35096 results about "Operations research" patented technology

A method is provided for estimating the number of distinct tokens in a text stream using a modified text-to-text variational autoencoder (T5VQVAE) model. The method includes receiving a continuous input of a text stream; dynamically maintaining a buffer that stores a probabilistic subset of tokens from the text stream; calculating a sampling probability for each token based on a condition related to the current state of the buffer; updating the buffer based on the sampling probability to include or exclude tokens; encoding the buffered tokens into a latent space using the T5VQVAE model; and estimating the number of distinct tokens in the text stream based on the tokens in the buffer and the corresponding sampling probabilities.

The invention discloses a production process state monitoring scheduling optimization method based on real-time data acquisition, relates to the technical field of manufacturing process scheduling, and is used for solving the problem of insufficient real-time performance and stability of process scheduling. According to the method, a process monitoring mechanism based on real-time acquisition and closed-loop scheduling is constructed, a time sequence structured data frame is formed under a unified time reference, operation stability and quality offset characteristics are extracted, a data credible label and a current process state factor vector are generated, an optimized scheduling model is input, and task conflicts and resource bottlenecks are identified according to the data credible label and the current process state factor vector. According to the method, path compression and sequence adjustment are implemented in combination with scheduling priority mapping and a resource path diagram, scheduling deviation vectors are constructed through task response time delay and process blocking in operation, rules and parameters are triggered to be updated online and locally rearranged, and solidification is performed after verification in a prediction window, so that equipment idling and switching fragmentization in the production process are reduced, and the production efficiency is improved. And the real-time performance of the production process, the resource utilization rate and the system stability are improved.

The invention provides a circuit board production yield root cause tracing method, which comprises the following steps of: acquiring process parameters, equipment states, environment variables and quality detection results of a whole production process, and constructing a multi-dimensional time sequence database; extracting a typical manufacturing process modeling unit through a sliding time window and dynamic time warping; establishing a cross-process dynamic causal relationship graph in combination with nonlinear Granger causal test, a structural equation model and a dynamic Bayesian network; an intervention and anti-factual reasoning method is applied, the causal effect and path stability under parameter disturbance of each process are evaluated, and the influence of a key causal path is quantified; according to the method, the accuracy of defect rate root cause positioning can be improved, and powerful support is provided for circuit board production process optimization and quality improvement.

The invention discloses a method for analyzing a matching degree between a demand and an output result based on text semantics. The method comprises the following steps: step 1, labeling a data set; step 2, technical document preprocessing; 3, training and predicting a single-parameter model; 4, integrating prediction results of the multi-parameter model; the method has the beneficial effects thatthe method is simple; deep learning and the NLP technology are applied to the field of project association degree calculation of enterprise project management for the first time. Calculating an association matching degree between the two projects according to project requirements and result description; the associated project positioning difficulty is effectively reduced; meanwhile, the demand side can be helped to quickly and efficiently locate high-quality projects adapting to the demand of the demand side; time and resource investment for achievement screening and matching are greatly reduced, the association matching degree between projects is calculated by means of text data of existing project achievement technical documents and project declaration guidelines, and then large enterprises are assisted in screening high-quality projects with the high matching degree in the project bidding and tendering link.

The invention relates to a power distribution system optimization method considering a space-time game under vehicle-station-network interaction, and the method employs a double-layer optimization framework integrating the space-time game and dynamic electricity price, an upper decision maker is a power distribution system operator, and the power distribution system operation cost is minimized and the renewable energy consumption rate is maximized. Generating a dynamic electricity price signal in a time-sharing and partition manner; the lower-layer main body comprises an electric vehicle user and a charging station operator, and the electric vehicle user optimizes a charging time period and a charging station and forms charging demand distribution through a non-cooperative game based on a dynamic electricity price signal and a charging service charge by taking the maximum self-charging decision utility as a target; and a charging station operator optimizes and adjusts a charging service fee decision by taking charging demand distribution as input and taking revenue maximization as a target. Compared with the prior art, the method can collaboratively optimize the economic operation of the power grid and the consumption of renewable energy sources, promotes the efficient flow and balanced distribution of resources among regions, and provides decision support for the dispatching of the power distribution network.

The invention provides an AI-based laboratory equipment scheduling optimization method and system, and the method comprises the steps: firstly obtaining a state monitoring data set containing the characteristics of equipment operation power consumption, idle time length, environment interference factors and the like in real time, and then carrying out the multi-dimensional analysis of the state monitoring data set; generating an availability evaluation index set containing characteristics of equipment load fluctuation, maintenance period prediction, compatibility matching and the like, and an experiment task priority queue, performing cross decision analysis on the availability evaluation index set and the experiment task priority queue based on a preset dynamic resource allocation model, and obtaining a scheduling strategy set containing a task allocation path, a cooperative operation rule and a conflict resolution mechanism; scheduling strategy parameters are calibrated according to experimental task operation log data, an optimized execution instruction set is generated, instructions are fed back to an equipment control system to adjust the equipment state, a dynamic resource allocation model is iteratively updated according to execution feedback data, and efficient scheduling optimization of laboratory equipment is achieved.

A system and method for integrated optimization of autonomous electric vehicle fleets across transportation and electricity networks which employs artificial intelligence to dynamically allocate autonomous electric vehicles between mobility services and electricity grid services based on real-time conditions. The platform acquires data including energy mix forecasts, earth observation measurements, vehicle owner schedules, and emission-based route penalties to generate coordinated allocation decisions. Vehicle owners specify availability through a scheduling interface. The system optimizes vehicle utilization through a hierarchical optimization approach implementing mobility demand-side flexibility and electricity demand-side flexibility simultaneously. Multi-objective genetic algorithm optimization balances revenue generation, energy costs, emissions reduction, and battery health. The integrated approach maximizes value creation across both transportation and energy domains, reducing urban emissions while enhancing grid stability through coordinated management of distributed energy resources in autonomous electric vehicle fleets.

The invention relates to the technical field of network security, in particular to a dynamic access blocking method based on zero trust. Comprising the following steps: step 1, collecting whole network flow data in real time in a bypass monitoring mode through a flow mirroring function of a network switch, performing deep packet inspection analysis on the collected original flow data, and extracting network flow characteristic parameters; 2, maintaining a dynamic identity information base; 3, performing real-time behavior analysis on each network session; 4, according to the risk assessment result and the real-time security context, generating a dynamic access control strategy based on a minimum permission principle; 5, implementing access control at the network execution point; and 6, continuously monitoring the network flow and the strategy execution effect, collecting feedback data, optimizing the risk assessment model and the strategy generation algorithm based on the feedback data, and forming closed-loop control. By dynamically updating the identity and asset information, the system can identify new assets or changes in real time, so that the adaptability and response capability of a network environment are improved.

The invention discloses an urban flood toughness evaluation method, apparatus and device, a medium and a computer program product. The method comprises the steps of obtaining hydro meteorological data of a to-be-evaluated region and geographic information data of an urban spatial form element layer; setting boundary conditions of a plurality of flood scenes according to the hydrometeorological data, constructing a multi-scene urban flood disaster simulation model in combination with geographic information data, and respectively generating flood inundation depth data of urban spatial form element layers of the plurality of flood scenes; and calculating a first toughness index, a second toughness index and a third toughness index of each flood scene so as to calculate a flood toughness comprehensive evaluation result of each flood scene of the to-be-evaluated region. According to the method, an evaluation system of multiple composite flood disaster scenes is established, different disaster factors and interaction thereof are comprehensively considered, and the effect of key space parameters in urban flood toughness is fully considered, so that the capability of coping with flood disasters in cities can be more comprehensively and accurately evaluated.

The invention discloses a railway station facility full life cycle information query method based on digital twinning. The method comprises the steps that a unified facility object with a unique logic identifier is generated based on multi-source heterogeneous data aggregation; constructing a facility tense version model containing time sequence nodes by using an event-driven rule; in combination with an engineering constraint knowledge base and statistical characteristics, evaluating physical feasibility and statistical association strength between events, introducing life cycle stage risk factors, and constructing a facility full life cycle causal chain model; and based on the model, responding to the explanatory query, executing a constrained path search in the facility full life cycle causal chain model, and generating a life cycle query result set containing a causal path. According to the method, the pseudo-correlation problem generated by pure data mining can be effectively solved, the cumulative influence of early-stage risks in the design and construction stage on later-stage operation is quantified, and accurate tracing and explanation of cross-stage fault causes are realized.

To predict the working time of sorting work for taking out merchandise and sorting it for each shipping destination.SOLUTION: The prediction device 100 includes a prediction unit 113 that predicts a work time of sorting work, which is work of taking out and sorting articles from a storage that stores the articles, based on a plurality of orders related to the articles (commodities), and calculates the work time as a predicted work time. The prediction unit 113 calculates the predicted working time based on the input information including the type of article to be taken out from the storage, the number of articles to be taken out from the storage, and the number of articles included in each order. The storage may be a storage shelf that the transport robot carries to a work place of the sorting work. The item may be transported along a predetermined path to a work location of a sorting operation. Further, a worker who performs the sorting work may move to a place where the storage is placed and perform the sorting work.SELECTED DRAWING: Figure 3

The invention discloses an agent collaborative optimization data center management system based on knowledge graph driving, and relates to the technical field of data center management. The system specifically comprises the following modules: a modeling and entity management module, a cross-regional global scheduling optimization module, an agent game negotiation optimization module, an agent trust management module, a game negotiation conflict identification module, a reasoning evidence management module and a cross-regional consistency verification module. By establishing a complete knowledge graph model, systematic modeling of resource attributes, constraints, historical decisions and strategy preferences is realized, a unified data basis is provided for negotiation among multiple agents, the problem of a suboptimal solution caused by information asymmetry is effectively solved, a hierarchical reasoning mechanism is adopted, and the probability of resource disruption is reduced. In combination with global-region-node three-level reasoning and multi-round game negotiation, recursive optimization from global to local is realized, the reasoning complexity is effectively reduced, and the negotiation efficiency is improved.

The invention discloses a cargo warehouse-in and warehouse-out management method and management system, and relates to the technical field of information management. Through dynamic storage position optimization, the warehouse-in and warehouse-out frequency, the associated sales relationship and the storage demand characteristics of cargos are combined with a warehouse three-dimensional space model; and generating a cargo storage location allocation scheme by using a multi-objective optimization algorithm. According to the scheme, the picking time of high-frequency warehouse-in and warehouse-out commodities is remarkably shortened, the average time consumption of single-time commodity picking is reduced, meanwhile, the combined picking error rate of associated commodities is reduced, the problem that in the prior art, storage positions are disjointed with warehouse-in and warehouse-out requirements is solved, through predictive process scheduling, cross congestion of operation channels is effectively avoided, and the working efficiency is improved. Therefore, smooth operation of the in-out warehouse process is realized, the occurrence frequency of congestion events of the in-out warehouse channel is reduced, the delay time is shortened, and the problem of congestion of the in-out warehouse process in the prior art is solved.

Systems for explainable large language model routing with immutable audit trails are disclosed. The system receives a query and determines its characteristics including complexity, domain, regulatory constraints, and performance requirements. It retrieves profiles for multiple LLMs from a model matrix containing performance attributes, resource consumption, and compliance parameters. The system selects a particular LLM by balancing resource consumption with performance requirements, evaluating regulatory compliance, ranking LLMs based on these factors, and prioritizing models with successful processing history. The system generates a human-readable explanation of the selection including decision factors, rationale, and alternatives considered. Finally, it records the selection and explanation in a tamper-evident, immutable audit trail data structure.

The invention provides an intelligent boxing method and system. The intelligent boxing method comprises the steps of obtaining boxing task information and generating a boxing scheme. The boxing task information comprises the size, the weight, the stacking limitation, the number and the priority of each cargo, and the size, the bearing capacity and the occupation state of each loading unit. And under the condition that the size, bearing and stacking constraint conditions are met, a plurality of candidate boxing combinations are generated, the loading utilization rate, the residual space distribution and the shape matching degree are evaluated based on a preset optimization target, and an optimal scheme is selected. The system comprises an input terminal, a task information receiving module, a data storage module, a task and scheme storage module, an operation processing module and a final boxing scheme generation module. According to the method, multi-dimensional constraint matching and weight balance are considered, and space waste and transportation risks are avoided; the maximum greedy algorithm and the adaptive large neighborhood search optimization are combined to realize the combination of automation and experience. The method can be expanded to batch task scheduling and grouping sequencing, so that the calculation complexity is reduced, and the overall boxing and transportation efficiency is improved.

The invention provides a privacy calculation dynamic strategy selection method and device based on data sensitivity identification. The method comprises the following steps: acquiring a privacy computing task request, and analyzing to obtain a target data set and a corresponding operation type; performing sensitivity identification on each field in the target data set to obtain a sensitivity level label corresponding to each field; retrieving a candidate privacy protection strategy set from a preset strategy mapping relation; for the candidate privacy protection strategy set, calculating resource consumption parameters and privacy budget occupation parameters of all strategies are evaluated; performing operator-level splitting and arrangement on the privacy computing task to generate a task scheduling plan; and if it is detected that the resource threshold value or the budget threshold value is triggered, re-executing the step of retrieving the candidate privacy protection strategy set to the step of generating the task scheduling plan based on the resource occupation data and the usage measurement data so as to adjust the target privacy protection strategy. According to the application, accurate identification of sensitivity, on-demand switching of privacy protocols and adaptive balance of resources and budget can be realized.

The invention discloses an urban water supply management data trend analysis method based on space-time analysis, and relates to the field of data processing, and the method comprises the steps: collecting data in real time through an urban water supply pipe network sensor network, building a space-time unified coordinate system, building a space-time Kriging interpolation model based on pipe network topology, and achieving the space-time alignment of multi-source data; dividing an adaptive space-time grid by using a Voronoi diagram and a sliding window mechanism, and calculating multi-dimensional features; constructing a dynamic space-time diagram by taking a grid as a node, performing multi-step prediction in combination with a space-time diagram convolution circulation network, fusing a Kriging interpolation result, and evaluating an abnormal probability and a confidence interval through a Bayesian neural network; a monitoring layer, a prediction layer and a risk layer are overlaid in a three-dimensional GIS, a dynamic thermodynamic diagram is generated, an early warning path is optimized based on a Dijkstra algorithm, and a minimum risk topology path is output. The method has the advantages that through space-time analysis and accurate prediction, the intelligence, stability and emergency response efficiency of urban water supply management are remarkably improved, and powerful support is provided for smart city construction.

The invention discloses an urban power grid real-time load collaborative peak regulation method based on multi-energy complementation and AI scheduling. The method comprises the following steps of multi-source data access and high-dimensional feature space construction, embedded entropy calculation and interactive network construction, domain knowledge and data-driven model fusion, hierarchical scheduling and dual-stage optimization, and real-time decision and closed-loop feedback. According to the method, the real-time performance and hierarchical scheduling thought are emphasized, and an organic closed loop is formed on the three aspects of intra-day scheduling, hour-level rolling correction and minute-level or second-level emergency response. Meanwhile, by means of a multi-stage optimizer switching mechanism, the model can complete rapid convergence of high-dimensional parameters in a short time, finer strategy fine adjustment is carried out in the later period, and the accuracy and reliability of a peak regulation scheme are guaranteed; the method can be applied to advanced power grid systems such as intelligent power grid dispatching, a multi-energy collaborative optimization platform and demand side response management, and has the characteristics of high real-time performance, strong adaptability and good expandability.

The invention discloses an urban drainage network overflow risk intelligent regulation and control system and method based on the Internet of Things, and relates to the technical field of urban drainage network monitoring, and the system comprises an Internet of Things sensing collection module which is used for fusing sensing data and carrying out the preprocessing, and obtaining an original data flow; the real-time diagnosis and early warning module is used for calculating the original data flow based on a nonlinear dynamic algorithm to obtain a fluid chaos degree index, and performing judgment in combination with a preset early warning threshold to obtain an early warning signal; the risk causal deduction module is used for fusing a graph neural network and a causal discovery algorithm and analyzing a fluid chaos degree index and an early warning signal to obtain a risk propagation space-time atlas; and the cooperative game decision execution module is used for analyzing the risk propagation space-time atlas by using a multi-agent adaptive game algorithm to obtain and execute a cooperative control instruction. According to the method, the Internet of Things, nonlinear dynamics, causal reasoning and the game theory are fused, and the problem of urban drainage pipe network overflow regulation and control is effectively solved.

The invention relates to the technical field of AGV control, in particular to a multi-AGV scheduling method. The method comprises the following steps: obtaining a real-time logistics instruction of a working area, carrying out logistics scheduling demand analysis and global task planning, and constructing a global execution task atlas; aGV real-time monitoring parameters are obtained, multi-state comprehensive evaluation is carried out, dynamic task demand allocation processing is carried out according to the global execution task atlas, and an AGV dynamic task set is constructed; carrying out AGV operation behavior intention identification one by one according to the AGV dynamic task set, carrying out multi-task cooperative carrying evolution, and constructing a multi-level space-time path network; performing potential AGV collision early warning according to the multi-level space-time path network, and marking local AGV space-time collision nodes; and performing dynamic task priority decision according to the global execution task atlas, performing intelligent conflict planning on local AGV space-time conflict nodes, and constructing a local conflict scheduling strategy. According to the invention, through cooperative control of multiple AGVs, the operation efficiency, the scheduling precision and the safety level of the operation area are improved.

The invention discloses an adaptive game-driven network defense method and system, and relates to the technical field of network security. The method comprises the following steps: generating a multi-modal bait according to network context information, and screening an optimal bait through credibility evaluation; constructing a time sequence feature tensor according to the network event sequence information of the bait, and obtaining predicted attack information by adopting a pre-trained attack prediction model; combining the network event sequence information of the bait and the predicted attack information to construct a defense income matrix, and carrying out iterative equilibrium solution to obtain an optimal strategy candidate pool; and taking the defense hybrid strategy of the optimal strategy candidate pool as an initial population, performing multi-objective optimization through a non-dominated sorting genetic algorithm to obtain a Pareto optimal strategy set, and performing screening to obtain an execution strategy set for dynamic defense decision making. According to the invention, the dynamic property, intelligence and self-adaptability of network defense are realized, and the ability of a network system to cope with complex attacks is effectively improved.

The invention discloses a bank loan business risk control system and method based on big data analysis, and relates to the technical field of financial risk control, and the method comprises the steps: collecting and preprocessing real-time behavior data, and obtaining a user behavior feature set; based on the user behavior feature set, calling a behavior map modeling engine to carry out structured mapping, matching with a risk anchor point rule base, identifying a potential risk mode and labeling an initial anchor point risk label; correcting the deviation between the initial risk anchor point tag and the actual default record by adopting a value function optimization method, and predicting the risk grade score of the current behavior of each user in combination with the historical behavior sample data and loan feedback data of the user; predicting probability distribution of migrating to a default state in the future through user risk grade scores and historical state evolution data; and in combination with the potential loss under each behavior path, evaluating the current loan business risk, and generating a risk control strategy through a risk level mapping rule and a strategy decision engine.

The invention relates to a dynamic regulation and control method and system for mine ventilation. The method comprises the following steps: acquiring multi-source data in a mine, and preprocessing the multi-source data to obtain a standardized data set; calculating the risk of each region in the mine based on the standardized data set to obtain a risk prediction matrix; based on the standardized data set and the risk prediction matrix, the air volume demand of each area is calculated, and an air volume demand table is obtained; based on the air volume demand table, an optimization proposition corresponding to the air volume demand is constructed and solved, and a solution set of the optimization proposition is obtained; mapping the solution set based on a preset rule to obtain a feasible allocation scheme; and based on the feasible allocation scheme, generating an equipment cooperation instruction, and obtaining a security instruction set. According to the method, dynamic factors can be considered, the air volume is dynamically adjusted, multi-fan cooperation is achieved, and the effects of real-time sensing and autonomous dynamic decision making of mine ventilation are achieved.

The invention discloses a logistics data credible evidence storage and tracing method based on a block chain, and the method comprises the following steps: collecting original logistics data, and executing data standardization processing; constructing a layered Merkle mountain structure, and calculating to obtain a main root commitment of the layered Merkle mountain; dividing a layered Merkle mountain range structure, and calculating to obtain a windowed subtree root set; configuring a revision branch record structure, generating a collision pair by using chameleon hash, and generating a revision proof record; a multi-index root structure is constructed, a cross-root sharing mechanism is established, and a multi-dimensional tracing index set is generated; and based on the hierarchical Merkle mountain range main root commitment, the windowed subtree root set, the multi-dimensional traceability index set and the revision proof record, executing anchoring consistency verification, and outputting a credible evidence storage result and a traceability verification result. According to the invention, the logistics data evidence storage integrity and the multi-dimensional tracing efficiency are improved.

The invention provides a credit risk dynamic assessment and early warning method based on multi-dimensional data analysis, and relates to the technical field of financial risk control and big data analysis, and the method comprises the steps: obtaining multi-dimensional data from borrower basic information, financial statements, credit records and an external market environment, carrying out cleaning, standardization processing and storage on the multi-dimensional data; based on the multi-dimensional data, through a credit scoring model, cash flow analysis, liability ratio analysis and market environment risk assessment, generating a financial risk level of the borrower; the financial and market data of the borrowers are updated in real time, the dynamic change of the risk state of the borrowers is monitored in combination with a time sequence prediction model and risk correlation analysis, and a dynamic monitoring result is generated; and according to the risk level and the dynamic monitoring result, triggering a grading early warning mechanism, and generating a corresponding control measure suggestion so as to adjust a credit strategy or adopt a risk slow release means.

The invention relates to the field of power systems and automation thereof. The invention relates to a power distribution network energy storage optimization configuration method considering dynamic uncertainty and multi-target cooperation. The method is characterized by comprising the following steps: 1) constructing a two-stage robust optimization model: constructing the two-stage robust optimization model with a min-max-min structure; in the first stage, the energy storage construction position and capacity are determined with the lowest annual investment cost of energy storage as the target; in the second stage, the system scheduling cost is minimized in the worst new energy output scene; 2) convex relaxation processing of network constraint; 3) implementation of an iterative solution algorithm: based on a KKT principle and a column constraint generation algorithm, decomposing an original problem into a mixed integer linear main problem and a sub-problem; the main problem optimizes an energy storage configuration scheme, and the sub-problems solve a scheduling strategy in the worst wind and light output scene and feed back to the main problem through cut plane constraint; and carrying out iterative calculation until the solutions of the main problem and the sub-problem converge, and obtaining an optimal energy storage configuration scheme. According to the method, more accurate and efficient energy storage planning can be realized.

The invention discloses a rail transit hub emergency regulation and control method and system based on real-time simulation, and relates to the technical field of intelligent traffic and rail transit emergency management, and the method comprises the steps: carrying out the time synchronization processing of real-time passenger flow data and train operation state data, and generating a hub state data set; constructing a multi-level simulation model, and simulating a passenger flow distribution evolution process by presetting passenger flow evacuation behaviors and train operation constraint conditions; based on an output result of the multi-level simulation model, positioning capacity limiting nodes in each evacuation path by adopting a bottleneck identification algorithm, calculating passenger flow carrying capacity, and generating an optimal path regulation and control parameter with shortest evacuation time as a target; and the optimal path regulation and control parameters are converted into a train automatic control system instruction and a passenger flow guide display instruction, the instructions are issued to each line control unit through a hub centralized dispatching system, and the train operation frequency and the passenger flow evacuation direction are adjusted. According to the invention, intelligence and collaboration of emergency regulation and control of the rail transit hub are realized.

The invention discloses a micro-service dynamic weight load balancing method and related equipment. The method comprises the following steps: asynchronously acquiring a multi-dimensional real-time load index of a micro-service instance, obtaining a load prediction value by virtue of a model, fusing the real-time index, the prediction value and a service health degree, calculating a dynamic weight by depending on service load data, distributing a request according to the weight, and feeding back an adjustment coefficient. According to the method, by asynchronously collecting the multi-dimensional real-time indexes, an accurate basis is provided for weight calculation, so that the request allocates the actual load of the matched instance, and the situation that the response of a high-load instance is slow due to too many requests is avoided; by introducing a load prediction value, load change can be pre-judged in advance, a weight strategy is adjusted in advance, overload when an instance load suddenly increases is prevented, and the fluctuation resistance of the system is improved; the weight is calculated by fusing multi-dimensional data, so that the comprehensive load of the instance can be comprehensively reflected, and evaluation deviation caused by a single index is avoided; a feedback mechanism can enable weight calculation to continuously adapt to a system state, and load unevenness caused by weight fixing or adjustment lag is avoided.

The invention relates to the technical field of big data analysis and mining, in particular to a construction progress deviation early warning method and system based on data mining, and the method comprises the following steps: integrating a construction task list, the spatial position of a component in a BIM, the number of operators in a construction log, and the operation state of a tower crane, and generating a multi-dimensional constraint construction map node set; according to the method, a multi-dimensional node set with time, space and resource attributes is constructed by integrating a construction task list, the spatial position of a BIM component, the number of operators in a construction log and the operation state of a tower crane, and structural expression of a construction site is achieved; on the basis, a causal association relationship among construction logic, resource occupation and space proximity is identified, and a time sequence causal conduction map with a deviation conduction coefficient value is further established, so that a risk conduction path between construction events has quantitative calculation capability.

The invention is suitable for the technical field of data asset management, and provides a data asset management system based on a block chain and big data analysis, and the system comprises a first terminal which carries out the storage of the ownership information of data assets through a block chain network, and generates a storage data package; generating an evaluation result based on a preset technical index analysis model; writing the hash value of the evaluation result into transaction data of the main chain of the block chain, and generating an evidence storage voucher matched with the block chain transaction; an interaction data sequence is packaged for identity signature, encryption and compression to generate a target code stream, and the target code stream is sent to the second terminal; the second terminal decrypts the encrypted data in the target code stream by using a private key, decompresses the data by using a compression algorithm matched with the first terminal, and restores the data into an interactive data sequence; verifying the access authority and the transaction condition of the evidence storage data packet; checking the anchoring state of the evidence storage voucher on the main chain of the block chain; and the data transaction is completed, the data asset state report is generated, and the data element marketization configuration efficiency is improved.