295 results about "Evaluation algorithm" patented technology

The invention provides a printing color consistency automatic calibration and management system and method, and the method comprises the steps: collecting a current environment temperature and humidity ink matching parameter density curve data dot increase compensation parameter in real time through a state detection device, and forming a current state data set; performing similarity analysis on the reference data set and the current state data set by adopting a matching degree evaluation algorithm, and judging a relationship between a matching degree numerical value and a preset threshold value; adjusting and correcting the calibration reference value and the color control parameter in the reference data set according to the offset compensation set to obtain a compensated and adjusted parameter set; on the basis of the compensated and adjusted parameter set, generating update code data by combining a current time identifier and an operator identifier, and forming new two-dimensional code information; and outputting the new two-dimensional code information to a subsequent work order carrier by adopting a code generation module, and establishing a parameter transmission tracing chain.

The invention belongs to the technical field of Internet of Things security, and particularly relates to a multi-level Internet of Things equipment security management and access control method, which comprises the following steps of: performing bidirectional verification on a digital certificate and a hardware fingerprint through an edge node when equipment is accessed; calculating credibility in real time and dynamically dividing security levels by using a hybrid evaluation algorithm based on equipment operation context data; a fine-grained access strategy is generated in combination with equipment attributes and environmental risks, and cross-level collaboration is realized through a lightweight protocol; abnormal behaviors are detected in real time in the access process, and high-risk operation is blocked within milliseconds; and strategy conflict resolution and adaptive optimization are realized through formalized verification and deep reinforcement learning. According to the technical scheme, the identity authentication reliability, the authority dynamic adaptability, the strategy consistency and the threat response speed of the Internet of Things equipment are remarkably improved, and a systematic security guarantee is provided for large-scale Internet of Things applications with high security requirements.

The invention relates to the technical field of Internet of Things gateways, and discloses an Internet of Things gateway device and system based on edge computing, and the device comprises a hardware heterogeneous computing module and a multi-interface protocol adaptation module. The edge calculation local processing module is used for performing preprocessing, feature extraction and local decision on the acquired terminal data; the network redundancy and self-healing module is used for realizing accurate network disconnection detection, seamless switching of a main network and a standby network and equipment fault self-recovery through a multi-dimensional network health degree evaluation algorithm; the local autonomous storage module is used for realizing hierarchical storage of high-frequency data and network disconnection emergency data and seamless synchronization after network recovery through network disconnection data hierarchical caching and an incremental synchronization algorithm; and the cloud collaborative management module is used for realizing remote configuration, OTA upgrading and state monitoring. The method is high in integration, low in delay and fast in response, adapts to real-time scenes, saves bandwidth cost, improves cloud efficiency, is high in reliability, and realizes network disconnection and continuous operation.

The invention relates to a rail transit engineering digital twin management and control method based on unmanned aerial vehicle honeycomb inspection, and solves the problem that a twin model is disjointed from the real-time state of an engineering field, and the method comprises the steps: firstly obtaining multi-source data containing BIM, GIS and Internet of Things data and an unmanned aerial vehicle initial image; carrying out BIM lightweight processing, carrying out registration with a GIS (Geographic Information System) and establishing an engineering entity-environment-event dynamic association network to form a digital twin reference model; based on the real-time state data of the model and external factors, a multi-dimensional evaluation algorithm is used to generate an inspection task priority; an adaptation rule is established in combination with an unmanned aerial vehicle honeycomb performance threshold, and unmanned aerial vehicle obstacle avoidance inspection is scheduled; the inspection data is preprocessed and then fused to the twin model, and the twin model is driven to dynamically iterate so as to realize real-time synchronization with the site. The method has the following effects: real-time and precise rail transit engineering management and control are realized through digital twin virtual-real mapping and unmanned aerial vehicle honeycomb intelligent inspection closed loop, and the core management and control efficiency is improved.

The invention relates to the technical field of quality regulation and control, in particular to a self-adaptive pile forming quality regulation and control system and method for soft foundation treatment pile construction. The method comprises the steps that a construction parameter acquisition unit acquires key construction parameters in real time through a multi-type sensor network; the data preprocessing and feature extraction unit is used for preprocessing the collected key construction parameters and extracting key feature parameters reflecting the construction stability and the pile forming uniformity; and the pile-forming quality judgment and deviation recognition unit is used for comparing the key characteristic parameters with a preset pile-forming quality threshold model in real time, judging the quality state of the pile-forming process and outputting a deviation category and a deviation strength index. According to the method, the limitation that judgment only depends on a single parameter threshold in the prior art is broken through, and a multi-mode coupling resonance intensity evaluation algorithm is provided for solving the problem that nozzle abrasion is high in concealment and great in influence.

The invention relates to the technical field of smart city information management, in particular to a smart city information management method and system based on GIS and AI fusion, and the method comprises the steps: carrying out the data mining and statistical analysis of the basic information of each economic entity in a city, and achieving the overview of economic development situations; collecting geographic position data of an economic body and matching coordinates, and constructing a three-dimensional digital city model associated with a GIS system; a hierarchical comprehensive evaluation algorithm is adopted to calculate the economic innovation value, the contribution degree of the economic innovation value is analyzed, and a long-short memory network is utilized to carry out space-time dynamic evolution analysis and trend prediction; and intelligent semantic understanding and personalized question and answer feedback are realized by analyzing a multi-modal query request of a user. The problems that an existing system is difficult in data fusion, static state analysis lags behind, and decision support is insufficient are solved, unified perception, intelligent analysis and scientific decision support of the urban operation state are achieved, and the urban fine management and service level is remarkably improved.

The invention belongs to the field of underground transportation, and particularly relates to a cascade failure-based subway freight network operation robustness evaluation method. Making a subway freight network cascade failure redistribution strategy; constructing a subway freight network cascade failure load redistribution model; constructing a subway freight network robustness evaluation index; and designing an evaluation algorithm for the robustness of the metro freight network based on the established cascade failure redistribution strategy of the metro freight network in combination with the constructed cascade failure load redistribution model and the proposed robustness evaluation index system, thereby realizing quantitative evaluation of the operation robustness of the network in the cascade failure situation. According to the invention, a scientific basis can be provided for planning and operation management of a subway freight system, and the reliability and safety of an urban comprehensive transportation system are effectively improved.

The invention discloses a highway intelligent risk monitoring method and system based on multi-modal information, and the method comprises the steps: collecting multi-modal traffic data, constructing a road network holographic portrait through a road network holographic portrait risk prediction model, positioning a potential risk point through a dual time-space mask anomaly detection model, and carrying out the detection of the potential risk point. Lane-level risk assessment is completed by means of a lane-level risk dynamic assessment algorithm, and risk information is output after multi-dimensional fusion analysis is carried out through a traffic risk intelligent research and judgment platform. All the models and algorithms are operated cooperatively, data integration, feature mining, anomaly recognition, risk assessment and fusion research and judgment are achieved step by step, a whole-process monitoring system from data collection to result output is constructed, refined and dynamic risk monitoring from the global road network to the local lane is achieved, the risk recognition accuracy and monitoring comprehensiveness are effectively improved, and the risk monitoring efficiency is improved. And technical support is provided for safe operation of the expressway.

The invention particularly relates to an intelligent monitoring method and system for the service life of an ion exchange resin mixed bed, and belongs to the technical field of ion exchange resin water treatment. The method comprises the following steps: collecting real-time flow and real-time conductivity of mixed bed inlet water; executing a mixed bed service life intelligent evaluation algorithm, and dynamically estimating a mixed bed exchange capacity consumption rate and a mixed bed residual operation duration; displaying the exchange capacity consumption rate of the mixed bed and the residual running time of the mixed bed in real time in the form of numbers, progress bars or trend curves; and when the exchange capacity consumption rate of the mixed bed and the residual operation duration of the mixed bed are lower than a preset threshold value, triggering an early warning prompt. The system implements the steps of the method. The invention provides the universal, convenient and economic intelligent monitoring method and system for the service life of the ion exchange resin mixed bed, and the real-time dynamic monitoring of the residual exchange capacity of the ion exchange resin mixed bed and the accurate prediction of the residual operation service life are realized.

The invention relates to the technical field of image processing, and discloses an AI-based lithium manganate process parameter regulation and control method and system. The method comprises the following steps: collecting lithium manganate preparation whole-process technological parameters through an infrared temperature measurement array, a pH sensor and an electrochemical workstation to establish a database, training and generating a technological parameter predictor by using an Actor-Critic deep reinforcement learning algorithm, quantifying a deviation degree output early warning level based on a fuzzy comprehensive evaluation algorithm, and determining the lithium manganate preparation whole-process early warning level according to the deviation degree output early warning level. And starting a self-adaptive PID control algorithm to generate a multi-equipment coordination control instruction set, synchronously driving an inclined mixer, a roasting kiln fan and an electrolytic bath temperature control system to adjust, detecting the product quality through an X-ray diffractometer, and feeding back an optimization control instruction. According to the invention, the process parameter control precision and the product quality stability in the lithium manganate preparation process are improved.

The invention relates to the field of energy storage and reactive compensation capacity optimization configuration, in particular to a new energy pooling station energy storage and reactive compensation configuration method based on big data analysis. The method comprises the following steps: firstly, acquiring data, respectively obtaining a sensitivity coefficient of active power and a sensitivity coefficient of reactive power through a disturbance response sampling linearization identification method, and introducing a dynamic active-reactive decoupling voltage improvement evaluation algorithm based on sensitivity weighting to obtain a voltage improvement amplitude of a node; then, based on the voltage improvement amplitude of the node, constructing a target function; and carrying out iterative solution on the constructed objective function, and outputting the optimal active power regulation quantity of each energy storage unit and the optimal reactive power regulation quantity of each reactive compensation device. The technical problems that the sensitivity coefficient is difficult to update in real time, the calculation scale is huge, the response speed of real-time optimization scheduling, unnecessary active loss and battery life loss are seriously influenced, and the physical balance of the voltage improvement amplitude between the nodes is ignored are solved.

The invention discloses a multi-modal data acquisition and processing system for dynamic pressure assessment, and the system comprises a multi-modal data acquisition module which synchronously acquires dynamic pressure signals of different modals through a plurality of high-frequency response pressure sensors; the data preprocessing module is used for carrying out real-time signal conditioning processing on the dynamic pressure signals in different modes to obtain preprocessed multi-mode dynamic pressure signals; the multi-modal data fusion module is used for carrying out space-time alignment and feature extraction on the preprocessed multi-modal dynamic pressure signals by adopting a multi-factor coupling model, and generating fusion pressure data through a fusion algorithm; and the dynamic pressure evaluation module is used for executing time-frequency domain analysis and mode recognition on the fusion pressure data through an analysis model, extracting key characteristic parameters, calculating a dynamic pressure index through a pressure evaluation algorithm, and generating an evaluation result containing the dynamic pressure index. According to the invention, high-precision and real-time dynamic pressure assessment can be realized, and the comprehensiveness and reliability of data acquisition and analysis are improved.

The application is suitable for the technical field of data storage, and provides a data disaster recovery method and device, comprising the following steps: determining a fault type in a distributed cluster system and a range of data to be recovered; acquiring state parameters of multiple normal nodes in the distributed cluster system, and service sensitivity features and data block access features of the data to be recovered; performing data preprocessing operations; respectively adopting a service sensitivity evaluation model and a node state evaluation model to determine a data recovery priority list and a node priority list; determining a fault processing strategy according to the data recovery priority list and the node priority list; and executing the fault processing strategy. Through the above method, the real-time evaluation algorithm of the heterogeneous big data node is increased on the basis of dynamically updating the data recovery priority based on the service sensitivity evaluation model, and compared with the traditional technology, the fault perception of the business system can be reduced while the data disaster recovery efficiency is improved.

The invention belongs to the technical field of photoelectric detection imaging, and particularly relates to a dim target enhancement device and method based on range gating polarization imaging, the device comprises a transmitting system and a receiving system, the transmitting system comprises a pulse laser, a signal modulation circuit and a transmitting optical system; the pulse laser is used for emitting laser beams; the signal modulation circuit is used for generating a high-frequency modulation electric signal and modulating a laser beam emitted by the pulse laser; the emission optical system is used for focusing and adjusting the light beam direction and the emergent light polarization state; the receiving system comprises a receiving optical system, a focal plane polarization detector, a synchronous control board, a signal processing board, an image processing board and display control equipment; according to the device, the pulse illumination module, the time synchronization control module and the polarization imaging module are integrated, and an image definition evaluation algorithm is combined, so that synchronous acquisition and fusion of target polarization information and distance information are realized.

The invention relates to the technical field of AI computing power evaluation scoring, and provides a console AI computing power evaluation scoring system which comprises a computing power data set management unit, a console task scheduling unit, a multi-hardware computing power execution unit, a computing power comprehensive calculation unit, a safety monitoring and adapting unit and a visual output unit. The data collection unit is responsible for collecting, cleaning and marking multi-type data sets related to console computing power evaluation and establishing association mapping of the data sets and console hardware parameters, and the console task scheduling unit dynamically creates evaluation tasks based on console hardware loads. Through automatic data set management and task scheduling, the evaluation period is greatly shortened, and the pain points that manual evaluation is low in efficiency and prone to making mistakes are thoroughly solved; by means of a hardware adaptation related module, the comparability of computing power scores among hardware of different brands and architectures is remarkably enhanced, and evaluation algorithm adaptation of newly-accessed small-scale industrial hardware can be rapidly completed.

The invention relates to the technical field of information processing and commanding and dispatching, and discloses an information commanding and disposing method and system based on multi-source information fusion. According to the method, information flow features are analyzed through an event emergency degree evaluation algorithm, and an emergency degree score is calculated in combination with an event type rule base and an arrival rate deviation degree; obtaining the queue length and the average waiting time of the processing queue, and calculating a queue congestion factor; a dynamic priority value is calculated according to the product relation of the emergency degree and the queue congestion factor, and when the system load is increased, the priority difference between the high and low emergency degree information flows is increased; reordering the processing queues based on the dynamic priority values; when the queue congestion factor exceeds a threshold, the sampling frequency of the low priority information stream is reduced and the processing node extension is triggered. According to the method, the technical problem that key information is delayed to be processed in a multi-source information flow concurrence scene is solved, and the real-time performance of information processing in an emergency scene is improved.

The invention belongs to the field of battery thermal runaway early warning, and particularly relates to an early warning method and device for thermal runaway of a lithium ion battery pack, and the method comprises the steps: firstly collecting the voltage, temperature and current data of a battery cell, and judging the grade of a current working condition according to a preset rule; self-adaptively matching a corresponding alternating current excitation strategy according to the working condition grade and applying excitation; synchronously acquiring the compensated excitation current and the voltage response of each battery cell through a sampling chip configured with a compensation strategy; based on the response information, a multi-dimensional impedance anomaly judgment strategy is combined, and a judgment parameter set containing impedance deviation, thermal runaway risk, voltage and temperature deviation flag bits is calculated and generated; and finally, determining the thermal runaway risk level of the battery pack through an evaluation algorithm according to the parameter set, and packaging and reporting the thermal runaway risk level through CAN communication. According to the invention, on-line and accurate sensing and early warning of the internal state of the battery under various working conditions such as standing and dynamic operation are realized.

The invention provides a vocational education teaching quality evaluation system, and belongs to the field of teaching quality evaluation, and the system comprises a data collection layer which is configured to collect multi-source evaluation data for teaching quality evaluation; evaluation subjects of the multi-source evaluation data comprise ideological and political course teachers, specialized course teachers, tutors, enterprise tutors and students; the micro-service layer is configured to evaluate the teaching quality based on a time sequence individual entropy weight evaluation algorithm according to the multi-source evaluation data; the user interface layer is configured for each evaluation main body to log in the evaluation system through a webpage or a mobile terminal and input evaluation data for each service unit of the micro-service layer to call; and the block chain layer is configured to package and store the multi-source evaluation data in a block chain according to a time sequence. The vocational education teaching quality evaluation method can solve the technical problems that only the teaching effect of the students during the school is considered, and the effect feedback in the aspects of practical application ability and quality after the students participate in work is not introduced in vocational education teaching quality evaluation.

The invention relates to a verification cascade failure early warning method and device for a complex simulation system. The method comprises the following steps: constructing a multi-layer verification network according to various verification tasks of the complex simulation system; and dividing each verification task into a plurality of continuous time slices according to the multi-layer verification network, wherein each time slice corresponds to a complete operation and index acquisition process of the complex simulation system. And calculating the state transition probability between the nodes of the multi-layer check network, so that the Bayesian network is adopted to obtain the failure probability of the check node state in the multi-layer check network. And performing failure evaluation on the joint state between the check nodes on the key propagation path to obtain a joint failure probability. And constructing a three-level early warning mechanism according to the failure probability and the joint failure probability, and performing early warning state sample iterative learning through an optimization evaluation algorithm to generate a cascade failure early warning scheme. By adopting the method, the verification efficiency and the self-adaptability of the complex simulation system can be improved.

The invention provides a network autonomous operation and maintenance method and device fusing topology discovery and situation awareness, and relates to the technical field of network operation and maintenance, and the method comprises the steps: achieving the automatic recognition of network equipment, the automatic construction of a connection relation, and the real-time updating of a topological graph through a distributed deployment IP probe and a protocol adaptive collection technology; then collecting multi-source heterogeneous data, and performing quantitative evaluation and security threat identification of the network situation based on grey correlation analysis and a threat evaluation algorithm; and finally, carrying out automatic repair and performance optimization on a network fault based on dynamic load balancing and an autonomous decision-making mechanism. According to the invention, a topology automatic discovery module, a situation awareness analysis module, an autonomous decision and control module and an operation and maintenance management module are integrated; according to the invention, through integration of multiple modules for cooperative work, automatic discovery of network topology, accurate perception of situation, self-repairing of faults and efficient management of operation and maintenance are realized, manual intervention is greatly reduced, and the intelligent and automatic level of network operation and maintenance is improved.

The invention discloses a spaceflight structure strength analysis method based on a multi-physics field, which comprises the following steps: establishing a parameterized finite element model integrated with a friction heat generation effect by acquiring in-orbit temperature time sequence data and material creep characteristic parameters, and simulating inelastic strain accumulation of a structure under a thermal cycle load; a nonlinear damage model considering a creep-fatigue interaction effect is adopted to calculate an accumulated damage degree, an effective stiffness matrix is constructed to deduce a structure inherent frequency drift distance and a shape retention capability parameter, and finally, through a safety margin evaluation algorithm, the effective strength of a material and the maximum equivalent stress of the structure are dynamically compared, so that the performance of the material is evaluated. And accurate prediction of quantitative residual strength and safe service life is realized. According to the method, the technical problems that the local thermal coupling effect is not depicted enough and the creep fatigue nonlinear interaction effect is ignored are solved, and the precision and reliability of on-orbit performance degradation prediction of the spaceflight flexible structure are remarkably improved.

The invention discloses a hybrid enhanced power distribution network business expansion power flow and reliability cooperative verification method and system, and the method comprises the steps: inputting the topological data and historical power flow data of a power distribution network into a power flow calculation engine for large model reasoning acceleration, and obtaining the data of the power distribution network; inputting the data of the power distribution network and the wind, light and storage output models into a reliability evaluation algorithm containing new energy randomness to obtain a reliability index; performing digital twinning and federal learning data correction processing on the reliability index and the equipment real-time measurement data to obtain a corrected reliability index and an equipment parameter update value; performing N-1 fault simulation and self-healing strategy generation processing on the corrected reliability index, the equipment parameter update value and the equipment importance degree to obtain a fault simulation index and a self-healing strategy; and performing feedback optimization closed-loop processing on the fault simulation index and the self-healing strategy to obtain a structured improvement suggestion. According to the invention, an innovative solution is provided for business expansion verification of the high-proportion new energy grid-connected power distribution network.

The invention belongs to the field of mechanical engineering, particularly relates to a pinwheel tooth form error detection system based on laser point cloud adaptive calibration, and aims to solve the problem of inaccurate tooth form error detection caused by mechanical deformation, point cloud distortion, coordinate registration misalignment and environmental disturbance in a traditional measurement mode. The system integrates a laser scanning array, a six-degree-of-freedom fine tuning platform, an environment sensing unit and a point cloud processing engine, and real-time updating of calibration parameters and offset correction of a coordinate system are realized through dual mechanisms of static internal reference self-calibration and dynamic external reference compensation; and a closed-loop detection process is constructed by combining multi-view point cloud adaptive weighted fusion and an improved Hausdorff distance evaluation algorithm. The gear surface point cloud integrity and the measurement stability can be effectively improved, multi-model rapid remodeling is supported, full-automatic and high-repeatability online detection is achieved in a non-constant-temperature workshop environment, and the quality control efficiency and the process consistency of the pin wheel transmission part are remarkably improved.

This invention provides a method for determining the carbon dioxide huff and puff mechanism in fractured reservoirs, belonging to the technical field of determining the carbon dioxide huff and puff mechanism in fractured reservoirs. This invention establishes a unified digital core by selecting outcrop cores to create cast thin sections and employing multi-scale fusion reconstruction technology. Topological connectivity is evaluated based on a seepage topological invariant fracture network connectivity evaluation algorithm. During well shut-in, a diffusion-convection hybrid encoding / decoding model is used to predict migration in real time, and the model learning rate is adjusted by a dynamic learning rate adjustment function. After well shut-in, a feedforward-feedback composite control strategy is used to gradually reduce back pressure to complete the production stage. The experiment ends after cleaning the chip with anhydrous ethanol. Finally, the degree of crude oil recovery is statistically analyzed using image processing software. This invention solves the technical problem of not being able to predict the migration process in fractures and matrix of fractured reservoirs in real time during well shut-in.

The invention discloses a pneumatic rapping ash removal efficiency optimization method under a high-ash working condition, and relates to the technical field of flue gas electrostatic precipitation, and the method comprises the steps: carrying out the time window time frequency feature extraction of a high-ash working condition rapping data set, obtaining a rapping multi-mode feature set, quantifying the rapping performance state through a rapping state evaluation algorithm, and generating a rapping comprehensive state set; constructing a double-constraint rapping optimization model based on the rapping comprehensive state set, and generating a regional rapping optimization strategy by performing optimal configuration on rapping strength parameters; an area rapping optimization strategy is executed, the marginal dust removal effect of pneumatic rapping is recognized through sound and vibration characteristics in combination with a high-ash working condition rapping data set, and a rapping execution result is obtained; and performing self-adaptive adjustment on the double-constraint rapping optimization model according to a rapping execution result, and performing ash removal performance trend analysis in combination with the rapping comprehensive state set to obtain an ash removal efficiency optimization report. By constructing the vibrating comprehensive state set, the ash removal efficiency is improved, and the unit ash removal energy consumption is reduced.

The invention relates to the technical field of computer vision, in particular to an end-to-end single target tracking method combining underwater frequency domain scene prompt and light parameter fine tuning. The method comprises the following steps: acquiring a tracking template graph and a search area image through a to-be-tracked video; generating a scene feature map by using a frequency domain enhancement algorithm; recoding the underwater target features by using a feature embedding extraction algorithm; taking the token mark sequence and the scene feature token mark sequence as the input of an underwater scene prompter, respectively calculating the attention scores of the two token mark branches by using a target perception attention algorithm and an auxiliary modal importance evaluation algorithm, and generating new scene features; introducing a cross-layer weight fusion mechanism, performing further feature extraction on the underwater scene in combination with shallow and deep features, and outputting target frame information through a prediction head network; in a network model training process, the performance of executing a specific underwater task is improved by adjusting a partial multi-head attention mechanism algorithm of a trunk vision Transform layer encoder.

The invention provides an enterprise management method and system and computer equipment, and the method comprises the steps: determining initial enterprise data of a plurality of enterprises and a corresponding enterprise management strategy, carrying out the preprocessing of the initial enterprise data, obtaining target enterprise data, generating an enterprise global database, and carrying out the calculation of the enterprise global database according to the enterprise management strategy. Determining at least one target evaluation algorithm and a corresponding service providing strategy, and based on each target evaluation algorithm, according to the target enterprise data of the plurality of enterprises in the enterprise global database, performing evaluation processing on the development conditions of the plurality of enterprises to obtain an enterprise development condition evaluation result corresponding to each target evaluation algorithm, and providing a service matched with the development condition of the target enterprise for the target enterprise based on the enterprise development condition evaluation result and the service providing strategy corresponding to each target evaluation algorithm. Therefore, operation complexity of enterprise management is reduced, timeliness and accuracy of service providing and enterprise management are improved, enterprise satisfaction is improved, and corresponding management of multiple enterprises is facilitated.

The invention relates to the technical field of data transmission, in particular to a processing and risk assessment method and device for mass files and asset data and a medium, and the method comprises the steps: dividing to-be-processed mass files and asset data into a plurality of data clusters; based on a duration evaluation algorithm and a risk evaluation model, calculating the expected total processing duration and the risk level of each data cluster, and processing each data cluster according to the expected total processing duration and the risk level; performing real-time feature extraction and vectorization representation on the asset data, and matching asset features with a risk information feature library to identify and evaluate the risk of the asset features; when it is monitored that the data cluster transmission state or asset data analysis is abnormal, an auxiliary processing thread is started to continue executing the next task in the data cluster, the average processing speed of the corresponding data cluster is updated, and the total processing duration of the remaining tasks is evaluated again. Therefore, the data processing efficiency and the data security of the power system are improved.

According to the multi-sensor fusion evaluation algorithm and device based on the continuous time sequence, modeling is carried out based on continuous time, physical reality is better met, motion modeling with better precision is achieved, intra-frame motion can be accurately described, smoother and more accurate track estimation can be provided, and the method and the device are suitable for being applied to multi-sensor fusion evaluation. Especially, the advantages are obvious in high-speed and high-frequency vibration scenes; meanwhile, according to the technical scheme disclosed by the invention, when LiDAR / Camera matching fails, the constraints of the IMU and the GNSS are seamlessly and continuously applied to the whole track through a continuous time model instead of only acting on a discrete frame, so that error accumulation is greatly inhibited, system collapse is avoided, the robustness of a degraded scene is enhanced, application in specific fields such as surveying and mapping is greatly facilitated, and the method is suitable for popularization and application. And the method has the capability of flexibly adding constraints.

The invention discloses a cultural tourism economic operation data monitoring method, which comprises the following steps of: constructing a multi-dimensional value radar index system: establishing a multi-dimensional evaluation framework, and quantifying bridge and tunnel values; deploying a multi-source data acquisition network: constructing an integrated data acquisition system, and performing real-time mapping between a physical space and a digital space; and adaptive fusion analysis: constructing a quantum state superposition classifier, representing bridge and tunnel characteristics by using quantum bits, constructing a real-time digital twin mirror image engine, carrying out dynamic evaluation algorithm loading through linkage of a data acquisition network, a mirror image body and an algorithm pool, and introducing an ecological correction coefficient and a hidden value correction coefficient to carry out negative adjustment on an economic contribution value. Dynamically distributing an index weight by combining an entropy weight method and a Delphi method with a particle swarm optimization algorithm, and outputting an economic equivalent value in combination with the index weight; building a four-dimensional visual decision platform; and displaying the interactive radar map based on WebGL three-dimensional rendering and data binding technology.