618 results about "Data Pooling" patented technology

In embodiments, systems and methods for improving machine-learning systems are disclosed. In embodiments, a system includes a data pool system that is configured to receive data from a plurality of different data sources and maintain a training data set that is used to train a specific machine-learning model based on the data from the plurality of different data sources. In embodiments, the system further includes a data scoring system that determines a data reliability score corresponding to the new data based on a set of intrinsic features of the new data and a data scoring model, wherein the data pool system selectively adds the new data to the training data set based on the reliability score of the new data. The system also includes a machine learning system that trains the specific machine-learning model based on the training data set.

The invention discloses a network security analysis early warning system based on artificial intelligence, and the system comprises a data collection layer which captures full flow based on DPI, aggregates firewall logs, terminal behaviors and threat intelligence, and constructs a structured data pool; through TLS fingerprint identification of AI driving, the encrypted traffic is penetrated, and a sampling strategy is dynamically adjusted in combination with reinforcement learning. The intelligent analysis layer is used for carrying out cross validation on known threats and abnormal behaviors; the time sequence CNN extracts encrypted traffic features, and a novel threat detector is rapidly generated by using historical attack fragments in combination with a meta-learning framework; sHAP value driving dynamic feature selection and optimization feature vector input; the decision-making early warning layer is used for fusing multi-source features through a Bayesian network and generating 0-100 score risk scores; a self-adaptive threshold module is combined to adjust a score threshold in real time, and a high-risk event is pushed; the collaborative response layer is used for triggering a preset decision tree, deploying a GAN dynamic honeypot to trap an attacker and reversely tracing; the Neo4j visually restores the attack path, and blocking is executed after the threat is confirmed by a progressive response mechanism.

The invention discloses an intelligent port crane operation monitoring method and system, and relates to the technical field of crane systems.The system comprises a data acquisition unit, an edge computing node, a cloud processor and an early warning monitoring unit, through deep fusion and efficient treatment of multi-source data, a dynamic twinborn data pool architecture is established, and the dynamic twinborn data pool architecture is established; cross-data-type space-time alignment is realized through multi-modal coding, and the feature fusion accuracy is improved; the dynamic twin data pool is intelligently analyzed, and the crane operation path is optimized in real time, so that the track of the crane operation path is dynamically adjusted, and the working efficiency is improved; through a digital twin model and integration of a multi-modal physical engine, the operation physical state deviation of the crane is evaluated, an operation plan is corrected in real time, and the operation precision is improved; video streams are deeply analyzed, virtual-real comparison is carried out, and model prediction video deviation is monitored, so that the analogue simulation precision is improved; by establishing a closed-loop early warning mechanism, full-process digital management and control are realized, and intelligent upgrading of a port is promoted.

The invention relates to the technical field of seed processing analysis, in particular to a big data-based rape seed quality evaluation model, which is characterized in that a data pool is constructed by converging multi-source heterogeneous rape data, and high-quality input is provided for subsequent cross-domain feature association; a cross-domain feature interaction graph is constructed, a graph neural network is used for learning, cross-domain feature interaction is visually presented, and a gene-environment interaction relationship is analyzed; through a four-branch multi-head and cross-modal attention analysis atlas, focusing key features, fusing optimization information and generating a high-quality feature vector, high-order feature nonlinear fusion is realized, and the characterization capability of the model on multi-source heterogeneous data is remarkably improved; an evaluation model is constructed through decoding vectors, and high-yield variety data is extracted, so that efficient screening of rape varieties is facilitated; by deploying the generative adversarial network and transferring the learning adaptive model, the evaluation capability is quickly expanded to a new variety, and breeding intelligence is promoted.

The invention relates to the technical field of oil field databases, and discloses a dynamic data management method, device, equipment and medium for an oil and gas field database, and the method comprises the steps: docking multi-source data of the oil and gas field database, the multi-source data comprising a shaft sensor real-time monitoring data flow, geologic modeling gridding structure data and an equipment management tree coding table; the method comprises the following steps: performing abnormal value cleaning and timestamp alignment on real-time monitoring data flow of a shaft sensor, converting geologic modeling gridding structure data into a reservoir unit set with a coordinate system label, extracting a set membership chain of well IDs and equipment IDs in an equipment management tree-shaped coding table, generating an identifier system with a unified coding format, and storing the identifier system in a database; the identifier system comprises a well identifier, a reservoir unit identifier and a device identifier; generating a structured data pool based on the identifier system; and when the data pool is updated, sending a change instruction to a plurality of subsystems of the oil and gas field database, and updating each subsystem based on the change instruction.

The invention relates to the technical field of operation data analysis, in particular to a multi-dimensional operation analysis index automatic adjustment and optimization management method, which comprises the following steps of: connecting a plurality of service systems through a distributed data acquisition gateway, and constructing a standardized data pool based on a dynamic pattern recognition algorithm; calling a dynamic index calculation engine, performing multi-dimensional feature extraction on the standardized data pool according to a preset index generation rule, and generating an initial index set; inputting the initial index set into an optimization model, generating an index optimization scheme through an association constraint analysis module, and outputting an optimization index set; and establishing an index dynamic update link. According to the invention, the health state of the system is monitored in real time in the release process of each business index, rollback control is triggered based on the change trend of the health index, and the updating safety is improved.

The invention discloses a remote printer monitoring and fault prediction system based on the Internet of Things, and relates to the technical field of the Internet of Things and artificial intelligence, and the system comprises a data collection module which is used for collecting equipment state data of a remote printer in real time and carrying out the preprocessing of the equipment state data; the data processing module is used for integrating the preprocessed equipment state data into a data matrix and a label vector; the model construction module is used for outputting a final feature set by using a random forest model and combining a Gumbel-Sigmoid activation function; and the fault prediction module is used for constructing a fault prediction model by using the bidirectional LSTM to output the predicted fault probability of the current time point and dividing a fault interval data pool. By introducing the combination of a random forest algorithm and a Gumbel-Sigmoid activation function, a feature selection process can be automatically optimized, a feature set which is crucial to fault prediction can be accurately screened out, and a bidirectional LSTM model is adopted to capture time sequence information, so that the fault prediction precision and the real-time response capability are remarkably improved.

The invention relates to the technical field of data processing, and discloses a multi-protocol fusion Internet of Things equipment intelligent gateway data conversion method and system. The method comprises the following steps: collecting a multi-protocol equipment data packet, and extracting protocol features to construct a vector library; protocol types are identified based on the vector library, data are analyzed, and a data object set containing semantic tags is constructed; semantic correlation is analyzed through an adaptive learning algorithm, and a dynamic protocol semantic mapping matrix is established; converting the data into a standard format according to the mapping matrix and recording a matching degree to form a target data pool; and extracting fusion data from the data pool, recoding according to a target protocol format, and outputting a data frame. The problem that an existing multi-protocol fusion data conversion method lacks protocol semantic understanding and self-adaptive learning ability is solved, and semantic consistency and conversion quality of data conversion among multi-protocol equipment are improved.

The invention discloses intelligent knowledge service electronic equipment for information retrieval, and the equipment comprises a data management layer which is responsible for providing original data for a cognitive calculation layer, and carrying out the closed-loop optimization through continuously monitoring the feedback of a man-machine interaction layer; the cognitive calculation layer is responsible for converting original data into a semantic tag system and a dynamic knowledge graph, applying BERT + GNN hybrid analysis, BERT processing text context and GNN analysis entity relationship in the knowledge graph, and dynamically adjusting a reasoning path according to user field preference by combining an SWRL rule engine and reinforcement learning; the service arrangement layer is responsible for extracting data from the multi-modal data pool, solidifying and arranging a complex query stream to perform logic decoupling and efficient multiplexing, and providing a visual interface to support non-technical personnel to configure a new scene; and the man-machine interaction layer is used for constructing a natural interaction bridge through the structured answers and the visual maps output by the service arrangement layer, carrying out personalized interface adaptation based on historical dialogues, and establishing a user feedback closed loop.

The invention relates to the technical field of data processing, in particular to a cross-unit data management method, which comprises the following steps of: performing format standardization processing on data in a data pool of each organ and unit, converting the data into a uniform standard format, extracting semantic features to construct a semantic association network, and recording a semantic relationship of data items and a dynamic authorization and authority mapping mechanism. Then, according to the inquirer identity information, the unit attributes, the business requirements and the data sensitivity level, dynamically generating inquiry authorization and establishing an authority mapping table; and when a query condition is received, analyzing by utilizing a natural language processing technology, and generating a query route and optimizing a statement in combination with the semantic association network and the permission mapping table. And finally, calling data from each data pool according to the route, performing fusion processing, displaying the data to a querier in real time, and recording a query log. The invention aims to solve the problems of data semantic isolation, authority management staticization, low query efficiency and insufficient cross-unit data conflict resolution capability in the existing cross-unit data management.

The invention relates to the technical field of environmental governance and remediation, in particular to a combined contaminated site soil remediation data management method, which comprises the following steps of: firstly, integrating multi-source heterogeneous data through a unified space-time coordinate system to generate a fusion data pool, and then driving a pollution assessment model to generate a multi-dimensional space-time pollution map; and then intelligently matching a candidate repair process chain based on an associated knowledge base, performing iteration deduction through a dynamic multi-objective optimization engine to obtain an optimal repair scheme, converting the optimal repair scheme into a digital regulation and control instruction, and finally monitoring an execution state and environmental response data in real time and feeding back and updating a data pool to realize closed-loop optimization. According to the method, the data quality and the pollution evaluation accuracy are improved, the adaptability of the repair process and the cost performance of the scheme are optimized, the repair effect is ensured to be stable and reach the standard, and the efficiency and reliability of the repair project are improved.

The invention relates to an electric power virtual simulation optimization method based on a digital twin technology, and the method comprises the following steps: S1, deploying sensors at key nodes of an electric power system, and integrating an intelligent electric meter, weather forecast, social media information and market data to form a comprehensive data pool; s2, constructing a digital twinborn model of the power system; s3, optimizing the digital twin physical model based on a Bayesian optimization method and an adversarial neural network; s4, applying a bee colony algorithm to formulate a cooperation strategy, and optimizing transmission and load adjustment of power in different areas; s5, introducing an adaptive neural fuzzy inference system, and further optimizing a cooperation strategy; and S6, based on the optimized digital twinborn model and the operation strategy, performing large-scale distributed simulation by using cloud resources. According to the invention, accurate construction of the electric power simulation model and accurate implementation of electric power virtual simulation are realized.

The invention discloses a charging user behavior pattern analysis system and method, and relates to the technical field of electric vehicle charging management, and the method comprises the steps: obtaining and building a multi-source data pool, fusing the multi-source data pool, analyzing a charging behavior pattern of a user, calculating the abnormal behavior probability, building a dynamic scheduling rule according to the charging behavior pattern of the user, and carrying out the dynamic scheduling according to the abnormal behavior probability. According to the method, through multi-source data collection and analysis, the user charging behavior is accurately predicted, resource distribution is dynamically optimized, the charging station operation efficiency and the user satisfaction degree are improved, virtual power plant integration is promoted, and the power generation efficiency is improved. According to the method, a vehicle-to-power grid strategy optimization module is constructed, the user participation power grid peak regulation potential is quantified, regional energy scheduling is optimized, in addition, an anomaly detection and response mechanism is provided, safety and stability are ensured, and the charging service is promoted to develop towards the efficient, intelligent and sustainable direction.

The invention discloses a garbage collection method and device, equipment and a storage medium, and relates to the technical field of electrical digital data processing.The method comprises the steps that when metadata of a cache pool is updated, internal object abstracts corresponding to the metadata are put into a garbage collection queue; according to the water level condition of the data pool and the disk utilization rate of the object storage device of the data pool, the single-round execution task number of the garbage collection queue is determined; according to the number of single-round execution tasks of the garbage collection queue and the garbage collection priorities of the internal objects, the internal objects are selected to execute garbage collection, so that effective data in the internal objects are aggregated into new internal objects, and the internal objects subjected to garbage collection are deleted, namely, the situation that the water level of the current data pool is too high, and the garbage collection efficiency is improved is avoided. And the overlarge disk utilization rate of the object storage equipment is avoided, and the two are balanced, so that the influence on other businesses is avoided as far as possible on the basis of ensuring the normal recovery of the garbage.

The invention discloses a grid-connected detection method and system for an energy storage converter, and relates to the technical field of electrics, and the method comprises the steps: collecting the operation data of the energy storage converter through a multi-source sensor, and carrying out the preprocessing; fusing the virtual grid-connected scene data with the operation data of the energy storage converter to form an enhanced data pool, and performing feature extraction on the enhanced data pool to form a feature vector; based on the feature vectors, extracting the feature vectors of each time point through a sliding window method, and combining the feature vectors into a three-dimensional array; and based on the three-dimensional array, obtaining a grid-connected state through a grid-connected detection model. According to the method, the operation data of the energy storage converter are collected and preprocessed through the multi-source sensor, virtual grid-connected scene data are generated by utilizing the generative adversarial network, the adaptability of the model to complex working conditions is enhanced through rich data samples, and finally more accurate and more stable grid-connected state detection of the energy storage converter is realized.

The invention discloses an intelligent construction method and system for a knowledge graph in the field of power marketing based on a large model Agent. The method comprises the following steps: constructing a to-be-trained small model and a pre-trained large model Agent; obtaining initial annotation data and performing supervised training on the small model to obtain a trained small model; constructing a performance evaluation function to evaluate the relation extraction performance of the trained small model; reviewing and correcting results generated by the trained small models to obtain corrected data; combining the corrected data with the initial annotation data to obtain an expanded data pool, and extracting training data to retrain the trained small model; according to a performance evaluation result, the large model Agent repeatedly expands the data pool and trains the small model until a preset standard is reached; and constructing a knowledge graph in the field of power marketing based on the trained small model. According to the method, the accuracy, generalization ability and optimization efficiency of the model in the relation extraction task are improved, and the method is particularly suitable for high-precision relation extraction requirements in the field of power marketing.

The invention discloses a method, a system and equipment for converting a natural language into an SQL (Structured Query Language) and a storage medium. The method comprises the following steps: selecting a relational database of a corresponding field according to a business scene, and reconstructing the database according to a business demand; utilizing a Prompt guide model to generate a sample and extracting a sample template; extracting field values from the reconstructed database to construct a data pool; preparing a data set by using the sample template and the data pool; performing instruction fine tuning on the large model by using a LoRA fine tuning algorithm to obtain a trained LoRA weight, and combining the original large model and the LoRA weight to obtain a fine-tuned model; using the fine-tuned model to generate a corresponding SQL statement according to the natural language query of the user; performing grammar verification and standardization operation on the SQL statement to obtain a final SQL statement; according to the method, the workload of manually annotating data can be greatly reduced, and the accuracy of converting the natural language into the SQL statement is improved.

The invention discloses a ground engineering construction quality supervision digital delivery system, which relates to the technical field of engineering supervision, and comprises the steps of generation and multi-source data checking, activity record structuring and anti-verification before hiding, space-time reference unification and point cloud fusion playback, rule checking and differential rectification, and object-level marshalling delivery and asset authority management. According to the method, a unified data pool, activities, events and evidence chains are constructed through object codes to achieve time-space consistent association, image, point cloud and detection consistency check is conducted before hiding, blocking difference and unified reference fusion point cloud are generated, indexes support three-dimensional playback, rule base batch check is conducted, and issuing rectification is conducted. And the object-level delivery list and fingerprints support cross-platform identification and auditing to form a traceable link.

The invention relates to a carbon emission prediction method based on electric power big data, and the method comprises the following steps: S1, collecting electric power production and consumption data, and constructing a data pool; s2, statistical features are extracted and generated through statistical analysis and an automatic encoder technology, spatial features are extracted by using a geographic information system technology, periodicity and tendency of time sequence data are analyzed to obtain time features, a causal inference method is introduced to identify key causal features, and finally a comprehensive feature set is constructed; s3, a regression model is adopted as a basic model, LSTM is introduced to process complex and long-sequence data, the nonlinear fitting ability of the model is enhanced, the generalization ability and accuracy of the model are improved through integrated learning, a carbon emission prediction model is constructed, and training is carried out based on a comprehensive feature set; s4, on the basis of the trained carbon emission prediction model, carbon emission prediction is carried out according to real-time data; and S5, displaying a prediction result through a visual tool, and supporting user-defined view and analysis. The accuracy of carbon emission prediction is improved.

The invention relates to the technical field of source network load storage intelligent scheduling, and discloses a source network load storage AI intelligent scheduling method and system, and the system comprises a data collection module, a fluctuation analysis module, a constraint calculation module, a load modeling module, an energy storage analysis module, a decision engine module, and a safety correction module. Output data of a photovoltaic power station, a wind power plant and a traditional power plant are collected in real time through an Internet of Things sensor, and a multi-source heterogeneous energy data pool is constructed; aligning the corrected source load data with the energy storage state and the power grid operation parameters according to a time sequence to generate a four-dimensional optimization combination matrix; matching an optimal scheduling algorithm through a bionic search strategy, and analyzing the data matrix through the deep reinforcement learning model to generate a three-section scheduling instruction; and finally, the power grid control system executes power generation adjustment, load regulation and control and energy storage charging and discharging instructions. According to the system, the edge computing gateway is adopted to realize data acquisition, the new energy consumption capability and the power grid stability are improved, and the risk of source-grid load-storage collaborative failure is reduced.

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 field of visual element recommendation, in particular to a personalized visual communication element recommendation method and system based on Internet big data. The method comprises the following steps: acquiring user multi-dimensional internet behavior data, and mining platform user interaction behaviors one by one to obtain a user multi-platform interaction content data pool; performing user interaction intention speculation on the user multi-platform interaction content data pool, and performing personalized interaction interest preference mining to obtain user interaction interest preferences; deep interaction demand mining is carried out according to user interaction interest preference, adaptive portrait evolution is carried out, and a personalized user demand portrait is constructed; and performing visual element analysis and multi-modal visual mining according to the user multi-platform interactive content data pool to obtain multi-modal visual element information of each homepage. According to the method and the device, the accuracy and the quality of visual element recommendation are improved through accurate user aesthetic demand analysis.

The invention provides a strategy model training method and device for a robot and a storage medium, and the method comprises the steps: in an offline learning stage, firstly collecting expert demonstration data, enabling the robot to carry out the imitation learning based on the expert demonstration data, and obtaining an initial strategy model and an offline data pool; and entering an online learning stage, enabling the robot to perform reinforcement learning on the basis of expert strategies, determining training data of a current time step according to manual intervention information, adding the training data into an online data pool, performing model parameter updating according to the offline data pool and the online data pool, and after all tasks are finished, performing online learning. And taking the obtained initial strategy model as a target strategy model. In the online reinforcement learning process of the robot, autonomous exploration and manual intervention of the model are combined, the robustness of the robot strategy network is further improved, and efficient transformation of the robot strategy from demonstration to autonomy is realized.

The invention relates to the technical field of three-dimensional modeling, in particular to a dynamic modeling method for a geological structure three-dimensional model. The method comprises the steps that multi-source data are acquired and preprocessed, a voxel semantic fusion algorithm based on variational optimization is introduced, after semantic information is extracted from the preprocessed multi-source data, the preprocessed multi-source data are mapped to a target three-dimensional space grid, and an optimal semantic fusion vector is obtained; based on the optimal semantic fusion vector, generating a standard voxel data pool, and constructing a geological structure three-dimensional model; monitoring data change, calculating the position of a newly added data point, combining the standard voxel data pool to obtain a space updating area, and modeling the space updating area to realize model updating; and after the modeling of the space updating region is completed, optimizing the boundary continuity. The problems that multi-source geological data cannot be directly used for structure construction and semantic fusion of a three-dimensional model, dynamic response to newly-added data is lacked, and geometric discontinuity and structural logic discontinuity exist at the boundary are solved.

The invention provides monitoring equipment and system for monitoring a reactor core of a nuclear power plant. The monitoring device comprises a data preprocessing module, a model updating module, a monitoring module, a shared data pool and a monitoring interface module. On one hand, on-line monitoring of a reactor core is realized by tracking actual operation data of a reactor, and the accuracy of a monitoring result is improved; and on the other hand, the reactor core three-dimensional power distribution reconstruction is carried out in combination with the actual measurement current, so that the monitoring result (such as the second reactor core physical parameter) obtained based on the reconstructed reactor core three-dimensional power distribution is more matched with the actual operation condition of the reactor core, and the accuracy is higher.

The invention relates to the technical field of meteorological data processing, in particular to a training data acquisition method and system for a power transmission line fault prediction model. The method comprises the following steps: constructing a multi-source heterogeneous original data pool containing severe convection meteorological data, power transmission line operation and fault data and geographic space data; performing time-space reference unified processing and grid division on the data; mapping the line paragraph to a grid unit, identifying a severe convection event and dividing an influence area through a density clustering algorithm based on a multi-parameter collaborative threshold value, calculating a grid exposure degree, and combining a strength grade weight and a terrain correction weight to calculate a space correlation strength; setting a differentiation time association window to calculate a time factor according to the severe convection dominant type, calculating association credibility based on the space association strength and the time factor, and screening high-credibility association results; and performing attribution labeling on the fault information to obtain a training data set. The problem of low training data quality is solved.

The invention belongs to the technical field of smart cities, and relates to a smart city public service opinion feedback multi-dimensional evaluation system, which comprises a data acquisition and processing module for analyzing object contour features in public service original data to generate a structured data pool; the space-time atlas modeling module is used for generating a demand migration feature vector with a weight coefficient; the dynamic scheduling engine module is used for calculating a municipal resource real-time scheduling path according to the demand migration feature vector, and generating an emergency execution scheme when a resource gap value exceeds a preset threshold value; the task execution monitoring module is used for splitting an emergency execution scheme into interactive task nodes, acquiring running state data of an execution terminal in real time, and triggering an automatic replacement path supplement mechanism when detecting that the overheating duration of equipment exceeds a safety critical value; and the feedback optimization evaluation module is used for generating an urban governance health index report containing a compensation scheme and a priority adjustment factor. According to the invention, the service can be recovered only through manual intervention when abnormal events are solved.

The invention belongs to the technical field of intelligent control, and discloses an energy optimization management system and method for hotel guest rooms, and the system comprises a central data pool unit which constructs a time sequence database cluster, and carries out the uploading and interaction of data between all units through a data bus; the existence sensing unit is used for integrating multiple sensors by adopting a sensing layer architecture to form a sensing network, capturing existence sensing data and generating a room existence state map based on the existence sensing data; the existence sensing data comprises guest room reservation information data, guest room environment data and guest information data; the partition environment control unit is used for obtaining customer preference data by analyzing a room existence state map; based on the guest preference data and the guest room environment data, selectively activating environment control of different guest room areas by adopting an intelligent optimization algorithm; and the hotel energy utilization rate and customer satisfaction are improved.

The invention discloses a traffic flow prediction method and device, a storage medium and electronic equipment. The method relates to the technical field of traffic flow analysis, and comprises the following steps: constructing a multi-source traffic data pool, and preprocessing historical traffic data in the multi-source traffic data pool to obtain a target multi-source traffic data pool; performing data enhancement processing on the historical traffic data in the target multi-source traffic data pool by adopting a pre-trained GANs model to obtain an enhanced sample set for training a target traffic flow prediction model; performing model training on an initial traffic flow prediction model by adopting a Dropout method based on the enhanced sample set to obtain a target traffic flow prediction model meeting a preset condition; and performing traffic flow prediction on multi-source traffic data acquired in real time by using the target traffic flow prediction model to obtain a traffic flow prediction result. According to the method, the accuracy of urban traffic flow prediction can be improved.

The invention discloses an automatic auditing business process control method based on rule optimization. The method comprises the following steps: S1, generating a unified auditing data pool; s2, constructing a multi-layer rule engine, and generating an initial rule set; s3, initializing an improved bee colony optimization algorithm, and dynamically defining a search space of a population; s4, dynamically optimizing the initial rule set by using an improved bee colony optimization algorithm, adjusting a single rule parameter in a search space in a worker bee stage, selecting an optimal rule direction in an observation bee stage, and introducing collaborative bees to optimize conflicts among multiple rules, and re-initializing a low-diversity population in a reconnaissance bee stage, and generating an optimized rule set; s5, generating a rule execution plan, adjusting the node priority of the auditing process and optimizing a task path; and S6, realizing continuous optimization iteration of the rule set and the audit process path based on implementation feedback of the rule execution plan. According to the invention, the multi-layer rule engine and the improved bee colony optimization algorithm realize automatic auditing business process control.