173 results about "Short path algorithm" patented technology

The invention provides a river water quality monitoring method based on multi-source remote sensing data, and belongs to the technical field of river water quality monitoring. The method comprises the following steps: establishing an inherent optical characteristic model of a river water body based on a Hybrid water body radiation transmission model, optimizing a calculation path by adopting a shortest path algorithm, and carrying out water body optical component inversion by adopting a quasi-analysis algorithm and a generalized inherent optical characteristic algorithm in combination with a multispectral characteristic enhancement model to obtain absorption coefficients of all components; and constructing a multi-band combination index to realize optical coupling effect decoupling, and applying the fine-tuned water quality parameter inversion basic model to a target river area to output a suspended matter concentration distribution diagram, a chlorophyll concentration distribution diagram and a transparency distribution diagram. The technical problem of low precision of remote sensing inversion of water quality parameters caused by mutual coupling of multiple optical active components in a river water body is solved.

The invention discloses a sea area dynamic communication method and system constructed based on a signal boat relay chain, and relates to the technical field of ocean mobile communication networks. The communication priority index matrix adopts a multi-target particle swarm optimization algorithm to carry out optimal configuration on the deployment position of the signal boat in the target sea area and output a signal boat network initial topology; based on the communication priority index matrix, constructing an optimal multi-hop communication path for each terminal task node, and adopting a bandwidth weighted shortest path algorithm to output a path stability score to perform path selection and updating; and evaluating the link health degree based on the path stability score, and continuously executing a link state monitoring task on all the running communication paths. According to the invention, intelligent deployment and path stable scheduling of the signal boat are realized, the coverage rate, fault tolerance and task adaptability of an ocean communication system are improved, and communication continuity and system flexibility are guaranteed.

The invention provides an expectoration auxiliary control method and device. The method comprises the steps that audio signals of breathing sounds and cough sounds at multiple positions of the chest of a patient are collected through an audio sensor array, and a standardized audio feature data set is obtained through noise reduction, segmentation and feature extraction; a dynamic weighted graph model is constructed, and a random edge sampling algorithm and a parallel batch processing dynamic algorithm are combined to analyze and obtain a sputum viscosity index and a sputum distribution position map; based on the result, mapping the vibration parameter space into an unweighted disk diagram, and obtaining a personalized vibration treatment scheme by using a shortest path algorithm; in combination with the real-time breathing cycle of the patient, working parameters of the sound wave vibrator and the negative pressure suction device are synchronously controlled, and a coordinated and consistent multi-mode treatment execution instruction is obtained; and collecting real-time feedback data in treatment, and dynamically adjusting working parameters through reinforcement learning to obtain an optimized expectoration adjuvant therapy scheme. Intelligent adjustment can be achieved based on the real-time breathing state and sputum characteristics of the patient, the expectoration efficiency is improved, and discomfort of the patient is reduced.

The invention discloses a document generation system and method based on multi-agent collaboration, and the method comprises the steps: understanding and extracting the high-level semantic information of a text input by a user, and dynamically generating a problem to guide the user to define a demand, so as to form a detailed document demand; recognizing the theme and key elements of the document from the detailed document requirements; the optimal sequence of task execution is determined based on a shortest path algorithm, potential conflicts in task execution are predicted and solved through parallel processing and a search algorithm, and optimized task planning is formed through feedback circulation; generating a document outline according to user requirements, retrieving associated contents of document chapters so as to write chapter contents, and performing grammar and format verification to form a preliminary document; defining a document quality evaluation index according to user requirements and industry standards, performing document quality evaluation on the preliminary document, and feeding back the document quality evaluation result to the user; and generating a complete final document and presenting the complete final document to the user. According to the invention, the document generation quality is improved.

The invention discloses a lecturer video automatic generation method based on AI speech synthesis and animation driving. The method comprises the following steps: performing structured analysis on a PPT or a text script through an improved interior point method and an incremental shortest path algorithm; performing semantic grouping by applying a full-dynamic parallel single-link clustering algorithm and generating an enhanced script with an expressive mark; a CosyVoice technology is combined with a low-rank approximation method to generate a high-quality voice data stream; establishing a mapping relation between contents and action expressions through semantic analysis, and generating a complete action expression instruction set; and driving the digital human model by using the msueTalk technology, and generating a final lecturer teaching video through a parallel rendering algorithm. According to the invention, the method achieves the efficient and automatic generation of the education video, remarkably improves the content production efficiency, reduces the production cost, and guarantees the specialty and expressive force of the teaching video.

The invention provides a knowledge base and business system collaboration method under an AI platform, and belongs to the technical field of AI platform digital data processing.The method includes the steps that triple semantic analysis is conducted on query by constructing a multi-level semantic vector representation module, a parallel data retrieval engine is started, vector retrieval, graph reasoning and real-time data pulling are executed at the same time, and the query efficiency is improved; establishing a dynamic confidence evaluation mechanism to evaluate the quality of the data source, executing a weight distribution algorithm based on reinforcement learning, dynamically calculating the weight of the data source according to a query type by adopting a graph convolutional network intelligent routing decision model, and implementing multi-source data fusion and consistency verification to solve data conflicts through a weighted voting mechanism. An intelligent result sorting and filtering system is established, a multi-dimensional evaluation strategy is adopted to output high-quality answers, a continuous learning and feedback optimization loop is constructed, system performance is continuously optimized through active learning and a graph shortest path algorithm, and the technical problem that knowledge base data and a service system cannot effectively and uniformly make decisions is solved.

The invention discloses a construction emergency early warning method and system, and the method comprises the following steps: collecting the three-dimensional coordinates of a constructor, combining a sliding time window with a wavelet packet energy entropy and other indexes, and generating a multi-scale movement disorder index through principal component analysis and fusion; continuously unstable persons are recognized according to the disorder index, after the trajectory of the persons is segmented, a weighted graph is constructed in combination with hidden Markov and building information model environment parameters, and the cognitive mismatch degree is calculated; mapping the cognitive mismatch degree to a space grid, calculating a local Moran index, fusing a density gradient and a mechanical operation sequence resonance result, and constructing a propagation weight matrix; constructing a heterogeneous graph based on weight matrix guidance, calculating risk influence propagation potential energy, combining historical disorder sequence analysis and relative entropy, and coupling to obtain a group-level instability pre-judgment value; and outputting a comprehensive critical level for the pre-judgment value over-limit individuals through motion trend prediction, spatial intersection and shortest path algorithms. The safety during building construction operation is improved.

The invention discloses a low earth orbit satellite network digital twin drive switching optimization method and system, and relates to the technical field of inter-satellite communication, and the method comprises the steps: building a digital twin system which is synchronously evolved with a physical LEO satellite network; predicting the link quality between each candidate satellite and the user terminal in a future time window based on a digital twin system; dividing a time window into a plurality of continuous time slices, constructing a directed weighted dynamic graph model which takes a satellite-time slice as a node and takes link comprehensive utility cost as an edge weight, and searching an optimal path in the directed weighted dynamic graph model by utilizing a shortest path algorithm so as to determine an optimal switching opportunity and a target satellite sequence; the optimal switching strategy is issued to a user terminal to be executed, and a link quality prediction result is corrected in real time through a feedback mechanism of the digital twin system to form closed-loop optimization; the method can effectively meet differentiated service quality requirements in a multi-service scene, and is suitable for a high-dynamic LEO satellite communication system.

The invention discloses a multi-source field-level blood relationship tracking method and system. The method comprises the following steps: extracting features from a multi-source heterogeneous system, matching templates, triggering a corresponding acquisition strategy, and outputting an original blood relationship log; reading an original blood relationship log to generate an abstract syntax tree, extracting a source / target field set to output a field-level mapping relation, further writing the field-level mapping relation into a graph database to form a blood relationship edge, writing metadata into a relational database, establishing a two-level index, and outputting a blood relationship graph capable of being quickly positioned; and taking the blood relationship map as input, returning a target path with the maximum sum of confidence coefficients by adopting a weighted shortest path algorithm, outputting a link-level traceability result, and displaying the link-level traceability result. According to the method, complete tracking of the field-level blood relationship is realized, the query response time is shortened to a millisecond level, rapid access to multiple types of data sources is supported, and the problems of insufficient blood relationship analysis precision, low query efficiency and weak multi-source adaptability in the prior art are solved.

The invention discloses a Text-to-SQL (Structured Query Language) generation method and device, a medium and a product, and relates to the field of geographic information technology application, the method comprises the following steps: analyzing a natural language query text input by a user to obtain a source field set and a target field set; determining corresponding nodes of each field in the source field set and the target field set in the river-lake long-system database mode knowledge graph to obtain a source node set and a target node set; the nodes in the source node set serve as starting points, the nodes in the target node set serve as terminal points, the knowledge graph is processed through the shortest path algorithm, and a path set is obtained; decomposing the natural language query text into a plurality of sub-problems by adopting a large language model and a thinking chain technology, and obtaining a dependency relationship among the sub-problems; according to the dependency relationship and the target field set, a public table expression is obtained, and then the SQL statement is obtained, and the accuracy of database query language generation under the river and lake long-term system scene can be improved.

The invention discloses an order logistics timeliness management system, and relates to the technical field of logistics intelligence, and the system comprises a risk prediction module which collects order logistics full-link data, carries out the preprocessing of the data, calculates the logistics node delay risk probability through a virtual intervention scene and a causal graph model, and obtains a delay risk prediction result; the visualization module is used for performing overlay analysis on the delay risk prediction result and the preprocessed order logistics full-link data to generate a delay risk thermodynamic diagram; the destroy-resistant module is used for carrying out vulnerability evaluation on the delay risk thermodynamic diagram to generate a vulnerability analysis report, and carrying out multi-objective optimization evaluation through a k-shortest path algorithm to generate a destroy-resistant strategy table; the scheduling module is used for carrying out transport capacity resource matching and scheduling scheme selection by utilizing a multi-target resource optimization model based on the survivability strategy table to obtain a scheduling instruction set; according to the invention, causal traceability and probability quantification of logistics delay risks are realized by constructing a collaborative prediction mechanism of a virtual intervention scene and a causal graph model.

The invention relates to the technical field of intelligent scheduling, and particularly discloses an intelligent scheduling decision control method and system for a distributed car washing robot, and the method comprises the steps: constructing a weighted graph model which comprises a vehicle position, obstacle distribution and path reachability, and dynamically adjusting the weight of an edge according to the path length, energy consumption and time; optimizing the graph structure by adopting an improved minimum spanning tree algorithm, and calculating a path efficiency index in combination with fuzzy logic and a neural network technology; on the basis of the optimized subgraph, applying a shortest path algorithm to obtain a local optimal path, and introducing a dynamic adaptation coefficient to evaluate the response capability of the path to environment change; fusing the path efficiency index and the dynamic adaptation coefficient into a comprehensive scheduling feature vector, inputting the comprehensive scheduling feature vector into a trained deep learning scheduling model, and predicting an optimal scheduling strategy in combination with a task priority and a robot resource state; and dynamically adjusting the moving path and the operation density of the robot according to a scheduling result, and realizing self-adaptive evolution and cooperative control of the system.

The invention discloses an irregular ship scheduling method and system considering uncertain harbor time, and a medium, and belongs to the technical field of shipping management. The method comprises the following steps: collecting ship, cargo, port and interference event data; generating an initial scheduling scheme by adopting a greedy strategy; generating multiple groups of random interference scenes based on Monte Carlo simulation; a two-stage stochastic programming model is constructed, in the first stage, income maximization serves as a target, and an initial plan is generated through damage and repair operator iterative optimization by means of a self-adaptive large neighborhood search algorithm; in the second stage, aiming at each interference scene, a recovery network is constructed for the influenced goods, and an optimal recovery strategy and adjustment cost are solved by utilizing a shortest path algorithm; and finally, outputting a scheduling scheme including a ship path, a navigational speed and a multi-scene recovery strategy through iterative optimization. According to the method, the earnings and the operation efficiency of the shipping companies are improved, the adjustment cost caused by uncertainty is reduced to the greatest extent, the benefits of the shipping companies and cargo owners can be balanced in a complex scheduling problem, and the service quality is improved.

The invention provides a structural engineering bearing capacity field detection method, and belongs to the technical field of structural engineering detection.The structural engineering bearing capacity field detection method comprises the steps that a multi-axial acceleration sensor and a strain gauge array are installed on the surface of a to-be-detected structural member, and an intelligent loading device is adopted to apply incremental loads; a mobile data acquisition terminal is utilized to acquire vibration response data and strain response data in real time, modal parameters are extracted based on a frequency domain analysis method to establish a load-modal parameter relation curve, the bearing capacity of a component is analyzed and calculated, a graph theory shortest path algorithm is adopted to optimize load transmission path analysis, and the load transmission path analysis is optimized. The bearing performance of the component is accurately evaluated through the structural response prediction model based on the Transform architecture, the technical integration of multi-dimensional detection data fusion, intelligent loading control, quantitative bearing capacity evaluation and accurate performance prediction is realized, and the technical problem of insufficient detection precision of the bearing capacity of the structural component is solved.

The invention discloses an automatic air rail scheduling method and system, and relates to air rail scheduling, and the method comprises the steps: obtaining air rail data; calculating the residual load of each vehicle according to the vehicle data set, and generating a to-be-distributed vehicle set V; performing priority ranking on the task data set to generate a task set U; generating a vehicle-task matching relationship according to the vehicle set V and the task set U; path planning is carried out on the vehicle-task matching relation through an improved Dijkstra algorithm, and an optimal scheduling scheme of vehicle scheduling is obtained; the improved shortest path algorithm Dijkstra is used for dynamically adjusting a path weight by introducing a track congestion degree coefficient; scheduling the sky rail vehicle according to the optimal scheduling scheme; aiming at the low path planning efficiency caused by the dynamic change of the track congestion of the sky track, the planning efficiency is improved by constructing a vehicle-task bipartite graph and combining a path planning strategy for dynamically adjusting the track congestion degree and the like.

The invention discloses a power network anomaly detection method and system, and relates to the technical field of intelligent power grid monitoring, and the method comprises the steps: constructing a neuron-like power network graph model, carrying out the node processing of equipment, introducing a multi-factor anomaly score function, and dynamically activating an abnormal node through combining with an improved Winner-Take-All mechanism; the connection weight is dynamically optimized through a topology-aware composite gradient descent method, and a multi-layer neural network structure is constructed to realize cross-layer forward propagation and feedback adjustment; and establishing an abnormal path model in combination with the propagation phase difference and the information entropy, identifying an abnormal path by using a propagation score function and a weighted shortest path algorithm, and carrying out fault tracing. According to the power network anomaly detection method provided by the invention, the neuron-like graph model is constructed, and a multi-factor anomaly activation mechanism is introduced, so that comprehensive perception of a power grid node state under a multi-dimensional time characteristic can be realized, and the anomaly recognition sensitivity and the multi-point simultaneous activation capability are effectively improved.

The invention discloses a carbon dioxide seepage dominant channel judgment and permeation enhancement effect evaluation method and system, and the method comprises the following steps: obtaining three-dimensional image data through the three-dimensional CT scanning of a rock sample before and after a CO2 mineralization storage experiment reaction, extracting a crack network as a topological structure center line skeleton, calculating the topological indexes of a network structure, and calculating the permeability enhancement effect of the network structure; a fracture network topological structure model constructed based on a graph theory is coupled with a discrete fracture network model for CO2 flow simulation, an equivalent permeability tensor is obtained, an improved Dijkstra shortest path algorithm is adopted, spatial distribution of CO2 seepage dominant channels is obtained, dominant channel characteristics before and after transformation are compared with the equivalent permeability tensor, the seepage enhancement effect is quantitatively evaluated, and the CO2 seepage enhancement effect is obtained. And changing injection conditions to perform multi-group simulation under different working conditions, calculating the seepage enhancement effect and the mineralization reaction rate under different injection conditions, establishing a quantitative evaluation model of the mineralization sealing permeation enhancement effect, and realizing sealing effect prediction and seepage enhancement effect evaluation.

The invention relates to the technical field of electric vehicle charging and discharging scheduling, in particular to an idle energy storage vehicle scheduling method and system, and the method comprises the steps: obtaining a charging and replacing task or a power grid power transmission task, and determining a target position; selecting idle energy storage vehicles according to vehicle states in a set range of an area where the target position is located; according to the current position, the vehicle speed, the battery state, the mileage information and the idle state information of the idle energy storage vehicles, the power supply capacity is determined, and the idle energy storage vehicle with the minimum distance from the target position is screened out; based on a shortest path algorithm, an optimal path between the idle energy storage vehicles and the target position is obtained, a scheduling instruction is sent to the corresponding idle energy storage vehicles, and the idle energy storage vehicles execute corresponding tasks; and after the task is finished, obtaining the actual charging and switching quantity, the service time and the user feedback score to form a reward function, storing the reward function in historical data, updating the state-action value function during the scheduling period based on the historical data, and enabling the scheduling algorithm to autonomously learn the optimal decision strategy through updating.

The invention discloses an electric power Internet of Things service scheduling method and device based on an SDN and an NFV, and a medium, and belongs to the field of virtual networks, and the method comprises the steps: receiving an electric power Internet of Things service request issued by a cloud network cooperative management platform through an SDN controller, and matching a corresponding VNF combination for the electric power Internet of Things service request; based on the control domain network topology structure, performing first path planning for the VNF combination through a weighted shortest path algorithm to obtain a first deployment path; constructing an optimization objective function of the first deployment path based on the dynamically adjusted time delay weight factor and load weight factor, and performing global optimization on the first deployment path based on the optimization objective function to obtain a second deployment path; and generating a deployment instruction based on the second deployment path, and issuing the deployment instruction to each corresponding physical node through the NFVO to perform VNF instance deployment so as to complete power Internet of Things service scheduling. Therefore, by implementing the method and the system, accurate scheduling of various services of the power internet of things can be completed according to the time delay requirement.

The invention discloses a fault identification and prediction method based on low-voltage distribution network topology modeling and an intelligent algorithm. Aiming at the problems of low fault positioning efficiency, insufficient intelligence and the like of the low-voltage power distribution network, the method realizes fault full-process processing through multi-module cooperation: firstly, collecting data such as voltage and current in real time through an Internet of Things terminal, and constructing a high-quality data set in combination with edge calculation and a four-step preprocessing technology; secondly, constructing a separated complex-valued neural network, and inputting a plurality of voltage and power vectors to realize accurate prediction of loop closing current and voltage deviation; reasoning fuzziness of equipment states and environmental factors through a fuzzy network, outputting TOP3 fault reasons, and tracking a fault propagation path for accurate positioning in combination with a shortest path algorithm and a D-S evidence theory; and finally, generating a switching operation sequence and a digital twinborn verification recovery scheme through an improved matrix algorithm. According to the method, the problems of dynamic modeling, uncertainty reasoning and the like are solved, and technical support is provided for intelligent operation and maintenance of the power distribution network.

The invention discloses a polar region ship navigation planning method based on sea ice prediction and multi-stage path optimization, and the method comprises the steps: obtaining a navigation risk map based on a to-be-navigated ship according to the updated single-day sea ice data in a time group; the method comprises the steps of obtaining a candidate path set based on a K shortest path algorithm, determining final U candidate paths according to path similarity of a result set of the candidate paths, further obtaining comprehensive scores of the paths in the result set, and finally determining a final driving path of a to-be-sailed ship according to the comprehensive score of each final candidate path. And completing the path planning of the polar region ship navigation. According to the method, the time, the risk and the path smoothness under the distance suboptimal constraint are taken as optimization targets, and similarity calculation is performed on the paths in the result set of the obtained candidate paths, so that diversified risk avoiding scheme paths can be provided. According to the method, the prediction time range is sliced, so that the high-frequency environment updating requirement in the prior art is met.

The invention relates to a bridge degradation identification and maintenance decision support method and system based on a knowledge graph, belongs to the technical field of traffic infrastructure intelligent operation and maintenance and engineering information processing, and solves the defects of weak data fusion capability, opaque causal modeling and non-traceable decision support in the prior art. The method comprises the following steps: acquiring and preprocessing multi-source heterogeneous data to generate a standardized data set; semantic alignment is carried out, and candidate entities and relations are extracted; constructing a knowledge graph body and loading the knowledge graph body into a knowledge graph; a self-adaptive quantile threshold value is adopted to binarize the factor intensity and the disease severity sequence, and a smooth point mutual information and a Spearman correlation coefficient are fused to learn a causal weight; complementing the influence relation of factors on the bridge through graph reasoning and calculating the weight; and finally, calculating a bridge degradation risk score, and outputting a high-confidence influence path based on a shortest path algorithm. According to the method, multi-source data fusion and degradation causal chain explicit modeling are realized, and a scientific basis is provided for a bridge maintenance decision.

The invention discloses a photovoltaic string fault positioning method based on a dynamic threshold value and topology analysis. The method comprises the following steps: firstly, collecting current, voltage, temperature and illumination intensity data of a string in real time; then, generating a fault judgment threshold dynamically adjusted along with the environment based on historical data and an LSTM model; then, comparing the real-time data with a dynamic threshold value to detect an abnormal node; then, constructing a string topology network taking electrical impedance as a weight, and accurately positioning a fault point by using a Dijkstra shortest path algorithm; and finally, multi-dimensional features are extracted, specific fault types such as hot spots and open circuits are identified through a fuzzy logic classifier, and a diagnosis report is output. According to the method, the problems that the static threshold false alarm rate is high and the traditional topology analysis precision is insufficient are solved, rapid and accurate positioning and diagnosis of photovoltaic faults are realized, and the operation and maintenance efficiency is improved.

The invention discloses an urban comprehensive park space-time network construction method and system based on crowd trajectory data. The method comprises the following steps: firstly, collecting, cleaning and dividing crowd trajectory data in a comprehensive park range; secondly, scenic spots in the comprehensive park are clustered, the number of optimal clusters is determined through clustering analysis, and all the clusters serve as all functional activity districts; then building a trajectory model through space projection and a shortest path algorithm, recovering a user moving trajectory path according to the crowd trajectory data, generating path node sequence data according to the timestamp of each trajectory point, and finally, taking the centroid of each functional activity area as each node of the space-time network, and combining each time period to obtain the user moving trajectory path. And constructing an undirected space-time network and a directed transfer network. The method has the advantages of efficient data processing, high modeling precision and perfect privacy protection, dynamic monitoring and space optimization analysis of the park people flow can be realized, and reliable data support and decision basis are provided for urban comprehensive park planning management.

The invention discloses a point cloud scene rod extraction method based on significance analysis, and the method comprises the specific steps: segmenting a point cloud scene into a plurality of super voxels, carrying out the neighborhood search of the super voxels, removing the plane super voxels through employing the region growing idea, and obtaining a target point cloud containing a rod; classifying the local saliency into a high-saliency point cloud and a low-saliency point cloud according to the FPFH descriptor of the target point cloud; clustering the low-saliency point clouds by using a mean shift algorithm, extracting structural points, and obtaining a structural point set; using a Dijkstra shortest path algorithm to classify the structure points into trunk structure points and non-trunk structure points; utilizing a Dijkstra shortest path algorithm simulating a tree reverse growth mode to extract a complete rod structure point; obtaining a trunk point cloud cluster according to the structural points of the rod, marking the high-significance point cloud, and obtaining an attachment point cloud cluster of the rod; and combining the trunk point cloud cluster and the attachment point cloud cluster to obtain a complete rod instance point cloud. The method provided by the invention can accurately segment and extract the rod-shaped object.

The invention discloses a medical robot autonomous path planning method based on convex optimization and a related device. The method comprises the following steps: acquiring an environment point cloud to construct a three-dimensional working space; performing alpha-shape segmentation on a collision-free region to obtain a convex set sequence; constructing a convex set graph by taking the convex set as a vertex and the intersection as an edge; shortest path mixed integer programming is established on the graph, and convex set coverage, continuity and acceleration constraint are added by taking the Bessel control point distance and the trajectory duration as targets; executing a convex set graph shortest path algorithm to obtain a discrete sequence, and then running mixed integer quadratic cone programming to output Bessel parameters; and circulating until the robot reaches the target. According to the method, the complex non-convex space is converted into the convex set sequence, efficient solving is achieved through convex optimization, the planning time is remarkably shortened, and the real-time performance and safety are improved.

The present invention discloses an AI intelligent operation and maintenance system and method based on digital twins, which belongs to the field of intelligent operation and maintenance technology. The present invention constructs a three-dimensional map, collects energy data and equipment data; constructs an energy digital twin model and an equipment digital twin model, and integrates them into the three-dimensional map; determines whether the current energy data and current equipment data are abnormal; marks the location of abnormal equipment or abnormal energy nodes on the three-dimensional map, performs correlation analysis on the digital twin model, and obtains the abnormal energy node source; when the energy output of the abnormal energy node source reaches a bottleneck, constructs an energy and equipment relationship diagram; analyzes the correlation path and closeness between the abnormal energy node source and the abnormal equipment through the shortest path algorithm to obtain a graph structure analysis result; determines the priority of the abnormal equipment, analyzes its adjustable parameters for abnormal equipment of different priorities, and preferentially adjusts the parameters of abnormal equipment with low equipment priority.

The invention discloses a water supply scheduling planning method, medium and equipment for a resident accumulation district, and the method comprises the steps: collecting basic water supply data of an adjacent water source, and carrying out the preprocessing to generate basic water supply data; adopting a constraint shortest path algorithm to generate an alternative scheduling route meeting pipe pressure bearing limitation and construction period constraint, and calculating a comprehensive cost weight of the alternative scheduling route; extracting water supply abrupt change characteristics through time domain analysis, and establishing a response strategy library containing typical water supply abnormal scenes; constructing an optimization decision model of multi-water-source collaborative scheduling and emergency switching; establishing a feature mapping relation between the reference water supply working condition and the emergency water supply mode; and finally, constructing a multi-dimensional water supply scheduling knowledge base including water source characteristics, pipe network topology, scheduling strategies and emergency responses. According to the invention, the response speed of the water supply system to emergencies is improved, the dynamic optimization scheduling of the water supply system is realized, the stability of multi-water-source collaborative scheduling is enhanced, and the emergency response efficiency and the system reliability are improved.

The invention provides a method for improving interfacial compatibility of PE (polyethylene) and PS (polystyrene), and belongs to the technical field of HI PS production. The method comprises the following steps: preparing compatilizer premixed master batches of a hydrogenated styrene-butadiene block copolymer and a styrene-butadiene-styrene block copolymer, and determining an optimal compatilizer dosage by adopting an interfacial tension prediction model; a multi-stage melt blending process is adopted to realize segmented addition and step-by-step mixing of components, inorganic powder and a processing aid are added to form a composite aid system to regulate crystallization behavior and improve thermal stability, and process parameters such as temperature and rotating speed of a twin-screw extruder are controlled to promote dispersion orientation of a compatilizer on an interface. And optimal cooling parameters are determined through a graded cooling temperature optimization experiment and iterative optimization of a shortest path algorithm to realize matching of polyethylene crystallization and a polystyrene curing process, so that the technical problem of unstable mechanical properties of an alloy material caused by poor interfacial compatibility of polyethylene and polystyrene is solved.

The invention provides a user operation recommendation method under an AI platform, and belongs to the technical field of large language models.According to the method, multi-dimensional operation data of a user are obtained in real time by building a distributed data collection system, and page operation records are processed through a clustering compression algorithm; natural language processing and a sequence identification algorithm are used for extracting semantic features and historical interaction behavior modes of inquiry content, a user operation intention graph is constructed, association strength is calculated through a shortest path algorithm, user intentions are accurately modeled, and a multi-modal fusion recommendation model based on a Transform architecture is established; an online learning system is constructed, user satisfaction is calculated through an intention correlation degree function, model parameters are optimized in real time by adopting a gating weight dynamic adjustment function, recommendation accuracy and response speed are ensured by applying a mode of combining offline pre-calculation and online retrieval optimization, and the technical problem that AI platform user operation recommendation is not accurate enough is solved.