitative index of the current cognitive state;

[0008] The current forest weather pattern and the current parameters corresponding to the current forest weather pattern are matched according to the quantitative index of the current cognitive state.

[0009] Based on the current forest weather pattern and the current parameters, a standardized control command is generated and simultaneously sent to the VR device and the multi-screen device. This allows the VR device to adjust the VR scene to the weather image corresponding to the current forest weather pattern according to the standardized control command, and adjust the parameters corresponding to the current forest weather pattern to the current parameters. At the same time, the multi-screen device is controlled to synchronously display the weather image from the same source as the VR device.

[0010] In one feasible embodiment, extracting the target feature wave of the current EEG signal includes:

[0011] The current EEG signal is filtered using a digital filter to obtain a purified current EEG signal.

[0012] The purified current EEG signal is amplified to obtain the amplified current EEG signal.

[0013] Extracting target feature waves from the amplified current EEG signal; wherein, the target feature waves include Wave, wave and Wave.

[0014] In one feasible embodiment, standardized control instructions are generated based on the current forest weather model and the current parameters, including:

[0015] Based on the current forest weather pattern and the current parameters, a standardized control instruction with a preset format including header fields and data segment fields is generated;

[0016] The header field includes a timestamp and a checksum, and the data segment field includes the current forest weather pattern and the set of current parameters.

[0017] In one feasible embodiment, the standardized control commands are simultaneously sent to both the VR device and the multi-screen device, including:

[0018] The standardized control command is set to the highest priority so that the transmission priority of the standardized control command is higher than the transmission priority of the screen data and status feedback data.

[0019] Standardized control commands marked with the highest priority are synchronously sent to the VR device and the multi-screen device via a gigabit local area network wired link;

[0020] During the issuance process, the integrity of standardized control instructions marked with the highest priority is verified using the verification code.

[0021] In one feasible embodiment, controlling the multi-screen device to synchronously display weather images originating from the same source as the VR device includes:

[0022] Adjust the parameters of multiple virtual cameras to perform edge blending processing on the weather images from the VR device;

[0023] Control the multi-screen devices to synchronously display the weather image after edge blending processing.

[0024] In one feasible embodiment, after controlling the multi-screen devices to synchronously display weather images originating from the same source as the VR device, the method further includes:

[0025] The brainwave signals of the user collected by the brain-computer interface device were reacquired, and the cognitive state quantification index corresponding to the brainwave signals was redefined.

[0026] Determine whether the quantitative indicators of cognitive status have met the preset requirements;

[0027] If so, control the VR device to maintain the current forest weather mode and adjust the current parameters by a preset adjustment range;

[0028] If not, proceed to the step of reacquiring the brainwave signals of the experiencer collected by the brain-computer interface device, re-determining the cognitive state quantification index corresponding to the brainwave signals, and re-matching the target forest weather pattern corresponding to the cognitive state quantification index and the target parameters corresponding to the target forest weather pattern.

[0029] The current forest weather mode of the VR device is adjusted to the target weather image corresponding to the target forest weather mode, and the current parameters of the current forest weather mode are adjusted to the target parameters. At the same time, the multi-screen device is controlled to display the target weather image synchronously until the cognitive state quantification index reaches the preset requirements.

[0030] In one feasible embodiment, matching the current forest weather pattern and the current parameters corresponding to the current forest weather pattern based on the quantitative index of the current cognitive state includes:

[0031] The quantitative index of the current cognitive state is input into the dynamic mapping model, and the current forest weather pattern corresponding to the quantitative index of the current cognitive state and the current parameters corresponding to the current forest weather pattern are matched based on the dynamic mapping model.

[0032] This application also provides an adaptive forest scene weather control system, including:

[0033] The acquisition module is used to acquire the current electroencephalogram (EEG) signals of the user collected by the brain-computer interface device;

[0034] The cognitive state quantification module extracts the target feature wave of the current EEG signal and inputs the parameters of the target feature wave into a preset association model to output the current cognitive state quantification index.

[0035] The matching module is used to match the current forest weather pattern and the current parameters corresponding to the current forest weather pattern based on the quantitative index of the current cognitive state.

[0036] The control module is used to generate standardized control instructions based on the current forest weather mode and the current parameters, and simultaneously send the standardized control instructions to the VR device and the multi-screen device, so as to control the VR device to adjust the VR scene to the weather screen corresponding to the current forest weather mode according to the standardized control instructions, and adjust the parameters corresponding to the current forest weather mode to the current parameters, while controlling the multi-screen device to synchronously display the weather screen of the same source as the VR device.

[0037] This application also provides an adaptive forest scene weather control device, including a memory for storing computer programs;

[0038] A processor is used to implement the adaptive forest scene weather control method when executing the computer program.

[0039] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the adaptive forest scene weather control method.

[0040] This application provides an adaptive forest scene weather control method, comprising: acquiring the current EEG signal of the user collected by a brain-computer interface device; extracting the target feature wave of the current EEG signal and inputting the parameters of the target feature wave into a preset association model to output a quantitative index of the current cognitive state; matching the current forest weather pattern and the current parameters corresponding to the current forest weather pattern according to the quantitative index of the current cognitive state; generating standardized control instructions based on the current forest weather pattern and the current parameters, and simultaneously sending the standardized control instructions to a VR device and a multi-screen device to control the VR device to adjust the VR scene to the weather image corresponding to the current forest weather pattern according to the standardized control instructions, and adjust the parameters corresponding to the current forest weather pattern to the current parameters, while controlling the multi-screen device to synchronously display the weather image of the same source as the VR device. The system extracts target feature waves from EEG signals, inputs the parameters of these waves into a pre-defined correlation model, and outputs a quantitative index of the current cognitive state. This process transforms physiological signals into cognitive states. Based on the quantitative index of the current cognitive state, it matches the corresponding current forest weather pattern and parameters, mapping the abstract cognitive state to specific scene elements. Standardized control commands are generated based on the current forest weather pattern and parameters, realizing the transformation of cognitive state data into control commands. These commands are then used to control VR devices and multi-screen devices to adapt to changes in the user's cognitive state and improve the personalized adaptability of the VR therapy scene. The system automatically selects the current forest weather pattern and adjusts parameters based on the user's current cognitive state quantification, replacing fixed rules or manual intervention, allowing the scene to adapt to changes in cognitive state. Standardized control commands are simultaneously sent to both VR devices and multi-screen devices. The VR device provides the user with a first-person immersive scene, while the multi-screen devices synchronously display the same weather imagery. This allows medical staff to observe the virtual environment in which the user is located in real time without wearing VR devices, achieving a balance between immersive experience and collaborative monitoring.

[0041] The beneficial effects and methods of the adaptive forest scene weather control system, device and medium provided in this application are as described above. Attached Figure Description

[0042] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 A flowchart illustrating an adaptive forest scene weather control method provided in this application embodiment;

[0044] Figure 2 A structural diagram of an adaptive forest scene weather control system provided in an embodiment of this application;

[0045] Figure 3 This is a structural diagram of an adaptive forest scene weather control device provided in an embodiment of this application. Detailed Implementation

[0046] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0047] The core of this application is to provide an adaptive forest scene weather control method, system, device, and medium for converting cognitive state data into scene control instructions to adapt to changes in the cognitive state of the user and improve the personalized adaptability of VR healing scenes, as well as to enable collaborative monitoring by medical staff.

[0048] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] Figure 1 A flowchart of an adaptive forest scene weather control method provided in this application embodiment is shown below. Figure 1 As shown, an adaptive weather regulation method for forest scenes includes:

[0050] S11: Acquire the current EEG signal of the user collected by the brain-computer interface device.

[0051] S12: Extract the target feature wave of the current EEG signal and input the parameters of the target feature wave into a preset association model to output a quantitative index of the current cognitive state.

[0052] S13: Based on the current cognitive state, quantitative indicators are matched with the corresponding current forest weather pattern and the current parameters corresponding to the current forest weather pattern.

[0053] S14: Generate standardized control instructions based on the current forest weather pattern and current parameters, and simultaneously send the standardized control instructions to the VR device and the multi-screen device to control the VR device to adjust the VR scene to the weather image corresponding to the current forest weather pattern according to the standardized control instructions, and adjust the parameters corresponding to the current forest weather pattern to the current parameters, while controlling the multi-screen device to synchronously display the weather image of the same source as the VR device.

[0054] To better understand the adaptive forest scene weather control method, the hardware components for implementing this method are described below: an adaptive forest scene weather control device, comprising: a brain-computer interface device, a PC host, a communication device, a multi-screen device, and a VR device; the brain-computer interface device includes an EEG helmet and a signal acquisition terminal, the EEG helmet being connected to the PC host via the signal acquisition terminal, and the PC host being connected to the multi-screen device and the VR device via the communication device. The PC host can be used to execute the steps of the aforementioned adaptive forest scene weather control method.

[0055] The non-invasive brain-computer interface (BCI) cognitive acquisition hardware includes an EEG helmet and a signal acquisition terminal. The EEG helmet features 8-channel dry electrodes covering the forehead, parietal lobe, and temporal lobe; electrode contact pressure is adaptively adjustable from 0.1 to 0.3 MPa; a built-in copper foil shielding layer (shielding effectiveness ≥40 dB); dual transmission via Bluetooth 5.0 and wired connection; battery life ≥8 hours; weight ≤500g; and adjustable head circumference from 54 to 62 cm. The signal acquisition terminal includes a 16-bit analog-to-digital converter (ADC) with an adjustable sampling rate of 250-500 Hz, supports real-time filtering and amplification, and has a switchable power supply.

[0056] PC-side control and multi-screen hardware include the PC host, three-screen devices, and auxiliary hardware. PC host: processor, graphics card, 32GB RAM, 1TB SSD; Three-screen devices: three 55-inch 4K screens, color gamut ≥99% sRGB (standard red, green, blue color space), brightness ≥500 nits (a unit of measurement for brightness), vertical splicing gap ≤2mm; Auxiliary hardware: gigabit Ethernet port, adjustable splicing bracket.

[0057] VR devices and communication hardware include VR equipment and communication hardware. VR equipment: headset, 2160×2160 resolution, 90Hz refresh rate, built-in audio module; Communication hardware: gigabit router, Cat 6 network cable, to build a local area network, supporting wired and wireless backup communication.

[0058] Before step S10, initialization is performed, specifically including: device connection, establishing a bidirectional linkage connection between the non-invasive EEG helmet, PC host, three-screen display device, and VR device via TCP (Transmission Control Protocol), ensuring smooth communication links between all devices, and supporting wired and wireless backup communication. Scene loading, the PC controls the host to load the same source forest healing scene 3D model, and synchronously pushes the scene resources to the VR device, ensuring that the scenes on both ends are consistent. Parameter calibration, completing multi-dimensional core parameter calibration, including EEG helmet electrode contact pressure calibration (0.1-0.3MPa), three-screen display brightness / contrast calibration (three-screen difference ≤5%), TCP communication latency calibration (target ≤20ms), and dual-end synchronization timing calibration (target synchronization latency ≤50ms). After calibration, the system enters the standby state.

[0059] In step S11, the brain-computer interface device acquires the subject's electroencephalogram (EEG) signals in real time, with a fixed sampling rate of 250Hz. The timestamp of each frame is recorded synchronously to ensure data consistency. During acquisition, environmental electromagnetic interference is filtered through a copper foil shielding layer (shielding effectiveness ≥40dB) built into the EEG helmet to ensure the purity of the original signal. The acquired raw signal is transmitted to the signal acquisition terminal in real time. The brain-computer interface specifically refers to a non-invasive brain-computer interface that acquires EEG signals through scalp electrodes, eliminating the need for invasive surgery.

[0060] In step S12, extracting the target feature waves of the current EEG signal includes: filtering the current EEG signal using a digital filter to obtain a purified current EEG signal; amplifying the purified current EEG signal to obtain an amplified current EEG signal; and extracting the target feature waves from the amplified current EEG signal. The target feature waves include alpha waves, beta waves, and theta waves. Specifically: Interference filtering: Using an Infinite Impulse Response (IIR) digital filter (filtering frequency band 0.5-30Hz) to filter electromyographic interference, electrocardiographic interference, and environmental noise, purifying the effective EEG signal. Signal amplification: Amplifying the purified EEG signal by 1000-5000 times to improve signal recognition. Feature extraction: Extracting three types of core target feature waves directly related to cognitive state from the amplified EEG signal, namely... Wave (8-13Hz, corresponding to a relaxed state) Wave (14-30Hz, corresponding to anxiety / focused state), Wave (4-7Hz, corresponding to low wake-up state).

[0061] After extraction, the parameters of the target feature wave are input into a preset association model to output a quantitative index of the current cognitive state. Specifically, the extracted... / / The amplitude, frequency, power spectral density, and other parameters are substituted into the preset correlation model to quantify and output cognitive state indicators ranging from 0 to 10 (0 being the lowest and 10 being the highest), namely anxiety level (0-10), concentration level (0-10), and relaxation level (0-10). The quantification error is strictly controlled within ±0.5 points. The cognitive state quantification indicators are transmitted to the PC host in real time via TCP at a frequency of once every 100ms to provide data input for subsequent mapping and matching.

[0062] Before inputting the parameters of the target feature wave into the preset association model, the process includes: constructing an association model between the feature wave and cognitive state based on the Support Vector Machine (SVM) algorithm. Specifically, this includes: inputting the feature vector, and extracting three types of feature wave parameters from the preprocessed EEG signal, including... Amplitude and power spectral density of the wave (8-13Hz) Amplitude, power spectral density and Amplitude and power spectral density of the wave (4-7Hz). Output cognitive indicators: three categories of cognitive state quantification indicators from 0-10: anxiety level (0 lowest, 10 highest), concentration level (0 lowest, 10 highest), and relaxation level (0 lowest, 10 highest). Model training and mapping: an SVM regression model is trained using a labeled EEG dataset to establish a nonlinear mapping relationship between feature wave parameters and cognitive state quantification. Cross-validation is used to optimize the kernel function parameters and penalty coefficient to ensure the model's generalization ability.

[0063] In step S13, matching the current forest weather pattern and the current parameters corresponding to the current forest weather pattern based on the quantitative index of the current cognitive state includes: inputting the quantitative index of the current cognitive state into the dynamic mapping model, and matching the current forest weather pattern and the current parameters corresponding to the current forest weather pattern based on the dynamic mapping model.

[0064] Specifically, the system receives quantitative indicators of the current cognitive state (anxiety level, focus, and relaxation level), inputs them into a preset dynamic mapping model of cognitive state and weather parameters, and matches them with the corresponding current forest weather mode and the current parameters corresponding to the current forest weather mode. For example, anxiety level (≥7 points) corresponds to the rainy day mode (parameters: light 100-200 lux, rain sound volume 40-50 dB, etc., used to soothe emotions); anxiety level (≤3 points) and focus (≤4 points) correspond to the sunny day mode (parameters: light 800-1000 lux, birdsong volume 50-60 dB, etc., used to enhance arousal); anxiety level (4-6 points) corresponds to the cloudy mode (parameters: light 400-600 lux, breeze volume 30-40 dB, etc., used to balance soothing and arousal); relaxation level (≥8 points) corresponds to the foggy day mode (parameters: light 200-300 lux, ambient sound volume 20-30 dB, etc., used to maintain a relaxed state). The dynamic mapping model is pre-built. The construction process includes defining input and output variables. Input variables: cognitive state quantification indicators (anxiety level, focus, relaxation level, each from 0-10 points); output variables: forest weather patterns and corresponding pattern parameters (light intensity, sound effect type and volume, transition animation duration, etc.). Based on the input variables and corresponding output variables, a mapping matrix from the cognitive state quantification indicators to the weather pattern parameters is constructed, resulting in the dynamic mapping model.

[0065] Of course, you can also search the preset mapping list to determine the current forest weather pattern that matches the current cognitive state quantitative index and the current parameters corresponding to the current forest weather pattern; the preset mapping list includes the correspondence between the cognitive state quantitative index, the forest weather pattern and the parameters of the forest weather pattern.

[0066] In step S14, generating standardized control instructions based on the current forest weather pattern and current parameters includes: generating standardized control instructions in a preset format containing header fields and data segment fields; wherein, the header fields include a timestamp and a checksum, and the data segment fields include a set of current forest weather patterns and current parameters. The timestamp is used for synchronization timing alignment between the two ends (VR device and multi-screen device), and the checksum is used for transmission integrity verification. Furthermore, it also includes: optimizing switching logic based on the rate of change of cognitive state quantitative indicators; if the cognitive state quantitative indicators change slowly (change amplitude ≤ 1 minute / 5 seconds), the instruction includes parameter adjustment logic (adjustment amplitude ≤ 10% / s); if the cognitive state quantitative indicators change abruptly (change amplitude ≥ 3 minutes / 1 second), the instruction includes rapid mode switching logic (with 1-2 seconds smooth transition animation parameters).

[0067] The simultaneous distribution of standardized control commands to both VR devices and multi-screen devices includes: setting standardized control commands as the highest priority, ensuring their transmission priority is higher than that of image data and status feedback data (the EEG signals and corresponding cognitive state quantification indicators subsequently fed back by the brain-computer interface); synchronously distributing the highest-priority standardized control commands to both VR devices and multi-screen devices via a gigabit LAN wired link; and verifying the integrity of the highest-priority standardized control commands using a checksum during the distribution process. Real-time verification is performed during transmission to ensure no commands are lost or corrupted, with transmission latency strictly controlled to ≤20ms. The command transmission loop is completed after the VR device and multi-screen device provide confirmation signals.

[0068] The VR device is controlled to adjust the VR scene to the weather image corresponding to the current forest weather mode according to standardized control instructions, and adjust the parameters corresponding to the current forest weather mode to the current parameters. Specifically, after receiving the standardized control instructions, the VR device adjusts the scene parameters in real time and renders the corresponding weather effects (such as the raindrop effect and rain sound effects in the rain mode), providing the user with a first-person immersive healing experience.

[0069] Controlling the synchronous display of weather footage from the same source as the VR device across multiple screens includes: adjusting the parameters of multiple virtual cameras to perform edge blending processing on the VR device's weather footage; and controlling the synchronous display of the edge-blended weather footage across multiple screens. Specifically, on the PC side, by adjusting the parameters of the left / center / right virtual cameras (65° field of view, 50mm focal length, 7° left / right offset), edge blending processing (blending area 10-20 pixels) is performed on the weather footage to eliminate splicing gaps, driving the three vertically spliced ​​screens to synchronously display the same weather footage from the VR device. A timestamp alignment mechanism is also used to ensure that the synchronization delay of weather effects between the VR device and the multi-screen devices is ≤50ms, achieving consistent perception between the user and accompanying personnel.

[0070] After step S14, and after controlling the multi-screen devices to synchronously display the weather image from the same source as the VR device, the process further includes: reacquiring the EEG signals of the experiencer collected by the brain-computer interface device, and re-determining the cognitive state quantification index corresponding to the EEG signals; determining whether the cognitive state quantification index meets the preset requirements; if yes, controlling the VR device to maintain the current forest weather mode, and adjusting the current parameters by a preset adjustment range; if no, proceeding to the step of reacquiring the EEG signals of the experiencer collected by the brain-computer interface device, and re-determining the cognitive state quantification index corresponding to the EEG signals, and re-matching the target forest weather mode corresponding to the cognitive state quantification index and the target parameters corresponding to the target forest weather mode; adjusting the current forest weather mode of the VR device to the target weather image corresponding to the target forest weather mode, and adjusting the current parameters of the current forest weather mode to the target parameters, while controlling the multi-screen devices to synchronously display the target weather image until the cognitive state quantification index meets the preset requirements.

[0071] The brainwave signals of the user collected by the brain-computer interface device are reacquired, preprocessed (filtered and amplified), and target features are re-extracted. The signals are then re-input into the preset association model to obtain updated quantitative indicators of cognitive state. The VR device and multi-screen device collect their own operating status (such as VR rendering frame rate and the integrity of the three-screen image) and synchronously feed it back to the PC host to ensure that the system operating status can be monitored.

[0072] The preset requirements are anxiety level ≤ 3 points, relaxation level ≥ 7 points, and focus level ≥ 6 points. When the cognitive state quantification indicators meet the preset requirements, control instructions are generated for parameter fine-tuning and state maintenance, making only minor adjustments to the parameters of the current weather mode (adjustment range ≤ 5% / s) to stabilize the user's healing state and avoid over-regulation. When the cognitive state quantification indicators do not meet the preset requirements, the updated cognitive state quantification indicators are matched with the target forest weather mode and the corresponding target parameters, and control instructions are generated to achieve continuous dynamic regulation until the healing goal is achieved.

[0073] Furthermore, the system determines whether the therapy process should terminate based on preset conditions (such as the participant completing a preset therapy duration, staff manually triggering a termination command, and the cognitive state quantitative indicators remaining stable for ≥10 minutes). If not terminated, the brain-computer interface device continuously monitors and adjusts the VR device and multi-screen device in real time. If the process terminates, it enters the model self-iterative optimization phase. All data from this process is collected (including the cognitive state quantitative indicator change curve, control command records, VR device and multi-screen device operating status data, and control effect feedback data). The parameter weights of the cognitive state-weather parameter dynamic mapping model are iteratively optimized using a gradient descent algorithm. The optimized model parameters are automatically saved and overwrite the initial parameters for use in subsequent therapy control processes for participants, improving the accuracy and personalized adaptation capabilities of subsequent controls.

[0074] The PC host saves all data from this treatment process (quantitative indicators of cognitive state, records of control commands, model optimization parameters, and device operation logs) in a preset compatible format for easy data traceability and analysis. A link disconnect command is issued via TCP communication to close the communication connection between the devices. The non-invasive EEG helmet, VR device, and three-screen display device are then sequentially shut down. Finally, the PC host shuts down itself, officially terminating the entire system workflow. In summary, the entire workflow forms a complete closed loop of preparation, data collection, control, feedback, optimization, and completion. Driven entirely by quantitative indicators of cognitive state, it achieves deep integration and automated collaboration between the non-invasive brain-computer interface, VR device, and PC three-screen display device. Its core advantages lie in the absence of human intervention, precise dual-end synchronization, and highly personalized control, effectively addressing the shortcomings of existing technologies such as passive lag, poor synchronization, and insufficient adaptability.

[0075] This application provides an adaptive forest scene weather control method, comprising: acquiring the current EEG signal of the user collected by a brain-computer interface device; extracting the target feature wave of the current EEG signal and inputting the parameters of the target feature wave into a preset association model to output a quantitative index of the current cognitive state; matching the current forest weather pattern and the current parameters corresponding to the current forest weather pattern according to the quantitative index of the current cognitive state; generating standardized control instructions based on the current forest weather pattern and the current parameters, and simultaneously sending the standardized control instructions to a VR device and a multi-screen device to control the VR device to adjust the VR scene to the weather image corresponding to the current forest weather pattern according to the standardized control instructions, and adjust the parameters corresponding to the current forest weather pattern to the current parameters, while controlling the multi-screen device to synchronously display the weather image of the same source as the VR device. The system extracts target feature waves from EEG signals, inputs the parameters of these waves into a pre-defined correlation model, and outputs a quantitative index of the current cognitive state. This process transforms physiological signals into cognitive states. Based on the quantitative index of the current cognitive state, it matches the corresponding current forest weather pattern and parameters, mapping the abstract cognitive state to specific scene elements. Standardized control commands are generated based on the current forest weather pattern and parameters, realizing the transformation of cognitive state data into control commands. These commands are then used to control VR devices and multi-screen devices to adapt to changes in the user's cognitive state and improve the personalized adaptability of the VR therapy scene. The system automatically selects the current forest weather pattern and adjusts parameters based on the user's current cognitive state quantification, replacing fixed rules or manual intervention, allowing the scene to adapt to changes in cognitive state. Standardized control commands are simultaneously sent to both VR devices and multi-screen devices. The VR device provides the user with a first-person immersive scene, while the multi-screen devices synchronously display the same weather imagery. This allows medical staff to observe the virtual environment in which the user is located in real time without wearing VR devices, achieving a balance between immersive experience and collaborative monitoring.

[0076] Furthermore, based on the SVM algorithm, a construction is made. / / A correlation model between wave and cognitive state achieves a measurement error of ≤±0.5 points for anxiety, focus, and relaxation, solving the problem of ineffective transformation of cognitive data. A dynamic mapping model of cognitive state and weather parameters enables cognitively driven weather control command generation, replacing manual and fixed-rule triggering and solving the problem of passive control. Through TCP low-latency communication (command transmission delay ≤20ms) and timestamp alignment, and three-camera linkage adaptation, a synchronization delay of ≤50ms is achieved between VR and PC three-screen weather effects, enabling homogeneous scene rendering based on the same cognitive command and multi-screen stereoscopic adaptation, solving the problems of lack of immersion and asynchrony between two devices in multi-screen viewing. Based on the cognitive state change data after control, the model parameters are iteratively mapped using a gradient descent algorithm to adapt to the cognitive characteristics of different users. A non-invasive, fully automated method for brain-computer interface acquisition, PC-side control, dual-device execution, and state feedback is constructed, clarifying the collaborative logic of each execution subject and filling the technological gap in the integration of brain-computer interface, VR, and multi-screen.

[0077] The above embodiments have described the adaptive forest scene weather control method in detail. This application also provides embodiments of an adaptive forest scene weather control system and device. It should be noted that the system is based on functional modules, while the device is based on hardware.

[0078] Figure 2 A structural diagram of an adaptive forest scene weather control system provided in this application embodiment is shown below. Figure 2 As shown, an adaptive forest scene weather control system includes:

[0079] Acquisition module 11 is used to acquire the current EEG signal of the user collected by the brain-computer interface device;

[0080] The cognitive state quantification module 12 extracts the target feature wave of the current EEG signal and inputs the parameters of the target feature wave into a preset association model to output the current cognitive state quantification index.

[0081] Matching module 13 is used to match the current forest weather pattern and the current parameters corresponding to the current forest weather pattern based on the quantitative indicators of the current cognitive state.

[0082] The control module 14 is used to generate standardized control instructions based on the current forest weather mode and current parameters, and simultaneously send the standardized control instructions to the VR device and the multi-screen device to control the VR device to adjust the VR scene to the weather screen corresponding to the current forest weather mode according to the standardized control instructions, and adjust the parameters corresponding to the current forest weather mode to the current parameters, while controlling the multi-screen device to synchronously display the weather screen of the same source as the VR device.

[0083] Based on the above embodiments, in an optional embodiment, the cognitive state quantification module includes:

[0084] The filtering unit is used to filter the current EEG signal using a digital filter to obtain a purified current EEG signal.

[0085] The amplification processing unit is used to amplify the purified current EEG signal to obtain the amplified current EEG signal.

[0086] The advance unit is used to extract target feature waves from the amplified current EEG signal; wherein, the target feature waves include Wave, wave and Wave.

[0087] Based on the above embodiments, in one optional embodiment, the control module includes:

[0088] The generation unit is used to generate standardized control instructions in a preset format, including header fields and data segment fields, based on the current forest weather pattern and current parameters.

[0089] The header field includes a timestamp and a checksum, while the data field includes the current forest weather pattern and a set of current parameters.

[0090] Based on the above embodiments, in one optional embodiment, the control module includes:

[0091] The setting unit is used to set the standardized control command as the highest priority, so that the transmission priority of the standardized control command is higher than the transmission priority of the screen data and status feedback data.

[0092] The distribution unit is used to synchronously distribute standardized control commands marked with the highest priority to VR devices and multi-screen devices via gigabit LAN wired links;

[0093] The verification unit is used to perform integrity verification on standardized control instructions marked with the highest priority during the issuance process using a verification code.

[0094] Based on the above embodiments, in one optional embodiment, the control module includes:

[0095] The fusion processing unit is used to adjust the parameters of multiple virtual cameras to perform edge fusion processing on the weather images of the VR device;

[0096] The display unit is used to control the synchronous display of weather images after edge blending on multiple screen devices.

[0097] Based on the above embodiments, in an optional embodiment, after controlling the multi-screen devices to synchronously display weather images of the same origin as the VR device, the method further includes:

[0098] The reacquisition module is used to reacquire the brain signals of the user collected by the brain-computer interface device and redetermine the cognitive state quantification indicators corresponding to the brain signals.

[0099] The judgment module is used to determine whether the quantitative indicators of cognitive state have met the preset requirements;

[0100] The adjustment module is used to control the VR device to maintain the current forest weather mode and adjust the current parameters by a preset adjustment range if the quantitative indicators of cognitive state reach the preset requirements.

[0101] The rematching module is used to reacquire the brain signals of the user collected by the brain-computer interface device if the cognitive state quantification index does not meet the preset requirements, and to redetermine the cognitive state quantification index corresponding to the brain signals, and to rematch the target forest weather pattern and target parameters corresponding to the cognitive state quantification index.

[0102] The switching module is used to adjust the current forest weather mode of the VR device to the target weather image corresponding to the target forest weather mode, and adjust the current parameters of the current forest weather mode to the target parameters. At the same time, it controls multiple screen devices to display the target weather image synchronously until the cognitive state quantification index reaches the preset requirements.

[0103] Based on the above embodiments, in an optional embodiment, the matching module includes:

[0104] The matching unit is used to input the quantitative index of the current cognitive state into the dynamic mapping model, and to match the current forest weather pattern and the current parameters corresponding to the quantitative index of the current cognitive state based on the dynamic mapping model.

[0105] Since the embodiments of the system part correspond to the embodiments of the method part, the embodiments of the device part are described in the description of the embodiments of the method part, and will not be repeated here.

[0106] Figure 3 A structural diagram of an adaptive forest scene weather control device provided in this application embodiment is shown below. Figure 3 As shown, the adaptive forest scene weather control device includes: a memory 20 for storing a computer program; and a processor 21 for executing the computer program to implement the steps of the adaptive forest scene weather control method as described in the above embodiment.

[0107] The adaptive forest scene weather control device provided in this embodiment can include, but is not limited to, smartphones, tablets, laptops, or desktop computers.

[0108] The processor 21 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 21 may be implemented using at least one of the following hardware forms: Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 21 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 21 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 21 may also include an Artificial Intelligence (AI) processor, which handles computational operations related to machine learning.

[0109] The memory 20 may include one or more computer-readable storage media, which may be non-transitory. The memory 20 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 20 is used to store at least the following computer program 201, which, after being loaded and executed by the processor 21, is capable of implementing the relevant steps of the adaptive forest scene weather control method disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202 and data 203, and the storage method may be temporary or permanent storage. The operating system 202 may include Windows, Unix, Linux, etc. The data 203 may include, but is not limited to, current electroencephalogram (EEG) signals.

[0110] In some embodiments, the adaptive forest scene weather control device may further include a display screen 22, an input / output interface 23, a communication interface 24, a power supply 25, and a communication bus 26.

[0111] Those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the adaptive forest scene weather control device and may include more or fewer components than shown.

[0112] The adaptive forest scene weather control device provided in this application includes a memory and a processor. When the processor executes the program stored in the memory, it can perform the following methods: acquire the current EEG signal of the user collected by the brain-computer interface device; extract the target feature wave of the current EEG signal and input the parameters of the target feature wave into a preset association model to output a quantitative index of the current cognitive state; match the current forest weather mode and the current parameters corresponding to the current forest weather mode according to the quantitative index of the current cognitive state; generate standardized control instructions based on the current forest weather mode and the current parameters, and simultaneously send the standardized control instructions to the VR device and the multi-screen device to control the VR device to adjust the VR scene to the weather screen corresponding to the current forest weather mode according to the standardized control instructions, and adjust the parameters corresponding to the current forest weather mode to the current parameters, while controlling the multi-screen device to synchronously display the weather screen of the same source as the VR device.

[0113] Finally, this application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the adaptive forest scene weather control method of the above-described method embodiment.

[0114] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they 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 all or 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 executes 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.

[0115] The above provides a detailed description of an adaptive forest scene weather control method, system, apparatus, and medium provided in this application. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

[0116] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. An adaptive weather control method for forest scenarios, characterized in that, include: Acquire the current electroencephalogram (EEG) signals of the user collected by the brain-computer interface device; Extract the target feature wave of the current EEG signal and input the parameters of the target feature wave into a preset association model to output a quantitative index of the current cognitive state; The current forest weather pattern and the current parameters corresponding to the current forest weather pattern are matched according to the quantitative index of the current cognitive state. Based on the current forest weather pattern and the current parameters, a standardized control command is generated and simultaneously sent to the VR device and the multi-screen device. This allows the VR device to adjust the VR scene to the weather image corresponding to the current forest weather pattern according to the standardized control command, and adjust the parameters corresponding to the current forest weather pattern to the current parameters. At the same time, the multi-screen device is controlled to synchronously display the weather image from the same source as the VR device.

2. The adaptive forest scene weather control method according to claim 1, characterized in that, Extracting the target feature wave of the current EEG signal includes: The current EEG signal is filtered using a digital filter to obtain a purified current EEG signal. The purified current EEG signal is amplified to obtain the amplified current EEG signal. Extracting target feature waves from the amplified current EEG signal; wherein, the target feature waves include Wave, wave and Wave.

3. The adaptive forest scene weather control method according to claim 1, characterized in that, Based on the current forest weather model and the current parameters, standardized control instructions are generated, including: Based on the current forest weather pattern and the current parameters, a standardized control instruction with a preset format including header fields and data segment fields is generated; The header field includes a timestamp and a checksum, and the data segment field includes the current forest weather pattern and the set of current parameters.

4. The adaptive forest scene weather control method according to claim 3, characterized in that, The standardized control commands are simultaneously sent to VR devices and multi-screen devices, including: The standardized control command is set to the highest priority so that the transmission priority of the standardized control command is higher than the transmission priority of the screen data and status feedback data. Standardized control commands marked with the highest priority are synchronously sent to the VR device and the multi-screen device via a gigabit local area network wired link; During the issuance process, the integrity of standardized control instructions marked with the highest priority is verified using the verification code.

5. The adaptive forest scene weather control method according to claim 1, characterized in that, Controlling the multi-screen devices to synchronously display weather images of the same origin as the VR device includes: Adjust the parameters of multiple virtual cameras to perform edge blending processing on the weather images from the VR device; Control the multi-screen devices to synchronously display the weather image after edge blending processing.

6. The adaptive forest scene weather control method according to claim 1, characterized in that, After controlling the multi-screen devices to synchronously display weather images of the same origin as the VR device, the method further includes: The brainwave signals of the user collected by the brain-computer interface device were reacquired, and the cognitive state quantification index corresponding to the brainwave signals was redefined. Determine whether the quantitative indicators of cognitive status have met the preset requirements; If so, control the VR device to maintain the current forest weather mode and adjust the current parameters by a preset adjustment range; If not, proceed to the step of reacquiring the brainwave signals of the experiencer collected by the brain-computer interface device, re-determining the cognitive state quantification index corresponding to the brainwave signals, and re-matching the target forest weather pattern corresponding to the cognitive state quantification index and the target parameters corresponding to the target forest weather pattern. The current forest weather mode of the VR device is adjusted to the target weather image corresponding to the target forest weather mode, and the current parameters of the current forest weather mode are adjusted to the target parameters. At the same time, the multi-screen device is controlled to display the target weather image synchronously until the cognitive state quantification index reaches the preset requirements.

7. The adaptive forest scene weather control method according to claim 6, characterized in that, Based on the quantitative indicators of the current cognitive state, the corresponding current forest weather pattern and the current parameters corresponding to the current forest weather pattern are matched, including: The quantitative index of the current cognitive state is input into the dynamic mapping model, and the current forest weather pattern corresponding to the quantitative index of the current cognitive state and the current parameters corresponding to the current forest weather pattern are matched based on the dynamic mapping model.

8. An adaptive weather control system for forest scenarios, characterized in that, include: The acquisition module is used to acquire the current electroencephalogram (EEG) signals of the user collected by the brain-computer interface device; The cognitive state quantification module extracts the target feature wave of the current EEG signal and inputs the parameters of the target feature wave into a preset association model to output the current cognitive state quantification index. The matching module is used to match the current forest weather pattern and the current parameters corresponding to the current forest weather pattern based on the quantitative index of the current cognitive state. The control module is used to generate standardized control instructions based on the current forest weather mode and the current parameters, and simultaneously send the standardized control instructions to the VR device and the multi-screen device, so as to control the VR device to adjust the VR scene to the weather screen corresponding to the current forest weather mode according to the standardized control instructions, and adjust the parameters corresponding to the current forest weather mode to the current parameters, while controlling the multi-screen device to synchronously display the weather screen of the same source as the VR device.

9. An adaptive weather control device for forest scenes, characterized in that, Includes memory used to store computer programs; A processor, configured to implement the steps of the adaptive forest scene weather control method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the adaptive forest scene weather control method as described in any one of claims 1 to 7.

Images