597 results about "Low Confidence" patented technology

The invention belongs to the technical field of intelligent navigation, and discloses a navigation obstacle avoidance method and system in a low-confidence and feature similar environment. The method comprises the following steps: acquiring multi-modal sensor data of obstacles in a detection area in a preset low-confidence environment by using a sensor; preprocessing the data of the multi-modal sensor; introducing a generative adversarial network to carry out image enhancement processing on the denoised image; performing cross-domain fusion on the data by using a converter architecture; obtaining an obstacle position, an obstacle type and a confidence degree distribution value based on a deep neural network, and constructing and obtaining an obstacle confidence degree map; dynamically planning an obstacle avoidance path by combining a deep reinforcement learning algorithm based on an obstacle confidence map obtained in real time, and performing auxiliary planning of the obstacle avoidance path based on an MPC model; the method can effectively cope with high challenge scenes such as tunnels and is widely applied to the fields of industrial automation, intelligent transportation and the like.

The invention belongs to the technical field of target detection and recognition, and particularly relates to a thinking chain-based large and small model collaborative target detection and recognition method. According to the method, the small model is responsible for most of easy-to-detect targets, the calculation pressure of the large model is reduced, the large model is responsible for suspected samples, vision and language multi-mode reasoning is combined, the overall false detection rate and the omission ratio are both reduced, confidence evaluation is conducted through the joint probability, automatic screening and manual rechecking of uncertain results are achieved, the reliability of key results is guaranteed, and the method is suitable for large-scale popularization and application. According to the'pseudo thinking chain + pseudo label 'method, by means of reasoning and labels generated by the model, data dependence on manual labeling is reduced, only low-confidence samples are manually confirmed, the human intervention range is narrowed, the human cost is remarkably saved, and semantic information with finer granularity is provided for the model by introducing phrase-level feature descriptors. And the identification capability of complex target attributes and states is improved.

The invention relates to the technical field of image processing, and provides a training data set labeling method and device for AI visual identification, and the method comprises the steps: obtaining and processing original image data, extracting feature mapping, inputting a target detection model to obtain a preliminary labeling result, carrying out the analysis, and determining a clustering center and a candidate target set; the candidate target set is classified based on the clustering center, the confidence degree of the classification labeling result is calculated, labeling data with the confidence degree higher than a threshold value serve as high-confidence-degree labeling data, rechecking and consistency checking are conducted on data with the confidence degree lower than the threshold value, and consistency labeling data are generated; and the target detection model is optimized in combination with the high-confidence data, the low-confidence data is re-labeled by using the optimization model, and the high-confidence data and the re-labeled data are combined to generate a complete labeled training set to train the recognition model. Through the above scheme, the consistency and confidence of the data are ensured, and the problems of inconsistent labeling and missed labeling and false labeling in complex scenes and multi-target recognition are effectively improved.

The invention belongs to the field of electric power and the technical field of artificial intelligence, and particularly relates to an electric power field operation safety detection method and system based on a hybrid expert model. According to the method, the visual language model and the YOLO target detection model are combined, and efficient and accurate electric power field operation safety detection is realized under the condition of few samples through a hybrid expert model architecture. In the training stage, through multi-model cooperative training, an expert model set for different scenes and targets is constructed. In the detection stage, a two-stage detection mechanism is adopted, YOLO is firstly used for rapid preliminary detection, then VLM is used for rechecking a low-confidence target, and finally detection results are integrated to ensure the accuracy of detection. The method aims to effectively cope with complex environments and potential safety hazards in electric power field operation, safety detection efficiency and accuracy are remarkably improved, and compared with a single model architecture, the method shows wider application scenes and higher detection quality in an electric power field operation scene.

The invention discloses a multi-path thinking chain reasoning generation method based on fine-grained knowledge retrieval, which comprises the following steps of: performing deep semantic analysis on a user question by adopting a large language model, and extracting a relationship between entities contained in a question text to generate a triple set; the triple is dynamically divided into different confidence sets by setting high and low confidence thresholds; constructing a thinking chain framework by using a predefined path to obtain an initial reasoning path set; optimizing the initial reasoning path, and sampling an optimized reasoning path set by using Top-k scoring to obtain a reasoning path set with high semantic relevancy; dividing the reasoning path set into a plurality of reasoning path groups with complementary internal path information to obtain a plurality of candidate answers; and adopting a majority voting mechanism to select a plurality of candidate answers, and finally obtaining consistent answers. According to the method, the reasoning accuracy and interpretability can be remarkably improved, and the reliability and robustness of a final conclusion are greatly improved.

The invention discloses an edge computing-oriented lightweight large-model intelligent customer service deployment method, and relates to the technical field of artificial intelligence and edge computing. The method comprises the following steps: acquiring a pre-trained large language model, and pruning based on a lottery hypothesis to obtain a winner ticket sub-network; remapping the sparse structure into a V: N: M structured sparse format, and loading the V: N: M structured sparse format together with the weight and the mask to an on-chip memory; inserting a lateral branch outlet in the converter layer and carrying out token pruning; monitoring the confusion degree and the confidence degree, and dynamically recovering the pruned weight which does not exceed 5% when the load is high; and uploading the 8-bit quantization hidden state of the low-confidence or sensitive token to the large cloud model for collaborative reasoning, and combining the result with local reasoning output. According to the method, the reasoning delay and the communication overhead of the edge equipment are reduced, and the response efficiency and the deployment economy of the intelligent customer service system in a low-computing-power environment are improved.

The invention relates to a text semantic conflict detection method for constructing context association based on a large model. The method comprises the following steps: S1, segmenting an original text by using a segmentation function; s2, performing triple information extraction on each segmented text by using a large model to generate a data set of an entity corresponding relationship; s3, extracting data groups of all entity pairs and relationships, and performing confidence evaluation; s4, aggregating all triads, constructing a affair graph, and pointing out a contradictory relationship with relatively low confidence in the affair graph; and S5, for the found contradictory relationship, performing semantic conflict repair in the text in combination with the large model. According to the method, the accuracy of information generation of the large language model is remarkably improved in a context association mode.

Methods and systems for managing data processing systems are disclosed. A data processing system may include and depend on the operation of hardware and / or software components. Inference models may be implemented to predict future system infrastructure outcomes (e.g., component failures) using information recorded in logs that reflect the operation of the components. However, the models may be complex “black boxes” and may generate critical outcome predictions for downstream consumers without explanations of how the predictions are determined, resulting in downstream consumers having low confidence in the predictions. Therefore, hidden knowledge (e.g., structured knowledge attributes) of the models may be extracted and / or used to understand the underlying processes that the models use to predict the system infrastructure outcomes. The hidden knowledge may be provided for interactively managing data processing system(s) failures in order to increase the likelihood of preventing and / or mitigating future data processing system failures.

The invention discloses a human body posture reconstruction and tracking method based on a neural network, and relates to the technical field of posture reconstruction and tracking. Target echo data are acquired by adopting a multiple-input multiple-output (MIMO) antenna array, and a low-resolution three-dimensional radar image is constructed by adopting space-time weighting in combination with wavelet transform noise reduction and self-adaptive gain compensation; in the feature extraction stage, a Transform-based feature extraction network is introduced, short-distance joint details are processed in combination with a local convolutional neural network (CNN), the spatial correlation of a far-end joint is calculated by adopting a global self-attention mechanism, and a global feature enhancement (GFE) module performs feature completion on a low-confidence region; a hybrid memory network HMN is adopted to carry out short-time and long-time information fusion, a short-time memory unit STM is used to store current frame features, a long-time memory unit LTM is used to store historical key point data, a drift error is corrected in combination with time sequence alignment, and time sequence consistency of attitude estimation is guaranteed.

Methods and systems for managing data processing systems are disclosed. A data processing system may include and depend on the operation of hardware and / or software components. Inference models may be implemented to predict future system infrastructure outcomes (e.g., component failures) using information recorded in logs that reflect the operation of the components. However, the models may be complex “black boxes” and may generate critical outcome predictions for downstream consumers without explanations of how the predictions are determined, resulting in downstream consumers having low confidence in the predictions. Therefore, hidden knowledge (e.g., structured knowledge attributes) of the models may be extracted and / or used to understand the underlying processes that the models use to predict the system infrastructure outcomes. The hidden knowledge may be provided for interactively managing data processing system(s) failures in order to increase the likelihood of preventing and / or mitigating future data processing system failures.

The invention provides an under-mine video stream dynamic denoising method based on multi-modal fusion, which comprises the following steps: constructing a time sequence synchronous fusion mechanism of visible light, infrared and laser radar data, and realizing time-space alignment of multi-source heterogeneous data; a dynamic noise model is established by introducing a fractional calculus optical flow field concept and combining a Gaussian mixture model, so that a dynamic noise region is accurately identified; an improved self-adaptive wavelet threshold function is constructed, a function threshold parameter can be linked with a dust concentration sensor in real time, and the de-noising intensity is dynamically adjusted according to the actual dust concentration; designing a dual-path feature enhancement neural network to effectively separate and enhance structural features and texture features in the video image; a cascaded detection decision system is created, a lightweight network is used as a primary detector, a high-confidence detection result is directly output, and a low-confidence detection result is input into a Transform correction module for secondary reasoning. According to the invention, dynamic denoising, feature enhancement and target intelligent monitoring of the video stream under the mine can be realized.

The invention discloses an intention recognition method based on cross attention and multi-scale uncertainty. The intention recognition method comprises the following steps: preprocessing multi-modal data; parallel multi-modal feature coding oriented to intention recognition; the invention relates to multi-scale uncertainty perception decoding. According to the method, a parallelized multi-modal feature extraction path is constructed, and a hierarchical fusion mechanism based on cross attention is designed, so that deep semantic alignment and complementary enhancement of four types of heterogeneous information including the posture, the motion track, the global scene and the local vision of a rider are realized; the problems of incomplete feature representation and insufficient cross-modal correlation modeling caused by dependence on a single information source or adoption of a shallow fusion strategy in a traditional method are solved, so that the accuracy and robustness of intention recognition in a complex traffic scene are remarkably improved. According to the method, a multi-scale uncertainty perception decoding framework is introduced, risk early warning or context auxiliary verification is carried out on a low-confidence identification result, and the reliability of an automatic driving system in a safety critical scene is improved.

The invention discloses a traditional Chinese medicinal material identification method based on image identification, and the method mainly comprises the following steps: firstly collecting an original image, and carrying out the preprocessing of the original image, and obtaining first image data; then, establishing an initial model by taking YOLOv8 as a basic model architecture, and adjusting to obtain a first recognition model; inputting the first image data into a first recognition model for training to obtain a trained second recognition model; image data to be recognized are input into a second recognition model, the model obtains high-dimensional feature representation of the image data through multi-layer convolution and pooling operation, a prediction layer outputs category probability and position information, low-confidence recognition results are filtered out, and a final recognition result is obtained. The method is characterized in that weight parameters are designed according to the characteristics of the traditional Chinese medicinal materials, the network structure and parameters are adjusted, and the recognition accuracy and reliability are improved. In addition, an anchor frame calculation method and a loss function design method are further provided, and the recognition efficiency is improved.

The invention discloses a multi-target tracking method based on track guidance, and the method comprises the following steps: carrying out the detection of a video frame through a target detector, obtaining the appearance information and position information of a target person, keeping a high-confidence detection frame and a low-confidence detection frame, and initializing a track; predicting the position of the target person in the next frame according to the track position information, wherein the corrected state vector and covariance matrix are used in the Kalman filter prediction stage; performing first matching on the high-confidence detection frame and all target candidate frames according to the motion similarity, the appearance similarity and the speed direction calculation cost matrix, and then performing second matching between the unmatched target candidate frames and the low-confidence detection frame by using the motion similarity and the speed direction calculation cost matrix; and performing unified management on matching results, and outputting target trajectory information. According to the invention, an end-to-end multi-target tracking algorithm can be realized, and the real-time rate and accuracy of tracking are improved.

Methods and systems for managing data processing systems are disclosed. A data processing system may include and depend on the operation of hardware and / or software components. Inference models may be implemented to predict future system infrastructure outcomes (e.g., component failures) using information recorded in logs that reflect the operation of the components. However, the models may be complex “black boxes” and may generate critical outcome predictions for downstream consumers without explanations of how the predictions are determined, resulting in downstream consumers having low confidence in the predictions. Therefore, hidden knowledge (e.g., structured knowledge attributes) of the models may be extracted and / or used to understand the underlying processes that the models use to predict the system infrastructure outcomes. The hidden knowledge may be stored in a repository and may be provided for downstream use in order to increase the likelihood of preventing and / or mitigating future data processing system failures.

The invention discloses a multipath ghosting suppression method for a millimeter-wave radar tunnel scene, which is applied to the field of millimeter-wave radar target detection and aims to solve the problem of poor multipath ghosting suppression effect in the tunnel scene in the prior art. The method comprises the following steps: firstly, constructing a confidence criterion by using multi-dimensional information point cloud data generated by radar original echoes, and performing density-based clustering on the point cloud data; performing linear fitting on the zero Doppler point cloud to obtain slope information of the tunnel wall, matching the slope of the perpendicular bisector of the dynamic point cloud pair with the slope set, and judging the target pair with lower confidence in the successfully associated target pair as a multipath ghosting; and finally, in consideration of motion characteristics of targets in the tunnel and a multipath reflection geometric model, performing transverse and longitudinal double-rectangular region division on the dynamic point cloud, performing Doppler information constraint on point cloud data in the region, and screening out the targets which meet conditions and are relatively low in confidence coefficient and judging the targets as multipath ghosts. According to the invention, vehicle targets in a semi-closed environment can be detected, and multipath ghosts generated by reflection of tunnel walls, handrails and the like can be effectively identified and inhibited while real targets are reserved.

The invention discloses a remote automatic quality control and calibration method and system for a pH meter, and the method comprises the following steps: S1, carrying out the initial temperature compensation correction of a pH value, and generating a corrected pH value; s2, environment parameters collected by an environment sensor and the corrected pH value are combined and formatted; s3, cleaning the standardized data set at an edge computing node, and generating an optimized measurement data set; s4, performing confidence evaluation based on a committee query algorithm, and triggering a calibration process for a detected low-confidence sample; s5, generating an enhanced calibration curve by using a double-neural network regression model, and optimizing measurement data in real time by combining particle filtering; s6, displaying the final data in real time through a visual tool, and uploading the final data to a cloud end; and S7, dynamically adjusting the calibration period, the acquisition frequency and the communication strategy of the sensor through the edge computing node. According to the method, double neural network regression, particle filtering, cloud edge cooperation technologies and the like are utilized, and accurate measurement and remote intelligent calibration of the pH meter are achieved.

The invention provides a semiconductor chip automatic test optimization system, and belongs to the technical field of electrical variable measurement. Comprising an acquisition module used for establishing an electrical variable time sequence arranged according to a time sequence, a drift analysis module used for calculating a short-time fluctuation amplitude value, a long-term drift slope and a high-frequency noise amplitude value based on the impedance time sequence arranged according to the time sequence, and a contact judgment module. The evaluation module is used for constructing a contact impedance coefficient based on a short-time fluctuation amplitude value, a long-term drift slope and a high-frequency noise amplitude value in a jth sliding window, and evaluating and optimizing the contact impedance coefficient, and the data marking module is used for marking electrical variable measurement data corresponding to a test channel which is evaluated to be instable in contact as low confidence. According to the system, the ith test channel of the temperature sensor chip can be tested more accurately, the short-time fluctuation amplitude value, the long-term drift slope and the high-frequency noise amplitude value are calculated at the same time, multi-source data participates, and the test accuracy is improved.

The invention provides an image text recognition method and system based on a mask diffusion model, a storage medium and equipment, and belongs to the technical field of image or video recognition or understanding. According to the method, multi-scale visual features of an image are extracted through a visual encoder, a mask diffusion decoder is combined, a diversified mask strategy and random character replacement disturbance are adopted in a training stage, denoising loss and auto-reflection loss are calculated respectively, and a model is optimized in a combined mode; in the reasoning stage, starting from a full mask state, a complete text sequence is recovered through multi-round iterative denoising. According to the method, the one-way modeling limitation of a traditional autoregression model is broken through, all-around context-dependent modeling is achieved, an autoreversion error correction mechanism and a block low-confidence mask strategy are introduced, and the recognition accuracy and reasoning efficiency in complex scenes such as shielding and fuzzy scenes are remarkably improved. The method provided by the invention reaches a leading level on a plurality of public data sets, and has the advantages of high precision and high speed.

The invention discloses a human-in-loop-based medical record data key information extraction and reconstruction method, which is characterized in that a method of introducing a human-in-loop technology into a large language model is adopted, and the large language model and expert priori knowledge are utilized to clean and standardize unstructured medical record data; the semantic retrieval and information understanding ability of the model is enhanced through a medical vector library, efficient extraction of key information is achieved, confidence is generated through self-evaluation, human expert verification is triggered under the condition of low confidence, the model is continuously optimized and updated through expert feedback of a human-in-loop, and the accuracy of human-in-loop verification is improved. And finally, medical record data structured output with higher credibility is realized. Compared with the prior art, the method has the advantages that the accuracy of automatic processing of medical record data is improved, the problem that an automatic processing system is difficult to meet high precision and high credibility is solved, and the method has wide application prospects in clinical decision support, intelligent medical record analysis, personalized diagnosis and treatment scheme making and medical scientific research data mining.

The invention relates to an archive credible question and answer method based on graph context retrieval and knowledge graph enhancement, and belongs to the field of computer software and artificial intelligence. According to the method, the relation between key entities and entities in a historical file is extracted by using a multi-modal large language model MLLM, and low-confidence fields are automatically identified to generate marks needing to be manually rechecked, so that the correctness of information extraction is ensured while the workload of manual rechecking is reduced, and then based on a given knowledge base, the information extraction efficiency is improved. Previous related questions and answers are retrieved according to query questions to achieve context enhancement, and finally, credible answers are generated through a large language model (LLM) in combination with extracted information in historical archives. On the premise of keeping the value of the archive voucher, the processing efficiency and the knowledge service capability are remarkably improved.

Methods and systems for managing data processing systems are disclosed. A data processing system may include and depend on the operation of hardware and / or software components. Inference models may be implemented to predict future system infrastructure outcomes (e.g., component failures) using information recorded in logs that reflect the operation of the components. However, the models may be complex “black boxes” and may generate critical outcome predictions for downstream consumers without explanations of how the predictions are determined, resulting in downstream consumers having low confidence in the predictions. Therefore, hidden knowledge (e.g., structured knowledge attributes) of the models may be extracted and / or used to understand the underlying processes that the models use to predict the system infrastructure outcomes. The hidden knowledge may be stored in a repository and may be provided for downstream use in order to increase the likelihood of preventing and / or mitigating future data processing system failures.

This disclosure introduces an automated computer system designed to detect fire and smoke in video frames. Within this system, an object detection model is trained to recognize fire and smoke patterns. In a post-training evaluation phase, the system utilizes the model to determine an instance-level detection threshold from a validation set, which dictates the minimum confidence score required for a fire or smoke designation. In addition, a frame-level detection rule is defined, considering both the cumulative count of detected fire or smoke instances and their associated confidence scores. During inference, the system processes video frames through the model, designates detected patterns with bounding boxes, and subsequently applies the instance-level threshold and frame-level rule. When a sequence of frames fulfills a predetermined criterion, the system activates an alarm.

The invention provides a self-adaptive information retrieval and optimization system based on retrieval enhancement generation, which comprises a prospective answer module used for receiving a query statement and context information and inputting the query statement and the context information into a large language model so as to generate a prospective preliminary answer; the confidence coefficient detection module is used for setting a confidence coefficient score threshold value to carry out keyword-level confidence coefficient evaluation on the content in the prospective preliminary answer, correcting a low-confidence-coefficient part in the answer according to an evaluation result, and outputting a complete answer content as a confidence coefficient answer; the knowledge graph extension query module is used for executing named entity recognition operation on the confidence answer and obtaining an extension query answer in combination with a pre-constructed small knowledge graph; and the context memory pool module is used for storing the context information input for the first time and related information fragments obtained in each round of retrieval process, preferentially performing information retrieval from the memory pool in the subsequent retrieval process, and accessing the external index database only when no matched content exists in the memory pool.

The invention discloses a data scarcity industrial vertical field-oriented knowledge-guided retrieval enhancement generation method, belongs to the technical field of natural language processing and industrial intelligence crossing, and can improve the retrieval accuracy and generation reliability of a large language model in an industrial scene. According to the method, a'sparse vector + dense vector 'mixed knowledge base is constructed, and general knowledge and industrial field texts are fused; after the model generates an initial text, judging whether external retrieval is needed or not through multi-dimensional evaluation token confidence; extracting attention weights for the low-confidence tokens, and screening key tokens to generate a retrieval query; dynamically adjusting the weight of a retriever based on a BGE-M3 model, and optimizing a retrieval result through reordering; the retrieval knowledge is converted into a context with an index, and a prompt template is constructed to generate a correction value iteration calibration text; and finally, optimizing the text format, and generating a structured response meeting industrial requirements. The method solves the problems of lack of professional knowledge of large language models in the industrial field, shallow retrieval and generation fusion, lack of knowledge calibration mechanisms and the like.

The invention provides a bridge structure vibration long-term monitoring algorithm and system based on computer vision, and relates to the technical field of beam monitoring. In the prior art, there is no vision-based bridge monitoring algorithm with high precision, strong robustness and shielding self-recovery capability. According to the method, matching point confidence is calculated, and matching points with low confidence scores are removed; calculating the displacement of the bridge at the plurality of feature points, and generating vibration time sequence data; the inherent frequency and the corresponding modal shape are extracted and compared with the reference frequency range and the reference modal shape, the monitoring result is obtained, shielding self-recovery is carried out according to the environmental interference influence, the precision and reliability of bridge monitoring are improved, and the robustness of bridge monitoring activities in a complex scene is improved.

The invention discloses a positioning calibration method of surface mounting equipment, equipment, a medium and a product, and relates to the technical field of printed circuit boards, a first position of a bonding pad on a circuit board and a second position of an element pin of an element to be mounted are obtained through recognition by using an image recognition model, the confidence coefficient is recorded, and when matching calculation is carried out, the positioning calibration accuracy is improved. And carrying out position transformation calculation on the positions of the bonding pad and the element pin matched in the current iteration, calculating a transformed position error in combination with the confidence coefficient, and outputting a final matching result of the bonding pad and the element pin and a corresponding final space mapping parameter after a first iterative calculation condition is met. According to the method, the influence of a low-confidence-coefficient position detection result on space mapping parameter calculation is effectively reduced, the stability and reliability of the final space mapping parameter are ensured, compared with traditional position error calculation, the method has higher anti-interference capability, and the accuracy of positioning matching of high surface mounting equipment is improved.

The invention relates to the technical field of information processing technologies, and discloses a method for acquiring an associated company based on enterprise and public institution information and a related device. The method comprises the steps of obtaining original data, performing cleaning and confidence calculation on enterprise and public institution information, and filtering low-confidence data to obtain high-confidence data; performing semantic analysis and confidence verification on the high-confidence data, and screening out credible associated data; the credible association data are input into the stable node processing language model, a final association result is obtained, the stable node processing model and the sensitive node processing model are trained mutually, the stable node processing model is used for training common nodes and high-confidence samples, and the stable node processing model is used for processing the high-confidence samples. The sensitive node processing model is used for training new nodes or low-confidence samples. The method has the advantage of balancing the precision of the high-confidence sample and the adaptation capability of the dynamic node.

The invention relates to the technical field of background investigation, and particularly discloses a talent background investigation method based on multi-source data evaluation, which comprises the steps of constructing a dynamic talent knowledge graph, performing semantic alignment processing on multi-source cross-modal data, evaluating data quality based on a multi-dimensional confidence model, and constructing a confidence closed-loop correction mechanism. Performing intelligent correction on the low-confidence data, and reversely updating the credibility weight of the data source according to a correction result; an event-driven incremental update architecture is adopted, and dynamic events of multi-source data are captured in real time through a distributed event awareness engine. According to the method, the dynamic knowledge graph is used as a unified carrier, and the multi-source data is upgraded from fragmented storage to a semantic association network, so that the talent background survey data is converted from static piling to a dynamically evolved knowledge network, and the accuracy and timeliness of evaluation are remarkably improved.

The invention discloses a character recognition method and system based on a large model and an OCR technology, and relates to the technical field of character recognition, and the method comprises the steps: extracting picture information, carrying out the unified preprocessing, generating a text detection box of a character region through a DBNet lightweight text detection model, and obtaining a text position; quickly identifying characters in the textbox by using a lightweight OCR model to obtain text content, and generating an identification information group; carrying out average value calculation on the confidence coefficient of the lightweight OCR model result, and analyzing the overall confidence coefficient; and for the result with low confidence coefficient, inputting the corresponding identification information group into the multi-modal large model, and carrying out secondary identification. According to the method, the text position set and the text character string sequence are combined into the identification information group, so that the effect of structured storage of detection and identification results is achieved, subsequent information retrieval and multi-modal fusion analysis are facilitated, and the effect of optimizing the subsequent processing efficiency and precision is achieved through the combination of the confidence coefficient screening step.