360 results about "Safety index" patented technology

The embodiments of the present disclosure belong to the technical field of data processing. Provided is a multimodal scenario risk determination method based on a generative AI large language model. The method specifically comprises: step 1, acquiring multimodal data to form a target data set, wherein the multimodal data comprises visual data and text data; step 2, using an ALBEF algorithm to extract key features corresponding to the target data set, and fusing the key features into a comprehensive scenario representation; and step 3, on the basis of a preset safety index and a large language model, evaluating a risk degree corresponding to the comprehensive scenario representation, comparing the risk degree with a risk threshold, and determining whether the scenario corresponding to the comprehensive scenario representation is a high-risk scenario. By means of the solution in the present disclosure, a high-risk scenario can be rapidly recognized and identified, so as to provide a basis for taking emergency measures, thereby enhancing the real-time response capability.

The invention specifically relates to the technical field of big data analysis, and discloses a water conservancy gate multi-parameter cooperative intelligent monitoring system, which comprises a multi-parameter acquisition module, a multi-parameter processing module, a comprehensive analysis module, an intelligent decision module, an operation and maintenance early warning module and a man-machine interaction module, the multi-parameter processing module is used for calculating flood control and discharge indexes, structure safety indexes and equipment health indexes; the comprehensive analysis module is used for judging gate risk levels; the intelligent decision-making module is used for generating an optimal gate scheduling scheme; the operation and maintenance early warning module is used for constructing a multi-level early warning mechanism; according to the method, parameter coverage is comprehensive, a gate digital twinborn model and a gate opening comprehensive evaluation model are constructed, the gate risk level is evaluated, an optimization strategy is dynamically adjusted through an intelligent decision module, the accuracy of gate risk judgment is improved, and the self-adaptive capacity of the system is improved.

An available transfer capability evaluation method and apparatus for a multi-region power system, belonging to the technical field of new energy grid connection. The method comprises the steps: in view of multi-dimensional uncertainty of new energy output and a load demand, on the basis of a conditional generative adversarial network method, determining a typical daily source-load scenario set; constructing an initial operation point set on the basis of the typical daily source-load scenario set, and determining a limit operation point of a multi-region power system; on the basis of the initial operation point set and the limit operation point, constructing an ATC evaluation model on the basis of safety indexes of multi-region power grid operation; and, on the basis of the ATC evaluation model and the typical daily source-load scenario set, determining available transfer capability probability distribution of the multi-region power system.

The invention relates to a dynamic planning method, device and equipment for a low-altitude route of an unmanned aerial vehicle and a medium. The method comprises the following steps: acquiring real-time dynamic environment change data and obstacle distribution information of a low-altitude flight area, constructing a preliminary data set, and generating an initial flight path; according to the initial flight path, predicting energy consumption data, and if the energy consumption data exceeds a threshold value, performing optimization to obtain an optimized path; performing risk assessment quantification, combining a safety margin adjustment mechanism to obtain a safety index, and if the safety index is lower than a threshold value, performing safety distance adjustment to generate a safety path; monitoring flight conditions, generating a condition monitoring update report, acquiring new obstacle position information, and generating a dynamic obstacle avoidance path; and generating an execution instruction set of the unmanned aerial vehicle according to the dynamic obstacle avoidance path. By adopting the method, the dynamic environment real-time adaptability of the unmanned aerial vehicle can be improved, and the route meeting the task requirement can be planned in time when the unmanned aerial vehicle faces sudden obstacles or environment changes.

The invention provides a small and medium-sized reservoir operation safety evaluation system based on sky-ground work, and belongs to the technical field of hydrological monitoring. The device comprises a space-time alignment layer, a feature weighting layer and a dynamic coupling layer which are connected in sequence, the space-time alignment layer is used for performing time alignment and space alignment on the input data of the sky-land water conservancy project; the feature weighting layer is used for quantifying contribution degrees of different monitoring features to safety assessment according to the data after time-space alignment; and the dynamic coupling layer adopts a dynamic weight distribution strategy for different modules through a multi-module coordination engine, fuses feature weighting to obtain a fusion result, and outputs a small and medium-sized reservoir operation safety index according to the fusion result. The evaluation result is more accurate, and the timeliness is better.

The invention relates to the technical field of data processing, and discloses a motor vehicle safety performance classification method and system based on multi-factor coupling influence. The method comprises the steps of collecting multidimensional safety data of a motor vehicle, calculating a mutual information value and transfer entropy to generate a coupling relation network diagram, constructing a three-layer safety index system to form a comprehensive score, obtaining low-dimensional features through phase-space mapping, determining a dynamic boundary value by applying density clustering, and classifying real-time states to output risk early warning information. According to the technical method for realizing accurate classification and early warning, the defects of neglect of factor coupling relation, lack of scene adaptability, insufficient dynamic prediction capability and the like in the prior art are overcome, and the accuracy and practicability of safety performance evaluation and early warning of the motor vehicle are improved.

The invention discloses a stress analysis method for bridge incremental launching construction, and particularly relates to the field of stress analysis. According to the method, a mechanical state matrix is generated by combining wavelet noise reduction and Kalman filtering through distributed optical fibers, a piezoelectric force sensor, a vibrating wire stress meter and other equipment and real-time data; a pushing system-temporary support-beam body coupling dynamic mechanical model is established, and a generalized alpha method is adopted for solving and Bayesian optimization calibration parameters are adopted; key parameters are identified on line through a three-level progressive framework, boundary conditions are dynamically updated in combination with a total station and a laser tracker, and supporting rigidity self-adaptive adjustment is achieved through fuzzy PID control. Model confidence evaluation adopts a residual norm-modal confidence criterion-Bayesian update three-level mechanism, finally, stress distribution, support reaction and displacement deviation are calculated based on a correction model, and a safety index, threshold alarm and risk analysis report is generated through a fuzzy comprehensive evaluation method and fed back to a construction control center in real time.

The invention provides a power equipment intelligent maintenance method and system, equipment and a storage medium, and relates to the technical field of fault monitoring, and the method comprises the steps: obtaining the use duration and historical fault data of target power equipment, and the first operation data of the target power equipment in a preset duration; determining a safety index of the target power equipment in combination with the use duration and the historical fault data; determining a first abnormal index of the target power equipment according to the first operation data; acquiring second operation data of associated equipment of the target power equipment, and adjusting the first abnormal index according to the second operation data to generate a second abnormal index; and adjusting the second abnormal index through the safety index, generating a target abnormal index, and when the target abnormal index is greater than a preset threshold value, determining that the target power equipment has a fault risk, and triggering fault early warning. The technical effect of the invention is that the accuracy of power equipment fault early warning is improved.

The invention provides an intelligent driving skill training method and system based on behavior cloning and reinforcement learning, and relates to the technical field, and the method comprises the steps: collecting the driving behavior data of a human driver, dividing a training set and a verification set to train a behavior cloning model, and obtaining an initial driving model; and determining to-be-optimized driving skill indexes such as safety, efficiency and comfort according to the performance of the initial model, and constructing diversified training scenes in the virtual training environment. And performing reinforcement learning training on the initial model through a deep reinforcement learning algorithm by using a reward function containing the indexes to obtain an optimized driving model. The optimized model is tested in the test scene set, and when the safety index, the efficiency index and the comfort index all reach preset threshold values, training is completed. By combining behavior cloning and reinforcement learning, the comprehensive performance of the intelligent driving model in the aspects of safety, efficiency, comfort and the like can be effectively improved.

The invention discloses an unmanned aerial vehicle adaptive path planning method and system based on double-layer search, and relates to the technical field of unmanned aerial vehicle inspection. At present, the unmanned aerial vehicle is poor in transformer substation inspection safety and autonomy and low in efficiency. The method comprises the steps that the position, type and safety distance data of electrical equipment in a transformer substation are collected in real time through a sensor carried by the unmanned aerial vehicle; dynamically calculating a three-dimensional safety distance between the unmanned aerial vehicle and the equipment according to the type and the position of the equipment in combination with the real-time state information and the environment data of the unmanned aerial vehicle; based on the edge computing nodes, generating an optimal security path by adopting a double-layer path planning algorithm; the planned path is sent to the unmanned aerial vehicle, the flight state is continuously monitored, and if a deviation path or a sudden obstacle is detected, a dynamic path adjustment or hovering instruction is triggered; after the task is completed, flight efficiency and safety indexes are evaluated, algorithm parameters are optimized, and system settings are updated. According to the technical scheme, accurate obstacle avoidance, efficient planning, real-time response and adaptive evolution are realized.

The invention discloses a BIM-based bridge high pier construction safety monitoring method and system, and relates to the technical field of BIM, a dust concentration prediction vector P is calculated through an environment feature set T, high-precision prediction of dust diffusion concentration is realized, and the dust distribution condition of a construction site is more prospective. Then, according to the scheme, the dust fall parameter set D is formulated in combination with the dynamic change of the dust concentration, the dynamic dust fall strategy is executed, the spraying scheme can be adjusted according to the real-time environment, the dust fall efficiency is effectively improved, and the problems of water resource waste and uneven coverage existing in a traditional spraying mode are solved. Besides, through a feedback mechanism of a dust concentration error D, an environment safety index ZS is calculated and compared with a preset construction area trigger threshold ST, iterative optimization of a dust fall parameter set D is achieved, it is ensured that the construction environment is always in a controllable range, and potential safety hazards caused by excessive dust are avoided.

The invention discloses a data driving-based route flight safety risk assessment method. The assessment method comprises the steps of obtaining historical QAR data and performing data preprocessing; constructing a flight safety risk index system which comprises a plurality of index events influencing the flight safety in the predetermined flight scene, dividing the index events into a plurality of risk levels, and setting a corresponding risk index coefficient for each risk level; carrying out standardization processing on the risk index coefficient; calculating information entropies of the plurality of index events, and determining weight coefficients of the plurality of index events according to an entropy weight method; and for the flight to be assessed, calculating a flight safety assessment risk value of the flight. According to the method, the QAR data can be comprehensively utilized to establish a safety index monitoring model of complete team operation, an airline and other related parties can be helped to more comprehensively grasp the safety risk of an aircraft in the course operation, and the safety risk in the course operation can be objectively and comparatively evaluated.

The invention discloses a food processing and production intelligent control method and system based on the Internet of Things, and relates to the technical field of food processing and production intelligent control. The system collects data of a supplier index data set and a food processing site in real time by integrating an Internet of Things platform and a sensor set, and preprocesses the collected data; uploading the data to a block chain; a raw material source credibility index, a processing environment fluctuation index, a machine operation consistency index and a packaging integrity index are calculated, a food production safety index is calculated in a summarized mode and compared with a preset production safety threshold value, and the production safety condition is preliminarily evaluated. The method comprises the following steps: extracting traceability data through an interface, calculating a quality traceability index, calculating a comprehensive production compliance evaluation index in combination with a food production safety index, performing secondary comparison with a preset production compliance evaluation threshold, generating a production adjustment and optimization suggestion, ensuring food safety and improving production compliance.

The invention discloses a LoRa-based ship environment multi-parameter intelligent wireless monitoring system, which is characterized in that environment state data and ship operation auxiliary data are acquired through a data acquisition module, and a data processing module is used for preprocessing and standardizing the acquired data to obtain a standardized data frame; the environment safety quantification module calculates a single parameter safety score, a comprehensive safety index and a safety situation level SL, the intelligent LoRa communication module dynamically selects a communication strategy according to the SL and the current link quality and generates and sends a safety data packet, the LoRa gateway receives the safety data packet, redundancy filtering is carried out, receiving metadata is added, a final data record is formed and uploaded, and the LoRa gateway sends the final data record to the intelligent LoRa communication module. And the data center performs visual display and data storage on the final data record, and generates a network optimization instruction based on the network performance data, so that the problem that transmission reliability, real-time performance and low power consumption are difficult to consider in ship environment monitoring is solved, and the intelligent level of ship safety management is improved.

The invention discloses a water environment safety early warning method and system based on the Internet of Things, and relates to the technical field of water environment monitoring and safety alarming, and the main scheme is as follows: firstly, collecting water quality physical and chemical parameters and water quantity parameters of a current detection period, then respectively calculating each subentry index, and calculating the water quality index; comprising temperature, turbidity, conductivity index and the like obtained by physical parameters, dissolved oxygen, pH value, lead content index and the like obtained by chemical parameters, and water quantity index, so as to obtain water quality physical index, chemical index and water environment safety index, and then judge whether to trigger safety real-time alarm or not; and acquiring data of a previous cycle to calculate a historical water environment safety index and an index growth rate, and comparing with corresponding threshold values to judge whether to give a delayed alarm. According to the method, multi-parameter and multi-cycle data are integrated, a comprehensive and accurate judgment basis is provided for water environment safety monitoring, the monitoring accuracy and reliability are effectively improved, and the water environment safety is guaranteed.

The invention discloses a tunnel structure full life cycle damage detection and safety evaluation method, and belongs to the technical field of damage detection and safety evaluation. The method comprises the following steps: completing installation configuration of a mobile detection platform along the axial direction of a tunnel; generating a moving track and a detection point location distribution scheme of the mobile detection platform; controlling the mobile detection platform to sequentially move to each detection section position according to the detection path, and synchronously starting each detection module to perform continuous data acquisition in the moving process; generating a tunnel three-dimensional mathematical model, a lining structure thickness distribution diagram, a heat radiation anomaly distribution diagram and a crack damage distribution diagram; equally dividing the tunnel three-dimensional model into N detection segments according to a preset interval, and counting the lining thickness mean value, the number of heat radiation abnormal points, the crack length density and the damage level of each segment as safety evaluation indexes; statistical analysis and trend judgment are conducted on the safety indexes of all the segments, abnormal segments exceeding a safety threshold value are recognized, and the risk level of the abnormal segments is determined.

The invention relates to the technical field of computer application, and particularly discloses a chemical industry park safety risk assessment method based on an agent model. The method comprises the following steps: constructing a multi-agent collaborative perception framework, and fusing multi-source heterogeneous data of equipment, environment, materials and personnel to form a park panoramic view under a unified space-time reference; establishing an agent behavior model library embedded with the chemical process mechanism and the safety interlocking logic; an intelligent agent decision strategy is optimized through deep reinforcement learning, and multi-target training is carried out by taking a safety index as a reward function; executing dynamic risk deduction and accident chain simulation in a digital twin environment, and quantifying risk probability and consequences in combination with Monte Carlo sampling; and finally, a visual thermodynamic diagram and an interpretable risk assessment report are generated, weak links are identified, and intervention suggestions are provided. According to the technical scheme, dynamic, accurate and prospective evaluation of the safety risk of the chemical industry park can be realized, and the early warning timeliness and the evaluation reliability are remarkably improved.

The invention belongs to the technical field of intelligent hydropower, and discloses a hydropower station operation period dam safety classification and grading early warning system and method, through man-machine collaborative inspection of an anti-interference optical fiber sensor and an unmanned aerial vehicle, in combination with edge calculation filtering, YOLO model detection and multispectral analysis, data acquisition precision and preprocessing quality are improved; dynamic risk assessment is realized by adopting a fusion formula, a finite element and LSTM time sequence prediction, and the limitation of a fixed threshold value is broken through; according to safety index grading early warning, the Mesh ad hoc network and the GIS system are linked, and it is ensured that response is timely and accurate; self-evolution optimization is achieved through digital twin iteration and sensor and equipment health management, the false alarm rate and maintenance cost are reduced, information isolated island is eliminated, the accuracy and reliability of dam safety early warning are improved, and the problems that in the prior art, a dam early warning system is single in monitoring means and insufficient in data analysis, multi-parameter coupling risks are difficult to dynamically evaluate, and the safety early warning effect is poor are solved. And missing report or false report is easy to occur.

The invention relates to the technical field of video recognition, in particular to a construction safety real-time high-precision detection method and system based on environmental characteristics. Arranging a plurality of sensors in a to-be-detected area to collect field environment data in real time; analyzing the illumination change of the to-be-detected area through the light and shadow analysis model, calculating a light-safety misjudgment index, and identifying a potential light misjudgment area; a visual processing model is utilized to analyze the worker video, human skeleton key points are recognized in real time, personnel behavior safety indexes are obtained, and potential dangerous behaviors are detected; detecting local wind speed and turbulence conditions and vibration and resonance phenomena of a to-be-detected area in real time through a local breeze and vibration detection model, and evaluating a local environment stability index; and the safety level of the to-be-detected area is calculated in real time by using the light-safety misjudgment index, the personnel behavior safety index and the local environment stability index, so that multi-dimensional and real-time construction safety risk assessment is realized.

The invention discloses an active and passive safety fusion evaluation weight coefficient determination method and system, and relates to the technical field of automobile safety evaluation, and the method comprises the steps: obtaining active safety data and passive safety data; combining the active and passive data to obtain total sample data; constructing an analytic hierarchy model and a judgment matrix of each hierarchy; calculating a total level sequence of all indexes in the scheme layer to obtain a weight value of each index; performing weighted scoring on each data sample to obtain a total score; constructing a multi-layer perceptron model; and extracting the final weight of each index. According to the method, the vehicle speed after braking of the vehicle active safety system is taken as the collision speed of human-vehicle collision simulation in the passive safety system, and an active safety index and a passive safety index are associated to carry out active and passive safety fusion evaluation; and the index weight coefficient value for fusion evaluation is extracted through an analytic hierarchy process and construction of a multi-layer perceptron model, so that the data accuracy is high, the objectivity is high, and the comprehensiveness is high.

The invention relates to an automatic driving automobile test method based on multi-traffic-participant collaborative hunting confrontation, which comprises the following steps: dividing a test scene into a plurality of space-time resource grids based on scene information, and constructing a conflict event set according to tracks of a tested automobile and traffic participants; defining a conflict degree index, a safety index and passing time; a multi-target speed optimization search model is constructed based on the conflict degree index, the safety index and the passing time, a plurality of traffic participants are utilized to cooperatively surround the tested vehicle, the multi-target speed optimization search model is solved in the cooperative surrounding process, and a space-time resource occupation table is obtained; carrying out collision detection, and if no conflict exists, generating an optimal track of all traffic participants; otherwise, returning to execute collaborative hunting; and obtaining a comprehensive index of the tested vehicle when the traffic participant executes the optimal trajectory, and carrying out performance evaluation. Compared with the prior art, a more accurate automatic driving test is realized by constructing a complex scene considering the collaboration between traffic participants.

The invention discloses a multi-dimensional multi-layer intelligent identification algorithm evaluation index system construction method, and belongs to the technical field of intelligent perception algorithm performance verification evaluation, and the method comprises the steps: constructing a traditional evaluation index based on the target identification precision, the target omission rate, the target false alarm rate and the time delay; constructing a generalization evaluation index based on the target adaptability index and the environmental adaptability index; constructing a safety evaluation index based on the confrontation sample safety index and the poisoning sample safety index; constructing a learning ability evaluation index based on the inversion learning ability index, the few-sample learning ability index and the incremental learning ability index; based on a traditional evaluation index, a generalization evaluation index, a safety evaluation index, a learning ability evaluation index and corresponding weights, a comprehensive evaluation result is obtained through calculation, so that the problem that a performance verification evaluation method of an intelligent sensing algorithm at the present stage is inaccurate, incomplete and unreliable in evaluation is solved.

The invention provides a traffic tunnel preventive monitoring and risk early warning system based on an unmanned aerial vehicle group, and relates to the technical field of traffic tunnel safety monitoring, and the method comprises the steps: laying reference mark points on the inner surface of a tunnel as monitoring nodes, collecting the crack width, surface temperature and deformation of each node through the unmanned aerial vehicle group, and calculating the crack width, surface temperature and deformation of each node; and constructing a multi-dimensional feature vector and calculating a space-time weighted distance in combination with the space coordinates and the timestamps. And adjusting a neighborhood search radius according to the tunnel curvature, identifying a risk region through a density clustering algorithm, and marking unclustered abnormal nodes as isolated risk points. And dividing risk levels based on comparison of the structure safety degree index and a dynamic threshold value, and registering a result with the BIM model to realize three-dimensional visualization. Trend early warning is triggered for an area with monotonically decreasing monitoring safety index for three continuous times, full-process automatic monitoring from data acquisition, intelligent analysis to risk early warning is realized, and the accuracy and timeliness of tunnel structure health monitoring are remarkably improved.

The invention discloses a BIM-based building construction monitoring method, and the method comprises the steps: arranging a data collection device at a construction site, and obtaining multi-source data, such as structural members, construction machinery, spatial positions of operators, construction states, safety indexes and environmental parameters; automatically mapping multi-source data to a BIM three-dimensional model through an adaptive coordinate transformation and semantic recognition algorithm, matching the multi-source data with construction plan nodes, and generating a BIM construction state model reflecting a field state in real time; the system automatically compares the model with a construction plan model, identifies a state and calculates deviations such as progress, position, size, quality and safety; the method comprises the following steps: displaying deviation parts and potential safety hazards in a BIM interface in a highlight manner, superposing rectification suggestions, issuing a construction adjustment instruction confirmed by a manager to a field terminal, and storing monitoring, comparison, early warning and processing records in a database according to a time sequence to form a digital file; and real-time intelligent monitoring and visual management of construction progress, quality and safety are realized.

The invention belongs to the technical field of hydraulic engineering, and provides a dam monitoring method based on data anomaly detection, which comprises the following steps: S1, acquiring multi-source heterogeneous data generated in a dam operation process, and performing time synchronization, data cleaning, missing value filling and normalization processing on the multi-source heterogeneous data; s2, aiming at the preprocessed multi-source heterogeneous data, extracting feature vectors of data modals respectively, and carrying out preliminary splicing and fusion on the feature vectors to form an initial multi-modal feature sequence; s3, inputting the initial multi-modal feature sequence into a mixed space-time diagram attention network, and outputting a high-dimensional space-time feature sequence containing dynamic relevance; s4, inputting the high-dimensional spatial-temporal feature sequence into a multi-view Transform decoder for prediction, and outputting a prediction time sequence of the key safety index; and S5, based on the prediction time sequence of the key safety indexes, comprehensively evaluating the future overall safety state of the dam through a multi-index decision evaluation model.

The invention discloses an abnormality monitoring method and system for a feed production control system, and relates to the technical field of abnormality monitoring. During operation of the system, safety data of raw materials, production processes and feed finished products are collected by deploying various high-precision sensors, analysis equipment and sensors; a first data set, a second data set and a third data set are obtained, the collected data are preprocessed and calculated to obtain a production safety index Slaq, the production safety index Slaq is compared with a preset first qualified threshold value M and a preset second qualified threshold value N, a grade strategy scheme is obtained, abnormal conditions in the production process are detected in real time, potential problems are recognized, and the production safety index Slaq is obtained. According to the abnormal condition identified by the abnormal detection module, preliminary emergency treatment measures are executed, including adjusting production parameters or starting an emergency plan, and when the abnormal detection module or the abnormal response module identifies an abnormal condition, the abnormal condition is fed back to an operator in time in a sound-light alarm or mobile terminal notification mode.

The present invention discloses a multi-dimensional information evaluation method and system for an enterprise security index, and relates to the technical field of data analysis. The present invention retrieves business data from an enterprise database to obtain current real-time business project generation data; constructs a business project data set in combination with data normalization processing; performs business feature transformation based on the business project data set, performs development feature value analysis on the business project data, and constructs a business project development feature curve; retrieves business project data feature prediction curve data, performs comparative analysis in combination with the current real-time business project development feature curve, and performs abnormal feature point judgment and classification; performs abnormal risk analysis on local adjacent feature points in combination with feature point classification results; performs risk assessment on the current business project in combination with the abnormal risk analysis data of each local adjacent feature point; visually outputs the risk analysis data of the corresponding business project of the enterprise, and provides comprehensive feedback on the risk assessment results.

The invention relates to the technical field of mine safety monitoring, solves the technical problems that miner behavior monitoring is insufficient, equipment fault diagnosis is not accurate enough, and dangerous condition judgment is not intelligent, and particularly relates to a mine safety production behavior monitoring and data analysis system. Acquiring miner position information, miner behavior image information and equipment operation state data in a mine; the behavior analysis module is used for analyzing the miner behavior image information based on an image recognition algorithm to obtain miner behavior safety indexes; the fault judgment module is used for judging whether the equipment breaks down or not by using a fault diagnosis algorithm; the danger judgment module judges whether a dangerous condition that the miner gets close to the fault equipment exists or not, and a dangerous condition index is obtained; and the safety evaluation module comprehensively evaluates mine safety production behaviors. The intelligent level of miner behavior monitoring is improved, the accuracy of equipment fault diagnosis is enhanced, and danger association analysis between miners and equipment is realized.

The invention provides a disassembly and reformation scheme optimization method and device considering the influence of space limitation. The method comprises the following steps: acquiring stress data and displacement data of a target disassembly and reformation part in a disassembly and reformation scheme; and weight factors of the stress data and the displacement data are determined based on the bearing capacity and the deformation capacity of the balance structure of the target dismounting and changing part. And generating a safety index based on the stress data, the displacement data and the weight factor. And determining a plurality of evaluation factors of the disassembly and reformation scheme in the space limitation corresponding to the target disassembly and reformation part according to the disassembly and reformation scheme. And determining a spatial influence index based on the corresponding relationship between each evaluation factor and the evaluation grade. And determining a target function based on the safety index, the space influence index and the target disassembly and change part. And with minimization of a safety index and minimization of a space influence index as a target, optimizing the disassembly and modification scheme according to a target function. According to the method provided by the invention, the disassembly and modification scheme can be comprehensively optimized under the condition of considering the influence of space limitation.

The present invention relates to the technical field of remote monitoring of emulsion explosive filling, and specifically to a remote monitoring control system and method for explosive filling based on the industrial Internet of Things. The method comprises: obtaining emulsion explosive production data in the emulsion explosive production process, obtaining thermal explosion performance data of emulsion explosive; being used to compare and analyze the emulsion explosive production data based on the emulsion explosive production index to obtain the emulsion explosive production safety index; calculating the thermal explosion performance index based on the thermal explosion performance data, judging whether the emulsion explosive meets the filling conditions based on the thermal explosion performance index, and if so, generating a signal that the emulsion explosive meets the filling conditions, obtaining the emulsion explosive production safety index, and analyzing and processing the emulsion explosive production safety index to generate a production safety warning signal; being used to receive a signal that the emulsion explosive does not meet the filling conditions, and generating a filling warning signal. The present invention can improve the production efficiency of emulsion explosives and reduce the safety risks of emulsion explosive filling.