753 results about "Decision unit" patented technology

The invention relates to the technical field of industrial robots, in particular to an industrial robot walking control system based on obstacle recognition. Comprising an environment sensing unit; the obstacle analysis and decision-making unit is used for processing the multi-dimensional data output by the environment sensing unit based on a deep reinforcement learning framework, accurately identifying static obstacle and dynamic obstacle types, motion trails and interaction influences, constructing a two-dimensional decision-making model of static obstacle avoidance and dynamic obstacle avoidance, and carrying out obstacle avoidance and obstacle avoidance on the basis of the two-dimensional decision-making model. A differential obstacle avoidance strategy is triggered; the path planning unit is based on a dynamic game path algorithm under space-time constraint; and an instruction transceiving unit. Through the multi-modal fusion sensing technology and the adaptive parameter adjustment module, space-time alignment and feature fusion of multi-source data such as three-dimensional point cloud, texture features and vibration spectrum are realized, a high-dimensional environment state model is constructed, and the problem of insufficient data fusion depth in the prior art is effectively solved.

The invention relates to the technical field of tunnel power supply and distribution, in particular to a tunnel modular prefabricated cabin power supply and distribution intelligent substation self-adaptive regulation and control system based on edge calculation. Comprising an edge calculation and AI decision-making unit which is used for realizing rapid acquisition, processing and instant decision-making of tunnel power supply and distribution multi-dimensional data, generating a power supply and distribution adaptive regulation and control strategy by deploying calculation resources and a machine learning algorithm at edge nodes close to a data source, and converting the strategy into an executable regulation and control instruction; a cloud platform collaborative management unit; and an intelligent sensing and internet-of-things unit. According to the invention, hierarchical decision control of the tunnel power supply and distribution system is realized by constructing a hybrid architecture of edge computing and cloud platform collaboration and a priority judgment mechanism; according to the invention, multi-modal data are integrated through the multi-protocol communication link module and the full-scene data fusion analysis module, and data association analysis is realized through Kalman filtering, D-S evidence theory and other algorithms.

The invention relates to the technical field of intelligent scheduling, in particular to an enterprise production real-time monitoring and intelligent scheduling system based on artificial intelligence, which comprises a multi-source heterogeneous data fusion unit, a priority resource coupling decision unit, a bottleneck prediction and tracing unit and a scheduling instruction generation unit, the multi-source heterogeneous data fusion unit collects multi-dimensional data such as equipment vibration, temperature, order delivery time and the like in real time and constructs a joint feature vector, and the priority resource coupling decision unit dynamically adjusts task priority and resource allocation through a dual-channel depth Q network to cope with order insertion tasks and equipment health degree fluctuation. The bottleneck prediction and tracing unit predicts production bottlenecks and traces root causes by using a process dependency graph, a multi-modal fusion model and a causal discovery algorithm, and supports preventive maintenance and dynamic scheduling, and the scheduling instruction generation unit synthesizes a preorder result to generate an adaptive scheduling instruction. And enterprise production equipment utilization rate and production efficiency are improved.

The invention relates to the technical field of agricultural internet of things and environment intelligent control, in particular to a greenhouse environment adaptive regulation and control system based on artificial intelligence, which comprises an environment acquisition module used for acquiring multi-dimensional environment data in real time through a distributed multi-source sensor; the central controller is used for generating an optimized regulation and control strategy; the regulation and control execution module is used for driving execution equipment to carry out regulation; the central controller comprises a multi-source data fusion unit, an AI decision-making unit and a dynamic optimization engine which are respectively responsible for data filtering and fusion, generating an initial regulation and control strategy based on a space-time joint AI model, and reconstructing and optimizing the initial regulation and control strategy through a multi-target optimization algorithm. According to the method, the response real-time performance is improved through multi-source sensing and data fusion, predictive regulation and control and multi-parameter cooperation are achieved through the AI model, balance of energy consumption, growth and carbon emission is achieved in combination with multi-target optimization, and long-term self-adaption and strategy iteration of the system are supported.

The present invention relates to a system and computer implemented method for cost-aware autoscaling of artificial intelligence workloads using predictive queueing models, designed to achieve proactive and economically optimized scaling of computational resources across cloud and edge environments. The invention introduces a predictive queueing-based technique that anticipates future workload congestion by modeling dynamic task arrivals and service times using a stochastic queueing process. A cost estimation unit computes the total projected operational cost of potential scaling actions by integrating real-time infrastructure pricing data, predicted delay penalties derived from service-level objectives, and estimated energy consumption. A scaling decision unit applies reinforcement learning-based optimization to select the scaling action that minimizes total cost while ensuring compliance with latency and throughput constraints. The system includes a hardware-integrated autoscaling controller device comprising a predictive computation processor, cost-decision processor, and scaling actuation interface configured for real-time execution of predictive and scaling operations.

The invention relates to the technical field of artificial intelligence, in particular to a real-time anti-fraud monitoring system and method based on behavior reasoning and sentiment analysis, and the system comprises a multi-modal data collection unit, an edge preprocessing unit, a feature fusion and behavior reasoning unit, a large language model context reasoning unit, a risk assessment and decision unit, and an intervention execution unit. A log recording and federal incremental learning unit; the method has the beneficial effects that the traditional isolated single-mode detection is evolved into an emotion and behavior dual-channel collaborative multi-mode recognition system through millisecond-level coaxial alignment of voice, video and user operation logs; the robustness of dialect, noise and expression shielding is greatly improved through the multi-modal fusion model, so that the cross-scene recognition accuracy is improved by nearly three percent compared with that of a traditional single-voice scheme, and high-sensitivity capture of hidden and emotion control type fraud is truly achieved.

The invention provides a Linux access control system based on attributes, and relates to the technical field of data access control. The system comprises a system monitor module, a data interaction module and a decision unit. The system monitor module collects attributes from a kernel and a user space and writes the attributes into the data interaction module; the access decision unit compiles the access control strategy into an eBPF program and mounts the eBPF program to a corresponding hook; executing the kernel to the hook, and triggering an eBPF program; an eBPF program queries a Flow rule; matching the attribute with the Flow rule, and if the matching is successful, executing a corresponding action; if all the Flow rules fail to match, executing a default action; the system can be expanded during operation, and can be loaded or unloaded based on dynamic loading characteristics and strategies of the eBPF program and the eBPF program during operation of the system, so that the problem that a kernel needs to be compiled in a traditional LSM scheme is solved; the method does not intrude the kernel, is completely based on an eBPF program, does not modify a kernel source code, and can guarantee the stability and compatibility.

The invention provides an unmanned aerial vehicle cluster dynamic topology communication network construction method and system. The method comprises the following steps: acquiring real-time spatial position information and environment obstacle distribution information of each unmanned aerial vehicle; predicting the change trend of the real-time space position information and the environment obstacle distribution information, generating a space trajectory prediction result between the unmanned aerial vehicles, inputting the space trajectory prediction result into a preset path decision unit to predict a collision risk, and generating an optimized flight path instruction; based on the change event of the optimized flight path instruction and a preset communication link quality parameter, screening out stable relay nodes from a preset relay node set, and generating a relay node update list meeting a preset stability condition; and performing topology reconstruction on the communication connection relationship between the nodes of the unmanned aerial vehicle cluster to generate a communication topology reconstruction instruction. According to the invention, cross-domain cooperation of physical path obstacle avoidance and communication topology stability of the unmanned aerial vehicle cluster in a high-speed motion scene is realized, and the cluster anti-interference capability and task continuity in a dynamic environment are significantly improved.

The invention relates to the technical field of intelligent war game deduction, in particular to an intelligent war game deduction simulation system based on a digital twin fusion large model. Comprising a digital twin modeling unit which adopts a dynamic precision adaptive modeling mechanism; a large model decision unit; a deduction execution unit; and a dynamic interaction unit. A dynamic precision self-adaptive modeling mechanism is adopted by the digital twin modeling unit, the model precision can be dynamically switched on demand, the detail simulation demand and the computing resource consumption are balanced, and meanwhile, the sub-second-level dynamic synchronization of a virtual model and a war game deduction scene is realized through a time sequence calibration algorithm; and space confrontation, time sequence decision and rule triggering features are fused through the multi-modal feature analysis module, and the Wargame rule verification module is combined to embed entity performance boundaries and scene rule constraints, so that compliance confrontation decision parameters conforming to Wargame deduction logic can be generated, and the problem of insufficient model precision adaptation and decision constraints is solved.

The invention discloses an intelligent obstacle avoidance system and method for a low-altitude aircraft. Efficient obstacle avoidance is realized by fusing an improved attention mechanism and a binocular vision sensing multi-dimensional analysis model. In the system, a binocular vision perception unit collects double-path images and establishes motion association, a feature enhancement attention unit performs dynamic weighting according to image features, and a multi-dimensional analysis processing unit constructs a three-dimensional feature space to accurately recognize obstacles. The path planning decision-making unit combines the aircraft state and a safety threshold value, an optimal obstacle avoidance path is screened through heuristic search and cost evaluation, and the motion control instruction generation unit converts the path into a posture and speed control instruction to drive obstacle avoidance. According to the invention, the defects of the traditional technology in obstacle identification and path planning are overcome, dynamic or hidden obstacles can be accurately identified in a complex environment, the obstacle avoidance path is efficiently planned, and the operation safety and reliability of the low-altitude aircraft are improved.

The invention relates to the technical field of metal structure detection, in particular to an intelligent special-shaped aluminum plate structure parameter detection method and system based on spectral analysis, and solves the problems of multi-parameter splitting, poor complex structure adaptability and inaccurate risk assessment in traditional detection. According to the method, data are synchronously collected through multi-spectral imaging and line laser scanning, surface component parameters such as the thickness gradient of an oxide film are inverted, the local curvature is calculated based on three-dimensional point cloud, a spiral encryption or grid sparse scanning track is dynamically switched, differential detection of a complex structure and a plane area is achieved, and the detection precision is improved. And then a space correlation model of the oxidation film thickness and the hole deformation quantity is constructed, a structure failure risk area is accurately recognized by combining a three-level risk judgment rule and cross-regional influence analysis, and the system effectively improves the accuracy and detection efficiency of defect recognition of the special-shaped aluminum plate through cooperation of dynamic scanning control, component deformation correlation and a composite structure decision unit.

The invention discloses automatic complete equipment for intelligent predictive maintenance of a primary and secondary fusion circuit breaker. The automatic complete equipment comprises a multi-sensor fusion unit, an edge calculation and analysis module; a parameter interaction module; a predictive maintenance decision unit; the primary and secondary converged communication architecture is used for managing control information and state information on the basis of an IEC61850 (International Electrotechnical Commission 61850) standard; wherein a noise covariance matrix and a feature weight coefficient of the adaptive Kalman filtering health assessment algorithm are dynamically adjusted according to a data quality index and prediction error feedback, and input features of the residual life prediction algorithm based on the LSTM comprise a health index, a change rate and component-level health state information from the health assessment algorithm. Accurate evaluation of the health state of the circuit breaker and accurate prediction of the residual life are achieved, the optimal maintenance strategy is generated, the operation reliability of the circuit breaker is improved, and the maintenance cost is reduced.

The invention relates to the technical field of edge computing, in particular to an intelligent parking unattended vehicle access management system and method based on edge computing. The method comprises the following steps: a vehicle detection positioning unit obtains multi-modal data based on a multi-modal sensor array module, and realizes real-time positioning of a vehicle and real-time updating of a parking space state through a Kalman filtering algorithm in combination with a QBCN information interference variable; the license plate recognition unit completes license plate feature extraction and recognition on the basis of a multispectral imaging technology and a lightweight CRNN model under the condition of abnormal illumination. The edge calculation decision unit combines a real-time rule engine and a space-time anomaly detection algorithm, optimizes a resource allocation strategy, and realizes low-delay decision and localization control; the payment authentication unit adopts a block chain intelligent contract, dynamic rate calculation, multi-factor identity authentication and abnormal payment fusing protection; and the edge cloud collaboration unit realizes model differential updating, cross-domain task scheduling and energy efficiency optimization through federated learning and space-time data compression technologies.

The invention relates to the technical field of intelligent fall detection, in particular to an old people fall detection and early warning system based on multi-modal data fusion, which comprises a dynamic weight fusion unit, a precursor trigger unit, a multi-level decision unit and a verification feedback unit, the dynamic weight fusion unit collects three-axis acceleration, surface electromyographic signals and millimeter wave radar data, multi-source data association analysis is achieved through a double-variable attenuation model and dynamic threshold calibration, and the precursor trigger unit activates electromyographic high-frequency sampling and joint three-dimensional space included angle monitoring when the exercise intensity is abnormal. And the multi-level decision-making unit constructs a tumble probability algorithm based on a random forest model, and performs three-level verification in combination with acceleration standard deviation, ground contact point density and historical mode matching, thereby realizing accurate discrimination of tumble events, providing a high-reliability intelligent solution for safety protection of old people, reducing the risk of tumble injury, and improving the safety of the old people. And the first-aid response efficiency is improved.

The invention relates to a customer service interaction method and system fusing AI digital employees and multi-agent decision, and the method comprises the steps: receiving a multi-mode interaction request from a user, converting the multi-mode interaction request into interaction text data in a unified format, and forwarding the interaction text data to a multi-agent decision unit. And performing intention recognition and sentiment analysis on the interactive text data through the multi-agent decision-making unit to obtain a recognition analysis result. And based on the identification analysis result, guiding the interaction process of the user in combination with the historical interaction content so as to determine the interaction task type, and feeding back the interaction task of the corresponding type to the AI digital employee. And in response to the interaction task, calling the AI digital employee to execute the interaction task according to the rules and knowledge in the knowledge base, and generating a task execution result. According to the task execution result and the recognition analysis result, reply content based on the multi-modal interaction request is generated, the reply content is fed back to the user side, and the flexibility and accuracy of the interaction process are improved.

The invention discloses an AI-based automatic production line scheduling system in an industrial internet, which relates to the technical field of production scheduling and comprises a production plan management module S1, a dynamic scheduling engine module S2, a resource scheduling module S3, a real-time monitoring system module S4, an exception handling center module S5 and a data optimization platform module S6. In the industrial internet, an AI-based automatic production line scheduling system, an X dynamic scheduling engine millisecond response and a multi-agent reinforcement learning engine based on a federated learning architecture realize millisecond response scheduling, each device is used as an autonomous decision-making unit, and dynamic coordination is performed through a distributed Q learning algorithm, so that the vacancy rate of the devices is greatly reduced, and the scheduling efficiency is improved. According to a long-short-term memory network deep analysis model of order delivery cycle compression, emergency order insertion response speed improvement, multi-modal AI quality monitoring, fusion of vibration, thermal imaging and current spectrum, the detection rate is greatly improved compared with a unified sensor, causal reasoning and root cause analysis are performed, a fault causal graph is constructed to position a deep problem, and the average repair time is shortened.

The invention relates to the technical field of transfer robots, in particular to an intelligent warehouse logistics automatic transfer robot. The robot comprises a scraper and a suction cup which are arranged on a robot body, the scraper is provided with specially-designed groove pressing teeth, an arc-shaped groove with a guiding function can be formed in the surface of a box body, and it is ensured that a frost layer effectively falls off along a preset path through the design. The system collects temperature gradient, surface appearance and contact stress data in real time through the integrated multi-mode environment sensing unit, the intelligent decision-making unit establishes a dynamic control model based on the data, and accurate evaluation and self-adaptive processing of the frost layer state are achieved. The whole system constructs a complete closed-loop control chain, the limitation of a traditional defrosting method is overcome, the adsorption stability is kept, meanwhile, the operation efficiency is improved, the system is particularly suitable for the low-temperature storage environment of-25 DEG C and below, and a reliable solution is provided for cold-chain logistics automatic operation.

The invention relates to the technical field of environmental sanitation vehicle dispatching, in particular to an intelligent environmental sanitation vehicle dispatching system based on the Internet of Things. The system comprises an Internet of Things sensing and data fusion unit which collects and preprocesses multi-dimensional data through a multi-source sensor to obtain state data; the intelligent task scheduling and distributing unit is used for receiving the state data and distributing tasks in combination with task priorities and time window constraints; the intelligent analysis and decision-making unit is used for predicting the garbage generation amount, optimizing a path and generating a dynamic scheduling scheme; and the vehicle condition monitoring and fault prediction unit is used for identifying abnormal working conditions and generating early warning. According to the system, task allocation is optimized through multi-source data fusion and an intelligent algorithm, and the problems of unreasonable task allocation and high empty driving rate caused by dependence on manual or simple positioning and insufficient data fusion in an existing mode are solved.

The invention discloses an autonomous decision-making system of a multi-level intelligent agent in an urban area and an operation method thereof. Each end-side decision-making unit broadcasts an equipment state variable to a side-side decision-making unit; the side decision-making unit determines a global reference state and generates a control instruction of each end decision-making unit; after the equipment executes the control instruction, each end-side decision-making unit updates an equipment state variable according to the real-time operation data of the equipment, and when an abnormal event is judged to occur, a side decision-making unit predicts a decision-making variable of each piece of equipment based on a collaborative optimization model of an equipment target and an urban area target; the cloud regulation and control platform predicts a global consistency variable based on the collaborative optimization model of all the side decision units; predicting a regulation and control instruction of each side decision-making unit according to a global dynamic optimization target under a system operation constraint; and the side decision-making unit decomposes the regulation and control instruction into the control instruction of each end decision-making unit, so that the problems that the global cooperative capability of the urban regional integrated energy system is weak and cooperative scheduling is not timely under extreme events are solved.

The invention discloses a laboratory whole-process intelligent management and control system based on AI and Internet of Things fusion, which relates to the technical field of laboratory management and comprises an equipment access unit, a central decision unit and a business application unit. According to the system, a risk model library and a real-time risk map can be constructed through the risk assessment module, accident risks caused by human errors are reduced through the real-time risk map, automatic emergency disposal and personnel behavior compliance monitoring, and when the equipment operation and maintenance module detects that the equipment is abnormal, predictive maintenance can be automatically carried out, so that the downtime is shortened, and the maintenance efficiency is improved. The resource scheduling module can be used for optimizing the inventory and scheduling the equipment, and carrying out global allocation based on an inventory sharing model, so that resource idleness is reduced through cross-courtyard inventory sharing, resource sharing is facilitated, cross-courtyard collaboration is completed, and the situation that data cannot be synchronized due to independent inventory management of each courtyard is avoided; and further, global optimization can be realized.

The invention provides a ship shaft power generation and lithium battery coordinated multi-working-condition energy optimization management system, which is applied to the technical field of ship power systems and energy management and comprises a data acquisition module, an energy optimization processing module and a power regulation and control module, wherein the energy optimization processing module is provided with a prediction unit, a working condition identification unit, a loss evaluation unit and a decision-making unit, the prediction unit is used for predicting a ship load demand in a preset period, the working condition identification unit is used for identifying a ship operation working condition, and the loss evaluation unit is used for determining a life loss factor by combining a prediction result and an energy storage unit state; the decision-making unit generates a regulation and control instruction based on the life loss factor and the state of the power generation unit to realize power distribution of the power generation unit and the energy storage unit; according to the system, multi-working-condition energy consumption requirements of the ship are met, meanwhile, comprehensive optimization of fuel consumption cost and energy storage unit service life loss cost is achieved, the energy utilization rate is increased, the service life of the energy storage unit is prolonged, and the full-life-cycle operation cost of the ship is reduced.

The invention discloses a multi-modal emotion calculation enabling adaptive interactive language learning system and a matching method thereof, and belongs to the technical field of artificial intelligence, and the system comprises a multi-modal emotion real-time perception and quantification module MHAE-Net, a personalized adaptive dialogue and intervention strategy module ADIS-RL, and a virtual dialogue partner interactive interface module EC-NLG. The multi-modal emotion real-time perception and quantification module MHAE-Net is composed of a voice signal acquisition and processing unit, a voice emotion analysis unit, a text emotion analysis unit, a facial expression emotion analysis unit, a physiological signal emotion analysis unit and a multi-modal emotion fusion and decision-making unit. The interactive strategy is dynamically adjusted, the oral anxiety of the language is effectively relieved, the oral confidence is improved, the system adopts the multi-mode emotion calculation technology, the accuracy and robustness of emotion recognition are improved, the targeted interactive strategy is designed for different emotion states, and personalized emotion support and language practice are provided for learners.

The invention relates to the technical field of equipment dust hazard maintenance, in particular to a hardware tool production equipment intelligent prediction maintenance system which comprises a dust cooperative sensing unit, a dust feature library construction unit, a dust weighted decision unit and a maintenance execution unit. The dust cooperative sensing unit collects dust concentration, a vibration energy peak value and a current fluctuation rate through a three-dimensional gridding sensor group, generates a dust-vibration-current baseline comparison table through self-calibration, associates a mapping relation with the dust cumulative gradient and dust feature library construction unit, and distinguishes an equipment operation stage optimization baseline. The dust weighted decision-making unit divides three levels of dust hazard levels, dynamically adjusts a threshold value and calculates a comprehensive influence index in combination with an equipment type weight, and the maintenance execution unit receives a grading instruction and outputs a maintenance scheme containing process compensation parameters, so that the problems of insufficient correlation of traditional maintenance parameters and one-sided risk assessment are solved; the fault early warning accuracy and the maintenance pertinence are improved, and the production continuity is guaranteed.

The invention discloses an artificial intelligence city emergency management system based on space-time prediction, which belongs to the technical field of city emergency management and comprises a data acquisition unit, a data preprocessing unit, a data processing unit, a model optimization unit, an emergency decision-making unit and an intelligent decision-making unit. Large-scale information from various sensors, monitoring systems and historical data is analyzed and processed by adopting a machine learning algorithm, potential risk modes and trends are identified, the information can be quickly screened and integrated by adopting a big data analysis technology, the real-time performance and accuracy of key data are ensured, and the real-time performance of the system is improved. The whole system generates early warning information by using a prediction result, and transmits the information to related departments in time through the emergency decision unit, so that rapid response and effective processing of emergencies are realized, the emergency response speed and accuracy can be improved, an emergency strategy can be flexibly adjusted according to real-time conditions, and the safety of the system is improved. And the requirements of cities for coping with complex and changeable conditions are met.

The invention relates to the technical field of power system automation, and particularly discloses a cross-domain heterogeneous computing power real-time calling and unified scheduling system for the power industry, which comprises a computing power resource global sensing unit, a business demand dynamic modeling unit, a cross-domain unified scheduling decision unit and a task execution and feedback control unit, according to the method, heterogeneous computing power states are collected in real time, task requirements are dynamically modeled, a scheduling scheme is generated based on multi-objective optimization, and task execution monitoring and load balancing are realized by means of closed-loop feedback, so that the utilization efficiency of global computing power resources is improved, and the real-time performance and reliability of power business are guaranteed.

The invention relates to the technical field of distributed computing and artificial intelligence optimization, in particular to an AI optimization system and method for a cloud-edge integrated distributed computing architecture, and the system comprises an environment sensing unit, a global optimization unit, a gradient decision-making unit, a hierarchical execution unit and a compensation effective unit. The global optimization unit aggregates historical data to generate a global data set with a topological mark, a full gradient matrix is generated through deep reinforcement learning, the gradient decision unit dynamically retains a key gradient according to a bandwidth, calculates a residual gradient influence factor and triggers edge re-optimization, and the hierarchical execution unit executes local optimization by taking the residual gradient as a constraint. Global data are fused to generate a precise solution and a space compensation value, the compensation effective unit issues the compensation value to update the edge model, the efficiency and precision under dynamic bandwidth are balanced by the system, the cloud and edge deviation is reduced, and the distributed scene optimization reliability is improved.

The invention relates to the technical field of biological recognition, in particular to a procambarus clarkii disease recognition edge computing system based on an improved YOLOv5 algorithm. According to the technical scheme, the system comprises a data acquisition and preprocessing module, a disease detection model based on an improved YOLOv5 algorithm, a multi-label association classification module and an edge cloud collaborative reasoning module. The definition of the obtained image is improved through the multispectral imaging unit, image details are enhanced through the improved MSRCR algorithm, meanwhile, water turbidity interference and algae shielding influence are eliminated, the false detection rate is reduced, in addition, the extraction capacity of small targets and complex features is improved through the backbone network module, and the detection accuracy is improved. According to the invention, basic disease identification can be realized in an off-network state through the local decision-making unit, the real-time requirement of edge equipment is met, an instant diagnosis result is provided, and the overall diagnosis accuracy is effectively improved in combination with the cloud enhancement unit.

The invention relates to the technical field of bridge engineering structure health monitoring, in particular to a service life prediction method and system for a bridge connection part, and the method comprises the steps: collecting the strain of a grouting sleeve, a reinforcement-grouting interface ultrasonic reflection signal and environment temperature and humidity data through a distributed sensor array; a three-dimensional dynamic void rate model is generated through fusion of a spatial topological mapping algorithm, a correlation function of load circulation and void expansion is established, the dynamic void rate is input into a bond strength coupling degradation model, after strength attenuation is quantized in combination with steel bar corrosion data and bond failure early warning is triggered, stress redistribution is calculated through multi-physics coupling simulation, and the dynamic void rate is obtained. Early warning is taken as a starting node, the actual load spectrum and temperature and humidity data are combined, Monte Carlo sampling is adopted to simulate rigidity degradation and output the residual life probability, the system comprises a multi-source collaborative sensing unit, a micro-void evolution analysis unit and a life prediction decision unit, and the safety control precision of bridge connection parts is improved.

The invention relates to the technical field of concrete curing, in particular to a concrete curing environment control system with intelligent temperature control and humidity adjustment functions. Comprising a data acquisition and storage unit; the regulation and control decision-making unit is used for calculating a deviation value between the current environment and a target maintenance condition through an improved sectional type PID (Proportion Integration Differentiation) regulation algorithm; a temperature and humidity execution unit; and a man-machine interaction unit. According to the invention, through the distributed air temperature and humidity sensor array and the embedded temperature sensor in the concrete, the air temperature and humidity of the curing space and the internal temperature data of the concrete are collected, and the initial setting stage, the final setting stage and the hardening stage are judged based on the internal temperature change rate and the internal surface temperature difference; the PID parameter adjustment interval corresponding to the concrete mark is called in combination with the stage, smooth parameter switching is achieved, in the concrete curing scene, the heat release rate and strength development requirements of different hydration stages can be accurately met, and the situation that a regulation and control strategy is disjointed with the actual concrete curing requirement is avoided.

The invention relates to the technical field of mine disaster monitoring and prevention and control, in particular to a deep mine disaster big data analysis and prevention and control decision-making system. Comprising a data pollution quantification unit used for collecting a multi-source heterogeneous data stream and reference prediction data and carrying out noise pollution quantitative analysis to obtain a data pollution index; the toughness entropy evaluation unit is used for collecting physical risk factors, resolving the basic safety entropy, correcting the basic safety entropy according to the data pollution index to obtain the toughness entropy of the mine system, and judging the toughness entropy of the mine system to obtain a risk level; the anti-fragility decision-making unit is used for dynamically adjusting a risk aversion coefficient according to the risk level, and performing utility function solution on a preset decision to be selected to obtain an optimal decision; and the closed-loop scheduling control unit is used for matching a preset scheduling instruction set according to the optimal decision and the risk level, and generating and executing a hierarchical control instruction. The problem that a traditional risk assessment model fails due to serious pollution of deep mine monitoring data is solved.