677 results about "Potential risk" patented technology

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 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 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 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 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 belongs to the field of traffic control, and relates to a high-risk scene-oriented risk, dynamics and intention collaborative trajectory prediction method, which comprehensively utilizes an outward risk clue, vehicle dynamics characteristics and driving intention information (historical trajectory characteristics), and constructs a priori enhanced risk attention mechanism and a three-body interaction micrograph module to predict the risk, dynamics and intention collaborative trajectory of a high-risk scene. Early perception of potential risks is realized; meanwhile, a multi-scale dynamic time sequence encoder is adopted to capture the dynamic change of the vehicle from short-term disturbance to long-term behavior trend; an explicit intention recognition auxiliary task is introduced, and semantic constraint and optimization are carried out on the multi-modal trajectory; through collaborative modeling of the modules, the trajectory prediction precision and robustness of the automatic driving system under complex high-risk situations such as sudden cut-in and sudden brake can be remarkably improved, more reliable prior support is provided for a subsequent decision and control module, and therefore the driving safety of a vehicle in a complex traffic environment is effectively improved.

The invention discloses a complex business project auditing system based on multi-dimensional historical data, and belongs to the technical field of business project auditing, and the system comprises a data collection and preprocessing module which is used for collecting business project auditing multi-source data and carrying out the preprocessing of the business project auditing multi-source data; the risk assessment anomaly detection module is used for identifying an abnormal mode and a potential risk in the auditing task and determining a business project auditing risk assessment result; the task sorting and intelligent distribution module is used for carrying out priority sorting on the tasks and intelligently distributing the tasks; and the real-time monitoring dynamic adjustment module is used for dynamically adjusting the task allocation scheduling scheme according to the real-time monitoring data feedback. According to the method and the device, the problem of low auditing efficiency caused by incapability of effectively identifying abnormal modes and potential risks in auditing tasks and incapability of optimizing resource allocation in the prior art is solved. According to the method, the abnormal mode and the potential risk in the auditing task can be effectively identified, resource allocation can be optimized, the risk can be reduced, and the auditing efficiency of complex business items can be improved.

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 invention discloses an import and export risk real-time early warning method and system based on multi-modal data fusion, and relates to the technical field of data analysis, and the method comprises the steps: obtaining multi-modal data in real time, and carrying out the preprocessing; designing a dynamic weight distribution algorithm, and endowing different types of data with differentiated weights to realize dynamic fusion according to quantitative indexes of timeliness, authority and target transaction association degree; fusion data features are extracted through a streaming computation engine, real-time risk calculation is carried out based on an Apache Flink cluster, and an association network is constructed in combination with a six-dimensional knowledge graph; on the basis of the extracted features and the association network, a CEP rule engine quickly identifies and calculates potential risk events and accurately locates risk points by relying on a predefined multi-class composite risk mode; and setting a grading early warning mechanism, enabling the identified risk mode to correspond to a corresponding early warning grade, generating a grading early warning signal, and synchronously prompting a dynamic disposal suggestion. According to the method, the risk identification accuracy can be improved, and the early warning response delay is shortened.

The invention discloses a substation operation and maintenance task risk digital assessment method and system, and belongs to the technical field of task risk digital assessment, and the method comprises the steps: carrying out the hidden risk dominant modeling based on the structural data of a data lake, generating a personnel and equipment dynamic coupling risk coefficient, and carrying out the hidden risk dominant modeling; the space-time diagram neural network captures a personnel-equipment-environment coupling relationship by fusing spatial topology and time sequence features, the model can be associated with a historical fault mode of adjacent equipment, potential risks are identified in advance, and the causal model can identify the potential risks in advance by calculating causal strength between nodes and revealing hidden risk driving factors. The attention weight mechanism dynamically adjusts the fusion proportion of the space and time features, the method adapts to the complex scene of the transformer substation, the dynamic coupling risk coefficient is combined with the frequency and similarity of the historical accident library, the risk weight is quantified, and the probability of artificial misjudgment is reduced.

The invention provides a dynamic risk prediction and optimization decision-making method and system for a deep foundation pit construction process. A deep foundation pit numerical simulation model is established, and the model is used for simulating soil mechanical behaviors and supporting system response parameters in the construction process; acquiring multi-dimensional real-time monitoring data of the foundation pit, comparing the monitoring data with a numerical simulation result, and calibrating parameters of the deep foundation pit numerical simulation model according to a comparison result; performing time series data modeling analysis on the real-time monitoring data by using a Transform algorithm, capturing possible risk modes in the construction process, and identifying potential risk signals in the data; on the basis of Transform algorithm analysis and numerical simulation feedback results, the foundation pit construction risk state is predicted in real time, and a real-time risk prediction result is obtained; and according to the prediction result, dynamically adjusting the construction scheme to complete decision optimization. According to the method, the risk management and control capability of the deep foundation pit construction process is remarkably improved, and the construction safety and efficiency are greatly improved.

The invention relates to the field of power transmission line channel risk hidden danger monitoring, and provides a data fusion power transmission line channel risk hidden danger monitoring method and system, and the method comprises the steps: collecting the multi-modal sensing data of a power transmission line channel, and generating a multi-modal data flow of a unified time-space coordinate; constructing a three-dimensional space point cloud through a phase unwrapping and stereo matching fusion algorithm, and fusing multi-modal data to generate a space probability tensor; extracting risk semantic latent variables, constructing a Bayesian network and identifying potential risks; performing tensor product on the potential risk and the environmental data to generate a dynamic risk enhancement feature matrix, and constructing a nonlinear dynamic threshold curved surface through quantum annealing and Gaussian process regression; a mechanical equation is constructed, Gaussian kernel density estimation and numerical simulation are combined, the evolution trajectory of the risk in the space-time dimension is predicted, and a risk thermodynamic diagram and early warning information are generated; and generating a structured risk early warning report by adopting a natural language processing method. And the accuracy of power transmission line channel risk hidden danger monitoring is improved.

The invention relates to an end-side malicious behavior security detection method, which comprises the following steps: acquiring multi-source data, carrying out standardization processing on the multi-source data, generating a unified quintuple sequence, and constructing a quaternary dynamic behavior graph; respectively extracting time sequence features, semantic features and context features by utilizing a quintuple sequence, calculating confidence coefficients of cross-modal association, mapping the confidence coefficients to a quaternary dynamic behavior graph, and generating an individual behavior graph, a current behavior graph and a group baseline graph; and performing projection processing on the individual behavior graph, the current behavior graph and the group baseline graph to calculate a deviation degree score and a structural anomaly contribution degree of graph-level comprehensive deviation characterization, and triggering a grading response strategy by taking the deviation degree score and the structural anomaly contribution degree as double indexes of safety detection to solve an anomaly root. According to the method, internal and external threats, especially abnormal behaviors and potential risks which are difficult to find by traditional security defense means, are detected by analyzing behavior modes of users and entities.

The invention provides a time-space evolution data processing method and system applied to tunnel deformation analysis, and the method comprises the steps: obtaining a time sequence monitoring sequence of different monitoring dimensions in a tunnel construction process, constructing a time-space evolution correlation model of tunnel deformation based on the time sequence monitoring sequence, and analyzing a dynamic coupling relation between structure response data and geological environment action data, generating a space-time correlation feature matrix, generating a feature evolution sequence of tunnel deformation based on the space-time correlation feature matrix, and performing nonlinear mutation feature extraction on the feature evolution sequence to obtain a mutation feature index set; according to the sudden change characteristic index set and a preset accumulated deformation rate threshold value, performing dynamic division of tunnel deformation safety levels, generating a safety level division result, and based on the safety level division result and the spatial distribution data of the tunnel construction section, executing spatial positioning and boundary contour extraction of the potential risk damage area. And obtaining a risk damage area distribution map. According to the invention, the tunnel construction safety risk can be accurately evaluated and effectively controlled.

The invention provides a big data-based constructional engineering quality safety early warning method and system. The method comprises the following steps of: firstly, acquiring a constructional engineering multi-source operation data set comprising building structure monitoring data, construction equipment operation data, constructor operation data and environmental influence data; carrying out cross-dimension association traceability processing on the multi-source operation data set, establishing potential risk association and obtaining a construction engineering risk association traceability map; then constructing a quality safety risk conduction chain based on the map, and identifying a risk factor transmission path and an influence range; verifying the initial risk conduction chain in combination with a constructional engineering historical risk event library, and generating a verified risk conduction chain; and finally, deriving a quality safety risk development trend based on the verified risk conduction chain, integrating the current operation state to generate a dynamic early warning scheme, and sending the dynamic early warning scheme to a monitoring terminal, thereby realizing real-time and dynamic early warning of the construction engineering quality safety, and effectively improving the management level and safety.

The invention discloses a transaction risk adaptive blocking system and method based on large model behavior chain analysis. The system comprises a data acquisition module, a behavior chain construction engine, a large model analysis module, a dynamic time sequence modeling module and a risk assessment and blocking module. According to the system, transaction behaviors of a transaction subject at different time points are connected in series to form a sequence, a time sequence risk feature vector is generated in combination with dynamic time sequence modeling, and real-time accurate assessment of risks is achieved. The system can quickly identify potential risk transactions and give an early warning in advance. Meanwhile, the risk assessment and blocking module dynamically adjusts a risk threshold according to market fluctuation and credit conditions of transaction subjects, adaptively blocks high-risk transactions, and ensures that prevention and control strategies are matched with actual demands. And meanwhile, multi-dimensional data including geographic position and operation behavior information are fused, so that the comprehensiveness and accuracy of risk identification are further improved. An innovative and effective technical means is provided for financial transaction risk prevention and control, and safe and stable operation of financial transactions is powerfully guaranteed.

The invention relates to the technical field of food safety tracing and early warning, and discloses a food safety whole-course tracing and risk early warning system based on big data analysis, and the system comprises a causal map construction module which constructs a causal knowledge map of a food supply chain; the risk function modeling module is used for establishing a risk transfer function for the causal relationship edge; the risk potential energy calculation module is used for calculating the total risk potential energy value of each entity node in the graph; the risk early warning and causal tracing module is used for monitoring the risk and early warning, and reversely tracing the key causal chain; the intervention strategy generation module is used for simulating and evaluating intervention actions and generating an optimal blocking strategy; and the system interaction presentation module is used for visually presenting maps, risks, causal chains and strategies. The food safety risk is converted into a quantifiable data index by calculating the dynamic total risk potential energy value of the entity node, and the potential risk accumulation process is recognized in advance by monitoring the real-time change and development trend of the risk potential energy, so that the conversion from passive response to active prevention is realized.

The invention discloses an equipment health state monitoring method and system based on the Internet of Things. According to the method, data in a multi-source sensor is obtained and preprocessed, a real-time operation data set of equipment is obtained, and a health quantification deviation value and a health state trend are determined through the real-time operation data set. And when the health state is abnormal, recording a starting point coordinate of an abnormal event, marking an abnormal event triggering timestamp, and analyzing the dependency relationship between the equipment by utilizing graph network modeling. Furthermore, the potential risk probability is analyzed through the long-short-term memory network, real-time monitoring, fault propagation prediction and risk assessment of the equipment health state are achieved, and the operation reliability and the maintenance efficiency of the industrial equipment are improved.

The invention is suitable for the technical field of automatic driving, and provides an automatic driving vehicle blind area track prediction and planning method based on Transform, and the method comprises the steps: firstly constructing a key entrance point, calculating a shielding region, and then generating and maintaining a belief state of a ghost target in combination with a risk score; then, real and ghost targets are coded into Token in a unified mode, the Token is input into Transform for interactive modeling, and multi-modal track distribution is output; and finally, a variable anisotropic repulsive force potential field is constructed based on the predicted risk, and a safety control instruction is obtained through optimization solution and hard safety filtering. According to the method, continuous probabilistic estimation and interactive reasoning of the potential risk of the blind area are realized, a flexible defensive track can be generated according to the risk confidence and the dynamic state of the vehicle, and the traffic efficiency is guaranteed while the safety is improved.

The invention relates to the technical field of tower crane control, and provides an aerial high-risk area identification and hoisting path reconstruction method and system. The method comprises the following steps of: respectively mounting two rotary radars on a rotatable bracket of the tower crane to form a radar sensing module comprising a double-radar module; dynamically scanning the potential risk area in the construction scene through a radar sensing module to obtain real-time position information of the potential risk area; receiving the real-time position information through a processing module, and converting the real-time position information into real-time three-dimensional coordinate data in a motion coordinate system of the hoisting equipment by adopting a three-dimensional coordinate conversion algorithm; constructing a three-dimensional model of the potential risk area based on the real-time three-dimensional coordinate data; and identifying the three-dimensional model to determine a high-risk area, adopting an improved fast exploration random tree algorithm as a path obstacle avoidance algorithm, combining the three-dimensional model to dynamically reconstruct an initial hoisting path set by a user, and generating an optimal risk-avoiding hoisting track avoiding the high-risk area.

The invention relates to the technical field of block chain cross-chain transactions, in particular to an AI-based interpretable risk early warning system for block chain cross-chain transactions, which comprises a dynamic feature extraction module, a risk factor identification module, a decision logic construction module and an early warning response generation module. According to the method, the dynamic characteristics of the transaction behaviors are analyzed, the historical data and the external environment are combined, the potential risk factor distribution diagram and the risk weight mapping table are generated, the risk early warning information is generated by using the neural network model, and the reliability evaluation is introduced to improve the prediction reliability. According to the invention, the risk node and weight distribution in the cross-chain transaction can be accurately identified, the risk time range is defined, the early warning accuracy and timeliness are improved, the user is helped to avoid economic loss, and the stability of the cross-chain transaction system and the user experience are enhanced.

The invention relates to the field of intelligent transportation, and discloses an intelligent monitoring and management system and method for an expressway and electronic equipment, and the system collects global spatial-temporal data of the expressway to construct digital twins synchronized with the physical world; in the twinborn body, performing prediction and deduction based on a space-time causal map to identify potential risks; responding to the risk, generating an optimal intervention strategy through anti-fact deduction and executing the optimal intervention strategy, and recording a predicted intervention effect of the optimal intervention strategy; and after intervention, comparing a real traffic state with a prediction effect, calculating an anti-fact error, and carrying out dynamic self-correction on the space-time causal map according to the anti-fact error. According to the invention, links of perception, prediction, decision making, execution and feedback are fused into a self-adaptive control loop, and a self-correction mechanism based on an anti-fact error is introduced, so that the system can continuously learn and self-evolve from interaction with the physical world, and the problems of model solidification and poor adaptability of a traditional traffic management system are solved.

Methods and systems for automatically mitigating risks of water damage to a property caused by insurance-related events are provided. A smart home controller and / or insurance provider remote processor may analyze data received from a smart device disposed on, within, or proximate to a property associated with monitoring water flow, as well as data received from an insurance provider. If it is determined that an actual or potential risk of water leakage exists, the smart home controller or remote processor may automatically issue commands to the smart device to take actions that mitigate the risk of water damage. The smart home controller may transmit information about the actual or potential water leakage risks and any mitigative actions to an insurance provider. The insurance provider remote processor may interpret the transmitted data and perform insurance activities, such as providing a discount or premium, and / or adjusting an insurance policy associated with the property.

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.