Earth-rock multi-target intelligent allocation method, equipment, medium and program product

Abstract

The invention relates to the field of earth-rock engineering scheduling, in particular to an earth-rock multi-target intelligent allocation method and device, a medium and a program product. The method specifically comprises the following steps: S1, collecting and sorting basic data; s2, constructing an earthwork allocation mathematical model; s3, using a sparrow search algorithm to optimize the earth-rock allocation scheme; and S4, scheme implementation and dynamic adjustment. The method comprises the following steps: firstly, constructing a mathematical model of earth-rock deployment, taking the shortest transportation time and the shortest transportation path as objective functions, comprehensively considering constraint conditions such as earth-rock supply and demand balance, transportation vehicle load limitation and construction progress requirements, and converting an actual deployment problem into a mathematical planning problem; and then, a sparrow search algorithm is introduced to carry out intelligent deployment, an optimal deployment scheme is obtained, efficient deployment of earthwork in time and path dimensions is realized, and a scientific and reasonable earthwork deployment decision scheme is provided for engineering construction.

Description

Technical Field

[0001] This invention relates to the field of earthwork engineering scheduling, and in particular to a multi-objective intelligent scheduling method, equipment, medium, and program product for earthwork. Background Technology

[0002] In large-scale engineering projects such as road construction, water conservancy projects, and mining, earthwork allocation is a crucial link, and its rationality directly affects project costs, schedules, and construction efficiency. Traditional earthwork allocation often focuses on a single objective, such as cost minimization or shortest transportation route optimization, which is insufficient to meet the demands of modern complex projects for simultaneous optimization of time and routes. Furthermore, as project scale continues to expand, earthwork allocation involves massive amounts of data and numerous constraints. Traditional methods of manual calculation and simple experience-based decision-making are not only inefficient but also prone to leading to unreasonable allocation schemes, resulting in resource waste and project delays.

[0003] In real-world engineering scenarios, earthwork allocation involves numerous complex factors. On one hand, projects have strict requirements for construction schedules, and the length of transportation time during allocation directly affects the overall project duration. On the other hand, differences in routes between different borrow pits and spoil heaps lead to variations in transportation and time costs. Furthermore, site topography, vehicle limitations, and changes in earthwork supply and demand across different construction areas all contribute to making earthwork allocation a complex problem with multiple constraints and objectives. Existing earthwork allocation methods cannot effectively balance multiple objectives such as time and route considerations, and are ill-suited to dynamically changing engineering environments.

[0004] Multi-objective earthwork allocation based on time and path optimization has unique requirements. Firstly, it requires establishing a mathematical model that simultaneously considers time and path factors, accurately describing the objective function and constraints in the earthwork allocation process to quantify the advantages and disadvantages of different allocation schemes in terms of time and path. Secondly, due to the complexity of the problem and the vast solution space, traditional optimization algorithms struggle to find the optimal solution within an acceptable timeframe. Therefore, a highly efficient intelligent algorithm is urgently needed to achieve rapid optimization of the multi-objective function to meet the needs of real-time engineering decision-making. Summary of the Invention

[0005] The purpose of this invention is to overcome the problems existing in the current earthwork allocation methods, such as the inability to effectively balance multiple objectives such as time and path, and the difficulty in adapting to dynamically changing engineering environments. This invention provides a multi-objective intelligent allocation method, equipment, medium and program product for earthwork, which solves the practical problems of complex on-site earthwork allocation management.

[0006] In a first aspect, the present invention provides a multi-objective intelligent allocation method for earthwork, characterized by comprising the following steps: S1, Basic data collection and organization; The basic data includes: project area information, earthwork supply and demand data, and transportation conditions data; S2, Construct a mathematical model for earthwork allocation; Define a variable to represent the volume of earth and rock transported from the borrow pit to the construction area; With the goals of minimizing transportation time and path, an objective function is constructed by comprehensively considering time cost, transportation volume, and site distance. Set constraints; wherein the constraints include: earthwork reserves constraints, cut-fill balance constraints, vehicle access uniqueness constraints, and vehicle load constraints. S3, the sparrow search algorithm is used to optimize the earthwork allocation scheme; Initialize the algorithm parameters and randomly generate an initial population, where each individual in the population represents an earthwork allocation scheme; Calculate and rank individual fitness; Update individual positions using a discoverer-follower strategy; The optimal allocation scheme was obtained through iterative calculation; S4, Implementation and Dynamic Adjustment of the Plan; According to the optimal allocation plan obtained in step S3, carry out earthwork allocation and transportation and record the progress. During the earthwork allocation process, monitor the changes in the basic data. If the changes in the basic data exceed the threshold, return to step S1.

[0007] According to a preferred embodiment, the objective function construction in step S2 includes: comprehensively considering the transportation time cost and transportation volume of each road segment to establish a transportation time objective; finding the allocation scheme that minimizes the total transportation time by calculating the total time spent transporting earth and stone from each borrow pit to the corresponding construction area; and establishing a transportation route objective based on the distance between each site.

[0008] According to a preferred embodiment, the constraints set in step S2 further include: site constraints, secondary loop elimination condition constraints, equipment quantity constraints, and time window constraints. Influencing factors introduced when setting constraints include: looseness coefficient, relationship between equipment configuration and efficiency, impact of project scale and scope, dynamic changes in the road system, requirements for vehicle tonnage and dispatch routes, and geographical environment.

[0009] According to a preferred embodiment, the earthwork allocation mathematical model expresses the earthwork allocation scheme as a vector consisting of a total transport distance target and a maximum transport intensity target. The total transport distance target is the sum of the transport distances at each stage of a single allocation task. The maximum transport intensity target is represented by the maximum daily average unit transport distance.

[0010] According to a preferred embodiment, the unit transport distance includes: The unit transport distance directly to the dam in this stage refers to the unit transport distance of the excavated material that can be transported directly to the dam body without transshipment in the current stage. Its value is defined as the actual transport distance minus the distance to the transshipment site and the converted distance for loading and unloading. Unit transport distance to the dam in subsequent stages: refers to the unit transport distance of the excavated material in the current stage after being temporarily stored at the transfer station and transported to the dam body in subsequent stages. Its value is the transport distance from the current location to the transfer station. Material transport distance per unit: refers to the unit transport distance when using material yard resources for filling. Its value is the actual transport distance from the material yard to the dam body plus the equivalent transport distance converted by the material yard mining cost; Unit transport distance of materials stored at the transfer station: refers to the unit transport distance from the reserve materials at the transfer station to the dam body in the initial stage of dam construction. Its value is the actual transport distance from the transfer station to the dam body.

[0011] According to a preferred embodiment, time constraints are imposed by setting a penalty number for the corresponding unit transport distance to ensure that the excavated and stored materials at each construction stage are used only in the current stage and in subsequent stages.

[0012] According to a preferred embodiment, the earthwork storage constraint includes: storage constraints for each stage and each work area, the storage of each stage and each work area being equal to the total storage and transportation volume, and the earthwork storage and transportation volume being non-negative. The cut-fill balance constraint includes: cut-fill balance constraints for each stage, the total excavation volume of each work area in each stage should be consistent with the storage volume of the spoil area. The vehicle access uniqueness constraint: the work object to be served by the transport vehicle in each dispatch process is only served once by each vehicle. The station constraint: involves the starting point, ending point, and possible intermediate stops in vehicle route planning, all vehicles returning to the depot after departing from the depot. The vehicle load constraint: the load of all vehicles cannot exceed the sum of the vehicle load capacities, and each delivery vehicle cannot exceed its maximum load capacity during transportation. The secondary loop elimination condition constraint: used to ensure the connectivity of vehicle routes and avoid the formation of local loops that do not include the starting point. The equipment quantity constraint: the setting of the number of excavating machines and the number of transport vehicles must meet the efficiency matching. The time window constraints include: the earliest allowed service time, the latest allowed service time, the vehicle's arrival time at the node, the vehicle's waiting time at the node, the vehicle's service time at the node, and the vehicle's travel time between two nodes.

[0013] The present invention also provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the multi-objective intelligent earthwork allocation method provided by the present invention.

[0014] The present invention also provides a computer-readable storage medium storing computer instructions, which are used to cause a processor to execute the multi-objective intelligent allocation method for earthwork provided by the present invention.

[0015] The present invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the multi-objective intelligent allocation method for earthwork provided by the present invention.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a multi-objective earthwork allocation method based on time and path optimization. First, a mathematical model for earthwork allocation is constructed, with the shortest transportation time and shortest transportation path as objective functions. Constraints such as earthwork supply and demand balance, vehicle load limits, and construction schedule requirements are comprehensively considered, transforming the practical allocation problem into a mathematical programming problem. Then, the Sparrow Search Algorithm (SSA) is introduced for intelligent allocation. This algorithm simulates the foraging and anti-predation behavior of sparrows, searching for the optimal allocation scheme in a complex solution space through population iterative updates. This achieves efficient earthwork allocation in both time and path dimensions, providing a scientific and reasonable earthwork allocation decision-making scheme for engineering construction. Attached Figure Description

[0017] Figure 1 This is a flowchart of a preferred embodiment of the intelligent allocation method for multi-objective earthwork and rock excavation according to the present invention. Detailed Implementation

[0018] The present invention will now be described in further detail with reference to specific embodiments. However, this should not be construed as limiting the scope of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0019] Unless otherwise specified, the use of terms such as "upper," "lower," "left," "right," "center," "inner," and "outer" to indicate orientation or positional relationships in the description of specific embodiments of the present invention is based on the orientation or positional relationships shown in the accompanying drawings, or the orientation or positional relationship in which the product / equipment / device is typically placed during use. These terms are merely for the purpose of facilitating the description of the present invention or simplifying the description in specific embodiments, enabling those skilled in the art to quickly understand the solution, and do not indicate or imply that a particular device / component / element must have a specific orientation, or be constructed and operated in a specific positional relationship. Therefore, they should not be construed as limitations on the present invention.

[0020] Furthermore, the use of terms such as "horizontal," "vertical," "suspended," and "parallel" does not imply that the corresponding device / component / element must be absolutely horizontal, vertical, suspended, or parallel, but rather that it can be slightly tilted or have a deviation. For example, "horizontal" merely means that its direction is more horizontal relative to "vertical," not that the structure must be completely horizontal, but that it can be slightly tilted. Alternatively, it can be simplified to mean that the corresponding device / component / element, when set in a "horizontal," "vertical," "suspended," or "parallel" direction, can have an error / deviation of ±10% relative to the corresponding direction, more preferably within ±8%, more preferably within ±6%, more preferably within ±5%, and more preferably within ±4%. As long as the corresponding device / component / element is within the error / deviation range, it can still achieve its function in the present invention.

[0021] Furthermore, the use of terms such as "first," "second," and "third" in terminology is merely for distinguishing descriptions of identical or similar components and should not be interpreted as emphasizing or implying the relative importance of a particular component.

[0022] Furthermore, in the description of the embodiments of the present invention, "several", "more than", and "a number of" represent at least two. The number can be any number, such as 2, 3, 4, 5, 6, 7, 8, or 9, and can even exceed nine.

[0023] Furthermore, in the description of the technical solution of this invention, unless otherwise explicitly specified / limited / restricted, the terms "set up," "install," "connect," "link," "provided with," "laid out," and "arranged" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to common connection methods in the art, such as welding, riveting, bolting, and threaded connections. Such connections can be mechanical, electrical, or communication connections; they can be direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components.

[0024] Example 1 This embodiment provides a multi-objective intelligent allocation method for earthwork and rock excavation. See also... Figure 1 The multi-objective intelligent allocation method for earthwork includes the following steps: S1, Basic data collection and organization; The basic data includes: project area information, earthwork supply and demand data, and transportation conditions data; S2, Construct a mathematical model for earthwork allocation; Define a variable to represent the volume of earth and rock transported from the borrow pit to the construction area; With the goals of minimizing transportation time and path, an objective function is constructed by comprehensively considering time cost, transportation volume, and site distance. Set constraints; wherein the constraints include: earthwork reserves constraints, cut-fill balance constraints, vehicle access uniqueness constraints, and vehicle load constraints. S3 uses the Sparrow Search Algorithm (SSA) to optimize the earthwork allocation scheme; Initialize the algorithm parameters and randomly generate an initial population, where each individual in the population represents an earthwork allocation scheme; Calculate and rank individual fitness; Update individual positions using a discoverer-follower strategy; The optimal allocation scheme was obtained through iterative calculation; S4, Implementation and Dynamic Adjustment of the Plan; According to the optimal allocation plan obtained in step S3, carry out earthwork allocation and transportation and record the progress. During the earthwork allocation process, monitor the changes in the basic data. If the changes in the basic data exceed the threshold, return to step S1.

[0025] Step S2 involves constructing the objective function by: comprehensively considering the transportation time cost and transportation volume of each road segment to establish a transportation time objective; finding the allocation scheme that minimizes the total transportation time by calculating the total time spent transporting earth and rock from each borrow pit to the corresponding construction area; and establishing a transportation route objective based on the distance between each site.

[0026] Preferably, the constraints set in step S2 also include: site constraints, secondary loop elimination condition constraints, equipment quantity constraints, and time window constraints.

[0027] Preferably, the influencing factors introduced when setting constraints include: looseness coefficient, relationship between equipment configuration and work efficiency, impact of project scale and scope, dynamic changes in the road system, requirements for vehicle tonnage and dispatch routes, and geographical environment.

[0028] The looseness coefficient describes the change in earth and rock volume during excavation, transportation, and compaction. It is divided into the initial looseness coefficient K. S (Ratio of loose volume to natural volume after excavation) and final looseness coefficient (Compacted volume to natural volume ratio). The looseness coefficient directly affects the configuration of transport vehicles and the planning of spoil disposal area capacity.

[0029] The impact of equipment configuration on work efficiency refers to the influence of the configuration of relevant transportation equipment and the working efficiency of each piece of equipment on the efficiency of earthwork allocation. This includes the excavation machinery efficiency model and the productivity Q of a single excavator. e With bucket capacity C e Job cycle time t cSoil type correction factor f s Related: (1) Transportation vehicle efficiency model and single-vehicle transportation efficiency Q t With load capacity W t Average velocity V t Loading and unloading time t load / unload Related: (2) Among them, D i (X) represents the distance traveled in the i-th stage.

[0030] The impact of project scale and scope refers to the fact that as the scope and scale of construction projects continue to expand, the problem of earthwork allocation becomes more complex. Large-scale earthwork projects involve more excavation, transportation, filling and other links. The coordination and optimization of these links in space and time becomes a huge challenge. The expansion of project scale directly increases the difficulty and complexity of earthwork allocation.

[0031] The impact of dynamic changes in the road system on the efficiency of earthwork allocation refers to the fact that the road system within the earthwork construction area is a dynamic system that changes continuously as the project progresses. The condition of the road system directly affects the efficiency of earthwork allocation. Affected by factors such as material stockpiling in the site, construction progress, and terrain, the traffic capacity and stability of the road system may change. When allocating earthwork, the dynamic changes of the road system must be fully considered to ensure the feasibility and efficiency of the allocation path.

[0032] Heavy-duty vehicles are crucial transportation tools in earthwork allocation. Their large tonnage necessitates strict route planning; improper route planning can lead to difficulties for heavy-duty vehicles, damage to machinery, and ultimately, delays in construction. Therefore, it is essential to plan allocation routes rationally to ensure the safe and efficient transport of earth and stone by heavy-duty vehicles. The average transport distance and travel time directly impact the total transportation cost.

[0033] The impact of geographical complexity on the efficiency of earthwork allocation refers to the fact that earthwork allocation often takes place in mountainous areas with complex geographical environments, high altitudes, large undulations, and poor road network conditions, which directly affects the transportation efficiency during the earthwork allocation process. The complex geographical environment not only increases the difficulty of allocation but may also bring safety hazards. When carrying out earthwork allocation, it is necessary to fully consider the complexity of the geographical environment and take corresponding measures to ensure the safety and efficiency of allocation.

[0034] Earthwork allocation is influenced by multiple factors, including the scale and scope of the project, dynamic changes in the road system, the tonnage of trucks and the requirements of allocation routes, as well as the complexity of the geographical environment. Therefore, in order to optimize the earthwork allocation process and improve transportation efficiency, it is necessary to comprehensively consider these factors and formulate a scientific and reasonable allocation plan.

[0035] According to a preferred embodiment, the earthwork allocation mathematical model expresses the earthwork allocation scheme as a vector consisting of a total transport distance target and a maximum transport intensity target. The total transport distance target is the sum of the transport distances of each allocation stage in a single allocation task. Preferably, each allocation stage corresponds to a continuous transport path from one work area to the next; for example, from excavation area A through B and C to fill area E, there are three allocation stages: A to B, B to C, and C to E. The sum of their transport distances is the total transport distance target for this allocation task. The maximum transport intensity target is expressed as the maximum daily average unit transport distance.

[0036] According to a preferred embodiment, the unit transport distance is divided into the following four categories based on the source of earth and stone and its allocation route: (1) Unit transport distance directly to the dam in this stage: refers to the unit transport distance of the excavated material directly to the dam body without transshipment in the current stage. Its value is defined as the actual transport distance minus the distance to the transshipment site and the converted distance for loading and unloading. (2) Unit transport distance to the dam in the subsequent stage: refers to the unit transport distance of the excavated material in the current stage after being temporarily stored in the transfer station and transported to the dam body in the subsequent stage. Its value is the transport distance from the current transfer station. (3) Unit transport distance of material yard: refers to the unit transport distance when using material yard resources for filling. Its value is the actual transport distance from the material yard to the dam body plus the equivalent transport distance converted by the material yard mining cost; (4) Unit transport distance of materials stored in the transfer station: refers to the unit transport distance from the transfer station to the dam body when the dam body is first filled. Its value is the actual transport distance from the transfer station to the dam body.

[0037] According to a preferred embodiment, time constraints are imposed by setting a penalty number for the corresponding unit transport distance to ensure that the excavated and stored materials at each construction stage are used only in the current stage and in subsequent stages.

[0038] According to a preferred embodiment, the earthwork storage constraint includes: storage constraints for each stage and each work area, the storage of each stage and each work area being equal to the total storage and transportation volume, and the earthwork storage and transportation volume being non-negative. The cut-fill balance constraint includes: cut-fill balance constraints for each stage, the total excavation volume of each work area in each stage should be consistent with the storage volume of the spoil area. The vehicle access uniqueness constraint: the work object that needs to be served by the transport vehicle in each dispatch process is only served once by each vehicle. Preferably, the work objects that need to be served by the transport vehicle in the dispatch process include, but are not limited to, excavation areas, transfer yards, and fill areas, which usually represent the work areas or locations with earthwork dispatch needs within a certain stage. The station constraint: involves the starting point, ending point, and possible intermediate stops in vehicle route planning, and all vehicles return to the depot after departing from the depot. The vehicle load constraint: the load of all vehicles cannot exceed the sum of the vehicle load capacities, and each delivery vehicle cannot exceed its maximum load capacity during transportation. The secondary loop elimination condition constraint: used to ensure the connectivity of vehicle routes and avoid the formation of local loops that do not include the starting point. The equipment quantity constraints are as follows: the number of excavating machines and transport vehicles must be set to meet the efficiency matching requirements. The time window constraints include: the earliest allowed service time, the latest allowed service time, the vehicle arrival time at the node, the vehicle waiting time at the node, the vehicle service time at the node, and the vehicle travel time between two nodes.

[0039] The multi-objective intelligent allocation method for earthwork provided in this embodiment is as follows: First, a mathematical model for earthwork allocation is constructed, with the shortest transportation time and the shortest transportation path as the objective functions. Constraints such as earthwork supply and demand balance, vehicle load limits, and construction schedule requirements are comprehensively considered, transforming the actual allocation problem into a mathematical programming problem. Then, the SSA (Sparrow Search Algorithm) is introduced for intelligent allocation. This algorithm simulates the foraging and anti-predation behavior of sparrows, and through population iterative updates, searches for the optimal allocation scheme in a complex solution space, achieving efficient allocation of earthwork in both time and path dimensions, and providing a scientific and reasonable earthwork allocation decision-making scheme for engineering construction.

[0040] Example 2 This embodiment is a further improvement on embodiment 1, and the repeated content will not be described again.

[0041] Basic data collection and organization includes: determining project area information, compiling earthwork supply and demand data, and obtaining transportation condition data.

[0042] Preferably, determining the project area information involves clarifying the scope of the project construction, including the location and coordinates of borrow pits, spoil heaps, and construction areas. Specifically, this can be achieved by using drones combined with BIM and GIS to create a map of the project area, marking the location and coordinates of each site. Preferably, compiling earthwork supply and demand data involves geological surveys and engineering design to calculate the earthwork demand in each construction area and the earthwork supply at borrow pits, accurately recording parameters such as the type and quality of earthwork at each site. Preferably, obtaining transportation condition data involves collecting vehicle information, measuring road conditions, and determining transportation time costs. Specifically, this can include collecting information such as the type, load capacity, and speed of transport vehicles; measuring road conditions between each borrow pit / spoil heap and construction area, including distance, road condition level, and traffic restrictions, and determining the transportation time costs for different road sections.

[0043] Constructing a mathematical model for earthwork allocation includes: defining decision variables, determining the objective function, and setting constraints.

[0044] Preferably, the decision variable is defined as follows: x is set as... ij Let represent the volume of earth and rock transported from borrow pit i to construction area j. This variable is the core expression of the allocation plan.

[0045] Preferably, the objective function is determined by comprehensively considering the transportation time cost and transportation volume of each road segment to construct a transportation time target. This is achieved by calculating the volume of earth and rock transported from each borrow pit i, x. ij Find the allocation scheme that minimizes the total transportation time by calculating the total time spent reaching the corresponding construction area j. Simultaneously, establish transportation route targets based on the distances between each site.

[0046] Preferably, the following constraints are set: In addition to the conventional constraints on the storage capacity of each work area at each stage of the earthwork allocation operation area and the constraints on the cut-fill balance at each stage, additional constraints such as the uniqueness of vehicle access, site constraints, vehicle load constraints, secondary loop elimination conditions, equipment quantity constraints, and time window constraints are added to take into account the spatiotemporal impact of earthwork allocation.

[0047] This embodiment provides a multi-objective earthwork allocation method based on time and path optimization. Addressing the complex need for simultaneous optimization of time and path in earthwork allocation during engineering construction, the mathematical model sets the shortest transportation time and path as objective functions, comprehensively considering multiple constraints such as earthwork supply and demand balance and vehicle load limits. Compared to traditional single-objective allocation methods, this model can comprehensively and accurately quantify the advantages and disadvantages of allocation schemes in terms of time and path dimensions, providing a reliable basis for scientific decision-making and effectively solving the problem of traditional methods struggling to balance multiple objectives simultaneously.

[0048] Intelligent allocation using the SSA algorithm includes: (1) Initialization parameters: Set the sparrow population size N, maximum number of iterations, proportion of discoverers, warning value ST and other algorithm parameters; randomly generate the initial population, with each individual representing an earthwork allocation scheme.

[0049] (2) Calculate fitness value: Substitute each individual in the initial population into the objective function to calculate the combined fitness value of its transportation time and transportation path, and sort the individuals according to the fitness value.

[0050] (3) Discoverer-follower update strategy: The discoverer updates its position according to the formula, prioritizes searching high-quality areas, and expands the search range; the follower adjusts its own position according to the discoverer's position and fitness, moving closer to a better solution. Some sparrows randomly change their positions according to the early warning mechanism to avoid getting trapped in local optima.

[0051] (4) Iterative optimization: Repeat the above update strategy and iterate continuously until the maximum number of iterations is reached or the stopping condition is met (such as the fitness value converges) to obtain the optimal earthwork allocation scheme.

[0052] This embodiment provides a time- and path-optimal multi-objective earthwork allocation method. When faced with problems of large amounts of earthwork allocation data and complex solution space, it introduces the Sparrow Search Algorithm (SSA) to simulate the foraging and anti-predation behaviors of sparrows, searching for the optimal allocation scheme through population iterative updates. With its powerful global search capability and fast convergence speed, compared to traditional optimization algorithms, it can find a solution that balances time and path optimization from a massive number of allocation schemes in a short time, greatly improving the efficiency of allocation scheme generation, meeting the needs of real-time engineering decision-making, and avoiding project delays caused by slow decision-making.

[0053] Preferably, after obtaining the optimal earthwork allocation plan, transport vehicles are arranged according to the final allocation plan, and earthwork is transported according to the planned transport route and time, with the transport progress and related data recorded in real time.

[0054] Preferably, during the construction process, factors such as changes in earthwork supply and demand, transport vehicle status, and road conditions at each site are continuously monitored. If there is a significant deviation between the actual situation and the model assumptions, the data is collected again, substituted into the mathematical model and SSA algorithm for calculation, and the allocation plan is dynamically adjusted to ensure that earthwork allocation is always kept in the optimal state.

[0055] Preferably, when constructing the mathematical model for earthwork allocation, the objective function is to minimize the total allocation and transportation time and distance of earthwork, comprehensively considering both quantitative and qualitative constraints during the allocation process. To express the spatiotemporal relationship of earthwork allocation, the allocation process is broken down into a series of construction sequences, statically describing the dynamic transfer of materials, treating the entire allocation process as a whole, and optimizing the allocation balance plan.

[0056] The overall decision objective can be written in vector form as follows: min{D(X),Q(X)}(3) Where D(X) and Q(X) are expressed as follows: The total transport distance target is the sum of the transport distances at each stage.

[0057] The transport distance in stage i is: (4) The objective function for the total transport distance is: (5) Q(X) represents the maximum transport intensity target. Since the selection of transport machinery is not considered, the maximum daily average unit transport distance is used to represent it.

[0058] Phase i transport intensity: (6) Maximum transport intensity at each stage: (7) (8) In the formula, This refers to the stage number; This refers to the material yard serial number; In the first Phase 1 The volume of materials supplied by each material yard; For each element in the unit distance matrix, the element represents the element at the th unit distance. The first phase adopts the first Unit transport distance for materials supplied from each material yard; For the first Stage filling time.

[0059] Preferably, the unit transport distance is crucial to achieving optimal daily targets. To reduce loading and unloading costs and round-trip transport distances caused by transshipment, the unit transport distance for excavated material directly to the dam in this stage is the actual transport distance minus the transport distance to the transshipment site and the transport distance converted from loading and unloading. In later stages, the transport distance to the dam is taken from the corresponding transshipment site. To fully utilize the excavated material, the unit transport distance for material to the material yard is the actual transport distance plus the transport distance converted from the material yard mining costs. When the dam begins filling, the transport distance for material stored at the transshipment site is taken from the actual distance.

[0060] Preferably, the unit transport distance is divided into the following four categories based on the source of earth and stone and its allocation route: (1) Unit transport distance directly to the dam in this stage: refers to the unit transport distance of the excavated material directly to the dam body without transshipment in the current stage. Its value is defined as the actual transport distance minus the distance to the transshipment site and the converted distance for loading and unloading. (2) Unit transport distance to the dam in the subsequent stage: refers to the unit transport distance of the excavated material in the current stage after being temporarily stored in the transfer station and transported to the dam body in the subsequent stage. Its value is the transport distance from the current transfer station. (3) Unit transport distance of material yard: refers to the unit transport distance when using material yard resources for filling. Its value is the actual transport distance from the material yard to the dam body plus the equivalent transport distance converted by the material yard mining cost; (4) Unit transport distance of materials stored in the transfer station: refers to the unit transport distance from the transfer station to the dam body when the dam body is first filled. Its value is the actual transport distance from the transfer station to the dam body.

[0061] Regarding the time constraint problem: excavated material from later stages cannot be used for backfilling in earlier stages, and stored material from later stages cannot be used for backfilling in earlier stages. This embodiment addresses this problem by applying a sufficiently large penalty number to the corresponding unit transport distance.

[0062] Preferably, the basic constraints set when constructing the mathematical model for earthwork allocation include: (1) Reserve constraints in each stage and operation area At each stage, the mining situation in the material yard, the storage of materials in the transfer yard, and the excavation planning of buildings are all dynamic and changing. Indicates the first Phase 1 The material storage status in the work area. The initial state of each stage depends only on the initial state and decision of the previous stage. The state transition equation for any stage is: (9) in, For the first Phase 1 The transport volume of each work area For the first Phase 1 Storage capacity of each work area It represents the looseness coefficient.

[0063] The decision variables satisfy: (10) Therefore, the earthwork reserves at each stage should take into account the relationship between the total reserves and the transportation volume, that is: (11) in, For the first Total storage capacity of the work area.

[0064] The earthwork reserves and cumulative transportation volume in each stage and work area cannot be lower than 0. Therefore, both earthwork reserves and transportation volume are non-negative, that is: (12) (2) Cutting and filling balance constraints at each stage For earthwork operations, the total excavation volume of each work area at each stage should be consistent with the storage volume of the spoil disposal area, that is: (13) in, For the first Total storage capacity of the phased spoil disposal area.

[0065] (3) Access uniqueness constraint (14) (15) This ensures that each customer is served only once per vehicle, guaranteeing that each customer's needs are met and that they are not served repeatedly.

[0066] (4) Site constraints (16) This constraint involves the starting point, ending point, and possible intermediate stops (i.e., customer points or service stations) in vehicle routing planning, and all vehicles return to the depot after departing from it.

[0067] (5) Vehicle load constraints (17) The total load of all vehicles must not exceed the sum of their load capacities. At the same time, each delivery vehicle must not exceed its maximum load capacity during transportation.

[0068] (6) Condition for eliminating secondary loops (18) in, Indicates vehicle From node Move to node (Usually the value is 0 or 1). This represents the set of all nodes after removing the starting point (such as the warehouse). It is any subset of nodes that does not contain a starting point, and contains at least 2 nodes. For vehicle assembly.

[0069] For any subset that satisfies the condition and any vehicle ,Require The total number of moves between internal nodes must be at least 1. The purpose is to prevent vehicles... The path forms an isolated sub-loop that does not contain the starting point. If the subset There are no edges inside (i.e., all) If the constraint is not met, then this illegal path is excluded. This constraint aims to ensure the connectivity of vehicle paths and avoid the formation of local loops that do not include a starting point. The correctness of the formula needs to be verified in conjunction with the model definition.

[0070] (7) Equipment quantity constraints Number of excavating machines With the number of transport vehicles Work efficiency must be matched: (19) in, For the first The excavation volume (e.g., earthwork volume) at each excavation point. The working efficiency of a single excavating machine (e.g., daily processing capacity). From the excavation point to unloading point The volume of transportation (e.g., number of trips or workload). The efficiency of a single transport vehicle (e.g., daily transport volume).

[0071] Number of excavating machines It must at least meet the requirement of total excavation volume divided by the efficiency of a single machine. Number of transport vehicles Must meet at least all transportation routes arrive The sum of squared transport volumes divided by the efficiency of a single vehicle .

[0072] (8) Time window constraint (20) in, Indicates the number of vehicles required. This indicates the vehicle's maximum load capacity. Indicates customer Demand per customer The time window is , To allow for the earliest possible start time, To allow for the latest service time. Indicates vehicle At the node Arrival time, Indicates vehicle At the node The waiting time Indicates that the vehicle is at the node Service hours, Indicates that the vehicle is at the node and Travel time.

[0073] For complex multi-objective decision-making problems, the constraint method can be used to find solutions. The core idea of ​​this method is to select one objective as the fundamental objective, that is, the objective that needs to be primarily focused on and optimized, and transform the other objectives into inequality constraints. In the earthwork allocation process, the total transport distance can be selected as the fundamental objective, with the aim of minimizing or optimizing the total transport distance. Simultaneously, the original objective of maximum transport intensity is transformed into an inequality constraint, thus reducing the original multi-objective problem to a relatively simple single-objective constraint problem. By setting different values ​​for the maximum transport intensity and solving the optimization problem under these constraints, a series of corresponding non-dominated solutions are obtained. The maximum transport intensity limit is taken as... The maximum transport intensity target is transformed into the following inequality constraint: (twenty one) in, To the excavation area To the fill area The actual volume of earthwork transported. This represents the maximum allowable transport volume from excavation zone i to fill zone j.

[0074] Preferably, after constructing the mathematical model for earthwork allocation, the collected and organized basic data can be input into the mathematical model to obtain the earthwork allocation scheme.

[0075] Preferably, after constructing a mathematical model for earthwork allocation, it is trained. Once the training is complete, it can be used to generate earthwork allocation schemes.

[0076] This embodiment uses the SSA algorithm to optimize the earthwork allocation scheme generated by the earthwork allocation mathematical model.

[0077] The Sparrow Search Algorithm (SSA) is based on the biomimetic principles of sparrow foraging and anti-predation. In the SSA algorithm, a sparrow population has two identities: the first is discoverer or joiner, and the second is scout. The discoverer directs and leads the joiners to search for foraging areas and directions; the remaining sparrows follow and monitor the discoverer to obtain food and other resources, and are called joiners; a portion of the first two groups also have a second identity—scout—who detect predators and other dangers and engage in anti-predation behavior.

[0078] The discoverer's location has been updated as follows: (twenty two) In the formula, Indicates the current iteration number. Indicates the maximum number of iterations. Indicates the first Only sparrows in the first The number of iterations is Location information values ​​at time are variables in earthwork transportation and allocation problems. It can represent the first In the next iteration, from the excavation area To the fill area The actual earthwork transport volume reflects the resource allocation of the dispatch plan under the current search status. for random numbers, Indicates the warning value. Indicates a safe value. For random numbers that follow a normal distribution, Represent a A matrix containing all elements equal to 1. When When the location is clear, it indicates that there are no predators nearby, and the discoverer can conduct a large-scale search; when... When the scouts spotted the predator, they immediately issued an alarm signal, and all the sparrows quickly flew to other safe areas.

[0079] The locations of the new members have been updated as follows: (twenty three) In the formula, This indicates the current worst-case position globally. , This indicates that an internal element is randomly assigned 1 or -1. matrix, for transpose, This indicates the best position occupied by the discoverer. When When, it indicates that the fitness value is poor. If a participant is hungry, it needs to fly in other directions to find food.

[0080] Scouts typically comprise 10% to 20% of the population, and their position update formula is as follows: (twenty four) In the formula, This indicates the current global optimal position. The step size control parameter is used to select random numbers that follow a normal distribution with a mean of 0 and a variance of 1. This indicates the direction of the sparrow's movement and is also a step size control parameter. This represents the current fitness value of the sparrow. and These represent the current global optimum and worst value, respectively. It is a constant used to avoid a denominator of 0. When When this occurs, it indicates that the sparrow is on the fringes of the population and vulnerable to predators; when When this occurs, it indicates that the sparrow in the middle of the population is aware of danger and therefore needs to move closer to other sparrows to reduce the probability of being preyed upon.

[0081] In the issue of earthwork transportation and allocation Indicates the first The allocation scheme with the worst fitness in the next iteration corresponds to the transportation allocation result with the least ideal overall effect. Indicates the first The optimal allocation position in the next iteration represents the earthwork transportation strategy with the best allocation quality at the current time.

[0082] SSA (Simultaneous Foraging Analysis) considers various scenarios of sparrow predation, promoting the entire sparrow population to approach and congregate near the optimal environmental location. However, SSA initializes the population using random generation, resulting in a uniform sparrow population distribution, which affects subsequent iterative optimization. Therefore, to enhance the optimization performance of SSA, algorithms such as chaotic mapping and Cauchy mutation can be used to further optimize and form an ISSA (Independent Standardized Segmented ...ation) model, enabling the solution of planning problems.

[0083] The multi-objective earthwork allocation method based on time and path optimization provided in this embodiment takes into account dynamic factors such as site topography, transportation vehicle limitations, and changes in earthwork supply and demand in various construction areas. By combining mathematical models and the SSA algorithm, it can quickly adapt to changes in the engineering environment and dynamically adjust the allocation plan. In practical engineering applications, it can effectively reduce resource waste caused by unreasonable allocation, lower transportation and time costs, improve the utilization efficiency of earthwork resources, and thus enhance the overall economic benefits and construction efficiency of the project.

[0084] Example 3 An electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the multi-objective intelligent earthwork allocation method according to Embodiments 1 and 2.

[0085] Example 4 This embodiment provides a computer-readable storage medium storing computer instructions that are used to cause a processor to execute the multi-objective intelligent allocation method for earthwork and rock excavation involved in Embodiments 1 and 2.

[0086] Example 5 This embodiment provides a computer program product, which includes a computer program that, when executed by a processor, implements the multi-objective intelligent allocation method for earthwork and rock excavation involved in Embodiments 1 and 2.

[0087] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-objective intelligent allocation method for earthwork, characterized in that, Includes the following steps: S1, Basic data collection and organization; The basic data includes: project area information, earthwork supply and demand data, and transportation conditions data; S2, Construct a mathematical model for earthwork allocation; Define a variable to represent the volume of earth and rock transported from the borrow pit to the construction area; With the goals of minimizing transportation time and path, an objective function is constructed by comprehensively considering time cost, transportation volume, and site distance. Set constraints; wherein the constraints include: earthwork reserves constraints, cut-fill balance constraints, vehicle access uniqueness constraints, and vehicle load constraints. S3, the sparrow search algorithm is used to optimize the earthwork allocation scheme; Initialize the algorithm parameters and randomly generate an initial population, where each individual in the population represents an earthwork allocation scheme; Calculate and rank individual fitness; Update individual positions using a discoverer-follower strategy; The optimal allocation scheme was obtained through iterative calculation; S4, Implementation and Dynamic Adjustment of the Plan; According to the optimal allocation plan obtained in step S3, carry out earthwork allocation and transportation and record the progress. During the earthwork allocation process, monitor the changes in the basic data. If the changes in the basic data exceed the threshold, return to step S1.

2. The method for multi-objective intelligent allocation of earthwork and rock excavation according to claim 1, characterized in that, Step S2 involves constructing the objective function, which includes: Taking into account the transportation time cost and transportation volume of each route, establish transportation time targets; By calculating the total time spent transporting earth and rock from each borrow pit to the corresponding construction area, the allocation scheme that minimizes the total transportation time can be found. Based on the distances between each site, establish transportation route targets.

3. The method for multi-objective intelligent allocation of earthwork and rock excavation according to claim 2, characterized in that, The constraints set in step S2 also include: site constraints, secondary loop elimination condition constraints, equipment quantity constraints, and time window constraints. Furthermore, the influencing factors introduced when setting constraints include: looseness coefficient, relationship between equipment configuration and work efficiency, impact of project scale and scope, dynamic changes in the road system, requirements for vehicle tonnage and dispatch routes, and geographical environment.

4. The method for multi-objective intelligent allocation of earthwork and rock excavation according to claim 3, characterized in that, The earthwork allocation mathematical model describes the earthwork allocation scheme as a vector consisting of the total transport distance target and the maximum transport intensity target. The total transport distance target is the sum of the transport distances of each stage of a single allocation task; The maximum transport intensity target is expressed as the maximum daily average unit transport distance.

5. The multi-objective intelligent allocation method for earthwork and rock excavation according to claim 4, characterized in that, Unit transport distance includes: The unit transport distance directly to the dam in this stage refers to the unit transport distance of the excavated material that can be transported directly to the dam body without transshipment in the current stage. Its value is defined as the actual transport distance minus the distance to the transshipment site and the converted distance for loading and unloading. Unit transport distance to the dam in subsequent stages: refers to the unit transport distance of the excavated material in the current stage after being temporarily stored at the transfer station and transported to the dam body in subsequent stages. Its value is the transport distance from the current location to the transfer station. Material transport distance per unit: refers to the unit transport distance when using material yard resources for filling. Its value is the actual transport distance from the material yard to the dam body plus the equivalent transport distance converted by the material yard mining cost; Unit transport distance of materials stored at the transfer station: refers to the unit transport distance from the reserve materials at the transfer station to the dam body in the initial stage of dam construction. Its value is the actual transport distance from the transfer station to the dam body.

6. The method for multi-objective intelligent allocation of earthwork and rock excavation according to claim 5, characterized in that, By setting penalty numbers for corresponding unit transport distances, time constraints are imposed to ensure that excavated and stored materials at each construction stage are used only in the current stage and in subsequent stages.

7. The method for multi-objective intelligent allocation of earthwork and rock excavation according to claim 6, characterized in that, The earthwork storage constraints include: storage constraints for each stage and each work area, storage for each stage and each work area, total storage and transportation volume, and earthwork storage and transportation volume are non-negative; The cut-fill balance constraint includes: cut-fill balance constraint at each stage, whereby the total excavation volume of each work area at each stage should be consistent with the storage volume of the spoil area; The vehicle access uniqueness constraint: each work object that needs to be served by a transport vehicle during the dispatch process is only served once by each vehicle; The station constraints involve the starting point, ending point, and possible intermediate stops in vehicle route planning, and all vehicles return to the parking lot after departing from it. The vehicle load constraints are as follows: the total load of all vehicles shall not exceed the sum of their load capacities, and each delivery vehicle shall not exceed its maximum load capacity during transportation. The secondary loop elimination condition constraint is used to ensure the connectivity of vehicle paths and avoid the formation of local loops that do not include the starting point. The equipment quantity constraint is that the number of excavating machinery and the number of transport vehicles must be matched to the efficiency requirement. The time window constraints include: the earliest allowed service time, the latest allowed service time, the vehicle's arrival time at the node, the vehicle's waiting time at the node, the vehicle's service time at the node, and the vehicle's travel time between two nodes.

8. An electronic device, characterized in that, The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the intelligent allocation method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the intelligent allocation method according to any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the intelligent allocation method as described in any one of claims 1 to 7.

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