Material port unloading plan generation, device, medium and program product

Abstract

The application relates to the field of material supply chains and provides a material port unloading plan generation device, medium, program product and method. The method comprises the following steps: acquiring ship shortage data and to-be-discharged train data, matching a target train set meeting a coal preparation condition; determining an available tripper set according to the reachability relationship between the ship and the stockyard and the mapping relationship between the tripper and the stockyard; determining a target available process string corresponding to the target train set according to the matching relationship between the target train and the available tripper; screening out a target discharge plan meeting preset constraints through multiple rounds of iteration based on a breadth-first search algorithm and in combination with index priority sorting, wherein the target discharge plan comprises a tripper, a process string and a stacking position; the preset constraints comprise a train allocation uniqueness constraint, a reachability constraint of the train and the available tripper, a reachability constraint of the available tripper and the stacking position and a process conflict avoidance constraint; and sending an unloading plan instruction generated based on the target discharge plan to a production system. The scheme can improve the unloading operation efficiency of the material port train.

Description

Technical Field

[0001] This application relates to the field of material supply chain, and in particular to a material port unloading plan generation device, medium and process product. Background Technology

[0002] In the coal transportation system, coal ports, as key connecting points between railway and waterway coal transport, occupy a core hub position in the entire coal supply chain. The main function of coal ports is to quickly and efficiently unload coal transported by rail and transfer it to subsequent stages. Currently, after a coal-loaded train arrives at the port's pre-shipment station, the unloading process typically involves the unloading supervisor comprehensively considering numerous factors such as train type, train height, coal type and unloading location, stacking rules, ship coal preparation, and operational procedures to select the appropriate tippler and determine the train's unloading sequence. It is evident that the efficiency of port operations directly impacts coal transportation costs and turnaround time. Any disruptions in the operational process not only increase enterprise operating costs but may also trigger production fluctuations in downstream industries such as power, steel, and chemicals. To ensure the efficient operation of the coal supply chain, continuous optimization of coal port operational processes (especially unloading operations) is crucial.

[0003] However, since the existing unloading operations mainly rely on the dispatcher's manual experience, this manual decision-making method has many limitations in terms of unloading efficiency and accuracy when the port operation scale is large and the frequency of train and ship arrivals and departures has increased significantly. This often leads to low unloading efficiency and uneven utilization of tippers. Summary of the Invention

[0004] This application provides a material port unloading plan generation device, medium and program product that can maximize the comprehensive utilization rate of tipplers and realize the real-time dynamic balanced allocation of each tippler's operation tasks, effectively eliminate local overload and idleness, extend equipment life and ensure stable system operation.

[0005] In a first aspect, this application provides a method for generating a material port unloading plan, the method comprising: The input / output module is used to acquire data on ship cargo shortages and vehicles awaiting dispatch. The processing module is used to match a set of target trains that meet the coal preparation conditions based on the ship shortage data and the waiting train data obtained by the input / output module. Based on the accessibility relationship between ships and storage yards, and the mapping relationship between tippers and storage yards, determine the set of available tippers corresponding to the target train set; Based on the matching relationship between target trains and available tipplers, the target available process strings corresponding to the target train set are determined; based on the breadth-first search algorithm, the target train scheduling scheme includes arranging tipplers, process strings, and stacking positions according to the priority of indicators; wherein, the target available process strings are the available tippler process strings and stacking position available process strings of each target train in the target train set; the preset constraints include train allocation uniqueness constraints, train and available tippler reachability constraints, reachable tippler and stacking position reachability constraints, and process conflict avoidance constraints; The unloading plan instruction generated based on the target vehicle scheduling scheme is sent to the production system through the input / output module.

[0006] Secondly, this application provides a material port unloading plan generation device, which has the function of generating material port unloading plans corresponding to the first aspect described above. The function can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above function, and the modules can be software and / or hardware.

[0007] In some embodiments, the material port unloading plan generation device includes: The input / output module is used to acquire data on ship cargo shortages and vehicles awaiting dispatch. The processing module is used to match a set of target trains that meet the coal preparation conditions based on the ship shortage data and the waiting train data obtained by the input / output module. Based on the accessibility relationship between ships and storage yards, and the mapping relationship between tippers and storage yards, determine the set of available tippers corresponding to the target train set; Based on the matching relationship between target trains and available tipplers, the target available process strings corresponding to the target train set are determined; based on the breadth-first search algorithm, the target train scheduling scheme includes arranging tipplers, process strings, and stacking positions according to the priority of indicators; wherein, the target available process strings are the available tippler process strings and stacking position available process strings of each target train in the target train set; the preset constraints include train allocation uniqueness constraints, train and available tippler reachability constraints, reachable tippler and stacking position reachability constraints, and process conflict avoidance constraints; The unloading plan instruction generated based on the target vehicle scheduling scheme is sent to the production system through the input / output module.

[0008] In some implementations, the ship cargo shortage data includes the loading status of ships at each berth, ship departure dynamics, number of ships with cargo shortages, number of cargo shortage trains, number of cargo shortage trains for the current coal type, and number of cargo shortage trains for the next ship at the same berth; the processing module is specifically used for: Based on the loading status of ships at each berth, ship departure dynamics, number of ships with cargo shortages, number of ships with cargo shortages, number of ships with cargo shortages for the current coal type, and number of ships with cargo shortages for the next ship at the same berth, multiple ships with cargo shortages are prioritized. The target train set that matches the multiple ships without cargo is obtained by sorting the ships by priority.

[0009] In some implementations, the processing module is specifically used for: The intersection of available stacking locations for each coal type and reachable stacking locations accessible by tippers is used to obtain the unloading stacking locations and scheduling periods for each target train in the target train set.

[0010] Obtain all coal types that are out of stock, traverse them sequentially by coal type, filter the ship out-of-stock records and available candidate train records for the current coal type, and match the candidate train records sequentially according to the ship priority to obtain the target train set.

[0011] In some embodiments, the processing module is specifically used to obtain the available stacking locations for the coal type according to the following steps: All currently empty stacks, stacks with real-time field inventory less than a preset threshold, and stacks that meet the clearing requirements are selected as the candidate empty stack set: If the number of station-stored trains, the number of cars waiting to be dispatched for the current coal type, and the number of cars entering the station for the current coal type are all determined to meet the preset conditions for opening the stack, then the list of unloading stack positions for each coal type in the current node stack position is compiled based on the number of unloading trains that can be unloaded at each stack position. If, based on the candidate stacking position set and the stacking status of each candidate stacking position in the candidate stacking position set, it is determined that the coal type corresponding to the current station's stockpile meets the preset stacking conditions, then a candidate available empty stacking set is determined from the candidate empty stacking set. Based on the coal type and the material handling line where each candidate available empty stack is located, select the available stack location for the coal type from the set of candidate available empty stacks.

[0012] In some implementations, the processing module is specifically used for: Obtain the material handling line where each candidate available empty stack is located; Select from the set of available empty stacks that meet the requirement of increasing the number of reachable picking lines by a preset number; If there is a candidate available empty stack in the candidate available empty stack set that satisfies the first condition, then select a candidate available empty stack that satisfies the first condition from the candidate available empty stack set; the first condition is that the candidate available empty stack meets the condition of increasing the number of reachable material picking lines by a preset number. If there is no candidate available empty stack in the candidate available empty stack set that satisfies the first condition, then the candidate available empty stacks are sorted by position. When the coal type is Class I coal, a candidate available empty stack within a preset range of the berth location is selected from the candidate available empty stack set as the available stack location for the coal type. When the coal type is Class II coal, a candidate available empty stack outside the preset range of the berth location is selected from the candidate available empty stack set as the available stack location for the coal type.

[0013] In some implementations, the processing module is specifically used for: Based on the matching relationship between the target train and the available tipplers, determine the target available process string corresponding to the target train set; Obtain the available process strings for the tipper and the available process strings for the stacking locations; The intersection of the available process strings for the tipper and the available process strings for the stacking position is taken to obtain the target available process string, which is the available tipper process string and the available process string for the stacking position for each target train in the target train set. Based on the target available process string, an initial node queue to be searched is obtained, the initial node queue including multiple nodes associated with the vehicle scheduling scheme; Traverse each node, and select nodes whose indicators are in the candidate threshold range of the optimal value from the initial node queue based on historical screening results and indicator priority sorting, so as to update the initial node queue to obtain the candidate node queue for the next round; until all indicators sorted according to indicator priority have filtered the candidate node queue to be empty, and the optimal vehicle scheduling scheme when it is empty is taken as the target vehicle scheduling scheme.

[0014] In some implementations, the processing module is specifically used for: Traverse each node and filter out invalid nodes from the queue of nodes whose car scheduling schemes do not meet the first constraint condition based on historical screening results and indicator priorities. Obtain the current optimal value and multiple candidate values ​​adjacent to the optimal value; The candidate threshold range is obtained based on the optimal value and the multiple candidate values; The candidate threshold range for each round is dynamically adjusted based on the number of remaining nodes and the number of vehicles in the queue. From the nodes in the initial node queue that satisfy the first constraint condition, candidate nodes for the vehicle scheduling scheme with indicators in the candidate threshold range are selected to obtain the candidate node queue.

[0015] In some implementations, the processing module is specifically used for: Based on the coal type restrictions and train attribute restrictions, traverse the set of available tipplers to obtain all available tippler process strings; The set of available empty stacks is obtained by taking all currently available stack locations and the candidate empty stacks obtained based on the coal type’s prohibition on stacking and the coal type’s unloading stack location restriction information. Traverse the set of available empty stacks to obtain the available process string for the stack position.

[0016] Thirdly, this application provides a material port unloading plan generation apparatus, the apparatus comprising: at least one processor and a memory; wherein the memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute any of the steps in generating a material port unloading plan provided in the first aspect and any embodiment of the first aspect.

[0017] Fourthly, this application provides a computer-readable storage medium having the function of generating a material port unloading plan corresponding to the first aspect described above. This function can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above function, and the modules can be software and / or hardware. Specifically, the computer-readable storage medium stores multiple instructions adapted for loading by a processor to execute the steps in generating a material port unloading plan provided in the first aspect or any embodiment of the first aspect of this application.

[0018] The solution provided in this application matches a set of target trains that meet coal preparation conditions based on the ship shortage data and the waiting car data; determines the set of available tippers corresponding to the target train set based on the reachability relationship between ships and storage yards and the mapping relationship between tippers and storage yards; obtains the train operation sequence corresponding to the target train set based on a breadth-first search algorithm, and generates a matching relationship between target trains and available tippers according to the train operation sequence; and generates an unloading plan instruction based on the train operation sequence and the matching relationship and sends it to the production system. It is evident that, on the one hand, by solving this operations research optimization model, the global optimization of unloading task allocation and tipper scheduling can maximize the comprehensive utilization rate of tippers; on the other hand, by using a breadth-first search algorithm to optimize the train unloading process, real-time dynamic balanced allocation of tipper operation tasks can be achieved, effectively eliminating local overload and idleness, extending equipment life, and ensuring stable system operation. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1This is a flowchart illustrating a method for generating a material port unloading plan in an embodiment of this application. Figure 2 This is a flowchart illustrating a method for generating a material port unloading plan in an embodiment of this application. Figure 3 This is a schematic diagram of a process for obtaining the optimal vehicle scheduling scheme based on breadth-first search in an embodiment of this application; Figure 4 This is a schematic diagram of a process for obtaining the optimal vehicle scheduling scheme based on breadth-first search in an embodiment of this application; Figure 5 This is a schematic diagram of a material port unloading plan generation device in an embodiment of this application; Figure 6 This is a schematic diagram of the physical equipment generated by implementing the material port unloading plan in the embodiments of this application. Detailed Implementation

[0021] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not necessarily limited to those explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. The module divisions appearing in the embodiments of this application are merely logical divisions; in actual applications, there may be other division methods. For example, multiple modules may be combined into or integrated into another system, or some features may be ignored or not performed. Furthermore, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces, and the indirect couplings or communication connections between modules may be electrical or other similar forms. These are not limited in the embodiments of this application. Moreover, modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed across multiple circuit modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of this application.

[0022] This application provides a material port unloading plan generation, device, medium, and program product, which can be used in scenarios such as bulk cargo ports, railway marshalling yards, smart warehousing, e-commerce warehousing, cold chain logistics, and cross-border warehousing. This application takes the transportation of coal, ore, and oil in bulk cargo ports as an example, and other scenarios will not be described in detail.

[0023] The servers involved in this application's embodiments (e.g., business servers, search engines) can be independent physical servers, server clusters or distributed systems composed of multiple physical servers, or cloud servers providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The material port unloading plan generation device involved in this application's embodiments can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, personal digital assistant, etc., but is not limited to these.

[0024] The embodiments of this application mainly adopt the following technical solutions: By comprehensively considering factors such as ship cargo shortages, train attributes, coal type distribution, tippler accessibility, process conflicts, and equipment balance, an optimization model is constructed and a breadth-first search algorithm is introduced to generate a reasonable unloading plan. This aims to automate and intelligently manage unloading operations, improve overall scheduling efficiency, optimize tippler task allocation to achieve dynamic load balancing and extend equipment lifespan, ensure timely coal preparation for ships, reduce loading delays caused by coal shortages, and enhance the stability and continuity of overall port operations, providing technical support for the construction of smart ports.

[0025] The following combination Figures 1-6 The technical solutions of the embodiments of this application will be described by way of example.

[0026] Since this application's method for generating material port unloading plans transforms multi-objective optimization into single-objective optimization, and uses a breadth-first search algorithm, it optimizes a single indicator in each round of optimization according to the priority of the indicators, filtering out scheduling schemes that are not within the candidate range, and narrowing down the candidate range of scheduling schemes round by round until the optimal solution is obtained, i.e., the optimal scheduling scheme is output. This breadth-first search algorithm is based on an operations research optimization model. Therefore, before introducing the material port unloading plan generation method of this application, we will first introduce the construction process of this operations research optimization model, and set the objective function for multi-objective optimization for this operations research optimization model, as follows: Step 1: Construct an operations research optimization model First, input data is acquired, including equipment master data, yard master data, coal type master data, process flow master data, wagon arrival order data, wagon waiting data, tippler operation status data, real-time yard inventory data, equipment planned maintenance data, process flow efficiency data, data on the material types (e.g., coal types) and tippler reachability of trains, data on train attributes and tippler reachability, data on coal type unloading locations, ship cargo shortage data, and wagon dismantling decisions. Then, an operations research optimization model is constructed based on this input data. In the port unloading field, this operations research optimization model can be a port area status model, and the materials can be coal, oil, etc.

[0027] Step 2: Setting up the operations research optimization model The multi-objective optimization scenario that meets the requirements of actual operation is transformed into a single-objective optimization problem. When scheduling vehicles in each round, the following objective function is constructed: in: The first item is the tipper operation load balancing item. The second item is penalties for ship shortages. The third term is the term that minimizes the number of stacking operations. , , Weighting coefficients for each item

[0028] Step 3: Set constraints for the objective function Train allocation uniqueness constraint:

[0029] Train-tipping machine accessibility constraints:

[0030] Tipper - Stacking location accessibility constraints:

[0031] Process conflict avoidance constraints:

[0032] Equipment maintenance plan constraints:

[0033] Coal type-stacking location matching constraints:

[0034] Stacking space capacity constraints:

[0035] Stacking decision constraints:

[0036]

[0037] Emergency ship coal preparation priority constraints:

[0038] In the above expressions, for Train assembly, For the collection of tippler machines, For the collection of storage yard stacks, For the collection of ship cargo shortage demand, For a set of process strings, It is a collection of coal types.

[0039] For train Should a tipper be used? and process string Do the homework. For train Should the load be unloaded to the stack location? , For stack position Has the stacking been opened? For tipper The amount of homework; The required field storage threshold for clearing stacks; For ships medium coal type The amount of stock shortage; For train Coal types; For train Is a tippler accessible and usable? ; For stack position The types of coal that are allowed to be stored are: empty stacks of coal that can be opened, and non-empty stacks of coal that are currently in stock. For stack position Maximum capacity; For stack position The current state of existence.

[0040] After constructing the above operations research optimization model, the material port unloading plan generation method in this application embodiment is implemented based on the operations research optimization model, that is, the optimal solution solution process of the operations research optimization model. For example... Figure 1 As shown, Figure 1This application provides a flowchart illustrating a method for generating a material port unloading plan, including steps 101-105 in its embodiments: 101. Obtain data on ship cargo shortages and pending vehicle dispatches; The vessel cargo shortage data includes the loading status of vessels at each berth, vessel departure dynamics, number of vessels with cargo shortages, number of cargo shortage columns, number of current coal type shortage columns, and number of cargo shortage columns for the next vessel at the same berth. Specifically, the vessel cargo shortage table can be summarized and sorted, distinguishing between urgent and non-urgent coal reserve needs, and prioritizing them according to the indicators of vessel loading status at each berth, vessel departure dynamics, number of vessels with cargo shortages, number of cargo shortage columns, number of current coal type shortage columns, and number of cargo shortage columns for the next vessel at the same berth.

[0041] The data for cars to be scheduled consists of available trains on ships that can reach and unload various materials. For example, first, all types of coal that are out of stock are obtained, and then the process is iterated through by coal type to filter for ship shortage records and available train records for the current coal type. Available trains are then matched sequentially by ship order to obtain the data for cars to be scheduled.

[0042] 102. Match a set of target trains that meet the coal preparation conditions based on the ship shortage data and the waiting car data; In some embodiments, step 102 includes: Based on the vessel loading status at each berth, vessel departure dynamics, number of vessels with cargo shortages, number of trains with cargo shortages, number of trains with current coal type shortages, and number of trains with cargo shortages for the next vessel at the same berth, multiple vessels with cargo shortages are prioritized; the target train set matching the multiple vessels with cargo shortages is obtained based on the vessel priority ranking.

[0043] In other embodiments, obtaining the target train set matching the plurality of ships with cargo shortages based on ship priority sorting includes: The intersection of available stacking locations for each coal type and reachable stacking locations accessible by tippers is used to obtain the unloading stacking locations and scheduling periods for each target train in the target train set.

[0044] Obtain all coal types that are out of stock, traverse them sequentially by coal type, filter the ship out-of-stock records and available candidate train records for the current coal type, and match the candidate train records sequentially according to the ship priority to obtain the target train set.

[0045] 103. Based on the accessibility relationship between ships and storage yards and the mapping relationship between tippers and storage yards, determine the set of available tippers corresponding to the target train set; 104. Based on the matching relationship between the target train and the available tippler, determine the target available process string corresponding to the target train set; based on the breadth-first search algorithm, sort according to the priority of the indicators, and perform multiple rounds of iterative screening to select the target train arrangement scheme that meets the preset constraints. The target train arrangement scheme includes arranging tippler, process string and stacking position. 105. Send the unloading plan instruction generated based on the target vehicle scheduling scheme to the production system.

[0046] As can be seen, in this embodiment, on the one hand, by solving the operations optimization model, the global optimization of unloading task allocation and tippler scheduling can maximize the comprehensive utilization rate of tipplers; on the other hand, by using a breadth-first search algorithm to optimize the train unloading process, the real-time dynamic balanced allocation of each tippler's operation tasks can be achieved, effectively eliminating local overload and idleness, extending equipment life and ensuring stable system operation.

[0047] Optionally, in some embodiments of this application, such as Figure 2 As shown, the coal type can be obtained from the stack location according to the following steps 201-204: 201. Select all currently empty stacks and all stacks with real-time field inventory less than a preset threshold and meeting the requirements for clearing stacks as the candidate empty stack set; Specifically, firstly, all currently empty stacks and all stacks with real-time inventory below a certain threshold and requiring clearing are selected as candidate empty stacks. Secondly, based on the number of small trains in the station, the number of cars waiting to be dispatched for the current coal type, and the number of cars entering the station for the current coal type, it is determined whether the small train can be opened. Based on the number of unloading trains at each stack, a list of unloading stacks for each coal type in the current node's inventory is compiled for each period. Based on the current existing stack status and the status of already opened stacks, it is determined whether the coal type corresponding to the current small train can be opened, thus completing the selection of available empty stacks.

[0048] 202. If the number of station-stored trains, the number of cars waiting to be dispatched for the current coal type, and the number of cars entering the station for the current coal type are determined to meet the preset conditions for opening the stack, then the list of unloading stack positions for each coal type in the current node stack position is compiled based on the number of unloading trains that can be unloaded at each stack position. 203. If, based on the candidate stacking position set and the already opened status of each candidate stacking position in the candidate stacking position set, it is determined that the coal type corresponding to the current station's stockpile meets the preset opening conditions, a candidate available empty stacking position set is determined from the candidate empty stacking position set. 204. Select the available stack location for the coal type from the set of available empty stacks based on the coal type and the material handling line where each candidate available empty stack is located.

[0049] In this embodiment, the step of selecting a usable stack location for the coal type from the set of candidate available empty stacks based on the coal type and the material handling line where each candidate available empty stack is located includes: 2041. Obtain the material handling line where each candidate available empty stack is located; 2042. Select from the set of available empty stacks a candidate available stack that satisfies the requirement of increasing the number of reachable picking lines by a preset amount; 2043. If there is a candidate available empty stack in the candidate available empty stack set that satisfies the first condition, then select the candidate available empty stack that satisfies the first condition from the candidate available empty stack set; the first condition is that the candidate available empty stack meets the condition of increasing the number of reachable material picking lines by a preset number. 2044. If there is no candidate available empty stack in the candidate available empty stack set that satisfies the first condition, then sort the candidate available empty stacks by position.

[0050] When the coal type is Class I coal, candidate available empty stacks within a preset range of the berth location are selected from the candidate available empty stack set as available stack locations for that coal type. When the coal type is Class II coal, candidate available empty stacks outside the preset range of the berth location are selected from the candidate available empty stack set as available stack locations for that coal type. Class I coal refers to coal types with a higher proportion than Class I coal; for example, when the coal type has a high proportion, empty stacks near the berth location are reserved. Class II coal refers to coal types with a lower proportion than Class II coal; for example, when the coal type has a low proportion, empty stacks far from the berth location are reserved. This application's embodiments are merely illustrative and do not limit the coal types. The coal type proportion refers to the percentage of a specific coal type's inventory or turnover in the overall stockpile inventory structure or operational plan. The proportion of Class I coal is greater than the proportion of Class II coal.

[0051] For example, when selecting the optimal range of candidate empty stacks from all empty stacks, if there is only one available empty stack, no selection is needed. Otherwise, first obtain the material handling line where the candidate empty stack is located. If there is an empty stack that can add a reachable material handling line, only the stack that can add a material handling line is retained. If not, the stack positions are sorted by position. When the coal type is high-proportion coal, the empty stacks near the berth are retained. When the coal type is low-proportion coal, the empty stacks far from the berth are retained.

[0052] Optionally, in some embodiments of this application, such as Figure 3 The diagram shown illustrates a process for obtaining a target vehicle scheduling scheme based on a breadth-first search algorithm. Embodiments of this application include: 301. Based on the matching relationship between the target train and the available tipplers, determine the target available process string corresponding to the target train set; obtain the available process string for the tippler and the available process string for the stacking position; The available process strings for tippers include tippers → belt conveyors → stackers → stacking positions. The function of the available process strings for tippers is to specify a specific tipper for each train of cars to be dispatched, to complete the entire operation chain from unloading to stacking, to determine the resource allocation of key equipment such as tippers and belt conveyors, and to select a branch in the tree corresponding to each available process string (i.e., to generate a search branch).

[0053] The available process flow for each stack location includes the material inlet → belt conveyor → stacker → stack location. The purpose of this available process flow is to specify the final stacking location of the coal, track the capacity, material type, and other status information of each stack location, ensure material compatibility with the stack location (such as coal type classification), and rationally allocate stack location resources to avoid space waste.

[0054] Specifically, the system iterates through the trains to filter available tippers. For trains that have been instructed and those with tippers assigned but not yet instructed, the tippers specified in the instructions are used. For trains without assigned tippers, all available tippers are filtered based on coal type and train attribute restrictions. The system then iterates through the available tippers to obtain all available process strings for each tipper.

[0055] Based on the limitations of the number of stacks that can be unloaded, high temperature conditions, and dust conditions, all available stacks in the current sub-column are filtered. Simultaneously, based on information regarding prohibited stack openings for different coal types and whether there are restrictions on unloading stacks for different coal types, reasonable empty stacks are added to the available stacks. The available stacks are then traversed to obtain all reachable process strings for each stack.

[0056] 302. Take the intersection of the available process string of the tipper and the available process string of the stacking position to obtain the target available process string; The target available process strings refer to the available tippler process strings and stacking position process strings for each target train in the target train set. These target available process strings are used to avoid equipment conflicts (such as stacker operation conflicts, tippler overload operation, and multiple process strings simultaneously occupying the same conveyor belt), space resource conflicts (such as insufficient stacking position capacity, mismatched material types at stacking positions, and overlapping stacking position operating spaces), and time window conflicts (such as overlapping process string operating times and insufficient equipment switchover time).

[0057] Specifically, because the objective function sets constraints on the tipper, the selection of the tipper constrains the selection of the stack location, and the stack location status in turn constrains the selection of the tipper. In other words, equipment capacity limits stack location selection, and stack location status restricts tipper operation. Infeasible combinations are identified and eliminated during the scheduling phase. Therefore, in each round of the breadth-first search, the collaboration between the tipper process string and the stack location allows for a significant reduction in invalid branches through early conflict detection, i.e., implementing a pruning strategy. For example, the original search space size is: the number of tipper process strings × the number of stack location process strings; the effective search space size is: the number of conflict-free process string combinations.

[0058] 303. Obtain the initial queue of nodes to be searched based on the available process flow of the target; The initial node queue includes multiple nodes associated with the vehicle scheduling scheme.

[0059] 304. Traverse each node, and select nodes from the initial node queue whose indicators are in the candidate threshold range of the optimal value based on the historical screening results and indicator priority sorting, so as to update the initial node queue to obtain the candidate node queue for the next round; until all indicators sorted according to indicator priority have filtered the candidate node queue to be empty, and the optimal vehicle scheduling scheme when it is empty is taken as the target vehicle scheduling scheme.

[0060] In some implementations, step 304 includes: 3041. Traverse each node and filter out invalid nodes from the queue of nodes to be searched that do not meet the first constraint condition based on historical screening results and indicator priority. 3042. Obtain the current optimal value and multiple candidate values ​​adjacent to the optimal value; That is, in each round of filtering, the optimal value of the current indicator and the nearby candidate values ​​are obtained.

[0061] 3043. Based on the optimal value and the multiple candidate values, a candidate threshold range is obtained; This candidate threshold range, also known as the candidate range, is used to retain car arrangement schemes that are close to the optimal value through each round of screening, so as to avoid discarding potential optimal solutions too early.

[0062] 3044. Dynamically adjust the candidate threshold range for each round based on the number of remaining nodes and the number of vehicles scheduled. 3045. From the nodes in the initial node queue that satisfy the first constraint condition, select candidate nodes for the vehicle scheduling scheme with indicators in the candidate threshold range to obtain the candidate node queue.

[0063] To facilitate understanding, a detailed explanation is provided below: Filter the queue to be searched, keeping the better-performing nodes and filtering out the poor-performing nodes.

[0064] Iterate through all nodes in the search queue, recording the scheduling range and KPI set for each node. Calculate the list of all nodes within the same scheduling range and for the same KPI. Determine if the scheduling scheme for each node strictly meets the scheduling order requirements. For the schemes retained in the previous step, obtain the Top-k indicators for each scheduling range and filter the schemes. Filter the schemes based on the remaining number of nodes and the number of scheduled vehicles, and update the search queue accordingly.

[0065] Iterate through the node schemes in the queue to be searched, and search to generate the node schemes for the next layer.

[0066] Obtain information such as the cars to be scheduled, the stockpiles, and the equipment status under the current scheme; update the available process strings for cars to be scheduled based on the stockpiles information; select the range of process strings to be considered in this search based on the available process strings for cars to be scheduled, and obtain the process string templates to be searched; traverse all process string templates to be searched, match the cars to be scheduled with the process string templates, and construct a bipartite graph with process strings and trains as nodes and the available relationship between process strings and trains as edges, and calculate all possible maximum matching results based on the bipartite graph; Iterate through all the maximum matching results, check whether the scheme meets the scheduling order, whether it meets the coal preparation requirements, and calculate the ranking of multi-dimensional KPI indicators. Then, filter according to the priority of the indicators. In each round of filtering, obtain the optimal value of the current indicator and the nearby candidate values, and select the scheduling scheme with the indicator within the candidate range.

[0067] The next round of filtering continues based on the results of the previous round until all indicators have been filtered and the final matching results are obtained. At the same time, it is necessary to determine whether there are homogeneous matching results and remove homogeneous structures. New search nodes are generated based on the filtered matching results. If all vehicles to be scheduled in the current results match the process, the solution node is added to the list of candidate feasible solutions; otherwise, it is used as the next-level node to continue searching. All next-level nodes generate a new queue to be searched.

[0068] Repeat the previous step until the search queue is empty. Verify whether the plan satisfies the scheduling order, coal preparation requirements, and calculate and rank multi-dimensional KPI indicators. Then, filter out poorly performing nodes according to indicator priority. In each round of filtering, obtain the optimal value of the current indicator and nearby candidate values, selecting plans with indicators within the candidate range. The next round of filtering continues based on the results of the previous round until all indicators have been filtered, yielding the optimal scheduling plan. The flowchart of the algorithm can be found in [reference needed]. Figure 4 As shown.

[0069] As can be seen, this solution mainly achieves the following technical effects: On the one hand, operations research optimization models enable intelligent decision-making for unloading plans within the port area. This allows for rapid and accurate comprehensive consideration of complex factors such as train arrival time, tipper performance, and coal type, scientifically allocating tipper resources, fully unleashing equipment potential, and significantly improving overall operational efficiency. Operations research optimization technology is profoundly changing port unloading operations. By establishing mathematical models and intelligent algorithms, it can efficiently handle multi-dimensional constraints such as train arrival time, tipper performance, and coal type, achieving optimal resource allocation. The implementation benefits and industry impact are significant: significantly improved operational efficiency, substantial reduction in operating costs, and dual improvements in safety and environmental protection. Through three-dimensional innovation—"data-driven + algorithm optimization + intelligent execution"—operations research optimization technology is reshaping port unloading operation standards. In the future, with the deep integration of technologies such as 5G and digital twins, port unloading plans will achieve a comprehensive leap from experience-based decision-making to intelligent decision-making, providing core support for the digital transformation of ports. On the other hand, by optimizing the unloading plan and train unloading process through the breadth-first search algorithm in this application embodiment, the operating load of each tippler can be effectively balanced, avoiding overuse or idleness of equipment, reducing maintenance costs, reducing equipment failure risks, and ensuring the stability of port operations. This forms a multi-dimensional fault prevention system and provides quantitative indicators for improving the failure rate, resulting in a breakthrough in stability for intelligent port scheduling.

[0070] Figures 1 to 4 Any technical feature mentioned in the embodiments corresponding to any one of the above also applies to the embodiments of this application. Figures 5 to 6 The corresponding implementation examples will not be repeated hereafter.

[0071] The above describes a method for generating a material port unloading plan in the embodiments of this application. The following describes a material port unloading plan generating device that performs the above method.

[0072] See Figure 5 ,like Figure 5 The diagram shows a structural schematic of a material port unloading plan generation device 40, which can be applied to scenarios such as bulk cargo ports, railway marshalling yards, smart warehousing, e-commerce warehousing, cold chain logistics, and cross-border warehousing. The material port unloading plan generation device 40 in this embodiment can achieve the above-mentioned... Figures 1-4The steps in the material port unloading plan generation method executed by the material port unloading plan generation device 40 in any corresponding embodiment. The functions implemented by the material port unloading plan generation device 40 can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions, and the modules can be software and / or hardware. The material port unloading plan generation device 40 may include an input / output module 401 and a processing module 402. The functional implementation of the input / output module 401 and the processing module 402 can be found in [reference needed]. Figures 1-4 The operations performed in any of the corresponding embodiments will not be described in detail here.

[0073] In some implementations, the input / output module 401 can be used to acquire ship cargo shortage data and waiting vehicle data; The processing module 402 can be used to match a set of target trains that meet the coal preparation conditions based on the ship shortage data and the waiting train data obtained by the input / output module. Based on the accessibility relationship between ships and storage yards, and the mapping relationship between tippers and storage yards, determine the set of available tippers corresponding to the target train set; Based on the matching relationship between target trains and available tipplers, the target available process strings corresponding to the target train set are determined. Using a breadth-first search algorithm, and sorted by priority of indicators, multiple rounds of iterative filtering are performed to select target train scheduling schemes that meet preset constraints. The target train scheduling schemes include the arrangement of tipplers, process strings, and stacking positions. The target available process strings are the available tippler process strings and stacking position process strings for each target train in the target train set. The preset constraints include train allocation uniqueness constraints, reachability constraints between trains and available tipplers, reachability constraints between reachable tipplers and stacking positions, and process conflict avoidance constraints. The unloading plan instruction generated based on the target vehicle scheduling scheme is sent to the production system through the input / output module.

[0074] In some implementations, the ship cargo shortage data includes the loading status of ships at each berth, ship departure dynamics, number of ships with cargo shortages, number of cargo shortage trains, number of cargo shortage trains for the current coal type, and number of cargo shortage trains for the next ship at the same berth; the processing module 402 is specifically used for: Based on the loading status of ships at each berth, ship departure dynamics, number of ships with cargo shortages, number of ships with cargo shortages, number of ships with cargo shortages for the current coal type, and number of ships with cargo shortages for the next ship at the same berth, multiple ships with cargo shortages are prioritized. The target train set that matches the multiple ships without cargo is obtained by sorting the ships by priority.

[0075] In some implementations, the processing module is specifically used for: The intersection of available stacking locations for each coal type and reachable stacking locations accessible by tippers is used to obtain the unloading stacking locations and scheduling periods for each target train in the target train set.

[0076] Obtain all coal types that are out of stock, traverse them sequentially by coal type, filter the ship out-of-stock records and available candidate train records for the current coal type, and match the candidate train records sequentially according to the ship priority to obtain the target train set.

[0077] In some embodiments, the processing module 402 is specifically used to obtain the available stacking location for the coal type according to the following steps: All currently empty stacks, stacks with real-time field inventory less than a preset threshold, and stacks that meet the clearing requirements are selected as the candidate empty stack set: If the number of station-stored trains, the number of cars waiting to be dispatched for the current coal type, and the number of cars entering the station for the current coal type are all determined to meet the preset conditions for opening the stack, then the list of unloading stack positions for each coal type in the current node stack position is compiled based on the number of unloading trains that can be unloaded at each stack position. If, based on the candidate stacking position set and the stacking status of each candidate stacking position in the candidate stacking position set, it is determined that the coal type corresponding to the current station's stockpile meets the preset stacking conditions, then a candidate available empty stacking set is determined from the candidate empty stacking set. Based on the coal type and the material handling line where each candidate available empty stack is located, select the available stack location for the coal type from the set of candidate available empty stacks.

[0078] In some embodiments, the processing module 402 is specifically used for: Obtain the material handling line where each candidate available empty stack is located; Select from the set of available empty stacks that meet the requirement of increasing the number of reachable picking lines by a preset number; If there is a candidate available empty stack in the candidate available empty stack set that satisfies the first condition, then select a candidate available empty stack that satisfies the first condition from the candidate available empty stack set; the first condition is that the candidate available empty stack meets the condition of increasing the number of reachable material picking lines by a preset number. If there is no candidate available empty stack in the candidate available empty stack set that satisfies the first condition, then the candidate available empty stacks are sorted by position. When the coal type is Class I coal, a candidate available empty stack within a preset range of the berth location is selected from the candidate available empty stack set as the available stack location for the coal type. When the coal type is Class II coal, a candidate available empty stack outside the preset range of the berth location is selected from the candidate available empty stack set as the available stack location for the coal type.

[0079] In some embodiments, the processing module 402 is specifically used for: Based on the matching relationship between the target train and the available tipplers, determine the target available process string corresponding to the target train set; Obtain the available process strings for the tipper and the available process strings for the stacking locations; The intersection of the available process strings for the tipper and the available process strings for the stacking position is taken to obtain the target available process string, which is the available tipper process string and the available process string for the stacking position for each target train in the target train set. Based on the target available process string, an initial node queue to be searched is obtained, the initial node queue including multiple nodes associated with the vehicle scheduling scheme; Based on the breadth-first search algorithm, each node is traversed. Based on the historical screening results and the index priority sorting, nodes with indexes in the candidate threshold range of the optimal value are selected from the initial node queue to update the initial node queue and obtain the candidate node queue for the next round. This process continues until all indexes sorted according to index priority have filtered the candidate node queue to be empty. The optimal vehicle scheduling scheme when the queue is empty is taken as the target vehicle scheduling scheme.

[0080] In some embodiments, the processing module 402 is specifically used for: Traverse each node and filter out invalid nodes from the queue of nodes whose car scheduling schemes do not meet the first constraint condition based on historical screening results and indicator priorities. Obtain the current optimal value and multiple candidate values ​​adjacent to the optimal value; The candidate threshold range is obtained based on the optimal value and the multiple candidate values; The candidate threshold range for each round is dynamically adjusted based on the number of remaining nodes and the number of vehicles in the queue. From the nodes in the initial node queue that satisfy the first constraint condition, candidate nodes for the vehicle scheduling scheme with indicators in the candidate threshold range are selected to obtain the candidate node queue.

[0081] In some embodiments, the processing module 402 is specifically used for: Based on the coal type restrictions and train attribute restrictions, traverse the set of available tipplers to obtain all available tippler process strings; The set of available empty stacks is obtained by taking all currently available stack locations and the candidate empty stacks obtained based on the coal type’s prohibition on stacking and the coal type’s unloading stack location restriction information. Traverse the set of available empty stacks to obtain the available process string for the stack position.

[0082] For other functions of the input / output module 401 and processing module 402 mentioned above, please refer to [the relevant documentation]. Figures 1-5The corresponding implementation examples will not be described in detail. It can be seen that, on the one hand, by solving the operations research optimization model, the global optimization of unloading task allocation and tippler scheduling can maximize the comprehensive utilization rate of tipplers; on the other hand, by using a breadth-first search algorithm to optimize the train unloading process, the real-time dynamic balanced allocation of each tippler's operation tasks can be achieved, effectively eliminating local overload and idleness, extending equipment life and ensuring stable system operation.

[0083] The above description, from the perspective of modular functional entities, describes the material port unloading plan generation device 40 in this application embodiment that executes the material port unloading plan generation method. The following description, from the perspective of hardware processing, describes the material port unloading plan generation device 40 in this application embodiment that executes the above-described material port unloading plan generation method. It should be noted that in this application embodiment... Figure 6 In the illustrated embodiment, the physical device corresponding to the input / output module 401 can be an input / output unit, transceiver, radio frequency circuit, communication module, and output interface, etc., and the physical device corresponding to the processing module 402 can be a processor. Figure 5 The material port unloading plan generation device 40 shown can have, for example, Figure 6 The structure shown, when Figure 5 The material port unloading plan generation device 40 shown has, as Figure 6 When the structure shown is used, Figure 6 The processor and transceiver in the device can perform the same or similar functions as the input / output module 401 and processing module 402 provided in the aforementioned embodiment of the material port unloading plan generation device 40. Figure 6 The memory storage processor in the memory is the computer program that needs to be called when executing the above-mentioned method for generating material port unloading plans.

[0084] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0085] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0086] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, apparatuses, or modules, and may be electrical, mechanical, or other forms.

[0087] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0088] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0089] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0090] The computer program product includes one or more computer instructions. When the computer program is loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium, or a semiconductor medium, etc.

[0091] The technical solutions provided in the embodiments of this application have been described in detail above. Specific examples have been used in the embodiments of this application to illustrate the principles and implementation methods of the embodiments of this application. The description of the above embodiments is only for the purpose of helping to understand the methods and core ideas of the embodiments of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the embodiments of this application. Therefore, the content of this specification should not be construed as a limitation on the embodiments of this application.

Claims

1. A method for generating a material port unloading plan, characterized in that, The method includes: Obtain data on ship cargo shortages and pending vehicle dispatches; Based on the ship shortage data and the waiting car data, a target set of trains that meet the coal preparation conditions is matched; Based on the accessibility relationship between ships and storage yards, and the mapping relationship between tippers and storage yards, determine the set of available tippers corresponding to the target train set; Based on the matching relationship between the target train and the available tipplers, determine the target available process string corresponding to the target train set; Based on the breadth-first search algorithm, the target vehicle arrangement scheme that meets the preset constraints is selected through multiple rounds of iterative filtering according to the priority of the indicators. The target vehicle arrangement scheme includes the arrangement of tippers, process sequences and stacking positions. The target available process strings are the available tippler process strings and stacking position available process strings of each target train in the target train set; the preset constraints include train allocation uniqueness constraints, train and available tippler reachability constraints, reachable tippler and stacking position reachability constraints, and process conflict avoidance constraints. The unloading plan instruction generated based on the target vehicle scheduling scheme is sent to the production system.

2. The method according to claim 1, wherein the ship cargo shortage data includes the ship loading status at each berth, ship departure dynamics, ship cargo shortage voyages, cargo shortage train numbers, current coal type cargo shortage train numbers, and the next ship cargo shortage train numbers at the same berth; the step of matching a target train set that meets the coal preparation conditions based on the ship cargo shortage data and the waiting train data includes: Based on the loading status of ships at each berth, ship departure dynamics, number of ships with cargo shortages, number of ships with cargo shortages, number of ships with cargo shortages for the current coal type, and number of ships with cargo shortages for the next ship at the same berth, multiple ships with cargo shortages are prioritized. The target train set that matches the multiple ships without cargo is obtained by sorting the ships by priority.

3. The method according to claim 2, characterized in that, The process of obtaining the target train set matching the multiple ships with cargo shortages based on ship priority sorting includes: The intersection of available stacking locations for each coal type and reachable stacking locations accessible by tippers is used to obtain the unloading stacking locations and scheduling periods for each target train in the target train set. Obtain all coal types that are out of stock, traverse them sequentially by coal type, filter the ship out-of-stock records and available candidate train records for the current coal type, and match the candidate train records sequentially according to the ship priority to obtain the target train set.

4. The method according to claim 3, characterized in that, The coal type can be obtained from the stack location in the following manner: All currently empty stacks, all stack positions with real-time field storage less than a preset threshold and meeting the requirements for clearing stacks are selected as the candidate empty stack set. If the number of station-stored trains, the number of cars waiting to be dispatched for the current coal type, and the number of cars entering the station for the current coal type are all determined to meet the preset conditions for opening the stack, then the list of unloading stack positions for each coal type in the current node stack position is compiled based on the number of unloading trains that can be unloaded at each stack position. If, based on the candidate stacking position set and the stacking status of each candidate stacking position in the candidate stacking position set, it is determined that the coal type corresponding to the current station's stockpile meets the preset stacking conditions, then a candidate available empty stacking set is determined from the candidate empty stacking set. Based on the coal type and the material handling line where each candidate available empty stack is located, select the available stack location for the coal type from the set of candidate available empty stacks.

5. The method according to claim 4, characterized in that, The step of selecting a usable stack location for a particular coal type from the set of candidate available empty stacks based on the coal type and the material handling line where each candidate available empty stack is located includes: Obtain the material handling line where each candidate available empty stack is located; Select from the set of available empty stacks that meet the requirement of increasing the number of reachable picking lines by a preset number; If there is a candidate available empty stack in the candidate available empty stack set that satisfies the first condition, then select a candidate available empty stack that satisfies the first condition from the candidate available empty stack set; the first condition is that the candidate available empty stack meets the condition of increasing the number of reachable material picking lines by a preset number. If there is no candidate available empty stack in the candidate available empty stack set that satisfies the first condition, then the candidate available empty stacks are sorted by position. When the coal type is Class I coal, a candidate available empty stack within a preset range of the berth location is selected from the candidate available empty stack set as the available stack location for the coal type. When the coal type is Class II coal, a candidate available empty stack outside the preset range of the berth location is selected from the candidate available empty stack set as the available stack location for the coal type.

6. The method according to claim 5, characterized in that, The process involves determining the target available process string corresponding to the target train set based on the matching relationship between the target train and the available tippler; and performing multiple rounds of iterative filtering based on the breadth-first search algorithm, according to the priority of the indicators, to select the target train scheduling scheme that meets the preset constraints, including: Based on the matching relationship between the target train and the available tipplers, determine the target available process string corresponding to the target train set; Obtain the available process strings for the tipper and the available process strings for the stacking locations; The intersection of the available process strings for the tipper and the available process strings for the stacking position is taken to obtain the target available process string, which is the available tipper process string and the available process string for the stacking position for each target train in the target train set. Based on the target available process string, an initial node queue to be searched is obtained, the initial node queue including multiple nodes associated with the vehicle scheduling scheme; Based on the breadth-first search algorithm, each node is traversed. Based on the historical screening results and the index priority sorting, nodes with indexes in the candidate threshold range of the optimal value are selected from the initial node queue to update the initial node queue and obtain the candidate node queue for the next round. This process continues until all indexes sorted according to index priority have filtered the candidate node queue to be empty. The optimal vehicle scheduling scheme when the queue is empty is taken as the target vehicle scheduling scheme.

7. The method according to claim 6, characterized in that, The step of traversing each node and selecting nodes whose indicators fall within the candidate threshold range of the optimal value from the initial node queue based on historical screening results and indicator priorities, in order to update the initial node queue and obtain the candidate node queue for the next round, includes: Traverse each node and filter out invalid nodes from the queue of nodes whose car scheduling schemes do not meet the first constraint condition based on historical screening results and indicator priorities. Obtain the current optimal value and multiple candidate values ​​adjacent to the optimal value; The candidate threshold range is obtained based on the optimal value and the multiple candidate values; The candidate threshold range for each round is dynamically adjusted based on the number of remaining nodes and the number of vehicles in the queue. From the nodes in the initial node queue that satisfy the first constraint condition, candidate nodes for the vehicle scheduling scheme with indicators in the candidate threshold range are selected to obtain the candidate node queue.

8. A material port unloading plan generation device, characterized in that, The material port unloading plan generation device includes: At least one processor and memory; The memory is used to store computer programs, and the processor is used to invoke the computer programs stored in the memory to execute the method as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, It includes instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1-7.

10. A computer program product containing instructions, characterized in that, The computer program product includes program instructions that, when executed on a computer or processor, cause the computer or processor to perform the method as described in any one of claims 1-7.

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