289 results about "Allocation algorithm" patented technology

The invention discloses a supply chain warehouse management system and method based on AI digital intelligence, and relates to the technical field of intelligent warehousing, and the method comprises the steps that a data processing unit collects supply chain system data through a data processing module, and constructs an associated data network containing goods information, shipping space information and order information based on the supply chain system data; the goods allocation unit executes a goods allocation algorithm according to the associated data network, extracts an association score based on goods information, analyzes a collaborative ex-warehouse frequency, and calculates an affinity matrix; the storage scheme unit generates a storage position scheme based on the affinity matrix; the job scheduling unit constructs a scheduling model, performs task optimization and outputs a task allocation sequence; the order picking navigation unit is provided with an order picking path planning module, presents a path and guidance, collects execution data, and optimizes the system through analysis. The problems that in traditional warehouse management, the goods allocation efficiency is low, task scheduling is not optimized, and the goods picking path is unreasonable are solved.

The invention relates to the technical field of cloud computing, and discloses an enterprise resource planning data efficient processing method based on cloud computing, and the method comprises the steps: collecting system data of finance, supply chain, production management and the like through a multi-source interface module, and carrying out the preprocessing through a hierarchical fusion framework, thereby obtaining a standardized data set; the method comprises the following steps of: selecting a multi-target scheduling model, inputting the multi-target scheduling model into a task decomposition model, generating task decomposition parameters based on a block processing mechanism and a service association rule, constructing the multi-target scheduling model aiming at the shortest processing duration and the minimum resource occupation, and outputting an optimal scheduling scheme by adopting a dynamic allocation algorithm. A hierarchical execution control model comprising a strategy layer, a coordination layer and an execution layer is established, the strategy layer performs global task planning, the coordination layer performs local resource adjustment, and the execution layer tracks tasks based on a load balancing algorithm and outputs a processing control instruction to realize efficient processing. The method improves the data processing efficiency and the resource utilization rate, and provides support for enterprise decision making.

The invention discloses a large vision-language model collaborative reasoning method for a cloud side end system, which is characterized by comprising the following steps: deploying a high-performance cloud LVLM model and a cloud archive library at a cloud end; an edge LVLM model and an edge archive library are deployed at an edge end, a retrieval enhancement generation algorithm is introduced into the edge LVLM model, and the edge archive library and a cloud archive library can perform dynamic knowledge interaction; and the task scheduler performs complexity evaluation on the received query task based on a task allocation algorithm, and allocates the query task to an edge end for local processing or allocates the query task to a cloud end for processing according to a complexity evaluation result. According to the method, the reasoning efficiency in the cloud edge end system can be improved, meanwhile, high precision is kept, and the technical problem that the reasoning efficiency is low due to the problems of limited computing resources, communication window intermittency, bandwidth limitation and the like of edge equipment is effectively solved.

The invention discloses an electric power engineering project management talent intelligent evaluation and dynamic training system, and the system comprises a multi-dimensional quality capability model construction module which constructs a four-dimensional capability framework based on the characteristics of an electric power engineering industry, generates a differentiated post capability model through a dynamic weight distribution algorithm, and carries out the dynamic training of the differentiated post capability model; constructing an industry ability dictionary library by utilizing a knowledge graph technology; the intelligent evaluation and matching module integrates a multi-modal evaluation tool to collect dominant and implicit capability data, dynamically adjusts capability index weights through a pre-trained AI model based on project characteristic parameters, and calculates man-post adaptation indexes by adopting a cosine similarity algorithm; and the dynamic training and development module is used for generating a personalized training scheme by quantifying the capability difference, tracking the training effect in a closed-loop manner based on a Korotkoff four-level evaluation model, and dynamically optimizing a training path by adopting a Bayesian network. The technical defects of single evaluation dimension, low man-post matching degree and insufficient cultivation efficiency of traditional power engineering human resource management can be solved.

The embodiment of the invention provides a multivariate data processing method and system.The method comprises the steps that original data are collected from multiple data sources by calling multiple APIs, and multi-source heterogeneous data are obtained; formatting the multi-source heterogeneous data according to a preset format conversion rule, wherein the formatting comprises timestamp alignment, space coordinate system conversion and key frame extraction; performing multi-dimensional feature extraction on the unified data to generate space-time aligned feature vectors, and performing cross-modal data fusion on the feature vectors based on a dynamic weight distribution algorithm to generate a fusion data packet; and performing data quality evaluation on the fused data packet, including verifying the integrity, consistency and real-time performance of the data, and dynamically adjusting a transmission path of a data acquisition interface according to a verification result. According to the method, an efficient and reliable solution is provided for collection, processing and fusion of multi-source heterogeneous data, and the data processing efficiency and the decision support capability are remarkably improved.

The invention provides a task parallel processing intelligent scheduling method and system oriented to large model services, and belongs to the technical field of service computation.The method comprises the steps that semantic features of instructions and texts of parallel subtasks are extracted through a LaBSE model, embedded vectors are generated, the vector dimension of the embedded vectors is reduced through a compression module, and the embedded vectors are distributed in parallel; inputting the compressed embedded vector into a regression device to predict the generation length; constructing a segmentation algorithm based on semantic integrity constraint, and segmenting the text into a text fragment list; dividing the segmented text fragments in the text fragment list into different batches of tasks by adopting a multi-constraint task batching algorithm according to a similar length constraint and a same instruction constraint to obtain a batch task set; and optimizing the batch task set by adopting a task allocation algorithm oriented to heterogeneous resources, and determining an optimized sub-task scheduling scheme. According to the method, efficient parallel processing of large model services is realized, and the reasoning speed, the resource utilization rate and the intelligent level of task scheduling are remarkably improved.

The invention discloses a multi-modal large-model multi-agent collaborative scheduling and distribution method, and relates to the technical field of agent deploying.The method comprises the steps that a complex task input by a user is received, the complex task is disassembled into atomic-scale subtasks, and a task decomposition graph is generated; establishing an intelligent agent capability evaluation model, and obtaining a comprehensive capability score of each intelligent agent according to the capability behavior of each intelligent agent in the candidate intelligent agent set; the intelligent agent set generation module is used for dynamically matching and distributing roles through a decision function according to subtask types and intelligent agent comprehensive capability scores, and generating a candidate intelligent agent set; and according to the task decomposition graph and the comprehensive capability score of each agent, constructing a task-agent ant colony allocation algorithm, and solving an allocation scheme. The problems that an existing multi-agent system lacks a dynamic role adjustment mechanism, flexible cooperation cannot be achieved according to task progress and agent states, and the efficient and flexible task processing requirement of a multi-mode large model application development platform is difficult to meet are solved.

The invention discloses a dynamic business data updating and querying method and system, and particularly relates to the technical field of information processing, and the method comprises the following steps: S1, real-time data acquisition and preprocessing; s2, updating the dynamic data; s3, intelligent query optimization; s4, result feedback and visualization are carried out; and S5, performing anomaly detection and self-repairing. According to the method, the real-time processing capability and query efficiency of massive heterogeneous data are remarkably improved, the distributed sensor network is combined with lightweight encryption and a differential privacy mechanism to realize localized cleaning and feature extraction of multi-source data, the load of a central server is effectively reduced, and the risk of sensitive information leakage is avoided; an incremental synchronization mechanism depends on a message queue and Hash chain log technology to ensure the atomicity and traceability of data updating in a high-concurrency scene, and by combining a dynamically generated space-time multi-dimensional index and an adaptive weight distribution algorithm, the system can intelligently analyze query semantics and optimize path selection, and the response time of complex query is greatly shortened.

The invention relates to the technical field of robots, and discloses a heterogeneous multi-machine cooperation method, a terminal and a readable storage medium, and the method comprises the steps: obtaining environment perception data, and obtaining a multi-modal fusion scene prompt according to the environment perception data; distributing a role for each agent according to the multi-modal fusion scene prompt, and generating an action plan of each agent according to the multi-modal fusion scene prompt and the role of each agent; and controlling each agent to execute the respective corresponding action plan. According to the method, the multi-modal fusion scene prompt is extracted according to the environment perception data, the parameters of different scenes are matched according to the multi-modal fusion scene prompt, the role of each agent is allocated through the role allocation algorithm, and the method does not depend on predefined roles and the parameters specific to the scenes; and the task execution capability of the system in a dynamic scene is improved.

The invention relates to the technical field of component management, and discloses an electronic component material identification management method, which comprises the following steps: synchronously acquiring a grayscale image, a three-dimensional point cloud and weight data through X-ray imaging, a laser radar and a weight sensor, and fusing and generating a multi-dimensional feature matrix based on a dynamic weight distribution algorithm; extracting physical characteristic parameters corresponding to the electronic components based on the multi-dimensional characteristic matrix, generating unique identification codes through multi-parameter matching in combination with a material physical characteristic database, and outputting material types and specifications; for a high-temperature or strong electromagnetic interference scene, the X-ray detector adjusts gain and sampling frequency in real time, image distortion is corrected through point cloud spatial correlation, and a high-fidelity image is output; the OCR technology automatically identifies label information to realize inventory dynamic updating; and through geometric consistency verification of the high-fidelity image and the multi-dimensional features, if conflicts occur, an early-warning material physical characteristic database is triggered. According to the invention, the precision and adaptability of electronic component identification management can be improved.

The invention discloses a hydropower station AGC intelligent optimization control method and system based on multi-unit dynamic load distribution and prediction and early warning, and relates to the technical field of AGC control, and the method comprises the steps: initializing system parameters, including a unit power range and upper and lower limits of a vibration region, and designing a dynamic vibration region crossing control algorithm, a prediction model and a multi-unit intelligent load distribution algorithm; acquiring real-time load and operation data, inputting the real-time load and operation data into the prediction model to obtain a prediction result, and triggering a load adjustment or early warning mechanism according to the prediction result; calling a vibration area crossing algorithm, and adjusting the unit power to a safe range; and a multi-unit intelligent load distribution algorithm is used, multi-unit loads are dynamically distributed, a control instruction is output, and real-time adjustment is completed. The method reduces manual intervention, improves the operation efficiency, prolongs the service life of equipment, reduces the operation cost through the optimization model, improves the economic benefits of a power plant, adapts to various working conditions, is short in response time, and adapts to complex scheduling scenes.

The invention discloses a computing task scheduling method and system based on time sequence diagram network resource state prediction, and relates to the technical field of computing resource allocation, and the method comprises the steps: collecting computing power resource state data based on computing power nodes, and modeling the computing power resource state data into time sequence heterogeneous diagram data; constructing a time sequence heterogeneous graph attention network THGAT model, and performing offline training on the THGAT model by taking node feature and edge feature prediction loss as a loss function and taking minimization of a prediction error as a target; inputting the time sequence heterogeneous graph data into a THGAT model, and predicting the resource state of a future time window; the calculation task is converted into a calculation demand vector, and a multi-objective optimization resource allocation THGATRM algorithm is adopted to perform calculation power resource allocation on the calculation task according to the resource state of the corresponding time window; and executing the computing task according to the computing power resource allocation scheme. Through the technical scheme of the invention, the accuracy of computing power resource prediction is improved, and efficient allocation and optimization of resources are realized.

The invention discloses a load balancing optimization method and device, electronic equipment and a storage medium, and relates to the technical field of load balancing optimization, and the method comprises the steps: carrying out the differentiation processing of a data migration task according to a file storage threshold value, and combining a migration performance index and server resource occupation information collected in real time, the task allocation strategy is adjusted through the dynamic task allocation algorithm, so that the reasonable allocation of the files with different sizes and the dynamic load optimization in the migration process are realized. The technical problems that in the prior art, server overload and resource idling coexist due to a single migration server or a static task allocation strategy, high delay and data inconsistency risks are generated due to network congestion and uneven task allocation in the migration process, and the overall performance and reliability of a system are affected can be solved. The technical effects of optimizing the load balance degree, reducing overload and idle conditions of the server, reducing migration delay, ensuring data consistency and improving the overall performance and reliability of the system are achieved.

The invention provides an intelligent computing center-oriented heterogeneous task scheduling method and system and electronic equipment. The method comprises the following steps of: acquiring computing power resource information of each computing power node in an intelligent computing center; determining task information and computing power resource requirements of each heterogeneous task; according to a preset scheduling rule, on the basis of the computing power resource information, the task information and the computing power resource demand, generating a scheduling strategy, and on the basis of the scheduling strategy, scheduling each heterogeneous task to each corresponding target computing node; wherein the preset scheduling rule comprises at least one of the following items: based on a priority allocation algorithm, performing computing power resource allocation on each heterogeneous task; based on a heuristic algorithm, carrying out computing power resource allocation on each heterogeneous task; and according to the preset load threshold value of each computing power node, carrying out computing power resource allocation on each heterogeneous task. According to the method, a proper scheduling mode can be flexibly selected for task scheduling, the computing power resource utilization rate is optimized, the service quality of large-scale heterogeneous tasks can be guaranteed, and the user experience is improved.

The invention relates to the technical field of intelligent manufacturing, particularly provides an AGV task allocation method, electronic equipment and a storage medium, and aims to solve the problems of low AGV starting rate and low production efficiency caused by the fact that an existing AGV scheduling system rarely pays attention to optimization of a dynamic task allocation algorithm. In order to achieve the purpose, the method comprises the steps that operation stages, including a first stage, a second stage and a third stage, of a current AGV task allocation scene are obtained; determining corresponding task allocation strategies based on the operation stage, wherein the task allocation strategies comprise an event-driven allocation strategy, a Hungary algorithm allocation strategy and a heuristic algorithm allocation strategy; and carrying out AGV task allocation based on the determined task allocation strategy. Through the above implementation mode, a coupling scheme of multiple task allocation algorithms is adopted, different task allocation strategies can be adopted in different operation stages, the requirement for dynamic adjustment of production tasks is met, the tasks can be reasonably formulated according to the production plan, and the AGV starting rate and the production efficiency are improved.

The invention discloses a service upgrading system, an electronic device, a method, a storage medium and a product, and relates to the technical field of computers, and the service upgrading system comprises a processor and a controller connected with the processor. Wherein the controller divides a monitored thread to obtain a first thread for carrying out an upgrade service and a second thread for carrying out a non-upgrade service, and the controller also obtains a total time slice and a retention time slice preset by the processor, and dynamically allocates an algorithm based on the total time slice, the retention time slice and the preset time slice; and determining a time slice allocated to the first thread, wherein the allocated time slice is used for representing the duration of the processor resources allocated to the first thread. And the processor generates a required clock signal in the time slice allocated by the first thread, wherein the clock signal is used for carrying out service upgrading. The stability of the clock signal generated by the controller is improved, and the success rate of service upgrading is further improved.

The invention provides a multi-satellite collaborative regional target observation scheduling method based on stripe coverage and ant colony improvement, and relates to the field of multi-satellite collaborative observation. According to the method, firstly, a to-be-observed task list and satellite resources are obtained, and a task conflict matrix is constructed; secondly, performing discretization processing on the regional target based on the task conflict matrix, and generating a stripe coverage scheme and a stripe priority; thirdly, generating a multi-satellite cooperative task allocation scheme by adopting a self-adaptive ant colony task allocation algorithm; thirdly, generating an optimization target based on the stripe priority and a multi-satellite cooperative task allocation scheme, and constructing a scheduling decision model in combination with an energy constraint, a time window constraint and a task unique execution constraint; and finally, solving the scheduling decision model by using an improved branch and bound algorithm, and obtaining a global optimal solution for deciding whether each satellite observes a task or not at different moments. According to the invention, by improving the ant colony algorithm and the dynamic stripe coverage strategy, the task execution efficiency and the resource utilization rate of multi-satellite collaborative observation are significantly improved.

The invention discloses an assembly interface shape error grouping method for three-dimensional matching, and relates to the field of precision assembly, and the method comprises the steps: obtaining the point cloud data of an assembly interface, and calculating five-dimensional morphology parameters; a greedy distribution K-Means algorithm is adopted to carry out clustering grouping on the five-dimensional morphology parameters; performing three-dimensional visualization on a grouping result in combination with principal component analysis; and based on a three-dimensional visualization result, selecting and matching reference parts and matching parts by adopting a grouping and selecting and matching method, and realizing assembling interface shape error grouping oriented to three-dimensional selecting and matching. The problem that an existing size grouping method faces a two-dimensional size chain model and is difficult to adapt to three-dimensional matching requirements is solved.

The invention provides a laboratory detection task intelligent allocation method and system based on big data, and relates to the technical field of laboratory management, and the method comprises the steps: collecting detection task data, carrying out the detachable analysis to construct a collaborative flow chart, calculating a collaborative efficiency index and a task priority, and extracting personnel collaboration features to generate an association weight matrix; and allocating task queues by adopting a double-layer adaptive task allocation algorithm, performing density peak clustering on the first layer to divide task clusters, and optimizing an allocation scheme in combination with personnel ability and cooperative affinity on the second layer. According to the invention, the distribution efficiency and cooperation quality of complex laboratory detection tasks are improved.

The invention discloses a high-performance CPU-GPU (Central Processing Unit-Graphics Processing Unit) cooperative processing architecture of an audio frequency integrated signal processor, belonging to the technical field of audio frequency integrated signal processing, the architecture is based on a dynamic priority scheduling model and realizes efficient collaboration of a CPU and a GPU through hierarchical resource management and protocol level optimization, a hardware layer adopts a multi-GPU cluster and a distributed storage node, and the CPU-GPU cooperative processing architecture has the advantages that the multi-GPU cluster and the distributed storage node are integrated; high-concurrency task processing is supported; the transmission layer fuses RapidIO and an Ethernet protocol, and adapts to a short frame control signal and a long packet data stream respectively; and the application layer calculates task resource demands through dynamic priority weights, and ensures low time delay of key tasks in combination with a normalized allocation algorithm. According to the architecture, in an audio signal processing scene, the task scheduling efficiency is improved by 40%, the short frame transmission delay is as low as 0.5, the throughput of a long data stream reaches 100 Gbps, and the requirements for real-time performance and calculation precision in a complex acoustic environment can be met.

The invention provides a VR game data synchronous transmission method based on a 5G network, and the method comprises the steps: obtaining a multi-type data stream generated during the operation of a VR game, extracting scene rendering data and player interaction data from the multi-type data stream, separating a feature value of each data type, and obtaining a data priority sequence; analyzing the real-time requirements of the scene rendering data and the interaction data according to the data priority sequence, and judging a key data packet which needs to be transmitted immediately; the method comprises the following steps: monitoring a current bandwidth fluctuation condition through a network transmission layer, calculating available bandwidth and delay parameters from network state data acquired in real time, and obtaining a resource allocation reference value in a dynamic scene; and for high-priority data packets, a dynamic resource allocation algorithm is adopted to adjust a transmission queue sequence, a resource allocation reference value is corrected through a bandwidth fluctuation parameter, and a transmission time slot allocation scheme of the key data packets is determined.

The invention discloses an AI digital human real-time rendering method based on GPU acceleration, and relates to the technical field of digital human rendering, and the method comprises the steps: S1, constructing a multi-GPU hardware feature and load monitoring module, and collecting hardware feature parameters and current load states of each GPU participating in collaborative rendering in real time; according to the method, by constructing the multi-GPU hardware feature and load monitoring module and combining a dynamic task allocation algorithm, dynamic allocation of the rendering sub-tasks to the optimal GPU is achieved, the problems of GPU performance bottleneck and resource idleness caused by traditional fixed task allocation are solved, the multi-GPU collaborative rendering efficiency is improved, and the multi-GPU collaborative rendering efficiency is improved. A scene complexity analysis module and a self-adaptive resource allocation algorithm are built, the proportion of GPU resources between an AI driving module and a rendering module is dynamically adjusted according to scene complexity, the AI digital human interaction naturalness is improved in a simple scene, the rendering frame rate and the picture quality are guaranteed in a complex scene, and the method is suitable for being used in a large-scale scene. And finally, dual optimization of rendering efficiency and picture quality in AI digital human real-time rendering is realized.

The invention relates to the technical field of mobile application continuous integration automation, and discloses a multi-channel APP automatic packaging system which comprises the steps that byte code files in a released mobile terminal APP installation package are acquired, and an occupied symbol space set is generated; generating an available confusion namespace by using set complementary operation; extracting method-level code change by using an abstract syntax tree difference algorithm, and generating a change method set; generating a patch special confusion rule for the change method set by using a namespace allocation algorithm; compiling and obfuscating the change method set by applying a patch special obfuscating rule to generate a safe obfuscating patch byte code; performing packaging and signature operation on the safe obfuscation patch byte code, and outputting a minimized obfuscation safe hot repair patch pack; according to the method, the technical problems that class definition conflicts or method calling errors occur after the hot repair patch is loaded, and the downloading success rate of a user is affected due to the overlarge size of the hot repair patch are solved.

The invention discloses an edge reasoning optimization method and system based on segmented knowledge distillation. Firstly, a segmented knowledge distillation framework is constructed, a teacher-student model is divided into corresponding sub-modules with balanced parameters, parallel distillation training is adopted, middle feature dimension reduction and space alignment are achieved in combination with a principal component analysis method, the knowledge transmission efficiency is improved, and convergence is accelerated. Secondly, proposing an equipment perception self-adaptive pruning strategy, dynamically distributing a differential pruning proportion according to the real-time calculation capability and resource state of heterogeneous edge equipment, and balancing the load of low-performance equipment and the precision of high-performance equipment; and finally, establishing a deep reinforcement learning dynamic scheduling mechanism, generating a module delay-energy consumption file through offline analysis, adaptively selecting a device combination by an intelligent agent in an online stage, determining an optimal partition and deployment strategy through a threshold value distribution algorithm, and realizing joint optimization of energy consumption and reasoning time while meeting delay constraint.

The invention relates to a layout wiring planning design method based on a 2.5 D core particle interconnection interposer, which is characterized by comprising the following steps of: defining signal logic interfaces for automatic wiring in a passive interposer according to signal transmission requirements among core particles and a communication relationship between the core particles and a packaging substrate, and creating a mapping relationship list among the logic interfaces; according to the method, layout planning is carried out on physical interface endpoints of a passive intermediate layer, the physical interface endpoints are matched with signal logic interfaces, and an optimal layout allocation scheme is quickly determined through a signal allocation algorithm based on a flow network in combination with a window matching method, so that the wiring resource loss of a 2.5 D integrated circuit is effectively reduced, and the wiring efficiency is improved. The system overall performance is improved. According to the invention, the method can cope with a complex interconnection wiring condition generated by a plurality of independently designed core particles in a 2.5 D integration process, and achieves the efficient interconnection design of the passive interposer.

The invention provides a Tiantong satellite transmission rate improving method and device based on a point-to-point protocol, relates to the field of satellite communication, and solves the technical problems of low bandwidth utilization rate and low multi-service concurrent processing efficiency. The method comprises the following steps: configuring an APN through an AT instruction set, starting PPP dialing, and establishing a physical connection with a Tiantong satellite; a plurality of virtual serial port channels are created on a single physical serial port channel by using a CMUX protocol, and each channel bears an independent PPP link. Compression type HDLC packaging is carried out on a PPP protocol stack, a frame header is reduced to 2 bytes, and a dynamic FCS verification mechanism is started. And sending the data fragments to the virtual channels through an adaptive time slot allocation algorithm according to the service priorities. And performing forward timestamp mark pre-compensation on the sending time sequence based on the 550ms satellite time delay prediction model. And sorting and recombining the channel data packets at a receiving end, and restoring the original data stream. The method is used in the Tiantong satellite transmission process.

The invention discloses a home appliance dual-network hub site selection planning method based on a hybrid heuristic algorithm. The method comprises the following steps: firstly, modeling a home appliance dual-network hub site selection planning problem into a mathematical model taking the minimum comprehensive cost as an optimization target; and then, solving by using a hybrid heuristic algorithm. The algorithm is a priority competition allocation algorithm based on a simulated annealing framework. Two attribute indexes of a comprehensive load coefficient and a distance sensitivity coefficient of a site are defined, the competition degree of the site for hub resources and hub distance sensitivity of the site are quantified, a reasonable distribution sequence is determined, resource conflicts are reduced, efficient configuration of hub resources of a sales service network is achieved, and the service efficiency of the sales service network is improved. The problem that a traditional clustering method is low in solving quality is effectively solved.

The invention relates to the crossing field of engineering cost management and building informatization (BIM) technology, and discloses a BIM-based multi-specialty collaborative engineering cost allocation and settlement auditing system, which comprises an engineering cost allocation and settlement auditing system, and the engineering cost allocation and settlement auditing system comprises a sensing layer, a platform layer, a core layer, an application layer and a guarantee layer. According to the BIM-based multi-specialty collaborative project cost allocation and settlement auditing system, a five-layer system architecture (a sensing layer, a platform layer, a core layer, an application layer and a guarantee layer) is constructed, so that the whole process from data acquisition, standardized processing and intelligent algorithm analysis to multi-role collaborative application is realized; according to the method, cost allocation and settlement auditing services under multiple specialties and multiple scenes are supported, and multi-dimensional cost intelligent allocation and automatic anomaly detection based on BIM components are realized by integrating a BIM semantization engine, a dynamic allocation algorithm engine and an AI auditing engine.

The invention discloses an intelligent dynamic security monitoring system and method based on multi-modal data fusion, and the method comprises the steps: capturing a behavior feature sequence of encrypted network traffic through a protocol analysis engine, and obtaining a hardware state vector of an equipment operation log through a security agent interface, extracting metadata fingerprints of the data session through a cross-network ferry monitor to form three types of standardized data streams; the three types of data streams are input into a space-time alignment engine for time window synchronization, feature level fusion is carried out based on an adaptive weight distribution algorithm, and a multi-modal feature matrix containing a network threat index, an equipment health degree score and a ferry risk value is generated; according to the method, an integrated safety monitoring system of cross-domain collaborative awareness and dynamic closed-loop evolution is constructed, and the risk identification precision, the response timeliness and the self-adaptive capability of the system are remarkably improved.

The invention relates to the technical field of data processing technologies, and discloses a method and system for analyzing user behavior data in a BOSS system, and the method comprises the steps: collecting multi-source behavior data of a user in the BOSS system; generating a dynamic feature set at least comprising role weight features and behavior attenuation features according to the user roles, the service stages and the real-time behaviors; according to a self-adaptive weight distribution algorithm based on a reinforcement learning framework, optimizing weight fusion of dynamic characteristics in real time according to a user retention rate and a path conversion rate; based on the fused features, performing collaborative analysis through a graph neural network and a multi-task learning model, and executing user grouping, path optimization and loss prediction; and the analysis result is pushed to the BOSS system in real time through the stream processing engine, and interface permission adjustment or resource pushing is triggered, so that the refinement degree of service decision is enhanced, and the user retention and conversion efficiency and the self-adaptive capability of system operation are remarkably improved through a real-time closed loop.