205 results about "Integer linear programming model" patented technology

The invention relates to a standardized project management and intelligent professional scheduling system, and belongs to the technical field of project management and human resource intelligent scheduling. The project standardization module is disassembled into standardization task steps through a business process association rule mining algorithm, and project complexity and resource tensity are adapted by using a task attribute dynamic weight algorithm; the archive matching module constructs professional archives, extracts feature vectors through a multi-criterion decision-weighted bipartite graph matching algorithm, and generates optimal matching pairs in combination with adaptive weight adjustment and an integer linear programming model; the dynamic scheduling module plans a task execution scheme according to a resource constraint scheduling mechanism, and realizes efficient scheduling in combination with a skill supply and demand prediction algorithm and calendar integration; and the quality optimization module adopts a deliverable anomaly detection algorithm to monitor compliance, feeds back an iterative matching and scheduling strategy through a time sequence prediction optimization algorithm, and perfects a skill map based on a map increment updating algorithm. The system realizes a project full-process closed loop.

The invention relates to an electricity-carbon cooperative scheduling optimization method and device for a low-carbon park integrated energy system, and the method comprises the steps: carrying out the cooperative prediction of a multi-state parameter through employing a panoramic situation deduction model, and generating a panoramic dynamic situation scene set; establishing an electricity-carbon cooperative scheduling model considering a carbon transaction mechanism, and deeply embedding the real-time carbon cost into a target function to carry out Pareto optimization of economic cost and carbon emission cost; an electricity-carbon cooperative scheduling model is converted into a standard mixed integer linear programming model, a situation deduction-day-ahead optimization-rolling correction hierarchical calculation framework is adopted to decompose a cooperative scheduling optimization problem to different time scales for decision making, and a global optimization plan is made on the day-ahead layer based on a panoramic dynamic situation. Deviation is corrected on line through rolling optimization in the intraday layer; and the integrated energy system executes the corrected scheduling plan. Compared with the prior art, the method has the advantages that the consumption rate of renewable energy sources can be remarkably increased and carbon emission can be effectively reduced while the operation economy of the system is ensured.

The invention relates to the technical field of high-performance computing resource management, and discloses a computing resource allocation method for a distributed supercomputing center. The method comprises the following steps: on the basis of obtaining real-time computing task and supercomputing center resource data and uniformly quantifying, integrally predicting resource requirements of future tasks; constructing a mixed integer linear programming model with the minimization of the total operation cost as a single target, wherein the total operation cost is the sum of the energy cost, the carbon emission cost, the data transmission cost and the SLA default penalty cost; solving the model by taking the time-varying electricity price, the green energy ratio, the resource capacity and the network parameters of each center as constraint conditions to generate an optimal resource allocation scheme; and then, by dynamically monitoring the resource state and the task progress, the model is triggered to resolve when the resource utilization rate is detected to be unbalanced or default risks, so that self-adaptive adjustment is realized. According to the invention, global collaborative resource allocation across super computing centers is realized, and operation economy, environmental sustainability and service reliability are considered.

The embodiment of the invention relates to the technical field of chemical products, in particular to a scheduling optimization method, device and equipment for chemical materials, and the method comprises the steps: initializing a plurality of mark sequences corresponding to a plurality of chemical material production devices; calculating the daily output of each batch of chemical materials according to the production rate and the production duration of each batch in each brand sequence; according to the daily output and the physical property parameters of each batch of chemical materials, determining the loss index of a chemical material production device and the daily consumption and the utilization rate of the chemical materials; constructing a mixed integer linear programming model by taking maximization of the utilization rate and minimization of the loss index as targets and taking conditions that the daily consumption meets daily inventory constraints and the starting time of each batch meets time window switching constraints, the mixed integer linear programming model comprises a production rate linear term and a production time linear term which are used for representing the daily output of each batch of chemical materials; and optimizing the plurality of mark sequences according to the mixed integer linear programming model.

The invention discloses a water energy accumulator and ground source heat pump combined control method, relates to the technical field of building environment and equipment engineering, accurately captures a time sequence change rule of a building cold load based on the load prediction capability of a time convolution network, overcomes the defect that a traditional method is insufficient in response to sudden fluctuation, and improves the control efficiency. The system can plan an energy storage strategy in advance; an electricity price-load change rate sensitivity index is introduced, and through an adaptive weight adjustment mechanism, economic operation and load tracking requirements are dynamically balanced, so that strategy stiffness caused by fixed priorities is avoided, and the utilization efficiency of time-of-use electricity price signals is remarkably improved; according to the joint optimization design of the mixed integer linear programming model, the equipment operation constraint and the cost target are comprehensively considered, the collaborative decision of the energy accumulator charging and discharging strategy and the heat pump frequency setting is realized, and the direct energy supply dependence in the midday high electricity price period is effectively reduced.

The invention discloses a reconfigurable flexible job shop scheduling optimization method with secondary clamping constraint, and relates to the technical field of intelligent manufacturing and production optimization. The method comprises the following steps of: 1) establishing a mixed integer linear programming model considering a reconfigurable flexible job shop scheduling problem of secondary clamping by taking minimization of maximum completion time and minimum number of chemical workers as targets; 2) designing a three-segment coding mode and a decoding mode corresponding to the mixed integer linear programming model based on process sorting, machine selection and worker selection; and 3) based on the three-segment coding mode and the decoding mode, adopting an improved multi-target genetic algorithm to solve an optimal scheduling scheme of the mixed integer linear programming model. According to the method, processing machine selection, auxiliary module selection, processing sequence sorting and secondary clamping worker selection of a manufacturing workshop can be considered at the same time, the workshop production efficiency is improved, and the method has the advantages of being good in model performance, small in result fluctuation and high in stability.

The invention discloses a site-level CCUS dynamic source-sink matching optimization method for an oil field enterprise, relates to the technical field of large-scale deployment of carbon capture, utilization and storage of the oil field enterprise, and particularly relates to the site-level CCUS dynamic source-sink matching optimization method for the oil field enterprise. Comprising the following steps: integrating a carbon source end full life cycle technical economy evaluation system and a storage target area'geology-potential-economy 'three-dimensional grading model to form a dynamic database; on the basis of the dynamic database, a mixed integer linear programming model fusing source sink dynamic priority coefficients is constructed, three scenes of cost minimization, oil displacement income maximization and carbon sink subsidy excitation are set, and a constraint system is coupled; according to the method, carbon source technology economic evaluation and storage target area three-dimensional grading are fused, the specific injection-production cycle, policy incentive and pipe network constraint of an oil field are converted into time-varying weight coefficients, traditional static optimization limitation is broken through, and site-level source-sink dynamic accurate matching is achieved.

The invention discloses a family medical care path planning and scheduling method based on deep reinforcement learning, and the method comprises the steps: obtaining HHCRSP instance data, modeling the HHCRSP instance data into a mixed integer linear programming model, carrying out the problem decomposition and sorting, and obtaining a plurality of VRP sub-problems with the types of services needed by patients as the grouping basis and the service dependency as the solving sequence; modeling the solving process of each VRP sub-problem into a Markov decision process, and solving the constructed Markov decision process according to a solving sequence through a strategy network to obtain a service path scheme and a service timetable thereof; and integrating a service path scheme obtained by solving each VRP sub-problem with a service timetable to form a planning and scheduling scheme including family medical care paths of all patients. According to the method, through key technologies such as problem decomposition, a neural network parameterization strategy, service embedding and constraint perception mask, the technical problems of the HHCRSP in the aspects of expandability, real-time performance, generalization ability and complex constraint processing are systematically solved.

The invention discloses a service scheduling method and system for a photoelectric hybrid low earth orbit satellite network, and relates to the technical field of satellite communication and network resource management.The method comprises the steps that the photoelectric hybrid low earth orbit satellite network is constructed, and topological information, node information and a to-be-scheduled service set of the photoelectric hybrid low earth orbit satellite network are obtained; establishing a mixed integer linear programming model containing node selection constraint, service scheduling sequence constraint and routing constraint by taking the minimum weighted sum of the total service completion energy consumption and the total service completion time as a target; in order to solve the problem of high model complexity, a heuristic algorithm based on simulated annealing is designed, and efficient solution is carried out by iteratively optimizing a scheduling sequence and a routing path of a service; and finally, implementing service scheduling according to the obtained optimal scheduling scheme. According to the method, heterogeneous characteristics of the photoelectric nodes and link resource conflicts are fully considered, dynamic balance of energy consumption and time delay is achieved, network energy efficiency and business service quality are remarkably improved, and the method is suitable for efficient operation of large-scale low-orbit satellite constellations.

The invention discloses a non-full-length beam steel bar material breaking optimization method based on a hybrid optimization algorithm, and aims to solve the problem that an existing non-full-length beam steel bar material breaking method cannot balance the steel bar utilization rate and the calculation efficiency. The method comprises the following steps: for required beam steel bars under the same beam steel bar diameter, dividing the required beam steel bars into two groups according to the length of the beam steel bars; for required beam steel bars with the length larger than 12 m, a cut excess material splicing method is adopted, an optimized mathematical model of steel bar excess materials is established, and the number of used raw material steel bars and the division length of the raw material steel bars are calculated and determined through a genetic algorithm; and for required beam steel bars with the length smaller than 12 m, directly cutting from a single raw material steel bar or combining and processing the raw material steel bars with different specifications, listing all cutting modes, calculating steel bar excess materials corresponding to the cutting modes, and minimizing the total steel bar excess materials by the integer linear programming model to obtain a steel bar broken material combination scheme.

The invention provides an energy storage power station joint market optimal regulation and control method and system, and is applied to the field of power system optimal scheduling. The method comprises the steps of constructing a double-layer optimization model, merging a lower-layer model into an upper-layer model according to the Carlo demand-Kuhn-Tuck condition, carrying out linear processing to form a mixed integer linear programming model, solving and obtaining an optimal strategy, and regulating and controlling energy storage. The income is improved.

The invention discloses a distributed heterogeneous flexible flow shop batch processing scheduling method and system, relates to the technical field of distributed production scheduling in the manufacturing industry, and aims to solve the problems that an existing scheduling method is not comprehensive in constraint consideration, poor in energy consumption optimization and low in algorithm efficiency. According to the method, a mixed integer linear programming model containing multiple constraints such as release time and sequence-related preparation time is constructed, a learning-assisted dual-objective co-evolution framework is established, and the maximum completion time and the total energy consumption are synchronously optimized by combining mixed initialization, global-local search collaboration, decision reinforcement learning operator selection and a collaborative energy-saving strategy. The release time, the sequence-related preparation time, the inter-stage transportation time and the batch processing scheduling are simultaneously considered in the distributed heterogeneous flexible flow shop scheduling for the first time, the established mixed integer linear programming model better fits the actual production scene, and the method fits the actual production scene, is good in energy consumption optimization effect and can be adapted to the non-ferrous metal metallurgy aluminum production process.

The invention relates to the technical field of production scheduling and intelligent manufacturing, and provides an assembly line scheduling optimization method considering generalized priority constraints, which comprises the following steps: constructing an integer linear programming model, performing generalized priority relation enhancement and propagation on an input instance of the integer linear programming model, and improving the capacity of a workstation; calculating a simple lower bound based on task processing time, a lower bound based on the longest path of the priority graph and a lower bound based on a maximum flow algorithm, and taking the maximum value of the three as a final lower bound; defining an action space, defining a state space to represent the change of a current solution on an objective function value and a distribution balance degree relative to a global optimal solution and a local optimal solution, and designing a reward function to adaptively select a neighborhood operation; constructing an initial scheme based on the final lower bound and a Q-learning dynamic selection module, and performing batch movement iteration through neighborhood operation adaptively selected by the Q-learning dynamic selection module; and outputting an assembly line scheduling scheme meeting the generalized priority constraint based on a result of the batch movement iteration local search.

The invention discloses an offshore wind power plant intensive current collection system planning method based on cable parallel laying quantification. The method comprises the following steps: acquiring node coordinates and engineering parameters of a wind power plant; an initial solution is obtained through clustering partition, candidate cables are screened, and the cross relation of the candidate cables is identified; a mixed integer linear programming model with the cable parallel laying length as a quantitative index is constructed, a target function of the mixed integer linear programming model fuses cable construction cost, full life cycle power loss cost and intensive excitation terms for parallel laying, and engineering constraints such as parallel laying logic, power conservation, node degree and crossing prevention are integrated; model solution and iterative optimization are carried out by setting and dynamically adjusting intensive excitation coefficients, finally, a reliable layout scheme which meets the intensive sea use policy and is better in total cost is automatically generated, and the defects that a traditional method depends on manpower, efficiency is low, and economy and intensive targets are difficult to plan as a whole are effectively overcome.

The invention belongs to the technical field of deterministic transmission, and particularly relates to a deterministic transmission method for a time division multiplexing passive optical network in computing power-power cooperative communication network networking, which comprises the following steps of: acquiring and identifying all time delay sensitive flows accessed to a TDM-PON (Time Division Multiplexing-Passive Optical Network), and pre-classifying periodic flows; clustering the similar periodic flow to form a shaping flow, and distributing a shaping window; calculating the least common multiple of all shaping flow periods as a super period; in a super cycle, an integer linear programming model is adopted to complete initial optimization time window distribution; carrying out secondary sorting and conflict elimination on the overlapped transmission windows, and scheduling the shaping stream by using the transmission windows after secondary optimization; the non-periodic flow is scheduled by using the idle time slot in the over-period; and continuously multiplexing a super-cycle scheduling scheme to complete a deterministic transmission process. According to the invention, high scheduling success rate, millisecond-level deterministic time delay and microsecond-level jitter control of large-scale periodic services are realized.

The invention relates to an intelligent calculation center optical network optical path survivability-oriented air-ground cooperative unmanned aerial vehicle distributed optimization deployment method and system, and belongs to the technical field of optical communication network protection. The invention aims to solve the technical problems of long switching time delay, limited coverage and poor stability of a traditional single ground or air protection mechanism. According to the technical scheme, an air-ground cooperative protection architecture is constructed, and firstly, an integer linear programming model with the aim of minimizing the total cost of a system is established to carry out precise deployment optimization of unmanned aerial vehicle nodes; in order to improve the solving efficiency, a spectral clustering and K-means clustering-based heuristic algorithm is designed to realize rapid intelligent division of the unmanned aerial vehicle service domain; the deployed unmanned aerial vehicle cluster adopts a master-slave architecture and has a failover mechanism, and a temporary relay network can be quickly established when a ground optical path fails. According to the invention, the survivability and fault recovery efficiency of the optical network are effectively improved, and good balance between deployment cost and protection performance is realized.

This invention discloses a global optimization method for differentiated traffic control measures that does not violate travel demand, relating to the field of intelligent traffic management technology, and includes the following steps: Step 1: Construct a global optimization framework for differentiated traffic control measures; Step 2: Define the urban traffic network and demand flow parameters; Step 3: Establish a mixed-integer linear programming model for optimizing road network flow distribution; Step 4: Consider factors such as flow demand, flow balance, and travel impedance, approximate the impedance function and objective function with secant lines, and construct constraints for the mixed-integer linear programming model; Step 5: Solve the model to obtain the optimal flow distribution result; Step 6: Obtain differentiated traffic control measures from the model based on the flow distribution result. This invention's method can construct a mixed-integer linear programming model that balances individual interests and overall efficiency based on the complex and diverse travel demands in large-scale urban road networks, design hierarchical control measures, and achieve synergistic optimization of control and path guidance, thereby improving the overall traffic efficiency of the road network.

The embodiment of the invention discloses a micro-grid two-stage robust optimization scheduling method and device, equipment and a medium, and the method comprises the steps: building a mixed integer linear programming model for a target micro-grid according to the equipment information of the target micro-grid by taking the minimization of the total operation cost of the target micro-grid as a first target function; solving the mixed integer linear programming model to obtain a first scheduling scheme; minimizing the adjustment cost in the target scene as a second target function, and constructing a robust optimization scheduling model for the target micro-grid, the adjustment cost including the adjustment abandoning cost and the load reduction cost in the target scene; based on the first scheduling scheme, solving the robust optimization scheduling model by using a preset target algorithm to determine a target scheduling scheme; and sending a control instruction to the micro-grid according to the target scheduling scheme, and scheduling the target micro-grid to operate. According to the scheme, operation of the micro-grid under significant uncertainty can be managed, and multi-device collaborative robust optimization scheduling of the micro-grid is realized.

The application discloses a password security evaluation method and system based on quadratic Boolean equation variable segmentation, and belongs to the technical field of password security evaluation. The method comprises the following steps: constructing a quadratic Boolean equation set about a password algorithm to be evaluated; constructing a mixed integer linear programming model of the quadratic Boolean equation set; solving the mixed integer linear programming model based on a set target function, to obtain an optimal segmentation result of x variables in the quadratic Boolean equation set; performing quadratic Boolean equation set solving based on the segmentation result of the x variables, to obtain a plurality of groups of candidate solutions; and decrypting the ciphertext according to the candidate solutions, to obtain a security evaluation result of the password algorithm to be evaluated. The application can provide security evaluation efficiency of the password algorithm.

The invention provides an asset combination optimization method which can be applied to the field of artificial intelligence. The asset combinatorial optimization method comprises the steps of obtaining a to-be-processed asset set, wherein each asset corresponds to a group of structured attribute data; receiving an optimization target and a business rule constraint defined by a user; constructing a mixed integer linear programming model according to the attribute data, the optimization target and the business rule constraint; wherein the optimization target is converted into a target optimization function of the model, and the business rule constraint is used as a linear constraint condition of the model; selecting a target engine from at least two mutually independent and pluggable optimization solution engines, transmitting the mixed integer linear programming model to the target engine for solution operation, and obtaining a final assignment result of the binary decision variable; and based on the final assignment result, screening out asset subsets conforming to business rule constraints, and forming an optimized asset combination. The invention further provides an asset combinatorial optimization device and equipment, a storage medium and a program product.

The invention discloses a constellation communication-navigation coupling task scheduling method based on load balancing, and belongs to the technical field of aerospace mission planning. The method mainly improves the resource distribution efficiency under the device-level constraint, and achieves the purpose of meeting the geometric precision requirement of navigation service while guaranteeing the completion of a communication task. According to the method, satellite ephemeris data and task demand parameters are used as input, a multi-target mixed integer linear programming MILP model is constructed, an optimal binary allocation variable is solved through an MILP solver, service satellites and service time slots of tasks are extracted from the optimal binary allocation variable, and then a scheduling table is generated; according to the method, the problem of navigation task failure caused by the fact that a communication task occupies a multi-satellite convergence window in separated planning is avoided, simulation shows that the overall task completion rate is averagely increased by 12.3%, and the load variation coefficient is reduced by 66%-88%.

The invention discloses a distributed photovoltaic aggregation frequency modulation standby optimal configuration method considering node frequency security constraints under transmission and distribution cooperation. The method comprises the following steps: describing uncertainty of photovoltaic aggregation frequency modulation capability by using a Gaussian mixture model, and establishing a power transmission network node frequency response model containing frequency space distribution characteristics; taking minimization of system power generation and frequency modulation reserve cost as a target, and combining opportunity constraint planning to construct an optimal configuration model; a response model is reduced based on a frequency divider theory, a Bernstein polynomial approximates frequency dynamics, and a differential algebraic equation is converted into an algebraic equation; then, binary expansion and Gaussian mixture model linear invariance are utilized to convert the model into a mixed integer linear programming model, and Bernstein polynomial convex hull property tightening constraint is combined; and finally, solving the model to obtain the optimal photovoltaic aggregation frequency modulation reserve capacity and control parameters. The frequency space distribution is accurately described, the problem of insufficient frequency safety risk identification is solved, and the frequency safety level of a power system is improved.

The present application relates to resource scheduling technical field, especially to a kind of agricultural resource scheduling method and system based on wisdom village, method includes steps: the road network distance of order destination to starting point and distribution path are acquired;By analyzing the distribution characteristics of road network distance and the similarity of distribution path, determine initial clustering center in two-stage screening mode, obtain multiple distribution subareas based on the initial clustering center clustering;Integer linear programming model is constructed considering vehicle loading utilization rate and estimated transportation cost, for each distribution subarea dynamically matching optimal vehicle combination.The present application solves the randomness and unreasonableness of initial clustering center selection of traditional clustering algorithm, ensures that distribution subarea result is optimal in physical distance and driving direction;For each distribution subarea dynamically matching optimal vehicle combination, reduce the additional cost generated due to transport capacity waste or improper vehicle selection.

This invention relates to an optimization method for an electric-gas coupled system based on adaptive piecewise linearization. This invention considers the coordinated operation of the power system and the natural gas system, and can improve the overall efficiency of the energy system through multi-energy complementarity, promote the absorption of new energy sources, and simultaneously improve the safe and reliable supply of electricity and natural gas. In the calculation process, the number of segments in the linearized pipeline airflow equation obtained through piecewise linearization is adaptively determined. Specifically, the number of segments and segmentation points are dynamically divided using an iterative method, gradually increasing the number of segments and segmentation points. This approach, while ensuring computational accuracy, introduces the minimum number of segmentation intervals and determines the optimal segmentation point location. This transforms the optimal energy flow calculation of the electric-gas coupled system into a mixed-integer linear programming model, achieving an effective trade-off between solution accuracy and computational efficiency. Ultimately, it provides a feasible and efficient technical means for the optimal energy flow analysis of electric-gas coupled systems.

The application discloses a kind of mixed pumping storage power station optimization scheduling method and system considering pumping cone effect, belong to the field of water power scheduling, comprising: the establishment of mixed pumping storage power station optimization scheduling model considering pumping cone effect;Adopt the three-dimensional surface dynamic interpolation method based on curve family sequence, through hierarchical selection strategy and weight variable collaborative determination algorithm, realize the linearization of pumping cone effect curve constraint condition;Build and solve multi-objective mixed integer linear programming model.The application is aimed at the operation state distortion caused by pumping cone effect of canyon river type mixed pumped storage power station, and the problem of difficult solution caused by complex nonlinear constraint, proposes a kind of accurate modeling and efficient solution scheme to overcome the problem of unreliable scheduling plan and difficult solution caused by ignoring or simplifying pumping cone effect of existing model.The application can improve the accuracy and engineering practicability of scheduling plan, and ensure the safe and efficient operation of complex hydropower system.

The invention relates to a mixed integer linear programming-based cabinet division optimization method and system, and the method comprises the steps: obtaining the related data of a delivered commodity and cabinet division configuration from a database based on a task ID, and the related data of the delivered commodity comprises delivery plan details, order information and SKU; splitting the delivery plan details into a plurality of delivery detail groups based on the order information; mapping the priority of the SKU in each delivery detail group based on a preset priority rule; obtaining a current sub-cabinet scene based on the sub-cabinet configuration, wherein the current sub-cabinet scene comprises a single-cabinet replenishment scene, a general high-priority sub-cabinet scene and a priority difference sub-cabinet scene; and constructing a mixed integer linear programming model of the current sub-cabinet scene based on the priority and the delivery plan details of each SKU, and calling a solver to solve the mixed integer linear programming model to obtain a sub-cabinet scheme.

The invention discloses a standby power supply priority dynamic optimization method, system and device based on mixed integer linear programming and a medium, and belongs to the technical field of power system automation. The method comprises the steps of obtaining operation state data and fault state types of each standby power supply in a target power grid; based on the operation state data of each standby power supply, a mixed integer linear programming model including a target function is constructed, and the target function takes comprehensive cost minimization as an optimization target; parameters in the mixed integer linear programming model are updated according to the operation state data and the fault state type of each standby power supply; and solving the updated mixed integer linear programming model by adopting an MILP solver to obtain an optimal solution of the priority of the standby power supply. The optimal power supply switching decision is realized, the reliability and economy of the system are improved, the change of the power grid can be quickly responded, and the power supply continuity is ensured.

This invention discloses a planning method and system for a hydrogen-containing integrated energy system. The method includes: constructing equipment instance models of various energy types in the system by configuring parameters; wherein the equipment includes electrolyzer equipment; constructing a load model in the system by configuring the load demand of the area to be planned, the load demand supporting configuration at multiple time scales; obtaining the energy price model of the area to be planned and completing the energy price configuration; constructing a system topology by graphically associating the equipment instance models, load models, and energy price models through a visual configuration interface, and configuring constraint rules on the system topology; wherein the constraint rules include start-up and shutdown optimization constraints for the electrolyzer; transforming the system topology with configured constraint rules into a mixed-integer linear programming model and performing optimization to obtain the configuration scheme and operation strategy of each device in the system. This invention solves the problems of insufficient characterization of equipment operating losses and low modeling efficiency in existing planning methods by introducing refined electrolyzer start-up and shutdown constraints and a visual modeling process, thus achieving efficient and accurate planning for hydrogen-containing integrated energy systems.

The invention discloses a container connection transportation path planning method based on deep reinforcement learning, and the method comprises the steps: building a mixed integer nonlinear programming model with the minimization of the total fuel consumption of all trucks as a target according to a container connection transportation system, performing linearization processing on the mixed integer nonlinear programming model to obtain a mixed integer linearization programming model; the method comprises the following steps: taking a container connection transportation path planning problem as a Markov decision process, and carrying out reinforcement learning training on a deep reinforcement learning network based on Transform in a container connection transportation simulation environment based on a deep reinforcement learning algorithm of Transform to obtain an optimal deep reinforcement learning network; and realizing container connection transportation path planning through the optimal deep reinforcement learning network. The method solves the problem that the existing method cannot effectively capture the structural features of the transportation process, limits the decision-making quality and generalization ability in high-dimensional and other complex scenes, and further causes the low container connection transportation efficiency.

The invention discloses a detection area division method of a railway detection vehicle, which takes a mixed integer linear programming model with task balance and structure compactness as double targets as a core, and comprehensively considers a matching relation between the detection vehicle and a railway line. Establishing a detection region division model suitable for a complex heterogeneity, downward compatibility and non-unique matching relationship, performing linearization processing to obtain a mixed integer linear programming model, and performing detection region division through the mixed integer linear programming model; the method can effectively solve the limitation that a manual compilation mode cannot balance detection load and reduce off-site business trips at the same time.