816 results about "Swarm algorithms" patented technology

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

The invention relates to the technical field of metasurface antennas, and provides a metasurface antenna parameter optimization method and system based on a convolutional neural network, and the method comprises the steps: collecting metasurface sample data; extracting a comprehensive electromagnetic feature set, and establishing a nonlinear mapping relation model; constructing an antenna performance comprehensive evaluation function, inputting the nonlinear mapping relation model and the antenna performance comprehensive evaluation function into a hybrid optimization algorithm to generate a parameter candidate set, and performing local optimization on the parameter candidate set by using a particle swarm algorithm to obtain a metasurface antenna parameter combination; and extracting electromagnetic characteristics of the metasurface antenna parameter combination, iteratively adjusting the height parameter of the resonant cavity until the height parameter meets a threshold value to obtain an electromagnetic simulation verification result, and feeding back the electromagnetic simulation verification result to the deep Q neural network model for parameter updating to obtain an optimal metasurface antenna parameter combination. According to the method, the optimization of antenna parameters is realized, the design efficiency of the metasurface antenna is improved, and the consumption of electromagnetic simulation calculation resources is reduced.

The invention discloses an unmanned aerial vehicle path planning and obstacle avoidance optimization method based on an improved elite colony algorithm, and the method comprises the following steps: carrying out three-dimensional grid environment modeling and obstacle generation, and obtaining a discrete model of a whole three-dimensional space according to a layering + two-dimensional grid method; elite strategy and path generation: ants select paths by using a random proportion strategy, set a transition probability function and introduce a fluctuation coefficient to prevent excessive concentration of path weights caused by pheromone volatilization; dynamic volatilization rate self-adaptive adjustment: carrying out self-adaptive adjustment according to pheromone distribution, and constructing a dynamic volatilization rate model; path cost calculation and dynamics constraint are integrated, a multi-objective planning method is applied, the path length and flight stability are optimized at the same time, and a path cost function is constructed; and performing path post-processing and smooth optimization, including processing a balanced path to improve the flyability and performing collision detection, thereby establishing an unmanned aerial vehicle dynamics constraint model, and ensuring the flight safety on the premise of path optimization.

The invention relates to the technical field of cloud computing, in particular to a network data encryption and privacy protection system in a cloud environment, which comprises a key management unit driven by a wolf pack algorithm, an encryption algorithm optimization unit, a privacy protection strategy dynamic adjustment unit and a safety monitoring and abnormity response unit. The invention discloses a cloud environment network data encryption and privacy protection system constructed based on a wolf pack algorithm. High-security keys are dynamically generated and distributed through a key management unit, security performance and resource consumption are balanced through an encryption algorithm optimization unit, multi-target dynamic gaming and compliance guarantee are achieved through a privacy protection strategy unit, distributed attack detection and cooperative defense are completed through a security monitoring unit, and the security performance is improved through a cooperative feedback mechanism between the units. Intelligent encryption protection, dynamic strategy adjustment and efficient attack response of the full life cycle of the data in the cloud environment are realized, and the system security, the resource utilization rate and the compliance capability are remarkably improved.

The present invention relates to the technical field of power source power allocation. Disclosed are a direct current power source power allocation method and system for a generator status monitoring apparatus. The method comprises the following steps: by means of system data, constructing a variational problem model; solving the constructed variational problem model, and using a particle swarm optimization algorithm to optimize computing parameters of the variational problem model; and, on the basis of a computing result of the variational problem model, allocating the total output power of a hybrid energy storage system to an energy-type storage device and a power-type storage device according to a ratio. By means of using the convergence-guaranteed particle swarm optimization algorithm, the present invention not only excels in the solving speed but also shows significant advantages in computational accuracy; furthermore, by means of solving the variational problem model, more accurate allocation ratios are obtained; variational mode decomposition can achieve adaptive matching of the optimal center frequency and bandwidth for each mode, thus effectively separating intrinsic mode components and achieving frequency domain partitioning of signals.

The invention provides a massage robot multi-mode fusion treatment system and method based on 3D vision, and relates to the technical field of massage robots. The region recognition module is used for establishing a human body recognition model and recognizing human body features in the processed image; the massage terminal is used for carrying out massage operation according to parts needing to be massaged and human body characteristics and monitoring skin pressure of the massaged parts in real time; the pain sense recognition module is used for human body pain sense recognition; the force adjusting module is used for adjusting skin pressure. According to the method, pertinence and safety of massage operation are remarkably improved through multi-modal image preprocessing and an initialized human body recognition model of a fusion segmentation model and a key point detection model; a global path strategy is generated and optimized through a hybrid ant colony algorithm, efficient and accurate motion control of the mechanical arm is achieved, through multi-modal data fusion analysis, the pain feeling of a user is sensed in real time, the massage strength is dynamically adjusted, and the comfort and the safety coefficient are improved.

The invention relates to the technical field of power system operation or management, in particular to a partial discharge monitoring strategy optimization method and system based on dynamic resource allocation, and the method comprises the steps: constructing a three-stage monitoring system comprising a sensor node, a sink node and a cloud processing center, firstly initializing monitoring parameters, and then obtaining system state information periodically or in a triggering manner, calculating the risk level of each monitoring point in combination with a dynamic risk evaluation model; constructing an efficiency-maximized resource allocation optimization model based on risk levels and resource constraints, solving an optimal scheme by adopting an improved multi-target particle swarm algorithm, and issuing the optimal scheme to each node to adjust monitoring behaviors to form closed-loop optimization; and meanwhile, model parameters are dynamically updated through an online learning mechanism. According to the method, dynamic matching of risks and resources is realized, the monitoring accuracy and the resource utilization rate are improved, the adaptability of the system to the equipment state and the environment change is enhanced, and the method is suitable for partial discharge monitoring scenes of various power equipment.

The invention provides a visual guidance type automatic disassembly method and system for a photovoltaic module, and relates to the technical field of module disassembly, and the method comprises the steps: obtaining real-time production data, constructing a multi-objective optimization decision model, and solving through an adaptive inertia weight particle swarm algorithm; verifying and dynamically optimizing the optimization scheme by adopting a deep reinforcement learning method in combination with a digital twinning environment; production line optimization control is realized and operation data is fed back to form a closed loop. According to the invention, the disassembly efficiency is improved, the energy consumption is reduced, the production quality is optimized, and intelligent automatic disassembly is realized.

The invention relates to a multi-distribution-center open type vehicle path intelligent optimization method and system, and belongs to the technical field of logistics distribution optimization and intelligent transportation, and the method comprises the steps: firstly obtaining the input data of a multi-distribution-center vehicle path optimization problem, selecting a multi-distribution-center processing strategy according to the problem scale and constraint conditions, and carrying out the optimization of the multi-distribution-center vehicle path; a vehicle path optimization model is constructed, the vehicle path optimization model comprises a single-target model and a multi-target model, a multi-algorithm collaborative optimization framework is adopted for solving, and the multi-algorithm collaborative optimization framework comprises an ant colony algorithm, a variable neighborhood search optimization ant colony algorithm and a non-dominated sorting genetic algorithm; and outputting an optimal vehicle path scheme, wherein the optimal vehicle path scheme comprises a distribution route, a distribution sequence and a corresponding objective function value of each vehicle. According to the method, strategy adaptive selection and algorithm collaborative optimization are carried out, global exploration, local optimization and multi-target equalization are carried out by combining the advantages of the ant colony algorithm, the variable neighborhood search algorithm and the non-dominated sorting genetic algorithm, and the method is good in reproducibility, high in scene adaptability and high in decision support capability.

The invention discloses an optimization design method for a control surface of a wide-speed-range large-attack-angle reusable carrier, and relates to optimization design of aerodynamic configuration of an aircraft. The method comprises the following steps: S1, setting a control surface aerodynamic configuration design variable range, and generating an initial sample library by adopting Latin hypercube sampling; s2, aerodynamic parameters are obtained through CFD simulation, and a Kriging proxy model is trained; s3, evaluating the precision of the proxy model by taking a U learning function as a criterion, and stopping adding points when the minimum U function value is smaller than a threshold value; s4, constructing an optimization model which takes maximization of the lift-drag ratio and the static stability margin under the hypersonic speed as a target and takes the condition that the aerodynamic parameters of the supersonic speed / subsonic speed are not lower than a reference value and the hinge moment as constraints; and S5, performing iterative optimization by adopting an improved multi-target particle swarm algorithm based on genetic algorithm crossover mutation operation, updating the proxy model after the optimal solution of each generation is subjected to CFD verification, and outputting an optimal solution set. The problem of wide-speed-range aerodynamic configuration contradictions is solved, and the comprehensive performance of the carrier is remarkably improved.

The invention belongs to the field of vehicle industry design, and discloses an electric bicycle form design method and system, electronic equipment and a storage medium, and the method comprises the steps: mining user emotion vocabularies through online comments, constructing an emotion lexicon in combination with an improved word frequency-inverse document frequency algorithm and a D-S evidence theory, and screening out key perceptual vocabularies; constructing a convolutional long-short-term memory neural network model optimized by a snake swarm algorithm to realize a mapping model between customer sensibility and product morphological characteristics; carrying out subjective and objective comprehensive evaluation on the design scheme in combination with an eye movement experiment and subjective evaluation so as to screen out an optimal design scheme; an optimal scheme is selected and input into a generative AI platform for multi-angle visual rendering, ergonomic modeling and aerodynamics simulation are assisted, and the structural feasibility and performance are verified. The method provides systematic technical support for emotional value improvement and design optimization of industrial products.

The invention discloses a multi-algorithm fusion transmission path planning method and device, terminal equipment and a storage medium, and belongs to the field of transmission path planning, and the method comprises the steps: obtaining state data of each node of a power grid, repeatedly executing a position updating operation until a convergence condition is satisfied, and obtaining a target particle position and a target ant position; the position updating operation comprises the step of updating the particle speed and the particle position according to the current particle speed, the particle position and the pheromone; according to the current ant position, the pheromone and the global optimal solution, the ant position and the pheromone are updated; when the convergence condition is not met, taking the updated parameter as the current parameter of the next iteration; otherwise, outputting the updated particle position and ant position; and determining a final transmission path according to the target position. By implementing the method, the advantages of the particle swarm algorithm and the ant colony algorithm can be combined, so that the problem that a single algorithm is easy to fall into local optimum or slow in convergence in transmission path planning in the prior art is solved.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides an intelligent recommendation method and system based on the plasticizing industry, and the method comprises the steps: obtaining multi-source heterogeneous data from a plurality of third-party data sources of the plasticizing industry; constructing the multi-source heterogeneous data into a dynamic knowledge graph in combination with a plasticizing industry knowledge base; performing demand prediction on the dynamic knowledge graph based on a knowledge-guided hybrid particle swarm algorithm to obtain a first plasticizing recommendation strategy; enhancing a recommendation model through causal reasoning, and performing anti-fact prediction on the dynamic knowledge graph in combination with industry real-time change data to obtain a second plasticizing recommendation strategy; the first plasticizing recommendation strategy and the second plasticizing recommendation strategy are dynamically fused based on Bayesian optimization, a plasticizing industry recommendation report is generated, the plasticizing industry recommendation report comprises the industry flow relation change trend and the corresponding industry hotspot recommendation, and a user is assisted in marketing decision making. The method can shorten the time consumption of the whole supply-demand docking process, effectively alleviates the information asymmetry problem, and remarkably improves the industry operation efficiency.

The invention relates to the technical field of parameter optimization, in particular to a data-driven hydraulic parameter real-time optimization method and system, and the method comprises the steps: obtaining drilling multi-source heterogeneous data, and carrying out the cleaning and multi-modal space-time alignment fusion, and generating a standardized multiphase flow parameter; inputting a pre-trained multiphase flow transient model through a data-model combined driving mechanism, dynamically correcting fluid density distribution and phase change parameters, and outputting a transient simulation result matched with a drilling working condition in real time; and determining hydraulic parameters to be optimized based on a simulation result, and generating a real-time optimization scheme of optimal displacement, throttling pressure and well killing parameters by combining wellbore pressure constraint and a multi-objective optimization function and adopting an adaptive particle swarm optimization algorithm for iterative calculation. According to the method, multi-source data space-time alignment and model dynamic correction are achieved, transient simulation precision is improved through a combined driving mechanism, and an optimal hydraulic parameter scheme meeting wellbore safety constraints can be generated by combining an adaptive optimization algorithm.

The invention discloses a warehouse AGV path planning method based on an improved wolf pack algorithm, and the method comprises the steps: a central control system receives tasks in a unified manner, builds a task table, and carries out the priority sorting of the tasks through a task value evaluation function; according to the task information in the task table and the state of each AGV, distributing the task to the AGV which meets the task condition and is closest to the task point; a map model is initialized, a central control system plans an optimal or suboptimal path for getting goods at a designated position according to task information and path conditions of a warehouse and selecting distribution of the goods on a goods conveying belt, and if the AGV conflicts with other AGVs in the path, avoidance is carried out according to a conflict avoidance rule. According to the invention, through improvements in multiple aspects such as dynamic path planning, multi-AGV collaborative optimization, energy management, real-time path updating and effective collision avoidance strategies, the operation efficiency, the task completion rate and the safety of the warehouse AGV system are significantly improved.

The invention belongs to the technical field of power system optimization scheduling, and particularly discloses a VPP real-time scheduling method based on cloud edge collaborative architecture and ADMM consistency optimization, a cloud operator uses a particle swarm algorithm to carry out global cost optimal calculation on real-time power deviation, and after an edge side transformer area group receives a scheduling instruction through a main node, the scheduling instruction is sent to the main node; the ADMM algorithm is adopted to realize the consistency iterative optimization of the cost increment rate of each transformer area, and dynamic coupling is carried out through default penalty constraint and an electric power spot market transaction strategy. According to the VPP real-time scheduling method based on the cloud edge collaborative architecture and the ADMM consistency optimization, the problem of real-time power mismatch caused by distributed resource prediction deviation and load abrupt change is effectively solved through a double-level collaborative mechanism, so that a virtual power plant meets the day-ahead contract response requirement, and the real-time scheduling efficiency of the virtual power plant is improved. Multiple deviation correction modes such as energy storage charging and discharging, resource regulation and control or market transaction can be flexibly selected according to real-time electricity price fluctuation, and finally the real-time scheduling target with the optimal operation cost is achieved.

The invention discloses a source-grid-load-storage integrated coordinated management and control system for an intelligent micro-grid, and relates to the technical field of intelligent micro-grids and comprehensive energy regulation and control. The system comprises the following components: a multi-energy-flow data acquisition unit, a multi-energy-flow coupling modeling and optimizing unit, a multi-energy-flow gradient utilization execution unit, a mode self-adaptive switching unit and a main control unit, according to the invention, through a multi-energy flow coupling modeling and optimization unit, an electric-thermal-gas multi-energy flow coupling model comprising a heat supply network transmission loss calculation sub-model is constructed, a renewable energy consumption constraint sub-model is additionally arranged, and an improved hybrid particle swarm optimization algorithm is adopted to carry out dynamic optimization solution, so that an optimal scheduling strategy is generated; according to the strategy, the consumption rate of renewable energy sources is increased, electricity-heat gradient utilization and efficient configuration are achieved through a heat energy distribution priority regulation and control mechanism and efficiency optimization control of the waste heat recovery module, and dependence of heat loads on electric energy is remarkably reduced.

The invention relates to a photovoltaic power generation system fault diagnosis method based on a dual-channel CNN and time-frequency characteristics. The method comprises the following steps: S1, collecting and preprocessing historical time sequence data of a photovoltaic power generation system; s2, copying the preprocessed data set to obtain a one-dimensional time sequence data set and a two-dimensional time-frequency graph data set; s3, inputting a dual-channel CNN for feature extraction, enabling a one-dimensional time sequence data set to enter a 1DCNN channel, and enabling a two-dimensional time-frequency graph data set to enter a 2DCNN channel; s4, performing dynamic fusion on the features extracted by the dual-channel CNN through an SE attention module; s5, constructing a classifier based on the SVM, and optimizing two key parameters of a kernel function of the classifier by adopting an adaptive inertia weight particle swarm algorithm to obtain a final SVM classifier; and S6, inputting the fused features into a final SVM classifier, and carrying out fault diagnosis classification. The complex fault diagnosis efficiency and reliability of the photovoltaic power generation system are effectively improved.

The invention relates to the field of unmanned ship control, and provides a flight path tracking method, system and device based on an improved bee colony algorithm and a medium, and the method comprises the steps: determining the position information of an unmanned ship through a navigation device; obtaining an expression of a reference course angle through a predetermined path, calculating an azimuth error and a foresight distance, calculating a virtual input calculation direction error, and generating an initial rudder angle; interference parameters are obtained, a model attitude controller is established, the motion attitude of the unmanned ship is predicted, a path prediction error is obtained, and an optimization function is constructed; calculating a dynamic weight coefficient and an adaptive scaling factor, carrying out initial solution migration to obtain an initial migration solution, selecting a current optimal solution, carrying out migration on the initial migration solution to obtain a target migration solution, and carrying out optimal solution solving of an optimization function to obtain a target rudder angle; and controlling the unmanned ship so as to complete the control of the unmanned ship. According to the invention, accurate track tracking of the unmanned ship can be realized.

The invention discloses a network construction type novel energy storage multi-objective optimization coordinated scheduling method, which comprises the following steps: S1, energy storage classification and data acquisition: (1) according to application scene function requirements, dividing energy storage types according to rated continuous discharge duration; (2) data acquisition; s2, calling an energy storage scene and combining with weight parameter setting to construct a multi-objective optimization model: (1) establishing the multi-objective optimization model with the purposes of minimizing the system operation cost, maximizing the energy storage utilization rate and improving the power grid stability; (2) setting constraint conditions; s3, a dynamic optimization algorithm: (1) adopting an improved multi-target particle swarm algorithm or a genetic algorithm, introducing an adaptive weight mechanism, and combining real-time data to dynamically adjust the weight of a target function; (2) aiming at uncertainty factors, embedding a scene analysis method or a robust optimization strategy, and judging whether the comprehensive efficiency is optimal or not; and S4, scheduling execution and monitoring.

The invention discloses an underwater gliding robot anchoring process energy consumption optimization method and system based on a subdomain grey box model. The energy consumption optimization method comprises the steps that a white box energy consumption mechanism model in the underwater gliding robot anchoring process is established; the whole anchoring energy consumption stage of the underwater gliding robot is divided into a plurality of sub-domains, and a Latin hypercube design method is adopted to simulate and generate a plurality of sub-domain samples meeting variable range constraints by using a hardware-in-the-loop simulation platform; selecting a Kriging model as a sub-domain agent model, fusing the white-box energy consumption mechanism model and constraint conditions of an oil bag volume and a movable mass position to form a sub-domain grey-box model, and performing segmented energy consumption fitting by using the sub-domain grey-box model; and with minimization of energy consumption fitted by the sub-domain grey box model as a target, performing iterative optimization on the sub-domain proxy model by adopting a dynamic guidance self-adjusting particle swarm algorithm, and outputting optimal planning parameters. The method realizes anchoring full-process low-energy-consumption control, and is suitable for marine resource exploration, hydrological monitoring and other tasks.

The invention discloses a trajectory planning and tracking method suitable for a small underwater robot, and belongs to the technical field of automatic control, and the method comprises the steps: generating a three-dimensional underwater environment global path of the small underwater robot through employing an improved adaptive ant colony algorithm; a dynamic obstacle is considered, a dynamic window method is fused to carry out local optimization on the generated global path, and an optimized path is obtained; the ocean current field environment is considered, a quantum particle swarm algorithm is used for optimizing the generated global path, and an optimized path is obtained; and improving the MPC model by using an improved particle swarm optimization algorithm, and tracking the trajectory generated by the small underwater robot through the improved MPC model. By adopting the method, the problems of low trajectory planning efficiency, poor path smoothness, high energy consumption, insufficient trajectory tracking precision and the like in a dynamic environment in the prior art are solved, and the task execution capability and adaptability of the underwater robot in a complex environment are remarkably improved.

The invention discloses a seismic signal denoising method based on PSO-VMD combined improved wavelet transform. The seismic signal denoising method comprises the following steps: S1, obtaining noisy and non-noisy seismic signals; s2, searching an optimal decomposition layer number K and a penalty factor alpha of VMD decomposition by using a particle swarm algorithm; s3, performing VMD decomposition on the seismic signal to obtain K modal functions; s4, kurtosis values of the K modal functions are calculated, and the modal functions containing noise and not containing noise are screened out; s5, retaining the original modal component of the modal function which is not smaller than the set kurtosis threshold value, and performing improved wavelet transform denoising on the modal function which is smaller than the set kurtosis threshold value to obtain an approximate function of the seismic signal and the wavelet function; and S6, reconstructing the modal function after noise reduction and the reserved modal function to obtain a seismic signal after noise reduction. According to the method, more effective signals can be reserved while similar calculation efficiency is kept, and the method has good stability.

The invention discloses a load transfer method, system, equipment and medium considering new energy and load power fluctuation, and belongs to the technical field of relay protection, and the method comprises the steps: taking a switch motion moment as a first stage of load transfer, taking a set time after the switch motion as a second stage of load transfer, building a dual-stage load transfer model, and taking the set time as a second stage of load transfer; obtaining a load transfer scheme; double-stage constraint verification is executed, and operation constraint verification is carried out on the load transfer schemes of the first stage and the second stage; introducing a constraint out-of-limit penalty factor, and quantifying the degree of the load transfer scheme deviating from the constraint condition; constructing a load transfer scheme comprehensive evaluation function, and performing weighted integration; and optimizing the load transfer scheme comprehensive evaluation function by adopting an improved binary particle swarm algorithm, and determining an optimal load transfer scheme. According to the method, the limitation of a traditional static model is overcome, and the problem of operation constraint violation caused by photovoltaic output change is avoided.

The invention relates to the technical field of new energy power generation control, and provides a new energy large base economic optimal control method and system, and the method comprises the steps: building a multi-temporal-spatial scale power prediction model, and outputting a power prediction value; constructing a time-varying multi-objective optimization model of an objective function including economic cost, equipment loss cost and power grid constraint cost and constraint conditions; adjusting weight coefficients of the economic cost, the equipment loss cost and the power grid constraint cost; and solving the time-varying multi-objective optimization model after the weight coefficient is adjusted through an improved multi-objective particle swarm algorithm to obtain an optimal control instruction set. According to the method, by constructing a collaborative architecture of a dynamic weight adjustment mechanism and a multi-target optimization model, multiple targets such as power generation efficiency, equipment life and power grid stability can be dynamically coordinated, multi-target dynamic balance is realized, the defect that multiple targets are difficult to coordinate by a traditional fixed weight coefficient is overcome, and the economical efficiency and operation benefits of a new energy large base are improved.

The invention provides a mobile robot path planning method based on an improved ant colony-guided optimization algorithm. According to the method, after a grid map environment is constructed, a dynamic selection factor is introduced to optimize a state transition rule of an ant colony algorithm, so that the efficiency at the initial stage of search is improved; a multi-step search strategy is adopted to enhance the flexibility of path planning, and an end point distance index is utilized to replace the traditional pheromone concentration, so that the risk of falling into local optimum is reduced; and carrying out smoothing processing on the path in combination with a guide optimization algorithm to realize iterative correction and global path optimization of a feasible region. The method has good path quality and convergence performance, and is suitable for a mobile robot path planning task. Simulation results show that compared with a traditional ant colony algorithm and an improved method thereof, the algorithm has obvious advantages in the aspects of path length, the number of corners, convergence efficiency and the like.

The invention discloses a cooperative control and dynamic resource scheduling method and system for a smart park, and relates to the technical field of program control. According to the method, the heterogeneous sensor network is deployed, and multi-source data is subjected to denoising, feature extraction and classified transmission; constructing a digital twinborn model, embedding equipment physical law constraints, optimizing the learning rate of an RCN prediction network in combination with historical data and a bee colony algorithm, and realizing high-precision state prediction; when the real-time data or the predicted data exceeds a threshold value, triggering global scheduling, generating an initial allocation scheme by using an improved contract network protocol, and generating a Pareto optimal scheme set of comprehensive cost, equipment utilization rate and service delay through a pea population algorithm; verifying the feasibility of the scheme based on digital twinning simulation, and dynamically distributing conflict resources by adopting virtual auction; the network load is reduced, the prediction progress is improved, the equipment load peak-valley difference is compressed, and the park resource scheduling efficiency is improved.

The invention discloses a travel map searching system and method based on deep learning and a Beidou database, and the method comprises the following steps: obtaining the position and track data of a user, loading a geographic object, and constructing a heterogeneous map; receiving a natural language request, extracting a semantic vector and writing the semantic vector into the graph; constructing a graph comparison learning network, generating a view and executing embedded learning; optimizing structural parameters by using a pigeon inspired algorithm, and outputting an optimal model; constructing a current position node, extracting a local sub-graph, and generating a sorting list; executing path reasoning, generating a recommendation result, and feeding back and updating. According to the method, deep semantic understanding and geographical trajectory modeling are fused, so that high-precision, interpretable and self-learning travel map intelligent search recommendation under the driving of the current position of the user is realized.

The invention provides an intelligent detection method and system for an urban sewer pipeline, and the method comprises the steps: synchronously collecting pipeline data through laser, vision, ultrasound and a pressure sensing array, and generating aligned multi-source data through space-time registration; edge end defect preliminary screening is realized by using a lightweight AI model, and high-risk data is screened and uploaded to a cloud end; a pipeline digital twin model is constructed based on point cloud splicing and feature recognition, a multi-modal fusion neural network is adopted to realize accurate defect diagnosis, and a detection path is dynamically optimized in combination with an ant colony algorithm. According to the method, the technical problems that the adaptability is poor and the omission ratio is high in a complex sewer pipeline environment and multi-source data fusion analysis and active early warning cannot be realized by a traditional method can be solved.

The invention relates to the technical field of cloud computing, and provides a cloud native assembly type application construction method and device supporting dynamic combination. According to the method, through component standardized packaging and distributed warehouse management, the high component reuse rate is achieved, the repeated development workload is reduced, a visual arrangement tool and a process template are reused, the application construction period is shortened by 50% or above, business personnel can directly participate in development, the'business-technology 'communication cost is reduced, and the development efficiency is improved. And during dynamic combination, an optimal loading path is calculated by adopting an optimization particle swarm algorithm of global search of a particle swarm algorithm and local kick of a simulated annealing algorithm, so that intelligent dependency analysis and optimal path calculation are realized, and the complexity of manual dependency sorting is avoided.