56 results about "Information gap" patented technology

The invention relates to a whole-process management method for internet asset vulnerability governance. The method comprises the following specific steps of task configuration, scanning task creation and execution, vulnerability list generation and work order distribution, vulnerability repair and multi-stage auditing, and data statistics and archiving. The efficiency is obviously improved; the whole process is online, so that the vulnerability management period is greatly reduced; the manpower cost is reduced: repeated docking of a third-party security team is not needed, the workload of security operation personnel is reduced, and manpower loss of offline communication is avoided at the same time; work order records, approval traces and scanning logs are archived in the whole process, a complete basis is provided for safety audit, an assessment report of a person in charge can be directly used for performance assessment, and the problem that no data exists in assessment is solved; and the rectification sheet state can be synchronized to all cadres in real time, information fault is avoided, and superior leaders can monitor the treatment progress in real time through the data statistics module and intervene in lagging links in time.

Methods and systems for automatic detection and filling of information gaps in a reference database are described. Responsive to a user query in an ongoing chat session, a query embedding associated with the user query is obtained. A synthetic question embedding is identified from a vector database, based on a similarity to the query embedding. Responsive to determining that the similarity between the synthetic question embedding and the query embedding does not meet a similarity threshold, the ongoing chat session is monitored to detect an answer to the user query. A prompt is provided to a large language model (LLM) to generate and display a textual content corresponding to the user query, based on the detected answer, for automatically updating the reference database. The disclosed methods and systems effectively incorporate new or undocumented information that is not currently captured within the reference database, as gaps are identified.

The invention relates to the technical field of natural language processing, in particular to a verifiable large model retrieval enhancement generation system and method based on an evidence chain, and the method comprises the steps: receiving an initial query, recognizing the fuzziness and information gap of the initial query in combination with an associated retrieval document, and generating a supplementary query set; based on the initial query, the supplementary query and the corresponding retrieval document, generating candidate answers with references and verifying the information supportability of the candidate answers; for the candidate answers passing the verification, extracting support information and constructing a hierarchical attribution mapping relation; and integrating the information to form to-be-evaluated information, and if the current verified to-be-evaluated information meets a preset sufficiency condition, integrating the generated preliminary answer and the to-be-evaluated information to synthesize a target answer. The method effectively overcomes the defects that a traditional RAG system is fragmented in information integration, has one-sided fuzzy query and answer, is low in attribution efficiency and excessively depends on retrieval content, and has the advantages of answer comprehensiveness, verifiability and deployment lightweighting.

The invention belongs to the technical field of ocean forecasting and artificial intelligence crossing, and discloses a physical equation driven ocean wave spectrum intelligent forecasting method and system.The method comprises the steps that a spectrum balance equation is deconstructed and divided into a propagation item and a source item, and modeling is conducted on the propagation item and the source item; dividing the source item into different sub source items according to different physical mechanisms; source item subnets are constructed for different sub-source items, guide variables are embedded, and decoupling of a nonlinear physical process and construction of a high-fitting-degree multi-source driving expression mechanism are achieved; through multi-step ground pushing training and in combination with main peak offset constraint, energy conservation control and propagation gradient supervision, prediction strategy construction of long-term prediction stability and physical consistency is realized. For ship motion response and other engineering applications taking spectrum as input, the method avoids an information gap between single-element forecasting and spectrum-driven application, realizes seamless connection from environment forecasting to engineering calculation, and greatly improves the practicability of the application.

The invention discloses a method for identifying personnel intentions through guided communication, belongs to the technical field of man-machine interaction and artificial intelligence, and aims to solve the problems of low communication efficiency and poor user experience caused by difficulty in processing fuzzy and incomplete user input and neglecting a user interaction state in an intention identification system in the prior art. The method comprises the following steps: analyzing initial input of a user to obtain an initial intention; analyzing and generating an information gap list based on a preset intention-information element mapping library; interaction states such as emotion and uncertainty of the user are perceived and quantified in real time; the core lies in that the information gap and the user state are fused, 'what is asked '(the information gap) and'what is asked' (the user state) are dynamically combined, more intelligent and humanized guidance can be carried out, the accuracy and the communication efficiency of intentional D graph recognition in a complex or emergency scene are remarkably improved, and the man-machine interaction experience is optimized.

The invention belongs to the technical field of code generation, particularly provides a large model tool calling method and system based on demand awareness, and aims to solve the technical problems that an existing code generation system lacks an active tool calling capability and cannot automatically select a proper tool according to information loss. According to the method, an information demand identification model, a tool mapping knowledge graph and a self-adaptive scheduling strategy are constructed, so that a large language model can actively identify an information gap in the code analysis and generation process, and an external tool is automatically selected and called to obtain required context information. The core of the system comprises four modules, namely a demand awareness engine, a tool selection decision maker, a multi-modal information fusion device and an iterative optimization feedback mechanism. By introducing an information missing detection algorithm based on an attention mechanism and a hierarchical tool calling planning strategy, the system can realize intelligent processing from a single file to a warehouse-level code task, and the accuracy and availability of code generation are remarkably improved.

The invention discloses a recursive deep text query method, system and device and a storage medium, and the method comprises the steps: firstly creating an initial query and initializing a hierarchical depth, executing a first-layer query, carrying out evolution processing, and then carrying out retrieval; if the information density is insufficient, sub-query is generated to execute second-layer query, and retrieval is performed after second evolution processing; if the second-layer retrieval is contradictory, third-layer query is executed, retrieval is performed after timeline query and information source tracing verification are passed, and a third-layer retrieval result is finally output; through multi-level query and targeted evolution processing, query can be optimized step by step, more information dimensions can be covered, information gaps can be filled up, and retrieval comprehensiveness and accuracy can be improved; and a third-layer verification mechanism can effectively check contradictions by means of timelines and source tracing, so that the accuracy and reliability of results are guaranteed, and better retrieval results are provided for users.

The application discloses an international trade intelligent customs declaration system and method, and relates to the technical field of international trade.The application comprises a data acquisition module for acquiring single certificate image data and associated form data of a batch to be declared; a multi-modal analysis and verification module for loading a trained multi-modal perception model; and the following steps: S1: acquiring single certificate image data and associated form data of a batch to be declared.The application has the advantages that single certificate information extraction and compliance logic verification are simultaneously completed in single semantic understanding, the recognition result directly carries a quantifiable and reliable verification conclusion, eliminating the information gap of the traditional separate architecture; at the same time, the maintenance work of creating fixed templates according to countries and single certificate types is eliminated, the application has self-adaptive capacity for format changes and unexpected abnormalities, and the model can be continuously optimized from the correction feedback of the customs officer, so that compliance conflicts can be effectively intercepted in the declaration preparation stage, and the single rejection rate and customs clearance time are reduced.

The application relates to the technical field of natural language processing, in particular to an evidence chain-based verifiable large model retrieval enhancement generation system and method, which comprises the following steps: receiving an initial query, identifying the ambiguity and information gap of the initial query in combination with associated retrieval documents, and generating a supplementary query set; generating a cited candidate answer based on the initial query, the supplementary query and the corresponding retrieval document, and verifying the information supportability of the cited candidate answer; extracting supportable information from the verified candidate answer and constructing a hierarchical attribution mapping relationship; integrating the above information to form to-be-evaluated information; if the to-be-evaluated information currently verified satisfies a preset sufficiency condition, integrating a generated preliminary answer and the to-be-evaluated information to synthesize a target answer. The application effectively solves the defects of the traditional RAG system, such as information integration fragmentation, one-sided query answering, inefficient attribution and excessive dependence on retrieval content, and has the advantages of comprehensive answer, verifiability and lightweight deployment.

The invention discloses an attendance abnormity real-time processing method and system, and relates to the technical field of attendance information processing, and the method comprises the steps: responding to a voice instruction input by a user, carrying out semantic analysis on the voice instruction, calling multi-source data associated with a to-be-queried target employee in real time based on the analysis content, and sending the multi-source data to the to-be-queried target employee; the multi-source data at least comprises employee identity data, time sequence attendance data, commuting feature data, scheduling data and business scene data; and extracting specified features of the multi-source data, and inputting the specified features into a pre-trained anomaly detection model to identify an attendance abnormal event of the target employee. The full-dimensional data complementation information gap is combined with the model accurate mining rule, the pain points of high misjudgment rate, more missed judgment, low efficiency and poor scene adaptation in the traditional single data and manual judgment mode are solved, and the labor intensive industry brings the values of recognition precision improvement, management efficiency optimization, business adaptation enhancement and employee experience improvement.

The present application belongs to the field of comprehensive energy planning, and provides a kind of comprehensive energy system planning robust optimization configuration method and system.The method comprises, obtaining the resource condition and load demand parameter of comprehensive energy system, establish the IES optimization configuration model with the maximum internal rate of return as optimization target;Establish the uncertain set model of wind and light output and cold and heat load demand parameter, based on IGDT combined with IES optimization configuration model, construct IES robust optimization model facing investment income;Simplify IES robust optimization model, solve the maximum uncertainty fluctuation radius and the uncertain scene parameter of the corresponding worst scenario;Under the condition that the uncertain scene parameter is determined, the optimization configuration scheme of maximizing investment income is solved;According to different expected income deviation calculation forms IES optimization configuration scheme set, adopt fuzzy decision, select optimal scheme.Maximize the investment rate of return as optimization configuration target, and based on information gap decision, under the premise of reaching expected income, solve configuration scheme.

The invention belongs to the technical field of data processing, and discloses a policy service accurate pushing method and system for medium and small enterprises. Comprising the following steps of: constructing a matching rule base covering four types of conditions of regions, scales, industries and products by performing clause-level atomization disassembly and multi-dimensional knowledge expansion on enterprise industry and commerce information on policy texts, and realizing cross-dimensional consistency matching of policy conditions and enterprise characteristics; according to the method, construction of a logical dependency relationship graph is introduced, atomic judgment conditions are used as graph nodes, a core admission chain is identified through dependency strength weights and topological sorting, matching confidence is quantified based on path weights and a logical propagation algorithm, and enterprise policy applicability and information gaps are accurately identified; the method has intelligence and accuracy, deep semantic analysis and applicability evaluation can be carried out on policy terms, and personalized difference pushing is effectively achieved.

The invention discloses an automatic medical record generation method and system based on multi-modal data and active perception, and belongs to the technical field of data processing.The method specifically comprises the steps that multi-modal original data of a patient and a doctor are collected, a field evidence graph is constructed, a constraint set is generated according to a clinical ontology and a time sequence rule, and configuring a priority and a dynamic threshold for each medical record field, generating an additional collection action based on information gain, and performing iterative update on the field evidence graph, the iterative update being to control the additional collection action of a target field until the target field reaches the dynamic threshold or meets a constraint set. Performing constraint decoding on medical record fields in the iteratively updated field evidence graph to obtain each target field value, and outputting a medical record with an evidence fingerprint chain; according to the method, the multi-modal information required by the medical record can be comprehensively covered, the information gap can be dynamically found and actively supplemented in the acquisition process, and the logic consistency and the time sequence reasonability between the fields are ensured.

An apparatus for determining a hierarchical listing of information gaps for a user is provided. The apparatus includes a processor and a memory connected to the processor. The memory contains instructions configuring the processor to receive an instance of an identification datum from a user device, where the identification datum describes an output type from the user device at a time, receive a target status datum from a database connected to the processor, where the target status datum describes an optimal output type between a minimal output type and a maximum output type, and to classify the identification datum and the target status datum to categories representing identification data. The processor may identify an instance of a gap between identification data and display an input field to the user capable of displaying a hierarchical listing of information gaps based on a user-input datum.

The application belongs to the technical field of data processing, and relates to a robot whole-machine joint calibration method, a robot and a storage medium. By constructing whole-machine calibration state expression information, the robot camera, the chassis and the environment information are uniformly represented as the basic input of the joint calibration process, realizing centralized management of multi-source information and avoiding information fragmentation in module calibration. In the calibration process, the target calibration data is evaluated to determine the calibration information gap, and when the constraint is insufficient, a whole-machine motion strategy is generated and new joint calibration data is collected. Then, the joint calibration data is uniformly optimized, the correlation of multi-source data is comprehensively considered, and the independent calibration error accumulation problem is reduced. At the same time, a verification mechanism is introduced to evaluate the calibration result and judge whether the termination condition is met. If not, the calibration process is re-entered to form a closed-loop control. Therefore, the integrity, controllability and result reliability of the calibration process are improved.

The invention discloses a node processing method of a server cluster and electronic equipment, and relates to the technical field of automatic operation and maintenance, a first node is used as a fault source, a fault type is firstly judged, information containing a node identifier and a fault reason is transmitted based on the fault type, and the delay of passive waiting detection is avoided; the third node relies on the hardware-level communication advantage of the back plate of the case, the fault signal is quickly transferred when the first node cannot directly communicate, and fault information fault caused by the fact that the first node cannot actively send the fault signal is avoided; and the second node receives and processes the fault message from the first node or the third node, accurately locates the fault node and executes disconnection operation, so that slow response depending on heartbeat timeout is avoided. Time consumption is reduced in the whole process from active fault reporting to rapid disconnection execution, the processing efficiency of the fault nodes in the server cluster is remarkably improved, and fault response time delay is effectively reduced.

The invention relates to the technical field of power grid side large-scale wind and light and energy storage configuration, in particular to a wind and light storage capacity distribution robust configuration method and system based on information gap decision, and the method comprises the steps: taking the lowest comprehensive cost as an objective function, and building a power grid side wind and light energy storage optimal configuration model considering inertia constraint; processing the data by using a multivariate time sequence density peak value clustering algorithm based on a principal component analysis method; uncertain factors in the optimal configuration model are considered, and a probability distribution fuzzy set based on a mixed norm is constructed; establishing a two-stage distribution robust optimization configuration model based on data driving, considering the uncertainty of wind and light output prediction, and converting the distribution robust optimization configuration model based on an information gap decision theory; and converting the distributed robust optimal configuration model into a mixed integer linear programming model by using a linearization technology, and solving the mixed integer linear programming model by using a column and number generation algorithm to obtain an optimal configuration and operation scheme of each device.

This application discloses a data processing method relating to the field of artificial intelligence, comprising: obtaining a first query corresponding to a first neural network and a second query corresponding to a second neural network; the first query is a query obtained by maximizing the information entropy of the query corresponding to the first neural network; the first neural network is a model obtained by compressing the second neural network; the first neural network and the second neural network are used for object detection; determining a first loss based on the first query and the second query; the first loss indicates minimizing the information difference between the second query and the first query; and updating the first neural network and the first query based on the first loss. This application improves the model accuracy of the compressed model by minimizing the information gap between the query queries of the student model and the teacher model, thereby minimizing the information gap between the student model and the teacher model.

The invention provides a multi-sensor vehicle target detection and identification method for smart parking, and belongs to the technical field of smart traffic. According to the method, accurate detection of vehicle targets in a parking lot and parking space occupation state identification are realized through fusion of various sensor data and the like. The method comprises the following steps: firstly, performing multi-modal fusion feature tensor mapping alignment on data of each sensor to a unified coordinate system; then, according to the environment and sensor signal quality, dynamically and adaptively adjusting the weight, fusing multi-source information to obtain a vehicle detection result, and introducing a virtual sensor compensation mechanism to fill up information vacancy when sensor data is missing; and finally, carrying out space-time consistency optimization on a detection result to ensure continuous and stable vehicle state output. Compared with a traditional single sensor scheme, the method improves the accuracy and robustness of vehicle detection, can adapt to complex conditions such as illumination changes and sensor faults, and remarkably reduces false detection and missing detection. The method can be used for vehicle monitoring, parking space guiding and other systems of a smart parking lot, and the parking management efficiency and the user experience are improved.

This application discloses a code generation method, apparatus, device, medium, and product, relating to the field of application development. The method includes converting front-end application design data into a structured dataset containing page element information, initial interaction topology, and visual design specifications, addressing the problem of insufficient interaction logic description capabilities. It then converts this data into an attributed state transition diagram, describing the complete interaction attributes of the front-end application's page-to-page interaction from pre-interaction preparation to the completion of the interaction's effect, replacing vague natural language descriptions and achieving machine-analyzable interaction logic, bridging the information gap between design and implementation. Finally, it combines a pre-trained language model to directly generate front-end development code, achieving automated and standardized output from design to code, addressing the lack of machine-analyzable formal representation of front-end interaction logic, thereby improving front-end development efficiency and code quality, and reducing communication and maintenance costs.

This invention provides a simulation experiment planning and decision-making intelligent agent and its method. This agent, through deep fusion of a large language model, achieves end-to-end intelligent generation and dynamic closed-loop optimization of experiment planning schemes. The agent possesses natural language interaction capabilities, automatically generating draft experiment task sheets and proactively inquiring about key details; it provides customized recommendations for equipment and algorithms based on real-time knowledge base queries; it automatically converts experimental conclusions using semantic understanding; and it continuously optimizes the knowledge base through an "execution-feedback-evolution" closed loop, upgrading traditional passive experiments into an agile decision-making system driven by proactive cognition. This invention solves the problems of information gaps, inefficient decision-making, and lack of evolutionary capabilities in simulation experiments, providing accurate, efficient, and sustainable intelligent support for simulation experiment planning, significantly improving the reliability and efficiency of key tasks such as top-level experimental planning, task decomposition, scheme generation, and experiment execution.

This invention proposes an automated questionnaire completion method based on progressive divide-and-conquer and multi-question fusion collaboration. It constructs a dedicated professional model for each question in the questionnaire; uses questions and options as queries to filter relevant clauses from the user's historical text to form a candidate set; performs independent one-stage training on each professional model based on the candidate clauses to obtain several expert-level models; sets a symptom-aware attention layer, freezes the parameters of the expert-level models, and then fuses their output encoding results and inputs them into the symptom-aware attention layer for two-stage collaborative training; finally, it uses the trained attention layer and the corresponding expert-level models to obtain the predicted results of the questions. This invention achieves accurate modeling of the degree correlation between questions through a "divide-and-conquer" strategy, then mines potential dependencies between questions through a "collaborative" fusion mechanism to alleviate information gaps, and uses task selection gates to avoid negative transfer effects, significantly improving the accuracy, robustness, and interpretability of automated questionnaire completion.

The invention discloses an intelligent diagnosis and optimization method for a resume. The method comprises the following steps: obtaining a resume file and a target post description corresponding to the resume file; performing information extraction on the resume file to obtain structured resume information; performing quantitative evaluation on the structured resume information to generate a diagnosis result; performing semantic matching analysis on the structured resume information and the target post description, and identifying an information gap; based on the diagnosis result and the information gap, performing reconstruction processing on experience description in the structured resume information; and based on the diagnosis result, the information gap and the reconstructed experience description, generating and outputting an optimization suggestion for the resume file. According to the method, accurate diagnosis and optimization of the resume are realized, and the adaptation degree of the resume and the post is improved.

Methods and systems for automatic detection and fdling of information gaps in a reference database are described. Responsive to a user query in an ongoing chat session, a query embedding associated with the user query is obtained. A synthetic question embedding is identified from a vector database, based on a similarity to the query embedding. Responsive to determining that the similarity between the synthetic question embedding and the query embedding does not meet a similarity threshold, the ongoing chat session is monitored to detect an answer to the user query. A prompt is provided to a large language model (LLM) to generate and display a textual content corresponding to the user query, based on the detected answer, for automatically updating the reference database. The disclosed methods and systems effectively incorporate new or undocumented information that is not currently captured within the reference database, as gaps are identified.

The application discloses a kind of self-optimization weakly supervised pathological tissue image segmentation model based on prototype guide, including classification stage and segmentation stage;In classification stage, a classification model is trained, and pseudo-labeling is generated for pseudo-supervised segmentation of segmentation stage;The segmentation performance of test set is evaluated using CAMs on BCSS-WSSS and LUAD-HistoSeg dataset respectively;Then the best checkpoint is selected as the best value on the test set, and cam, similar graph, fused cam and refined pseudo-mask are generated;In the segmentation stage, a segmentation model is trained through the pseudo-mask;The performance of the model is evaluated on the test set and the validation set.The application compensates for the information gap caused by weak supervision in a more effective way, thereby solving the underactivation problem, and introduces a self-optimization method based on multi-layer feature affinity to denoise, while using a reliable region learning strategy to reduce noise influence.

The invention discloses a spot goods reliability clearing and settlement method and system based on an information gap theory, and belongs to the technical field of power market clearing optimization, and the method comprises the steps: collecting the declaration cost information of a thermal power generating unit and a renewable energy power plant, carrying out the market optimization clearing, generating a marketization starting sequence, and carrying out the optimization clearing of the marketization starting sequence; the bid-winning data is transmitted to the reliability unit combination clearing module; a reliability unit combination clearing module scheduling mechanism carries out first clearing based on the marketization starting sequence and the bid-winning data, determines a target function base value of the reliability unit combination, considers randomness of the power of the renewable energy power plant based on IGDT, carries out second clearing, and determines the target function base value of the reliability unit combination; obtaining a final thermal power generating unit starting sequence and an output fluctuation range of the renewable energy power plant; and constructing a reliable unit commitment settlement mechanism based on IGDT. According to the invention, reasonable allocation of the reliability unit combination scheduling cost between the user and the renewable energy source main body is realized, and the demand of the high renewable energy source ratio system for the capacity adequacy in the day is satisfied.

The application belongs to the technical field of multi-energy flow systems, and provides a multi-energy flow system optimization configuration method and system considering source and load uncertainty. The method comprises the following steps: determining an equivalent uncertainty fluctuation range; based on information gap decision and conditional risk value theory, combining a deterministic optimization configuration model, a multi-energy flow system optimization configuration model is constructed; taking the maximum loss that a decision maker can bear as a limiting condition, according to the set risk coefficient and avoidance coefficient, a target function of total planning cost is constructed; based on the electric heat cold gas load data, the wind power photovoltaic output data and the equipment parameter data, under the condition that the equivalent uncertainty fluctuation range is the largest, according to the target function of total planning cost and the uncertain variable model, combining the constraint condition and the multi-energy flow system optimization configuration model, a multi-energy flow system optimization configuration model considering source and load uncertainty is constructed; the multi-energy flow system optimization configuration model considering source and load uncertainty is solved, and a multi-energy flow system optimization configuration scheme is obtained.

An object of a system according to an embodiment is to eliminate digital divide in a company and activate communication between employees.SOLUTION: A system according to an embodiment includes a conversation analysis unit, a topic extraction unit, a visualization unit, and a display unit. The conversation analysis unit analyzes a conversation of the communication tool in real time. The topic extraction unit extracts a hot topic from the conversation analyzed by the conversation analysis unit. The visualization part expresses the size of the subject of the topic extracted by the topic extraction part by the size of bubbles. The display unit displays the bubbles represented by the visualization unit in chronological order.SELECTED DRAWING: Figure 1

The present application belongs to the technical field of comprehensive energy, and discloses a networked hydrogen-based micro-grid multi-period planning method based on probability constraint and information gap decision. A two-stage multi-period planning framework is constructed, a hybrid chance constraint-information gap decision theory (CCP-IGDT) co-modeling method and an adaptive prediction-correction Benders decomposition (APC-BD) algorithm are designed, the robustness of the system to unexpected hydrogen load growth is maximized under fixed budget constraints, and the heterogeneous characteristics of short-term random fluctuations and long-term structural uncertainties are effectively handled.

The invention discloses an E-SOP optimal scheduling method and system based on unscented transformation and an information gap decision theory, and belongs to the field of E-SOP optimal scheduling. According to the method, historical data are obtained, unscented transformation is used for carrying out joint uncertainty modeling on loads and new energy output, fluctuation characteristics and correlation of the loads and the new energy output are captured, and the unscented transformation and the new energy output are combined; and performing information gap uncertainty modeling on the price cost of the transformer substation market by using an information gap decision theory, quantifying the maximum tolerance of the system to the price fluctuation, finally comprehensively representing the characteristics of source-load random fluctuation and price interval fluctuation, solving a function taking the minimum operation cost as an objective function, and obtaining an optimal E-SOP regulation and control strategy. Cooperative processing of source load randomness and price fluctuation is realized, scheduling accuracy of the E-SOP is improved, and the problem of low scheduling accuracy of the E-SOP caused by incapability of cooperatively processing source load randomness and price information gap uncertainty due to independent processing of a certain type of uncertainty in the prior art is solved.