2579 results about "Dynamic data" patented technology

The invention provides a fatigue life prediction and residual life evaluation method for a high-power heavy-duty gearbox, and belongs to the technical field of intelligent operation and maintenance based on computer data processing. Comprising the following steps: acquiring dynamic data in an operation process, and performing multi-scale decomposition to form multi-source multi-scale data; inputting the multi-source multi-scale data into a designed multi-scale fatigue feature extraction module and a health state prediction module to obtain a multi-scale health index sequence and a health state label; establishing a fatigue damage evolution model, introducing the generated health index sequence for self-adaptive updating, outputting a comprehensive damage value, performing staged evaluation of fatigue degradation to obtain a damage label set, and performing multi-scale health index sequence and health state labels as well as the comprehensive damage value and the damage label set to obtain a multi-scale health index sequence and health state labels; inputting into a designed double-source fusion fatigue life prediction model, and outputting residual life prediction quantity; according to the invention, high-precision prediction and residual life evaluation of the fatigue life of the high-power heavy-duty gearbox are realized.

The invention relates to the technical field of power distribution network evaluation, and discloses a power distribution network bearing capacity evaluation system based on dynamic correction. The system comprises a dynamic data acquisition module, a multi-dimensional state space construction module, a security domain analysis module, a partition coupling degree calculation module and a bearing capacity evaluation engine module. The dynamic data acquisition module acquires a power injection quantity sequence, a voltage deviation ratio sequence and uncontrollable parameter fluctuation data of each partition node of the power distribution network; a multi-dimensional state space construction module performs dimension raising mapping on the sequence to generate a linearized power flow state space model containing a power-voltage Jacobian matrix; the security domain analysis module corrects the boundary of the model according to uncontrollable parameter fluctuation and generates a dynamic security operation constraint set; the partition coupling degree calculation module quantifies an electrical independence index by means of a spectrum radius; and the bearing capacity evaluation engine constructs a chance constraint optimization model, outputs the photovoltaic maximum accessible capacity of each partition and a safety guarantee supply control strategy set, and improves the evaluation accuracy and practicability.

The embodiment of the invention provides a computer communication method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-level system architecture, carrying out the preprocessing and feature extraction of an original data flow, recognizing the data characteristics through a time sequence analysis method, and constructing a dynamic data model; deploying a monitoring agent at a transmission node to collect network performance indexes in real time, and constructing a network quality evaluation model; for an incomplete sensor data flow, prior probability distribution is constructed based on a dynamic data model and a network quality grade, and an optimal estimation value of missing data is calculated by adopting a Bayesian reasoning framework and an iterative algorithm; establishing a mapping relation between a network state and an optimal parameter through reinforcement learning to realize self-adaptive adjustment and optimization; and grading the data according to reliability, extracting high-reliability data points as anchor points, designing an iterative refinement algorithm to realize information propagation, and fusing to obtain a complete sensor data stream. According to the method, the problems of poor data recovery accuracy, static parameter configuration and insufficient adaptability in a complex network environment are solved.

The invention discloses an intelligent water affair monitoring and management system based on digital twinning, and relates to the technical field of intelligent water affair. The intelligent water affair monitoring and management system comprises a water affair monitoring and management platform, and the water affair monitoring and management platform is in communication connection with the following modules: a multi-source data acquisition module; the water affair monitoring system is used for collecting and preprocessing water affair monitoring data from a plurality of monitoring points of the water affair system, monitoring changes of a pipe network topological structure and obtaining dynamic data of a pipe network connection relation and geometric parameters. Through the digital twinborn technology, data of multiple monitoring points can be integrated in real time, abnormal events such as water quality pollution, equipment faults, water shortage and hydraulic change can be rapidly recognized in combination with the abnormal trend analysis module, early warning signals are automatically generated, the response time is remarkably shortened through an instant early warning mechanism, and the early warning efficiency is improved. Therefore, the management personnel can take measures at the initial stage of the abnormal event, the problem expansion is effectively prevented, and the timeliness and accuracy of water management are improved.

The invention relates to a defect positioning method based on fusion of weld defect features and trajectory tracking data, and belongs to the technical field of weld defect detection and positioning. The method comprises the following steps: capturing welding seam track dynamic data and defect feature data, constructing a dynamic coordinate system based on a welding seam initial feature point, and establishing double-data-set reference mapping; performing multi-physics field interference decoupling correction on the trajectory data, and performing cross-modal feature purification and core feature consistency verification on the defect data; converting the preprocessed data into a feature form adaptive to fusion, and constructing a welding process-defect formation mechanism association network to regulate and control fusion weight; and finally, reconstructing a three-dimensional dynamic contour of the welding seam, calling dynamic positioning logic to position the defect, and outputting a result carrying the process-defect causal confidence coefficient. The positioning precision is improved through multi-dimensional data fusion and mechanism association, and technical support is provided for welding quality management and control.

The invention relates to the technical field of data processing, and discloses a knowledge graph incremental updating and consistency checking method and system. The method and the device are used for solving the problem of low incremental updating and consistency checking efficiency of a large-scale knowledge graph. The method comprises the following steps: collecting a to-be-updated data source, sorting and separately storing data, and marking priority labels; performing change detection on the marked data source, identifying change items by comparing entities and relationships, and generating a log; incremental updating is executed based on the log, and nodes and edges are processed in a hierarchical fusion mode; preliminary consistency verification is carried out, and attribute uniqueness and relation directivity are checked; expanding a verification range, traversing an association path through cascade check, and recording problems; and optimizing storage according to the record, merging the update area, updating the index and cleaning the log. The method solves the problem of low efficiency of incremental updating and consistency verification of the large-scale knowledge graph, improves the response speed and the data accuracy of the system, and is suitable for a high-frequency dynamic data environment.

The invention relates to the technical field of petroleum and natural gas engineering, and discloses an oil and gas reservoir optimized mining method based on multi-modal data, which comprises the following steps: constructing an oil and gas reservoir multi-modal data acquisition system, seismic wave field data, logging interpretation data, production dynamic data, micro-seismic monitoring data, underground temperature and pressure time sequence data and shaft structure parameter data are obtained through the acquisition system; and performing space-time alignment processing on the acquired multi-modal data, establishing a unified geological coordinate system and a time reference, and eliminating data isomerism caused by different acquisition frequencies and spatial resolutions. A six-dimensional heterogeneous data acquisition system covering a seismic wave field, well logging interpretation, production dynamics, micro-seismic monitoring, an underground temperature and pressure time sequence and shaft structure parameters is constructed, so that the depiction precision of a reservoir porosity field, a permeability field, a saturation field and a pressure field is essentially improved.

The invention discloses a system and method for estimating the charging capacity of an energy storage lithium battery, and particularly relates to the technical field of energy storage battery management, and the method comprises the following steps: collecting dynamic parameters in the charging process of the energy storage lithium battery, and forming a dynamic data set; calculating an environmental disturbance influence index and a charging response consistency factor based on time sequence analysis and feature decoupling; constructing a two-dimensional working condition mapping matrix, identifying a current working condition area and generating a scene label; determining parameter weights of the plurality of estimation models by adopting a probabilistic reasoning mode; according to the scene state, selecting a single model output result or fusing a plurality of model output results for estimation; according to the method, the environment disturbance influence index and the charging response consistency factor are constructed, so that the complex working condition is accurately identified; scene labels are automatically generated based on two-dimensional working condition mapping and a clustering algorithm, and a plurality of estimation models are dynamically selected or fused in combination with model confidence, so that the accuracy, robustness and intelligent level of estimation are improved.

The invention relates to the technical field of data governance, and discloses a data governance method based on an intelligent decision engine, which comprises the following steps: carrying out business semantic classification and marking on preliminarily processed real-time streaming data, and constructing a data portrait library; constructing a dynamic topological graph, learning an abnormal propagation rule based on a graph neural network, analyzing an influence range and establishing an influence grading mechanism; performing multi-dimensional quality evaluation on the data, and generating a dynamic data quality score and a grading strategy; constructing a data quality historical problem and reason case library, and generating a quality anomaly root cause judgment and influence quantification report by using a large language model agent; generating a candidate strategy set, and selecting an optimal governance strategy from the candidate strategy set by establishing a multi-objective optimization model; and performing compliance test and conflict identification on the optimal governance strategy by using a large language model agent, and dynamically adjusting the decision weight of a rule engine by using a reinforcement learning algorithm to realize a closed loop of data governance and dynamic learning.

The invention discloses a bus and station interactive scheduling method and system based on V2X and deep reinforcement learning decision, a medium and equipment, and the method comprises the steps: collecting bus operation dynamic data, station passenger flow and environment data in real time, and combining V2X network interaction information to construct a full-dimension perception system; a deep reinforcement learning model is adopted for dynamic decision making, intelligent closed-loop control of vehicle scheduling, station service and traffic signal cooperation is achieved, the bus punctuality rate is remarkably increased, the waiting time of passengers is shortened, operation safety is guaranteed through active early warning of abnormal conditions, and intelligent upgrading of a bus system from passive response to active prevention is achieved.

The invention discloses a dynamic strategy data access method and device, electronic equipment and a storage medium, and the method comprises the steps: determining a dynamic strategy parameter corresponding to a data access object according to a data access request under the condition of obtaining the data access request corresponding to the data access object; determining a dynamic data access strategy corresponding to the data access object according to the dynamic strategy parameter; and determining target access data corresponding to the data access request based on the dynamic data access strategy, and sending the target access data to the data access object. Based on the technical scheme, the dynamic strategy corresponding to the data access request is determined, and then the target access data is dynamically determined according to the dynamic strategy, so that the data access strategy can be dynamically changed, the strategy flexibility problem in a specific scene of urban and rural fusion is solved, and the data credibility is improved.

The invention provides a multi-channel contact resistance intelligent test device and method, the contact resistance intelligent test device is integrated with a plurality of different types of resistance test channels, and the resistance test channels comprise a conduction test channel, a static resistance test channel and a dynamic resistance test channel. The device comprises an upper control module used for receiving a channel test parameter of a contact resistor and generating a channel selection test instruction based on the channel test parameter; the multi-channel switching module switches to a target test channel of a corresponding type based on the channel selection test instruction; the resistance measurement module applies current of a corresponding type according to the type of the target test channel, collects the voltage of the target test channel, and obtains a resistance value according to the voltage and the current; and the signal processing module performs noise reduction processing on the resistance value to obtain a target resistance value, and evaluates the change trend of the target resistance value, so that the problems of low test efficiency, dynamic data missing and personal error accumulation of a traditional method are solved, and the subjective deviation of manual interpretation is eliminated.

The invention relates to the technical field of lithium ion battery testing, and discloses a lithium ion battery life loss evaluation method and device, a medium and equipment, and the method comprises the following steps: S1, collecting multi-modal dynamic data of a lithium ion battery in a charge-discharge cycle process; s2, performing time-frequency domain conjoint analysis on the multi-modal dynamic data, and extracting a battery aging sensitive feature set; s3, constructing a multi-scale coupling model of battery life loss; by constructing a multi-modal data fusion mechanism and a dynamic feature extraction system, during lithium ion battery life loss evaluation, change trends of electrochemical impedance spectroscopy and heat distribution key parameters are captured in real time based on time-frequency domain conjoint analysis, sensor signal distortion and drift problems can be identified, the extraction precision of aging sensitive features is improved, and the accuracy of lithium ion battery life loss evaluation is improved. The problem of characteristic errors caused by signal interference in traditional evaluation is solved, and the accuracy and reliability of life loss evaluation are ensured.

The invention relates to the field of edge computing data security, in particular to an integrity auditing method and system in an edge computing environment. The problem that an existing cloud auditing model is high in delay and large in bandwidth consumption in an edge scene is mainly solved. A lightweight audit challenge is generated through random sampling, data block aggregation operation is executed at an edge node, Merkel Hash tree path evidence is extracted, and integrity proof of dual verification is formed; and the aggregated evidence and the path evidence are verified by using bilinear mapping, so that efficient and credible auditing is realized. Dynamic data updating and accurate positioning of damaged blocks are supported, communication overhead is remarkably reduced, and real-time credibility of edge data is guaranteed.

The invention provides a dynamic data desensitization method which can be applied to the field of privacy computing and the field of financial science and technology. The method comprises the steps of obtaining original data in response to a data access request of a user, and performing sensitive field identification on the original data through a natural language processing model to obtain a sensitive field and a sensitive field type; collecting user access information in real time, analyzing the user access information through a context scoring model, and generating a current desensitization intensity grade; dynamically selecting a desensitization strategy based on the sensitive field type and the current desensitization intensity grade, determining a target desensitization strategy, and processing the sensitive field through the target desensitization strategy to obtain first desensitization data; performing irreversible desensitization processing on the first desensitization data to generate second desensitization data; and determining first request data based on the original data and the second desensitized data, and returning the first request data to the user. The invention further provides a dynamic data desensitization device and equipment, a storage medium and a program product.

The invention relates to the technical field of computers, and discloses a deployment strategy adjustment method and system, electronic equipment and a storage medium, and the method comprises the steps: configuring a multi-dimensional index set, collecting and processing dynamic data, generating a deployment adjustment strategy based on the synergistic effect of a dynamic weight distribution algorithm and a prediction model, and executing the deployment adjustment strategy. And selecting a progressive adjustment mode or an aggressive adjustment mode based on the change amplitude of the index in the processed dynamic data to carry out resource or configuration change, monitoring a corresponding state after execution of the deployment adjustment strategy to generate feedback data, and when the feedback data exceeds a preset recovery threshold, recovering to a state before execution of the deployment adjustment strategy. And the adjustment effect is quantitatively evaluated based on feedback data, and a dynamic weight distribution algorithm and prediction model parameters are optimized. According to the method, the problems that a traditional deployment strategy depends on manpower and response lags behind are solved, and the operation and maintenance efficiency and the service toughness can be remarkably improved in a network request high-concurrency scene.

The invention discloses an urban occupational health risk real-time supervision method and system based on data analysis, and the method comprises the steps: continuously collecting the behavior data flow of a target employee and the physical environment dynamic data flow of an associated working region, and generating an individual-environment dynamic data sequence; detecting a behavior abnormal signal based on the historical behavior mode of the target employee; in combination with the physical environment dynamic data flow at the current moment, the association strength between the physical environment dynamic data flow and the occupational health risk is evaluated, and a personalized micro-intervention instruction is generated and triggered; capturing the instant feedback response of the employee to the micro-intervention instruction in real time, updating the personal behavior baseline model of the target employee in real time, and calibrating the evaluation rule of the association strength; and dynamically generating a real-time occupational health risk portrait of the employee based on the individual behavior baseline model, the association strength evaluation rule and the individual-environment dynamic data sequence. According to the embodiment of the invention, accurate early warning and personalized intervention of occupational health risks can be realized.

Disclosed in the present invention is an aircraft engine maintenance policy optimization method based on nonparametric reinforcement learning. Firstly, for sparse aircraft engine operation data, constructing an aircraft engine model by means of a Bayesian network and a Gaussian process; then, establishing a policy network and a value network, interacting with the aircraft engine model to form a state / action value group, storing same into a replay buffer, and performing random sampling for use in a training set; updating the policy network and the value network, and updating the training set; and finally, performing aircraft engine maintenance optimization policy decision-making. Provided in the present invention is, for the first time, a Gaussian process-based nonparametric reinforcement learning method for an aircraft engine, which improves the degree of fit between overall training data and a model by means of dynamic data updating while integrating system uncertainty, thereby improving the sampling efficiency of an algorithm. Action selection is performed on the basis of prior maintenance experience data fused with uncertainty, improving the safety and sampling efficiency of a system and thereby solving the problem of predictive maintenance of an aircraft engine system.

The invention discloses a drainage basin intelligent flood control scheduling method and system based on digital twinborn, and relates to the technical field of flood control and disaster mitigation, and the method comprises the steps: collecting static data and dynamic data of a drainage basin, building a hydrological and hydrodynamic coupling model based on the static data and the dynamic data, and forming a drainage basin digital twinborn body; inputting the received numerical weather forecast into the digital twin of the watershed for simulation, generating a plurality of flood routing scenes in a future time period, and calculating a dynamic flood risk probability graph; the method comprises the following steps: constructing a simulation training environment by using historical flood data and a high-precision drainage basin digital twinborn body, carrying out offline training on a scheduling strategy network in the simulation training environment based on a reinforcement learning algorithm, and outputting a scheduling instruction according to a real-time drainage basin state to complete training of the scheduling strategy network. According to the method, the core problem that the traditional method is insufficient in decision timeliness and weak in adaptive capacity in an uncertain environment is effectively solved.

The invention discloses a server heat dissipation module control optimization method and system based on analogue simulation, and relates to the technical field of multi-physics field coupling modeling, and the method comprises the steps: constructing a multi-physics field simulation twin space, carrying out the heat flow simulation to obtain a heat dissipation simulation data set, collecting the real-time operation parameters of a server, mapping the real-time operation parameters to the space, and carrying out the heat dissipation simulation data set; the heat dissipation simulation data set is combined to deduce and obtain the heat dissipation temperature change trend, heat dissipation requirements are analyzed and determined according to the trend, a feedback data set is obtained through multi-target control and parameter adjustment, the space is corrected through the feedback data set, and a strategy is made to optimize heat dissipation module control. The technical problems that existing server heat dissipation control lacks twinborn simulation, accurate mapping and dynamic data processing support of the server heat flow state are difficult, complex changes of heat flow cannot be adapted, and consequently the heat dissipation regulation and control precision is insufficient are solved, accurate pre-judgment and dynamic regulation and control of the heat dissipation trend of the server are achieved, and the service life of the server is prolonged. And the control accuracy and adaptability of the heat dissipation module of the server are improved.

The invention relates to the field of building construction safety, in particular to a risk prediction method and system for building construction. Aiming at the defects of multi-source data isolated analysis, dynamic risk response lagging, insufficient prediction precision and the like in the prior art, a unified analysis base is formed by constructing a space-time fusion data space and integrating multi-dimensional dynamic data such as structure micro-deformation monitoring, environmental parameters, three-dimensional live-action scanning, personnel positioning, a building information model and the like; based on a deep neural network architecture, designing a multi-modal feature extraction mechanism to quantify the coupling risk, and generating a partition risk probability distribution diagram; and in combination with a construction stage characteristic matching security policy library, implementing a three-level early warning mechanism and an automatic avoidance instruction. A closed-loop optimization mechanism is introduced, model parameters and decision threshold values are dynamically adjusted through actual accident feedback, and continuous evolution of a prediction system is achieved. According to the method, the active prevention and control capacity of compound accidents such as collapse and high-altitude falling is remarkably improved, and a self-adaptive intelligent protection system is constructed for a construction site.

The invention relates to the technical field of water conservancy informatization and intelligent early warning, in particular to a river network water regimen big data intelligent analysis early warning system which comprises a dynamic data fusion module, a topology sensing module, an overload propagation analysis module, an intelligent early warning decision module and an early warning execution module. Wherein the dynamic data fusion module is used for collecting multi-dimensional data of a river network area in real time; the topology sensing module is used for dynamically constructing a real-time topological relation graph of the river network connectivity; the overload propagation analysis module is used for extracting an overload coefficient of each node and outputting a risk diffusion path set; and the intelligent early warning decision module is used for generating a three-level early warning instruction by combining the positions of the engineering vulnerabilities in the real-time topological relation graph. According to the method, through fusion of the multi-source water regimen data and the dynamic topological structure, linkage of accurate identification and hierarchical response of the risk path is realized, and the intelligent early warning and rapid disposal capability of the sudden water regimen of the river network is remarkably improved.

Techniques for executing privacy-preserving aggregation queries on a database table include receiving a query, obtaining the true output, and generating deterministic pseudorandom noise based on the query and current database state. This noise is added to the true output to create a privacy-protected result. For subsequent queries, this process is repeated, potentially with updated state data, ensuring that privacy protection adapts to changes in the underlying data. The approach maintains consistency for repeated queries on unchanged data while providing fresh noise when data changes. It balances privacy protection with data utility, allowing for various query types while guarding against privacy attacks. The techniques include displaying the noisy output to a user interface. The techniques offer adaptive privacy protection, query flexibility, and efficient resource utilization, making them suitable for dynamic data environments requiring both privacy and analytical capabilities.

The invention discloses a reinforcement learning model training method and device based on dynamic data selection and electronic equipment, and the method comprises the steps: carrying out the screening from an original mathematical problem data pool according to preset data quality, diversity and difficulty evaluation indexes, and obtaining a candidate training set; calculating a data validity score of each training sample in the candidate training set; difficulty filtering is carried out on the candidate training set, and an adaptive training course is constructed; further filtering the candidate training set according to the solution reasoning process and the reward score of the candidate training set; dynamically selecting a data subset for the current training period by setting a temperature parameter attenuated along with the training process and a probability threshold based on the further filtered candidate training set and the data validity score; the selected data subsets are put into a reinforcement learning model for training, and the reinforcement learning model is trained in an optimized mode according to the correctness reward and the format reward. The model training period can be remarkably shortened, and the performance equivalent to or even better than that of full data training is achieved.

The invention relates to the technical field of response time testing of electro-hydraulic proportional valves, and discloses a response time testing method and system of an electro-hydraulic proportional valve, and the method comprises the steps: collecting multi-point pressure, step response, pipeline length and medium parameters, and unifying the time reference to obtain an original pressure sequence; constructing an inverse filter spectrum to remove damping interference so as to recover a sharp peak value; lagging offset is calculated according to pipeline and medium parameters, time delay compensation is carried out, and an alignment sequence is obtained; intercepting a non-stationary segment to eliminate residual delay deviation; constructing a response dynamic data set, extracting harmonic frequency domain representation, and reconstructing a pressure sequence through inverse transformation and superposition of compensation factors; and calculating a deviation integral quantity, adjusting a dynamic compensation parameter, performing weighted correction, and outputting an optimized reconstruction response sequence. According to the method, accurate reconstruction of the real pressure response of the proportional valve can be realized, so that the accuracy and the reliability of response time testing are improved.

The invention discloses a knowledge graph updating method and device generated by graph retrieval enhancement, equipment and a medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring different types of dynamic data information based on a preset data acquisition strategy, and converting the dynamic data information into candidate graph elements which can be identified by a knowledge graph; whether the candidate graph elements need to be updated or not is judged through a preset updating judgment strategy to generate an updating judgment result, and information conflicts in the candidate graph elements are detected through a preset conflict detection strategy to generate a conflict detection result; and determining a target updating instruction according to the updating judgment result and the conflict detection result, and performing incremental updating on the knowledge graph database based on the target updating instruction. By means of the technical scheme, the problem that only static information can be kept in existing graph retrieval enhancement generation is solved.

The invention provides a dynamic data traceability method and system oriented to network security, and relates to the technical field of network security and data governance crossing, and the method comprises the steps: carrying out the real-time visual monitoring of a user interaction interface of a data transaction platform, and capturing the visual behavior information related to data calling; extracting feature parameters based on the visual behavior information to obtain a visual feature data set, and performing structured conversion on the visual feature data set to obtain a structured call log record containing visual behavior associated information; based on the structured call log record, extracting a time sequence, a frequency and a spatial distribution feature of a call behavior as basic indexes; a plurality of feature sampling states are dynamically selected in a monitoring time window, and a dynamic feature evaluation set is constructed based on the feature sampling states. According to the invention, the accuracy and controllability of network security protection in a data transaction scene can be improved.

The invention relates to an intelligent logistics scheduling and resource distribution system based on multi-source data fusion, and relates to the technical field of logistics management technologies, and the system comprises the steps: collecting vehicle operation state data, road condition data, logistics order dynamic data and warehouse inventory change data in real time based on a multi-source heterogeneous interaction platform; constructing a multi-modal traffic logistics time series data set; based on a maximum priority scheduling algorithm, giving priority weights to the logistics orders in combination with logistics order timeliness demands and cargo features; based on the logistics order priority weight, path optimization is carried out by using a space-time hierarchical planning algorithm, and an initial transportation path set is constructed; real-time monitoring of the transportation process is achieved based on the digital twinborn technology, the path condition is sensed dynamically, the optimal transportation path is obtained through continuous adjustment, and an intelligent logistics scheduling and resource allocation scheme based on multi-source data fusion is generated. The method has the advantages that the resource allocation efficiency is improved and the transportation cost is reduced by optimizing the priority path and continuously adjusting the transportation path.

The invention relates to the field of ecological monitoring, in particular to a marine ecological anomaly detection method and system based on multi-source data. Comprising the following steps: acquiring multi-source dynamic data of a target sea area; constructing a dynamic quality evaluation system, inputting multi-source dynamic data, and outputting a quality control report; based on the multi-source dynamic data conforming to the preset quality threshold value, establishing a marine ecological dynamic database in combination with a spatio-temporal indexing mechanism; based on the marine ecological dynamic database, space-time anomaly features are extracted, and candidate anomaly regions are determined; and generating a marine ecology comprehensive abnormal index by adopting the dynamic weight calculation model, outputting a prediction result through a preset abnormal trend prediction model based on the marine ecology comprehensive abnormal index, and generating an early warning measure. Through a distributed crawler and sensor gateway collaborative architecture, comprehensive acquisition of macroscopic data and near-shore real-time data is realized, and high-quality basic data is provided for subsequent detection.

The invention relates to an artificial precipitation enhancement operation area identification method and system based on multi-source data, and relates to the technical field of meteorological intervention operation, and the method comprises the steps: building a multi-resolution dynamic data grid system, and enabling a coarse-grained grid layer to cover the whole operation area, and to be used for bearing the macro data of a satellite cloud picture and a conventional radar; the fineness grid layer is used for receiving and mapping real-time data provided by the high-resolution radar; receiving real-time data of all cloud blocks, mapping the real-time data to corresponding grid layers, and projecting the data collected at different time points to a unified time reference frame to obtain a cloud system state view; predictive interpolation is carried out by using the latest data of the conventional radar and the macroscopic trend of the satellite cloud picture in combination with the motion vector and confidence weighting so as to obtain cloud system information; and forming a visual cloud system three-dimensional view, identifying and tracking a target cloud block with an artificial precipitation enhancement potential, and generating an operation instruction. The method has the effect that the target cloud block with the operation value can be identified and tracked to generate the accurate operation instruction.