1653results about "Logic circuits" patented technology

The invention relates to the technical field of computing power scheduling, and discloses a heterogeneous resource computing power intelligent scheduling method and system. According to the method, real-time state monitoring is conducted on heterogeneous computing resources, and resource state parameters such as the computing unit utilization rate and the memory occupancy rate are obtained; task attributes and user request parameters of the task queue are collected, historical task data are processed based on the genetic algorithm optimization model to execute task demand prediction, and predicted demand parameters are generated. A dependency graph containing resource unit nodes and communication link roadsides is constructed through a resource topology analysis tool, predicted demand parameters are input into a scheduling priority classifier trained by a graph neural network, and an actual scheduling priority is identified. And executing resource conflict prediction based on the priority, inputting task feature vectors into a conflict resolution module of a fuzzy logic decision maker, outputting actual conflict resolution parameters, and finally integrating to generate a scheduling scheme containing a resource allocation sequence and an execution time table.

An output drive circuit includes: a totem-pole output including: a high-side transistor (HST) with drain and source, an output stage power supply voltage applied to the drain, the source connected to the first node (N1); and a low-side transistor with source and drain, a ground voltage applied to the source, the drain connected to N1; and a bootstrap part including a capacitor supplying charge to a gate of HST when on, the charge being charged when HST is off, and one terminal of the bootstrap part connected to N1, the output drive circuit further including: a first transistor (T1) that conducts when HST is to be on, T1 connected between a drive circuit power supply voltage and the gate of HST; and a second transistor conducting when HST is to be turned on, the second transistor connected between the other terminal of the capacitor and HST gate.

The invention provides a BIM intelligent management platform oriented to a whole life cycle of project construction. A building information model, Internet of Things sensing data and a block chain evidence storage mechanism are integrated through a multi-source data fusion technology, and a whole-process data chain of association planning, design, construction, operation and maintenance is associated. The platform adopts space optimization Huffman coding to realize model lightweight, combines a constraint genetic algorithm to optimize a construction path, and applies a bidirectional long-short-term memory network to analyze an equipment state. A three-chain block chain system is reconstructed on the architecture, intelligent association of engineering quantity and payment nodes is realized through cooperation of a main chain, a calculation quantity side chain and an auditing side chain, and mobile terminal offline interaction is supported based on a digital-analog separation technology. The platform covers an intelligent design management unit, a block chain investment management unit, a dynamic correction management unit, a quality safety responsibility tracing unit, an NLP risk management unit and a digital twin operation and maintenance unit. The units achieve cross-system cooperation through a unified data bus, and a closed-loop management architecture covering the whole life cycle of project construction is formed.

The invention discloses a multi-region collaborative power grid planning system and method based on an improved multi-target particle swarm optimization algorithm, relates to the technical field of power system planning, and solves the problems of multi-target coupling and cross-region coordination in traditional power grid planning by constructing an economical, environment-friendly and reliable multi-dimensional target function and introducing a game theory method to quantify a multi-target constraint relation. The system comprises a data acquisition module, a multi-objective optimization model construction module, an improved particle swarm algorithm execution module, a collaborative decision module and a result output module, the improved particle swarm algorithm adopts dynamic adaptive inertia weight, time-varying acceleration coefficient and differential mutation operation, and the convergence speed and Pareto frontier distribution quality are remarkably improved; and the collaborative decision-making module realizes cross-regional parameter interaction and scheme optimization through a hierarchical collaborative mechanism and a fuzzy entropy theory. According to the method, collaborative optimization of calculation efficiency and scheme balance is realized in multi-regional power grid collaborative planning, and technical support is provided for scientific planning of a complex power grid system.

The invention relates to the technical field of intelligent water affairs, in particular to an intelligent water affairs management and control system based on the Internet of Things, and aims to solve the problems that in the prior art, multi-source water quality data and user behavior characteristics can be fused to construct a risk assessment model based on fuzzy logic and a Bayesian network, dynamic assessment of a complex water quality state cannot be achieved, and the risk assessment accuracy is poor. Abnormity cannot be quickly recognized through a similarity matching mechanism, and the response speed and accuracy of early warning are reduced; multi-source water quality data and user behavior characteristics are fused through the water quality intelligent early warning module, a risk assessment model based on fuzzy logic and a Bayesian network is constructed, the method has high nonlinear modeling and uncertainty processing capacity, dynamic assessment of a complex water quality state is achieved, abnormity is rapidly recognized through a similarity matching mechanism, and the risk assessment efficiency is improved. The method improves the early warning response speed and accuracy, combines the countercurrent tracking and GIS technology, accurately locates the pollution source, and enhances the emergency disposal and decision support capability.

The invention provides an urban building group carbon emission real-time monitoring and visualization system and method, and the system comprises a carbon emission data collection module which integrates multi-source data and carries out the denoising through a multi-mode perception sensor; the carbon emission calculation module is used for constructing a dynamic factor library, determining a fixed weight and accurately calculating carbon emission; the data transmission and storage module adopts a distributed account book and fog computing to ensure that the data is safe and efficient; and the carbon emission analysis module analyzes and predicts the carbon emission data by using a deep reinforcement learning algorithm based on transfer learning. The visual display module is used for forming a visual platform based on a digital twinning technology; and the early warning and decision support module is used for setting a dynamic carbon emission threshold value based on risk assessment and providing personalized emission reduction suggestions and decision support. According to the method, the accuracy of carbon emission calculation can be improved, multi-dimensional visual display is provided, and a user can conveniently analyze data and make decisions from different angles.

The invention discloses a building engineering data information intelligent management method, and relates to the technical field of information management, and the method comprises the steps: collecting and aligning construction site multi-source heterogeneous data, forming an engineering alignment data set, extracting construction state features based on the alignment data set, and calculating a comprehensive deviation index; according to the method, the multi-source heterogeneous data and the dynamic twinborn model are fused, real-time monitoring, prediction and optimization control of the construction site state are achieved, the dynamic twinborn scene is constructed, the health state mapping of the key component is generated, and the construction optimization strategy is generated based on the health state mapping and the deviation index and is controlled. The structural deviation is quantitatively analyzed, the optimal construction strategy is automatically generated, model self-evolution and parameter self-optimization are achieved through feedback learning, the safety and intelligent level of constructional engineering are remarkably improved, the energy consumption and the construction period delay rate are reduced, and conversion from static monitoring to active decision making is achieved.

The invention relates to a quality risk assessment method for a highway bridge engineering construction stage and a medium, and the method comprises the steps: determining the quality influence factor indexes of the highway bridge engineering construction stage, carrying out the dimension reduction through employing a PCA method, and constructing a quality risk index system, wherein the quality risk index system comprises a risk factor layer and a corresponding index layer; based on the quality risk index system, adopting a DEMATEL method to calculate the subjective influence weight of each index; based on the quality risk index system, an improved CRITIC method is adopted to calculate the objective weight of each index; based on the subjective influence weight and the objective weight, determining a comprehensive weight of each index by adopting a minimum identification information theory; and based on the comprehensive weight, combining the determined grade standard interval, and based on a variable fuzzy set theory, carrying out quality risk assessment, and outputting a quality risk assessment result. Compared with the prior art, the method has the advantages of improving the reliability of risk grade evaluation and the like.

The invention discloses a marine ranching raising site selection method and system, applied to the field of data processing, and the method comprises the steps: obtaining seabed environment parameters of each target sea area; based on the time sequence change information of the submarine environment parameters, at least obtaining a temperature field distribution result and a biological field distribution result; determining full life cycle information of a to-be-bred object, and determining a corresponding growth demand parameter set; performing space-time matching analysis on the temperature field distribution result, the biological field distribution result and the growth demand parameter set, and calculating a comprehensive raising index corresponding to each target sea area; and obtaining a site selection decision result of each target sea area at least based on the shipping channel information, the ecological protection information and the corresponding comprehensive raising index of each target sea area. According to the marine ranching raising site selection method provided by the invention, scientific and effective marine ranching raising site selection can be realized by comprehensively considering seabed time sequence sensing data, biological image analysis and full life cycle requirements of cultured objects and combining a multi-objective optimization algorithm.

The invention relates to a trade data analysis and logistics resource intelligent optimization method and related equipment, and belongs to the technical field of trade data analysis and logistics resource management. According to the method, trade data is collected, data de-noising and structured processing are carried out, a logistics demand prediction model based on a ChatGLM big language model is adopted to carry out semantic analysis and mining on structured trade data, and logistics demands are predicted; then, according to a logistics demand prediction result and in combination with a specific state of real-time logistics resources, an intelligent optimization algorithm is adopted to generate a logistics resource allocation scheme; besides, by comparing actual logistics implementation data with a logistics demand prediction result in real time, identifying differences and generating feedback signals to adjust and optimize parameters of the logistics demand prediction model, the sensitivity and prediction accuracy of the logistics demand prediction model to logistics demand changes are enhanced. The logistics cost can be remarkably reduced, the logistics service efficiency and quality are improved, and the market competitiveness of enterprises is enhanced.

The invention discloses a switch load balancing method based on fuzzy logic control, and the method comprises the following steps: S1, monitoring a network state in real time through quantum sensing equipment, and obtaining related parameters; s2, transmitting the parameters to a fuzzy logic controller for processing; s3, generating a load distribution strategy according to a predefined rule; s4, transmitting the preliminary strategy to each switch node in the block chain network, and recording related data; s5, verifying the data by using a distributed consensus mechanism of the block chain; s6, dynamically evaluating a node trust value based on an enhanced fuzzy trust model, and selecting nodes meeting requirements to participate in a load balancing game decision; s7, carrying out cooperative calculation on the selected nodes by utilizing a game theory cooperation mechanism, and generating a globally optimized load distribution strategy; and S8, optimizing a load distribution strategy through a quantum genetic algorithm, and performing automatic execution through an intelligent contract. According to the method, the advantages of fuzzy logic, the block chain and quantum computing are combined, and the intelligence and safety of load balancing are improved.

The invention discloses an improved food sensory quality evaluation method and system based on a fuzzy mathematical method, and relates to the technical field of food inspection and detection. Performing a single-factor test on the improved food sample, and performing a response surface test on the basis of the single-factor test; performing comprehensive sensory evaluation on each sample in the response surface test by adopting a fuzzy mathematical analysis method; and physical and chemical indexes of the improved food under the optimal process are determined. According to the invention, the technology of the improved food is optimized, and the physicochemical indexes of the improved food with the optimized response surface are measured; a single-factor test and a response surface test are carried out in sequence, sensory scores are taken as evaluation standards, and factors influencing the quality of the improved food are determined; the optimal formula of the improved food is finally determined through a verification experiment; a fuzzy mathematics comprehensive sensory evaluation model is established for the improved food, and the obtained sensory evaluation result is used for response surface optimization evaluation.

The invention discloses a deep concealed ore deposit detection method based on multi-source geological data fusion, and the method comprises the following steps: 1, carrying out a dynamic multi-source data fusion algorithm: introducing a fuzzy logic algorithm, dynamically adjusting the data fusion weight according to the feature significance of different geological units, and developing a cross-scale feature pyramid network, hierarchical feature extraction and fusion are carried out on different detection depth data; 2, optimizing an intelligent model based on prior knowledge; step 3, constructing a real-time interactive three-dimensional modeling system; 4, establishing an efficient multi-means verification system; and step 5, intelligent integration of the exploration process. According to the method, multi-source data are fused through fuzzy logic dynamic weight adjustment, the mineralization mode and the reinforcement learning optimization model are combined, three-dimensional modeling and multi-means combined verification are updated in real time, and the effects of accurate fusion of the multi-source data, dynamic model adjustment and optimization, efficient and accurate modeling, short verification period and low cost are achieved; and the detection efficiency and scale of the deep concealed ore deposit are improved.

The invention belongs to the technical field of power system risk assessment, and particularly relates to a new energy station broadband oscillation risk redisk assessment method based on multivariate data, which comprises the following steps: performing data acquisition and data preprocessing on a new energy station-power grid coupling system to obtain a historical data set; extracting oscillation characteristic indexes to form index vectors; carrying out oscillation frequency source positioning analysis, calculating a time-frequency spectrum energy function to obtain an oscillation energy spectrum, carrying out main frequency energy track extraction, and introducing a frequency-power cross analysis mechanism to realize accurate identification of an oscillation source device; constructing a source-network coupled transfer function, performing stability margin calculation, and judging the stable state of the coupled system; and based on the index vector and the stability margin, carrying out oscillation risk grade division, and combining a source positioning result to generate an oscillation redisk map. According to the invention, panoramic recovery and risk quantitative evaluation of the broadband oscillation problem of the new energy field station can be realized.

The invention discloses an electrical automation detection device for power distribution automation equipment, and belongs to the technical field of intelligent detection of power distribution equipment. Comprising a multi-source data acquisition module which is used for acquiring multi-dimensional electrical parameters and environment information data of the power distribution automation equipment in real time and realizing comprehensive perception of the operation state of the equipment; the load prediction module is used for executing short-term and medium-term prediction of the equipment load based on historical data and the real-time operation state; the prediction error analysis module responds to the prediction result output by the load prediction module and generates a corresponding error compensation coefficient; the self-adaptive threshold calculation module is used for dynamically calculating an optimal detection threshold adaptive to the current operation condition; and the fault detection and judgment module is used for intelligently evaluating and judging the running state of the equipment based on the optimal detection threshold value output by the self-adaptive threshold value calculation module so as to realize accurate identification and positioning of a fault.

The invention discloses an emergency patient information data matching method and system based on the Internet, and the method comprises the steps: obtaining multi-source heterogeneous data in real time according to an electronic medical record, image data and real-time vital sign data of an emergency patient, and generating a time-synchronized multi-source data set through a self-adaptive data format conversion algorithm; for the multi-source data set, using a feature extraction network to extract illness state features of patients, and generating patient priority scores; adjusting a resource allocation scheme in real time by adopting a medical resource matching algorithm based on a graph neural network according to the patient priority score and the illness state feature, and generating a preliminary medical resource matching result; and for a medical resource matching result, generating a final emergency patient information data matching scheme through a differential privacy protection mechanism by adopting a data security transmission protocol based on homomorphic encryption. According to the embodiment of the invention, the method can meet the requirements of quick, safe and effective processing of data in an emergency scene, and improves the quality and efficiency of emergency medical service.

The invention discloses a full-closed-loop automatic spraying method, system and device based on geometrical characteristic driving and multi-physical quantity feedback and a storage medium. The method comprises the following steps: performing three-dimensional scanning on a workpiece and extracting geometric features; generating a spraying track and a spraying posture for keeping a topological structure based on the geometrical characteristics; motion, process and coating quality parameters are collected in real time through a sensor, and are processed and fused to form a state vector; inputting the state vector into an intelligent control module, performing optimization by combining model predictive control, fuzzy logic and a neural network, and outputting trajectory and process parameters; a control strategy is updated according to the quality deviation of online detection, and closed-loop correction is carried out; spraying is automatically executed in the closed spraying cabin, and unmanned operation of the whole process is achieved. According to the method, complex curved surface spraying can be completed without manual programming, and the method has the advantages of high flexibility, multi-parameter closed loop, self-adaptive optimization and the like.

The invention discloses a forest fire risk assessment method based on interpretable deep learning, and relates to the technical field of forestry disaster prevention, and the method comprises the steps: collecting and preprocessing multi-source heterogeneous data, and generating a multi-dimensional data cube; based on the multi-dimensional data cube, extracting and forming a logical relationship of the forest fire danger, and based on the logical relationship of the forest fire danger, constructing a forest fire danger domain knowledge graph; based on a multi-dimensional data cube as a training sample, a concept bottleneck interpretable deep learning model is constructed and trained, and the multi-dimensional data cube input into the concept bottleneck interpretable deep learning model is mapped to a semantic concept layer defined by a forest fire danger domain knowledge graph. The method comprises the following steps: collecting multi-source heterogeneous data to generate a multi-dimensional data cube, constructing a forest fire danger domain knowledge graph, training a concept bottleneck interpretable deep learning model, mapping input data to a semantic concept layer, and outputting a fire danger grade and a concept activation result.

The invention relates to the technical field of power electronics and power system control, and discloses an intelligent control method and system for a multi-network converter, and the method comprises the steps: collecting the frequency deviation, the power change rate and the voltage amplitude of a power grid in real time, carrying out the filtering and feature extraction, and analyzing the coupling relation, thereby obtaining the parameter coupling strength. And if the intensity exceeds the threshold value, an inertia adjusting signal is generated through a fuzzy logic controller. And calculating a deviation correction value by combining the state evaluation response performance of the converter, and generating an optimization instruction by fusing the frequency deviation. And solving a voltage stability threshold based on the instruction, updating a coupling index, and issuing and executing after verification. And finally, updating the fuzzy rule base by adopting neural network iterative learning according to feedback data, and forming an inertia support frame with self-learning capability. According to the method, the inertia response speed and the operation stability of the multi-network converter system under high-proportion new energy access can be effectively improved, and adaptive cooperative control is realized.

The invention provides a method for rapidly detecting the content of nitrogen oxides in air. The method comprises the following steps: acquiring original signal data of nitrogen oxides in air in a target detection area in real time through a plurality of nitrogen oxide sensors; performing noise reduction filtering processing on the original signal data to generate target signal data; performing signal drift compensation correction on the target signal data based on a preset sensor calibration model to generate standardized concentration data; performing space-time correlation analysis on the standardized concentration data through a nitrogen oxide concentration prediction algorithm model in combination with environment parameter data collected in real time to generate a target prediction concentration value; and comparing the target predicted concentration value with a preset concentration standard threshold value based on a fuzzy logic judgment algorithm, and constructing a detection report. The whole process from data acquisition to report generation is intelligent, and the detection flexibility is remarkably improved.

The invention discloses a reservoir dam body deformation monitoring system, and relates to the technical field of hydraulic engineering safety monitoring. The problems that the data acquisition dimension is single, the real state of a dam body under the complex working condition is difficult to comprehensively present, the depth detection capability on the internal structure of the dam body is lacked, and the deformation trend of the dam body is difficult to accurately predict are solved. Through comprehensive consideration of interaction of a displacement field, a stress field, a temperature field and a hydraulic field, the dam body mechanical state and the deformation trend are accurately evaluated, the internal defects of the dam body are rapidly and accurately detected by means of an elastic wave tomography technology, a key basis is provided for dam body maintenance and reinforcement, the defect identification capability of the system is improved, and the system has a wide application prospect. A dynamic risk assessment technology is adopted, historical and real-time data are combined to update the dam body deformation state transition probability, potential safety hazards are found in advance, a complete closed-loop monitoring system is constructed, all-weather intelligent guarantee is achieved, monitoring accuracy, timeliness and reliability are remarkably improved, and safe operation of a reservoir is effectively guaranteed.

The invention relates to the technical field of cloth bag processing, and discloses a non-woven fabric bag processing parameter regulation and control method and system based on deep learning, and the method comprises the steps: obtaining the real-time rotation speed and tension data of equipment, carrying out the signal filtering and state calculation, obtaining a cloth traction state, carrying out the fuzzy logic processing, and determining the adjustment amount; obtaining a target rotation speed adjustment amount; according to the target rotation speed adjustment amount, threshold judgment and parameter dynamic updating are carried out, a new tension fluctuation index is obtained, fitness evaluation is carried out, and the product specification fitness is obtained; according to the product specification fitness, a dynamic adjustment decision is made, a parameter regulation and control scheme is obtained, risk quantification is carried out, and a production interruption risk level is obtained; and according to the production interruption risk level, risk threshold verification and optimization algorithm correction are carried out, a defective percentage inhibition value is obtained, tension stable signal analysis and global fusion output are carried out, and final processing equipment parameter configuration is obtained. The method can improve the qualified rate of products.

The invention relates to the technical field of vehicles, in particular to an intelligent cabin man-machine cooperative control method and device, a vehicle, a medium and a product. Performing fusion processing on the voice signal data, the visual signal data and the tactile signal data of the current driver by using a preset dynamic weight distribution model to obtain a multi-modal fusion control instruction; and under the condition that the multi-mode fusion control instruction meets a preset sight-voice dual-mode condition, synchronously issuing the multi-mode fusion control instruction to a plurality of target execution devices in the intelligent cabin so as to drive each target execution device to perform cooperative control. Therefore, by constructing a cooperative control mechanism of multi-mode sensing and dynamic weight distribution, the problems that the false wake-up rate is high, the voice and graphical interface cooperative efficiency is low and the interaction mode cannot adapt to a complex driving environment due to a single or separated interaction mode in the related technology are effectively solved.

The invention discloses a multi-source data fusion method based on a water conservancy rain measuring radar, and belongs to the technical field of meteorological observation. Historical rainfall inversion, short temporary rainfall forecast and short-term rainfall forecast are realized based on multi-source data fusion, and a space-air-ground integrated rainfall observation and prediction system is constructed. According to the method, innovative technologies such as multi-source data, multi-parameter inversion, AI intelligent optimization and manual correction are fused, the limitation of a traditional rainfall inversion method is broken through, and the precision and service efficiency of quantitative rainfall estimation are remarkably improved; through multi-modal fusion and full-link integration, the method has significant technical advancement and business value in application in the fields of water conservancy and meteorology.

The invention belongs to the field of knowledge graph updating, and provides a human social knowledge graph adaptive optimization method and system based on uncertain information, and the technical scheme is as follows: associating each piece of data to a corresponding policy label according to a policy index, and constructing an initial multi-dimensional graph structure according to service connection and shared fields; establishing a multi-field weighted membership function and initializing a fuzzy set, and performing comprehensive credibility combination on cross-department entities and relationships; performing cross-department difference degree calculation on the suspicious entities and relationships, and correcting a state transition probability based on a multi-stage Markov process in combination with a cross-department interaction factor and a policy aging factor; carrying out aggregation on the high-confidence nodes and the edges after state transition by applying a multi-hop attention layer; performing versioning management on the optimized graph structure and establishing an incremental updating queue; according to the method, the credible evolution of the knowledge graph tracking nodes and edges in the time sequence dimension can be remarkably improved.

The invention belongs to the technical field of fatigue testing, and discloses a testing machine phase difference optimization method and system and a rubber product fatigue testing machine. The method comprises the steps of presetting a loading path signal, collecting sensor data, and performing feature extraction on the sensor data to obtain phase difference features; taking the parameters of the test piece and the phase difference characteristics as input of a dual-channel lightweight model, and obtaining a phase difference predicted value of each channel; based on a multi-target dynamic parameter optimization algorithm, control parameters of the test machine are optimized and adjusted in combination with the phase difference predicted value and the inter-channel coupling characteristics; the strain distribution of the test piece is monitored in real time, and when the strain unevenness is larger than a preset strain threshold value, channel load redistribution is triggered, and the phase difference is optimized again; according to the invention, test piece characteristics and loading path changes can be sensed in real time, the phase difference prediction error is reduced, the test efficiency is improved, the energy consumption is reduced, and the authenticity of multi-axis load simulation, the phase control precision and the comprehensive performance of the system are remarkably improved.

The invention discloses a building construction safety risk assessment method and system, and relates to the technical field of building construction safety management, and the method comprises the steps: recognizing a plurality of risk factors affecting the construction safety based on multi-source construction data collected at a construction site, constructing a multi-layer fuzzy ANP network based on the fuzzy dependency relationship between the risk factors, and carrying out the construction safety assessment of the multi-layer fuzzy ANP network. Calculating the fuzzy weight of each risk factor; the multi-layer fuzzy ANP network is converted into a Bayesian graph, and initial conditional probability of each risk factor is generated by using fuzzy weight; constructing a failure propagation path set of the risk factors in the Bayesian network, and calculating the propagation probability and accumulated risk intensity of each path in combination with the preset severity level of each risk factor; and in combination with real-time construction data, dynamically correcting the conditional probability and the severity level in the Bayesian network, updating the sorting result of the failure paths and the risk factors, and outputting the risk assessment result of the current construction stage. The real-time assessment of the construction safety risk is realized, and the precision of the assessment result is improved.

The invention discloses a power system distribution robust scheduling optimization method based on optimal configuration, and relates to the field of power system robust scheduling, and the method comprises the steps: carrying out the coupling modeling of a power transmission network extension plan, a DPFC and an energy storage device, and solving a model to generate an optimal configuration scheme; constructing an uncertainty set capable of being dynamically updated, and obtaining a day-ahead scheduling scheme by adopting a dual-stage distribution robust optimization method in combination with the optimal configuration scheme; on the basis of a day-ahead scheduling scheme, a hybrid robust and adaptive model prediction control framework is utilized, and a real-time control parameter of the DPFC and a rapid power adjustment instruction of energy storage are generated through rolling optimization; according to the invention, by combining optimal configuration, dual-stage distribution robust optimization and hybrid robust adaptive model prediction control, collaborative optimization of long-term planning and short-term scheduling is realized, and through real-time model parameter updating and dynamic uncertainty processing, real-time scheduling is realized. The problems that a scheduling scheme is insufficient in robustness, and economical efficiency and real-time adaptability are difficult to consider are solved.

The invention discloses a heterogeneous Internet of Vehicles network selection method based on fuzzy logic and a related device. The method comprises the following steps: acquiring network attributes of candidate networks; inputting the network attribute of each candidate network into a membership function to obtain input membership of low, medium and high levels of the network attribute; performing fuzzy reasoning on the input membership degrees of the low, middle and high levels of the network attribute to obtain an aggregation membership degree of the output level of the network attribute; performing defuzzification on the aggregation membership degree of the output level of the network attribute to obtain a network evaluation value of each candidate network; according to the method and the related device, the network selection problem of a V2I communication scene can be solved, the optimal network can be selected, and efficient information transmission with road infrastructures is realized.

The invention relates to the technical field of computer simulation, and discloses a coal mine underground reservoir environment risk assessment method and system, and the method comprises the steps: constructing a digital twinborn body of a coal mine underground reservoir; establishing a risk assessment index system; collecting monitoring data corresponding to the index system in real time; calculating risk coupling factors among different dimension indexes; according to the risk coupling factor and the real-time numerical value of each index, dynamically adjusting the weight of the index system, and generating a dynamic weight set; calculating a current comprehensive risk level; and extracting physical field parameter values in the real-time monitoring data, inputting the physical field parameter values into a multi-physical field coupling simulation engine for simulation, and outputting a prediction report. According to the invention, dynamic assessment and advanced early warning of the environmental risk of the coal mine underground reservoir can be realized, the risk identification precision and the emergency response capability are improved, and the method is suitable for safety management and control of the whole life cycle of the underground reservoir under complex geological conditions.