1023 results about "Potential risk" patented technology

The invention provides an industrial environment monitoring and accident prediction method fusing multi-modal data, and relates to the technical field of data processing, and the method comprises the steps: carrying out the semantic collection and causal association preprocessing of multi-modal heterogeneous data collected in real time through constructing a dynamic industrial knowledge graph; a customized deep learning model is adopted to extract deep abstract features of each mode, and weak signals and potential risks are accurately represented and uncertainty is quantified; a high-fidelity digital twin model is utilized to drive a deep reinforcement learning algorithm, and dynamic optimization and verification are performed to generate a multi-level and multi-target preventive intervention strategy combination; an intervention strategy is executed through an edge-end-cloud three-layer collaborative intelligent architecture, and online learning and system sustainable evolution are realized by using a closed-loop data feedback mechanism. According to the method, the sensing and early warning capability of the early weak and complex abnormal state of the industrial environment can be remarkably improved, the accident evolution path is accurately predicted, and credible explanation is provided.

The invention provides a knowledge graph generation method and system for science and technology project risk control, and the method comprises the steps: obtaining a multi-source heterogeneous data set of a target science and technology project, converting structured index data into a standard vector sequence through a heterogeneous data fusion mechanism, and converting unstructured text data into a semantic vector sequence; converting the time sequence behavior data into a behavior pattern vector sequence, inputting the three into a risk quantitative evaluation model, generating a risk entity feature matrix and a risk association strength matrix, and determining a node distribution topology of the knowledge graph according to entity feature vectors in the risk entity feature matrix; and according to association strength values in the risk association strength matrix, determining an entity relationship topology of the knowledge graph, generating a dynamic knowledge graph of the target science and technology project, and identifying a potential risk propagation path in the dynamic knowledge graph. According to the invention, the risk identification result has the dynamic characteristic of real-time updating, and the traceability of the multi-dimensional risk characteristic is maintained.

Systems and methods for providing a space launch service platform (SLSP) that integrates artificial intelligence and data analytics to support launch operations. The SLSP may connect to multiple data sources, collect data, and standardize the collected data according to regulatory and operational standards. The SLSP may evaluate launch safety and risks using standardized data and generate a situational analysis for decision-making. The SLSP may provide graphical overlays and decision-support tools to highlight optimal launch windows and potential risks.

The invention provides an enterprise employee behavior analysis and safety risk early warning monitoring method and system, and relates to the technical field of enterprise risk management, and the method comprises the steps: collecting employee terminal operation behavior data, building an activity thermal distribution diagram based on office area grid behavior association intensity, and training a behavior evaluation model. And inputting the behavior scoring matrix into a deep neural network to extract target behavior characteristics, calculating an abnormal behavior risk weight coefficient in combination with a department security policy, performing classification and analyzing a risk diffusion probability, and generating a risk situation index to determine an early warning level. Starting a response strategy according to the early warning level, blocking high-risk early warning in real time, tracking associated accounts, establishing a risk association map, identifying potential risk propagation nodes and performing active protection, finally generating an early warning report and feeding back effective protection rules to a behavior baseline model, and realizing continuous optimization of a risk early warning mechanism. And the accuracy and effectiveness of safety risk early warning in the enterprise are improved.

The invention discloses a comprehensive service system and method for intelligent safety protection, and relates to the technical field of safety service systems.After state data of a park are intelligently analyzed, potential risk event types are identified, the risk propagation trend is judged through a space-time association algorithm, the risk level of each area in the park is evaluated, and the risk level of each area in the park is evaluated. And generating a risk level map, performing path planning and scheduling optimization on the inspection equipment according to the current available inspection resources and the risk level map, and cooperatively controlling all the inspection equipment to operate based on path planning and scheduling optimization results. The service system generates a high-credibility regional risk level map, takes a current available resource state and the risk level map as input conditions, dynamically generates a routing inspection path and task allocation, effectively improves reasonable utilization and allocation of routing inspection resources, automatically adjusts a scheduling task under the condition that individual routing inspection equipment fails, has sudden obstacles or is limited in path, and improves the routing inspection efficiency. And stable proceeding of the inspection task is ensured.

The invention relates to the field of enterprise risk early warning analysis, in particular to an intelligent risk early warning method and system based on multi-dimensional data analysis. The method comprises the following steps: acquiring a multi-dimensional enterprise data stream, performing heterogeneous index information analysis and logic hierarchy reconstruction, and constructing an enterprise running state sensing map; performing multi-index local fluctuation amplitude calculation on the enterprise operation state sensing map, and performing dynamic disturbance feature mining to construct a risk disturbance spectrogram; performing deep semantic analysis and node state abrupt change feature analysis on the risk disturbance spectrogram to generate a dynamic transition type of an abrupt change node; and performing full-period time sequence tracing on the risk disturbance spectrogram, performing potential risk node prediction based on the dynamic transition type, and identifying other potential risk propagation ports. Through accurate and efficient enterprise risk perception, the risk intervention decision is made in advance, and the anti-risk capability and the operation stability of the enterprise are improved.

An AI-powered agile project management system for automatic risk assessment and mitigation, including: a risk identification module configured to extract project data from agile project management tools, including but not limited to Jira, Trello, Asana, and Monday.com, and identify potential risks using machine learning and NLP; a risk analysis module configured to assign risk scores based on probability, impact, and severity using predictive analytics, where the module uses historical data, real-time project status, and external dependencies to determine the severity and impact of risks; a real-time monitoring module configured to continuously track project conditions, detect anomalies, and generate immediate notifications via email, mobile apps, or messaging platforms when high-risk events are identified; a risk mitigation module configured to dynamically recommend and execute proactive corrective actions such as task reassignment, resource optimization, schedule adjustments, and implementation of alternative strategies using AI-driven decision-making, where the actions can be automated through predefined workflows or manually adjusted based on AI recommendations; an AI-driven task prioritization module configured to dynamically prioritize tasks based on risk factors, project deadlines, and team workload distribution; an integration module configured to ensure seamless compatibility with existing Agile project management frameworks through API-based synchronization; a learning and continuous improvement module configured to refine risk prediction models over time using machine learning and historical risk mitigation results, with AI-driven models continuously updating risk assessment mechanisms based on evolving project conditions, industry-specific risk factors, and historical performance metrics; a compliance and audit reporting module configured to automatically generate risk reports, audit logs, and compliance checklists; a customizable risk assessment configuration module configured to allow project managers to define custom risk parameters, probability thresholds, and mitigation preferences; an interactive dashboard module configured to visualize data in real time, including risk trends, effectiveness of remedial actions, and analysis of project progress; and a scalability feature configured to be used in industries such as IT, healthcare, finance, construction, and manufacturing.

The invention discloses an engineering project supervision real-time visual decision-making platform based on multi-source heterogeneous data fusion, and relates to the technical field of engineering supervision. The visual decision platform is in communication connection with a data acquisition module, a project risk feature module, a risk identification model construction module, a real-time data stream processing module, a risk early warning module and a decision support module. According to the invention, the risk in the engineering project is accurately identified in combination with historical multi-source heterogeneous data and project risk characteristics, and meanwhile, the real-time data stream processing module can analyze data in real time, identify potential risks and problems and generate early warning information, so that the accuracy and timeliness of risk identification are greatly improved, and the risk identification efficiency is improved. According to the invention, powerful decision support is provided for supervisors, and through the preset early warning level and the early warning signal, managers can rapidly understand the risk condition and take corresponding measures, thereby effectively reducing the project risk.

The embodiment of the invention provides a BIM-based hydraulic engineering digital management method and system. The method comprises the following steps: performing multi-dimensional monitoring system deployment on a to-be-monitored area to obtain multi-dimensional hydrological data; constructing a first BIM based on the topographic data of the to-be-monitored area, the real-time work area image, the hydraulic engineering construction information of the design stage and the equipment deployment information; fusing the multi-dimensional hydrological data, the water area change data, the construction progress data and the operation and maintenance monitoring data into the first BIM, and constructing a second BIM including a full life cycle; and through the equipment characteristic curve and the water conservancy project physical model, in combination with the multi-dimensional hydrological data and the water area change data, predicting a water conservancy project structure change trend and potential risk factors in the second BIM, fusing the water conservancy project structure change trend and the potential risk factors into the second BIM, and displaying an implementation effect and improvement suggestions in real time in the second BIM. A user is assisted to realize hydraulic engineering digital management, and the engineering management efficiency is improved.

The invention discloses an intelligent contract risk recognition method and system, and relates to the technical field of text recognition, and the method comprises the steps: extracting a semantic vector of a to-be-recognized file; obtaining a first risk identification result based on the semantic vector and the review list; performing semantic matching on the semantic vector and the review knowledge graph to obtain a potential risk; obtaining a first risk category based on the potential risk and the review knowledge spectrogram, obtaining a preset risk judgment rule based on the first risk category, and obtaining a second risk category based on the preset risk judgment rule; obtaining a second risk identification result based on the contract category and the second risk category; constructing a clause rule base, and obtaining a compliance result based on the semantic vector and the clause rule base; obtaining a complete result based on the semantic vector and the standardized contract template library; and obtaining a total risk identification result based on the above identification result, thereby solving the problems of low risk identification efficiency and low accuracy caused by the fact that an existing contract term risk identification method depends on the determination of the license experience of professionals.

The invention relates to the technical field of path planning scheme design based on deep reinforcement learning, in particular to a path planning method and system based on deep reinforcement learning and electronic equipment. According to the method, a reward function is designed by comprehensively considering path length, smoothness, feasibility and multi-objective optimization weight through a deep reinforcement learning model, the model is trained by using experience playback and exploring and utilizing a balance strategy, and the generated path is used for real-time planning after being subjected to smoothness and feasibility correction. The system comprises an environment perception module, an action definition module, a reward function design module, a model construction and training module, a path optimization module, a plan updating module and the like. The electronic device covers a processor, a memory, and input and output modules to implement real-time path planning. According to the method, the quality and efficiency of path planning can be improved, the method adapts to a complex dynamic environment, potential risks caused by frequent turning or sudden change are effectively avoided, the practicability and safety of the path are improved, and the method has a wide application prospect.

The invention discloses an integrated collaborative management method for construction site risk operation data, and belongs to the technical field of data management. The method comprises the following steps: constructing and calibrating a space-time risk reference database; deducing a potential risk conduction link set, and generating an associated intervention knowledge base; during operation, through multi-sensor cooperative verification, an abnormal signal is confirmed as a risk event; matching the risk event with a conduction link to calculate a risk upgrade level and dynamically adjust an early warning threshold; and when the early warning is triggered, generating and issuing a dynamic collaborative response instruction, and feeding back a processing result to correct the reference database to form a management closed loop. According to the method, the technical means of constructing the space-time risk reference, deducing the conduction link, cooperatively verifying the risk and dynamically regulating and controlling the threshold are adopted, so that the predictability of project risk management and control, the efficiency of resource cooperative scheduling and the scientificity of overall management decision are improved.

A prefabricated building component stability monitoring and early warning method and system, relating to the technical field of engineering project management. The method comprises: on the basis of a target building engineering drawing, constructing a construction node sequence; generating a plurality of engineering construction simulation models; acquiring a sample data set of a plurality of construction nodes, performing stability-associated factor analysis, and constructing a node factor mapping sequence; by using the node factor mapping sequence as monitoring data, performing construction monitoring, and determining construction monitoring data of a first construction node; on the basis of the construction monitoring data, performing mapping and acquiring a first engineering construction simulation model, performing error analysis by means of a twin comparison channel, and determining a first construction error; and on the basis of the first construction error, generating a construction early warning signal, and, on the basis of the construction early warning signal, acquiring a first construction compensation parameter and performing subsequent construction compensation for target building engineering, thereby achieving the technical effects of improving the safety and reliability of a prefabricated building structure and reducing potential risks and losses.

The invention discloses an AI-driven robot working environment safety sensing method, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting the visual, laser radar, force sense and environment parameter data of a working environment in real time through a multi-mode sensor; performing feature extraction and semantic understanding on the acquired data by using a deep learning algorithm to generate multi-dimensional perception information of the environment; based on a preset security policy model, in combination with the multi-dimensional perception information, predicting and classifying potential risks; according to a risk classification result, a behavior strategy of the robot is dynamically adjusted, and active safety decision is realized; and through the continuous learning module, the deep learning model and the security policy model are optimized online by using newly collected data so as to adapt to environmental changes.

The invention discloses an operation data risk intelligent identification and evaluation method based on a large language model. The method comprises the following steps: S1, collecting operation data of a data source; s2, preprocessing the unstructured data in the operation data; s3, performing semantic analysis on the unstructured data by using an improved BERT large language model, and identifying potential risk signals; s4, preprocessing the structured data, extracting feature information, and fusing a risk signal identification result with the feature information; s5, risk assessment is carried out based on an improved XGBoost algorithm; s6, performing quantitative evaluation on the identified risk factors; s7, when the operation data is updated, performing real-time risk identification and evaluation on the new operation data; and S8, when the risk assessment result triggers a set early warning threshold value, generating risk early warning. According to the method, the improved BERT model and the XGBoost algorithm are combined, industrial data risk identification and evaluation are realized, and the method has the advantages of high accuracy, high real-time performance and high automation degree.

A coal mine risk early warning system based on big data analytics, the coal mine risk early warning system comprising: a data collection module, used for collecting data in real time during coal mine operation; a data storage module, configured to store historical data records collected by the data collection module; a data processing module, which uses big data analytics technology to process the stored data and identify potential risk factors; a risk assessment module, which assesses the risk level of coal mine operation on the basis of analysis results of the data processing module, there being three risk levels: low, medium, and high; and an early warning module, which sends an early warning signal to relevant personnel when the risk level reaches a preset threshold.

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 the technical field of bridge construction, in particular to a swivel bridge ectopic support cast-in-place construction method which comprises the following steps: S1, constructing a three-dimensional model of a bridge and a support by using a BIM technology, performing virtual simulation, obtaining a simulation result by simulating a construction process, and outputting a construction scheme; s2, monitoring the support structure and the overall state of the bridge in real time on the swivel bridge construction site through monitoring equipment, and collecting real-time data of the construction site; when the system is used, equipment idling is reduced, personnel are reasonably distributed, material transportation is optimized, the construction efficiency is improved, intelligent optimization of data driving is achieved through BIM + AI, the construction automation level is improved, accurate analysis of supports and construction data is facilitated, the problem finding efficiency is improved, construction personnel can conveniently and rapidly intervene, construction delay is reduced, and the construction efficiency is improved. Potential risks can be predicted before faults occur, potential safety hazards are reduced, the construction sequence is adjusted through intelligent analysis, and delay caused by abnormal data is reduced.

The invention discloses a security management method and system for data asset transaction, and provides a new normal form of distributed evidence storage and automatic management for data assets through deep fusion of a block chain technology and an intelligent contract. Specifically, embedded coding on a semantic level is carried out on an asset use behavior log recorded in an intelligent contract and a preset data use strategy, and a fine-grained semantic verification mechanism is utilized to realize consistency automatic comparison of an actual behavior and the strategy. And once the deviation from the preset strategy or the existence of abnormal access is detected, the alarm prompt can be triggered in real time. Through the mode, the response capability of the system to violation operation or potential risk events is improved, each time of data calling can be ensured to be in a controllable and traceable state, unauthorized access and malicious operation are effectively prevented, and a safer, transparent and reliable operation environment is created for the whole data element market.

The invention discloses a digital content tracing system based on a block chain technology, and particularly relates to the technical field of block chains. The tampering risk of the digital content in the distributed storage process is evaluated by analyzing the multi-node data synchronization behavior; the transaction overstock risk judgment module evaluates the transaction overstock and local congestion hidden danger according to the transaction verification rate in the propagation process; and the reliability evaluation module judges the reliability of the digital content tracing chain in combination with the tampering risk and the local congestion hidden danger. The abnormal risk assessment module analyzes operation behavior abnormity through the intelligent contract log, and the conflict possibility assessment module detects consistency deviation of circulation records; and the potential risk assessment module synthesizes the multi-dimensional risk, generates a potential risk assessment result of the tracing chain, and determines whether to trigger an on-chain responsibility node tracing verification mechanism, so that potential hidden dangers in the tracing chain can be comprehensively identified, and an efficient and reliable solution is provided for block chain digital content tracing.

The invention provides a multi-modal LLM-based building risk prediction management and control method and system, and relates to the technical field of building safety management, and the method comprises the steps: analyzing sensor data, a text report, an image video and a voice instruction of a building construction site through a multi-modal feature extraction module, and generating a structured feature vector set; performing cross-modal semantic fusion and risk coupling analysis by using a multi-modal LLM inference engine to generate a potential risk identification set and a risk level assessment result; dynamically matching a management and control rule of the building safety specification library based on the risk identifier, and outputting a strategy set consisting of an equipment regulation and control instruction, a personnel early warning notification and a regional management and control suggestion; driving a field execution device to implement a control action, and collecting a multi-modal feedback data stream; and calculating a strategy execution efficiency index through a closed-loop optimization module, dynamically updating LLM model parameters and rule weights, and forming a self-adaptive optimization link. The system correspondingly comprises a multi-modal feature extraction and fusion module, an LLM inference engine module, a dynamic strategy generation module, an execution feedback module and a closed-loop optimization module. According to the method, the problems of key feature omission and risk response lag in traditional single-mode analysis are solved, and the risk prediction accuracy and the management and control real-time performance are remarkably improved.

The invention discloses an unmanned agricultural machine path navigation method based on farmland environment perception judgment, and relates to the technical field of path navigation, and the method comprises the steps: continuously perceiving farmland topographic relief features, gradient changes, and agricultural machine current attitude and wheel speed information before and during the operation of an agricultural machine; identifying a terrain disturbance potential risk area based on the sensing data, and predicting the offset and rollover risk value of the terrain fluctuation to the agricultural machine trajectory; according to the method, a pre-correction model fusing terrain prediction data and agricultural machinery dynamic characteristics is called, a pre-correction path containing the terrain compensation amount is generated, and navigation control parameters are adjusted in advance, so that the pre-correction path containing the terrain compensation amount is actively generated by sensing terrain fluctuation and predicting risk and offset in advance; the problem of causal malposition of risk prior to control is avoided from the source.

The invention relates to the technical field of semantic decision management and control, in particular to a multi-modal semantic alignment intelligent decision and risk management and control system, which comprehensively and accurately captures cross-modal semantic association through multi-modal semantic alignment processing so as to generate a plurality of possible reasoning chains with reliability and interpretability. And combining dimensions such as knowledge conflicts and historical risks, calculating reasoning overlapping values to evaluate inter-chain association, realizing multi-dimensional and multi-angle analysis of potential risks, bringing risk fingerprint values and the reasoning overlapping values into a quantitative calculation framework of decision response values, dynamically setting a decision response threshold value, and realizing quantitative calculation of the risk fingerprint values and the reasoning overlapping values. The system can flexibly trigger emergency, early warning or monitoring response according to different risk levels, and refinement and differentiation of risk management and control are realized. The mechanism not only ensures timely disposal in a high-risk scene, but also avoids resource waste in a low-risk scene, and effectively balances risk prevention and control and execution efficiency.

The invention relates to the technical field of big data, in particular to an intelligent operation and maintenance management method based on a big data algorithm, and the method comprises the steps: constructing and continuously updating a dynamic fault association graph through inputting multi-source heterogeneous operation and maintenance data; starting full-graph scanning based on a predefined period, detecting an abnormal topological structure through a graph pattern recognition algorithm, and marking potential risk nodes; executing dynamic influence diffusion simulation on the potential risk nodes, calculating a business influence severity quantized value after the fault, and marking fault propagation vulnerabilities according to the quantized value; taking the potential risk node as a starting point, executing a reverse traceability algorithm for preferentially exploring a path pointing to a fault propagation vulnerable point, and outputting a fault propagation path and a source fault node identifier; and finally generating and executing a fault processing strategy. The process solves the problem that traditional operation and maintenance cannot quantitatively evaluate and discriminate the highest priority disposal object from numerous potential risks, and realizes accurate positioning and active prevention and control of weak links of fault propagation.

The invention discloses an equipment preventive maintenance and diagnosis method based on multi-dimensional data verification, relates to the technical field of equipment preventive diagnosis, and solves the technical problems that fault reasons are difficult to comprehensively and deeply analyze, and maintenance measures are lack of pertinence due to the fact that the fault reasons are often misjudged or cannot be positioned. According to the method, the fault diagnosis model is constructed, the adaptation algorithm is adopted for different types of data, the diagnosis accuracy of the model to equipment faults and the adaptability of the model to complex working conditions are improved, the equipment fault type and the abnormal state can be rapidly and accurately recognized, and the fault diagnosis accuracy is improved through the methods of fault phenomenon matching, parameter correlation analysis, historical data deep backtracking and the like. According to the method, fault reasons can be accurately determined, the performance degradation trend and potential risks of the equipment can be effectively identified in combination with a hybrid similarity measurement method, correlation among data is further mined through a secondary analysis mechanism, the potential risks are accurately pre-judged in combination with historical data, and real preventive maintenance is achieved.

The invention discloses a BIM-based water conservancy project construction quality intelligent management method and system, and relates to the technical field of intelligent management. During operation of the system, various parameters related to the water conservancy project construction quality are acquired based on construction of a BIM model, data from a data acquisition module are preprocessed and integrated, and a BIM model is established; the method comprises the following steps: establishing a mathematical model for water conservancy project quality monitoring, establishing a dynamically updated and optimized model through regression analysis of historical data and a machine learning algorithm, predicting potential risks and quality problems in a construction process, and calculating a dynamic monitoring index Dtjk according to parameters output by a BIM model establishment module, the real-time monitoring and early-warning module is used for monitoring a BIM model, comparing the real-time monitoring and early-warning module with a preset qualified threshold value in the BIM model, giving out early warning according to the preset threshold value, reminding management personnel to take intervention measures, and providing improvement suggestions and optimizing a construction management scheme according to feedback information of the real-time monitoring and early-warning module and changes of a dynamic monitoring index Dtjk.

The invention relates to the technical field of enterprise data encryption, and discloses an enterprise data encryption method and system based on artificial intelligence, and the method comprises the steps: collecting internal and external heterogeneous data streams of an enterprise, carrying out the standardization processing, and recognizing potential risk features through combining time sequence alignment and vulnerability analysis; a risk probability analysis tool is utilized to carry out quantitative evaluation from two dimensions of data sensitivity and an influence range, an encryption parameter adjustment scheme is dynamically generated, calculation and storage resources are dynamically allocated according to a scene risk level, an encryption strategy is updated in real time, and hierarchical protection is carried out on sensitive information. According to the method, corresponding encryption strength can be adopted for data of different risk levels, resource utilization is optimized while safety is guaranteed, the data leakage risk is effectively dealt with, and the enterprise information safety protection capability is improved.

The invention relates to a safety production equipment risk monitoring method and system, and belongs to the technical field of risk monitoring, and the method comprises the steps: obtaining historical operation data and real-time operation data of to-be-monitored safety production equipment; setting a dynamic threshold according to the historical operation data, predicting the range of the dynamic threshold according to preset target process operation data and the historical operation data through a bidirectional long-short-term memory network, and updating the range of the dynamic threshold in combination with the real-time operation data to obtain an updated dynamic threshold; the comprehensive risk index is calculated according to the updated dynamic threshold value and the real-time operation data through the analytic hierarchy process, the risk of the safety production equipment to be monitored is evaluated according to the comprehensive risk index, an evaluation result is obtained, dynamic monitoring and evaluation of the risk of the safety production equipment are achieved, and the safety production equipment risk evaluation efficiency is improved. The actual operation state of the equipment can be reflected more accurately, potential risks can be found in time, corresponding measures can be taken, and the operation safety and reliability of the equipment are improved.

The invention relates to the field of crane management, and discloses an intelligent agent control method and system.The method comprises the steps that operation parameters and operation environment data are collected in real time through multiple sensors arranged on a crane and subjected to standardization processing; transmitting the processed data to an operation state analysis model, and outputting an operation state evaluation value so as to analyze the current operation state of the crane and predict potential risks; based on the analysis result, an optimization control instruction is generated and issued to each execution mechanism controller, and corresponding mechanisms are driven to execute precise actions; through multi-source data fusion and intelligent analysis, real-time monitoring and risk assessment of the running state of the crane are achieved, the self-adaptive capacity and safety of the control system are improved, the manual intervention requirement is effectively reduced, and the method is suitable for intelligent crane control in the complex working environment.

The invention discloses a construction safety management method based on multi-source information driving, and relates to the technical field of construction safety. The method comprises the following steps: acquiring multi-source information of a construction site; inputting the video monitoring image into a pre-trained artificial intelligence model for analysis, identifying a personnel behavior state and an environment feature of a construction site, and obtaining image feature information; performing preprocessing and fusion on the sensor data and the image feature information by using an edge computing device to generate multi-source fusion data; analyzing the multi-source fusion data by using a safety risk identification algorithm, evaluating the potential risk of the construction site and generating safety early warning information; calculating a safety score value of the construction site through a safety score model according to the evaluation result; and when the safety score value is lower than a preset safety threshold value, automatically triggering a corresponding safety control measure. According to the invention, the timeliness and accuracy of construction risk management and control are greatly improved.