12007results about How to "Improve adaptability" patented technology

The invention relates to a multi-objective optimization scheduling method and system for a multi-AGV flexible job shop. The method comprises the steps of collecting job shop layout data, AGV operation parameters and production task data, and constructing a scheduling system basic model; performing recurrent neural network training and physical constraint model integration to obtain a hybrid scheduling prediction model; subtask decomposition and path search processing are carried out, and a dual-objective optimization model is obtained through constraint linearization conversion; performing particle swarm optimization solution on the dual-objective optimization model, and obtaining a non-dominated solution set through two-stage iterative calculation of an adaptive weight strategy and constraint conversion; performing multi-dimensional evaluation calculation on the non-dominated solution set to obtain an optimal scheduling execution scheme; and performing model feedback updating on the hybrid scheduling prediction model to obtain a target scheduling prediction model. According to the invention, multi-target balance of task completion time and energy consumption is realized, and the real-time response capability and operation stability of the system are improved.

The invention discloses an energy storage energy management system for improving a demand protection algorithm, and the system comprises a data collection module which is used for obtaining energy storage system load data, distributed energy operation data, and energy storage system operation data; the correlation feature analysis module is used for acquiring a correlation feature set between the load and the energy supply; the control strategy generation module is used for adjusting a charging and discharging threshold value and formulating a charging and discharging control strategy; the control strategy execution module is used for formulating a charging and discharging control strategy and adjusting the working mode of the energy storage converter; the cooperative execution module is used for dynamically optimizing a power distribution strategy by dispatching distributed energy and power grid cooperative work; and the dynamic power adjustment strategy adjustment module is used for adjusting the charging and discharging protection threshold value and the power adjustment strategy through a feedback mechanism. Through an intelligent control and scheduling mechanism, the charging and discharging efficiency of the energy storage system is improved, the stability and flexibility of the power grid are enhanced, and effective guarantee is provided for safe operation of the power grid.

The invention provides a three-dimensional point cloud data segmentation method and system based on multi-scale point features, and relates to the technical field of point cloud processing, and the method comprises the steps: obtaining original point cloud data, constructing a three-dimensional Hash index table, carrying out the self-adaptive hierarchical division through a local maximum value of a density distribution curve, and obtaining a local maximum value of a density distribution curve; noise point elimination is carried out based on a region density count value and a geometric distribution coefficient, an enhanced feature vector containing a main feature direction, a main curvature value and the like is constructed for non-eliminated points, a local geometric descriptor is constructed by adopting four spherical search regions with increasing radiuses, a three-branch space attention network is input to extract spatial features, and the spatial features are extracted; and fused feature data are obtained through a multi-level geometric feature extraction network and a bidirectional feature propagation network. And obtaining a final segmentation result through segmentation energy function optimization and a region merging strategy.

The invention provides a computing system and method for GPU computing power scheduling, and the method comprises the following steps: 1, obtaining computing task data, and carrying out the information collection of each task through receiving a to-be-processed computing task queue; according to the invention, through task information collection and deep analysis, and GPU resource omnibearing monitoring and workload prediction, deep understanding of tasks and GPU resources is realized, highly accurate computing power distribution can be carried out, resource waste is avoided, and the resource utilization rate is improved; advanced technologies such as a knowledge graph, mixed integer programming and deep reinforcement learning are fused through the intelligent scheduling model, the fusion mode is innovative in the GPU computing power scheduling field, the limitation of singleness of a traditional scheduling model is overcome, the method can better adapt to a complex and changeable computing environment, the task execution efficiency is optimized, and the task scheduling efficiency is improved. The adaptability and competitiveness of the whole system in a complex environment are remarkably improved, and an excellent solution is provided for the GPU computing power scheduling problem in the field of high-performance computing.

The invention discloses an object labeling method and system based on laser point cloud, particularly relates to the technical field of laser point cloud processing, and is used for solving the problem of inaccurate labeling result caused by a complex reflection phenomenon caused by a non-Lambertian surface in the prior art. The method comprises the steps of obtaining original point cloud data collected by a laser radar and performing preprocessing; identifying a non-Lambertian reflection region based on the reflection intensity information and the geometric topology feature, and carrying out filtering correction; correcting false mirror image points through multi-layer iteration correction, and reconstructing boundaries to generate corrected point cloud data; grouping and classifying target objects in the corrected point cloud data based on a clustering algorithm combining global features and local features; finely dividing the boundary of the preliminary segmentation result by adopting an attention mechanism network, and evaluating the uncertainty of a boundary region through a local mutual information entropy; and adjusting a boundary division strategy according to an uncertainty evaluation result, and generating an optimized segmentation result.

The invention discloses an AGV task allocation method and system, and relates to the technical field of automatic navigation trucks, and the method comprises the steps: carrying out the initialization of AGV state information; acquiring environment information in real time through ground sensor equipment; performing priority evaluation on the task instructions, and adjusting task priorities in real time according to emergencies; task merging and splitting are carried out; task allocation is carried out by using an ant colony algorithm, and a task allocation result is output to the corresponding AGV to guide the AGV to execute operation according to the allocated task; an optimal path is planned for each AGV, and path planning is dynamically adjusted in the task execution process of the AGVs; and optimizing a task allocation algorithm by adjusting a pheromone volatilization coefficient. Limited AGV resources are reasonably scheduled by arranging a task instruction obtaining module, a task information processing module and a task planning and distributing module, high environment perception and adaptability are achieved, and task priorities and path planning can be rapidly adjusted.

The invention relates to the technical field of data processing, and discloses an industrial one-stop multi-source data acquisition and monitoring system and equipment, and the system comprises a data acquisition module which is used for obtaining multi-source industrial monitoring data and generating a data stream input node; the data preprocessing module is used for processing the input data in the data flow model to generate a standardized data flow; the data conversion module is used for dynamically converting data nodes in the standardized data stream according to different data protocols to generate a compatible data stream; the task scheduling module is used for optimizing the data flow and distributing the data flow to a target node according to the compatible data flow in combination with a node priority and a dynamic scheduling strategy in the data flow model; and the exception detection module is used for carrying out exception state analysis on the optimized data stream and judging whether an exception exists or not according to a preset rule. According to the technical scheme, the compatibility and reliability of the monitoring system can be improved.

The invention relates to the technical field of intelligent spectrum regulation and control, and discloses an intelligent spectrum regulation and control system based on multi-modal data fusion and dynamic optimization, which comprises a multi-modal data acquisition module, a real-time data processing module, a dynamic weight distribution algorithm, a deep reinforcement learning regulation and control algorithm and a distributed collaborative optimization module. According to the system, real-time fusion and dynamic priority adjustment of multi-dimensional data are realized through acquisition of multi-modal data (environment spectrum, temperature and humidity, carbon dioxide concentration and user behavior) and dynamic weight distribution based on LSTM (Long Short Term Memory); dynamic optimization of a spectrum regulation and control strategy is realized by combining a deep reinforcement learning model and utilizing a weighted reward function of a spectrum output error and energy consumption, and efficient light supplement and low energy consumption requirements are balanced; a distributed optimization algorithm with edge computing and cloud collaboration is adopted, computing resources are dynamically allocated, task processing efficiency is greatly improved, and system power consumption is reduced.

The invention provides a robust multi-modal image segmentation method and system based on instance perception query, and the method comprises the steps: obtaining original image data and text description data, and generating training data through semantic analysis and causal graph construction; extracting image features and text features by adopting a multi-layer coding network, and establishing a feature dependency relationship through causal reasoning; generating instance clustering features based on attention calculation, and obtaining fusion features in combination with a multi-scale fusion strategy; and calculating query confidence and generating candidate segmentation masks, and outputting a final segmentation result through post-processing optimization. According to the method, by introducing causal reasoning and instance awareness query mechanisms, the cross-modal feature alignment precision and the robustness of the segmentation result are improved, and the method has better segmentation performance and generalization ability in a complex scene.

The invention provides a power supply energy analysis method and system for a medium-voltage power distribution network. The method comprises the following steps: collecting multi-modal data of the medium-voltage power distribution network; constructing a health state evaluation model, and predicting a health score and a fault risk of the transformer of the medium-voltage power distribution network according to the fusion features after dimension reduction; constructing an optimization target of the power electronic equipment of the medium-voltage distribution network according to the health score and the fault probability of the transformer; constructing a multi-modal load prediction model to predict the load change of the power electronic equipment in the future; constructing a multi-objective optimization function, and generating an optimal scheduling strategy; and executing the optimal scheduling strategy to actually control the medium-voltage power distribution network equipment. According to the method, the defects of a traditional method in the aspects of equipment monitoring, load prediction and scheduling optimization are overcome, and the overall management level and coping capacity of a power grid are improved by introducing a leading-edge artificial intelligence algorithm and a data fusion technology.

The invention provides an unstructured database federated learning collaboration method and system based on swarm intelligence, and relates to the technical field of structured data, and the method comprises the steps: training a query pattern recognition sub-model, a path planning sub-model and a resource allocation sub-model through federated learning, and constructing a swarm intelligence collaboration network. After a user query request is analyzed by the query mode recognition sub-model, candidate query paths are generated and optimized by fusing an ant colony algorithm and a particle swarm algorithm, and a query execution scheme is generated. And the resource allocation sub-model performs resource scheduling accordingly. And generating a performance optimization target for updating local model parameters of each node, and fusing the updated parameters through a security aggregation algorithm to guide the next query optimization. According to the method, the query efficiency of the unstructured database is improved, and the resource consumption is reduced.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a flight mission planning and management system and method for cooperation of multiple unmanned aerial vehicles, and the method comprises the steps: decomposing a flight region of an unmanned aerial vehicle based on a flight mission of the unmanned aerial vehicle, and dividing the flight region; path planning is carried out in the flight area of the unmanned aerial vehicle, global flight path planning is carried out by adopting a genetic algorithm, and local flight path planning is carried out by adopting an A * algorithm after the global path planning; an obstacle avoidance strategy of the unmanned aerial vehicle in the flight process is constructed, and after the obstacle avoidance strategy is triggered, local flight path planning is carried out again; according to unmanned aerial vehicle flight task constraints, a priority judgment criterion of multiple unmanned aerial vehicle tasks is formulated, and unmanned aerial vehicle resources are allocated and dispatched. The intelligent level of unmanned aerial vehicle task planning can be remarkably improved, the adaptability to complex environments is enhanced, flight safety and efficient task execution are ensured, and a landing technical support is provided for actual application of the unmanned aerial vehicle.

The invention relates to the field of shield tunneling, and discloses a shield tunneling control method and system based on a rock-soil particle motion trail, and the method comprises the steps: fusion deployment of various types of high-precision sensors, intelligent anti-noise data collection and preprocessing, precise physical model construction, efficient numerical simulation optimization, and multi-parameter comprehensive detection and correlation analysis. And real-time data-driven adaptive control model construction, control instruction rapid generation and transmission, closed-loop feedback and continuous optimization are realized. According to the method, multiple links including fusion deployment of multiple types of high-precision sensors, intelligent anti-noise processing, physical model construction and the like are covered, all the links have a synergistic effect, data can be accurately obtained, data quality can be guaranteed, a simulation foundation can be provided, comprehensive evaluation, intelligent control and rapid instruction transmission can be achieved, continuous optimization is achieved through closed-loop feedback, and the method is suitable for large-scale popularization and application. And finally, the construction level in all aspects is improved, and complex engineering requirements are met.

The invention provides a precise interference method and system for pilot signals of a remote controller of an unmanned aerial vehicle, and the method comprises the steps: receiving and processing an image transmission signal and a remote control signal of the unmanned aerial vehicle, and analyzing a starting time, a transmission period and different types of image transmission protocol classification results of the image transmission signal; extracting the time period of the remote control signal and the occurrence time of a pilot symbol in the remote control signal; generating optimized interference signal parameter configuration by utilizing a reinforcement learning algorithm, analyzing historical data by utilizing a long short-term memory network to obtain a historical analysis result, and predicting an optimal interference opportunity; monitoring a wireless communication environment around the unmanned aerial vehicle by using a real-time spectrum sensing technology so as to identify a potential conflict frequency band, and selecting a target interference frequency band by using a frequency agility technology; determining a precise interference method based on the historical analysis result, the optimal interference opportunity and the target interference frequency band; according to the invention, the interference precision and effect on the pilot signal of the remote controller of the unmanned aerial vehicle are improved.

The invention relates to the field of water quality evaluation, in particular to a rain source type river water quality evaluation method and system.The method comprises the steps that a sensor of a water quality monitoring system monitors micro-rain detection data of a micro-rain event, and the water quality monitoring system adjusts the sampling frequency of the sensor through an adaptive algorithm; obtaining water quality monitoring data through a micro-pollution mutation detection method; constructing a water quality recovery model of the micro-rain event through nonlinear kinetic analysis, and predicting a peak value and recovery time of water quality change of the micro-rain event; identifying a significant sudden change point of water quality change through a chaos theory algorithm, and adjusting the sampling frequency of the sensor through fuzzy logic control; key pollutants are obtained according to the peak value of the water quality change and the recovery time, and the key pollutants and pollution source data are subjected to data correlation analysis to judge the pollution source. The problems that the impact and the influence of a micro-rain event on the river water quality are not fully considered, so that the water quality evaluation is not timely and accurate enough, and the water quality change cannot be effectively captured and a pollution source cannot be analyzed are solved.

The invention discloses an unmanned aerial vehicle group cooperative inspection control method for power station inspection, and the method comprises the steps: determining an unmanned aerial vehicle group according to a power station inspection task; performing task allocation of the heterogeneous unmanned aerial vehicle cluster by using a multi-objective optimization algorithm; performing unmanned aerial vehicle path planning by adopting an improved dynamic multi-population particle swarm algorithm; based on a graph theory and master-slave control, unmanned aerial vehicle group attitude consistency control is realized; integrating a target detection technology based on deep learning, and constructing an inspection anomaly detection model; performing remote fault diagnosis analysis on the real-time inspection data through an inspection anomaly detection model to obtain an inspection anomaly result; optimizing the inspection anomaly detection model according to the target condition of the real-time inspection data; and dynamically controlling task allocation and path planning of the unmanned aerial vehicle group according to an abnormal inspection result. Through the scheme, potential problems can be found and processed in time, the intelligent control level of power station inspection is improved, and the efficiency and accuracy of unmanned aerial vehicle group cooperative inspection are improved.

The invention discloses an air conditioner energy-saving control method and system based on multi-objective optimization, a medium and a product, and relates to the field of data processing. The method comprises the steps that optimization targets for conducting energy-saving control on the air conditioner and corresponding constraint conditions are determined, and the optimization targets comprise target air conditioner energy consumption and target comfort degree; acquiring current environment data of the air conditioner; the equipment operation parameter combination of the air conditioner is used as particles in the multi-target particle swarm algorithm; determining an optimal solution through a multi-target particle swarm algorithm; the air conditioner is controlled according to the parameter combination corresponding to the optimal solution; the actual air conditioner energy consumption and the actual comfort degree are obtained; based on the target air conditioner energy consumption, the target comfort level, the actual air conditioner energy consumption and the actual comfort level, the equipment operation parameters of the air conditioner are adjusted through a PID control algorithm, and final equipment operation parameters are obtained; and controlling the air conditioner to work according to the final equipment operation parameters. According to the method, both the energy-saving effect and the comfort level of the air conditioner are balanced.

The invention discloses a data communication method and system based on information security, and belongs to the technical field of data communication, and the data communication method comprises the stages of preparation, transmission, intrusion detection and defense, receiving verification and cleaning ending. The two parties authenticate and exchange keys during preparation; encrypting data by using a symmetric key and an AES algorithm; automatic defense is performed after intrusion detection; the receiver verifies the data; and cleaning the resources at the end, and performing log optimization on the system. According to the invention, through innovatively improving the intrusion detection and defense method, based on real-time network flow monitoring, intrusion analysis based on machine learning and an automatic defense mechanism, the detection accuracy and efficiency are greatly improved, and false alarms and missing alarms are reduced; defense capability and flexibility are enhanced, attacks are intelligently blocked and isolated, and cooperative emergency response is rapid; data communication security technology progress and expansion are promoted, and network security is powerfully guaranteed.

The invention discloses a large model knowledge base construction method based on multi-granularity retrieval. The method comprises the steps of obtaining a target text and analyzing a document structure, splitting and reorganizing text data according to three levels of titles, fragments and sentences, and constructing three levels of knowledge of the titles, the fragments and the sentences; using a large language model to construct a generative index as question-level knowledge according to the sentence-level knowledge; using a vector model to generate a first vector representing index keys of fragment-level knowledge and / or sentence-level knowledge, and a second vector representing index keys of title-level knowledge and / or question-level knowledge; and respectively storing the first vector and the second vector into a vector database, and storing the index values and the index types corresponding to the first vector and the second vector into a relational database. According to the method, multi-granularity document knowledge base construction is achieved, the expression modes of knowledge under different granularities are expanded, the accuracy of user query and knowledge matching is improved, and the problem that information is lost or incomplete in an existing method is solved.

The invention provides an unmanned aerial vehicle route layout method and device for complex terrains, equipment and a medium, and relates to the technical field of unmanned aerial vehicle aerial photogrammetry. The method comprises the following steps: carrying out preliminary region division on a terrain region to be surveyed, and determining a plurality of region units; clustering the area units according to topographic observation data to obtain survey sub-areas; determining a survey priority corresponding to each survey sub-region, and matching a basic flight route corresponding to the unmanned aerial vehicle according to the survey priority; and splicing the different types of basic flight routes corresponding to the survey sub-regions, and performing optimization adjustment on the spliced basic flight routes to obtain a flight route of the target unmanned aerial vehicle so as to perform topographic survey on the topographic region to be surveyed. According to the technical scheme, the route planning of the unmanned aerial vehicle can adapt to various types of landform features, and the safety and aerial survey efficiency of the unmanned aerial vehicle are ensured while the integrity and accuracy of aerial survey data are improved.

The invention relates to the field of intelligent titration control, and discloses a high-precision intelligent titration control method and system.The method comprises the steps of sample information input and preprocessing planning, sample automatic preprocessing, multi-sensor system calibration and preparation, intelligent titration parameter setting and scheme optimization, and multi-sensor data real-time collection and fusion in the titration process. Titration end point prediction and judgment based on machine learning, result calculation, verification and storage, system self-cleaning, maintenance and state monitoring. Through multi-module collaborative operation and a series of intelligent technical means, the accuracy, the reliability, the automation degree and the adaptability of high-precision titration in each link are comprehensively improved, and the high requirements on the titration precision and the titration efficiency in a complex and diversified chemical analysis scene are met.

The invention discloses a passive unsupervised domain adaptive medical image segmentation method and device based on pseudo labels, and relates to the technical field of computers, and the method comprises a supervised source domain model training stage, an unsupervised adaptive target domain model training stage and a segmentation stage. The method specifically comprises the following steps of: performing pixel-level self-marking on a source domain medical image by applying pseudo-tag pixel-level uncertainty estimation to generate a reliable pseudo-tag; and on this basis, a strong and weak enhanced teacher-student target domain model is used, and a high-quality pseudo tag is fully utilized to train a target domain medical image, so that the network adapts to representation specific to the target domain medical image. According to the method, unsupervised segmentation is carried out on the target domain data graph on the premise that the source domain model and the target domain unmarked data are known, source domain data do not need to be accessed, the features of the source domain model can be reasonably utilized, and the problem existing in SFUDA can be well relieved; and the accurate segmentation capability on the target domain is still maintained under the condition that less data is available compared with the UDA method.

The invention relates to the technical field of silt dam monitoring, and discloses a silt dam monitoring information sensing integration method and system, and the method comprises the steps: collecting the monitoring data of a silt dam, carrying out the short-time risk prediction at an edge node according to the monitoring data, triggering local early warning based on the short-time risk prediction, and starting a response mechanism; a self-adaptive cooperation mechanism is established between edge nodes, and sensor abnormity and local disaster chains are identified through exchange and comparison of monitoring data between the nodes; and the cloud integrates the monitoring data uploaded by the edge nodes, generates a global risk prediction model through dynamic data fitting, and performs hierarchical emergency response according to a risk prediction result. According to the method, the safety management efficiency of the silt dam is improved, and a scientific support and an intelligent decision foundation are provided for flood prevention and fighting.

The invention discloses an intelligent working place dust monitoring and early warning system. The system comprises a dust sensor module, a data processing and storage module, a wireless communication module, an alarm module and a visual interface. The dust sensor detects the dust concentration in the air in real time, dust with different particle sizes is included, the data processing module carries out concentration calculation, trend analysis and acceleration monitoring, and dynamic threshold adjustment and trend prediction are supported. The system has a multi-stage alarm function, and when the dust concentration reaches a preset threshold value, early warning signals with different intensities are triggered to remind a user to take protective measures in time. The system not only improves the real-time performance and accuracy of dust monitoring, but also has automatic learning and intelligent early warning functions, is suitable for various high-dust environments such as mines, construction sites, workshops and the like, and remarkably improves the safety management level of workplaces.

The invention discloses an emergency power supply quick access method based on scene recognition, which relates to the technical field of emergency power supplies, and comprises the following steps: in an emergency power supply system, firstly obtaining electrical input parameter information through a preset detection frequency; by introducing a load change evaluation mechanism based on machine learning, the system can identify load fluctuation in real time and dynamically adjust the detection frequency, and the response speed to the rapid change of the load is improved. Compared with a traditional detection method, the load state is intelligently evaluated and accurately divided, it is ensured that the power factor is adjusted in time when the load fluctuates violently, and power loss and waste are reduced. Correction is carried out according to the traditional frequency during stable load fluctuation, the frequency is dynamically adjusted during rapid fluctuation, the electric energy use is optimized, the electric power quality is improved, and harmonic distortion and overload risks are reduced.

The invention provides a multi-agent cooperation system, which comprises a multi-agent cooperation engine module, a task decomposition and distribution module and a tool calling normal form module, and is characterized in that the task decomposition and distribution module is used for decomposing tasks and distributing decomposed sub-tasks to agents; the multi-agent cooperation engine module is used for managing cooperation and communication between agents; and the tool calling module enables the intelligent agent to dynamically call different tools. According to the multi-agent collaboration system, the collaboration efficiency among the agents, the flexibility of task decomposition, the adaptivity of tool calling and the intelligent level of user interaction are remarkably improved, and remarkable technical advantages and effects are shown in complex task processing and dynamic environment processing.

The invention provides a task allocation and optimal scheduling method and system for a multi-robot collaborative industrial assembly line, and relates to the technical field of robots, comprising the steps of constructing a task allocation strategy network through deep reinforcement learning, encoding workpiece types and attribute information into a state vector input network, a multi-objective optimization function including total assembly time minimization, load balance degree maximization and energy consumption minimization is designed, a near-end strategy optimization algorithm is adopted to train network parameters, a robot task mapping matrix is output as a distribution scheme, and the network parameters are dynamically optimized and updated in an online learning mode. The task allocation efficiency of the multi-robot collaborative assembly production line can be remarkably improved, the dynamic optimization scheduling of the production line is realized, the system energy consumption is reduced, and the assembly efficiency is improved.

The invention discloses a novel electric power system source network load storage coordinated optimization planning method, and relates to the technical field of electric power system optimization, and the method comprises the steps: S1, building a load prediction model based on various user side load dynamic adjustment data, and improving the prediction precision, S2, determining parameters and constraint conditions of each link of source network load storage of an electric power system, s3, carrying out source network load storage collaborative optimization scheduling by utilizing an optimization algorithm; S4, evaluating and verifying an optimization result; according to the novel power system source-network-load-storage coordinated optimization planning method, the precision of load prediction is greatly improved, so that the problem that supply and demand matching is difficult is effectively solved, rapid fluctuation or sudden change of user loads can be rapidly responded, the adaptability and flexibility of a scheduling strategy are remarkably improved, and the scheduling efficiency is improved. The problems of supply and demand matching and scheduling flexibility caused by low load prediction precision under the condition of dynamic adjustment of various user side loads in the prior art are solved.

The invention discloses a risk early warning method and device constructed based on dynamic rules, equipment and a medium, and relates to the technical field of risk early warning, and the method comprises the steps: extracting a user static attribute and a dynamic behavior sequence, constructing an original data set, carrying out time sequence analysis, generating a behavior time sequence response feature, carrying out cross-modal feature fusion, and constructing a space-time perception behavior vector; constructing a dynamic rule graph structure according to the risk rule and the corresponding standard space-time perception behavior vector, iteratively updating a risk rule weight based on a graph convolutional network, constructing a wavelet domain risk quantification model, and generating a multi-scale risk value; and inputting the newly generated space-time perception behavior vector into the wavelet domain risk quantification model, generating a to-be-judged multi-scale risk value, and performing risk early warning when a threshold condition is not met. Through time sequence analysis and a multi-scale risk quantification model, the accuracy of risk identification is improved, and multi-dimensional and multi-scale risk analysis is supported, so that the accuracy and real-time performance of risk management are effectively improved.

The invention relates to the technical field of distributed file processing, in particular to a distributed file processing method and system, and the method comprises the following steps: based on a task distribution node and a data exchange interface, extracting path characteristics of a task input / output file, analyzing a file exchange sequence of a task path, and calculating a topological structure and length of the path. According to the method, the flexibility and the adaptability of task allocation are realized by combining task state monitoring and real-time adjustment of dependent path weights and task priority marks, resource allocation and a short-time task sequence are optimized by utilizing request frequency and time period records, high-frequency request resource conflicts are effectively relieved, the resource allocation efficiency is improved, and the resource allocation efficiency is improved. Through node historical resource distribution data and time distribution trend analysis, task distribution is adjusted and resource loads are balanced in combination with differences, the stability and balance of the system are enhanced, a file transmission path is optimized based on a load state and a task execution path, and a concurrent task distribution strategy is improved.