555 results about "Weight adjustment" patented technology

The invention relates to a standardized project management and intelligent professional scheduling system, and belongs to the technical field of project management and human resource intelligent scheduling. The project standardization module is disassembled into standardization task steps through a business process association rule mining algorithm, and project complexity and resource tensity are adapted by using a task attribute dynamic weight algorithm; the archive matching module constructs professional archives, extracts feature vectors through a multi-criterion decision-weighted bipartite graph matching algorithm, and generates optimal matching pairs in combination with adaptive weight adjustment and an integer linear programming model; the dynamic scheduling module plans a task execution scheme according to a resource constraint scheduling mechanism, and realizes efficient scheduling in combination with a skill supply and demand prediction algorithm and calendar integration; and the quality optimization module adopts a deliverable anomaly detection algorithm to monitor compliance, feeds back an iterative matching and scheduling strategy through a time sequence prediction optimization algorithm, and perfects a skill map based on a map increment updating algorithm. The system realizes a project full-process closed loop.

The invention relates to the technical field of temperature control of high-precision industrial heating equipment, in particular to an anti-interference precise temperature control method and system for heating an upper die, and the method comprises the steps: starting a multi-region heating array, heating the upper die to a target temperature according to a preset temperature rise curve, and building a uniform thermal field; before the high-pressure medium is introduced, heat load change caused by the high-pressure medium is predicted based on medium process parameters; in the medium introduction process, dynamic anti-interference compensation control is executed, a feedforward power compensation instruction based on a thermal load and a feedback power fine tuning instruction based on temperature deviation are generated respectively, and the feedforward power compensation instruction and the feedback power fine tuning instruction are weighted and fused into a composite power driving signal for adjusting a heating array; when the temperature fluctuation converges to a stable threshold value, the feedforward compensation weight is gradually reduced, smooth switching to steady-state control is achieved, transient thermal shock caused by a high-pressure medium is effectively restrained through feedforward and feedback cooperative compensation and dynamic weight adjustment, the temperature fluctuation is reduced, and the temperature control precision and stability are improved.

The invention discloses a dynamic portrait construction method and system fusing a large model and user behavior data, belongs to the technical field of artificial intelligence and big data analysis, and aims to solve the problems of insufficient real-time performance, difficulty in multi-source data integration and high privacy risk in the traditional technology. The method comprises the following steps: collecting basic attributes, behavior sequences and unstructured data by burying points, and processing through an Apache Flink session window and a dynamic watermark; multi-modal feature extraction (discrete feature embedding, bidirectional LSTM coding behavior sequence and BERT coding text) is carried out, and joint embedding is generated through cross-modal contrast learning; generating three types of labels, namely a static label (rule engine), a dynamic label (1.3 B parameter quantity Nano-vLLM) and a predictive label (XGBoost), and dynamically adjusting weights; and realizing global model updating through federated learning and differential privacy. The system comprises a data acquisition layer, a feature extraction layer and a label generation updating layer. The real-time performance and accuracy of the portrait are improved, the privacy of the user is protected, and the commercial value in e-commerce, finance and other scenes is remarkable.

The invention discloses a multi-source heterogeneous data flow real-time fusion and weight adaptive correction system and method, and relates to the technical field of multi-source data fusion. Comprising a path monitoring data extraction module, an initial fusion weight determination module, an abnormal transportation event analysis module, an initial correction strategy table generation module, a response updating module and a real-time weight adjustment fusion module. According to the method, a multi-dimensional data quality scoring system is established, so that strong association between weight distribution and data quality is ensured, and fusion precision loss caused by'fixed weight 'is avoided. For an abnormal monitoring point, through indexes such as a data missing rate, a deviation change rate, an auxiliary positioning deviation rate and the like, an abnormal data source type is accurately identified, a weight adjustment scheme of'target data source decreasing amplitude + increasing amplitude data source equal proportion complementation 'is formulated until abnormity is eliminated, and closed-loop correction of'abnormity positioning-weight adjustment-error elimination' is realized.

The invention relates to the technical field of intelligent parking management, and discloses a parking reservation management method and system. The system obtains a parking space state data set containing a parking space number, an occupation state time sequence and a reservation request frequency through a parking management platform; performing spatio-temporal feature extraction on the set to generate a multi-dimensional feature matrix, and determining a parking space association network in combination with topology analysis; screening a candidate parking space set satisfying reservation stability and space reachability from the matrix; inputting the candidate parking space set into a pre-trained priority ranking model, and generating a parking space priority score by the model through a dynamic weight adjustment algorithm; fusing the priority score and the real-time parking space occupation data to construct a dynamic distribution matrix, and simulating a parking space distribution spatio-temporal evolution process based on a queuing theory model; and separating an optimal distribution scheme and a candidate parking space sequence from the matrix, and generating final parking space reservation confirmation information. According to the reservation parking management method and system provided by the invention, the reliability and user satisfaction of the reservation system can be improved.

The invention discloses a knowledge-enhanced multi-granularity causal fault diagnosis method, and the method comprises the steps: constructing a hierarchical bearing fault domain knowledge graph which comprises a structural layer, a fault layer and a feature layer, and systematically integrating multi-dimensional domain knowledge; designing a three-level causal chain structure covering a microscopic layer, a mesoscopic layer and a macroscopic layer, and realizing cross-scale reasoning from a microscopic signal feature to a macroscopic fault decision; a knowledge-causal dynamic fusion mechanism based on working condition perception is provided, and adaptive coupling of domain knowledge and causal reasoning is realized through dynamic weight adjustment; and finally, a diagnosis result containing fault types, positions and severity is output, so that the defects of an existing method in the aspects of knowledge fusion, cross-granularity reasoning and interpretability are effectively overcome, and the precision and credibility of bearing fault diagnosis under complex working conditions are improved.

The invention discloses an adaptive evaluation method for the health degree of an electrolytic cell, and the method comprises the following steps: collecting the multi-dimensional operation parameters of the electrolytic cell in real time, and carrying out the preprocessing of the collected time series data, so as to construct a training sample with a time window; extracting a multi-scale time sequence feature from the training sample to form a feature vector; inputting the feature vector into a weight adjustment network, and outputting a dynamic weight vector; weighting the feature vector by using the dynamic weight vector to generate a weighted feature vector; inputting the weighted feature vector into a performance prediction model, and outputting a short-term performance prediction value of the electrolytic cell at a future moment; after the corresponding real performance value is obtained, calculating a prediction error of the short-term performance prediction value, and constructing a reinforcement learning reward signal based on the prediction error; updating a strategy of the weight adjustment network through a reinforcement learning algorithm by utilizing a reward signal, thereby optimizing dynamic weight vector generation at a subsequent moment; based on the dynamic weight vector and the feature vector at the current moment, a comprehensive health degree index of the electrolytic bath is obtained through calculation; according to the method, main factors influencing the equipment health degree in different stages are intuitively revealed, and a basis is provided for operation and maintenance decision making.

The invention discloses an operation and maintenance workflow cooperation system and method, and relates to the technical field of business process.The method comprises the steps that after a natural language operation and maintenance requirement is received, a subtask set containing task attributes is extracted through a semantic model built based on a pre-training operation and maintenance field language model; inputting the sub-tasks into a causal mining model, capturing an implicit dependency relationship between the tasks through an attention mechanism which takes task types and resource demands as weight regulation factors, and generating an operation and maintenance relationship graph which contains dependency confidence coefficients and dependency types and does not have cyclic conflicts; splitting the atlas into a task chain set and a free task point set by adopting a causal-oriented greedy pruning algorithm based on a directed edge association subtask maximum aggregation and task chain set scale minimization principle; task chains are distributed through a weighted matching algorithm in combination with the chain overlap ratio and the to-be-handled task amount of the intelligent agent, remaining free task points are distributed according to the balance principle, and accurate disassembly and efficient cooperation of operation and maintenance tasks are achieved.

The invention discloses an AI-based digital project performance evaluation data processing method and system, and relates to the technical field of artificial intelligence and digital project management, and the method comprises a multi-source data collection module which is used for obtaining data; the entity alignment module is used for establishing an association relationship among cross-system data entities; the dynamic index generation module is used for dynamically adjusting the evaluation index weight by utilizing a reinforcement learning framework; the efficiency prediction module is used for modeling a task dependency relationship according to the time sequence diagram convolutional network and outputting a delay risk probability; and the visual interface is used for displaying the data. According to the method, cross-system entity alignment, dynamic index weight adjustment, task delay risk prediction and root cause analysis are realized through AI technologies such as BERT semantic matching and a graph neural network, visual data display and interaction functions are provided by virtue of a visual interface, the problems of data dispersion, index static state, risk lag and the like in traditional project evaluation are solved, and the project evaluation efficiency is improved. And the decision support capability of project management is improved.

The invention discloses a scene adaptive recommendation method in smart home based on multi-modal data, and relates to the technical field of multi-modal data processing. According to the method, firstly, scene classification is carried out based on multi-mode sensing data for a smart home scene, a current life scene of a user is accurately recognized, meanwhile, an equipment linkage state is analyzed in combination with a classification result, whether equipment operation is matched with a scene requirement or not is judged, and then a scene mode is recognized according to a linkage analysis result to generate preliminary recommendation; whether the dynamic weight space relation model of the user is updated or not is judged, finally, the recommendation effect is quantified according to the weight adjustment judgment result, the recommendation strategy is optimized through two-way feedback, the adaptability to the user requirement is improved, and the user experience is improved. A full-process closed loop of multi-modal data classification, equipment linkage analysis, mode recognition recommendation, weight dynamic updating and effect quantitative feedback in a smart home scene is realized, and the problem that the intelligent degree of scene recommendation in smart home is not high in a user behavior dynamic change scene is effectively solved.

The invention provides a trailing suction dredger loading operation parameter optimization real-time recommendation system and method, and relates to the technical field of trailing suction dredger dredging engineering.The system comprises a data collection layer, a real-time calculation layer, an intelligent recommendation layer and a feedback control layer, the data collection layer is used for collecting trailing suction dredger loading operation parameters in real time, and the real-time calculation layer is used for calculating the real-time calculation layer; the real-time calculation layer can calculate energy consumption and yield in real time based on edge calculation equipment, the intelligent recommendation layer can generate a Pareto optimal solution set through a multi-objective optimization algorithm, and the dynamic weight adjustment module adjusts energy consumption and yield weight coefficients in real time according to the construction stage. The feedback control layer issues the collected loading operation parameters of the trailing suction dredger to an execution mechanism in real time through a PLC, and monitors the operation state of equipment to update a historical database, so that collaborative optimization of energy consumption and yield can be achieved, and the optimization precision is continuously improved through historical data.

The invention discloses an anomaly detection method and system for transaction flow data, and relates to the technical field of big data analysis. The method comprises the following steps: based on transaction flow data, acquiring operation behavior data of a target user in a transaction process and interaction behavior data after the transaction is completed, and sorting according to timestamps to form a user operation behavior sequence and a user interaction behavior sequence; inputting the sequence into a pre-trained abnormal risk prediction model, and outputting an initial risk coefficient, wherein the model integrates an individual behavior baseline and an adaptive weight module; acquiring a group behavior baseline of the similar user group, and correcting the initial risk coefficient in combination with a matching result of the sequence and the group baseline to obtain a final risk coefficient; and performing classification abnormity early warning operation on the final risk coefficient based on a preset risk threshold. According to the method, through whole-process behavior sequence analysis, individual and group baseline dual calibration and dynamic weight adjustment, the accuracy and timeliness of transaction flow data anomaly detection are effectively improved.

The invention discloses a terminal meter reliability dynamic evaluation method, system and device based on multi-source data features and a medium, and relates to the technical field of terminal meter fault prediction.The method comprises the specific steps that multi-source data of a metering terminal are obtained, and the multi-source data are integrated to form a structured feature matrix; and introducing a mixed feature screening mechanism to carry out mixed feature screening to obtain a key feature set of the terminal meter. And carrying out weight distribution by adopting a Bayesian algorithm to obtain a comprehensive weight. And constructing a comprehensive reliability scoring model, and outputting a comprehensive reliability score of the terminal meter. And dynamically setting a risk threshold according to the environmental parameters, and outputting a comprehensive risk level of the terminal meter. According to the method, terminal meter reliability dynamic evaluation under multi-source data fusion is realized, the accuracy of reliability evaluation is improved through dynamic feature screening and an adaptive weight adjustment mechanism, and fault risks and operation and maintenance weak links under different scenes are effectively identified.

The invention discloses a water energy accumulator and ground source heat pump combined control method, relates to the technical field of building environment and equipment engineering, accurately captures a time sequence change rule of a building cold load based on the load prediction capability of a time convolution network, overcomes the defect that a traditional method is insufficient in response to sudden fluctuation, and improves the control efficiency. The system can plan an energy storage strategy in advance; an electricity price-load change rate sensitivity index is introduced, and through an adaptive weight adjustment mechanism, economic operation and load tracking requirements are dynamically balanced, so that strategy stiffness caused by fixed priorities is avoided, and the utilization efficiency of time-of-use electricity price signals is remarkably improved; according to the joint optimization design of the mixed integer linear programming model, the equipment operation constraint and the cost target are comprehensively considered, the collaborative decision of the energy accumulator charging and discharging strategy and the heat pump frequency setting is realized, and the direct energy supply dependence in the midday high electricity price period is effectively reduced.

The invention discloses a real-time positioning method, system and device supporting multi-frequency Beidou signals and a medium. The method comprises the steps that multi-frequency observation is collected in parallel, and quality features are generated; performing distribution alignment of risk perception based on the quality features to obtain equivalent observation; generating a causal weight by adopting anti-fact evaluation based on influence approximation; in the cross-frequency fusion model, taking multiple paths as potential disturbance quantities, only implementing group stripping on suspicious stars, frequencies or time periods, and solving and outputting a positioning result in combination with robust constraint and increment; a confidence radius is generated on line, the confidence radius is used as a main control signal to execute risk-driven self-adaptive control, and the risk-driven self-adaptive control comprises weight adjustment, measurement subset selection and front-end loop and frequency chain working state adjustment; and implementing topological consistency goalkeeping on a multi-frequency phase level, and triggering time domain virtual array suppression and feeding back to the fusion model when abnormity occurs. The system can significantly improve the precision, robustness and integrity of Beidou multi-frequency real-time positioning.

The invention relates to the technical field of data processing, in particular to a BN-based UHPC mix proportion data analysis method and a strength prediction system.The intelligent level of ultra-high performance concrete mix proportion design is improved by introducing a Bayesian network model and a multi-objective optimization algorithm, and the method comprises the steps that firstly, an enhanced feature set is generated through feature engineering; a nonlinear relationship and a cross-level interaction effect among material parameters are fully excavated, and the characterization capability of the model on a complex material system is enhanced; secondly, the Bayesian network model is combined with causal reasoning and a hierarchical regularization strategy, so that the overfitting risk is reduced while the compressive strength prediction precision is ensured; in the multi-objective optimization link, through dynamic weight adjustment and Pareto frontier search, carbon emission and strength requirements are effectively balanced, and candidate schemes with low carbon and excellent mechanical properties are output; and finally, dynamic evaluation and risk analysis are carried out to further screen out a mix proportion with high stability and strong feasibility, and a reliable decision basis is provided for engineering practice.

The invention discloses an intelligent error correction and style optimization system and method for business English writing, and relates to the technical field of natural language processing and artificial intelligence, and the system comprises the following components: a user interaction module, a data storage module, a language ability evaluation module and a self-adaptive error correction feedback and style optimization module. Through a multi-dimensional evaluation algorithm based on dynamic weight adjustment, historical writing data of a user is deeply analyzed, an evaluation system including four dimensions of grammar errors, vocabulary application, sentence structures and business profession is constructed, the system comprehensively evaluates the language ability of the user, and the user experience is improved. And the evaluation weight is dynamically adjusted according to the capability improvement rates of the users in different dimensions, so that the language capability levels of the users are more accurately divided, and the system can provide personalized error correction feedback and style optimization suggestions for the users with different language levels based on the evaluation result.

The invention relates to the technical field of navigation control, in particular to a path planning method and system based on an AGV, and the method comprises the following steps: a laser radar obtains a point cloud grid segmentation monitoring normal vector, calculates a gradient, collects a wheel pressure, generates a risk ratio, calls the risk ratio to calculate an inclination angle distribution entropy value, monitors an energy consumption generation cost coefficient, and calculates an inclination angle distribution entropy value; according to the method, slope gradient and pressure difference are combined to form a bearing risk ratio, path selection avoids a potential instability area, an inclination angle distribution entropy value depicts terrain uncertainty, a dynamic weight distribution table is generated through double-threshold judgment, a coordinate screening passing grid is input to form candidate nodes, and an AGV path sequence is generated through combination of an A * algorithm and weight adjustment. The environmental complexity is quantified through path evaluation, the energy efficiency level is dynamically measured through an energy consumption cost coefficient, weight distribution is regulated and controlled through a double-threshold mechanism, the direction is corrected in real time through path planning, comprehensive balance of stability, energy efficiency and safety is achieved, and reliability and adaptability in the complex slope environment are improved.

The embodiment of the invention discloses a personalized video generation model construction method and device, a medium and a program product. The method comprises the steps that an original weight of a pre-training model is combined with an initial identity base weight derived from an initially introduced low-rank matrix to generate an initial fine tuning model weight and freeze the original weight; training an initial fine-tuning model weight based on a disordered video frame set extracted from an input video and a static text prompt, obtaining a middle-stage fine-tuning model weight, generating a later-stage fine-tuning model weight in combination with an initial weight adjustment amount derived from a post-introduced low-rank matrix, and freezing the initially introduced low-rank matrix; and training a later fine-tuning model weight based on an ordered complete video frame set extracted from an input video and a motion text prompt, obtaining a target fine-tuning model weight, and unfreezing a frozen weight to obtain a video generation model. According to the method, through extraction of two video frame sets and staged training, a personalized video generation model which can effectively learn and has static and motion concepts is constructed.

The invention relates to a precise health monitoring method based on multi-source data fusion. The method comprises the following steps of 1, collecting multi-source data; step 2, data preprocessing; step 3, multi-source data fusion; 4, health analysis and decision making; step 5, dynamic weight adjustment; and step 6, feedback optimization. The hierarchical fusion strategy is adopted to process heterogeneous data, a machine learning algorithm is combined to establish an individualized health model, physiological state changes are tracked in real time, a targeted intervention scheme is generated, and in terms of technical implementation, the system supports collaborative decision making of various analysis models, including rule-based behavior reasoning, time sequence feature analysis and environment threshold judgment, and the system has the advantages of being simple in structure and convenient to use. The contribution degrees of information of different sources are balanced through an adaptive weight distribution mechanism, time-frequency analysis and dimension reduction technologies are fused in the feature extraction process, the dynamic mode of the health state is effectively captured, cross validation optimization is adopted in model training, and the reliability of an evaluation result is ensured.

The invention relates to the technical field of data processing, and particularly provides an intelligent decision-making method and system for multi-source data fusion, and the method comprises the steps: carrying out the parallel collection of environment data and equipment state data under the same timestamp, obtaining multi-source data, transmitting the multi-source data to a PLC at an equipment end, and carrying out the local preprocessing, performing time sequence feature extraction and anomaly detection on the preprocessed multi-source data to obtain labeled multi-source data and an abnormal event list, and inputting the labeled multi-source data and abnormal event list into a preset weight adjustment model to obtain a dynamically adjusted weight vector; and carrying out weighted fusion on the labeled multi-source data by using the dynamically adjusted weight vector to obtain fused data, and carrying out intelligent decision making based on the fused data. By carrying out dynamic weighted fusion on the environment data and the equipment state data, the overall intelligent regulation and control capability of the system is improved, and the problems that the reliability of multi-source data fusion is insufficient and the weight cannot be adaptively adjusted are solved.

The invention discloses a method for evaluating the security performance of an information system. The method comprises the following steps: S1, matching an asset security baseline and compliance; s2, a dynamic security situation awareness step; s3, a multi-agent collaborative evaluation step; according to the information system safety performance assessment method, through real-time perception and trend prediction, the crossing from static assessment to dynamic early warning is realized, the adaptability to dynamic risks is improved, meanwhile, a multi-dimensional assessment system integrating asset compliance, dynamic situation and service toughness is established, and the assessment efficiency is improved. According to the method, firstly, multi-agent collaboration and topological correlation analysis are adopted, system key paths and deep correlation risks are accurately recognized, the risk recognition depth is improved, finally, evaluation full-process automation and model self-learning optimization are achieved, the evaluation efficiency is greatly improved, and the method is suitable for large-scale popularization and application. And the continuous advancement of the method is ensured.

The invention discloses a distributed big data real-time processing and analysis system, which belongs to the technical field of big data real-time processing, and comprises a multi-dimensional data flow acquisition module, an elastic resource scheduling module, an increment state management module, an intelligent fault-tolerant coordination module and a real-time analysis output module, refined dynamic scheduling of computing resources is realized through an adaptive weight adjustment strategy, and the resource utilization rate is improved by more than 40%. Through a fine-grained increment state management technology, only change data is recalculated, the processing delay is reduced by 60%, the throughput is improved by 150%, and through an intelligent fault-tolerant coordination mechanism, the fault recovery time is controlled within 200ms, and the availability reaches 99.99%. The problems of inflexible resource scheduling, low data processing efficiency and weak fault-tolerant capability in the prior art are solved, efficient, low-delay and high-reliability processing of mass real-time data streams is realized, and the method is suitable for application scenes such as financial risk control, telecommunication monitoring and Internet of Things analysis.

The invention discloses a multi-mode nonlinear model predictive control method and system for a visual servo-driven arm-carrying unmanned aerial vehicle, and belongs to the technical field of intelligent control of arm-carrying unmanned aerial vehicles. The objective of the invention is to solve the problems of unstable control mode switching and difficulty in unified control modeling of different target structures in existing unmanned aerial vehicle grabbing control. According to the technical scheme, a task-driven modal judgment mechanism is introduced, the current task modal can be judged in real time based on the target type, the image features and the flight state information, control strategy self-adaptive switching between a rod-shaped target and a plane target is achieved, and continuity and stability of control logic are guaranteed. For a rod-shaped structure target, a remote approaching, servo adjustment and compliant contact three-stage control strategy is designed, and a dynamic weight adjustment mechanism is introduced into NMPC control solution, so that smooth transition of control performance from quick response to fine compliant is realized, and the control precision and compliance of the arm-carrying unmanned aerial vehicle in different operation stages are remarkably improved. For a plane structure target, the invention provides an adsorption control strategy based on impact time prediction and angle feedforward compensation, the contact time can be accurately estimated, the attitude of the aircraft can be adjusted in advance, the adsorption stability is effectively improved, and the contact failure probability caused by inclination of a target surface is reduced.

The invention provides a circuit breaker monitoring method and system based on multi-state sensing, a medium and equipment, and relates to the technical field of electrical equipment monitoring. According to the technical scheme provided by the invention, the initial influence weight of the directed edge is dynamically updated into the target influence weight according to the weight adjustment rule corresponding to the event type of the operation event, so that the fault propagation map accurately reflects the actual change of the influence relationship of the current operation event on the components. And calculating the first node health degree of each node based on the updated target influence weight and the multi-dimensional state data, accurately evaluating the current health state of the circuit breaker, predicting the second node health degree within the duration of the operation event, and realizing the pre-judgment of the development trend of the health state. According to the first node health degree and the second node health degree, the monitoring mode is determined, and the early warning response of the corresponding level is triggered, so that the timeliness and pertinence of early warning are ensured, and the accuracy of monitoring and early warning of the circuit breaker can be improved.

The invention discloses a lightweight monocular three-dimensional target detection method, system and device and a storage medium, and relates to the field of computer vision and artificial intelligence, and the method comprises the following steps: a teacher model construction step: constructing a teacher model based on a CNN and complex Transform fused backbone network, multi-task output capabilities of three-dimensional target detection, pseudo 3D target detection and depth estimation are configured; a student model construction step: the student model adopts a backbone network fused by a lightweight CNN and a simple Transform, and only three-dimensional target detection is reserved; a mixed knowledge distillation design step of constraining training of a student model through feature semantic distillation, probability distribution distillation and task supervision distillation; a dynamic weight adjustment step: dynamically adjusting the weight of each loss function in distillation; and a model training and end-side deployment step: obtaining a lightweight model through training in the above steps, and deploying the lightweight model on an end-side hardware platform to carry out real-time three-dimensional target detection so as to realize high-precision three-dimensional target detection under low calculation cost.

The invention discloses a rail transit continuous positioning method realized by combining Beidou and inertial navigation, and particularly relates to the technical field of rail transit localization, comprising the following steps: completing weight adjustment and error synchronization before failure by evaluating a navigation signal environment state in real time, predicting a shielding risk and identifying a signal degradation trend; the precision of the inertial navigation system is guaranteed. Constructing a trajectory prediction window to constrain an inertia trajectory during shielding, judging the stability of a recovery signal after the recovery signal is received, and supplementing a trajectory error based on a bidirectional residual error correction mechanism after the recovery is confirmed, so as to realize whole-course continuous positioning of rail transit; according to the invention, through navigation signal degradation trend pre-judgment, inertial navigation system dynamic switching preparation and a bidirectional residual error compensation mechanism, continuous track positioning of a train inside and outside a shielding area is realized, the stability and robustness of a positioning system are improved, and non-interruption and non-jump output of the whole track traffic process is ensured.

The invention belongs to the technical field of fabric process chain control, and particularly relates to a multi-process cooperative control system and method based on an open-width processing process chain. According to the method, the downstream is compensated in advance through disturbance source identification and feed-forward prediction, response lag and overshoot caused by disturbance transmission are remarkably reduced, the cloth cover is restored to a target state more quickly, and full-line tension fluctuation and breadth deviation are effectively reduced by coupling feed-forward and feedback and combining self-adaptive control of fabric humidity and temperature weighting; the defect rates of breadth breaking, wrinkling, edge warping and the like are reduced, the qualified rate and consistency of finished products are improved, a good control effect can be kept under different raw material, humidity and temperature conditions based on dynamic weight adjustment of historical data and fabric states, manual intervention and frequent parameter resetting are reduced, a disturbance source positioning mechanism can rapidly indicate the problem occurrence procedure, and the working efficiency is improved. And maintenance and root cause analysis are facilitated.

According to the financial risk control risk assessment method based on the dynamic list library, the problems that a traditional method is poor in real-time performance, low in precision and insufficient in adaptability are solved through dynamic weight self-calibration list library construction, multi-model fusion of a self-adaptive scene, real-time assessment of dual-channel cross validation and a closed-loop self-adaptive response mechanism. The core innovation of the method lies in that a list library and model evaluation are deeply coupled, risk attenuation factors, dynamic weight adjustment and feedback closed loops are introduced, the fraud recognition rate is improved, the misjudgment rate is reduced, and the method can be widely applied to financial scenes such as banks and payment platforms.

The invention provides a deep evidence fusion-based target detection and tracking method for an intelligent maritime investigation instrument, and belongs to the technical field of multi-modal sensor fusion. The method comprises the following steps: performing feature extraction on data of each modal by using a deep learning model, generating a corresponding evidence quality function, then correcting the weight of a sensor through context discount, and finally performing evidence fusion through a Dempster-Shafer rule to generate a global decision result. Another technical innovation point is that the system designs a mechanism for dynamically adjusting the weight of the sensor, and the fusion process is dynamically optimized according to a real-time environment and historical data so as to ensure the self-adaptability of the system in different environments. According to the invention, through combination of a real-time weight adjustment mechanism and a multi-modal fusion algorithm, the precision of sea target detection is improved, sudden environmental changes can be dealt with, and the robustness and high efficiency of the system in long-time operation are ensured.