1967 results about "Algorithm optimization" patented technology

The invention discloses a traffic supervision system applied to an intelligent street lamp and an intelligent supervision method thereof, relates to the technical field of intelligent traffic, and solves the problems that an existing intelligent street lamp system lacks a physical-digital mapping relation, edge computing resource allocation is low in efficiency and cloud computing delay is high. According to the scheme, on the basis of multi-sensor data fusion, space-time reference unification is carried out by adopting an atomic clock and a GNSS, and a dynamic causal graph is constructed through a graph neural network, so that abnormal event detection is optimized; an improved Jaccard space-time similarity algorithm is adopted to optimize calculation task allocation, an edge calculation cluster is constructed based on 5G-V2X, and high-risk region identification and traffic flow prediction are carried out; a LiFi or 5G-UWB communication medium is adaptively selected through a multi-modal fusion reinforcement learning algorithm, and efficient early warning information synchronization is realized; according to the method, the multi-source data fusion value and the early warning precision are remarkably improved, the computing power resource utilization rate is optimized, and the instruction real-time performance and the system self-adaptive capability in a complex environment are enhanced.

The invention relates to the technical field of data security management, in particular to a self-adaptive data security management and risk early warning system based on intelligent analysis under a cloud platform. Comprising a multi-dimensional data acquisition module; an intelligent analysis module; a self-adaptive strategy generation module; a risk early warning module; and a user behavior portrait construction module. In the design, the security policy can be dynamically adjusted along with the risk situation of the cloud platform, the problem that a static policy cannot adapt to real-time change is solved, and dynamic mapping of risk characteristics-policy parameters is realized; according to the design, the one-sidedness of single-dimension analysis is broken through, multi-modal feature association modeling of user behaviors is achieved, an abnormal behavior triggering threshold value is accurately recognized, and the integrity and accuracy of risk feature analysis are improved; the security policy can be continuously optimized through historical event data, so that protection efficiency attenuation caused by long-term static operation is avoided, and an autonomous lifting link of data driving, algorithm optimization and policy evolution is realized.

The invention relates to the technical field of intelligent building energy-saving control, and discloses a green building equipment intelligent energy-saving control method and system based on artificial intelligence, and the method comprises the steps: collecting building environment data, and carrying out the preprocessing; and simulating the energy consumption of the building equipment by using the digital twin model, and training the baseline neural network model to obtain an initial control strategy. And adopting a genetic algorithm to optimize hyper-parameters of the baseline model, and generating a plurality of candidate energy-saving control strategies. And inputting the optimized strategy into the deep reinforcement learning model, carrying out strategy iteration, calculating the energy-saving efficiency and comfort score after each iteration, screening the optimal strategy through a reward mechanism, and improving the accuracy of energy consumption prediction of the building equipment. The hyper-parameter of the neural network is optimized by using the genetic algorithm, and the intelligent degree of the energy-saving control strategy is improved. On the basis of deep reinforcement learning, the intelligent agent can adaptively adjust an energy-saving strategy under different environmental conditions, and the user comfort is guaranteed while the optimal building energy consumption is ensured.

The invention discloses a BIM-based electromechanical installation project progress planning and resource scheduling method and system, and belongs to the technical field of intelligent construction. The method comprises the following steps: 1) collecting multi-dimensional parameters (real-time construction parameters, prediction model parameters and external constraint parameters) in construction in a classified manner, and constructing a dynamic knowledge graph; 2) detecting progress and resource deviation based on a preset threshold value of the BIM model, and dynamically correcting a resource demand curve through the model; 3) in combination with constraint conditions such as policies and weather, optimizing an equipment scheduling path by adopting an algorithm, and screening compliance candidate schemes; 4) performing multi-objective optimization (minimizing progress deviation, maximizing resource utilization rate and controlling supply chain risk) on the scheme by using a genetic algorithm, and verifying through simulation iteration; and 5) outputting the optimal scheme and synchronizing the optimal scheme to a visual interface. According to the invention, real-time closed loop of data is realized through hardware-algorithm cooperation, and an efficient, dynamic and extensible intelligent management scheme is provided for electromechanical engineering.

The invention provides a large-scale network node scene construction method and system based on a network target range, and relates to the technical field of network security, and the method comprises the steps: dividing network nodes into a plurality of attack and defense levels through calculating the vulnerability association degree between the nodes, and screening a target network node template based on a vulnerability chain-type propagation coefficient. Secondly, optimizing a network topology structure by adopting a graph theory clustering algorithm, and constructing a connection relation between nodes based on service affinity; and finally, according to the resource utilization rate index, constructing a node operation environment, deploying network services, implanting a vulnerability program and carrying out anomaly detection and repair, and finally generating a large-scale network node scene meeting resource optimization scheme constraints. According to the method, the large-scale network target range with a complex topological structure and a real vulnerability environment can be automatically constructed, and the efficiency and authenticity of network security attack and defense drilling are improved.

The invention discloses a non-energy-storage off-grid photovoltaic hydrogen production system and a control method. The control method comprises five steps of environmental data acquisition and preprocessing, photovoltaic cell output power estimation, electrolytic cell power management, converter control parameter optimization and system performance evaluation and adaptive adjustment. The method comprises the following steps: firstly, processing environmental data by adopting an isolation forest algorithm, Kriging interpolation and an empirical mode decomposition method, and extracting time sequence features; and then combining with the estimated photovoltaic output power. And then the genetic algorithm and dynamic programming are utilized to realize electrolytic cell power optimization distribution. And then converter control parameters are optimized through subspace identification, pole assignment and a hybrid optimization algorithm. And finally, system performance evaluation and adaptive adjustment are realized by adopting a sliding time window method, singular spectrum analysis and a fuzzy prediction control method. The technical problems of the energy-storage-free photovoltaic hydrogen production system in the aspects of environmental data processing, power estimation, energy management, control optimization and the like are solved, and the operation efficiency and reliability of the system are improved.

The invention relates to the technical field of traffic management, and discloses a road obstacle clearance rescue enterprise automatic scheduling method comprising the following steps: S1, through multi-source data fusion and a dynamic convolutional neural network, generating a three-dimensional road model, and predicting accidents and congestion; s2, dynamically adjusting the priority of the rescue task by using a multi-target genetic algorithm in combination with real-time data; s3, path planning and cooperative scheduling are optimized through deep reinforcement learning and game theory algorithms; s4, dynamically correcting the path by using a vehicle-mounted sensor, and optimizing the flow and the energy consumption; s5, in an emergency, an emergency module based on reinforcement learning starts a plan and adjusts resources; and S6, after the task, analyzing an optimization strategy through collaborative learning, and carrying out self-improvement. According to the method, multi-source dynamic data fusion is carried out, a dynamic convolutional neural network is used for processing data in real time, a high-precision three-dimensional road environment model is generated, accidents and congestion are predicted, and it is ensured that a rapid and accurate scheduling decision can be made in a complex and changeable road environment.

The invention relates to the technical field of information, in particular to an error compensation method for multi-axis linkage machining of a mold blank hole system, which comprises the following steps: judging the time range of a tool changing window based on an error accumulation trend, if error accumulation reaches a preset threshold value, triggering a compensation action, otherwise, continuously monitoring error change; aiming at the complexity of a motion track, a nonlinear optimization algorithm is adopted, an execution path of a compensation action is optimized, and conflicts with a processing action are reduced; according to the optimized compensation path, the control parameters of the servo motor are adjusted in real time, and it is ensured that the compensation action is stably executed in the machining process; dynamically updating an error model through data of a main shaft temperature sensor and a tool wear monitoring device, and continuously optimizing a time point and an execution path of a compensation action; in the machining process, the error accumulation condition is monitored in real time, if the error exceeds the preset range, the compensation action is triggered again, and the machining precision is ensured.

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 relates to the technical field of traffic flow regulation and control, and discloses an electronic information traffic flow automatic regulation and control system based on a wireless sensor network. The multi-source sensing module collects road network multi-dimensional traffic data through a wireless sensing network, and traffic situation characteristics are generated through a specific data fusion method. The spatio-temporal feature fusion module utilizes a spatio-temporal attention mechanism to associate cross-modal features, and the collaborative decision-making module inputs fusion features into a pre-trained distributed decision-making model to generate a regulation and control instruction set. The dynamic game optimization module constructs a multi-target game optimization model, and traffic signal parameters are optimized by adopting a dynamic game strategy decomposition algorithm. And the hierarchical execution module executes regulation and control instructions in a distributed manner through a three-level control architecture of a central decision-making layer, a regional coordination layer and an intersection execution layer. The system can comprehensively collect and fuse traffic data, realizes scientific decision making and accurate regulation and control, effectively improves the road passing efficiency, balances the road network load, and relieves traffic jam.

The invention discloses a high and steep slope dangerous rock falling intelligent monitoring and early warning method and system, and the method comprises the following steps: S1, collecting and preprocessing real-time monitoring data, and generating a standardized input sample set; s2, constructing a long-term prediction network model, and outputting a state prediction sequence of multiple time steps in the future; s3, optimizing structure parameters and training parameters of the prediction network model based on a bald eagle search algorithm; s4, executing multi-step state prediction by using the optimized model, and outputting a crack trend, a displacement trend and an abnormal probability value; s5, constructing an early warning risk scoring function, and fusing multiple prediction indexes to generate a risk scoring value; and S6, setting a multi-level early warning threshold value, outputting an early warning level, and issuing the early warning level in linkage with voice, a terminal and a platform. According to the invention, through constructing the intelligent monitoring and early warning method fusing the long-term prediction network model and the bald eagle search algorithm, accurate prediction and multi-stage linkage early warning of the high and steep slope dangerous rock falling risk are realized.

The invention is suitable for the technical field of life evaluation, and provides an enhanced high-voltage circuit breaker life evaluation method, which comprises the following steps: constructing a dynamic evolution model of a contact material wear rate; constructing a nozzle degradation dynamic prediction model; constructing residual life probability distribution of the insulating material; according to the dynamic evolution model of the wear rate of the contact material, the dynamic prediction model of nozzle degradation and the residual life probability distribution of the insulating material, constructing a comprehensive life evaluation index; inputting the comprehensive life evaluation index into a preset layered prediction architecture for evaluation; wherein the first layer generates basic life distribution through a Bayesian network, the second layer outputs posterior life distribution correction parameters through a convolutional neural network, and the third layer optimizes posterior distribution through a KL divergence minimization algorithm to obtain a probability density function and a confidence interval of residual electrical life; according to the method, the error of life evaluation can be reduced on the basis of complex working conditions.

The invention relates to the technical field of system control data processing, in particular to a waste heat recovery heat supply system based on an injection type heat pump, which comprises a multi-source data acquisition module, a genetic algorithm optimization module, a dynamic mode switching module, a thermal stress suppression module and a self-learning updating module. The multi-source data collection module collects industrial waste heat temperature, power grid load and heat supply network backwater flow data through a temperature sensor array and a flow metering device, and feature vectors are extracted through sliding window standardization and principal component analysis. And heat transfer end difference fluctuation is restrained through closed-loop feedback. And the self-learning updating module constructs a multi-parameter incidence matrix to dynamically adjust the weight of a fitness function, and realizes parameter iterative optimization in combination with a historical optimal solution caching mechanism. The problems that the industrial waste heat recovery efficiency is low, thermoelectric coupling adjustment is delayed, and thermal stress of equipment is accumulated are solved.

The invention provides a pipe jacking construction digital twinning optimization system and method based on high-precision physical simulation, and relates to the technical field of underground pipe network pipe jacking construction. Construction data of multiple physical entities during pipe jacking construction are collected in real time through a physical simulation engine module and then transmitted to an established digital twin model, virtual mapping synchronously updated with the construction data of the physical entities is established, and an optimization and decision module optimizes construction parameters by adopting a joint simulation algorithm fusing finite element analysis (FEA) and computational fluid dynamics (CFD), so that the construction parameters are optimized. The construction feedback module realizes automatic parameter adjustment based on PLC control according to optimized construction parameters, the analysis report module analyzes time sequence data and generates a construction efficiency evaluation report, a stress concentration area is accurately simulated, the simulation precision is improved, high synchronization of a virtual model and physics is realized, and the precision and real-time performance of system execution are improved.

The invention relates to the technical field of business data insight, and discloses a method for disassembling insight business data through indexes. The method comprises the following steps: firstly, constructing a multi-dimensional index system, determining key business indexes and associated dimensions, and outputting an index disassembling framework; the method comprises the following steps: collecting multi-source data in a business process, performing cleaning and standardization processing, extracting index characteristics and dimension attributes, and constructing a business data association graph; introducing an adaptive weight distribution mechanism and a hierarchical iterative algorithm to optimize a preset attribution model, and generating a preliminary index contribution degree sequence based on an index disassembly framework; setting a monitoring threshold value of each dimension index, and tracking service data in real time; dynamically adjusting an index weight and a model parameter according to a tracking result, and recalculating and updating an index contribution degree sequence; and evaluating the service health degree, identifying abnormal index nodes, and starting a root cause analysis process for the abnormal index nodes. According to the method, multi-dimensional and dynamic insight of the business data can be realized.

The invention discloses a medical clinical decision support method and system based on a knowledge graph, and the method comprises the following steps: S1, collecting structured and unstructured medical data, and constructing an initial medical knowledge graph; s2, performing term standardization and semantic alignment on the graph to generate a fusion knowledge graph; s3, constructing a time-labeled medical record graph structure based on the medical record data, and aligning the time-labeled medical record graph structure with the fusion graph; s4, inputting the fusion atlas and the medical record graph into the hypersphere graph neural model, and generating semantic representation; s5, calculating a gravitation vector by using a path traction module, and guiding the propagation direction of the reasoning path; s6, generating a diagnosis and treatment candidate set and a corresponding recommended path according to the node state; s7, optimizing a model structure and initial parameters through a black widow spider optimization algorithm; and S8, outputting diagnosis and treatment suggestions and reasoning paths. According to the method, intelligent organization of medical knowledge and accurate diagnosis and treatment path recommendation are realized, and the auxiliary decision making efficiency and reliability are improved.

The invention discloses an industrial visual detection system based on multi-modal fusion, which belongs to the technical field of industrial data acquisition, processing and processing, and comprises a multi-modal data acquisition and calibration module, a scene image capture module, a visual information synthesis module, an algorithm optimization and training module and a multi-modal image output module, the multiple modules are integrated into the industrial visual detection system for multi-modal data acquisition, processing and fusion, high-quality original data are provided for subsequent image analysis, images of industrial scenes can be automatically captured under different illumination conditions, an original image database containing various illumination and scene conditions is constructed, and the visual detection system has the advantages of being simple in structure, convenient to operate and high in practicability. The system can operate stably, high-precision analysis and processing of the image data set in the industrial scene are realized, the corresponding fusion image is generated, the industrial visual inspection report is generated based on the fusion image, and the adaptability and reliability of the industrial visual inspection system are improved.

The invention relates to a deep integrated electric drive assembly design method and system, and the method comprises the steps: obtaining the parametric modeling data of an electric drive assembly, and carrying out the digital twin synchronous motor modeling, and obtaining a drive three-dimensional model; topological structure data of the electric drive assembly are obtained, and mixed algorithm optimization calculation is carried out on the topological structure data and the drive three-dimensional model to obtain a system optimization solution set; performing model prediction control construction based on the system optimization solution set, and performing gear contour line modification processing on the electric drive assembly to obtain a speed reduction transmission strategy; performing heat dissipation management extraction optimization on the system optimization solution set according to a preset variable cross-section micro-channel heat dissipation algorithm to obtain a dynamic heat dissipation strategy; preliminarily integrating the speed reduction transmission strategy and the dynamic heat dissipation strategy to obtain an initial design scheme; eMI filtering optimization is carried out on the initial design scheme, multi-stage electromagnetic shielding construction is carried out, and a system performance optimization scheme is obtained. The comprehensive performance of the system can be improved through integral design, regulation and control.

The invention relates to a server application architecture migration method and device and a storage medium, and the method comprises the steps that an automatic detection module analyzes and checks a to-be-migrated application under an X86 architecture, and the to-be-migrated application is subjected to static code analysis, dynamic performance analysis and system compatibility check; according to analysis and check results of the automatic detection module, the automatic adjustment module replaces an X86 specific instruction and a library function, performs memory allocation optimization and memory mapping adjustment, and generates an automatic adjustment report; and the automatic optimization module executes a performance benchmark test, and executes memory access optimization, algorithm optimization, task scheduling optimization and multi-core parallelization adjustment according to a test result. According to the server application architecture migration method and device and the storage medium, the architecture adaptation process can be simplified, performance optimization support and automatic dependency relationship management can be provided, the migration efficiency can be improved, and the cost can be reduced.

The invention discloses a driving style quantitative evaluation method based on multi-modal perception and a knowledge graph, and the method comprises the steps: employing a multi-modal data source, which comprises various sensor data, vehicle motion data, and physiological reaction and behavior data (such as heart rate change and operation intervention frequency during emergency braking) of a driver and a passenger in a real scene; a user emotion mapping algorithm is constructed by the model, subjective experience is converted into quantifiable numerical indexes (such as a trust index and a comfort score), a driving style deviation index (SDI) is calculated by using an algorithm in combination with a constructed driving behavior knowledge graph, quantitative evaluation of brand driving genes is realized, and a data driving basis is provided for function and algorithm optimization. And a personalized intelligent driving mode is realized. Therefore, the driving style quantitative evaluation method based on the multi-modal perception and the knowledge graph has the advantages of being high in intelligent degree and high in individuation degree.

The invention relates to the technical field of data processing, in particular to an earthwork balancing method based on discrete elevation points and a free-form surface creation technology, and the method comprises the steps: generating topographic feature partition tags through an improved DBSCAN density clustering algorithm, and constructing a regularized elevation field in combination with octree and R * tree mixed indexes; generating an elevation standard deviation thermodynamic diagram based on Gaussian process regression, and outputting a multi-target digging and filling scheme set in combination with an FEM-DEM coupling model; a multi-layer graph model fused with soil bearing capacity is constructed, a collision-free construction path is optimized through an improved D * Lite algorithm, a positioning track and a planning deviation are fused by using Kalman filtering, and an online sequence extreme learning machine is triggered to realize dynamic updating of the model. According to the method, the complex terrain modeling efficiency and the earthwork volume calculation precision are improved, the resource allocation path is optimized, the construction cost is reduced, and the problem of excavation and filling imbalance caused by data discreteness and modeling staticization in a traditional method is solved.

The invention discloses a multi-modal X-ray image diagnosis report generation method based on reinforcement learning optimization. The method comprises the following steps: acquiring a multi-source public data set and a private data set in a specific range; performing format standardization processing on the X-ray image, performing normalization processing on text information, and constructing a structured public training data pair and a private enhanced data pair; training an end-to-end multi-modal neural network by using the public training data pair in a supervised fine tuning mode; carrying out reinforcement learning on the first-stage end-to-end multi-mode neural network by adopting a strategy gradient algorithm optimization mode of GRPO and using candidate reports and weighted rewards to obtain a second-stage end-to-end multi-mode neural network; and generating a diagnosis report by using the two-stage end-to-end multi-modal neural network. The method is suitable for personalized application scenes of X-ray image diagnosis, can quickly adapt to specific clinical requirements on the basis of limited professional data, and has good practical value and popularization prospect.

The invention relates to a hybrid architecture mechanical property prediction method and system for an additive manufacturing lattice structure, and the method comprises the steps: converting a lattice structure node three-dimensional coordinate into a normalized distance from a lattice center, introducing a self-attention mechanism, and constructing a feature prediction model in combination with residual connection and layer normalization; constructing a generative adversarial network comprising a generator and a discriminator; a strategy network based on prediction error rewards is constructed, and a depth deterministic strategy gradient algorithm is adopted for optimization; jointly training the models, and constructing a performance prediction feedback model; finally, the node coordinates of the target structure serve as input, and mechanical property prediction output is achieved. According to the method, the generative adversarial neural network, reinforcement learning and an attention mechanism are mutually combined to generate a hybrid architecture, the problems of gradient disappearance and mode collapse of a traditional GAN can be effectively relieved, the model can accurately recognize implicit association between node positions and mechanical properties, and then the mechanical properties of target lattice structure parameters are rapidly predicted.

The invention relates to an agricultural biomass characteristic intelligent detection method, system and device based on multi-frequency conductance coupling and a medium, and the method comprises the following steps: applying an electric excitation signal to an agricultural biomass sample, and synchronously collecting multi-physics coupling response data; based on the multi-physics field coupling response data, constructing a multi-scale equivalent circuit model comprising a resonance unit, a constant phase unit and a diffusion impedance unit; synchronously optimizing a resonance unit parameter, a constant phase unit parameter and an impedance unit parameter of the multi-scale equivalent circuit model through a Bayesian joint inversion algorithm to obtain an optimal parameter combination; and according to the optimal parameter combination, calculating a preliminary detection result of biomass components containing cellulose crystallinity, lignin crosslinking degree and water content, combining the collected environment temperature and humidity sensing data to implement dynamic compensation, and outputting a final detection result. Therefore, the detection precision, the environment robustness and the operation reliability are synergistically improved in three aspects of hardware design, model construction and algorithm optimization.

The invention discloses an intelligent load management and allocation method for an electric meter box, relates to the technical field of intelligent power grid management and energy optimization, and solves the problems that the adaptability of load management in an existing electric meter box is insufficient, the prediction accuracy is influenced by data prejudice, and the dynamic adjustment capability is limited. According to the method, diversified data are collected and analyzed through a real-time data interface and a gradient boosting tree technology, a power consumption prediction model is constructed in combination with a long-short term memory (LSTM) network, and meanwhile, prediction performance is optimized by adopting a data enhancement algorithm and an adaptive enhancement algorithm; fast response and dynamic adjustment are realized by using a sliding window prediction method based on Kalman filtering, a load distribution strategy is optimized through an adaptive depth deterministic strategy gradient algorithm, and collaborative distribution scheduling and management are performed on cross-regional electric meter boxes through a distributed collaborative scheduling system; according to the invention, the stability and efficiency of the power system are greatly improved, and the energy utilization efficiency and economic benefits are remarkably improved.

The invention relates to the technical field of robot intelligent control, in particular to a concrete construction digital intelligent robot control system. Comprising a concrete data acquisition module, a construction path planning module, a robot control module, a quality monitoring module and an interactive feedback module. The data acquisition module acquires information such as concrete height and humidity of a construction area through a sensor; the path planning module generates an optimal construction path by using an artificial bee colony algorithm; the robot control module accurately executes a path instruction through sliding mode control and a fuzzy adaptive algorithm; the quality monitoring module adopts a support vector machine to evaluate construction quality and generate a report; and the interaction feedback module realizes path adjustment and information interaction through a visual page. Through combination of digital intelligence and algorithm optimization, integration of intelligent planning of a construction path, precise control of the robot and quality monitoring and evaluation is realized, and the intelligent level and engineering quality of concrete construction are remarkably improved.

The invention discloses a temperature collecting, monitoring and alarming method for palm fire fighting, relates to the technical field of temperature monitoring for fire fighting, and is used for solving the problem of inaccurate temperature monitoring and prediction. According to the method, environmental factor data are synchronously collected through temperature monitoring points, the correlation between each factor and the temperature is calculated after denoising, multi-factor characteristics are extracted to generate an output characteristic vector of temperature prediction, the output characteristic vector is constructed into a time sequence matrix to be input into a long-short-term memory network, and temperature trend characteristics are extracted; the method comprises the following steps: predicting a future trend and comparing the future trend with a safety threshold to judge whether early warning is triggered or not, meanwhile, identifying temperature changes of different frequencies by adopting fast Fourier transform, a weighted moving average method and an exponential smoothing method through short-term, medium-term and long-term scale temperature trend analysis, setting a three-level early warning mechanism and dynamically adjusting the threshold, and finally, carrying out early warning. Based on early warning feedback data, a Q-learning algorithm is adopted to optimize a threshold value and an alarm strategy, a self-adaptive learning closed loop is formed, and the early fire early warning capability is improved.

The invention discloses an urban water supply management data trend analysis method based on space-time analysis, and relates to the field of data processing, and the method comprises the steps: collecting data in real time through an urban water supply pipe network sensor network, building a space-time unified coordinate system, building a space-time Kriging interpolation model based on pipe network topology, and achieving the space-time alignment of multi-source data; dividing an adaptive space-time grid by using a Voronoi diagram and a sliding window mechanism, and calculating multi-dimensional features; constructing a dynamic space-time diagram by taking a grid as a node, performing multi-step prediction in combination with a space-time diagram convolution circulation network, fusing a Kriging interpolation result, and evaluating an abnormal probability and a confidence interval through a Bayesian neural network; a monitoring layer, a prediction layer and a risk layer are overlaid in a three-dimensional GIS, a dynamic thermodynamic diagram is generated, an early warning path is optimized based on a Dijkstra algorithm, and a minimum risk topology path is output. The method has the advantages that through space-time analysis and accurate prediction, the intelligence, stability and emergency response efficiency of urban water supply management are remarkably improved, and powerful support is provided for smart city construction.

The invention discloses a metal plate laser cutting control method, and relates to the technical field of laser cutting, material parameters, thickness information and cutting path data are obtained, a mixed enveloping rectangle algorithm is combined with a genetic algorithm to optimize a cutting sequence, and a path sequence containing idle stroke avoidance and nested graph priority is generated; constructing a laser parameter prediction model based on the material, thickness and path characteristics, and dynamically adjusting the power gradient; the slag splashing state is judged through data fusion of a capacitive sensor and an encoder, a quality feedback signal is extracted in combination with machine vision, and laser parameters are dynamically adjusted through a fuzzy control algorithm; wavefront distortion is monitored in real time, the correction frequency is adjusted according to the slag splashing state, and a deformable mirror is used for compensating the phase; when the quality is abnormal, historical parameter combinations are retrieved through a cosine similarity algorithm for joint compensation. According to the method, the path cutting efficiency is improved, and the problems of low efficiency of path planning and parameter adjustment lagging in the prior art are solved.

The invention discloses an intelligent target distribution method and system for collaborative interception, particularly relates to the field of radar networking, is used for solving the problem of correction of phase noise accumulation and array error propagation, and comprises the following steps: establishing a high-dimensional node phase distribution model, determining a noise mode and extracting key features; in combination with calculation of a phase drift index and a signal distortion index and construction of a multi-dimensional noise matrix, fine quantization of node errors is realized; on the basis, the array surface information is corrected in real time by using a dynamic interpolation and phase offset correction technology, node cooperation is optimized in combination with an energy measurement algorithm, and the stability and precision of the array surface information are improved; and finally, through a multi-dimensional decision matrix, target features and node states are comprehensively analyzed, and intelligent allocation and optimization of resources are realized. Therefore, the observation precision and the cooperation efficiency of radar networking are remarkably improved, and reliable technical support is provided for high-precision target monitoring and resource optimization allocation in a complex environment.