124 results about "Score vector" patented technology

The invention provides a geological data driving path optimization and settlement prediction method for pipe jacking construction, and relates to the technical field of artificial intelligence. The method comprises the following steps: firstly, acquiring sparse drilling point location geological data, constructing a geological data set including soil layer types, forming continuous geological feature tensors through multi-dimensional interpolation, and generating a path vector sequence by combining starting and ending points and building distribution to represent crossing tracks of different paths under geological conditions; and constructing a path graph topological structure through a graph coding neural network, performing feature propagation, obtaining a path comprehensive score vector, and screening an optimal path meeting structural integrity and settlement response constraints. Further identifying settlement trend sensitive points by utilizing the settlement risk prediction values and the accessibility scores of the nodes, constructing a settlement trend map and calculating settlement transaction coefficients. And the transaction coefficient is fed back to a path generation link and is used for iteratively optimizing path sampling density and node distribution so as to realize dynamic correction and stable convergence of a path scheme.

The invention relates to the technical field of personnel examination scoring, in particular to an end-to-end-based multi-agent personnel examination scoring method, which comprises the steps of generating fusion features based on image data of an examinee answer sheet, inputting the fusion features into a heterogeneous OCR engine for processing, and outputting an examinee answer text; establishing a knowledge base, analyzing a question requirement text, and screening out a reference answer text from the knowledge base; constructing a plurality of dimension scoring agents, determining an original state based on the question requirement text and the reference answer text, training each dimension scoring agent through the original state, inputting the examinee answer text into the dimension scoring agents, outputting dimension scoring scores, and forming dimension scoring vectors; carrying out self-consistency analysis by integrating the dimension score vector and the original state, and outputting a final score; the whole-process closed-loop processing from paper answer sheet scanning to high-precision, automatic and interpretable scoring is realized, and the marking efficiency, fairness and credibility are improved.

The invention provides an intelligent training system and method for a portable control console, and relates to the technical field of man-machine interaction and intelligent training, and the method comprises the steps of original trajectory data collection, operation feature extraction, capability score vector construction, user capability vector generation, capability trend analysis and personalized training path construction. The method comprises the following steps: acquiring attitude angular velocity, displacement acceleration and key data of a user in a training process through a portable console, extracting multi-dimensional operation characteristics, performing alignment and evaluation in combination with a scoring rule and a dynamic time warping method, and generating ability scores and user ability vectors; and further analyzing a capability development trend, identifying a capability short board, and matching a training task to generate a personalized path. The technical problems that an existing training system does not have portability, data acquisition dimensions in the training process are insufficient, capability evaluation is rough, and trend analysis and personalized training path recommendation are lacked are solved.

The invention discloses an intelligent education robot question answering system based on voice recognition and a knowledge graph, and particularly relates to the technical field of artificial intelligence. The method comprises the following steps: performing feature extraction on a user voice signal to obtain a text sequence and a multi-modal context feature; recognizing subject domain judgment and question answering intentions based on supervised classification and keyword rule fusion, and outputting a subject domain prior probability and a question answering target vector; determining polysemy words in the text sequence by using a context window, generating a semantic item sequence and semantic item confidence, constructing a subject domain sub-graph based on a subject domain prior probability, and obtaining a candidate reasoning path and a path scoring vector; determining a target teaching concept and an optimal reasoning path by combining Bayesian inference and consistency verification; generating a personalized question answering result aiming at the question asked by the user through fact retrieval and knowledge derivation in combination with the question answering target vector; accurate and efficient intelligent teaching question answering can be realized, and question answering accuracy and intelligent interaction capability of the education robot are effectively improved.

The invention relates to the technical field of airport operation management, in particular to a data-driven airport scene sliding path intelligent generation method, which comprises the following steps: acquiring a plurality of taxiway sequences to form a taxiway sequence set; establishing a neural network model and a target function, and training the neural network model to obtain a taxiway embedded vector generator; starting and ending point pair coding, environment feature coding and related flight coding are carried out based on the taxiway embedded vector generator, and heterogeneous fusion features are obtained; carrying out autoregression generation based on Transform to obtain a plurality of candidate taxiways and corresponding score vectors; the multiple candidate taxiways are expanded and screened according to the score vectors of all the candidate taxiways, multiple candidate paths are generated, and the high-quality airport scene sliding path can be generated.

The invention discloses a brain-controlled rehabilitation equipment-oriented control strategy generation method, device and equipment and a storage medium, and relates to the technical field of signal processing, and the method comprises the following steps: acquiring a multi-channel electroencephalogram signal, and preprocessing the multi-channel electroencephalogram signal to obtain a target electroencephalogram signal comprising a steady-state visual evoked potential signal and a motor imagery signal, the target electroencephalogram signal corresponds to a preset action category; decoding the steady-state visual evoked potential signal by adopting filter group task related component analysis to obtain a correlation score vector; decoding the motor imagery signal by adopting a Mangban dynamic routing space-time network model to obtain a classification score vector; performing posterior probability distribution conversion and weighted fusion on the correlation score vector and the classification score vector to obtain fusion probability distribution; and generating a target control strategy according to the action category corresponding to the highest probability value in the fusion probability distribution. The control strategy obtained by the invention can consider both intention recognition precision and rehabilitation nerve activation effect.

The invention discloses a mental disorder intelligent discrimination system based on a brain structure image similarity map, and belongs to the technical field of artificial intelligence medical image analysis. The system firstly collects brain structure magnetic resonance image data of a multi-center mental disorder patient and a healthy control and carries out standardization preprocessing; then constructing a brain structure standardized distribution model of the cross-age gender, and extracting individualized deviation degree features; establishing a typical brain structure characteristic spectrum database of various mental disorders through a neural network; after to-be-diagnosed individual features are vectorized, multi-dimensional parallel comparison calculation is carried out on the to-be-diagnosed individual features and the atlas database by using a special similarity quantization algorithm; and the system outputs a quantitative similarity score vector containing the matching degree with various mental disorders and health modes, and generates a structured differential diagnosis report. According to the invention, diagnosis normal form transformation from absolute classification to flexible matching is realized, the problem of identification of mental disorder heterogeneity and common diseases is effectively solved, and the interpretability and clinical credibility of an intelligent diagnosis system are remarkably improved.

The invention provides an automatic driving system reliability self-diagnosis method and platform based on multi-modal sensor fusion. The method comprises the following steps: acquiring image modal data, performing feature extraction by using a visual Transform model, generating an environment scene label, and representing the type of a current environment; carrying out statistics on key modal characteristics in actual automatic driving operation data to obtain a modal score vector corresponding to each environment scene label to reflect the prior credibility of each sensor modal in the environment, and calculating a modal trust weight through softmax to obtain a confidence weight vector; performing weighted fusion on the key modal features based on the confidence weight vector to obtain fusion features; generating a modal reconstruction feature according to the fusion feature, and generating a modal consistency error vector based on the modal reconstruction feature; and in combination with the confidence weight vector and the modal consistency error vector, determining an early warning signal of the current system for judging whether the system has a fusion degradation or modal failure risk.

The invention relates to the technical field of natural language processing, in particular to a retrieval system and method based on retrieval enhancement generation, and the method comprises the steps: obtaining a user query text through a user interaction interface, and carrying out the embedding processing of the user query text to obtain a query vector; inputting the query vector into a vector knowledge base, performing approximate nearest neighbor search on the query vector and a storage vector in the vector knowledge base, and inputting the storage vector meeting a preset semantic distance into a candidate text block sequence; analyzing the semantic association strength between each text block in the candidate text block sequence and the query vector; performing descending sorting on each text block in the candidate text block sequence according to the similarity score vector; and inputting the enhanced prompt text into the large language model for reasoning analysis to obtain a retrieval result. According to the method, the text semantic similarity and the context correlation can be considered at the same time in the retrieval process, and the accuracy of the retrieval result is improved through a dynamic weighting mechanism.

The embodiment of the invention provides a model optimization method, electronic equipment and a storage medium. The method comprises the following steps: acquiring an initial quantization bit width and an initial pruning rate of each convolutional layer in a BEV perception model; on the basis of the initialized quantization bit width of each convolution layer, quantizing the weight of the convolution layer, and according to the initial pruning rate of each convolution layer and the importance score vectors of the multiple output channels, obtaining pruning masks of the output channels; based on the loss function, the quantized weight and the pruning mask training model, obtaining an initial deployment model; and carrying out online quantitative sensitivity evaluation training on the initial deployment model, and when an evaluation condition is met, taking the initial deployment model as a final deployment model. Therefore, the problems that the domain adaptation and generalization ability is limited, the detection precision is reduced, the domain adaptation and generalization ability is difficult to be efficiently utilized by NPU of edge chips such as horizon lines, model evolution in the training process cannot be responded, and error accumulation is serious are solved, and structured sparsity, dynamic adaptation and end-to-end collaborative optimization are achieved.

The invention discloses an urban road tunnel reconstruction and extension scheme comparison and selection method, which belongs to the field of tunnel engineering, and comprises the following steps: establishing a universal standardized comparison and selection condition parameter library, and quantizing and encoding parameters in the comparison and selection condition parameter library to construct an urban road tunnel reconstruction and extension scheme database matrix; constructing an expert authority coefficient matrix based on the expert basic information; training a random forest model by using the urban road tunnel reconstruction and extension scheme database matrix, and generating a preliminary scheme suggestion based on a proposed project demand; independently scoring the preliminary scheme suggestion and giving an independent total score to form an expert scoring tensor and an independent total score vector; weighting the expert score tensor based on the expert authority coefficient matrix, training a random forest model by using the weighted data and the independent total score vector, obtaining the comprehensive score of each scheme, and determining the optimal scheme; and adjusting the expert authority coefficient matrix according to the scoring result of the optimal scheme, and bringing the optimal scheme data into the scheme database matrix for continuous learning and optimization.

The invention relates to the technical field of computer vision and performance evaluation, and discloses a license plate recognition system performance evaluation method and device. The method comprises the steps that a scene image of a target scene is collected and preprocessed; inputting the preprocessed scene image into a scene element extraction model, and outputting an initial scene element score vector; performing anomaly detection and correction on the initial scene element score vector to obtain a standard scene element score vector; generating a disturbance score vector by applying predetermined disturbance to each dimension score of the standard scene element score vector; inputting the standard scene element score vector and the disturbance score vector into a regression prediction model to obtain a plurality of identification rate prediction values; and determining a predicted recognition rate of the external license plate recognition system in the target scene and a confidence interval of the predicted recognition rate according to the statistical distribution of the plurality of recognition rate predicted values. According to the method, reliable mapping from scene visual elements to an LPR system recognition rate is established, and uncertainty evaluation is carried out on a prediction result.

The invention relates to a model data processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: converting text sequence feature data into a query vector, a key vector and a value vector through a decoder-only converter model; then multi-head attention vectors are determined; performing non-parameter shielding processing on the multi-head attention vector to obtain an attention score vector shielded by a pseudo attention parameter; performing normalization processing on the attention score vector through a normalization exponential function to obtain a normalized attention score vector; performing shielding score replacement processing on the normalized attention score vector to obtain a target attention score vector; and determining model output data of the text sequence features according to the product of the target attention score vector and the value vector. According to the method, the problem of unproportionate attention distribution can be solved in a non-parameter shielding manner, and meanwhile, absolute position information is coded in a self-attention processing process.

The invention discloses a WMS adaptive putaway decision-making method and system, and a computer readable storage medium. The method comprises the following steps: receiving a racking request, and collecting dynamic data including an operation state and business pressure in real time; inputting the data and the basic strategy weight into a trained deep learning model, and outputting a normalized dynamic weight vector; calculating score vectors of each candidate storage location under different strategy targets, and carrying out weighted summation on the score vectors and the dynamic weights to obtain a comprehensive score; recommending the storage location with the highest score for racking; and continuously optimizing the model by using a deep reinforcement learning algorithm based on operation effect data generated after actual shelving. According to the method, a'perception-decision-optimization 'closed loop is constructed, so that the problems of static stiffness, incapability of responding to real-time change and difficulty in multi-target tradeoff of a traditional racking strategy are solved, dynamic self-adaption and autonomous persistent evolution of a racking decision are realized, and the intelligent level and the overall efficiency of warehousing operation are remarkably improved.

The invention relates to the technical field of employee evaluation, in particular to a high-risk industry multi-dimensional dynamic weight employee evaluation method, which comprises the following steps of: obtaining quantitative scores of three-level indexes of employees and normalizing the quantitative scores by constructing a multi-level evaluation index system; inputting the standardized score vector into a Transform-based weight learning model, dynamically learning an index weight through a self-attention mechanism, carrying out step-by-step aggregation through a multi-layer perceptron, and outputting a comprehensive score of the employee on each first-level index; inputting the comprehensive score and the historical background information into a pre-established large language model, and performing fusion analysis according to a structured template to generate a personalized evaluation text; and finally, based on the score, the text and the employee information, automatically synthesizing a visual comprehensive evaluation report containing the radar map, the information bar and the text. According to the invention, objectiveness, individuation and operability of employee evaluation are realized.

The invention provides a safety monitoring method and system for an underground pipeline of a municipal road, and the method comprises the steps: arranging multiple types of sensors along the underground pipeline, and collecting the original monitoring data, such as stress, vibration, gas concentration, soil humidity and displacement; the original data is sent to a preprocessing module, time synchronization, abnormity elimination, deletion completion and normalization are completed, and a standardized feature matrix is generated; indexes such as structure variability, trend change rate and sensitivity responsivity are calculated based on the feature matrix, and a health score vector is generated through a feature fusion mechanism; performing density clustering and boundary segmentation in combination with the spatio-temporal index of the score vector, and extracting a high-risk section which changes violently or has an abnormal trend; the section features are combined with historical data, risk levels are calculated through a rule judgment mechanism, and two-dimensional layers of corresponding positions are generated; and accessing the layer to an operation and maintenance platform, completing early warning linkage, instruction generation and log archiving, and synchronously writing into a security evaluation database.

The invention discloses an action sports scoring method and device based on big data analysis, and relates to the technical field of computer vision, and the method comprises the following steps: S1, constructing a human body posture tensor manifold; s2, generating aligned action track characteristics; s3, generating symmetrical positive definite manifold features; s4, generating deep manifold distribution parameters; s5, quantifying global distribution deviation characteristics; s6, calculating a residual vector based on the deep manifold distribution parameters and the standard action distribution parameters, inputting the residual vector into the improved AGCN model for processing, constructing an adaptive topology based on the residual vector, and performing aggregation analysis to obtain a joint-level physical angle error after multi-scale space-time convolution and manifold enhancement; and S7, outputting a comprehensive score vector. According to the method, the limitation that a traditional method only depends on local geometric features and ignores global topological structure constraints and statistical distribution priori is overcome, and an efficient solution is provided for intelligent scoring of sports actions.

The invention provides a dialogue method and system based on context awareness, electronic equipment and a storage medium, and the method comprises the steps: carrying out the vectorization of a current dialogue instruction, obtaining a current dialogue instruction vector, calculating the correlation between the current dialogue instruction and N historical dialogue instructions, and obtaining a plurality of first correlation; determining whether to call the context based on the plurality of first correlations; calculating the correlation between the current dialogue instruction and the N historical dialogue instructions to obtain a multi-dimensional correlation score vector, and determining a plurality of second correlations based on the multi-dimensional correlation score vector; for each second correlation, performing multi-dimensional feature extraction on the historical dialogue instruction based on the second correlation to obtain a multi-dimensional feature vector corresponding to the historical dialogue instruction; obtaining a corresponding target input vector based on the target input sub-vector corresponding to each dimension; and determining a target answer based on the target input vector and the current dialogue instruction vector. The accuracy of reply in the dialogue process can be improved.

The invention relates to a multi-causal model integrated abnormal root cause tracing method and device and computer equipment. The method is applied to industrial equipment, and comprises the following steps: acquiring multivariable time sequence data of the industrial equipment in an abnormal state; based on the multivariable time series data, training at least three causal inference models adopting different prediction functions; determining a unified root cause score vector based on the root cause score vector of each causal inference model after training; and determining a root dependent variable based on the unified root cause score vector. By adopting the method, the root cause identification efficiency and accuracy can be improved.

The invention provides a supplier credit assessment early warning method and system based on a time sequence neural network in the technical field of artificial intelligence and supplier management crossing. The method comprises the following steps: S1, acquiring supplier data through a crawler cluster; s2, inputting the data of each supplier into a dynamic alignment model constructed based on a knowledge graph, performing entity disambiguation and relationship alignment on the data of each supplier through the dynamic alignment model, and constructing a time sequence feature matrix; s3, reasoning the time sequence characteristic matrix based on a double-flow time sequence neural network, and generating a credit score vector; s4, reasoning the time sequence characteristic matrix and the credit score vector based on a risk conduction simulation model to obtain a supplier risk conduction path; and S5, generating an early warning notification based on the credit score vector and the supplier risk conduction path. The method has the advantages that the timeliness, the reliability and the comprehensiveness of supplier credit assessment early warning are greatly improved.

The invention discloses a supplier settlement risk management and control method based on AI big data, and the method comprises the steps: obtaining enterprise data, carrying out the preprocessing, obtaining a global feature matrix, constructing a global knowledge graph, and obtaining an optimized time sequence global graph; the method comprises the steps of aggregating node features and local model parameters based on local data and a time sequence global atlas, generating a multi-dimensional risk score vector and generating a personalized intervention strategy based on the multi-dimensional risk score vector, generating a supplier intervention strategy vector based on the intervention strategy, generating an ERP system instruction based on the supplier intervention strategy vector, and collecting instruction feedback data to update a risk score. And optimizing a global model after federal aggregation, and realizing risk visualization, automatic report generation and interactive query by combining a BERT model based on a multi-dimensional risk score vector, a supplier intervention strategy vector and instruction feedback data. According to the scheme, through dynamic modeling, accurate prediction, safe cooperation and efficient storage, an intelligent and reliable risk management tool is provided for enterprises, and the method has remarkable practicability and commercial value.

The invention provides a personnel portrait construction method and system based on a multi-dimensional talent evaluation model, and belongs to the technical field of human resource management and data processing, and the method comprises the steps: collecting the original data of talents, constructing a tree-level comprehensive quality index system, and outputting the index score vectors of the talents; performing dimension reduction processing on the original data to extract key features, calculating objective weights of the key features, and outputting weight vectors; inputting the key features and the weight vectors into a random forest machine learning model to calculate a preliminary evaluation score; on the basis of the key features, an analytic hierarchy process is adopted to calculate expert correction scores; integrating the two scores to calculate a comprehensive evaluation score, and constructing a personal portrait based on the index score vector, the comprehensive evaluation score and the key features; and updating the personal portrait, monitoring the index score vector or the comprehensive evaluation score, and generating growth early warning information for the talents meeting the early warning conditions. According to the method, multi-dimensional data are integrated, the weight is objectively calculated, and the portrait is dynamically updated.

An intent recognition method based on a large language model and a customer portrait classification model, the method comprising: obtaining a customer's current relevant order and background information according to customer ID information; a customer portrait classification model obtains a first score vector according to the relevant order and the background information; the first score vector is used to indicate a current customer portrait classification score; a fine-tuned large model performs semantic recognition on the context of an input customer conversation to obtain a second score vector; the second score vector is used to indicate a customer current emotional feedback score; the fine-tuned large model is obtained by fine-tuning a large language model; a comprehensive score vector is obtained by fusing the first score vector and the second score vector; the comprehensive score vector is used to indicate different tendency weights in different business scenarios; an inference large model performs inference according to the comprehensive score vector, and outputs a customer intent; a corresponding business logic is driven to be executed according to the customer intent, and the corresponding business logic comprises providing customized suggestions or triggering a specific service process.

The application discloses a kind of based on timing characteristic analysis one section converter operating abnormality detection method, to be in by analyzing the timing characteristic of one section converter sampling data, complete the real-time abnormality detection of one section converter operating state.Specifically, the method of the present application carries out timing characteristic analysis on multiple time nodes for the sample data collected when one section converter is in normal operation, establishes data model for online real-time abnormality detection after learning corresponding timing characteristic, and then detects whether the operation of one section converter is abnormal through a comprehensive abnormality detection index.Compared with prior art, the method of the present application obtains corresponding feature transformation matrix through timing characteristic analysis process capable of minimizing the difference between score vectors in time sequence, and completes the real-time online abnormality detection of one section converter on the basis of monitoring through the comprehensive abnormality detection index of three different abnormality detection indexes.

The invention discloses an employment psychological analysis model construction method and an employment psychological analysis method, and belongs to the field of artificial intelligence, and the method comprises the steps: distributing a corresponding dynamic distribution weight for each psychological traits dimension according to a center vector of each psychological traits dimension and an original embedded vector of each word, combining the projection matrix and the original embedded vector of each word to generate a characteristic projection embedded vector of each word; calculating an intensity score vector of each psychological traits dimension according to the traits projection embedding vector of each word in combination with high and low expression intensity reference vectors, and adjusting a universal language model in combination with a position coding vector, the traits projection embedding vector and an attention head output vector; and training the adjusted model by using the employment psychological assessment question and answer data set to obtain an employment psychological analysis model. By implementing the application, the problem that a general language model in the prior art cannot decouple the employment psychological dimensions of the college students and cannot capture the employment psychological traits of the college students can be solved.

The application provides an instance retrieval method based on a cross-semantic attention model and related equipment. The method comprises the following steps: selecting any shot as a target shot from multiple shots, and obtaining an action prediction score vector and a location prediction score vector in the target shot; correcting the location prediction score vector and the action prediction score vector to obtain a corrected location prediction score vector and a corrected action prediction score vector; calculating an association retrieval score based on the corrected action prediction score vector, the corrected location prediction score vector, a to-be-queried action and a to-be-queried location; repeating the above steps until the multiple shots are selected to obtain multiple association retrieval scores; and obtaining a retrieval result corresponding to the to-be-queried action and the to-be-queried location based on the multiple association retrieval scores. Through the application, a shot with a semantic contradiction in a retrieval result can be effectively avoided, and the precision of "action-location" compound semantic retrieval is improved.

The invention relates to the technical field of information security, in particular to a penetration test effect evaluation method and system based on an attack mode framework, and the method comprises the steps: firstly, constructing an evaluation model comprising a target layer, a criterion layer and an index layer; the evaluation indexes under each criterion are weighted through a large language model, and the indexes are quantitatively scored by experts. And for each criterion, on the basis of a grey theory, calculating a comprehensive evaluation vector of the criterion by using index scores, weights, a preset comment set and a whitening weight function so as to obtain a quantitative score and a qualitative evaluation result. And finally, calculating a final comprehensive evaluation result of the target layer by integrating the quantitative score vectors and the criterion weights of all the criteria. During decision making, the final result needs to be combined with the qualitative evaluation conclusion of each criterion so as to formulate a comprehensive and targeted security reinforcement strategy. According to the invention, the accuracy of penetration test effect evaluation is improved.

The invention provides a multi-agent collaborative illusion suppression method and device, equipment, a medium and a product, and the method comprises the steps: carrying out the multi-label classification of a user request through a dominant large language model, and generating a request classification weight vector; then, combining the ability score vector of each cooperative large language model to calculate the adaptation degree with the current request, and screening out a plurality of target cooperative models according to the adaptation degree; after the target models process the same request in parallel and generate responses, the dominant model carries out modular division on the responses, and for each module, weighted fusion is carried out on output content of the module according to the adaptation degree of the target models to obtain final response content of the module; and finally, combining fusion results of all the modules according to an original module sequence to form a unified collaborative response. In conclusion, the stability, the response efficiency and the generalization ability of the large model on illusion suppression are effectively improved.

This invention belongs to the field of data processing, specifically disclosing a clustering method, system, terminal, and storage medium for mass spectrometry data. The method includes: acquiring a target mass spectrometry dataset, performing state freezing processing and affinity calculation to obtain an affinity score vector; determining a preset similarity selection strategy, and partitioning the target mass spectrometry dataset according to the preset similarity selection strategy and affinity score vector to obtain the optimal assigned cluster; updating the centroid of the optimal assigned cluster to obtain the updated centroid, and performing deterministic structure editing processing on the optimal assigned cluster to obtain the target assigned cluster; constructing a kinetic potential function, and performing iterative clustering processing on the target assigned cluster to obtain the final clustering result. This invention, by performing state freezing processing on mass spectrometry data, enables parallel processing of mass spectrometry data, effectively improving the efficiency of mass spectrometry data clustering analysis and the accuracy of clustering result output.

The invention discloses a depth sensor anomaly detection method and system, a storage medium and electronic equipment. The method comprises the steps that an absolute depth value collected by a depth sensor and a relative depth value output by a monocular depth estimation model are acquired; global sorting is carried out on the absolute depth value and the relative depth value, a sorting difference matrix between the absolute depth value and the relative depth value is further calculated, and a sorting inconsistency score vector is obtained; obtaining a first-stage detection threshold value by adopting a fused adaptive threshold value strategy based on the sorting inconsistency score vector, and forming an initial anomaly set; for each point in the non-abnormal point set, constructing a comprehensive similarity scoring matrix after fusion; and accumulating the comprehensive similarity scoring matrix according to rows to obtain a second-stage scoring vector, and determining a final refined abnormal point set. According to the invention, by introducing the relative depth information generated based on the monocular image, the robustness and environmental adaptability of the depth sensing system can be significantly improved.