397 results about "Configuration optimization" patented technology

The invention relates to the technical field of energy scheduling, in particular to an energy system capacity optimization and mobile resource space-time decoupling cooperative scheduling method, system and device under extreme disaster disturbance and a medium. Load change parameters are coupled to form a disaster-causing response database; renewable energy output fluctuation and user load demand deviation are converted into fuzzy variables, and a capacity configuration optimization model is established in combination with a disaster-causing response database to generate multi-energy complementary capacity configuration; mobile resources are introduced to establish an elastic lifting scheduling model, and an elastic lifting scheme is generated by implementing damaged road network topology reconstruction of the mobile resources, space-time transfer chain modeling and action state coordinated regulation and control. According to the method, collaborative decision-making of capacity optimization of equipment and dynamic scheduling of mobile resources is driven through the disaster-causing response database, and dual elastic gains of fixed resource optimization and after-disaster quick response of the mobile resources under extreme disaster events are achieved.

The invention relates to the technical field of power data processing, in particular to an energy storage configuration optimization method, which comprises the following steps: acquiring new energy output time sequence data and computing power load characteristic data; generating a space-time correlation coupling evaluation result of the new energy output volatility and the computing power load volatility; inputting a result into a hybrid power supply double-layer optimization model, recursively correcting parameters through a two-stage collaborative solution algorithm, and outputting a Pareto optimal solution set; a computing power task elastic regulation and control mechanism is embedded, and the task priority is dynamically adjusted according to the energy storage charge state and the new energy output level to generate a scheduling strategy; finally, an energy storage configuration scheme and a dynamic scheduling strategy are output, and collaborative optimization of cost effectiveness and power supply reliability is achieved. The method breaks through the coupling conflict of the economic target and the robust constraint in the traditional bilevel planning, remarkably reduces the energy storage configuration cost, and improves the system stability.

The invention relates to the technical field of design service, in particular to a roof waterproof structure intelligent design optimization system which comprises a node contour extraction module, a contact segment recognition module, a close boundary evaluation module, a disturbance area labeling module and a waterproof configuration reconstruction module. According to the invention, by extracting the direction vector change of the boundary point cloud sequence, the continuity and stability of the turning trend in the contour structure can be accurately obtained, the boundary of the roof structure can be identified, and the contact type of the boundary joint can be determined in combination with the direction change rate distribution of the contact surface, so that the boundary sealing state has quantitative index output; in addition, local positioning information of a fitting path and a structure defect can be provided, a node contour reconstruction path with an adjustment space is provided based on a pressing path continuity judgment and simulation edge shift strategy, and the adaptability of a node structure under the action of complex stress and the configuration optimization capability of the overall waterproof performance of a roof are improved.

The invention discloses a deep-sea mining VTS configuration optimization design method based on data driving, and belongs to the technical field of deep-sea mining engineering and optimizing.The method comprises the steps that firstly, a mechanical response model of a VTS is constructed through a VFIFE method, and mechanical response is calculated in combination with VTS structure parameters and borne external force; then generating a prediction agent model based on CPO-BP, and realizing relation modeling between the configuration design variable and the mechanical response of the VTS; and finally, the prediction agent model is embedded into the MODA-DE, the VTS configuration is optimized based on the MODA-DE, and VTS configuration design variables meeting the multi-objective optimization requirements are finally obtained through multi-generation iteration. According to the scheme, the three links of VFIFE, CPO-BP and MODA-DE are closely combined, an efficient multi-target optimization technology is formed, collaborative improvement from high-precision prediction to global optimization is achieved in mining pipeline configuration design, and the method has high actual popularization and application value.

According to the transition prediction method suitable for the wide Mach domain of the high-speed aircraft provided by the invention, temperature and Mach number coupling correction is performed on key parameters in time scale modeling of a second mode of a transition mode, and simplified processing of a flow compression effect and a heat transfer process by traditional single parameter association is broken through; the influence of an incoming flow disturbance effect is embedded in the mode, and dynamic association between test environment parameters and transition triggering criteria is achieved; according to the improved mode, accurate prediction results are obtained on a plurality of examples with the Mach number range of 4.7-14, the research results provide a more reliable theoretical tool for aerodynamic configuration optimization of a high-speed aircraft with a wider speed range, and the method has important engineering guiding significance for thermal protection system design of a new-generation reusable aircraft.

The invention discloses a server configuration optimization method and device, a storage medium and electronic equipment, and relates to the technical field of computers.The method comprises the steps that firstly, configuration data of a server is obtained; configuration data of the servers are input into a reinforcement learning model, change benefits corresponding to different configuration change operations of the servers are predicted, and the reinforcement learning model is obtained by grouping server clusters and training based on sample data generated in the running process of multiple groups of servers; and generating a configuration optimization strategy of the server according to the change income. Compared with the prior art, the method has the advantages that grouping management and operation and maintenance monitoring are performed on the server cluster, reinforcement learning modeling is combined, the reinforcement learning model is utilized to generate the intelligent server configuration optimization strategy, a traditional server operation and maintenance configuration mode is optimized, and the server operation and maintenance efficiency and benefits are effectively improved.

The invention discloses an energy storage optimal configuration method and system based on data-driven model fusion. The method comprises the following steps: extracting feature data with important influence in load data and operation and maintenance data for data fusion; constructing a load prediction model for predicting the load state of the energy storage equipment and a predictive maintenance model for predicting the health state and the remaining service life state of the energy storage equipment; training a load prediction model and a predictive maintenance model of the energy storage system through iteration; and the load state, the health state and the remaining service life state of the energy storage equipment are predicted in real time, an energy storage system configuration optimization model is constructed, and the overall configuration of the energy storage system is optimized. The method is not only used for short-term power load prediction, but also used for health state prediction and residual service life prediction of the energy storage system, through the accurately predicted energy storage equipment state, an energy storage system configuration optimization model is cooperatively constructed in a complex power grid scene, a maintenance plan is reasonably arranged, and efficient and stable operation of the energy storage system is ensured; and the reliability and the economical efficiency of power supply are improved.

The invention provides a configuration optimization method before capturing of a space double-arm robot, and the method comprises the steps: constructing a kinematics and dynamics model of the space double-arm robot according to a three-dimensional model of the space double-arm robot; constructing a kinetic model of the to-be-captured target by using the motion parameters of the to-be-captured target in the space; according to the dynamic model of the space double-arm robot, the relative mass of the space double-arm robot under non-centering collision is obtained, and a maximum pre-contact collision force performance evaluation index is constructed; further constructing a double-arm closed chain inertia matching index performance evaluation index; constructing a configuration optimization model before the space double-arm robot captures the target; and through a multi-target optimization algorithm, the optimal configuration of the space double-arm robot before capturing the target is solved. The pre-contact collision force peak value of the double-arm robot during target capturing can be effectively reduced, and meanwhile the operability of a closed chain system formed after capturing is guaranteed.

The invention relates to an energy system configuration optimization method considering equipment variable working condition characteristics and energy storage life attenuation, which comprises the following steps: establishing a mathematical model of an energy system, and calculating equipment cycle aging indexes in the mathematical model of power storage equipment by considering the energy storage life attenuation, in mathematical models of the cold-heat-electricity combined supply system, the gas-fired boiler and the electric refrigerating unit, the corresponding efficiency is calculated by considering the variable working condition characteristics of the equipment; and based on the established mathematical model, resource configuration optimization of the energy system is carried out by using a double-layer optimization method, the upper layer optimizes equipment capacity design by taking minimization of annual cost as a target, and the lower layer carries out operation optimization by taking the sum of minimization of operation cost, battery life degradation cost and demand electricity price as a target. The battery life degradation cost is calculated based on the equipment cycle aging index. Compared with the prior art, battery life attenuation and equipment variable working condition operation are considered at the same time, and the overall efficiency and reliability of the system are improved.

The invention relates to the technical field of optical line terminal configuration, in particular to a method and system for automatically configuring optical line terminals of different manufacturers based on artificial intelligence. The method comprises the steps of collecting multi-source heterogeneous data to train a deep learning model, constructing a Transform or RNN-based architecture of a TensorFlow framework, performing cross validation and model compression, then performing optimization through a TensorRT inference engine, developing an intelligent configuration application system, deploying the intelligent configuration application system to a control host, and providing algorithm support for automatic configuration. By constructing an automatic configuration system based on artificial intelligence, the problems of heterogeneous compatibility and intelligent configuration of OLT equipment of multiple manufacturers are systematically solved. In a configuration optimization link, a virtual simulation environment is constructed based on a real network topology, a configuration effect is evaluated through multi-dimensional indexes such as throughput and time delay, potential conflicts are detected in combination with an AI algorithm, an optimization scheme is generated, and the configuration error rate of manual operation is reduced.

The invention discloses a novel telescopic pipe structure of a high-temperature and high-pressure well and an optimal configuration method of a pipe column of the novel telescopic pipe structure, belongs to the technical field of oil and gas exploitation, and aims to solve the problem of buckling failure of the pipe column caused by piston force load of an existing telescopic pipe under high-temperature and high-pressure working conditions. The novel telescopic pipe is composed of an inner cylinder mechanism, an outer cylinder mechanism and a balance chamber mechanism. The inner cylinder mechanism and the outer cylinder mechanism are connected through threads, and structural integrity and reliable connection are guaranteed. The balance chamber mechanism comprises inner and outer pressure cavities and a hydraulic hole which are respectively communicated with the inner and outer pressure of the oil pipe to dynamically balance the piston force. The optimized configuration of the pipe column comprises the following three steps: S1, analyzing the mechanical behavior of the telescopic pipe column to provide a mechanical basis for configuration optimization; s2, the position of the telescopic pipe is designed, the well depth range where the telescopic pipe works is determined, and the optimal position is locked through iterative calculation; and S3, designing the number of the telescopic pipes, and determining the optimal number of the telescopic pipes meeting the compensation requirement. The stability of the high-temperature and high-pressure well pipe column can be remarkably improved, and technical support is provided for safe and efficient development of ultra-deep oil and gas wells.

Disclosed in the present invention are a shared energy storage configuration optimization method and system for a wind farm cluster. The method comprises: constructing a shared energy storage double-layer programming model; on the basis of the output of a wind farm cluster, an electricity price in an electric energy market and a compensation price in a frequency-modulation auxiliary service market, obtaining multiple uncertainty sets, using an improved K-means clustering method to perform joint clustering on the multiple uncertainty sets so as to obtain a typical scenario, and on the basis of the annual occurrence frequency of the typical scenario, calculating a typical scenario probability; and using a distributionally robust optimization method to model the multiple uncertainty sets on a short-time scale so as to obtain an extreme uncertainty scenario, using the typical scenario probability and the extreme uncertainty scenario to update the shared energy storage double-layer programming model so as to obtain an updated shared energy storage double-layer programming model, and using a particle swarm algorithm to perform solving so as to obtain a configuration result. By means of the method, shared energy storage configuration optimization is performed by means of taking various uncertainty factors into consideration, thereby improving the accuracy of shared energy storage configuration optimization.

The invention discloses a user demand-driven distributed energy supply system capacity configuration method based on a large language model. The method comprises the following steps: collecting user demand information; performing semantic analysis and understanding on the acquired user demand information by utilizing a large language model, and outputting structured and quantitative user demand description; parameterizing the structured and quantized user demand description to obtain a mathematical model for capacity configuration optimization; and solving a mathematical model for capacity configuration optimization by adopting a numerical optimization algorithm, generating a plurality of optimization schemes, and performing performance evaluation and visual display to assist a user in selection. According to the method, the accuracy and the intelligent level of user demand understanding are improved through the large language model, automatic processing and personalized capacity configuration of unstructured demands are achieved, the capacity configuration efficiency and the user satisfaction degree of the distributed energy supply system are remarkably improved, and the system performance is effectively improved.

The invention relates to the technical field of control and adjustment, in particular to an energy storage configuration point-based power consumption cost calculation method, which comprises the following steps of: acquiring power supply data, renewable energy output fluctuation data and computing power load fluctuation data, and generating a coupling influence feature vector; splicing the electricity price features and the coupling features to form configuration optimization input features; the input features are processed through a dual-objective optimization model, and robustness configuration parameters and economical efficiency operation parameters are output; calculating the reserve cost compensation amount of the extreme scene based on the robustness parameter, and calculating the power purchase cost and maintenance cost of the power grid based on the economical efficiency parameter; and aggregating the cost component and outputting a power consumption cost calculation result. According to the method, the interactive influence of wind-light fluctuation and load mutation is quantified through coupling features, the target conflict between robustness and economy is coordinated by adopting the Pareto algorithm, the risk compensation amount is generated in combination with a pressure test, a feature updating mechanism is fed back in a closed loop mode, and the problem that the operation cost deviates from expectation due to quantization deviation of an existing model is solved.

The invention relates to the technical field of micro-grid configuration, in particular to a wind and light storage micro-grid capacity optimization configuration method based on a PGWO algorithm, and the method comprises the following steps: building a wind and light storage micro-grid capacity optimization configuration model according to the output power model of each distributed power supply in a micro-grid; setting a micro-grid operation strategy; under the micro-grid operation strategy, solving the capacity optimization configuration model through a PGWO algorithm to obtain a micro-grid optimal configuration scheme; according to the method, the PGWO algorithm combining Bernoulli chaotic mapping initialization, individual historical memory and a border crossing processing strategy is provided, the method has higher global optimization capacity, capacity configuration optimization can be carried out on the premise of stable operation of the microgrid, and the operation cost is reduced.

The invention discloses a collaborative configuration optimization method for an intelligent self-reconfiguration robot. The invention relates to the field of modular robot configuration-motion collaborative optimization in extraterrestrial planet surface inspection and detection tasks, constructs a multi-constraint dynamic graph optimization model for unified representation of configuration and motion control, and aims to improve the configuration reconstruction efficiency of a modular robot and obtain an optimal configuration-motion reconstruction scheme. The method comprises the following steps: carrying out restrictive configuration characterization on a robot by utilizing a rooted tree structure; constructing a sensing-driving integrated neural network to perform robot motion control representation; constructing a multi-constraint dynamic graph optimization model to uniformly represent configuration and motion control; constructing a fitness function for reconstruction optimization according to the evaluation function corresponding to the task; and performing configuration-motion optimization solution by using a non-dominated sorting genetic algorithm with an elitist strategy.

The invention belongs to the technical field of industrial boiler control and parameter configuration, and particularly provides a boiler multi-modal data collaborative configuration optimization method based on the industrial internet, and the method mainly comprises the steps: obtaining boiler operation data through the industrial internet; dividing the boiler operation data into operation modal characteristic data and working condition characterization data; generating a boiler parameter optimization relation matrix according to the operation mode characteristic data; performing parameter fusion recombination and cooperative state coding processing on the boiler parameter optimization relation matrix to generate a cooperative configuration parameter set; and outputting a final optimization control instruction set of boiler operation according to the collaborative configuration parameter set and the working condition representation data. The energy efficiency and emission indexes of the boiler can be improved, linkage adjustment of global balance temperature and pressure parameters is achieved, system oscillation and safety conflicts are avoided, the load fluctuation response precision is enhanced through a real-time closed-loop mechanism, stable operation is ensured, and safe, efficient and low-emission collaborative optimization is achieved.

The invention discloses an energy storage system capacity configuration optimization method and device and a storage medium, and relates to the technical field of energy storage system capacity configuration.The energy storage system capacity configuration optimization method comprises the following steps that daily total electricity utilization power information of a factory is continuously collected, corresponding weather information is obtained, and factory power weather data is obtained; denoising processing is carried out, an electricity consumption power prediction model is constructed, and the daily total electricity consumption power of the factory is predicted; calculating a prediction error, carrying out error rule analysis, and correcting the predicted total power consumption power to obtain corrected power prediction data; calculating predicted electricity consumption, and performing configuration optimization on the capacity of the energy storage system of the factory according to the predicted electricity consumption; the method is used for solving the problems that when an existing energy storage system capacity configuration technology configures the capacity of an energy storage system of a factory through a power consumption prediction model, the rule of prediction deviation of the model cannot be analyzed, meanwhile, a prediction result cannot be corrected, and the accuracy of capacity configuration of the energy storage system cannot be improved.

The invention discloses a game theory-based capacity configuration optimization method for a water, wind and light storage micro-grid, and relates to the technical field of energy system operation optimization configuration. According to the method, four types of energy operators including water, wind, light and storage serve as game subjects, and the problems of multi-subject benefit conflict and capacity configuration are solved through a cooperative game model and a double-layer optimization framework. The method comprises the following steps: constructing an alliance total revenue function and verifying the super-additivity; fairly distributing earnings by adopting a Shapley value in combination with a capacity correction factor; an economical, stable and environment-friendly multi-objective optimization model is established, an improved NSGA-III algorithm is utilized to dynamically generate a Pareto optimal solution set, and an optimal capacity configuration scheme is selected through three-dimensional tradeoff. According to the invention, the economical efficiency of the system is improved by 51.6%, the output fluctuation is reduced by 47.4%, the carbon emission is reduced by 43.2%, and an efficient and balanced capacity optimization method is provided for the multi-energy microgrid.

The invention discloses a ventilation cabinet parameter configuration management method based on big data, and relates to the technical field of big data processing, and the method comprises the following steps: collecting historical data of a ventilation cabinet; performing parameter analysis on the historical data; performing performance prediction on a parameter analysis result; performing configuration optimization on the prediction result; setting parameters according to an optimal configuration result; carrying out equipment calibration according to a parameter setting result; and performing system health assessment according to a calibration result. According to the method, by introducing multi-dimensional real-time data acquisition, a health assessment algorithm and a self-adaptive real-time feedback mechanism, the problems of feedback lag and inaccurate health assessment in the prior art are solved. And an intelligent algorithm is utilized to comprehensively analyze the operation state of the equipment, and adjustment is made in time, so that accurate prediction and optimal management of the performance of the equipment are realized. And through a data-driven feedback mechanism, the operation efficiency of the equipment is remarkably improved, and the safety and the stability of the equipment are ensured.

The invention belongs to the technical field of cold and heat source configuration methods, and particularly relates to a cold and heat source configuration optimization method and system based on a full life cycle, and the method comprises the steps: S1, building use characteristic analysis; s2, designing a staged unit configuration scheme; s3, optimizing an intelligent control strategy; s4, carrying out full-life-cycle cost-benefit analysis; s5, optimal scheme decision making and dynamic adjustment; according to the method, a dynamic prediction model of a time sequence analysis and Transform-TCN hybrid architecture is adopted, historical load data can be deeply mined through time sequence analysis, and time characteristics and periodic rules in the data are captured; the long sequence modeling capability of the Transform and the local feature extraction capability of the TCN are combined by the Transform-TCN hybrid architecture, so that the long and short term dependency relationship in the load data can be better processed, and the prediction accuracy is improved.

The invention discloses a capacity configuration optimization method for an energy storage system, relates to the field of energy storage planning, solves the problem of energy waste of the energy storage system, and comprises the following steps: collecting historical environmental data of a region where a power generation facility corresponding to the energy storage system is located and historical power consumption of a power grid corresponding to the energy storage system; analyzing the power generation performance of the power generation facility of the energy storage system and the power consumption performance of a power grid corresponding to the energy storage system to obtain a peak value operation time period, an average value operation time period and a valley value operation time period corresponding to the energy storage system; setting a system capacity configuration scheme of the energy storage system corresponding to the peak value operation time period and the valley value operation time period; analyzing the operation state of the energy storage system corresponding to the average operation time period, and setting a system capacity configuration scheme of the energy storage system in the average operation time period; the actual energy storage mode of the energy storage system is adjusted based on the system capacity configuration schemes corresponding to the peak value operation time period, the average value operation time period and the valley value operation time period, and stable operation of the energy storage system is guaranteed by improving the energy utilization rate.

The invention discloses a space target monitoring constellation configuration optimization method and system considering complex constraints, and belongs to the technical field of aerospace mission design and optimization. The method comprises the following steps: firstly, constructing a discrete orbit modal library meeting regression orbit dynamic resonance conditions; constructing generalized hybrid design variables consisting of integer indexes and real number fine tuning quantities on the basis of the library; establishing an optimization model taking the space volume coverage rate, the maximum revisit time and the system construction cost as targets and taking illumination, dynamics and maneuvering capability as constraints; performing rapid analysis and coarse screening on invalid links through track geometry; and finally, performing global optimization by using an improved non-dominated sorting genetic algorithm to obtain a Pareto optimal constellation configuration set. According to the method, accurate evaluation of the three-dimensional space target coverage efficiency is realized, the physical feasibility and long-term stability of a configuration scheme are ensured, and a high-dimensional multi-target optimization problem is efficiently solved.

The invention belongs to the technical field of resource optimization configuration and decision support, and discloses an equipment configuration optimization method, which comprises the following steps of: acquiring multi-source heterogeneous data such as task demand information, available equipment resource information and environment situation information, constructing a standardized configuration basic data set, and establishing a standardized configuration basic data set; constructing a task demand dynamic prediction model, an equipment capability evaluation model and an environmental impact analysis model, and defining a multi-dimensional configuration constraint condition and an optimization objective function; the method comprises the following steps: performing iterative optimization on an equipment configuration scheme, generating an initial optimal configuration scheme, triggering a dynamic reconfiguration mechanism according to real-time feedback information and environment change in a task execution process, and performing adaptive adjustment and online optimization on the optimal configuration scheme. According to the invention, the intelligent, dynamic and refined configuration of the equipment is realized, the configuration efficiency, the resource utilization rate and the task success rate are obviously improved, and the configuration challenge in a complex dynamic environment is effectively dealt with.

The invention discloses a configuration method of a wind-solar-thermal storage coupling system with wind-solar load uncertainty, and the method comprises the steps: obtaining the predicted output power of a fan, the predicted output power of a photovoltaic generator set, and the predicted electric load demand of a user; obtaining the actual output power of the fan, the actual output power of the photovoltaic generator set and the actual electric load demand of the user according to the corresponding deviation; generating a plurality of initial scenes by using a Latin hypercube sampling method based on the actual output power of the fan, the actual output power of the photovoltaic generator set and the actual electric load demand of a user; performing clustering processing on the initial scene by using a k-means clustering method to obtain a reduced typical scene and a probability of the typical scene so as to obtain wind and light output and electric load requirements in the typical scene; and solving the configuration optimization model of the wind-solar-thermal storage coupling system based on wind-solar output and electric load requirements in typical scenes to obtain a capacity configuration result of the system.

The invention relates to the technical field of coating liquid configuration optimization control, and discloses a coating liquid configuration optimization method and system based on adaptive control and a storage medium. The method comprises the following steps: collecting coating liquid parameter data by utilizing multiple sensors, and obtaining a feature vector and an environmental influence factor data set; establishing a multi-layer mapping model of parameters and coating quality, and generating a sensitivity weight matrix; inputting the weight matrix and the constraint condition into an annealing algorithm, and calculating an optimal formula instruction; and driving the metering pump and the stirrer to execute configuration through sliding mode control based on the instruction. According to the application, accurate modeling of the relationship between the coating liquid parameters and the coating quality is realized, and accurate regulation and control of the coating liquid preparation process are realized through adaptive control on the basis, so that the consistency and stability of the coating quality are improved.

The invention relates to a wind-solar optimal configuration ratio-based capacity configuration optimization method for an off-grid wind-solar hydrogen production system, which comprises the following steps of: firstly, solving an optimal wind-solar ratio by taking a wind-solar complementary principle as a basis and taking economic cost and fluctuation of a wind-solar power generation overall curve as optimization targets according to the wind-solar complementary principle; then, on the basis of the optimal configuration ratio, an improved particle swarm optimization algorithm is adopted for an off-grid wind-solar hydrogen production system which has been successfully modeled according to hydrogen production demand data and historical data such as wind speed and illumination of a target area, so that the optimal configuration ratio of the wind-solar hydrogen production system is obtained, and the optimal configuration ratio of the wind-solar hydrogen production system is obtained. In the optimization process, optimization parameters such as inertia weights and learning factors change adaptively according to system parameters, capacity configuration optimization of the off-grid wind and light hydrogen production system is carried out, and finally an optimal capacity configuration result is obtained.

The present invention belongs to the field of complex equipment system design and verification. It discloses a model-based integrated design and verification method for complex equipment systems. With the four levels of "demand-behavior-structure-parameter" as the framework, combined with mission scenario requirements, a full-process design and analysis method for reusable space vehicle systems is constructed; starting from the demand analysis, the complex system is decomposed, and the model is calibrated and performance verified by combining theoretical and experimental data to construct a reusable space vehicle system model, and carry out system comprehensive analysis and verification; combined with the model's three-dimensional virtual environment and configuration optimization algorithm, various configurations of the space vehicle are quickly optimized to evaluate the feasibility and performance of the design scheme; through the two-way interaction of design and verification models, and the integration of system-level design and professional-level design, the problems of design and verification separation and insufficient model collaboration in the existing technology are solved, and the efficiency and accuracy of complex equipment system design and verification are improved.

The invention discloses a personalized configuration method for a cloud mobile phone and related equipment, and relates to the technical field of cloud computing, and the method comprises the steps: obtaining user operation behavior data; based on the user operation behavior data, determining a user behavior model through a time sequence analysis model and an association rule mining algorithm; determining a target configuration parameter based on the user behavior model; and controlling the cloud mobile phone system to perform dynamic configuration operation according to the target configuration parameter. The user behavior analysis module is used for collecting multi-dimensional data such as the touch track and the application switching frequency, and the accurate user behavior model is constructed by using the LSTM time sequence model and the Apriori algorithm, so that personalized configuration optimization based on fine-grained user behaviors is realized, and the adaptability of cloud mobile phone system configuration and user behavior habits is effectively improved.

The invention provides a river-sea combined transportation wharf resource intelligent dynamic configuration method and system based on deep learning, and the method comprises the steps: firstly obtaining a wharf resource type, a ship arrival demand and a historical configuration result set, then capturing the time sequence correlation between the ship demand and resource use through a deep learning model, generating a resource demand feature sequence, and carrying out the recognition of the ship arrival demand. Generating multiple groups of resource configuration initial schemes according to resource types and feature sequences, pre-relieving potential conflicts through an interaction module, inputting the multiple groups of resource configuration initial schemes into an evaluation module, generating an evaluation result in combination with a historical configuration result, and adjusting the schemes according to the evaluation result to generate a resource configuration optimization scheme; and triggering a wharf resource allocation operation, recording an application result, and feeding back the application result to the model updating logic, thereby realizing efficient and intelligent dynamic configuration of the wharf resources, and improving the wharf operation efficiency.