A ship-shore cooperative sand green intelligent fusion transfer control system and method

Abstract

The application relates to the technical field of construction logistics and green construction of large traffic engineering such as a river-crossing and sea-crossing bridge and a tunnel, and particularly relates to a ship-shore cooperative sandstone green intelligent fusion transfer control system and method, which comprises the following steps: acquiring real-time data and plan data of a water transportation module, a shore base conveying module, an intelligent unloading module and each stock bin in real time; constructing a sandstone green transfer optimization model; the sandstone green transfer optimization model is a multi-objective optimization model containing a truck carbon compensation item; the truck carbon compensation item is carbon emission generated by truck transportation; solving the sandstone green transfer optimization model according to the real-time data and the plan data to obtain a target control strategy; and controlling the intelligent unloading module according to the target control strategy.

Description

Technical Field

[0001] This disclosure generally relates to the fields of construction logistics and green construction technology for large-scale transportation projects such as cross-river and cross-sea bridges and tunnels, and specifically relates to a ship-shore collaborative green intelligent integrated transfer control system and method for sand and gravel. Background Technology

[0002] Large-scale cross-river and cross-sea transportation projects, as key nodes in the national transportation network, are massive in scale and place extremely high demands on resource allocation and construction organization. Taking the Shiziyang Channel, currently under construction, as an example, concrete mixing plants for such projects are typically located along waterways to meet the massive concrete production demands. The initial intention behind this site selection strategy was to leverage the inherent advantages of waterway transportation—its large capacity and low cost—by transporting raw materials such as sand and gravel from their production sites to the vicinity of the project site by ship. However, in long-term engineering practice, the traditional "water transport + land transshipment" model has gradually revealed its inherent and insurmountable defects, forming a bottleneck that restricts the green and efficient construction of these projects. Summary of the Invention

[0003] In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to provide a ship-shore collaborative green intelligent integrated transfer control system and method for sand and gravel.

[0004] In a first aspect, embodiments of this application provide a ship-shore collaborative green intelligent integrated transfer control method for sand and gravel, including: Real-time data and planned data are acquired from the water transport module, shore-based conveying module, intelligent unloading module, and each silo. An optimization model for green transportation of sand and gravel is constructed. The optimization model for green transportation of sand and gravel is a multi-objective optimization model that includes a truck carbon offset term. The truck carbon offset term is the carbon emissions generated by truck transportation. Based on real-time and planned data, the optimization model for green transportation of sand and gravel is solved to obtain the target control strategy; The intelligent unloading module is controlled according to the target control strategy.

[0005] In some embodiments, the optimization model for green transportation of sand and gravel is solved based on real-time data and planned data to obtain the target control strategy, including: Based on real-time and planned data, an initial hybrid coding chromosome is generated; the hybrid coding chromosome consists of two chromosome segments that use different coding methods and genetic operations but are ordered in the same way. Based on the initial mixed-encoded chromosomes, a genetic algorithm based on improved constrained non-dominated sorting is used to solve for the target control strategy.

[0006] In some embodiments, generating an initial hybrid coding chromosome based on real-time data and planned data includes: Based on the planned data, determine the total number of transport ships; Based on real-time data, determine the start time for unloading each transport ship; The initial mixed coding chromosome was determined based on the total number of transport ships and the start time of unloading.

[0007] In some embodiments, it also includes: For each hybrid coding chromosome individual in each genetic iteration round, the target value and constraint violation degree corresponding to the hybrid coding chromosome individual are obtained; the target value includes multiple values ​​consistent with multiple targets corresponding to the sand and gravel green transportation optimization model. Genetic iteration is performed on at least one mixed-coding chromosome individual that satisfies the genetic conditions based on the target value and constraint violation degree.

[0008] In some embodiments, obtaining the constraint violation degree corresponding to a hybrid coded chromosome individual includes: Based on real-time data, obtain simulation data for each silo; Based on simulation data and planned data, calculate the constraint violation degree corresponding to the hybrid coded chromosome individual.

[0009] In some embodiments, the hybrid coding chromosome includes discrete decision segment chromosomes and continuous decision segment chromosomes, wherein the discrete decision segment chromosomes employ sequential crossover genetic operations, and the continuous decision segment chromosomes employ simulated binary crossover genetic operations.

[0010] Secondly, embodiments of this application provide a ship-shore collaborative green intelligent integrated transfer control system for sand and gravel, including: The waterborne transfer module includes a dedicated unloading vessel equipped with a hydraulic grab bucket and a head-enclosed belt conveyor, used to transfer sand and gravel from the transport ship to the shore system; The shore-based conveyor module is used to receive materials from the water and deliver them into the silo area; The intelligent unloading module is used to move to the designated bin to complete the material distribution in response to the control commands of the intelligent control module; The intelligent control module is used to execute the aforementioned ship-shore collaborative green intelligent integrated transfer control method for sand and gravel.

[0011] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in embodiments of this application.

[0012] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in embodiments of this application.

[0013] Fifthly, embodiments of this application provide a computer program product, including a computer program, characterized in that, when the computer program is executed by a processor, it implements the method described in embodiments of this application.

[0014] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0015] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 A schematic flowchart of a ship-shore collaborative green intelligent integrated transfer control method for sand and gravel provided in an embodiment of this application is shown. Figure 2 This paper shows a schematic diagram of the structure of a ship-shore collaborative green intelligent integrated transfer control system for sand and gravel provided in an embodiment of this application; Figure 3 A schematic diagram of the structure of a computer system suitable for implementing an electronic device or server according to embodiments of this application is shown. Detailed Implementation

[0016] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0017] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0018] The land-based transportation link constitutes a significant weakness in the entire transportation chain. The riverside areas where the mixing plants are located are mostly traditional village settlements, such as Xinsha Village in Dagang Town, where the mixing center for the T3 contract section of the Shiziyang Channel is located. The internal road networks in these areas are not designed to handle high-intensity, high-frequency engineering logistics; the roads are generally narrow, and the load-bearing capacity of bridges and roadbeds is limited. When hundreds of heavy trucks shuttle daily through these village roads and bridges for the "last mile" transfer of sand and gravel, they cause continuous crushing and damage to local infrastructure, resulting not only in high road repair costs but also creating far-reaching safety hazards. Even more serious is the continuous roaring noise, pervasive dust, and large amounts of exhaust emissions from the dense traffic, severely disrupting the tranquility and cleanliness of the riverside communities and posing a direct threat to residents' quality of life and physical and mental health. Especially at road bends and residential entrances, the mixing of pedestrians and vehicles dramatically increases traffic safety risks, easily triggering engineering and social conflicts.

[0019] From a life-cycle environmental impact perspective, the carbon emission costs of traditional transportation methods are enormous. Although waterway transportation itself is a relatively low-carbon mode, the indispensable land-based connecting links keep the carbon footprint of the entire transportation chain high. Heavy-duty trucks, as the main land transport vehicle, have a much higher energy consumption and carbon emission intensity per ton-kilometer than inland waterway vessels. When the transport distance is long, the fuel consumed and the corresponding carbon emissions of this "last-mile transport" largely offset the environmental benefits brought by the upstream waterway transport.

[0020] Furthermore, the operational efficiency and management level within the mixing plant area are severely constrained by traditional transportation methods. The silo area is typically a major challenge and pain point in the overall management of the mixing plant. Long lines of heavy trucks waiting to unload not only occupy valuable space but also create a complex "people-vehicle crossover" situation with loader operations and personnel inspections within the plant, making management and coordination extremely difficult and posing numerous safety hazards. In addition, traditional unloading points are usually fixed and lack flexibility. When aggregate needs to be transported to different silo areas, transport vehicles either need to repeatedly move and adjust their positions within the narrow space, or additional transfer equipment must be used for secondary transport. This cumbersome and passive operating process not only significantly reduces unloading efficiency and prolongs vehicle turnaround time within the plant but also further increases operating costs and the accident rate.

[0021] Meanwhile, the lack of systemic coordination is a deeper problem. Under traditional management frameworks, shipping, yard inventory, and concrete production planning are often three relatively isolated information silos. Ship scheduling may fail to fully consider the actual inventory levels and emptying speed of silos, and adjustments to production plans are difficult to promptly relay to the logistics scheduling end. This disconnect directly leads to two common resource waste phenomena: either ships arrive early but have to anchor and wait because the silos ahead haven't been cleared, i.e., "ships waiting for materials"; or the production line is facing the risk of supply disruption, but the scheduled transport ships are delayed, i.e., "materials waiting for ships." Both situations reflect a failure to achieve optimal global resource allocation, resulting in a huge waste of transport capacity, time, and capital.

[0022] While some projects have attempted to utilize pure shipping, they have failed to achieve a seamless, green end-to-end connection from the source of transportation to the storage yard. After materials are unloaded from ships at the dock, they still rely on trucks to complete the final warehousing process, essentially failing to escape ultimate dependence on land transportation. Therefore, there is an urgent need for an innovative transfer system that can completely eliminate land-based limitations and achieve a low-carbon, efficient, and intelligent process throughout.

[0023] To further illustrate the technical solutions provided in the embodiments of this application, a detailed description is provided below in conjunction with the accompanying drawings and specific implementation methods. Although the embodiments of this application provide method operation instruction steps as shown in the following embodiments or drawings, the method may include more or fewer operation instruction steps based on conventional methods or without inventive effort. In steps where there is no logically necessary causal relationship, the execution order of these steps is not limited to the execution order provided in the embodiments of this application. In actual processing or when the device executes the method, it may be executed sequentially or in parallel according to the method shown in the embodiments or drawings.

[0024] Please refer to Figure 1 , Figure 1 A schematic flowchart of a ship-shore collaborative green intelligent integrated transfer control method for sand and gravel provided in an embodiment of this application is shown. Figure 1 As shown, the method includes: Step 101: Acquire real-time and planned data from the water transport module, shore-based conveying module, intelligent unloading module, and each silo.

[0025] It should be noted that the water transport module, shore-based conveying module, intelligent unloading module, and intelligent control module together constitute the ship-shore collaborative green intelligent integrated transfer control system for sand and gravel described in this application. This system constructs a seamless sand and gravel transfer channel and organically integrates the water transport module, shore-based conveying module, and intelligent unloading module to achieve fully enclosed, continuous, and automated transfer of sand and gravel from ships to silos.

[0026] The waterborne transshipment module is the starting point of the entire system, replacing fixed, costly deep-water wharves with a dynamic combination of specialized vessels. Large transport ships handle the mainline logistics, with a single transport capacity of 2,000-3,000 tons, equivalent to replacing more than 50 heavy trucks. Dedicated unloading is key to achieving ship-to-shore transport, equipped with hydraulic grabs and a bow-mounted enclosed conveyor belt. During operation, the grab transfers sand and gravel from the transport ship to its own receiving hopper, and then the conveyor belt directly and encloses the material to the shore system, avoiding reliance on fixed deep-water wharves.

[0027] The shore-based conveyor module receives materials from the water and smoothly and continuously delivers them into the silo area. Temporary storage hoppers, secured to the shore by driven piles, receive materials from the unloading vessel, acting as a buffer and temporary storage unit to effectively balance the rhythm of ship and shore operations. The conveyor belt system consists of longitudinal and transverse sections, enabling right-angle turns of the materials and smoothly and continuously guiding sand and gravel from the shore into the silo area. This entire process is completed automatically by mechanical structures, requiring no manual intervention.

[0028] The core equipment of the intelligent unloading module is a mobile unloading machine, which relies on an innovative cross-sliding track system. This system employs a double-layer design: the lower layer consists of two fixed transverse tracks, while the upper layer is a transversely movable longitudinal track, thus forming a complete planar movement capability. The track uses a modular box-beam structure, with the drive rack embedded within the beam, combining high rigidity, dustproofing, and durability. The unloading machine is equipped with a bidirectional unloading hopper, allowing for flexible unloading into the hoppers on both sides of the track. The entire operation is controlled by an intelligent control module, which automatically plans the optimal path and controls the unloading machine to move to the designated hopper for precise material placement. The entire process is unmanned, safe, and highly efficient.

[0029] Specifically, the application acquires real-time data and technical data from the water transport module, shore-based conveying module, intelligent unloading module, and each silo, including at least the real-time location and speed of each dedicated unloading vessel, the real-time inventory volume of each silo, the time-by-time material demand plan for each mixing plant in the next T hours, and the real-time location, estimated arrival time, and type and tonnage of the transport vessels. This application does not make specific limitations on these aspects.

[0030] It should also be noted that after obtaining real-time and planned data, these data are aggregated, cleaned, and standardized to provide a solid data foundation for subsequent optimization.

[0031] Step 102: Construct a green transportation optimization model for sand and gravel; the green transportation optimization model for sand and gravel is a multi-objective optimization model that includes a truck carbon compensation term; the truck carbon compensation term is the carbon emissions generated by truck transportation.

[0032] It should be understood that, since this application is applied to a system composed of a water transport module, a shore-based conveying module, and an intelligent unloading module, the entire sand and gravel transfer process does not require the use of high-carbon-emission trucks for transportation. Therefore, this application innovatively introduces a "carbon compensation" mechanism into the sand and gravel green transfer optimization model, that is, deducting the carbon emissions generated by traditional truck transportation that are completely avoided by adopting this system in the calculation, thereby directly quantifying the green benefits into an optimizable decision variable.

[0033] Specifically, the sand and gravel green transportation optimization model constructed in this application is a multi-objective optimization model that takes into account economy, environment, and efficiency. That is, it is a multi-objective optimization model that minimizes total carbon emissions, minimizes total operating costs, and minimizes operation time. Minimizing total operating costs aims to comprehensively consider ship waiting times, inventory holding costs, and equipment energy consumption costs; minimizing total operation time aims to compress the entire process cycle from ship arrival to unloading completion. It should be understood that the achievement of the above objectives is subject to a series of practical constraints, including the inventory limits of each silo, the minimum safety stock required to ensure continuous production, and the equipment uniqueness constraint that can only unload for one ship at a time.

[0034] In one specific embodiment, the optimization model for green transportation of sand and gravel can be expressed as:

[0035] Step 103: Solve the green transportation optimization model for sand and gravel based on real-time data and planned data to obtain the target control strategy.

[0036] Specifically, an initial hybrid coding chromosome is generated based on real-time data and planned data; based on the initial hybrid coding chromosome, a genetic algorithm based on an improved constrained non-dominated sorting is used to solve for the target control strategy.

[0037] Among them, the mixed coding chromosome consists of two chromosome segments that use different coding methods and genetic operations but are arranged in the same order.

[0038] It should be noted that existing genetic algorithms typically employ a single encoding, such as a binary string or a real number vector, which is insufficient to effectively represent the combination of permutation order and continuous time. Particle swarm optimization, on the other hand, is naturally suited for continuous optimization but is less effective at handling discrete variables. Based on this, Benson proposes a hybrid encoding approach to handle both discrete and continuous variables separately.

[0039] In one feasible embodiment, the hybrid coding chromosome includes a discrete decision segment chromosome and a continuous decision segment chromosome. Generating an initial hybrid coding chromosome based on real-time data and planned data includes: determining the total number of transport ships based on planned data; determining the start unloading time for each transport ship based on real-time data; and determining the initial hybrid coding chromosome based on the total number of transport ships and the start unloading time.

[0040] Specifically, based on real-time and planned data, the number of transport ships en route or already arrived is determined, the earliest possible start time for unloading of each ship is recorded, and ship sequence chromosomes, material allocation chromosomes, and start time chromosomes are randomly generated. For example, using three transport ships to construct a hybrid coded chromosome, after obtaining the total number of transport ships, a ship sequence chromosome is randomly generated. This indicates that ship 3 berths first, followed by ship 1, and finally ship 2. A material allocation chromosome is randomly generated for each transport ship. This indicates that material from ship 3 (first in sequence) is fed into silo 2, ship 1 into silo 1, and ship 2 into silo 3. The start time chromosome is of the same length as the total number of transport ships, representing the start unloading time of each transport ship. To ensure consistency with the sequence, during decoding... The sorted time must be monotonically non-decreasing.

[0041] It should be understood that the initial hybrid coding chromosome contains multiple initial hybrid coding chromosome individuals, each corresponding to a complete scheduling scheme. Subsequently, the target control strategy is obtained by performing genetic iterative analysis on the individuals in the hybrid coding chromosome.

[0042] In a preferred embodiment, the hybrid coding chromosome includes discrete decision segment chromosomes and continuous decision segment chromosomes, wherein the discrete decision segment chromosomes employ sequential crossover genetic operations and the continuous decision segment chromosomes employ simulated binary crossover genetic operations.

[0043] Therefore, this application uses a hybrid coding chromosome to jointly determine which ship, when, and to which silo to unload cargo. During parsing, based on... A unique and complete scheduling schedule can be determined. Through classification, discrete, and continuous encoding, genetic operations can employ appropriate operators for different variable types.

[0044] Furthermore, in solving the sand and gravel green transportation optimization model to obtain the target control strategy, for each hybrid coding chromosome individual in each genetic iteration round, the target value and constraint violation degree corresponding to the hybrid coding chromosome individual are obtained respectively; the target value includes multiple values ​​consistent with multiple targets corresponding to the sand and gravel green transportation optimization model; genetic iteration is performed on at least one hybrid coding chromosome individual that satisfies the genetic conditions based on the target value and constraint violation degree.

[0045] The constraint violation degree is used to quantify the severity of a candidate solution violating constraints. Specifically, in this embodiment, the constraint violation degree is used to quantify the severity of a candidate solution violating hard constraints. Hard constraints include, but are not limited to, maintaining safe inventory levels in each warehouse and allowing only one transport ship to unload at a time.

[0046] For example, the constraint violation degree can be calculated using the following expression:

[0047]

[0048] Furthermore, the improved constraint non-dominated sorting is to sort all solutions in ascending order of constraint violation degree before the traditional non-dominated sorting, and to separate feasible and infeasible solutions into layers.

[0049] Therefore, this application employs a constraint-prioritized non-dominated sorting method, where the dependency constraint depends on the constraint violation degree corresponding to each hybrid coding chromosome. This eliminates the need for manually tuning penalty coefficients, completely avoiding infeasible solution residues caused by inappropriate coefficients. By thoroughly stratifying feasible and infeasible solutions, it ensures that evolutionary pressure always gravitates towards the feasible region. Furthermore, by directly embedding constraint satisfaction as a comparison criterion, the output solution satisfies the rigid constraints of safety stock and device uniqueness.

[0050] In a preferred embodiment, to further improve the convergence speed of genetic iteration, this application further proposes a gap-driven local search. Specifically, based on simulation data, the real-time inventory gap of each silo is obtained, at least one hybrid-coded chromosome individual is randomly selected, and it is identified whether the hybrid-coded chromosome individual meets preset conditions. If it does, a local adjustment is performed to obtain a new hybrid-coded chromosome individual. If the constraint violation degree of the new hybrid-coded chromosome individual is not 0, the local adjustment is canceled. Correspondingly, if the hybrid-coded chromosome individual does not meet the preset conditions, no local adjustment is performed, or if the constraint violation degree of the new hybrid-coded chromosome individual is 0, the hybrid-coded chromosome individual before the local adjustment is replaced with the new hybrid-coded chromosome individual.

[0051]

[0052] Furthermore, the normalized gap exponent can be calculated using the following expression:

[0053]

[0054] Therefore, this application, through gap-driven local search, directly injects engineering experience into the evolutionary process, avoiding blind search and significantly accelerating convergence. By prioritizing the most scarce material silos, the risk of production disruptions is reduced, while also lowering ship waiting costs caused by waiting.

[0055] Step 104: Control the intelligent unloading module according to the target control strategy.

[0056] It should be noted that the target control strategy is issued to the intelligent unloading module via scheduling instructions. The scheduling instruction distribution step is crucial for translating the digital solution into concrete action. Based on the selected target control strategy, three types of precise instructions are automatically generated and issued to the corresponding execution units. These three types of instructions include transport ship scheduling instructions, material allocation instructions, and optimal unloading machine path instructions. The transport ship scheduling instructions specify the precise berthing sequence and time window for each transport ship, maximizing berth utilization efficiency and eliminating disorderly waiting. The material allocation scheme intelligently distributes thousands of tons of stone arriving from a ship to different target silos according to type and quantity, based on inventory gaps and production needs. The optimal unloading machine path is determined by the shortest movement path among all target silo compartments, significantly reducing empty runs and improving unloading efficiency.

[0057] It should be understood that after receiving the instruction, the unloading machine automatically moves along the cross-shaped sliding track to the first target bin. Upon reaching the designated position, it opens the corresponding side of the bidirectional unloading hopper and follows a unloading strategy of advancing from the innermost part of the bin outwards, ensuring uniform material accumulation and avoiding segregation. Throughout the unloading process, the level gauge continuously monitors inventory changes and feeds the data back to the central controller in real time. Once a bin is full, the next movement instruction is immediately triggered, guiding the unloading machine to automatically move to the next bin until the task is completed, forming a complete intelligent closed loop of "perception-decision-execution-feedback," truly achieving unmanned, precise, efficient, and safe operation within the site.

[0058] It should be noted that although the operation of the method of the present invention is described in a specific order in the accompanying drawings, this does not require or imply that the operations must be performed in that specific order, or that all the operations shown must be performed in order to achieve the desired result.

[0059] Figure 2 A schematic diagram of the structure of a ship-shore collaborative green intelligent integrated transfer control system for sand and gravel provided in an embodiment of this application is shown.

[0060] like Figure 2 As shown, the ship-shore collaborative sand and gravel green intelligent integrated transfer control system 10 includes: The water transfer module 11 includes a special unloading vessel equipped with a hydraulic grab bucket and a head-enclosed belt conveyor, used to transfer sand and gravel from the transport ship to the shore system. The shore-based conveyor module 12 is used to receive materials from the water and deliver them into the silo area; The intelligent unloading module 13 is used to move to the designated bin to complete the material distribution in response to the control command of the intelligent control module; The intelligent control module 14 is used to execute the aforementioned ship-shore collaborative green intelligent integrated transfer control method for sand and gravel.

[0061] It should be understood that the intelligent control module 14 and the reference Figure 1 The steps in the described method correspond to each other. Therefore, the operations and features described above for the method also apply to the intelligent control module 14 and the modules contained therein, and will not be repeated here. The intelligent control module 14 can be pre-implemented in the browser or other security applications of the electronic device, or it can be loaded into the browser or other security applications of the electronic device by means of downloading. The corresponding modules in the intelligent control module 14 can cooperate with the modules in the electronic device to implement the solution of the embodiments of this application.

[0062] The division of modules or units mentioned in the detailed description above is not mandatory. In fact, according to the embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0063] The following is for reference. Figure 3 , Figure 3 A schematic diagram of the structure of a computer system suitable for implementing the embodiments of this application is shown. like Figure 3 As shown, the computer system 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 302 or programs loaded from storage section 308 into random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the system's operating instructions. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0064] The following components are connected to I / O interface 305: an input section 306 including a keyboard, mouse, etc.; an output section 307 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card, modem, etc. The communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0065] Specifically, according to embodiments of this application, the flowchart above refers to... Figure 2 The described process can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such an embodiment, the computer program contains program code for performing the methods shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the functions defined in the system of this application.

[0066] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0067] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operational instructions of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two connected blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified functions or operational instructions, or using a combination of dedicated hardware and computer instructions.

[0068] In another aspect, this application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments, or may exist independently and not assembled into the electronic device. The computer-readable storage medium stores one or more programs that, when used by one or more processors, execute the ship-shore collaborative green intelligent fusion transfer control method for sand and gravel described in this application.

[0069] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A ship-shore collaborative green intelligent integrated transfer control method for sand and gravel, characterized in that, include: Real-time data and planned data are acquired from the water transport module, shore-based conveying module, intelligent unloading module, and each silo. Construct an optimization model for green transportation of sand and gravel; The green transportation optimization model for sand and gravel is a multi-objective optimization model that includes a truck carbon offset term; the truck carbon offset term is the carbon emissions generated by truck transportation. Based on real-time and planned data, the optimization model for green transportation of sand and gravel is solved to obtain the target control strategy; The intelligent unloading module is controlled according to the target control strategy; Based on real-time and planned data, the optimization model for green transportation of sand and gravel is solved to obtain the target control strategy, including: Based on real-time and planned data, an initial hybrid coding chromosome is generated; the hybrid coding chromosome consists of two chromosome segments that use different coding methods and genetic operations but are ordered in the same way. Based on the initial mixed-encoded chromosome, a genetic algorithm based on improved constraint non-dominated sorting is used to solve the target control strategy. The improved constraint non-dominated sorting is to sort all solutions in ascending order of constraint violation degree before the traditional non-dominated sorting, and to separate feasible solutions from infeasible solutions. In the process of solving the sand and gravel green transportation optimization model and obtaining the target control strategy, for each hybrid coding chromosome individual in each genetic iteration round, the target value and constraint violation degree corresponding to the hybrid coding chromosome individual are obtained respectively; the target value includes multiple values ​​that are consistent with multiple targets corresponding to the sand and gravel green transportation optimization model; genetic iteration is performed on at least one hybrid coding chromosome individual that satisfies the genetic conditions based on the target value and constraint violation degree, and the constraint violation degree is used to quantify the severity of the candidate solution's violation of constraints; The constraint violation degree is calculated using the following expression: ； 。 2. The ship-shore collaborative green intelligent integrated transfer control method for sand and gravel as described in claim 1, characterized in that, Based on real-time and planned data, an initial hybrid coding chromosome is generated, including: Based on the planned data, determine the total number of transport ships; Based on real-time data, determine the start time for unloading each transport ship; The initial mixed coding chromosome was determined based on the total number of transport ships and the start time of unloading.

3. The ship-shore collaborative green intelligent integrated transfer control method for sand and gravel as described in claim 1, characterized in that, Obtain the constraint violation degree corresponding to the hybrid coded chromosome individual, including: Based on real-time data, obtain simulation data for each silo; Based on simulation data and planned data, calculate the constraint violation degree corresponding to the hybrid coded chromosome individual.

4. The ship-shore collaborative green intelligent integrated transfer control method for sand and gravel as described in claim 2, characterized in that, Hybrid coding chromosomes include discrete decision segment chromosomes and continuous decision segment chromosomes. Discrete decision segment chromosomes use sequential crossover genetic operations, while continuous decision segment chromosomes use simulated binary crossover genetic operations.

5. A ship-shore collaborative green intelligent integrated transfer control system for sand and gravel, characterized in that, include: The waterborne transfer module includes a dedicated unloading vessel equipped with a hydraulic grab bucket and a head-enclosed belt conveyor, used to transfer sand and gravel from the transport ship to the shore system; The shore-based conveyor module is used to receive materials from the water and deliver them into the silo area; The intelligent unloading module is used to move to the designated bin to complete the material distribution in response to the control commands of the intelligent control module; The intelligent control module is used to execute the ship-shore collaborative green intelligent integrated transfer control method for sand and gravel as described in any one of claims 1-4.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the ship-shore collaborative green intelligent integrated transfer control method for sand and gravel as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the ship-shore collaborative green intelligent fusion transfer control method for sand and gravel as described in any one of claims 1-4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the ship-shore collaborative green intelligent integrated transfer control method for sand and gravel as described in any one of claims 1-4.

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