97 results about "Response vector" patented technology

The invention relates to the technical field of bridge bearing capacity evaluation, and discloses an existing continuous beam bridge bearing capacity comprehensive evaluation method which comprises the steps that multi-source data are collected to construct a three-dimensional damage voxel field, and an initial finite element model is generated; simulating a heavy load identification control section, and designing an equivalent static load test scheme according to an equivalent internal force principle; executing a field static load test, collecting elastic response data and constructing an actual measurement response vector; calculating errors and iteratively adjusting parameters in a voxel field constraint range to finish model correction; and generating an operation space-time security envelope based on the correction model, and comparing the state in real time to output a control instruction. A three-dimensional damage voxel field is constructed, voxels are used as a normalized data carrier, discrete apparent and internal damage data such as three-dimensional laser scanning point cloud, ultrasonic velocity and rebound strength are mapped into a continuously distributed space field, and the space variability of the rigidity of an existing bridge material can be represented in a refined mode. And the accuracy of describing the physical current situation of the structure by the initial finite element model is improved.

The invention discloses a network traffic anomaly detection and identification method based on artificial intelligence, and the method comprises the following steps: obtaining original network traffic data, carrying out the preprocessing, and carrying out the fusion construction of a multi-layer traffic language field tensor; behavior semantic modeling and path structure coding are carried out; constructing a behavior event sequence tensor, inputting the behavior event sequence tensor into the improved RetNet model, and outputting a behavior drift response vector; performing deviation analysis on the behavior drift response vector and the target behavior intention trajectory tensor, and constructing an abnormal evolution graph of abnormal growth and propagation; performing type classification and causal analysis according to the abnormal evolution graph, and outputting an abnormal type label and a causal path set; constructing a minimum disturbance sample, and performing comparative reasoning to generate an abnormal credibility change interval and an anti-robust score; and updating the abnormal knowledge graph and executing incremental updating. According to the invention, high-precision, interpretable and robust anomaly detection and classification analysis in a complex network environment can be realized.

The invention discloses an underwater sound environment sensing method based on multi-array-element sparse channel estimation. An underwater sound receiving end receives signals transmitted through an underwater sound multipath channel through a multi-array-element array; performing Hilbert transform on the receiving signal of each array element to obtain an analysis signal, and calculating a cross-correlation function of the analysis signal and the transmitting signal; based on the cross-correlation function, sparse channel parameters, including path amplitude and time delay, of each array element are estimated by adopting an orthogonal matching pursuit algorithm combined with a constant false alarm detection dynamic threshold value; the method comprises the following steps: constructing an array response vector by using sparse channel parameters of a multi-array element array, and searching and estimating angles of arrival, including a direction angle and a pitch angle, of a multipath signal through a spatial spectrum peak value; and based on the arrival angles, amplitudes and time delays of the direct path and the reflection path, inverting an underwater environment structure through a ray acoustic theory, including a reflection point distance and a reflection surface normal vector, so as to realize three-dimensional perception of the underwater reflector. According to the invention, multipath resolution can be improved, and false alarm and missing detection can be effectively reduced.

A method for calculating domain relevance scores for responses generated by LLMs is disclosed. The method includes receiving a response generated by LLM corresponding to user query. The user query is associated with a domain. The method further includes splitting the response into a plurality of response chunks using a splitting technique. The method further includes generating a plurality of response vector embeddings based on the plurality of response chunks using at least one sentence transformer. The method further includes computing a plurality of cosine distances between the plurality of response vector embeddings and a corresponding plurality of training data vector embeddings, wherein the plurality of training data vector embeddings corresponds to domain-specific training data of the LLM. The method further includes calculating a domain relevance score corresponding to the response, based on a sum of the plurality of cosine distances and a number of the plurality of chunks.

The invention is suitable for the field of portal frames, and discloses a portal frame dynamics analysis method which comprises the following steps: establishing a full-order finite element model of a portal frame, and obtaining a mass matrix and a stiffness matrix of the full-order finite element model; dividing the full-order finite element model into a plurality of substructures based on the modular structure characteristics of the portal frame, and decomposing the degree of freedom of each substructure into an internal degree of freedom and a boundary degree of freedom; constructing a fixed boundary main modal matrix and a constraint modal matrix of each substructure, and constructing a reduced-order basis matrix; performing modal polycondensation on a mass matrix and a stiffness matrix of the full-order finite element model by using the reduced-order basis matrix to generate a reduced-order mass matrix and a reduced-order stiffness matrix, and constructing a reduced-order kinetic equation; and solving the reduced-order kinetic equation to obtain reduced-order modal coordinates, carrying out inversion reduction in combination with the reduced-order basis matrix and the reduced-order modal coordinates to obtain a displacement response vector of the full-order finite element model, and retaining key kinetic information of the portal frame to the greatest extent through reduced-order efficiency improvement.

The invention discloses a converter transformer modal prediction method, system, equipment and medium, which are applied to the field of vibration modal prediction, and comprises the following steps: establishing a theoretical frequency response relationship between an excitation vector and a response vector based on an oscillatory differential equation of a target converter transformer; training the neural network model through the excitation data and response data of the target converter transformer under a plurality of operation conditions, constructing a first converter transformer modal prediction model, and performing optimization to obtain a second converter transformer modal prediction model, and collecting real-time operation parameters of the target converter transformer and inputting the real-time operation parameters into the second converter transformer modal prediction model to obtain corresponding modal prediction data. According to the method, the modal prediction efficiency is remarkably improved on the basis of ensuring the physical rationality by constructing the hybrid framework combining the vibration analysis model and the deep learning, and the prediction precision and generalization ability of the model among transformers with different sizes are effectively enhanced by introducing geometric and structural mechanics similarity constraints.

The invention provides a circular curve burr detection method and system based on multi-scale phase consistency and a contour model, and a medium, and the method comprises the steps: constructing a multi-scale Log-Gabor filter group, and carrying out the convolution calculation of an input image, and obtaining a response vector; calculating phase consistency measurement of each pixel point, extracting an initial edge point set, fitting an initial circular contour, performing energy minimization evolution on the initial circular contour based on an active contour model, and fitting a real contour of burrs; constructing a two-dimensional feature vector; carrying out fusion discrimination on the two-dimensional feature vectors based on a linear weighted score function, and outputting a burr detection result; according to the method, phase consistency is used as a core image feature, so that the detection algorithm has natural invariance for uneven illumination, brightness change and contrast difference, and under the scene of failure caused by poor image quality in the prior art, the contour edge can still be stably and completely extracted, and the omission ratio is remarkably reduced.

The invention relates to the technical field of proportioning control, and discloses an intelligent proportioning control method for activated carbon and a deodorant in an air purification system, which comprises the following steps of: dividing the pore size distribution of the activated carbon into three types of micropores, mesopores and macropores, and obtaining pollutant response vectors of various typical pollutants and consumption weight vectors of the pollutants to the three types of pore sizes; constructing an agent mapping knowledge base; environmental pollutants are monitored in real time, a current pollutant response vector is obtained, and an activated carbon dynamic ability portrait is generated by matching a knowledge base; identifying a new pollutant event, acquiring a response vector and an aperture consumption weight of the new pollutant event, performing efficiency prediction calculation with the dynamic capability portrait, executing three-section decision logic according to a prediction result, and outputting dynamic ratio data of the activated carbon and the deodorant; accurate prediction of functional saturation of the activated carbon is realized through multi-dimensional capability characterization, purification efficiency reduction caused by'saturation illusion 'is effectively avoided, and the comprehensive purification efficiency of an air purification system is remarkably improved.

The invention belongs to the field of geophysical exploration, and particularly relates to a very-low-frequency three-component coil calibration method based on frequency sweep excitation, which comprises the following steps: acquiring an alternating-current excitation current and a time domain signal of a three-component coil, and performing frequency domain transformation on the time domain signal to obtain a plurality of frequency domain response vectors covering a frequency sweep range; constructing a linear equation set for describing a coil system error based on a plurality of frequency domain response vectors measured under each attitude and the calculated excitation magnetic field reference vector, and solving the linear equation set by using a least square method to obtain a calibration matrix for correcting measurement data; and the calibration matrix is adopted to correct the measurement data of the three-component coil in the use state, so that systematic errors of the very-low-frequency three-component coil can be accurately calibrated and calibrated, and the measurement precision and data reliability of a very-low-frequency electromagnetic method are effectively improved.

The invention provides a trapped person position state analysis method and system, and relates to the field of data processing. According to the method, building structure distribution, conductive fluid flow channels and metal component distribution in a disaster target area are constructed to form a background entropy flow ground state, and noise intensity probability distribution, a channel joint fluctuation relation and a time stability index are counted to generate a background entropy flow field. In the search and rescue stage, a background entropy flow field is converted into a controlled mode through active disturbance, multi-physical channel noise responses before and after disturbance are collected through a multi-field passive sensor network, and a multi-dimensional local entropy response vector is constructed. And combining the consistency, directivity and duration of the multi-noise channel to identify an initial suspected region and extract an entropy anomaly core region, finally inferring metabolic activity and posture limitation conditions according to a local entropy response cooperative relationship in different disturbance modes, and outputting a position state result of the trapped person. By implementing the technical scheme provided by the invention, the position state of the trapped person can be accurately identified.

The invention belongs to the technical field of power system control, and provides a source-grid-load interactive renewable energy supply flexibility control method and system, and the method comprises the steps: obtaining a power grid state deviation according to related physical information; generating and broadcasting synthetic pheromones according to the power grid state deviation to obtain a pheromone gradient field; secondly, extracting field features of a position pheromone gradient field where the distributed flexible resources are located; the field features comprise pheromone concentration and pheromone concentration gradient; according to the field feature and the state of the flexible resource, calculating a power regulation amount through a preset response vector graph; and finally, according to the power regulation quantity, controlling the flexible resource to carry out power output regulation. According to the method, the communication and calculation bottlenecks of centralized control can be avoided, and the limitation of global coordination deficiency of distributed control can be overcome, so that rapid, efficient and stable cooperative control of massive distributed flexible resources is realized.

This invention discloses a forest fire risk early warning system based on Internet of Things (IoT) communication, belonging to the field of data processing technology. The system comprises a data acquisition module that collects forest area data from edge devices; an environmental feature module that uses an adaptive forgetting factor recursive method to obtain environmental anomaly response vectors; a feature fusion module that extracts image feature tensors, maps these vectors to scaling matrices, combines them with a forced masking weighted feature tensor, and outputs predicted bounding box parameters; a feature inversion module that obtains a penalty matrix based on the parameters, constructs a partial differential equation for canopy drag force, and solves for the fire plume feature matrix and flow field uncertainty tensor matrix by minimizing the inverse source-finding objective function; and a communication optimization module that extracts elements as fire risk mapping weights, arranges them in descending order, and allocates time resources so that the terminal only sends fire plume elements to the decision center. This invention effectively isolates meteorological interference, improves the accuracy of locating small fire points under dense smoke, and solves the communication congestion problem during extreme fire events.

The invention discloses an unmanned aerial vehicle intelligent equipment detection and fault diagnosis method based on computer vision, and the method comprises the following steps: 1, obtaining multi-modal image data and synchronous sensor data of key parts of an unmanned aerial vehicle, and carrying out the preprocessing of the data; 2, constructing a component state topological graph at the current observation moment; 3, performing local disturbance enhancement operation to generate a multi-scale disturbance image set; 4, extracting physical response vectors to form a component physical response vector set; step 5, on the basis of a Gromov-Wasserstein distance, an evolution path of optimal matching is determined; step 6, fault inversion simulation is carried out, a push-back state image is generated, and a structure residual error between the push-back state image and the initial state image at the current observation moment is calculated; and step 7, generating a natural language diagnosis report based on the optimal matching evolution path. According to the invention, through image disturbance and virtual fault evolution, fault evolution simulation and accurate diagnosis of the unmanned aerial vehicle are realized.

The present application relates to the technical field of vehicle-mounted hydrogen system safety, and discloses a hydrogen leakage detection and positioning method and system for a fuel cell vehicle-mounted hydrogen system, which comprises establishing a preset candidate leakage source set, collecting concentration data of each hydrogen-sensitive sensor and wind field data of a wind speed and direction acquisition unit, forming a measured response vector at the same sampling time, calculating a predicted response vector of the candidate leakage source at each hydrogen-sensitive sensor, performing matching operation on the predicted response vector and the measured response vector, screening out a target candidate leakage source with a matching degree satisfying a preset condition, determining a target leakage position based on stability or convergence conditions of cumulative results, reading hydrogen-sensitive coating state information arranged at the target leakage position, and obtaining a final leakage position and corresponding alarm information. The present application can guide operation and maintenance personnel to quickly locate and handle the leakage point by accurately identifying the leakage source and generating alarm information, thereby reducing manual investigation time and operation and maintenance cost.

The invention relates to the technical field of image detection, and discloses a floor processing quality detection method and system based on machine vision. The method comprises the following steps: collecting multiple frames of images of the surface of a floor through multi-angle structured light illumination, constructing a pixel-level luminosity response vector, and decomposing the pixel-level luminosity response vector into a diffuse reflectance graph, a specular reflectance graph and a normal vector graph based on a physical illumination model; and respectively detecting material flaws, coating unevenness and geometric scratches and pits in each component graph by adopting a targeted algorithm, and finally fusing results to generate a high-precision detection report. The system comprises a high-speed camera and a dome-type multi-light-source module which are synchronously controlled, a data processing unit and a defect decision fusion module. According to the method, interference suppression is realized through physical attribute decoupling, and the accuracy and robustness of defect identification are remarkably improved.

This invention belongs to the field of artificial intelligence technology and relates to a method and system for optimizing visual language model prompts. The method includes: S1: feature acquisition; S2: response vector generation of candidate regions; S3: candidate region segmentation; S4: region reliability score calculation; S5: region feature construction and fusion; S6: training based on fused features; S7: classification decision calibration during the inference stage; S8: outputting category prediction results. It enables a paradigm shift from static whole-image prompt optimization to region-aware, adaptive guidance, and multi-granularity dynamic fusion optimization, thereby significantly improving the classification performance, robustness, and interpretability of visual language models in tasks such as fine-grained recognition, cross-domain transfer, and new category generalization.

The invention relates to a smart power grid deep learning model classification method and device based on model response fingerprints and computer equipment. The method comprises the steps of obtaining a model sample set according to smart grid data, performing disturbance processing on the model sample set to obtain a plurality of disturbance sample sets, inputting the disturbance sample sets to a to-be-verified model, obtaining disturbance response vectors corresponding to the disturbance sample sets, determining a target sample set from the disturbance sample sets according to all the disturbance response vectors, and verifying the target sample set according to the target sample set. And based on the disturbance response vector corresponding to the target sample set, constructing a fingerprint sample set of the to-be-verified model, and inputting the fingerprint sample set into a pre-trained classifier to obtain a classification result of the to-be-verified model. The method can accurately identify the attribution of the power grid model.

The application discloses a kind of question and answer recommendation method, system, equipment, storage medium and program product, the method includes: real-time acquisition first information, first information contains first inquiry information and multiple second inquiry information, the inquiry time corresponding to second inquiry information is earlier than the inquiry time corresponding to first inquiry information;First information is carried out semantic analysis, determine first demand feature, first demand feature is used to describe first information in from at least one of the following characterization dimensions: first characterization dimension, second characterization dimension and third characterization dimension;First demand feature is searched in first vector knowledge base based on, determine multiple response result data for first inquiry information;Wherein, the similarity between response result data and first demand feature is greater than similarity threshold, and first vector knowledge base is formed by the response vector data corresponding to the response data of multiple different modalities.The present scheme can realize automatic and fast accurate question and answer recommendation.

The application discloses a kind of based on multi-array sparse channel estimation underwater acoustic environment perception method, underwater acoustic receiving end is received through multi-array array signal that is propagated through underwater acoustic multipath channel;The received signal of each array element is subjected to Hilbert transform, and analytic signal is obtained, and the cross-correlation function of analytic signal and transmitting signal is calculated;Based on cross-correlation function, using the orthogonal matching pursuit algorithm of combining constant false alarm detection dynamic threshold, the sparse channel parameters of each array element are estimated, including path amplitude and time delay;Utilize the sparse channel parameters of multi-array array, construct array response vector, estimate the angle of arrival of multipath signal, including direction angle and pitch angle by space spectrum peak value search;Based on the angle of arrival, amplitude and time delay of direct path and reflected path, the underwater environment structure is inverted by ray acoustics theory, including reflection point distance and reflection surface normal vector, realize the three-dimensional perception of underwater reflector.The application can improve multipath resolution, effectively reduce false alarm and miss detection.

This invention discloses a camera-sensing adaptive dimming method and system for LED lighting, comprising: controlling an LED light source to output at least two different detection illumination states; acquiring environmental images of the same scene under each detection illumination state; preprocessing the environmental images to obtain a target area image; extracting a subset of reliable pixels from the target area image after interference suppression processing; constructing a multi-source response vector of the target area based on the reliable pixel subsets under each detection illumination state; selecting a locally optimal sample subset from a preset sample library based on the multi-source response vector; calculating reflection characteristic characterization parameters; correcting the illuminance and color temperature estimation results of the target area to obtain the target brightness, target color temperature, and dimming parameters; and adjusting the LED light source using the generated dimming parameters. This invention can improve the accuracy of target area light environment perception under complex reflection environments and enhance the accuracy and scene adaptability of LED brightness and color temperature adjustment.

The invention relates to the technical field of spectral measurement and data correction technologies, in particular to a reflectivity reconstruction method based on adaptive weighted multi-core support vector regression, which comprises the following steps: calling cross-aperture spectral measurement data, aligning and normalizing, comparing channel response vector direction differences, calculating neighborhood spacing and combining, and calculating the reflectivity of the cross-aperture spectral measurement data. According to the method, dynamic analysis of spectrum form indexes and local structure indexes is combined, kernel function weights are adjusted in a self-adaptive mode, spectrum form differences and local changes between different samples are accurately captured, and the reflectivity prediction curve sequence is obtained through dynamic analysis of the spectrum form indexes and the local structure indexes. According to the method, a dynamic weighting kernel function based on similarity between samples and neighborhood density is utilized to solve the problem that local complexity cannot be effectively incorporated into modeling, so that under different optical sampling conditions, the reconstruction result of spectral data is more stable and accurate and adapts to the change of complex spectral data, and the accuracy and consistency of reflectivity reconstruction are improved.

The invention discloses a power distribution network emergency resource scheduling strategy generation method and system, and relates to the technical field of power distribution network operation and management. Operation situation data of a power distribution network are acquired, a preset time attenuation function is adopted to carry out dynamic influence evaluation on the operation situation data to obtain a plurality of time response values, and a plurality of time response values are obtained based on a preset coupling function; and generating a corresponding response vector by adopting each time response value, performing risk assessment on the response vector according to each time response value to obtain a corresponding risk energy index, and performing emergency resource optimization processing on the risk energy index and a pre-acquired historical risk energy index according to a preset initial multi-objective optimization function to obtain an emergency resource optimization result. And obtaining an optimization emergency strategy corresponding to the power distribution network. The technical problems that a conventional emergency resource scheduling strategy generation method mainly formulates a fixed scheduling scheme based on historical fault cases, but resource redundancy or insufficiency is easily caused, and the emergency disposal efficiency of the power distribution network is reduced are solved.

This invention relates to a mapping-based MIMO vibration testing zero-response control method and system, belonging to the field of vibration testing technology. It includes: acquiring the frequency response matrix, response degrees of freedom, current response vector, and driving spectrum vector of the vibration testing system; constructing an unmapped error vector based on the difference between the current response vector and a preset target response vector; determining the active degrees of freedom and zero degrees of freedom based on the response degrees of freedom; determining a zero-mapping operator by combining the active and zero degrees of freedom; obtaining an effective error vector by combining the zero-mapping operator and the unmapped error vector; and iteratively updating the driving spectrum vector by combining the effective error vector and the frequency response matrix. This invention improves system stability, reduces sensitivity to noise, and minimizes unnecessary actuator output.

The embodiment of the invention discloses an end-to-end compressed sensing imaging method and device, a storage medium and a program product. The method comprises the steps that an image sample set is acquired, a compressed sensing imaging model is trained based on the image sample set, and trained network parameters are packaged into a global network model; receiving an image size specified by a user, and constructing a zero vector and a plurality of standard basis vectors based on the image size; inputting the zero vector into a global network model, and obtaining an offset vector output by the global network model; inputting the plurality of standard basis vectors into a global network model, and obtaining a plurality of basis response vectors output by the global network model; constructing a global matrix equivalent to the global network model based on the bias vector and the plurality of base response vectors; and deploying the global matrix to the target device. According to the method, a global matrix equivalent to a global network model is constructed and deployed to target equipment, so that the technology is promoted to be industrialized and commercialized, and wide application of the method in a real world scene is realized.

The present application relates to well logging technology, and discloses a multilayer formation inversion method of a while-drilling electromagnetic boundary detection instrument, which comprises two stages of offline training and online inversion. In the offline stage, first, a plurality of formation structure types are constructed and training samples are generated; then, the original response vector of the forward modeling of the samples is enhanced and reconstructed to improve the data dimension and sensitivity; then, for each formation type, a dedicated gradient optimization set is trained by a supervised descent method. In the online stage, the original response measured in real time is subjected to the same feature reconstruction, and its belonging formation type is intelligently judged, and the matched gradient optimization set is automatically called to perform rapid iterative inversion. In the inversion iteration, dynamic attenuation regularization and geological prior constraint are integrated to effectively suppress noise, guarantee stability and geological rationality. The present application realizes rapid, accurate and stable inversion of complex multilayer formation, and significantly improves the reliability of while-drilling geosteering.

This invention discloses a method and system for graded detection of aspirating smoke fires based on wireless networking, comprising the following steps: deploying nodes within the target monitoring area and extracting air samples from monitoring sub-areas; performing baseline smoke concentration learning, short-term disturbance removal, and continuous response segmentation on the original sampling sequence; extracting segment features, constructing local smoke response vectors for nodes, and determining local response states; transmitting data and performing time-series alignment on the uploaded data to form a node collaborative response matrix; determining the causal relationship, direction, and intensity of response transmission between monitoring sub-areas, and constructing a smoke propagation chain; performing regional aggregation and fire grade determination to generate graded detection results for the target abnormal area; and transmitting data to the nodes and outputting regionalized fire alarm information. This invention, based on wireless networking and Granger causality analysis, achieves multi-node collaborative graded detection, possessing advantages such as high positioning accuracy and strong anti-interference capability.

This invention relates to the field of medical device sensor calibration and error compensation technology, and particularly to a method for medical device sensor calibration error compensation based on electronic measuring instruments, comprising the following steps: S1, performing multi-measurement topology switching and acquiring bridge circuit signals under zero external load conditions; S2, constructing a topology response vector and identifying electrical parameters; S3, repeatedly identifying and determining compliance ratio coefficients under different constraint conditions; S4, establishing a calibration matrix; S5, online reconstruction compensation to obtain compensated measurement values; S6, reviewing residual projection and selectively updating parameters. This invention achieves differentiated compensation for bridge circuit electrical errors and mechanical errors caused by assembly preload and changes in constraint conditions.

A system and method for objectively determining interpersonal compatibility by correlating synchronized multimodal biometric responses to humorous stimuli. The system presents categorized media content to a user while simultaneously capturing asynchronous data streams, including facial video, audio vocalizations, and galvanic skin response (GSR). A synchronization engine aligns these streams, applying a latency correction algorithm to correlate delayed physiological arousal peaks (phasic SCR) with antecedent visual micro-expressions (Action Units 6 and 12). By validating visual smiles with physiological arousal, the system distinguishes genuine Duchenne responses from social masking. These validated responses are fused via a tensor network into a multidimensional “Humor Response Vector.” Compatibility is determined by calculating the cosine similarity between users' vectors in a latent space, providing a quantification of psychological alignment that transcends self-reported bias. Embodiments include privacy-preserving edge extraction and homomorphic encryption of profile vectors.

A system may receive four-dimensional ultrasound image data generated by an ultrasound device directed a heart. The system may generate, with a machine learning model, a plurality of response vectors respectively corresponding to anchor points along a boundary of the heart. The anchor points may measure an angle and distance with respect to an axis of the heart. The system may generate, based on the response vectors, a mesh comprising displayable locations of the anchor points at one or more times during a cardiac cycle. The system may display a graphical user interface including the image data and the anchor points rendered from the mesh.

The present application relates to the technical field of water environment treatment, solves the technical problems that the existing buffer zone design generally has the problems of width / structure determined by experience, lack of multi-scenario adaptation and quantifiable ecological target constraint, and particularly relates to a method for constructing a composite buffer zone at a lake inlet based on a hydrology-water ecology response model, a multi-scenario fingerprint library is constructed based on multi-source data of a target lake inlet area, a hydrology-water ecology response model is established for quantifying ecological indexes such as transparency, algal risk and submerged plant adaptability probability into an ecological response vector, a functional unit library is constructed and structure gene coding is used to describe the topology and parameters of units such as dissipation allocation, sedimentation dredging, reaction bed fixation, vegetation rhizosphere reaction and bottom protection and motion suppression; multi-objective inversion optimization and robustness evaluation are carried out under multi-scenarios, and a composite buffer zone spatial layout, size parameters and operation and maintenance trigger threshold values that meet flood discharge and stability constraints are output, so that parameterization and deliverable design of the lake inlet buffer zone are realized.