35results about How to "Improve decision making" patented technology

The invention discloses an end-to-end planning method fusing mixed trajectory representation and curriculum reinforcement learning. The method comprises the following steps: constructing a discrete-continuous mixed representation end-to-end pre-training network; constructing a course strengthening fine tuning framework based on interactive deduction; and designing a hard and soft constraint coupled hierarchical course award mechanism. According to the method, a discrete intention and continuous residual error coupling mixed trajectory characterization mechanism is introduced, and on the basis that a driving intention is quickly locked by using discrete primitives, subgrid-level geometric correction is performed on a coarse-grained trajectory through parallel regression branches. According to the invention, on the premise of not increasing the complexity of the search space, accurate trajectory planning with both long-time-sequence intention consistency and dynamics smoothness is realized. According to the method, the reinforcement learning training efficiency is effectively improved, catastrophic forgetting of a long-tail risk scene is prevented, a safety boundary is established in a strategy planning decoder, and the robustness and decision-making ability of an automatic driving system under extreme working conditions are improved.

The invention provides a virtual simulation training system and method for a forest fire fighting commander. The virtual simulation training system comprises a physical layer, a service layer, an element simulation layer and an application layer. The physical layer provides a hardware infrastructure; the service layer provides a data processing function; the element simulation layer simulates key elements in the forest fire fighting process and comprises a fire investigation module, a field communication module, an information submission module, a situation research and judgment module, a tactical making module, an instruction issuing module and a tactical execution module. The application layer provides a user interaction interface and comprises a training end situation display function, a training end interaction function and a guiding end function. According to the system, real-time analysis of the fire scene situation is realized by adopting multi-source data fusion and a deep learning technology, and the training effect is improved through dynamic scene injection and an automatic evaluation function. The system can provide a highly vivid training environment for forest fire suppression commanders, effectively improve command decision and coordination ability, reduce training cost, and enhance emergency disposal effect.

The application belongs to the technical field of fault identification, and discloses an equipment processing device fault identification method and system based on artificial intelligence. The method comprises the following steps: in a cloud data center, using an artificial intelligence algorithm, an equipment processing device fault identification engine is constructed, and a multi-modal data acquisition device is connected through an edge computing gateway; using the multi-modal data acquisition device, real-time monitoring multi-modal data of the equipment processing device is acquired, and is uploaded to the cloud data center through the edge computing gateway; in the cloud data center, according to the real-time monitoring multi-modal data, using the equipment processing device fault identification engine, fault identification is carried out, and real-time fault identification results and real-time fault maintenance strategies are obtained. The application solves the problems of single data dimension, limited model depth and precision, and lack of response mechanism in the prior art.

This invention discloses a method, apparatus, electronic device, and storage medium for training an intelligent agent policy network. The method includes: determining the linear score of a first training sample; inputting the current first training sample into a state-action value network, a state value network, and a policy network respectively, and determining a first actual output, a second actual output, and a third actual output; determining a first loss value based on the second actual output, the cumulative reward at a first historical time, the linear score, and a first loss function, and adjusting the parameters of the state-action value network; determining a second loss value corresponding to the state value network, and adjusting the parameters of the state value network accordingly; determining a third loss value corresponding to the policy network, and adjusting the parameters of the policy network based on the third loss value, to obtain the target policy network. This technical solution improves the training effect and speed of the policy network, enabling more accurate and convenient training of the target policy network.

The application discloses a kind of self-operation and maintenance methods for floating photovoltaic, it is related to operation and maintenance management technical field, comprising the following steps: S1, acquisition multi-source data, and pre-processing, obtain wave characteristic parameter and wave event mark;S2, according to wave spectrum, construct comprehensive risk index, and determine the course of unmanned ship autonomous decision;S3, according to wave characteristic parameter and wave event mark, reinforcement learning is carried out, and total control instruction is obtained;S4, according to the course of unmanned ship autonomous decision and total control instruction, complete autonomous operation and maintenance.The present application makes unmanned system have higher task level plan and decision-making ability, the ability of self-sensing real-time wave distribution map on its planning path, can autonomously modify task plan, select the risk minimum scheme.

The application discloses an obstacle screening method, device and automatic driving system, and belongs to the technical field of automatic driving. The method comprises the following steps: determining the game interaction intensity of each dynamic obstacle based on the trajectory overlap measure of each dynamic obstacle in the multiple dynamic obstacles around the ego vehicle and the predicted trajectory of the ego vehicle within a future preset time period, and the time gap acceptance of the ego vehicle; determining the behavior uncertainty score of the dynamic obstacle based on the determinant of the covariance matrix of the trajectory prediction of each dynamic obstacle, and the probability entropy of performing different possible actions; determining the comprehensive score of the dynamic obstacle based on multiple evaluation indexes of each dynamic obstacle, including the game interaction intensity and the behavior uncertainty score; and outputting the dynamic obstacle with the highest comprehensive score as the target obstacle. The application can accurately identify the key game obstacle which has a greater impact on the decision of the ego vehicle in the multi-obstacle game scene.

The application provides a UAV combat intention judgment method based on a Bayesian model and human-machine complementarity, and the technical scheme is as follows: a human-machine hybrid intelligent framework combining Bayesian inference and human-machine complementarity theory is constructed, real-time situation information of enemy UAVs is collected and analyzed, situation data of the enemy UAVs are subjected to feature extraction, independent intention judgment is carried out by machine intelligence and human intelligence respectively, and relevant parameters are dynamically obtained through Bayesian inference; a confidence feedback mechanism is used to fuse the prediction results of human and machine by combining a human-machine complementarity coefficient; and through dynamic weight distribution and parameter adjustment, more robust and accurate intention recognition results are generated. The application has the beneficial effect that in a complex and dynamic battlefield environment, the complementary advantages of the intuitive judgment of human intelligence and the high-speed calculation of machine intelligence are effectively fused.

The application discloses an electronic brake intelligent test simulation method and system based on big data analysis, and belongs to the technical field of intelligent test simulation; brake response delay data, steering angle change data and load change data in a historical simulation process are acquired; a running time period corresponding to each test scene is acquired, and a time sequence response data set is constructed; load-brake efficiency ratio and steering angular velocity of a single running time period corresponding to a single test scene are calculated; based on the load-brake efficiency ratio and the steering angular velocity, steering-load coupling efficiency index is calculated; a benchmark score value is constructed, brake performance score and caliper wear degree of a single running time period corresponding to a single test scene are calculated in combination with the steering-load coupling efficiency index; a preset threshold value is used for intelligent test simulation analysis, so that dynamic monitoring and intelligent scheduling of a test process are realized, and the effects of improving test efficiency, optimizing test resource configuration and enhancing system durability and safety are finally achieved.

This invention discloses a data compliance dynamic detection method and system based on deep learning, belonging to the field of data security processing technology. It addresses the problem that existing methods, which focus primarily on text data processing when performing compliance detection based on extracted comprehensive semantic words, lack the ability to comprehensively process multimodal data and comprehensively evaluate complex compliance issues, leading to inaccurate compliance assessments. The method includes preprocessing the original data stream, extracting features from the preprocessed data stream based on data stream type, and using a compliance analysis model to identify and analyze multimodal feature sets based on a standard rule base. In this invention, preprocessing the original data stream filters and reduces noise in the multimodal data, ensuring the authenticity and validity of the data. Furthermore, the compliance analysis model's identification and analysis of multimodal feature sets ensures comprehensive processing capabilities for multimodal data and comprehensive evaluation capabilities for complex compliance issues.

The application discloses an interpretable analysis and decision sharing verification method and system for a colorectal cancer prognosis model, and relates to the field of medical artificial intelligence interpretability. The method comprises the following steps: performing gradient weighted class activation mapping analysis on the prognosis model to generate an image heat map; calculating the contribution degree of multi-modal features by using a SHAP interpreter; constructing an integrated visualization interface to present the patient data, model prediction and the above-mentioned explanation results to doctors; the doctors perform independent risk assessment based on the interface information; and finally, the decisions of the doctors and the model are compared to evaluate the auxiliary performance of the model. Through multi-level explanation and innovative doctor-model decision sharing verification mechanism, the transparency and clinical credibility of the complex AI prognosis model are significantly improved, the value of time series data in dynamic risk assessment can be verified, and the clinical landing application of the AI model is effectively promoted.

A kind of AGV robot vision acquisition, avoidance control method based on deep thinking, it is related to the control or regulation system field of non-electric variable, this method includes: collecting the real-time motion parameter of automatic guided vehicle, calculates key path point set and constructs feature vector;Collect vision image and depth data, extract obstacle features through grid division and generate three-dimensional feature model;The path point is mapped to three-dimensional model to generate dynamic scene sequence, calculate and dynamic target distance value determine risk level;Using large language model analyzes scene and generates the obstacle avoidance scheme containing obstacle avoidance trajectory, speed and steering is stored in scheme library;Obtain current scene characteristics, select candidate scheme by similarity matching, select optimal scheme execution by safety score. By implementing the method, the obstacle avoidance success rate of automatic guided vehicle can be improved.

The invention relates to the technical field of industrial intelligent robots, in particular to a self-adaptive industrial robot with multi-modal sensing and online learning capabilities, which comprises a robot module and a master control module, the multi-modal sensing module, a robot execution module and the master control module are in bidirectional communication connection, and the robot execution module is used for receiving a final action instruction and feeding back an execution state; the robot module comprises a base, and the robot module is installed on one side of the top of the base through bolts. According to the method, the heterogeneous data routing unit is arranged to independently process perception data of different modes such as vision, force touch and ontology at a bottom layer and route the perception data to the corresponding exclusive edge control nodes, so that direct fusion of cross-modal data or characteristics is avoided at a perception layer, and mutual interference between multi-modal noise and uncertainty is effectively inhibited; and the stability and reliability of subsequent action generation and decision making are improved, so that the robot can better adapt to an unstructured and dynamically changing working environment.

The invention relates to the technical field of power fault diagnosis, and discloses a power fault diagnosis method and system based on cross-modal semantic alignment and agent coordination decision, and the method comprises the steps: obtaining multi-modal data needed in the operation and maintenance process of power equipment, and constructing a multi-modal power professional knowledge base; extracting multi-modal features contained in the multi-modal electric power professional knowledge base based on a feature representation technology, calculating dynamic weights of the multi-modal features, and generating an intelligent agent in combination with the electric power fault diagnosis instruction data set; an electric power fault diagnosis task is input, the modal weight of the electric power fault diagnosis task is calculated, and the intelligent agent outputs a fault diagnosis result of the electric power fault diagnosis task and a corresponding processing flow according to the modal weight; and generating a fault diagnosis report based on the fault diagnosis result of the power fault diagnosis task and the corresponding processing flow. According to the invention, intelligence and real-time performance of decision making are greatly improved, and dependence on artificial experience is reduced.

This invention discloses a robot obstacle avoidance method based on dynamic feature perception, aiming to solve the obstacle avoidance problem of robots in complex environments where unknown static and dynamic obstacles coexist. The method first acquires sensor scan data and the robot's own pose data, and then performs feature decoupling: a multi-scale residual convolutional network is used to extract static environment features, while a long short-term memory network is combined to learn the motion trend features of dynamic obstacles. Subsequently, an attention mechanism layer fuses the static environment features, dynamic obstacle motion trend features, and the robot's own pose data into a comprehensive perception feature vector, which is then input into a deep reinforcement learning model to output the optimal obstacle avoidance action. This invention can effectively distinguish between static and dynamic obstacles, and by predicting dynamic trends, it significantly improves the accuracy, safety, and efficiency of obstacle avoidance decisions.

The invention relates to the technical field of damage detection of oil and gas transportation pipelines, in particular to a marine pipeline damage degree intelligent identification method based on multi-source feature deep fusion. The method comprises the following steps: S1, acquiring damage data of a damaged pipeline; s2, performing noise reduction on the damage data by adopting an improved wavelet threshold noise reduction method; s3, performing normalization processing on the data after noise reduction; s4, inputting the normalized data into a preset multi-feature deep fusion white box model for training; and S5, calculating the accuracy to reflect the accuracy of model identification. The method overcomes the defects of a hard threshold function and a soft threshold function, flexibly switches between the hard threshold function and the soft threshold function by adjusting the factor alpha, reduces discreteness, avoids the problem of constancy, is high in adaptability, can be automatically adjusted according to different signal characteristics, effectively removes the noise of experimental acquisition data, improves the signal quality, and greatly improves the identification accuracy of the damage degree.

This invention relates to the field of air traffic management and intelligent control technology, specifically to an airport gate control system and method based on dynamic control of the airport surface; it includes the following steps: constructing a dynamic digital twin model capable of predicting future situations by real-time fusion of flight, surface surveillance, high-precision meteorological, and aircraft status data; predicting multi-dimensional operational conflicts, including weather mismatch and abnormal flight schedules, based on this model; intelligently generating and evaluating a coordinated control plan that integrates impact domain diffusion adjustment and fault-oriented safe gate strategies when receiving events such as sudden weather changes or aircraft malfunctions; parsing the optimal plan into differentiated instructions for roles such as control tower, apron, and ground services, and issuing them, tracking the execution status throughout the process; and finally, enabling the system rule base to self-optimize through a closed-loop feedback mechanism. This invention achieves efficient, coordinated, and early response to complex dynamic disturbances, improving the efficiency, safety, and resource utilization of airport ground operations.

The invention belongs to the technical field of laser precision machining and intelligent manufacturing, and particularly relates to a laser machining parameter transfer learning method, system and equipment and a medium, and the method comprises the following steps: S1, specifying a machining requirement, and inputting known physical characteristics and an initial laser parameter range of a to-be-machined material; s2, establishing a basic model library containing a multi-physics field coupling model, and constructing a laser parameter-electronic dynamics-processing result mapping database based on historical experimental data and model simulation results; and S3, based on the physical model library and the mapping database, training a machine learning or deep learning model as a prediction model. According to the method, a multi-physics field coupling model and a data driving model can be fused, the interpretability of the model is ensured by utilizing a physical mechanism, and the prediction precision is improved through mass data training; and the three-stage feedback mechanism realizes real-time adaptive adjustment in the machining process, so that the key index deviation of the machining result can be within an effective control range.

This invention discloses an intelligent operation and maintenance method and system for ship software platforms based on big data. By integrating multi-source heterogeneous data from the entire ship lifecycle, it can achieve comprehensive perception of the platform's operational status, predictive early warning of potential faults, and intelligent diagnosis of root causes of faults. The system utilizes historical data to offline train a health assessment model and a fault prediction model based on time-series networks. During online operation, the system analyzes real-time data streams, continuously quantifies the platform's health status, and issues early warnings of faults. When a warning or fault occurs, the system can automatically activate a knowledge graph-based inference engine to quickly locate the root cause and provide decision support for operation and maintenance personnel, realizing a shift from passive response to proactive intervention. This invention provides key technical support for realizing intelligent operation and maintenance and autonomous navigation of ships, and has been verified in a simulation environment, demonstrating its application potential in actual ship deployments.

The invention provides a strip mine mining and selection collaborative plan dynamic optimization method and system for coping with weather disturbance, and relates to the technical field of strip mine mining and selection collaborative production plan optimization. The method specifically comprises the following steps: for any to-be-planned production cycle, acquiring weather forecast data of all planning periods in the production cycle, and respectively extracting time characteristics and meteorological characteristics of each planning period; constructing a bilevel programming model based on data-driven prediction and optimization decision coupling; and based on the time characteristics and the meteorological characteristics of all the planning periods in the production cycle, generating an optimal production plan of the production cycle by using a bilevel planning model based on data-driven prediction and optimization decision coupling. According to the method, weather changes can be dynamically responded, so that productivity fluctuation caused by weather uncertainty to strip mine production is dealt with, and ore supply stability and production economic benefits are guaranteed.

This invention discloses an intelligent assisted driving control method and device for automatically recognizing traffic lights, belonging to the field of intelligent assisted driving technology. The method comprises the following steps: acquiring the distance between the current vehicle and the intersection ahead from a road traffic information database and a vehicle-mounted camera; when the distance is less than a set distance threshold, retrieving traffic light and lane information from the road traffic information database and real-time traffic light and lane information from the vehicle-mounted camera; generating first and second signals based on successful acquisition, and converting the two signals into voice and data information respectively; selecting between driver decision-making and human-machine co-driving decision-making based on whether the vehicle is in an emergency state; in non-emergency states and with the second signal, driver decision-making is selected, while in emergency states and with the first signal, human-machine co-driving decision-making is selected. This invention provides the possibility for red-green color blind driving by combining voice recognition with assisted driving control and driver decision-making.

The application discloses an agent autonomous decision-making method and system based on embedded edge computing, comprising the following steps: constructing an initial probability decision space according to an observation state, and performing localized simplification processing on state characteristic parameters under the calculation power and storage constraints of an embedded edge device; sampling the initial state value by using an improved Posen sampling algorithm, generating a decision value update sequence, and iteratively obtaining an updated state value; dynamically adjusting the sampling intensity according to the convergence trend of the updated state value to obtain a state value threshold; correcting the decision value update sequence according to the state value threshold and generating a decision path; feeding back the decision path to the initial probability decision space to calibrate the state transition probability; and finally obtaining a real-time optimal action strategy based on the calibrated state transition probability. The application effectively improves the real-time performance and reliability of the autonomous decision-making process in the embedded resource-constrained environment.

The invention relates to the technical field of glass seal welding process optimization and defect prediction, in particular to a glass seal welding process optimization and defect prediction method based on deep learning. The method comprises the following steps: firstly, acquiring multi-source sensor electric signal data of a welding furnace in real time, and synchronizing and preprocessing to form a standard time sequence data sequence; and then, inputting the data sequence into a pre-trained multi-task deep learning model, and synchronously realizing dynamic process optimization and early defect prediction by the model through a shared feature extraction network and two parallel task branch networks. After the process is finished, the system associates an actual quality result with process data to form an incremental sample, and performs online fine adjustment on the model based on an intelligent trigger mechanism and an anti-forgetting algorithm, so that the system can adapt to changes of equipment and materials. According to the invention, the transformation of the glass seal welding process from fixed parameter control to real-time closed-loop intelligent optimization is realized, and the product yield, the process stability and the production intelligence level are effectively improved.

This invention belongs to the field of information processing technology and provides a cloud-edge collaborative method for sharing and processing accounting data. To address the poor performance of traditional accounting data sharing technologies, the method acquires raw accounting data based on a preset user terminal and determines whether the raw accounting data is preset important information data. If the determination is yes, the raw accounting data is sent to a locally deployed preset edge terminal. Based on the preset edge terminal, edge computing is performed on the raw accounting data to obtain preliminary accounting information data. This preliminary accounting information data is then encrypted and sent to a preset cloud, achieving cloud-edge collaborative accounting data sharing. This not only ensures the security of accounting data sharing by leveraging the locally deployed edge terminal and its edge computing capabilities but also achieves convenience and efficiency through cloud-edge collaboration, thereby improving the effectiveness of accounting data sharing.

The invention relates to the technical field of intelligent agriculture, in particular to a fig water and fertilizer digital management method based on a low-code platform. The core of the method comprises the following steps: constructing a water and fertilizer decision model integrated with time sequence prediction and an attention mechanism through a low-code platform, processing an environmental data sequence by adopting an improved Transform architecture, and capturing periodic characteristics in combination with learnable time perception codes; establishing a segmented attention weight distribution strategy based on a growth stage, and dynamically adjusting the attention degrees of different environment characteristics; and configuring a model updating system comprising an online learning mechanism, and realizing continuous optimization of model parameters through an elastic weight consolidation algorithm. Accurate prediction and self-adaptive regulation and control of fig water and fertilizer requirements are realized, and the water and fertilizer management precision and the system availability are effectively improved.

The invention discloses a deep reinforcement learning method based on a collaborative attention auto-encoder, and the method comprises the steps: collecting an environment image, carrying out the preprocessing, and generating an environment observation frame sequence; performing feature extraction on the environment observation frame through a shared weight encoder; taking the first frame of the environment observation frame sequence as a main frame, taking the characteristics of the main frame as a reference, and calculating the collaborative attention characteristics of the main frame and subsequent frames; fusing the collaborative attention features through a collaborative attention mechanism to obtain cross-frame features of the environment observation frames; splicing the main frame feature and the cross-frame feature to generate a potential feature of the environment observation frame; and finally, reconstructing a tail end frame of the environment observation frame sequence through a decoder, calculating reconstruction loss, splicing potential features and static features, inputting the spliced features into a strategy network, and outputting action distribution for guiding decision of a reinforcement learning agent, thereby realizing the deep reinforcement learning method based on the collaborative attention auto-encoder. And the learning efficiency and the strategy performance can be obviously improved.

The invention discloses a continuous image recognition method based on model fusion and multi-level distillation, and the method comprises the steps: firstly pre-training an image recognition model, and obtaining an initialized task adapter in a multi-task image data training image recognition model; secondly, constructing a teacher model and a student model based on an image recognition model, and performing incremental learning to obtain feature representation and soft labels of the teacher model and the student model; then feature representation and soft labeling are carried out, a loss function is constructed, adaptive parameters of a student model and a classification head are subjected to back propagation gradient updating, a verification set is input into the student model, and a prototype classifier is obtained; and finally, after incremental task training is completed, the Adapter trained in the student model is used for prediction, and image categories are classified. According to the method, a unified reasoning space for all tasks is constructed, the expandability of continuous learning tasks is improved, and accurate and efficient image recognition is carried out.

The invention discloses a hybrid large model cascade intelligent decision-making method, and relates to the technical field of artificial intelligence decision-making. The method comprises the following steps: receiving demand data of a complex decision scene, and constructing a decision risk matrix and generating a hierarchical risk type decision demand topological conformation by disassembling decision dimensions and defining precision and risk tolerance; calling the first large model and the second large model to execute cascade operation by adopting a large model cascade-risk-oriented matching architecture, and generating a risk-controllable fine decision intermediate feature code table by eliminating redundant links; the risk threshold adaptability is assessed through a decision risk reciprocating check-optimization architecture, reverse optimization is carried out on an overproof link, complete derivation is carried out on a logic fault, and a final intelligent decision correlation topological graph is generated; and acquiring execution effect data of the final intelligent decision correlation topological graph, updating the decision risk matrix, and precipitating the decision template to the decision feature code knowledge base to complete iterative tuning of the decision ability. And the decision pertinence and the risk controllability are improved.

This invention claims protection for a high-speed laser scanning method for small-volume structures based on path optimization, belonging to the field of high-speed laser scanning and precision optomechanical control technology. The core of this method lies in constructing a complete closed-loop control system encompassing "data acquisition - model building - parameter adjustment - real-time feedback - dynamic suppression - closed-loop verification," focusing on high-order vibration suppression under high-frequency drive of MEMS micromirrors. By accurately acquiring vibration characteristic data to establish a dedicated database, and combining adaptive filtering and dynamic modeling to create a compensation model that fits the characteristics of the micromirror, the driving current adjustment parameters are finely configured. Vibration and trajectory data are acquired in real time during high-speed scanning, and the adjustment current is dynamically output. The suppression effect is simultaneously verified and iteratively optimized, achieving accurate perception and efficient suppression of internal dynamic vibrations. This method stabilizes the scanning trajectory without sacrificing scanning efficiency, overcoming the problem that traditional technologies relying solely on external parameter adjustments cannot simultaneously achieve high speed and accuracy, thus ensuring high-precision point cloud output for small-volume structures.

The application relates to the technical field of vehicle-mounted data processing, and provides a vehicle-mounted multi-source data fusion processing method and system, which comprises the following steps: according to logical association, identifying contradictory items existing in an environment understanding graph structure, and quantifying the degrees of the contradictory items to obtain contradiction degrees; obtaining the degree of conformity between a preliminary environment interpretation result and a preset rule; according to reliability scores, contradiction degrees and the degree of conformity, judging the root sources causing the contradictory items; applying weight reduction processing to the root sources to reduce the reliability scores corresponding to the root sources, and updating the confidence labels of nodes or logical associations of the environment understanding graph structure based on the reduced reliability scores to obtain the weight-reduced environment understanding graph structure, so as to complete vehicle-mounted multi-source data fusion processing; the nodes of the environment understanding graph structure are environment element nodes, which are used for representing the categories and spatial attributes of corresponding environment elements. The application has the effect of improving the decision-making capability of an intelligent driving vehicle.

The invention provides a federated learning-fused cross-department data collaborative analysis method and system, and relates to the technical field of federated learning, and the method comprises the steps: firstly, dynamically constructing a cross-department federated learning collaborative link, and generating a configuration scheme; endowing the local data processing unit of each department with federal cooperation capability based on the configuration scheme, and generating a federal enabling type local data processing unit; generating a cross-department federal collaboration data link flow through interaction between the cross-department federal collaboration data link flow and the federal learning collaboration center; and constructing a cross-department data collaborative analysis model based on the cross-department federal collaborative data link flow, completing cross-department data collaborative analysis by using the model, and outputting a result. According to the method, the problems of privacy protection and security compliance in cross-department data collaboration are effectively solved, and efficient fusion and deep analysis of data are realized.