Quantitative evaluation method and system based on superimposed effect of matter wave and biological wave

Abstract

The invention relates to the technical field of material wave and biological wave superimposed effect, provides a quantitative evaluation method and system based on material wave and biological wave superimposed effect, and aims to solve the problems of low evaluation precision and poor reliability of biological and physical interface energy coupling effect in the prior art. The method comprises the following steps: synchronously acquiring an electroencephalogram signal of a contact interface of a living body and a resonant frequency signal of a contact medium, and carrying out dynamic resonance compensation to generate a matter wave data set; mapping coding is carried out, and a physical coupling feature set representing the superimposed effect of the two is generated; constructing an energy transfer network of a contact medium based on a graph structure analysis model in combination with a frequency deviation gradient and an energy propagation path between adjacent nodes, extracting a propagation rate parameter and an energy attenuation coefficient, and performing coupling; and matching the coupling result with a preset medium characteristic database to generate a quantitative evaluation result of the superimposed effect of the matter wave and the biological wave. According to the invention, the evaluation precision and reliability of the energy coupling effect of the biological and physical interfaces are improved.

Description

Technical Field

[0001] The present application relates to the technical field of the superposition effect of matter waves and bio-waves, and in particular to a quantitative evaluation method and system based on the superposition effect of matter waves and bio-waves. Background Art

[0002] In the fields of biomedical engineering and smart materials, assessing the energy coupling effects between organisms and contact media is crucial. For example, scenarios such as the biocompatibility of electrode materials in brain-computer interfaces and the interaction between medical implants and human tissue require precise quantification of the combined effects of bioelectrical signals and the physical response of the medium. Traditional methods struggle to achieve collaborative analysis and dynamic evaluation of multimodal signals, necessitating a new assessment technology that can integrate the characteristics of biowaves and matter waves and quantify their coupling strength.

[0003] Current research uses spectrum analysis to assess the interaction between bioelectric signals and medium vibrations. This method uses Fourier transforms to extract the frequency domain characteristics of EEG signals and the spectral characteristics of the medium's resonant frequency, then calculates the degree of energy coupling between the two using linear correlation. This method constructs an evaluation model based on frequency domain energy distribution, outputting a quantitative indicator of the interaction between the organism and the medium.

[0004] Spectral analysis methods focus solely on the static matching of signal frequency domain characteristics, ignoring the dynamic correlation between bio-waves and matter waves in the spatial and temporal dimensions. Due to a lack of modeling of signal propagation paths and inter-node energy transfer characteristics, the evaluation results fail to reflect local variations and nonlinear effects of interfacial interactions, leading to deviations between quantitative indicators and actual coupling strength. Summary of the Invention

[0005] The present application provides a quantitative evaluation method and system based on the superposition effect of matter waves and bio-waves, which is used to solve the problems of low evaluation accuracy and poor reliability of the energy coupling effect of the biological and physical interface in the existing technology.

[0006] In a first aspect, the present application provides a quantitative evaluation method based on the superposition effect of matter waves and biowaves, comprising:

[0007] Collect the EEG signals generated by the contact interface of the organism and the resonant frequency signals in the contact medium;

[0008] performing dynamic resonance compensation processing on the resonant frequency signal to generate a matter wave data set;

[0009] Spectral encoding of the EEG signal and the matter wave data set according to the acquisition position and time sequence to generate a physical coupling feature set, wherein the physical coupling feature set is used to represent the superposition effect of matter waves and biowaves;

[0010] Based on the graph structure analysis model and the physical coupling feature set, combined with the frequency offset gradient and energy propagation path between adjacent nodes, an energy transfer network of the contact medium is constructed;

[0011] extracting a propagation rate parameter and an energy attenuation coefficient from the energy transfer network, and coupling the propagation rate parameter with the energy attenuation coefficient;

[0012] By matching the coupling results with the preset medium physical property parameter database, a quantitative evaluation result of the superposition effect of matter wave and bio-wave is generated.

[0013] Optionally, extracting a propagation rate parameter and an energy attenuation coefficient from the energy transfer network and coupling the propagation rate parameter with the energy attenuation coefficient comprises:

[0014] Extracting the starting node and the ending node of the energy propagation path in the energy transfer network;

[0015] Calculating a path delay time of the resonant frequency signal propagating from a starting node to an ending node;

[0016] generating a propagation rate parameter according to a ratio of a target distance to the path delay time, wherein the target distance is a distance between the starting node and the ending node;

[0017] Extracting frequency offset gradients between adjacent nodes along the energy propagation path, performing a linear fit between the frequency offset gradients and the energy loss of the corresponding energy propagation path segment to generate an energy attenuation coefficient, where the energy propagation path segment is a local interval of the energy propagation path;

[0018] assigning a first weight coefficient to the propagation rate parameter and a second weight coefficient to the energy attenuation coefficient;

[0019] The propagation velocity parameter and the energy attenuation coefficient are numerically combined according to the first weight coefficient and the second weight coefficient to generate a composite coupling parameter, which is a coupling result.

[0020] Optionally, extracting a frequency offset gradient between adjacent nodes along the energy propagation path, performing linear fitting on the frequency offset gradient and the energy loss of the corresponding energy transfer path segment to generate an energy attenuation coefficient includes:

[0021] Traversing each energy propagation path segment in the energy transfer network, and calculating a frequency offset gradient between a starting node and an ending node in each energy propagation path segment;

[0022] Obtaining energy loss of the energy propagation path segment within a preset time window;

[0023] In a two-dimensional plane with the frequency offset gradient as the horizontal axis and the energy loss as the vertical axis, a corresponding two-dimensional coordinate point is determined for each energy propagation path segment;

[0024] Performing least squares straight line fitting on all two-dimensional coordinate points, and calculating the energy absorption factor of the energy propagation path segment according to the slope value of the best fitting straight line;

[0025] The arithmetic mean of the energy absorption factors of all energy propagation path segments is taken as the energy attenuation coefficient.

[0026] Optionally, performing least squares straight line fitting on all two-dimensional coordinate points and calculating the energy absorption factor of the energy propagation path segment according to the slope value of the best fitting straight line includes:

[0027] Count the two-dimensional coordinate points corresponding to all energy propagation path segments to generate a two-dimensional coordinate point set;

[0028] Analyzing the distribution trend of the two-dimensional coordinate point set in the two-dimensional plane, and determining the best fitting straight line passing through the two-dimensional coordinate points by minimizing the sum of squared deviations;

[0029] Calculating an extension direction parameter of the optimal fitting straight line in a two-dimensional plane, and converting the extension direction parameter into a slope value;

[0030] The slope value is normalized with a preset medium energy absorption reference value to generate an energy absorption factor.

[0031] Optionally, the step of graphically encoding the EEG signal and the matter wave data set according to acquisition positions and time sequences to generate a set of physical coupling features includes:

[0032] Dividing the EEG signal into a plurality of time segments, and assigning a corresponding spatial coding mark to each time segment, wherein the spatial coding mark reflects the collection position of the EEG signal at the contact interface of the biological body;

[0033] calculating a plurality of resonant frequency offsets corresponding to the time segment in the matter-wave data set;

[0034] Binding the maximum resonant frequency offset corresponding to each time segment to the corresponding spatial coding mark;

[0035] Traverse all bound time segments, superimpose the EEG signal amplitude change curve and the matter wave frequency offset change curve under the same spatial coding mark to generate a fusion feature unit;

[0036] According to the distribution order of the spatial coding marks, the fusion feature units are spatially topologically arranged to form a physical coupling feature set.

[0037] Optionally, the step of constructing an energy transfer network for a contact medium based on the graph structure analysis model and the physical coupling feature set, combined with the frequency offset gradient and energy propagation path between adjacent nodes, includes:

[0038] Determining a conduction direction between adjacent nodes according to a spatial topological arrangement order of the physical coupling feature set, and setting the conduction direction as a positive direction of an energy propagation path;

[0039] Using the frequency offset gradient as a connection weight, connecting adjacent nodes along the positive direction into directed edges;

[0040] An energy transfer network is generated based on the directed edges.

[0041] Optionally, generating a quantitative evaluation result of the superposition effect of matter waves and bio-waves by matching the coupling result with a preset medium physical property parameter database includes:

[0042] Decompose the coupling results into rate eigenvectors and decay eigenvectors;

[0043] Comparing the rate characteristic vector and the attenuation characteristic vector with the rate parameter interval set and the attenuation coefficient interval set stored in a preset medium physical characteristic parameter database, respectively;

[0044] When the rate characteristic vector falls within the first parameter interval of the rate parameter interval set and the attenuation characteristic vector falls within the second parameter interval of the attenuation coefficient interval set, activating the corresponding medium physical characteristic tag in the preset medium physical characteristic parameter database;

[0045] The similarity values ​​between the rate feature vector and the attenuation feature vector and the vector corresponding to the medium physical characteristic label are calculated respectively, and all the similarity values ​​are weightedly superimposed to generate a quantitative evaluation result.

[0046] In a second aspect, the present application provides a quantitative evaluation system based on the superposition effect of matter waves and bio-waves, comprising:

[0047] An acquisition module is used to collect the EEG signals generated by the contact interface of the organism and the resonant frequency signals in the contact medium;

[0048] a processing module, configured to perform dynamic resonance compensation processing on the resonant frequency signal to generate a matter wave data set;

[0049] an encoding module for graphically encoding the EEG signal and the matter wave data set according to the acquisition position and time sequence to generate a physical coupling feature set, wherein the physical coupling feature set is used to represent the superposition effect of matter waves and biowaves;

[0050] A construction module is used to infer the node relationship in the physical coupling feature set based on a graph structure analysis model, and to construct an energy transfer network of the contact medium by combining the frequency offset gradient and energy propagation path between adjacent nodes;

[0051] an extraction module, configured to extract a propagation rate parameter and an energy attenuation coefficient from the energy transfer network, and couple the propagation rate parameter with the energy attenuation coefficient;

[0052] The generation module is used to generate a quantitative evaluation result of the superposition effect of matter wave and bio-wave by matching the coupling result with the preset medium physical property parameter database.

[0053] In a third aspect, the present application provides a computing device comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any quantitative evaluation method based on the superposition effect of matter waves and bio-waves as described in the first aspect.

[0054] In a fourth aspect, the present application provides a computer storage medium having computer program instructions stored thereon, which, when executed by a processor, implements a quantitative evaluation method based on the superposition effect of matter waves and bio-waves as described in any one of the first aspects.

[0055] In the present application, a quantitative evaluation method based on the superposition effect of matter waves and bio-waves is provided, which includes: collecting EEG signals generated by the contact interface of a biological body and resonant frequency signals in a contact medium; performing dynamic resonance compensation processing on the resonant frequency signals to generate a matter wave data set; graphically encoding the EEG signals and the matter wave data set according to the collection position and time sequence to generate a physical coupling feature set, and the physical coupling feature set is used to represent the superposition effect of matter waves and bio-waves; based on a graph structure analysis model and the physical coupling feature set, combined with the frequency offset gradient and energy propagation path between adjacent nodes, an energy transfer network of the contact medium is constructed; propagation rate parameters and energy attenuation coefficients are extracted from the energy transfer network, and the propagation rate parameters are coupled with the energy attenuation coefficient; and a quantitative evaluation result of the superposition effect of matter waves and bio-waves is generated through the matching relationship between the coupling results and a preset medium physical property parameter database.

[0056] The technical solution provided by this application has the following beneficial effects:

[0057] This application realizes the synchronous capture of multi-source signals of the interaction between organisms and contact media, providing the raw data basis for subsequent analysis. It eliminates the disturbance of environmental interference on the matter wave signal and improves the stability and reliability of the resonant frequency data. It establishes the correlation between bio-waves and matter waves in the time and space dimensions, and forms a structured characteristic expression that characterizes the superposition effect of the two. It reveals the dynamic path and intensity distribution of energy transfer inside the medium and quantifies the mechanism of the influence of bio-waves on the physical properties of the medium. It comprehensively reflects the dynamic response characteristics of the medium under the action of bio-waves and generates a composite index that can be quantified and compared. Through standardized comparison, it realizes the objective evaluation of the interaction strength, providing a basis for material optimization and biocompatibility design.

[0058] Furthermore, the present application also selects the starting node and the ending node from the energy transfer network, and obtains the propagation rate parameter by calculating the ratio of the path delay time to the node spacing; at the same time, the energy attenuation coefficient is generated based on the linear fitting of the frequency offset gradient and the energy loss between adjacent nodes; finally, dynamic weights are assigned to the two types of parameters and combined to form a composite coupling parameter.

[0059] In addition, through path delay analysis and gradient-loss fitting, the key physical parameters of medium energy transfer are accurately extracted; a weighted fusion method is used to construct a comprehensive evaluation index, which not only retains the independence between parameters but also reflects the synergistic effect, and comprehensively characterizes the coupling strength of biowaves and matter waves.

[0060] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0062] Figure 1 A flowchart of a quantitative evaluation method based on the superposition effect of matter waves and bio-waves provided in an embodiment of the present application;

[0063] Figure 2 A schematic diagram of the structure of a quantitative evaluation system based on the superposition effect of matter waves and bio-waves provided in an embodiment of the present application;

[0064] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0065] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0066] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0067] Among current bio-physical interface interaction assessment technologies, spectrum analysis can match the frequency-domain characteristics of bioelectrical signals with medium vibrations. However, its core flaw lies in fragmenting multimodal signals. This method calculates linear correlations solely through static frequency-domain energy distributions, failing to capture the dynamic coupling characteristics of biowaves and matter waves in time and space, while also ignoring the nonlinear effects of interfacial energy transfer. This approach results in assessment results that fail to truly reflect the local interaction strength at the contact interface. This is particularly true in scenarios such as brain-computer interface electrode optimization and medical implant material design, where the quantitative indicators of existing methods deviate from the actual bio-physical coupling effect.

[0068] In response to the above limitations, this application proposes a quantitative evaluation method based on the superposition effect of matter waves and bio-waves, which realizes the accurate characterization of interface interactions by constructing a spatiotemporal coupled energy transfer network. Its innovation lies in: after synchronously collecting EEG signals and medium resonant frequency signals, graphical coding is used to generate a physical coupling set that integrates spatiotemporal features, and based on the graph structure analysis model, the frequency offset gradient and energy propagation path between adjacent nodes are dynamically tracked, thereby extracting the dual parameters of propagation rate and energy attenuation for weighted fusion. This method breaks through the static limitations of traditional frequency domain analysis, and accurately quantifies the energy coupling intensity of each area of ​​the interface through dynamic correlation modeling of bio-waves and matter waves in the spatiotemporal dimension. It not only solves the problem of insufficient characterization of nonlinear interactions in existing technologies, but also realizes standardized evaluation through matching the medium physical properties database, providing a reliable basis for biocompatible material design and medical equipment optimization.

[0069] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0070] Figure 1 A flowchart of a quantitative evaluation method based on the superposition effect of matter waves and bio-waves provided in an embodiment of the present application is shown as follows: Figure 1 As shown, the method includes:

[0071] Step 101: Collecting the EEG signal generated by the contact interface of the biological body and the resonant frequency signal in the contact medium.

[0072] In step 101, the organism refers to a living organism that generates bioelectric signals, and in this method specifically refers to a human or animal body that can generate detectable EEG signals. The contact interface refers to the physical boundary area where the organism is in direct contact with the external medium, and in this method it is the surface of the skin or tissue of the organism and the medium material where energy exchange occurs. EEG signals represent bioelectric signals generated by neuronal electrical activity, reflecting the functional state of the brain, and are usually collected through a multi-electrode array. The contact medium refers to a non-living material that is in contact with the organism and can transmit matter waves, and in this method specifically refers to a solid or liquid material that can generate a measurable resonant frequency signal and couple with the bioelectric signal. The resonant frequency signal represents the natural vibration frequency signal generated by the contact medium under the action of external excitation (such as mechanical vibration, electromagnetic wave), which can be used to characterize the physical properties of the medium.

[0073] In the embodiment of the present application, a multi-channel electrode array is first deployed at the interface between the organism and the medium to synchronously collect EEG signals and resonant frequency signals. The EEG signals are conditioned by a high-precision bioelectric amplifier to ensure signal fidelity; the resonant frequency signals are captured by a piezoelectric sensor or optical interferometer, and their frequency offset is recorded in real time. During the acquisition process, a time synchronization mechanism is used to ensure that the timestamps of the two signals are strictly aligned.

[0074] For example, in a brain-computer interface electrode optimization experiment, a 64-channel EEG cap was used to collect EEG signals from the subject's frontal lobe. Simultaneously, a micro-resonant sensor was embedded at the electrode-scalp interface to monitor changes in the electrode material's resonant frequency. These two signals were synchronously collected and stored as a time-aligned data stream for subsequent processing.

[0075] Step 102: Perform dynamic resonance compensation processing on the resonance frequency signal to generate a matter wave data set.

[0076] In step 102, dynamic resonance compensation processing involves suppressing ambient noise and resonant frequency drift through a feedback adjustment mechanism to improve signal stability. The matter-wave dataset represents the resonant frequency signal after compensation, including physical parameters such as frequency offset and phase change.

[0077] In this embodiment, the collected resonant frequency signal is analyzed in real time to detect its frequency offset trend. Phase-locked loop technology is then used to dynamically adjust the reference frequency to stabilize the signal within the target frequency band. Furthermore, an adaptive filtering algorithm is used to eliminate environmental noise interference, ultimately generating a matter-wave dataset containing information such as timestamps and frequency offsets.

[0078] For example, in medical silicone material evaluation experiments, the resonant frequency signal drifts due to the influence of body temperature. The dynamic compensation algorithm is used to correct the frequency offset in real time, thereby improving the frequency stability of the data set and ensuring the accuracy of subsequent analysis.

[0079] Step 103: Graph-encode the EEG signal and the matter wave data set according to the acquisition position and time sequence to generate a physical coupling feature set, which is used to represent the superposition effect of matter waves and biowaves.

[0080] In step 103, the graphical encoding converts the spatiotemporal multimodal signal into a structured feature expression. The physical coupling feature set represents the data set of the interaction between bio-waves and matter waves, including features such as signal amplitude and frequency offset.

[0081] In this embodiment, the EEG signal is segmented into time segments and associated with corresponding spatial coding tags. Frequency offsets within the same time window in the matter wave dataset are simultaneously extracted. The EEG signal amplitudes and resonant frequency offsets at the same spatial location are waveform-superimposed to form fused feature units, which are then arranged according to spatial topology to generate a set of physically coupled features.

[0082] For example, in brain-computer interface experiments, the EEG signals in the frontal lobe area are aligned with the electrode resonance frequency data in time to generate fusion feature units, and then arranged according to the spatial distribution of the electrodes to form a complete set of physical coupling features.

[0083] Step 104: Based on the graph structure analysis model and the physical coupling feature set, combined with the frequency offset gradient and energy propagation path between adjacent nodes, an energy transfer network of the contact medium is constructed.

[0084] In step 104, the graph structure analysis model maps the fused feature units in the physical coupling feature set into network nodes and constructs a dynamic topological structure through the connection relationships between the nodes. This is used to quantify the energy transfer between biowaves and matter waves in the contact medium. The frequency offset gradient refers to the difference in the resonant frequency offsets corresponding to adjacent nodes, reflecting the rate of change of frequency along the energy transfer path. Its value is obtained by calculating the difference in frequency offsets between adjacent spatial locations in the physical coupling feature set. The energy propagation path refers to the dynamic transmission trajectory formed by the conduction direction of the bioelectric signal in the medium. Its direction is determined by the attenuation trend of the EEG signal amplitude from high to low. The path topology is constructed using directed edges between nodes. The energy transfer network represents the dynamic topological structure describing the energy transfer path within the medium. Nodes represent spatial locations, and edges represent energy transfer intensity.

[0085] In an embodiment of the present application, the fusion feature units in the physical coupling feature set are mapped to network nodes, the frequency offset gradients of adjacent nodes are calculated as connection weights, and the energy propagation path is determined based on the conduction direction of the bioelectric signal, and finally a weighted directed network is constructed.

[0086] For example, in the evaluation of medical gels, nodes represent the signal characteristics of different contact areas, edge weights reflect the efficiency of energy transfer, and the network topology shows the trend of energy propagation from high-amplitude areas to low-amplitude areas.

[0087] Step 105: extracting a propagation rate parameter and an energy attenuation coefficient from the energy transfer network, and coupling the propagation rate parameter with the energy attenuation coefficient.

[0088] In step 105, the propagation velocity parameter represents the transmission speed of the signal in the medium and reflects the energy diffusion efficiency. The energy attenuation coefficient represents the degree of energy loss of the signal during the transmission process.

[0089] In an embodiment of the present application, a critical path in the network is selected, and the ratio of the signal propagation delay to the node spacing is calculated to obtain the propagation rate; the attenuation coefficient is calculated by linear fitting of the frequency offset gradient and the energy loss, and finally a weighted fusion is performed to generate a composite coupling parameter.

[0090] For example, in the silicone material test, the propagation rate parameter is 15 mm / 10 ms = 1.5 mm / ms, and the attenuation coefficient is determined to be 0.9 by the ratio of the fitting slope of 0.18 to the reference value of 0.2.

[0091] Step 106: Generate a quantitative evaluation result of the superposition effect of matter wave and bio-wave by matching the coupling result with a preset medium physical characteristic parameter database.

[0092] In step 106, the medium physical property parameter database stores the response data of standard materials under different biowave conditions. The quantitative evaluation result is a standardized score generated by matching the composite coupling parameters with the medium physical property database. This score objectively measures the interaction strength between biowaves and matter waves at a specific interface, for example, using an "excellent" rating to indicate the biocompatibility of a medium.

[0093] In an embodiment of the present application, the composite coupling parameters are compared with the standard range in the database, the similarity is calculated and a quantitative score, such as "excellent", is output.

[0094] For example, after the coupling parameters of the medical gel (1.5 mm / ms, 0.9) are matched with the database, the evaluation result "biocompatibility: excellent level" is output.

[0095] This method achieves dynamic quantitative evaluation of bio-physical interface interactions through synchronous multimodal signal acquisition, dynamic compensation, graphical encoding and energy network modeling, solving the problem of insufficient characterization of spatiotemporal coupling characteristics by traditional methods and providing a reliable basis for the optimization of biocompatible materials.

[0096] To solve the problem of extracting and coupling key parameters in the energy transfer network, in some embodiments, step 105: extracting a propagation rate parameter and an energy attenuation coefficient from the energy transfer network and coupling the propagation rate parameter with the energy attenuation coefficient includes:

[0097] Step 201: extracting the starting node and the ending node of the energy propagation path in the energy transfer network.

[0098] In step 201, the starting node and the ending node of the energy transmission path refer to the starting point and the ending point position nodes of the signal transmission in the energy transfer network. The starting node corresponds to the energy input area, and the ending node corresponds to the energy output area. The two together define the complete energy transmission path.

[0099] In an embodiment of the present application, all nodes of the energy transfer network are first traversed, the main propagation direction of the signal is determined according to the node connection weight, and the two ends of the path with the largest connection weight are selected as the starting node and the ending node to complete the positioning of the critical path.

[0100] Step 202: Calculate the path delay time of the resonant frequency signal propagating from the starting node to the ending node.

[0101] In step 202 , the path delay time refers to the time difference required for the resonant frequency signal to propagate from the starting node to the ending node, and reflects the energy transfer efficiency in the medium.

[0102] In an embodiment of the present application, by comparing the timestamp data of the starting node and the ending node, the absolute time difference between the signal reaching the two nodes is calculated to obtain the path delay time, which is directly related to the energy conduction characteristics of the medium.

[0103] Step 203: Generate a propagation rate parameter according to a ratio of a target distance to the path delay time, wherein the target distance is the distance between the start node and the end node.

[0104] In step 203, the propagation rate parameter refers to the speed at which energy is transferred in a medium and is determined by the ratio of the target distance to the path delay time. The target distance is the physical distance between the starting node and the ending node.

[0105] In an embodiment of the present application, the spatial coordinates of the two nodes are first measured and the Euclidean distance is calculated as the target distance, which is then divided by the path delay time to obtain the distance of energy transfer per unit time, which is the propagation rate parameter.

[0106] Step 204: Extract the frequency offset gradient between adjacent nodes along the energy propagation path, linearly fit the frequency offset gradient with the energy loss of the corresponding energy propagation path segment, and generate an energy attenuation coefficient, where the energy propagation path segment is a local interval of the energy propagation path.

[0107] In step 204, adjacent nodes refer to nodes that are directly connected according to spatial topological relationships in the constructed energy transfer network; the starting node and the ending node are the two end nodes of a specific path selected from the network, and together with the adjacent nodes, they constitute a complete energy transmission path. The energy attenuation coefficient refers to the degree of energy loss during the transmission process, which is calculated by the linear relationship between the frequency offset gradient and the amount of energy loss. As a specific example of an energy transmission path segment, when evaluating the interaction between EEG signals and a silicone medium, the energy transmission path segment is specifically manifested as follows: in the head contact area collected by the multi-electrode array, on the complete energy transmission path from the frontal node (starting point) through the parietal node (midpoint) to the occipital node (ending point), the local interval formed between the frontal node and the parietal node is an energy transmission path segment. The frequency offset gradient within this interval is calculated by the difference in the resonant frequency offset of the parietal node relative to the frontal node, and the corresponding energy loss is the absolute value of the initial energy value of the frontal node minus the residual energy value of the parietal node.

[0108] In an embodiment of the present application, the frequency offset difference of each pair of adjacent nodes along the energy propagation path is extracted as the gradient value, and the input and output energy difference of the corresponding path segment is synchronously calculated as the loss amount. The linear relationship between the gradient and the loss is fitted by the least squares method, and the slope is the energy attenuation coefficient.

[0109] Step 205: Allocate a first weight coefficient to the propagation rate parameter and allocate a second weight coefficient to the energy attenuation coefficient.

[0110] In step 205, the first weight coefficient and the second weight coefficient are respectively used to adjust the contribution of the propagation rate parameter and the energy attenuation coefficient in the final evaluation, and the weight distribution is dynamically adjusted according to the node energy value.

[0111] In an embodiment of the present application, the initial energy distribution of all nodes in the network is statistically analyzed, and the parameters corresponding to the high-energy areas are given greater weights to ensure that the parameters in the key areas have a greater impact on the final result.

[0112] Step 206: numerically combining the propagation velocity parameter and the energy attenuation coefficient according to the first weight coefficient and the second weight coefficient to generate a composite coupling parameter, which is a coupling result.

[0113] In step 206 , the composite coupling parameter is a comprehensive index obtained by combining the propagation velocity parameter and the energy attenuation coefficient according to a weight, and is used to comprehensively characterize the dynamic response characteristics of the medium under the action of the biowave.

[0114] In the embodiment of the present application, the normalized propagation velocity parameter is multiplied by the first weight coefficient, and the energy attenuation coefficient is multiplied by the second weight coefficient, and the two are added together to generate the final composite coupling parameter.

[0115] Here's a specific example:

[0116] In a brain-computer interface electrode material evaluation experiment, a 64-channel EEG system was used to collect EEG signals from the temporal lobe of subjects. Micro-resonant sensors were integrated into the electrode array to monitor the frequency response characteristics of the electrode material. The center of the electrode was selected as the starting node of the energy propagation path, and the edge of the electrode was selected as the ending node. The target spacing of 12 mm was calculated by measuring the coordinates of the two nodes. Analysis of the resonant frequency signal propagation process revealed a 15 millisecond delay between the starting and ending nodes. Using the propagation rate calculation formula (rate = spacing / delay time), the propagation rate parameter was 0.8 mm / millisecond. Three groups of adjacent nodes were selected along the energy propagation path, and their frequency offset gradients were calculated to be 0.05 kHz / mm, 0.07 kHz / mm, and 0.06 kHz / mm, respectively. The energy loss of the corresponding path segments was simultaneously measured to be 0.12 dB, 0.15 dB, and 0.13 dB. A least-squares fit was used to obtain a slope of 1.8 dB / kHz as the energy attenuation coefficient. According to the node energy distribution characteristics, the propagation rate parameter is assigned a first weight coefficient of 0.6, and the energy attenuation coefficient is assigned a second weight coefficient of 0.4. The two parameters are normalized and weighted and summed to obtain a composite coupling parameter of 0.72.

[0117] In the embodiments of the present application, by systematically extracting key parameters of energy transfer and scientifically coupling them, a multi-dimensional quantitative evaluation of the interaction intensity between bio-waves and matter waves is achieved, which solves the problems of single parameters and insufficient characterization of local characteristics in traditional methods, and provides a more accurate decision-making basis for biomaterial optimization.

[0118] To further improve the calculation accuracy of the energy attenuation coefficient, in some embodiments, step 204: extracting the frequency offset gradient between adjacent nodes along the energy propagation path, performing a linear fit between the frequency offset gradient and the energy loss of the corresponding energy transfer path segment to generate the energy attenuation coefficient, includes:

[0119] Step 301: traverse each energy propagation path segment in the energy transfer network, and calculate the frequency offset gradient between the starting node and the ending node in each energy propagation path segment.

[0120] In an embodiment of the present application, the starting and ending nodes of all energy propagation path segments in the energy transfer network are first determined, and then the resonant frequency difference between the nodes at both ends of each path segment is calculated, and then divided by the node spacing to obtain the frequency change per unit distance, that is, the frequency offset gradient.

[0121] Step 302: Obtain the energy loss of the energy propagation path segment within a preset time window.

[0122] In step 302, the energy loss refers to the energy value lost during the transmission of the propagation path segment, which is obtained by comparing the energy intensity difference between the starting node and the ending node of the path segment.

[0123] In an embodiment of the present application, signal energy data within a fixed time window is selected, the energy peaks of the start and end nodes of the path segment are extracted respectively, and the difference between the two is calculated as the energy loss of the path segment.

[0124] Step 303: In a two-dimensional plane with the frequency offset gradient as the horizontal axis and the energy loss amount as the vertical axis, determine the corresponding two-dimensional coordinate point for each energy propagation path segment.

[0125] In step 303, the two-dimensional coordinate points refer to data points constructed with the frequency offset gradient as the abscissa and the energy loss as the ordinate, and are used to characterize the energy attenuation characteristics of each path segment.

[0126] In an embodiment of the present application, the calculated frequency offset gradient is matched one-to-one with the obtained energy loss amount, and the data point position corresponding to each path segment is marked in a two-dimensional coordinate system.

[0127] Step 304: perform least squares straight line fitting on all two-dimensional coordinate points, and calculate the energy absorption factor of the energy propagation path segment according to the slope value of the optimal fitting straight line.

[0128] In step 304, the slope of the best-fit line represents the rate at which energy loss changes with the frequency offset gradient. Its physical meaning is the degree of energy loss per unit frequency offset of the medium. A larger slope indicates a greater energy absorption capacity of the medium. The energy absorption factor, the slope parameter obtained through the linear fit, reflects the proportional relationship between frequency offset and energy loss.

[0129] In the embodiment of the present application, the least squares method is used to perform straight line fitting on all two-dimensional coordinate points, and the slope value of the optimal fitting straight line is calculated. The slope is the energy absorption factor.

[0130] Step 305: The arithmetic mean of the energy absorption factors of all energy propagation path segments is taken as the energy attenuation coefficient.

[0131] In an embodiment of the present application, the energy absorption factors of each path segment are summed and divided by the total number of path segments to obtain a global energy attenuation coefficient.

[0132] Here's a specific example:

[0133] In the performance test of brain-computer interface electrode materials, based on the obtained energy transfer network data, an in-depth analysis was conducted on the three main energy propagation paths from the center to the edge of the electrode. For the first path, the frequency offset difference between the starting and ending nodes was measured to be 0.6 kHz, with a node spacing of 12 mm. Using the formula frequency offset gradient = frequency offset difference / node spacing, a gradient value of 0.05 kHz / mm was calculated. The energy intensity of the starting node, 1.2 dB, and the energy intensity of the ending node, 1.08 dB, were simultaneously recorded within a 10-millisecond time window. The difference between the two yielded an energy loss of 0.12 dB. Using the same method, the second path obtained a frequency offset gradient of 0.07 kHz / mm, corresponding to an energy loss of 0.15 dB. For the third path, a frequency offset gradient of 0.06 kHz / mm was calculated, with an energy loss of 0.13 dB. The data points for the three paths, 0.05, 0.12, 0.07, 0.15, and 0.06, 0.13, were plotted in a two-dimensional coordinate system. A least-squares linear fit algorithm was used to find the optimal fit equation, y = 1.8x. The slope of 1.8 decibels per kilohertz was the energy absorption factor for each path segment. The arithmetic average of the absorption factors (1.8, 1.9, and 1.7) for the three paths was taken as 1.8, which was used as the overall energy attenuation coefficient for the electrode material.

[0134] In the embodiments of the present application, by systematically analyzing the attenuation characteristics of each energy propagation path segment and performing comprehensive calculations, accurate quantification of the medium energy attenuation behavior is achieved, providing a reliable parameter basis for evaluating the interaction between biowaves and matter waves.

[0135] To further improve the accuracy of energy absorption factor calculation, in some embodiments, step 304: performing least squares straight line fitting on all two-dimensional coordinate points and calculating the energy absorption factor of the energy propagation path segment based on the slope value of the best fitting straight line includes:

[0136] Step 401: Count the two-dimensional coordinate points corresponding to all energy propagation path segments to generate a two-dimensional coordinate point set.

[0137] In step 401, the two-dimensional coordinate point set refers to a group of data points consisting of frequency offset gradient and energy loss data of each energy propagation path segment, and each data point represents the energy attenuation characteristics of a path segment.

[0138] In an embodiment of the present application, the frequency offset gradients and corresponding energy loss data of all energy propagation path segments obtained by calculation are collected, and according to the rule that the horizontal axis is the frequency offset gradient and the vertical axis is the energy loss, the data of each path segment are converted into discrete points in a two-dimensional coordinate system to form a data set to be analyzed.

[0139] Step 402: Analyze the distribution trend of the two-dimensional coordinate point set in the two-dimensional plane, and determine the optimal fitting straight line passing through the two-dimensional coordinate points by minimizing the sum of squared deviations.

[0140] In step 402 , the best fitting straight line refers to a straight line that can best characterize the overall distribution trend of the two-dimensional coordinate point set, and the criterion for determining the best fitting straight line is to minimize the sum of the squares of the vertical distances from all data points to the straight line.

[0141] In an embodiment of the present application, the least squares method is used to analyze the distribution law of a two-dimensional coordinate point set, and the straight line parameters that minimize the sum of squared deviations are found through iterative calculation. This straight line is the optimal fitting straight line reflecting the energy attenuation characteristics.

[0142] Step 403: Calculate the extension direction parameters of the optimal fitting straight line in the two-dimensional plane, and convert the extension direction parameters into slope values.

[0143] In step 403, the extension direction parameter refers to a quantitative index of the inclination degree of the best fitting straight line in the two-dimensional plane, and the slope value of the straight line can be derived from the parameter.

[0144] In an embodiment of the present application, based on the position of the best fitting straight line in the two-dimensional coordinate system, the tangent value of the angle between the straight line and the horizontal axis is calculated, and it is converted into a standard slope form to obtain a slope value reflecting the rate of change of energy loss with frequency.

[0145] Step 404: normalize the slope value with a preset medium energy absorption reference value to generate an energy absorption factor.

[0146] In an embodiment of the present application, the calculated slope value is divided by the baseline slope value of the preset standard medium under the same conditions to obtain a dimensionless energy absorption factor, which is convenient for horizontal comparison between different materials. The specific implementation process is: the measured optimal fit straight line slope value is divided by the baseline slope value of the standard medium (such as pure silica gel) under the same experimental conditions. The resulting ratio is the normalized energy absorption factor. When the ratio is greater than 1, it indicates that the energy absorption characteristics of the measured medium are stronger than those of the standard medium. When it is less than 1, it is weaker than the standard medium. For example, when evaluating the energy absorption characteristics of a new medical gel medium under the action of an EEG signal, the measured optimal fit straight line slope is 0.25, and the baseline slope measured using the same specification pure silica gel medium under the same EEG signal excitation is 0.20. The normalization process is 0.25 / 0.20=1.25, and the final energy absorption factor generated is 1.25.

[0147] Here's a specific example:

[0148] In the evaluation experiment for medical brain-computer interface electrode materials, based on data from three energy propagation paths, a two-dimensional coordinate point set was constructed using the data from path one (0.05 kHz / mm frequency offset gradient and 0.12 dB energy loss), path two (0.07 kHz / mm frequency offset and 0.15 dB energy loss), and path three (0.06 kHz / mm frequency offset and 0.13 dB energy loss). This coordinate point set was analyzed using the least squares method, and the equation of the best-fit straight line was determined to be y = 1.8x by solving for the minimum sum of squared deviations. The slope of 1.8 dB / kHz represents the rate of change in energy loss per unit frequency offset. Using the baseline slope of 1.5 dB / kHz for a standard silicone medium under the same test conditions, a normalized energy absorption factor of 1.2 was calculated using the formula: Energy Absorption Factor = Measured Slope / Baseline Slope.

[0149] In the embodiments of the present application, objective quantification of energy absorption characteristics is achieved through systematic data fitting and normalization processing, providing a reliable evaluation basis for the study of the interaction between biowaves and matter waves.

[0150] To further improve the accuracy of extracting the coupling features of biowaves and matter waves, in some embodiments, step 103: graphically encoding the EEG signals and the matter wave dataset according to the acquisition location and time sequence to generate a set of physical coupling features includes:

[0151] Step 501: Divide the EEG signal into multiple time segments, and assign a corresponding spatial coding mark to each time segment, where the spatial coding mark reflects the collection position of the EEG signal at the contact interface of the biological body.

[0152] In step 501, a time segment is a discrete time segment that divides a continuous EEG signal into fixed time segments. A spatial coding mark is a unique code used to identify the signal acquisition location, reflecting the spatial distribution of electrodes at the contact interface of the biological body.

[0153] In an embodiment of the present application, the collected EEG signal is first segmented into equal time segments, each segment containing a fixed number of sampling points. Then, according to the physical arrangement of the multi-electrode array, a corresponding spatial position code is assigned to each time segment to establish a correspondence between time and space.

[0154] Step 502: Calculate a plurality of resonant frequency offsets corresponding to the time segment in the matter-wave data set.

[0155] In step 502 , the resonant frequency offset refers to the difference between the resonant frequency measured in each time segment in the matter-wave data set and the reference frequency, reflecting the dynamic response of the medium affected by the bio-wave.

[0156] In an embodiment of the present application, a matter wave data set is analyzed in the same time window, the frequency offsets of all sampling points in each time segment are calculated, and frequency offset data that can characterize the vibration characteristics of the medium in this time period are extracted.

[0157] Step 503: Bind the maximum resonant frequency offset corresponding to each time segment to the corresponding spatial coding tag.

[0158] In step 503, the maximum resonant frequency offset refers to the peak value of the frequency offset within a single time segment, and is used to represent the maximum extent to which the medium is affected by the biowave in this time segment.

[0159] In the embodiment of the present application, all frequency offsets within each time segment are compared, and the offset with the largest absolute value is selected as the representative value of the time segment, and an association relationship is established with the corresponding spatial coding mark.

[0160] Step 504: Traverse all bound time segments, perform waveform superposition on the EEG signal amplitude change curve and the matter wave frequency offset change curve under the same spatial coding mark, and generate a fusion feature unit.

[0161] In step 504, the EEG signal amplitude variation curve is constructed by extracting the amplitude of the EEG signal sampling points within each time segment and connecting the amplitude data points at each sampling point in chronological order to form a continuous curve reflecting the temporal fluctuation of the EEG signal intensity at the location corresponding to the spatial coding marker. The matter wave frequency offset variation curve is constructed by calculating the frequency offset of the resonant frequency signal within each time segment. The offset data points are obtained by subtracting the measured frequency value at each time point from the initial resonant frequency of the medium. These offset data points are then connected in chronological order to form a continuous curve reflecting the temporal variation of the matter wave frequency offset at the location corresponding to the spatial coding marker. The fusion feature unit is a feature expression that integrates the EEG signal amplitude variation and the matter wave frequency offset variation at the same spatial location, reflecting the interaction characteristics between biowaves and matter waves at that location.

[0162] In an embodiment of the present application, for a time segment with the same spatial coding mark, the curve of the EEG signal amplitude changing with time and the curve of the matter wave frequency offset changing are superimposed point by point to generate a new waveform feature data unit.

[0163] Step 505: Arrange the fusion feature units in a spatial topological manner according to the distribution order of the spatial coding marks to form a physical coupling feature set.

[0164] In step 505, the distribution order of the spatial coding markers refers to the topological order determined based on the actual physical arrangement position of the multi-electrode array at the contact interface of the biological body. This order is derived from the manufacturing specifications and installation layout design of the electrode array, and is usually determined by a systematic arrangement from the central area to the peripheral area or by anatomical position (such as forehead, temporal lobe, occipital lobe, etc.) according to the electrode number.

[0165] In the embodiment of the present application, according to the actual spatial distribution order of the electrode array, the fusion feature units corresponding to each spatial position are arranged and combined according to the positional adjacent relationship to form a feature set with spatial structural characteristics.

[0166] Here's a specific example:

[0167] In an experiment evaluating the biocompatibility of novel brain-computer interface electrode materials, a 32-channel EEG acquisition system was used to synchronously record EEG signals from the parietal region of subjects and resonant frequency data at the electrode-scalp interface. The experiment divided 10 seconds of EEG signals into 100 time segments, each 100 milliseconds long. Position codes were assigned to each time segment based on the spatial distribution of the electrode array, such as P1 to P8 representing eight electrode sites in the parietal region. The resonant frequency data were analyzed within the same time segment, and the maximum frequency offset for each segment was calculated. For example, the maximum offset measured at P1 within the 0-100 millisecond time window was 0.3 kHz. This value was obtained by comparing the difference between the real-time frequency of all sampling points within this time period and a reference frequency of 1.5 kHz. The EEG amplitude curves for all time segments at P1 were then overlaid with the corresponding frequency offset curves. The EEG amplitude curves consisted of the voltage values ​​at each time point, while the frequency offset curves consisted of the frequency offsets at each time point. This overlay generated a fused feature unit for the P1 electrode site. Following the spatial arrangement of the electrode array from central P1 to peripheral P8, the fused feature units of the eight electrode points were sequentially arranged to form a complete set of physical coupling features. This set served as input data for the subsequent construction of the energy transfer network. The maximum frequency offset of 0.4 kHz recorded at P3 within the 300-400 millisecond time window, when superimposed with the EEG amplitude fluctuation curve, revealed clear bio-physical coupling characteristics.

[0168] In the embodiment of the present application, through signal decomposition and feature fusion in the time and space dimensions, the refined extraction of the interaction characteristics of biowaves and matter waves is achieved, providing high-quality input data for the subsequent construction of the energy transfer network.

[0169] To further improve the accuracy of energy transfer network construction, in some embodiments, step 104: constructing the energy transfer network for the contact medium based on the graph structure analysis model and the physical coupling feature set, combined with the frequency offset gradient and energy propagation path between adjacent nodes, includes:

[0170] Step 601: Determine the conduction direction between adjacent nodes according to the spatial topological arrangement order of the physical coupling feature set, and set the conduction direction as the positive direction of the energy propagation path.

[0171] In step 601, the spatial topological arrangement order of the physical coupling feature set refers to the order determined by the actual physical arrangement of the multi-electrode array at the biological contact interface. This order is derived from the numbering rules preset during electrode manufacturing and the spatial distribution scheme during installation. It is typically determined based on the anatomical position of the electrodes on the scalp surface, systematically arranged from front to back, left to right, or from center to periphery, to ensure consistency with the actual contact structure of the biological body. The conduction direction refers to the main propagation trend of the bioelectric signal in the medium and is determined by analyzing the spatial attenuation pattern of the EEG signal amplitude in the physical coupling feature set.

[0172] In an embodiment of the present application, the amplitude distribution characteristics of the EEG signals in each spatial position fusion feature unit are first analyzed, the attenuation trend of the signal amplitude from high to low is identified, the trend direction is determined as the positive direction of energy propagation, and the conduction relationship between nodes is established.

[0173] Step 602: Using the frequency offset gradient as a connection weight, adjacent nodes along the positive direction are connected into directed edges.

[0174] In step 602, a directed edge refers to a directional connection line connecting adjacent nodes, and its weight is determined by the frequency offset gradient, reflecting the intensity of energy transfer.

[0175] In an embodiment of the present application, the frequency offset difference between each pair of adjacent nodes is calculated as a gradient value, and the gradient value is used as a connection weight to establish a directed connection edge from the high-amplitude node to the low-amplitude node according to the determined conduction direction.

[0176] Step 603: Generate an energy transfer network based on the directed edges.

[0177] In an embodiment of the present application, all nodes and their weighted directed connection edges are integrated to construct a network model that reflects the energy transfer path under the coupling of bio-waves and matter waves.

[0178] Here's a specific example:

[0179] In a brain-computer interface (BCI) electrode material performance evaluation experiment, an energy transfer network was constructed based on data from six electrodes in the frontal lobe region, F1 to F6, selected based on a set of physical coupling characteristics. First, based on the spatial arrangement of the electrode array, the node order was determined to be F1 to F6. Analysis of the EEG signal amplitude characteristics at each node revealed a decreasing amplitude trend from F1 to F4, confirming that the positive direction of energy conduction was from F1 to F4. The frequency offset gradients between adjacent nodes were calculated. The frequency offset difference between F1 and F2 was 0.4 kHz, and the node spacing was 8 mm. Using the formula gradient = frequency offset difference / node spacing, the gradient between F1 and F2 was 0.05 kHz / mm. Similarly, the gradient between F2 and F3 was 0.06 kHz / mm, and the gradient between F3 and F4 was 0.04 kHz / mm. Using these gradient values ​​as connection weights, directed edges were established along the F1-F4 direction, while retaining the reverse edges between F4 and F5 and F5 and F6. This resulted in an energy transfer network consisting of six nodes and five directed edges. The network clearly shows that energy is mainly transferred along the path from F1 to F2 to F3 to F4, among which the edge weight connecting F2 and F3 is the largest, indicating that the energy transfer efficiency of this path segment is the highest.

[0180] In the embodiment of the present application, through the precise determination of the conduction direction and the rational construction of weighted directed edges, a network model is formed that can truly reflect the energy transfer law under the coupling of bio-waves and matter waves, laying a solid foundation for subsequent parameter extraction and quantitative evaluation.

[0181] To further improve the accuracy of the quantitative evaluation results, in some embodiments, step 106: generating a quantitative evaluation result of the superposition effect of matter waves and bio-waves by matching the coupling results with a preset medium physical property parameter database includes:

[0182] Step 701: Decompose the coupling result into a rate eigenvector and an attenuation eigenvector.

[0183] In step 701, the rate characteristic vector and attenuation characteristic vector refer to the characteristic expressions after decomposing the composite coupling parameter according to the two dimensions of propagation characteristics and attenuation characteristics, which are used to respectively characterize the dynamic response behavior of the medium under the action of biowaves. The relationship between the propagation rate parameter and the rate characteristic vector: the propagation rate parameter is the original physical quantity calculated, and the rate characteristic vector is the standardized characteristic expression formed by normalizing and dimensionalizing the parameter. The two are numerically linearly proportional. The relationship between the energy attenuation coefficient and the attenuation characteristic vector: the energy attenuation coefficient is a physical parameter obtained by linear fitting, and the attenuation characteristic vector is a dimensionless characteristic value obtained by performing logarithmic transformation and dimension normalization on the coefficient. The two are converted through a nonlinear mapping relationship.

[0184] In an embodiment of the present application, the composite coupling parameters are firstly subjected to characteristic decomposition, and the component reflecting the energy transfer speed is extracted as the rate characteristic vector, and the component reflecting the energy loss characteristics is extracted as the attenuation characteristic vector, thereby forming two independent evaluation dimensions.

[0185] Step 702: traverse and compare the rate characteristic vector and the attenuation characteristic vector with the rate parameter interval set and the attenuation coefficient interval set stored in the preset medium physical characteristic parameter database.

[0186] In step 702, the rate parameter interval set and the attenuation coefficient interval set refer to parameter value ranges of different types of media under standard test conditions pre-stored in a database, and are used to provide a comparison benchmark.

[0187] In an embodiment of the present application, the decomposed rate feature vector is compared one by one with the standard rate range of each medium type in the database, and the attenuation feature vector is compared with the standard attenuation coefficient range to screen out candidate intervals that meet the conditions.

[0188] Step 703: When the rate characteristic vector falls into the first parameter interval of the rate parameter interval set and the attenuation characteristic vector falls into the second parameter interval of the attenuation coefficient interval set, activating the corresponding medium physical characteristic tag in the preset medium physical characteristic parameter database.

[0189] In step 703, the first parameter interval refers to the numerical range for the propagation velocity parameter in a preset medium physical property parameter database. This range is pre-set based on standard experimental data on propagation characteristics of different types of media. The second parameter interval refers to the numerical range for the energy attenuation coefficient in the same database. This range is pre-set based on standard test results of energy absorption by the medium. The medium physical property label is a material property descriptor associated with a specific parameter interval in the database, used to characterize the specific physical response characteristics of the medium.

[0190] In an embodiment of the present application, when the rate characteristic vector and the attenuation characteristic vector simultaneously meet the parameter range requirements of a certain type of medium, the corresponding characteristic label in the database, such as "high conductivity" or "low loss", is automatically activated.

[0191] Step 704: Calculate similarity values ​​between the rate feature vector and the attenuation feature vector and the vector corresponding to the medium physical characteristic label, perform weighted superposition on all the similarity values, and generate a quantitative evaluation result.

[0192] In step 704, the similarity value refers to a quantitative indicator of the degree of matching between the measured feature vector and the standard vector in the database, reflecting the degree of similarity between the measured medium and the standard medium.

[0193] In the embodiment of the present application, the cosine similarities of the rate feature vector and the attenuation feature vector with the standard vector corresponding to the activation label are calculated respectively, and weighted summation is performed according to preset weights to finally generate a comprehensive quantitative evaluation result.

[0194] Here's a specific example:

[0195] In the medical brain-computer interface electrode material evaluation experiment, based on the composite coupling parameters of 1.5 mm / ms and 0.9 obtained in Example 1, the parameters were first decomposed into two independent components: a rate eigenvector (1.5 mm / ms) and an attenuation eigenvector (0.9). The rate eigenvector was compared with a set of rate parameter intervals in a preset database, revealing that 1.5 mm / ms fell within the first parameter interval of 1.2 to 1.8 mm / ms. Simultaneously, the attenuation eigenvector (0.9) was compared with a set of attenuation coefficient intervals, confirming that it fell within the second parameter interval of 0.8 to 1.1. Because both eigenvectors met the parameter interval requirements for medical-grade silicone electrode materials, the system automatically activated the corresponding "Medical-Grade Biocompatibility" label in the database. The similarity between the rate eigenvector (1.5 mm / ms) and the labeled standard value (1.6 mm / ms) was then calculated, at 0.94, using the formula similarity = 1 - |measured value - standard value| / standard value. The attenuation eigenvector (0.9) was also calculated, at 0.94, to the standard value (0.85). The two similarity values ​​were weighted and superimposed at a ratio of 6:4, with a weight of 0.6 for rate similarity and 0.4 for decay similarity, resulting in a final overall score of 0.94. According to the scoring criteria, a score above 0.9 corresponds to "excellent," and the system outputs an excellent biocompatibility assessment result for this electrode material.

[0196] In the embodiments of the present application, a scientific quantitative evaluation of the superposition effect of bio-waves and matter-waves is achieved through the precise matching of multi-dimensional feature decomposition and database, providing an objective and reliable decision-making basis for material performance optimization.

[0197] Figure 2 This is a schematic diagram of the structure of a quantitative evaluation system based on the superposition effect of matter waves and bio-waves provided in an embodiment of the present application, such as Figure 2 As shown, the system includes:

[0198] The acquisition module 21 is used to acquire the EEG signals generated by the contact interface of the biological body and the resonant frequency signals in the contact medium.

[0199] The processing module 22 is configured to perform dynamic resonance compensation processing on the resonance frequency signal to generate a matter wave data set.

[0200] The encoding module 23 is used to perform graphical encoding on the EEG signal and the matter wave data set according to the acquisition position and time sequence to generate a physical coupling feature set, which is used to represent the superposition effect of matter waves and biowaves.

[0201] The construction module 24 is used to infer the node relationship in the physical coupling feature set based on the graph structure analysis model, and to construct an energy transfer network of the contact medium by combining the frequency offset gradient and energy propagation path between adjacent nodes.

[0202] The extraction module 25 is configured to extract a propagation rate parameter and an energy attenuation coefficient from the energy transfer network, and couple the propagation rate parameter with the energy attenuation coefficient.

[0203] The generation module 26 is used to generate a quantitative evaluation result of the superposition effect of matter wave and bio-wave by matching the coupling result with a preset medium physical characteristic parameter database.

[0204] Figure 2 The quantitative evaluation system based on the superposition effect of matter wave and bio-wave can be performed Figure 1 The implementation principles and technical effects of the quantitative assessment method based on the superposition effect of matter waves and bio-waves described in the illustrated embodiment will not be elaborated on here. The specific manner in which the various modules and units in the quantitative assessment system based on the superposition effect of matter waves and bio-waves in the above embodiment perform their operations has been described in detail in the relevant embodiments of the method and will not be elaborated on here.

[0205] In one possible design, Figure 2 The quantitative evaluation system based on the superposition effect of matter wave and bio-wave in the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0206] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0207] The processing component 32 is used to perform the above Figure 1 The embodiment provides a quantitative evaluation method based on the superposition effect of matter waves and bio-waves.

[0208] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0209] The storage component 31 is configured to store various types of data to support operations in the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random-access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0210] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0211] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0212] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0213] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0214] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is a quantitative evaluation method based on the superposition effect of matter waves and bio-waves.

[0215] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0216] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0217] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0218] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A quantitative evaluation method based on the superposition effect of matter waves and bio-waves, characterized in that: include: Collect the EEG signals generated by the contact interface of the organism and the resonant frequency signals in the contact medium; performing dynamic resonance compensation processing on the resonant frequency signal to generate a matter wave data set; Spectral encoding of the EEG signal and the matter wave data set according to the acquisition position and time sequence to generate a physical coupling feature set, wherein the physical coupling feature set is used to represent the superposition effect of matter waves and biowaves; Based on the graph structure analysis model and the physical coupling feature set, combined with the frequency offset gradient and energy propagation path between adjacent nodes, an energy transfer network of the contact medium is constructed; extracting a propagation rate parameter and an energy attenuation coefficient from the energy transfer network, and coupling the propagation rate parameter with the energy attenuation coefficient; By matching the coupling results with the preset medium physical property parameter database, a quantitative evaluation result of the superposition effect of matter wave and bio-wave is generated.

2. The method according to claim 1, characterized in that The extracting a propagation rate parameter and an energy attenuation coefficient from the energy transfer network and coupling the propagation rate parameter with the energy attenuation coefficient comprises: Extracting the starting node and the ending node of the energy propagation path in the energy transfer network; Calculating a path delay time of the resonant frequency signal propagating from a starting node to an ending node; generating a propagation rate parameter according to a ratio of a target distance to the path delay time, wherein the target distance is a distance between the starting node and the ending node; Extracting frequency offset gradients between adjacent nodes along the energy propagation path, performing linear fitting on the frequency offset gradients and the energy loss of the corresponding energy propagation path segment to generate an energy attenuation coefficient, where the energy propagation path segment is a local interval of the energy propagation path; assigning a first weight coefficient to the propagation rate parameter and a second weight coefficient to the energy attenuation coefficient; The propagation velocity parameter and the energy attenuation coefficient are numerically combined according to the first weight coefficient and the second weight coefficient to generate a composite coupling parameter, which is a coupling result.

3. The method according to claim 2, characterized in that Extracting the frequency offset gradient between adjacent nodes along the energy propagation path, performing linear fitting on the frequency offset gradient and the energy loss of the corresponding energy transmission path segment to generate the energy attenuation coefficient includes: Traversing each energy propagation path segment in the energy transfer network, and calculating a frequency offset gradient between a starting node and an ending node in each energy propagation path segment; Obtaining energy loss of the energy propagation path segment within a preset time window; In a two-dimensional plane with the frequency offset gradient as the horizontal axis and the energy loss as the vertical axis, a corresponding two-dimensional coordinate point is determined for each energy propagation path segment; Performing least squares straight line fitting on all two-dimensional coordinate points, and calculating the energy absorption factor of the energy propagation path segment according to the slope value of the best fitting straight line; The arithmetic mean of the energy absorption factors of all energy propagation path segments is taken as the energy attenuation coefficient.

4. The method according to claim 3, characterized in that The least squares straight line fitting is performed on all two-dimensional coordinate points, and the energy absorption factor of the energy propagation path segment is calculated according to the slope value of the optimal fitting straight line, including: Count the two-dimensional coordinate points corresponding to all energy propagation path segments to generate a two-dimensional coordinate point set; Analyzing the distribution trend of the two-dimensional coordinate point set in the two-dimensional plane, and determining the best fitting straight line passing through the two-dimensional coordinate points by minimizing the sum of squared deviations; Calculating an extension direction parameter of the optimal fitting straight line in a two-dimensional plane, and converting the extension direction parameter into a slope value; The slope value is normalized with a preset medium energy absorption reference value to generate an energy absorption factor.

5. The method according to claim 1, wherein The step of graphically encoding the EEG signal and the matter wave data set according to the acquisition position and time sequence to generate a set of physical coupling features includes: Dividing the EEG signal into a plurality of time segments, and assigning a corresponding spatial coding mark to each time segment, wherein the spatial coding mark reflects the collection position of the EEG signal at the contact interface of the biological body; calculating a plurality of resonant frequency offsets corresponding to the time segment in the matter-wave data set; Binding the maximum resonant frequency offset corresponding to each time segment to the corresponding spatial coding mark; Traverse all bound time segments, superimpose the EEG signal amplitude change curve and the matter wave frequency offset change curve under the same spatial coding mark to generate a fusion feature unit; According to the distribution order of the spatial coding marks, the fusion feature units are spatially topologically arranged to form a physical coupling feature set.

6. The method according to claim 1, characterized in that The graph structure analysis model and the physical coupling feature set are combined with the frequency offset gradient and energy propagation path between adjacent nodes to construct an energy transfer network for the contact medium, including: Determining a conduction direction between adjacent nodes according to a spatial topological arrangement order of the physical coupling feature set, and setting the conduction direction as a positive direction of an energy propagation path; Using the frequency offset gradient as a connection weight, connecting adjacent nodes along the positive direction into directed edges; An energy transfer network is generated based on the directed edges.

7. The method according to claim 1, characterized in that The matching relationship between the coupling result and the preset medium physical characteristic parameter database is used to generate a quantitative evaluation result of the superposition effect of matter wave and bio-wave, including: Decompose the coupling results into rate eigenvectors and decay eigenvectors; Comparing the rate characteristic vector and the attenuation characteristic vector with the rate parameter interval set and the attenuation coefficient interval set stored in a preset medium physical characteristic parameter database, respectively; When the rate characteristic vector falls within the first parameter interval of the rate parameter interval set and the attenuation characteristic vector falls within the second parameter interval of the attenuation coefficient interval set, activating the corresponding medium physical characteristic tag in the preset medium physical characteristic parameter database; The similarity values ​​between the rate feature vector and the attenuation feature vector and the vector corresponding to the medium physical characteristic label are calculated respectively, and all the similarity values ​​are weightedly superimposed to generate a quantitative evaluation result.

8. A quantitative evaluation system based on the superposition effect of matter waves and bio-waves, characterized in that: include: An acquisition module is used to collect the EEG signals generated by the contact interface of the organism and the resonant frequency signals in the contact medium; a processing module, configured to perform dynamic resonance compensation processing on the resonant frequency signal to generate a matter wave data set; an encoding module for graphically encoding the EEG signal and the matter wave data set according to the acquisition position and time sequence to generate a physical coupling feature set, wherein the physical coupling feature set is used to represent the superposition effect of matter waves and biowaves; A construction module is used to infer the node relationship in the physical coupling feature set based on a graph structure analysis model, and to construct an energy transfer network of the contact medium by combining the frequency offset gradient and energy propagation path between adjacent nodes; an extraction module, configured to extract a propagation rate parameter and an energy attenuation coefficient from the energy transfer network, and couple the propagation rate parameter with the energy attenuation coefficient; The generation module is used to generate a quantitative evaluation result of the superposition effect of matter wave and bio-wave by matching the coupling result with the preset medium physical property parameter database.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a quantitative evaluation method based on the superposition effect of matter waves and bio-waves as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, a quantitative evaluation method based on the superposition effect of matter wave and bio-wave as described in any one of claims 1 to 7 is implemented.

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