436 results about "Risk prevention" patented technology

The invention discloses a financial data risk control system and method based on big data, and relates to the technical field of financial science and technology, and the system comprises a multi-source data collection module which achieves cross-mechanism safety collection through federal learning; the data cleaning and preprocessing module is used for processing abnormal values and missing values by using an improved algorithm; the knowledge graph construction module is used for constructing a dynamic knowledge network based on an innovative algorithm; the risk assessment engine fuses various models to assess risks; the real-time monitoring and early warning module is used for realizing second-level response by utilizing multi-scale analysis; the decision support module is used for optimizing a strategy based on reinforcement learning; and the audit tracking module guarantees evidence storage and privacy through zero-knowledge proof, and all the modules cooperate to improve the risk control capability. According to the financial data risk control system and method, risks are accurately recognized, real-time monitoring and early warning are achieved, data security sharing is achieved, risk control strategies are dynamically optimized, risks and business development are balanced, the risk prevention and control capacity and economic benefits of financial institutions are improved, and data privacy and risk control transparency are guaranteed.

The invention discloses an intelligent enterprise financial data analysis system and method based on artificial intelligence, and belongs to the technical field of enterprise informatization management and intelligent finance. The objective of the invention is to solve the technical problems of low processing efficiency, insufficient data insight, lagging risk identification, weak intelligent decision-making assistance capability and the like in the existing enterprise financial data analysis process. The system generally integrates a data automatic acquisition and integration module, a financial data deep analysis and modeling engine based on artificial intelligence, a key financial index and operation performance intelligent calculation module, a multi-dimensional financial risk intelligent identification and dynamic early warning module and an interactive visual analysis report and decision support module. The core of the method is that internal and external multi-source heterogeneous financial and operation data of an enterprise are automatically collected and fused, deep processing and intelligent analysis are performed on the data by applying artificial intelligence technologies such as machine learning and natural language processing, and accurate portrait of the financial condition of the enterprise, dynamic evaluation of operation performance and real-time monitoring and prediction of financial risks are realized. And analysis reports and optimization suggestions with insight are generated, so that enterprise managers can make efficient and scientific operation decisions. According to the method, the automation and intelligence level of enterprise financial data analysis can be remarkably improved, the financial information quality and decision support efficiency are improved, the risk prevention capability of enterprises is enhanced, and the enterprises are assisted to realize refined operation and value creation.

The invention provides an intelligent risk identification and analysis method based on multi-modal heterogeneous data fusion, which relates to the technical field of risk management, and comprises the following steps of: acquiring multi-source data, constructing symbol-nerve double-space processing, generating semantic vectors, extracting features through orthogonal matrix decomposition and a bidirectional long-short-term memory network, and obtaining an intelligent risk identification and analysis result; according to the method, multi-modal information is fused by using an adaptive weight mechanism, and a risk propagation topology network is constructed by applying a graph embedding algorithm, so that accurate identification and early warning of risks are realized, and the risk prevention and control capability and prediction accuracy are effectively improved.

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 discloses an AI-based tendering and bidding full-process intelligent generation and review method and system. The method comprises the steps of tendering full-process intelligent generation, bidding full-process intelligent generation and bidding review. An AI scoring model is adopted for review, the technical scheme of each bidding document is deeply analyzed through a natural language processing technology, and real-time cross validation is carried out with a knowledge graph, so that the score of each sub-item is provided with detailed comments and a definite evidence chain, the objectivity, transparency and credibility of review are greatly improved, and the review efficiency is improved. The review quality is improved from experience judgment to a data and knowledge driven level; secondly, potential violation behaviors are identified through a surrounding bidding risk report generated through multi-dimensional correlation analysis, and the risk prevention and control capability is effectively enhanced; and finally, based on comprehensive data analysis, automatically generating a bid-winning candidate sorting and comprehensive review report, and ensuring that the calibration result is optimal.

The invention discloses a big data abnormal behavior risk detection method and device, equipment and a medium, and the method comprises the steps: constructing a time-space behavior tuple containing a timestamp, a geographic position and behavior characteristics through user behavior data, environment sensor data and external event data collected by terminal equipment; inputting the time-space behavior tuple into a neural network model, and generating a portrait feature vector representing a user behavior pattern; performing time sequence conversion on the portrait feature vector, and outputting an abnormal probability value through a multi-scale Transform model; based on the abnormal probability value, clustering behavior data of the same region and the same time period, and combining the external event data to construct a causal association map; and calculating contribution weights of environmental factors according to the causal association map, and determining risk tags according to the contribution weights. By analyzing the time-space behavior tuple of the user, the abnormal behavior detection and risk prevention and control capabilities are improved.

The invention discloses a supply chain data-oriented enterprise upstream and downstream collaborative risk control prediction method and system, and relates to the technical field of supply chain financial risk management, and the method comprises the steps: extracting a payment time deviation value between enterprises to construct a payment behavior sequence, calculating a supply chain collaborative credit rating based on a fluctuation amplitude and a historical default record, and recognizing a risk source enterprise; adopting a recursive partition time window to extract a period index and a sudden change index of the payment behavior, and constructing a payment behavior portrait; identifying associated enterprises through the phase overlapping degree of the payment behavior track to form a risk conduction sequence; and based on the evolution law and the risk conduction sequence of the payment behavior portrait, predicting a risk triggering node, and generating a risk prevention and control scheme including a credit line regulation and control instruction and a behavior monitoring strategy. The method can effectively identify the supply chain risk source, predict the risk conduction path, and improve the financial risk control capability of the supply chain.

The invention discloses an urban financial service full-link risk prevention and control management system, and belongs to the technical field of risk prevention and control. Comprising a multi-source heterogeneous data acquisition and treatment module, a dynamic risk portrait construction module, a real-time risk monitoring and early warning module, a risk disposal and decision support module, a cross-scene risk linkage analysis module, a compliance and privacy protection module and a system adaptive optimization module. Automation and manual cooperation of risk disposal are realized; a low-risk event automatically executes a preset action through a formula, so that the disposal efficiency is improved; medium and high risk events are pushed to an expert system, a disposal scheme is generated in combination with historical cases, and a block chain records an operation log to ensure transparency and traceability; the module balances machine decision and manual auditing boundaries, and the misjudgment risk is reduced.

The invention discloses a three-dimensional digital twin modeling method, system, equipment and medium taking a reservoir as an object, and relates to the technical field of reservoir monitoring, and the method comprises the steps: constructing a three-dimensional space model facing a reservoir entity, and forming a visual simulation environment; accessing multi-source data related to reservoir operation, and establishing a unified data aggregation and management mechanism; integrating a real-time operation state in the three-dimensional space model, and dynamically mapping perception information and business activities of the reservoir; and based on a set analysis model and a determination rule, carrying out intelligent processing on the reservoir operation data, identifying potential risks and generating an early warning result. According to the invention, by establishing a multi-type analysis model and a judgment rule system, real-time calculation and risk identification are carried out on key indexes, intelligent early warning of risks such as emergencies, trend anomalies and historical extreme value overrun is realized, and the risk prevention and control capability of the reservoir is effectively improved.

The embodiment of the invention discloses a landslide disaster evolution analysis method and system based on a coupling model, and the method comprises the steps: carrying out the multi-source data integration of geological structure monitoring data, landform distribution data and hydrological environment monitoring data of a target seismic region, and generating a comprehensive monitoring data set; based on the data set, establishing an interaction association relationship of the target equation set, and constructing a geology-landform-hydrology coupling model; performing dynamic simulation and quantitative evaluation on the instability probability and the landslide scale of the potential landslide mass by using the coupling model according to a preset scene parameter combination, and generating an image-text annotation quantitative evaluation report; and finally, carrying out vulnerability analysis and risk assessment on the disaster-bearing body based on the report, generating a comprehensive guidance report containing a disaster-bearing body vulnerability spatial distribution diagram, landslide risk prevention and control measure suggestions and a regional space planning optimization scheme, and providing a scientific basis for landslide disaster prevention and control and regional planning.

The invention discloses a mucky soil foundation intelligent monitoring method based on multi-source data fusion, and particularly relates to the technical field of foundation monitoring and early warning. The method comprises the following steps: constructing an original soil body state data set of a mucky soil foundation, and carrying out anomaly elimination and time calibration based on a rheological constitutive model to generate a foundation state characteristic matrix; performing dynamic causal association tracking analysis on the feature matrix in a sliding window mode to generate a water-soil coupling drift parameter set; extracting a stress historical dependency coefficient set by using a nonlinear scale analysis method; and according to the water-soil coupling drift parameter set and the stress historical dependency coefficient set, the stability risk level of the mucky soil foundation is judged, and corresponding early warning information is selected from a preset graded early warning instruction database and output, so that the accuracy and adaptability of anomaly recognition and risk early warning are improved, and the accuracy and the adaptability of abnormal recognition and risk early warning are improved. The method is suitable for long-period stability monitoring and intelligent risk prevention and control of high-soft foundations or silt stratums.

The invention discloses a water quality monitoring method and system based on large model data analysis, and the method comprises the steps: collecting multi-dimensional environment index real-time data through a remote sensing device and an underwater mobile sensor, completing the noise filtering and format standardization through an edge calculation node, and inputting the data into a machine learning model; the method comprises the following steps: automatically extracting space-time distribution characteristics and intelligently identifying potential abnormal modes, immediately starting multi-parameter correlation analysis when an abnormal degree exceeds a threshold value, constructing a deviation correlation map to accurately invert pollution source space coordinates, further performing time sequence prediction modeling in combination with a historical data sequence, and deducing a future diffusion path track of pollutants. And finally, deeply overlapping and fusing the predicted trajectory and the ecological sensitive area map, quantitatively calculating the comprehensive risk score distribution of each intersection area by using a risk assessment algorithm, automatically generating a visual environment monitoring report, and updating a historical sequence to form closed-loop learning at the same time. According to the invention, the emergency response speed, the traceability accuracy and the risk prevention and control capability of the sudden pollution event of the water environment are greatly improved.

The invention relates to the technical field of sewage treatment, and discloses a risk early warning method and system for a sewage treatment trusteeship operation project. The method comprises the following steps: acquiring multi-source operation data of sewage treatment equipment, and generating a time sequence data set after time synchronization processing; identifying abnormal data points based on the data set and generating an abnormal mark sequence; performing time sequence fluctuation and persistence analysis on the sequence, and extracting features to construct an abnormal trend feature vector; performing correlation analysis in combination with medicament dosage records, and determining a cost risk assessment result; generating a layered early warning signal according to the risk level, and matching a response strategy; and integrating related data to form a push data set, sending a notification to an operator and updating a processing record, and finally generating a traceable exception processing file. According to the invention, timely early warning and accurate response in the sewage treatment process can be realized, and the efficiency and safety of operation management and the accuracy of risk prevention and control are improved.

The invention discloses an intelligent early warning system for safety of buildings around foundation pit construction, and relates to the technical field of construction safety monitoring. The platform adopts a four-layer architecture of a sensing layer, a transmission layer, a platform layer and an application layer. The sensing layer collects data in real time through a multi-class sensor array; the transmission layer wirelessly transmits data by using the Internet of Things technology; the platform layer serves as a core, comprises a data management module, a digital twin modeling module and an intelligent analysis early warning engine, and is responsible for data preprocessing, three-dimensional model driving and intelligent risk study and judgment based on machine learning and a dynamic threshold value; and the application layer provides visual display and early warning push service through a Web terminal and a mobile APP. All-weather automatic monitoring, intelligent advanced early warning and accurate risk prevention and control of the surrounding environment of the foundation pit are achieved, a complete intelligent management closed loop is formed, and the construction safety level is effectively improved.

The invention relates to the field of environment management, and discloses a watershed digital twinning environment real-time simulation and interaction platform fusing cloud edge collaboration and intelligent modeling, and the platform comprises the steps: carrying out the dynamic collection of the Yangtze River and Yellow River watershed, and generating environment state information organized according to the temporal-spatial resolution; associating the generated environment state information with multi-source environment data to form a multi-source knowledge basis of the drainage basin pollution field; establishing a rule knowledge base covering pollution identification, early warning and prevention and control business processes by utilizing a generative knowledge representation method, and generating a pollution event candidate identification result; carrying out fusion calculation on the pollution event candidate recognition result and multi-source heterogeneous data, constructing a digital twin simulation model, and eliminating data islands between modules and data sources; and executing linkage calculation on the pollution area and the risk node calibrated in the digital twinborn simulation model, generating environment state interaction information and synchronously updating the environment state interaction information to the digital twinborn environment. The method has the advantage that the regional water environment risk prevention and control capability is improved.

The invention is suitable for the technical field of fire prevention and control, and relates to a regional fire risk assessment method based on a fire risk prevention and control unit, which is based on a specific risk prevention and control unit, integrates static risk assessment and dynamic risk adjustment, and can realize refined assessment and visual display of regional fire risks. Compared with an existing assessment method, the method can more accurately reflect the distribution characteristics of specific risk points, makes up for the defect that the overall assessment result of a region cannot reflect details in a traditional method, and effectively solves the problem that assessment unit division is disjointed from actual fire-fighting management work. According to the assessment method based on the risk prevention and control unit, the efficiency and precision of regional fire risk assessment are remarkably improved, and fire risk prevention and control work can be carried out more scientifically and efficiently.

The invention belongs to the technical field of construction management, and particularly relates to a construction data processing method and system based on big data, and the system comprises a data collection and editing preprocessing module, a time-space correlation modeling module, a multi-dimensional risk deduction module, a decision feedback optimization module and a construction supervision terminal. Multi-source heterogeneous data are efficiently accessed and processed through the data collecting and editing preprocessing module, the efficiency and accuracy of data processing are improved, the space-time correlation modeling module constructs dynamic digital twinborn bodies of construction elements, real-time mapping and correlation analysis of a physical space and a digital space are achieved, powerful support is provided for risk prevention and control, and the method is suitable for large-scale popularization and application. The multi-dimensional risk deduction module carries out risk quantitative evaluation and scene simulation based on the digital twinborn body, potential risks can be found in advance and early warning signals can be generated, the decision feedback optimization module converts risk early warning into a specific disposal scheme, the construction supervision difficulty can be reduced, and a powerful guarantee is provided for smooth proceeding of a construction project.

The invention provides a slope displacement monitoring system and method based on multi-parameter progressive analysis, and belongs to the technical field of slope monitoring. The system comprises a central processing module, a first-stage monitoring module, a second-stage monitoring module, a third-stage monitoring module and a risk elimination module. The first-level monitoring module collects slope surface displacement data and identifies a potential sliding area. The secondary monitoring module collects stress data of the deep anchor cable and judges the depth of a sliding surface; the third-level monitoring module collects vegetation root system soil fixation force, reinforced material deformation and soil moisture content data and evaluates the displacement inhibition efficiency of ecological slope protection. The system triggers detection according to a progressive sequence of'surface displacement-deep stress-ecological effect ', abnormity of a previous stage is a necessary condition for starting a next stage, the central processing module judges a risk level according to a detection result, and the risk elimination module outputs a corresponding risk elimination strategy. According to the method, closed-loop management of early discovery, fine positioning and strong treatment is realized, and the accuracy and risk prevention and control capability of slope monitoring are improved.

The invention relates to the technical field of risk analysis, in particular to a dynamic risk analysis method and system based on a knowledge graph and a digital twin model. The method comprises the following steps: acquiring a preset three-dimensional twin model of a hydrogen cylinder; determining a key weak area of the hydrogen cylinder according to a preset three-dimensional twinborn model; predicting a potential failure mode of the hydrogen cylinder according to the key weak area on the basis of a preset knowledge graph; and according to the failure mode, simulating a risk evolution state of the hydrogen cylinder, and according to a simulation result, determining a transportation strategy. Meanwhile, a foundation is laid for the risk evolution state, and it is ensured that the input data is accurate and timely. The overhead of comprehensive scanning is reduced, knowledge graph query and risk simulation are ensured to be concentrated on high-probability failure points, and the overall analysis efficiency is improved. Operable input is provided for risk evolution state simulation, and the reliability and pertinence of risk identification are enhanced. The dynamic association of risk prevention and control and transportation decision is realized, and the initiative of safety management is improved.

The invention relates to the technical field of new energy systems, and particularly discloses a new energy engineering cost progress two-dimensional dynamic prediction and risk prevention and control system, which comprises a data processing module, a dynamic prediction module, a risk prevention and control module and a visualization module. Cost and progress linkage prediction is realized based on the coupling relationship between the cost subject tree and the progress node network, the error is reduced by more than 40% compared with the traditional single-dimensional prediction, and the cost hyper-branched and progress lagging risks of the future N cycles can be identified in advance; a multi-level early warning mechanism automatically triggers risk prevention and control suggestions including super-budget attribution analysis, cost compression and progress compression strategies, the response speed is increased, and human decision delay is reduced; supplier full-process management quantifies the performance capability, quality and delivery cycle through a weighted scoring model, an elimination mechanism is automatically triggered based on an accumulated performance problem or a scoring threshold, and the performance risk is reduced.

The invention relates to the technical field of virtual power plant optimization scheduling, in particular to a low-carbon virtual power plant collaborative optimization method based on digital twinning and deep reinforcement learning, which comprises the following steps: intelligently coupled digital twinning collaborative modeling is carried out, and an aging-efficiency bidirectional coupling mechanism is developed; carbon energy flow coupling dynamic tracking is carried out, a block chain network is established to store carbon emission data, a carbon intensity database is established to guide low-carbon scheduling, and a space-time carbon footprint thermodynamic diagram is generated; constructing a hybrid optimization architecture to realize multi-temporal-spatial-scale collaborative optimization, generating a risk parameter packet and converting the risk parameter packet into risk model features; and establishing a risk assessment system, and developing a risk hedging strategy to realize risk dynamic protection. According to the method, the bottleneck of existing virtual power plant operation management is broken through, efficient equipment management, energy low-carbon scheduling, multi-temporal-spatial-scale collaborative optimization and effective risk prevention and control can be achieved, and the safety, reliability and resource utilization efficiency of virtual power plant operation are improved.

The invention discloses a risk assessment method for power grid cascading failures, and the method comprises the steps: firstly determining an assessment range and a target, and carrying out the data collection after the assessment range and the target are determined; establishing a system model, and constructing a digital model capable of accurately reflecting physical characteristics, operation states and control behaviors of a real power grid in a computer; constructing an initial fault scene; analyzing a cascading failure propagation path; quantifying risk indexes; and evaluating the cross-domain association influence, supplementing and evaluating the linkage influence on the association system in combination with the cross-domain association characteristics, and proposing a risk prevention and control and optimization strategy. According to the method, potential cascading failure inducements in a power grid can be actively mined through risk assessment of power grid cascading failures, a traditional passive mode of failure reprocessing is broken through, weak links are positioned, key equipment which easily triggers the cascading failures is found out through initial failure scene screening and propagation path simulation, and long-term latent hidden dangers are avoided.

The invention discloses an intelligent delivery targeted penetration decision-making method for precision marketing, particularly relates to the technical field of intelligent delivery targeted penetration decision-making, and comprehensively identifies potential anomalies through unified processing of a standardized index set and dynamic association construction of multi-level thresholds; by means of the constraint of a delivery strategy oscillation index on a delivery behavior, dynamic bidding, material optimization and flow distribution can be smoothly adjusted in a fluctuating environment, the controllability of budget consumption and the continuity of a delivery effect are ensured, and a multi-dimensional penetration strategy of competition, scenes, crowds and materials is combined, so that the delivery efficiency is improved. According to the method, high-value user groups are more accurately reached, the material matching degree is improved, the return on investment and the conversion rate are stably kept increased, oscillation and resource waste in the putting process are reduced, and the cross-platform collaborative optimization capability and the risk prevention and control level are improved.

The invention discloses an intelligent prediction and risk prevention and control method and device for an intelligent construction site, and a medium, and relates to the technical field of intelligent construction sites. The method comprises the steps of collecting environment data and motion data of a worker through a multi-mode sensing network deployed on a construction site; performing time synchronization and noise adaptive fusion on the motion data and the environment data at an edge computing node to generate a three-dimensional space position; according to the three-dimensional space position, a graph structure is constructed with the worker as a node and the space relation between the worker and the environment entity as an edge, a multi-head attention mechanism is coupled to capture a time sequence behavior evolution rule, and a behavior track of a preset time period is output; mapping the real-time position and the predicted trajectory to a digital twin platform, performing spatial matching with a construction task node, and rendering a task progress; and calculating a risk score according to the behavior track and the construction progress deviation. According to the method, a dynamic graph structure and spatial-temporal feature separation mechanism is realized, and the behavior trajectory prediction error is reduced.

The invention discloses a blast furnace smelting safety reinforcement learning decision optimization method based on expert strategy constraint. According to the method, an optimization strategy can be learned from an off-line expert track on the premise that operation safety is ensured. Specifically, a conditional generative adversarial mechanism is introduced to realize the alignment of strategy distribution and expert decision, and the exploration ability in a security domain is retained while the expert experience is inherited. And an independent state-action safety evaluation network is designed and a discount factor is introduced, so that the long-term accumulation risk caused by the large hysteresis characteristic of the blast furnace is effectively dealt with. In addition, memory is adopted to enhance network coding historical information, incomplete state observation is supplemented, and information deviation in decision is reduced. According to the method, effective integration of triple guidance mechanisms is realized, knowledge inheritance is realized through distributed alignment, performance improvement is driven through reward optimization, and risk prevention and control are ensured through explicit security constraints.

The invention discloses an internet user behavior accurate analysis method based on artificial intelligence, which belongs to the technical field of internet and comprises the steps of S1, data acquisition and privacy enhancement processing, S2, behavior modeling and transparent analysis, S3, real-time risk prevention and control and abuse blocking and S4, feedback closed loop and continuous optimization. On the basis of analyzing user behaviors, the traditional contradiction between privacy and efficiency can be broken through, and systematic improvement of safety and efficiency can be achieved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses an anti-fraud method, device and equipment for information verification, and a medium, and the method comprises the steps: obtaining multi-dimensional verification data associated with an information verification request, and carrying out the preprocessing of the multi-dimensional verification data to generate standardized data, inputting the standardized data into a preset data analysis model to obtain a first abnormal value, inputting the first abnormal value and a predefined business strategy corresponding to the information verification request into a business logic processor to generate a second abnormal value, and fusing the first abnormal value and the second abnormal value to obtain a comprehensive abnormal score, and comparing the comprehensive abnormal score with a preset abnormal threshold value, and executing an anti-fraud operation when the comprehensive abnormal score exceeds the threshold value. According to the method, the comprehensive anomaly score is formed by fusing model anomaly and the business strategy result, and judgment is performed based on threshold comparison, so that the defects of single data or a general model in the prior art are effectively overcome, the identification accuracy and real-time performance are improved, and business risk prevention and control are realized.

The invention discloses an uncertainty guided few-sample harmful speech detection method (U-GIFT). According to the method, a pre-training language model is finely adjusted based on a small number of labeled samples, and a semi-supervised self-training and uncertainty guiding strategy is combined. Monte Carlo Dropout is started in the reasoning stage, multiple times of random forward propagation are carried out to obtain sample posterior distribution, prediction entropy and information gain are calculated, pseudo-label samples are sorted and screened, and only high-confidence samples are selected to be added into a training set. And in order to reduce the influence of a pseudo labeling error, designing a stability weighting mechanism, giving a sample weight according to a prediction variance, and constructing a joint loss function, so that the model preferentially learns a stable sample to improve the detection performance. According to the method, the semantic and attention mechanism of the pre-training model is utilized, the detection effect is remarkably improved under the conditions of few samples, imbalance, multiple languages and cross domains, models such as BERT, RoBERTa, XLM-R, LLaMA2 and DeepSeek-R1 are compatible, and the method is suitable for content auditing and risk prevention and control.

The invention relates to a dynamic early warning and maintenance strategy adjusting method based on residual life, and belongs to the technical field of equipment management in the oil refining and chemical engineering field. The method comprises the following steps: acquiring multi-source operation data of equipment through an online acquisition module, and constructing a structured equipment state parameter set; based on the parameter set, establishing a residual life admission judgment model and generating a residual life accounting result of the current equipment; further adopting a corrosion rate-residual life two-factor coupling model to generate an equipment risk influence rate curve, and constructing a dynamic risk prevention and control boundary; and according to the risk level of boundary division, a dynamic maintenance strategy adjustment module is linked to perform early warning response, strategy execution, response recording and self-learning optimization are realized through a closed-loop management module, real-time evolution of a fortification value and a maintenance plan along with an equipment state is realized, and the accuracy of risk response and the intelligent level of equipment maintenance are improved.

The invention discloses a field operation video monitoring risk prevention and control method, system and device and a medium, and the method comprises the steps: analyzing the reference operation risks of the whole network over the years, and constructing a reference operation risk library according to the analysis result; establishing a first risk model through the reference operation risk library, and identifying risk influence factors; constructing a second risk model through the risk influence factors, determining weight values of the risk influence factors, outputting risk values, and performing risk grade division according to the risk values; deploying video monitoring equipment to identify operating personnel, synchronously monitoring an operation site, and analyzing and judging an abnormal condition through a pixel difference value of front and back frame images; and when the abnormal situation is judged, determining an early warning level in combination with the risk level, and giving an alarm. According to the method, historical risk data analysis and an intelligent video monitoring technology are deeply fused, so that risk assessment is fundamentally converted from static experience judgment to dynamic quantitative prediction.