478 results about "Decision matrix" patented technology

The invention discloses an AI agent design platform decision optimization method based on big data, and particularly relates to the field of artificial intelligence, comprising multi-modal data sensing layer construction, a streaming feature calculation engine, a dynamic index fusion center and an adaptive decision matrix. According to the method, accurate synchronous monitoring of the utilization rate of hardware resources and dynamic collaborative optimization of heterogeneous computing units are achieved, and the resource scheduling efficiency in a complex computing scene is remarkably improved; knowledge system degradation caused by long-term learning is effectively prevented, and the continuous reliability of a cognitive system is ensured. The provided multi-dimensional decision credibility verification system is fused with interpretability penetration analysis, environment coupling modeling and logic drift detection technologies, the limitation of a traditional single credibility index is broken through, the risk prediction and fault-tolerant capability of the decision process is remarkably enhanced, and a full-dimensional safety decision guarantee system is constructed for an intelligent agent.

The invention relates to the technical field of network routing, and discloses a dynamic load balancing method and system for network routing, and the method comprises the steps: carrying out the periodic sampling and feature extraction of a processing load of a network node and a transmission load of a network link, and obtaining a load state vector and a network load degree; constructing a load balancing decision matrix, and performing comparison operation on the load balancing decision matrix and the multi-stage load threshold to obtain a load optimization instruction sequence; identifying a high-load source node set and a low-load target node set based on the load optimization instruction sequence, and performing load prediction modeling to obtain a task migration scheme; performing path optimization of reinforcement learning based on the task migration scheme to obtain a multi-routing path set; and carrying out load partition clustering and routing strategy distribution on the multi-routing path set to obtain a regional routing mapping table, and the method reduces the network delay, improves the resource utilization rate and realizes dynamic load balance.

The invention relates to the technical field of data exchange, in particular to a data exchange system of a nested tag structure, which comprises a data carrier adaptation module, a structure analysis module, a field mapping module, a logic driving module and a script generation module. According to the method, carrier identifiers are generated through comparison of MIME types, file header features are positioned through pattern matching, the recognition accuracy and adaptability are improved, a nesting relation is calculated through depth-first traversal of file paths, node sequences are generated through analysis in combination with a DOM tree, the label analysis efficiency is optimized, and a mapping table is generated through algorithm matching of field identifiers and database codes; the method comprises the following steps: dynamically constructing a field mapping relation, reducing manual configuration, constructing a decision matrix based on a mapping table, executing logical operation to generate a status bit and triggering a threshold write-in instruction, enhancing strategy dynamic adjustment capability, reducing semantic ambiguity through multi-algorithm collaboration and a dynamic decision mechanism, improving data exchange robustness, and supporting real-time state response. And the transmission timeliness of the industrial Internet of Things is ensured.

The invention provides an infrared thermal imaging building facade defect intelligent diagnosis method based on multi-modal fusion, and relates to the technical field of building detection.The method comprises the steps that infrared thermal imaging, visible light images and three-dimensional point cloud data are synchronously collected to construct a multi-modal data set; segmenting a hot spot region by adopting an improved morphological watershed algorithm and extracting contour and temperature features; recognizing a surface crack and peeling area based on a double-branch attention network to generate a texture defect feature map; curvature distribution and thermal deformation gradient are calculated through space registration constrained by a heat conduction equation; multi-source features are fused to calculate a hot spot form dispersion TSMD and a structure risk quantification factor SRQF; constructing a defect risk decision matrix to output defect types, positions and risk levels; and superposing the diagnosis result to a BIM model to generate a three-dimensional visual report and predicting a thermodynamic evolution trend. The multi-modal data collaborative analysis is realized, the defect risk is accurately quantified, and the problems of poor anti-interference performance, inaccurate segmentation and large registration error of a traditional method are solved.

The invention provides a multi-source heterogeneous distributed computing power fusion scheduling method and system, and the method comprises the steps: carrying out the multi-dimensional collection and preprocessing of heterogeneous scheduling source data, generating a scheduling joint data set, carrying out the key demand prediction processing of a task set based on a preset quantum channel topology, generating a key pre-distribution matrix, and carrying out the key pre-distribution matrix, and generating a real-time task queue according to a task set of the scheduling joint data set, and performing multi-target matching degree calculation processing on the key pre-distribution matrix and the real-time task queue based on a dynamic priority quantification model to generate a distribution decision matrix. Then adaptive key supplement and migration path optimization processing are performed on the allocation decision matrix to obtain a migration task set, and finally hidden node resource prediction and preset quantum channel topology updating are performed on the migration task set, so that the rationality of key allocation is improved, the problem of compatible conflicts of resource scheduling is solved, and the resource scheduling efficiency is improved. Therefore, the task migration success rate is improved.

The invention discloses a multi-source heterogeneous data fusion pipe network intelligent scheduling decision-making system, which comprises a multi-modal data acquisition cabin module, a space-time alignment fusion center module, a digital twin deduction cabin module, a self-adaptive decision-making matrix module, an elastic execution feedback chain module and a credibility tracing platform module, the multi-modal data acquisition cabin module comprises a heterogeneous protocol analysis unit, an unstructured processing engine and an edge preprocessing mechanism, and the space-time alignment fusion center module comprises a space-time reference mapping engine, a federal learning cleaning tower and a dynamic semantic association library. The problem that data of a traditional system cannot be effectively integrated is solved, fusion of multi-source heterogeneous data is achieved, data islands are broken, the response speed is increased, the dynamic response capacity is enhanced, in addition, decision making efficiency and accuracy can be improved, decision making intellectualization can be enhanced, optimal configuration of pipe network energy efficiency can be achieved, and the system is suitable for popularization and application. And the energy efficiency of the pipe network is greatly optimized.

The invention relates to the technical field of charging pile group load intelligent regulation and control, and particularly discloses a charging pile group load intelligent regulation and control method and system based on dynamic power equalization, and the method comprises the steps: collecting the oil temperature of a transformer in real time, calculating a total output power threshold value, and correcting the actual output power of a charging pile through combining the line loss and the equipment aging degree; a multi-dimensional load regulation and control decision matrix including user priority, equipment health degree and power regulation sensitivity is established, a dynamic power strategy is allocated through matrix weight, power allocation is inclined to high-priority users and health equipment, emergency power allocation and progressive readjustment during newly-added demands are supported, and the power regulation and control efficiency is improved. Impact of sudden power change on a power grid is avoided, system stability is ensured when newly added loads are accessed, refined intelligent regulation and control of loads of the charging pile group are finally realized, transformer safety, charging efficiency and user experience are improved, and power grid stability and resource optimization configuration are ensured.

The invention discloses a fine-grained access control method and system based on risk identification, and belongs to the technical field of information security. According to the method, user subject attributes, behavior attributes and system environment attribute information are collected in real time, a standardized decision matrix is constructed, an interval type-2 fuzzy set (IT2FS) is used for conducting fuzzy modeling on the attributes, and an upper membership matrix and a lower membership matrix are generated. And calculating the dynamic weight of the attribute index in combination with a CRITIC method, introducing a time decay factor to dynamically correct a risk score through an improved TOPSIS method, calculating the Euclidean distance between an access request and a positive / negative ideal solution, and generating a normalized risk closeness degree. And based on the risk score and a preset threshold value, dynamically matching a hierarchical permission strategy, and adopting a static rule and a priority coverage mechanism to eliminate permission conflicts. According to the method, multi-dimensional risk assessment and dynamic weight adjustment are fused, the problems of insufficient real-time performance, subjective weight dependence and weak uncertainty processing capability in a traditional method are solved, the accuracy and security of access control are remarkably improved, and the method is suitable for scenes with high security requirements such as cloud computing and finance.

The invention discloses a grassroots society digital governance method and system, and the method comprises the steps: collecting multi-source heterogeneous data in real time through an Internet of Things sensing network disposed in a grassroots community, and generating a standardized multi-mode sensing data flow; based on the multi-modal perception data stream, outputting a time-space associated cleaned data set; inputting the cleaned data set into a multi-scale space-time encoder, and generating a feature tensor containing regional hotspot distribution and a risk propagation path; based on the feature tensor, constructing a dynamic risk knowledge graph, and outputting a decision matrix including a risk level and an optimal intervention path; inputting the decision matrix into a strategy optimization engine to obtain a hierarchical governance instruction set; and on the basis of real-time governance feedback data after the hierarchical governance instruction set is executed, a conflict instruction in the strategy chain is autonomously corrected through a fuzzy reinforcement learning algorithm. By utilizing the embodiment of the invention, an efficient, intelligent and traceable digital governance scheme can be realized, so that the social governance efficiency and the service quality are improved.

The invention provides a machine vision defect real-time detection and classification method and system based on deep learning, and relates to the field of machine vision detection.The method comprises the steps that regional enhancement weights are determined by calculating local entropy and gradient direction consistency, and regional self-adaptive enhancement is carried out; establishing a feature transfer sequence and progressively fusing features; generating and correcting a defect area probability distribution diagram; and constructing a dynamic decision matrix to calculate a comprehensive score for defect grading. According to the method, the defect detection accuracy under a complex background can be improved, false detection and missing detection are reduced, and real-time defect positioning and accurate classification are realized.

The invention provides a dynamic scheduling management method and system for spaceflight measurement and control task resources, and relates to the technical field of spaceflight measurement and control resource scheduling. Defining a task feature vector according to the time sensitivity, the resource demand intensity and the coupling degree between the tasks of the tasks; satellite health parameters, equipment working conditions and environment data are collected in real time through a satellite-ground Internet of Things terminal, and digital twin bodies are constructed; performing priority ranking, resource matching and conflict resolution mechanism on the tasks according to the task feature vectors, performing global optimization scheduling on the spaceflight measurement and control tasks, and executing cross-domain two-stage scheduling during global optimization scheduling; based on bitmap decision matrix calculation, weighting calculation is carried out in combination with task matching values and conflict depths, the priority of conflict tasks is dynamically adjusted, conflicts generated in the scheduling process are dynamically eliminated, and the method can be applied to resource scheduling and optimization of low-orbit satellites and ground stations.

The invention relates to the technical field of intelligent transportation control, in particular to a tomato transportation speed self-adaptive adjustment method based on path road condition feedback, which comprises the following steps: acquiring road images, vibration waveforms and altitude data through a multi-source sensing unit, and constructing real-time road condition information; identifying a driving mode and extracting a corresponding bumping parameter; detecting the maturity grade of the tomato by combining multispectrum and thermal imaging, and querying a maturity-compressive strength corresponding table to calculate a cargo damage threshold value; constructing a dynamic mapping model under multiple working conditions, predicting vibration response and converting the vibration response into equivalent pressure; comparing the equivalent pressure with a damage threshold value to obtain a safety margin, constructing a speed adjustment decision tree and generating a maximum allowable speed value of each road section; and dynamically generating a segmented variable-speed control instruction based on the speed decision matrix, and controlling a throttle valve and a braking system to cooperatively change speed. The method has the advantages of accurate working condition identification, dynamic fruit adaptation, closed-loop speed regulation and control and the like, and is suitable for fine speed control of a high-sensitivity fruit and vegetable transportation scene.

The invention relates to the technical field of data analysis, in particular to a road traffic intelligent operation and maintenance method and system based on multi-source data fusion, and the method comprises the steps: constructing a three-dimensional model through a credibility calculation module by integrating three types of factors, namely a road environment, an equipment state and historical verification, generating a dynamically updated credibility index, and correcting a weight in real time based on feedback; the problem that the reliability of the multi-source data fluctuates dynamically due to the influence of environmental interference, equipment aging and historical deviation is solved; the road feature conflict resolution module maps to feature dimensions corresponding to road topology conflict features through dynamic credibility indexes, constructs a road conflict decision matrix, and fuses and generates a road anomaly decision set with credible labels according to the feature dimensions; the resource scheduling module is used for dividing three-level response areas based on credible indexes, executing accurate scheduling of triple constraints in combination with device priorities and credible labels and generating a resource-decision association graph, and self-adaptive optimal configuration of limited maintenance resources according to data reliability is achieved.

The invention discloses an Internet of Things test platform adaptation method, system and device based on post quantum cryptography. The method comprises the following steps: constructing a quantum threat perception model; establishing a multi-dimensional adaptive decision matrix, and generating a candidate post-quantum cryptography algorithm set; dynamic injection of a cryptographic protocol is executed, and lossless replacement of a communication layer encryption suite is achieved; deploying a runtime performance monitoring engine, and collecting encryption and decryption delay, memory occupancy rate and electromagnetic radiation characteristic data in real time; and constructing an algorithm switching decision tree based on reinforcement learning, and when a quantum attack feature or an equipment performance threshold is detected to break through, triggering cryptographic algorithm dynamic migration. A quantum threat perception driven Internet of Things security adaptation system is constructed, and by introducing a dynamic password switching mechanism, a hardware acceleration optimization scheme and a trusted execution environment, the security upgrading problem of traditional Internet of Things equipment in the post-quantum era is solved. According to the invention, the execution efficiency of the PQC algorithm of the resource-constrained equipment can be improved, and the key updating delay is reduced.

The invention relates to the technical field of communication, in particular to a dynamic priority dual-mode communication fault-tolerant switching method and equipment, and aims to solve the problems of communication interruption and service quality reduction when a main link is degraded in a complex electromagnetic environment. According to the method, a dynamic priority evaluation model and a dual-mode protocol cooperation mechanism are constructed, throughput, bit error rate, delay jitter, task semantic priority and aging constraint data of a main link and a standby link are collected in real time, a priority weight and a communication mode decision matrix are generated through combined modeling, an atomization switching instruction is executed according to the priority weight and the communication mode decision matrix, and a communication mode is switched. And completing path reconstruction and resource redistribution. The system further introduces link health prediction, a resource reservation pool and an online learning feedback loop, and supports fault pre-judgment, key task preemption and model adaptive optimization. According to the method, the limitation of a static threshold is broken through, the task semantics and channel state joint scheduling is realized, and the communication reliability, the service continuity and the intelligent fault-tolerant capability of a distributed system in a high-risk scene are remarkably improved.

The invention provides a multi-agent cooperative processing system and method for multi-scene legal complaint consultation, and relates to the technical field of artificial intelligence, and the method comprises the steps: receiving a legal or complaint consultation text inputted by a user, and generating structured consultation request data; automatically generating an evidence obtaining template, and forming a standardized evidence obtaining data packet; constructing a demonstration tree and outputting a multi-scheme decision matrix; performing multi-objective optimization by combining the success rate, the timeliness, the cost and the regional qualification index, and outputting a comprehensive recommendation result; generating a knowledge unit set, and updating a regulation index and agent prompt template library; predicting a re-complaint risk and generating a remedy and optimization suggestion; and outputting review result data, and reversely updating the review result data to the agent configuration strategy in the step of the knowledge construction module. According to the method and the device, the technical targets of multi-agent cooperative processing and dynamic optimization management in the whole legal complaint consultation process can be realized, and the technical effects of improving consultation processing efficiency, enhancing scheme recommendation accuracy, reducing manual participation cost and ensuring data compliance and risk controllability are achieved.

The invention relates to the technical field of network resource allocation, and discloses a network node resource dynamic allocation method and system. The method comprises the following steps: collecting multi-dimensional resource use data of network nodes and executing preprocessing to obtain a standardized resource monitoring matrix and a topological entropy weight matrix; performing nonlinear topology perception calculation to obtain a resource demand prediction matrix; based on the resource demand prediction matrix, performing sectional progressive allocation decision on the network node resources to obtain a first resource allocation decision matrix; performing chaos disturbance fine tuning on the first resource allocation decision matrix to obtain a second resource allocation decision matrix and a resource locking time table; and performing network node resource multi-level scheduling based on the second resource allocation decision matrix and the resource locking time table, and generating a resource execution result log and performance monitoring data. According to the invention, the prediction accuracy of the real resource demand of the network node is improved, and a differentiated, multi-level and smooth transition resource scheduling execution strategy is realized.

The invention belongs to the field of data storage, and discloses a frequency access data storage migration method and device for big data, and the method comprises the steps: obtaining a data access log, carrying out the preprocessing of the data access log, and obtaining a time window statistical matrix; carrying out timeliness attenuation weighting calculation on a plurality of data access times in the time window statistical matrix to obtain a weighted access frequency vector; obtaining a multi-dimensional feature matrix based on the weighted access frequency vector; clustering the feature vector of each time window slice in the multi-dimensional feature matrix through a Gaussian mixture model to obtain a clustering tag vector; inputting the clustering label vector into an access frequency prediction model to obtain an access frequency prediction value of the data in a future preset time step; the migration decision matrix is determined based on the access frequency predicted value and the data storage cost parameter, data migration is carried out based on the migration decision matrix, the data migration complexity can be reduced, and the access speed and the storage cost are balanced.

The invention relates to the technical field of industrial platform data analysis, in particular to a renewable resource recovery data management system based on the Internet of Things, which comprises the steps of synchronously acquiring weight, spectrum and microwave characteristic data of a tested resource through a data acquisition module, executing moisture weight decoupling operation by utilizing a resource matching module, and performing data analysis; then, an inventory evolution module tracks performance loss of resources in real time by using an evolution model of an integrated long and short-term memory network, generates a resource attenuation weight, and constructs a two-dimensional decision matrix containing scheduling priority and preprocessing strength grade instructions in combination with real-time inventory saturation; and finally, the clearance scheduling module executes dynamic pruning and bidirectional optimization through a resource scheduling model, accurately allocates the loading share and the access sequence of each node, and generates a dynamic instruction set containing a delivery sequence. According to the method, feeding homogenization is realized through industrial data multi-dimensional collaborative analysis.

The invention discloses an old people safety monitoring method and equipment based on multi-modal sensor fusion, which are applied to the technical field of data processing, and the method comprises the steps: collecting multi-modal monitoring, environment state and equipment operation data, and carrying out synchronous correction, feature extraction and edge end encryption caching to generate a standardized multi-dimensional feature vector; by means of a dynamic weight multi-modal fusion engine, in combination with adaptive weight distribution and scene association analysis, suspected tumble events are recognized, and then the real tumble situation is confirmed through AI agent voice interaction. Based on an emergency linkage decision matrix, a priority notification and a home custom control rule are fused, an emergency linkage scheme is generated, and finally, based on an edge-cloud collaboration architecture and an adaptive adjustment strategy, the security dynamic protection and continuous optimization of the old people are realized, and the home security of the old people is comprehensively guaranteed.

The invention provides an automatic management system for tower crane ground control, and relates to the technical field of tower crane ground control management. A dynamic multi-dimensional cost model is constructed by collecting spatial obstacle data, environmental risk indexes and time efficiency parameters, and an environmental protection reference value is generated and transmitted to a credit evaluation unit. The credit evaluation unit calculates an equipment health green credit score based on an environmental protection reference value and real-time operation data, and dynamically adjusts a green weighting factor of task allocation. And the task allocation unit constructs an economic decision matrix in combination with the cost parameter and the credit evaluation result, generates an optimal task allocation scheme through priority ranking, and triggers path optimization. And the task constraint unit optimizes the operation path and feeds back the energy recovery benefit to the cost model in real time. The visual interaction unit controls and manages in real time through a three-dimensional thermodynamic diagram and supports manual intervention. According to the system, resource optimization configuration and dynamic risk management and control of tower crane operation are achieved, and the construction efficiency and economical efficiency are improved.

The invention discloses a security check door security check process optimization scheduling system and method based on big data, and belongs to the technical field of intelligent security check, a whole security check area is divided into a plurality of functional areas and mapped into network nodes according to security check process links and physical space distribution, directed edges among the nodes represent passenger flow paths, and the flow paths of passengers are distributed in the network nodes. A weight matrix of edges is calculated through historical flow data, and a security check area network topology model is constructed; fusing ticket business, historical security check and real-time sensor data, constructing a load evaluation model, and calculating a node load index and an influence conduction coefficient; based on passenger ticket business time and historical behavior records, establishing a time urgency index and risk level model, and forming a two-dimensional decision matrix to dynamically divide priorities; constructing and training a congestion propagation model, predicting a future congestion path in combination with priority distribution, and identifying bottleneck nodes; and according to a prediction result, cooperatively implementing a grooming strategy from four dimensions of personnel, channels, equipment and processes.

The invention discloses a drilling construction intelligent decision support and risk early warning method, device and equipment, and the method comprises the steps: collecting drilling sound wave response data, carrying out harmonic analysis to recognize a reservoir resonance node, and constructing a reservoir energy conduction network; a fluctuation propagation path is tracked based on an energy conduction network, and a construction risk area is identified through stress wave spectrum features; porosity and permeability data are extracted for phase difference analysis, a seepage-construction mismatch factor is generated, and a construction optimization window is actively generated; constructing a geological potential energy gradient field to determine an overflow influence range, and performing tensor coupling on multi-dimensional risk factors to generate a three-dimensional construction decision matrix; an optimal construction scheme is generated through energy minimization and risk-efficiency dynamic balance processing; construction parameter response changes are monitored in real time, abnormal parameter combination symptoms are recognized, a grading risk early warning instruction is generated, and intelligent decision support and risk early warning of drilling construction are achieved.

The invention relates to the technical field of tunnel engineering automation and intelligent control, and discloses a TBM tunneling parameter intelligent optimization decision-making system based on an LSTM network, and the system comprises a data collection and preprocessing module which obtains external data and outputs a tunneling parameter sequence; the probabilistic tunneling trend prediction module is used for outputting a prediction expected value and prediction uncertainty; the prospective geological precursor sensing module is used for matching and identifying known risks and outputting alarm events; and determining an optimal tunneling mode by the dynamic risk avoidance decision matrix. When the prediction uncertainty is too high, activating the prospective template driven by the uncertainty to excavate a new precursor template and update the template library; meanwhile, the decision-efficiency relevance evaluation and strategy self-optimization engine optimizes the decision rule according to the actual tunneling efficiency. According to the method, decision making is carried out through quantitative risk prediction and fusion of multi-source information, and a double learning closed loop of knowledge discovery and strategy optimization is established, so that the reliability, the adaptability and the long-term efficiency of system decision making are remarkably improved.

The invention discloses a memory anomaly prediction method based on multi-algorithm decision, and belongs to the technical field of fault prediction. The method comprises the steps that data information generated in the running process of a server is obtained, preprocessing and feature extraction are conducted on error data, a feature set is obtained, and the feature set comprises counting dimension features, component dimension features, statistical dimension features and equipment configuration dimension features; the feature set is input into a trained basic prediction model, a first prediction probability, a second prediction probability and a third prediction probability are obtained, and the basic prediction model comprises a random forest sub-model, a lightweight gradient elevator sub-model and an extreme gradient lifting sub-model; obtaining a first prediction probability value based on the first prediction probability, the second prediction probability and the third prediction probability; inputting the feature set into a depth time sequence extraction network, and obtaining target features based on a feature masking method; inputting the target features into a gating circulation unit to obtain a fourth prediction probability value; obtaining a decision matrix based on the first prediction probability value and the fourth prediction probability value; and inputting the decision matrix into the trained decision optimizer logistic regression model to obtain a memory anomaly prediction result. According to the method, the accuracy and reliability of memory anomaly prediction are improved.

The invention discloses an aspect emotion triple extraction method for a large language model annotation data set, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out the initial triple labeling of a standardized text data set, calculating a cognitive bias index based on a labeling reference set, and generating a data set with a bias label; a dynamic attenuation suppression method is adopted to generate a bias mask matrix, exponential mask enhancement is performed on samples exceeding a preset bias threshold value, and bias mask enhancement representation is obtained; inputting the bias mask enhanced representation into a differentiable grammar parser, calculating a grammar dependency matrix by using an attention mechanism, optimizing a topological structure of the grammar dependency matrix in combination with structural entropy loss, and generating a boundary judgment matrix; and extracting candidate triple embedding based on a boundary judgment matrix, calculating a contradiction coefficient by using a quantum emotion entangled state, and generating a correction triple set. According to the method, the accuracy and availability of emotion recognition results in the fields of medical evaluation and the like are enhanced.

The invention discloses a multi-modal interaction method and interaction system applied to an intelligent robot. The method comprises the following steps: collecting a scene image and processing the scene image into a three-dimensional point cloud and a two-dimensional texture feature; the Gemini Robotics-ER model is used for extracting features, and the vision-language-action model is used for analyzing a language instruction into a sequence capable of being recognized by a machine; and fusing the features to generate an interactive decision matrix, planning a trajectory, calculating kinetic parameters, driving the robot to execute actions and feeding back in real time. The system comprises a multispectral visual information acquisition and preprocessing unit, a Gemini Robotics-ER model processing unit, a natural language instruction analysis unit, a vision-language-action cooperative processing unit, a trajectory planning and dynamics calculation unit and a motion control and feedback unit, and all the units work cooperatively. According to the method and the system, through multi-modal fusion and closed-loop control, interaction accuracy and real-time performance are improved, and industrial scene requirements are met.

The invention discloses a hydropower station income risk measurement method based on hydraulic energy value, which comprises the following steps: S1, constructing a hydraulic energy value dynamic evaluation model, calculating the hydraulic energy available amount under different space-time scenes by combining a distributed hydrological model, converting the hydraulic energy available amount into an economic value, and obtaining a dynamic hydraulic energy value; s2, establishing a multi-risk factor coupling network, and calculating a synergistic effect weight between risk factors; s3, constructing an income risk dynamic prediction model, updating and predicting a power generation income probability distribution curve in a future preset time period in real time, and identifying a key risk source; s4, constructing a risk-income tradeoff decision matrix through a prediction result and a key risk source output by the income risk dynamic prediction model, and generating income optimization schemes under different risk coping strategies; and S5, displaying the dynamic water energy value, the income risk prediction result and the income optimization scheme by using a visual interface. Accurate evaluation of the dynamic water energy value and real-time prediction of the power generation income risk are achieved.

The invention provides a hybrid model fault early warning method and system based on time sequence prediction and fuzzy logic, and the method comprises the steps: collecting operation and maintenance data of equipment in a continuous operation period, constructing an equipment state feature set, carrying out the feature correlation analysis of the equipment state feature set, generating an equipment state potential vector which reflects a dynamic coupling relation between parameters, and carrying out the fault early warning of a hybrid model. Calling a pre-trained fuzzy logic reasoning model to carry out fuzzy rule matching, and generating a fuzzy state set containing a multi-dimensional fuzzy subset; performing space-time correlation processing on the fuzzy state set to generate a multi-dimensional decision cloud picture, and performing spatial reconstruction on the multi-dimensional decision cloud picture through a quantization weight distribution mechanism to obtain an optimized decision matrix; and generating a fault early warning signal containing a fault early warning level and fault position positioning information based on the confidence distribution characteristics corresponding to the fault types. According to the invention, the accuracy and timeliness of fault early warning can be improved, and reliable decision support is provided for equipment operation and maintenance management.

The invention discloses a data acquisition method and device for an edge internet-of-things oiling machine, and relates to the technical field of data acquisition, and the method comprises the steps: carrying out the verification of a sensor through three groups of heterogeneous sensors in combination with a confidence model and an automatic calibration program; edge node clocks are synchronized, a mixed protocol and an FPGA nanosecond-level hardware time scale are adopted, and a quantum time reference and a distributed synchronization enhancement mechanism are combined; collecting and scheduling, constructing a state decision matrix based on network quality, electric quantity and CPU load, dynamically adjusting a sampling rate and a transmission mode, integrating an LSTM model to predict a network state, calculating an abnormal score through an entropy weight method, and triggering a fault-tolerant strategy. According to the method, the data acquisition precision and reliability are remarkably improved, the energy efficiency and the transmission efficiency are optimized, and the system robustness is enhanced.