Slope earthwork deployment method and system under low carbon constraint

Abstract

The application discloses a slope earthwork deployment method and system under low-carbon constraint. Firstly, the earthwork construction area is divided into several deployment blocks, and each block is represented by an information binary tuple; the minimum carbon emission and the lowest transportation cost are taken as the optimization target of the earthwork deployment scheme, and a slope earthwork deployment model under low-carbon constraint is established; then, the slope earthwork deployment model under low-carbon constraint in the step 3 is solved based on a multi-objective optimization algorithm or a programming solution method; after the above optimization deployment, the remaining or insufficient earthwork in the field is treated by the way of discarding the earth to a designated earth discarding field or borrowing the earth from outside the field, and finally, the filling and digging amount balance in the slope construction area is realized. Compared with the previous earthwork deployment scheme, the optimal earthwork deployment scheme determined by the application is an environmentally friendly and most economical scheme.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of earthwork allocation optimization in civil engineering construction, and particularly relates to a slope earthwork allocation method and system under low-carbon constraints. BACKGROUND

[0002] Earthwork allocation is an indispensable work in large-scale civil engineering construction, and the pros and cons of its scheme not only affect the engineering progress, but also have different degrees of influence on engineering quality, cost, and surrounding environment. For a long time, scholars have carried out a lot of researches on the optimization problem of earthwork allocation scheme, and have achieved a lot of meaningful results. For example, a channel earthwork allocation method based on distance priority (authorized number: CN101702181B) divides the channel into several equal distance sections, and then takes the nearest distance as the earthwork allocation principle, and discloses a channel earthwork allocation method based on distance priority. In order to improve the optimization efficiency of the scheme, the method for optimizing the earthwork allocation scheme by using BIM technology (application number: CN201710398061.1) converts the collected initial terrain data into electronic initial terrain data by using BIM technology related software, and on this basis, calculates the earthwork quantity and center of gravity, and optimizes the earthwork allocation scheme with the minimum transportation distance (cost) as the target. An earthwork allocation optimization method for rockfill dams based on improved interval multi-objective optimization (authorized number: CN112001547B) uses interval numbers to represent the earthwork allocation parameters of rockfill dams, and takes the minimum construction period and cost as the target, and discloses an improved interval multi-objective optimization earthwork allocation optimization method for rockfill dams.

[0003] From the existing research results, it can be found that the existing technology pays more attention to the cost and progress of scheme optimization, but the difference in environmental impact caused by different allocation schemes is not considered. After all, the earthwork construction stage is the stage where various types of construction machinery are used more intensively, and it is also the stage with larger carbon emissions. Different earthwork allocation schemes also have different carbon emissions, especially under the construction conditions of slope (including mountainous, hilly and other landforms), the difference in carbon emissions caused by different earthwork allocation schemes cannot be ignored. SUMMARY

[0004] In view of the technical problem that the difference in environmental impact caused by different allocation schemes is not considered in the above existing technology, the present application provides a slope earthwork allocation method under low-carbon constraints, which comprises the following steps:

[0005] A slope earthwork allocation method under low-carbon constraints, characterized by comprising the following steps:

[0006] Step 1: divide the entire earthwork construction area into n earthwork allocation blocks;

[0007] Step 2: use one information pair T for each earthwork allocation area block i i (Q i ,H i ), i = 1, 2, …, n represents; wherein, Q i is the filling amount or excavation amount of earthwork allocation area block i, Q i ≤ 0 is the filling amount, Q i > 0 is the excavation amount; H i represents the average elevation of earthwork allocation area block i;

[0008] Step 3: taking the minimum carbon emission and the lowest transportation cost as the optimization target of the earthwork allocation scheme, establishing a slope earthwork allocation model under low carbon constraint and determining the constraint condition;

[0009] Step 4: solving the slope earthwork allocation model under low carbon constraint in step 3 based on multi-objective optimization algorithm or programming solution method;

[0010] Step 5: after the above optimization and allocation, the remaining or insufficient earthwork in the field is treated by dumping to the designated spoil field or borrowing earth from outside, so as to realize the balance of filling and excavation amount in the slope construction area.

[0011] Further, according to the planar position of the building to be constructed on the slope site, and the type and quantity of the leading construction machinery to be put into the site, the entire earthwork construction area is divided into n earthwork allocation blocks, n ≥ 2 and is an integer.

[0012] Further, the slope earthwork allocation model under low carbon constraint in step 3 is: min{Emd, F cost};

[0013] Wherein, the carbon emission target function Emd:

[0014]

[0015] The cost target function F cost :

[0016] Wherein, S fc is the fuel consumption rate, kg / kwh; C w is the vehicle weight, kg; L d is the density of earthwork in loose state, kg / m 3 ; T hc is the maximum volume of the car, m 3 ; R s is the road condition factor reflecting the road firmness, %; p ijTo adjust the slope between blocks i and j for earthwork, ρ ij >0 indicates that the transition from allocation block i to allocation block j is a downhill slope; ρ ij <0 indicates that the path from allocation block i to allocation block j is uphill; ρ ij =0 indicates that the section from allocation block i to allocation block j is a slope-free section; S av C represents the average speed of the vehicle, in km / h; hp Represents the horsepower conversion factor; cc represents the empirical constant; E mcf The energy conversion factor of fuel, expressed in kg / L; ρ fuel Fuel density is expressed in kg / L; T pr Indicates vehicle transportation efficiency, m 3 / h;C ij L represents the quantity of earth and rock transported from earth and rock allocation block i to block j; ij The average transport distance for earth and stone to be moved from earth and stone allocation block i to block j.

[0017] Furthermore, the constraints in step 3 are as follows:

[0018]

[0019] Among them, Q T Q W These represent the total fill volume and total excavation volume of the earthwork construction area, respectively.

[0020] Furthermore, the multi-objective optimization algorithm in step 4 includes the following sub-steps:

[0021] Step 4.1: Find the Pareto solution using a multi-objective optimization algorithm;

[0022] Step 4.2: Based on the Pareto solution obtained in Step 4.1, assign weights to each objective using the entropy weight method;

[0023] Step 4.3: Use evidence reasoning algorithms to make the optimal solution decision.

[0024] Further, step 4.1 includes:

[0025] Step 1. Model initialization: Randomly generate an initial population of N individuals, and calculate P for all individuals. k The fitness value f of k = 1, 2, ..., N for the carbon emission objective function and the cost objective function kl l = 1, 2; and set the initial iteration step number g = 0;

[0026] Each of the above individuals P k All are two-dimensional arrays, that is

[0027]

[0028] Step2. Variation operation, according to the variation strategy The adjustment value of all individuals in the population is subjected to the variation operation:

[0029] Wherein, V ij (g+1) is the adjusted value after variation, g is the iteration number, respectively, three individuals selected from the population at iteration step g , i.e. the adjustment value, F is the variation factor and F ∈ [0, 1], r1≠ r2≠ r3≠ i, and r1, r2, r3 ∈ [1, N], N is the population size;

[0030] Step3. Crossing operation, the individual after variation is subjected to crossing operation with the individual of the last generation population to generate candidate individual U ij (g+1), the operation strategy is as follows:

[0031]

[0032] In the formula, CR is the crossing probability factor, CR ∈ [0, 1]; j rand is a randomly generated dimension index number, j rand ∈ [1, 2,..., n], which ensures that at least one element in the candidate individual is contributed by the variation value;

[0033] Step4. Selection operation, the candidate individual and the individual of the last generation population are combined into a population with a size of 2N, the objective function fitness value of each individual in the population is calculated, and the obtained objective function fitness value is sorted by using the Pareto non-dominated level hierarchical and crowding distance sorting criteria, and the N individuals with high ranking are selected to form a new population;

[0034] Step5. Determine whether the loop end condition is met, if yes, output the result and stop the loop; otherwise, the iteration step number g = g + 1, return to Step2 for iteration until the loop end condition is met.

[0035] Further, the step 4.2 comprises:

[0036] Step1. According to the strategy The target fitness value of all individuals under each target is subjected to normalization processing, wherein k = 1, 2,..., N, and l = 1, 2;

[0037] Step2. Calculate the target entropy value E l :

[0038]

[0039] Step3. Calculate the target normalization weight

[0040]

[0041] where ω l is the target normalization weight.

[0042] Further, the step 4.3 includes:

[0043] Step1. Arrange the N Pareto solutions under each target in the order of fitness value f kl from small to large, and further divide each target series into H levels, different levels mean that the solutions are also different in the degree of excellence, divide each target series into five different levels of optimal, good, general, poor and worst, and take the target fitness values arranged in the first, N / 4, N / 2, 3N / 4 and N positions as the level standard values of the five levels respectively;

[0044] Step2. Express all target fitness values with confidence structure according to the following method:

[0045]

[0046]

[0047] wherein, f kl represents the fitness value f represents the confidence degree belonging to level r,

[0048] Step3. Take the two target fitness values of each Pareto solution as the evidence that the solution belongs to different levels, and use the following evidence reasoning algorithm for evidence fusion, so as to obtain the credibility distribution of each Pareto solution belonging to different levels:

[0049]

[0050] In the above formula, H represents the number of levels; m = 1, 2, …, H, L represents the number of targets;

[0051] Step4. Let the utility value of each level be U(r), and calculate the comprehensive utility value of each Pareto solution: The solution with the maximum comprehensive utility value and the credibility distribution acceptable to the decision maker is the optimal solution.

[0052] Further, the planning solution method in step 4 includes the following substeps:

[0053] Step 4.1': Solve the programming problem using the minimum carbon emissions as the objective function 1 alone, and obtain the optimal value Emd for objective function 1. * The value of the decision variable at time is denoted as

[0054] Step 4.2': Solve the programming problem with minimizing transportation costs as the objective function 2 alone, and obtain the optimal value of objective function 2. The value of the decision variable at time is denoted as

[0055] Step 4.3': Take and The total number of allocations is denoted as This represents the earthwork allocation quantity when both objective functions are simultaneously optimal, and the values ​​of the decision variables are calculated separately. When the two objective function values ​​Emd 0 ,

[0056] Step 4.4': Let the total allocation quantity be... Add on the basis Afterwards, the value of objective function 2 is... That is, due to the increase Increase the value of objective function 2 Due to the increase Increase the value of objective function 1 by Emd * -Emd 0 ,set up for The contribution to objective function 1 is set as follows: for Contribution to objective function 2;

[0057] Step 4.5': Let the total allocation quantity be... Add on the basis Afterwards, the value of objective function 1 is Emd. 1 That is, due to the increase Increase the value of target 1 by Emd 1 -Emd 0 Due to the increase Increase the value of objective function 2 set up for Contribution to Objective 1, set for Contribution to objective 2;

[0058] Step 4.6': Rank according to contribution level and The redistribution is carried out as follows;

[0059] Let The assigned weight for objective function 1 is The assigned weight for objective function 2 is The total earthwork amount is The earthwork amount assigned to objective function 1 is , and the earthwork amount assigned to objective function 2 is ;

[0060] Then The earthwork amount assigned to the two objectives is and

[0061] Step 4.7': The final earthwork allocation scheme is objective function 1; Objective function 2:

[0062] On the other hand, the present application also provides a slope earthwork allocation system under low-carbon constraint, comprising:

[0063] Block division module: for dividing the entire earthwork construction area into n earthwork allocation blocks;

[0064] Information binary tuple representation module: for representing each earthwork allocation block i with an information binary tuple T i (Q i ,H i ), i = 1, 2, …, n; wherein Q i is the fill or excavation amount of earthwork allocation block i, Q i ≤ 0 is the fill amount, Q i > 0 is the excavation amount; H i represents the average elevation of earthwork allocation block i;

[0065] Objective model construction module: for taking the minimum carbon emission and the lowest transportation cost as the optimization target of the earthwork allocation scheme, establishing a slope earthwork allocation model under low-carbon constraint and determining the constraint conditions;

[0066] Model solving module: for solving the slope earthwork allocation model under low-carbon constraint in the objective model construction module based on multi-objective optimization algorithm or programming solving method;

[0067] After the above optimization and allocation, the remaining or insufficient earthwork in the field is treated by dumping to a designated spoil field or borrowing earth from outside the field, so as to finally realize the balance of fill and excavation amount in the slope construction area;

[0068] The slope earthwork deployment system under low carbon constraint is used for executing the steps in the slope earthwork deployment method under low carbon constraint.

[0069] Compared with the prior art, the present application has the following beneficial effects:

[0070] Considering that the carbon emissions generated by different earthwork deployment schemes are different under the slope construction condition, the minimum carbon emission and the lowest transportation cost are taken as the optimization targets of the earthwork deployment scheme. When solving the multi-objective problem, the present application proposes two solving schemes: scheme one: genetic algorithm and evidence reasoning combination optimization scheme. Since the final solution obtained by the traditional genetic algorithm is usually a group of Pareto solutions, not the optimal solution, the present application takes the two target fitness values of each Pareto solution as the evidence supporting the solution belonging to different optimization levels, uses the evidence reasoning algorithm for evidence fusion, thereby obtaining the credibility distribution of each Pareto solution belonging to different optimization levels, and finally, the optimal scheme is determined by comparing the comprehensive utility value and the credibility distribution of each Pareto solution. Scheme two: the method for obtaining the optimal solution by using the "planning solving" tool. The calculation process of this method is simple, and it does not need complicated iteration, and it is easier for construction technical personnel to master. Compared with the previous earthwork deployment scheme, the optimal earthwork deployment scheme determined by the present application is an "environment-friendly" most economical scheme. BRIEF DESCRIPTION OF DRAWINGS

[0071] Figure 1 The present application is a whole flow chart;

[0072] Figure 2 The present application is a model solving flow chart in embodiment 1. DETAILED DESCRIPTION

[0073] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by the person skilled in the art without making creative efforts belong to the protection scope of the present application.

[0074] Embodiment 1

[0075] As shown in the drawings, Figure 1 The present application provides a slope earthwork deployment method under low carbon constraint,

[0076] The specific steps are as follows:

[0077] Step 1: According to the planned location of the building (structure) on the slope site, and the type and quantity of the main construction machinery to be put into the site, the entire earthwork construction area is divided into n earthwork allocation blocks, n≥2 and is an integer.

[0078] The allocation block division should follow three principles: (1) The division of earthwork allocation blocks should not damage the integrity of the building (structure) as much as possible, that is, the location of one or more complete buildings should be divided into the same block; (2) The range of each block should match the applicable working range of the main construction machinery; (3) The carbon emissions of the earthwork transport vehicles driving within the same block are negligible relative to the minimum carbon emissions of the vehicles driving between blocks.

[0079] Step 2: Each earthwork allocation block i is represented by an information binary tuple T i (Q i ,H i ), i = 1, 2, …, n. Where Q i is the fill volume (Q i ≤ 0) or excavation volume (Q i > 0) of earthwork allocation block i; H i represents the average elevation of earthwork allocation block i. Let the amount of earthwork transported from earthwork allocation block i to block j be C ij , and the average distance be L ij .

[0080] Step 3: Establish a slope earthwork allocation model under low-carbon constraints

[0081] min{Emd, F cost}

[0082]

[0083] Where Q T and Q W represent the total fill volume and total excavation volume of the earthwork construction area, respectively.

[0084] Carbon emission objective function:

[0085]

[0086] S fc — fuel consumption rate, kg / kwh

[0087] C w — vehicle weight, kg

[0088] L d — density of earthwork in loose state, kg / m 3

[0089] T hc — the maximum volume of the vehicle cabin, m 3

[0090] R s — the road condition factor reflecting the degree of road firmness, %

[0091] ρ ij — the slope between the earthwork deployment area block i and j, When ρ ij > 0, it means that the slope from the deployment area block i to the deployment area block j is downhill; when ρ ij < 0, it means that the slope from the deployment area block i to the deployment area block j is uphill; when ρ ij = 0, it means that the slope from the deployment area block i to the deployment area block j is a flat section.

[0092] S av — the average driving speed of the vehicle, km / h

[0093] C hp — the horsepower conversion coefficient, taking 0.735

[0094] cc— empirical constant, taking 273.75

[0095] E mcf — the energy conversion factor of fuel, kg / L

[0096] ρ fuel — the density of fuel, kg / L

[0097] T pr — the vehicle transportation efficiency, m 3 / h

[0098] Cost objective function:

[0099] Step 4: Model solving, as shown in the following: Figure 2

[0100] 4.1 Seek Pareto solution through multi-objective optimization algorithm

[0101] Preferably, the differential evolution algorithm is used to seek the Pareto solution, and the steps are as follows:

[0102] Step 1. Model initialization. Randomly generate an initial population consisting of N individuals, calculate the fitness values f k (l = 1, 2) of all individuals P kl (k = 1, 2, …, N) for two objective functions, and set the initial iteration step number g = 0.

[0103] Each of the above individuals P k is a two-dimensional array, namely​

[0104] Step2. Mutation operation. The allocation values of all individuals in the population are mutated according to the following mutation strategy:

[0105] wherein V ij (g+1) is the mutated allocation value, g is the iteration number, respectively represent the allocation values of three individuals randomly selected from the population at iteration step g, F is a mutation factor and F ∈ [0, 1], r1≠ r2≠ r3≠ i, and r1, r2, r3 ∈ [1, N], N is the population size.

[0106] Step3. Crossover operation. The mutated individuals are crossed with the individuals of the previous generation population to generate candidate individuals U ij (g+1), the operation strategy is as follows:

[0107] wherein CR is a crossover probability factor, CR ∈ [0, 1]; j rand is a randomly generated dimension index number, j rand ∈ [1, 2, …, n], which ensures that at least one element in the candidate individual is contributed by the mutated value.

[0108] Step4. Selection operation. The candidate individuals and the individuals of the previous generation population are combined into a population with a size of 2N, the objective function fitness values of each individual in the population are calculated, and the obtained objective function fitness values are sorted based on the Pareto non-dominated level stratification and crowding distance sorting criteria, and the top N individuals are selected to form a new population.

[0109] Step5. Determine whether the loop termination condition is met. If the loop termination condition is met, output the result and stop the loop; otherwise, the iteration step number g = g + 1, and return to Step 2 for iteration until the loop termination condition is met.

[0110] 4.2 Setting target weights

[0111] Preferably, according to the Pareto solution obtained in the foregoing, the entropy weight method is used to assign weights to each target, and the steps are as follows:

[0112] Step1. Preferably, the target fitness values of all individuals under each target are normalized according to the following strategy

[0113] wherein k = 1, 2, … N, and l = 1, 2.

[0114] Step2. Calculate the target entropy value

[0115]

[0116] Step3. Calculate the target normalization weight

[0117]

[0118] 4.3 Optimal scheme decision by using evidence reasoning algorithm

[0119] Step1. Arrange the fitness values f kl (k=1,2,...,N,l=1,2) in ascending order, and further divide each target series into H levels, and different levels mean that the schemes are also different in degree of excellence. Preferably, each target series is divided into five different levels of optimal, good, general, poor and worst, and the target fitness values arranged in the first, N / 4th, N / 2th, 3N / 4th and Nth positions are taken as the level standard values of the five levels.

[0120] Step2. Express all target fitness values by using confidence structure according to the following method:

[0121]

[0122] In the formula, f kl confidence degree belonging to level r, respectively represent the standard values of r level and r+1 level under the target series l.

[0123] Step3. Take the two target fitness values of each Pareto solution as the evidence supporting the solution belonging to different levels, and use the following evidence reasoning algorithm for evidence fusion, so as to obtain the credibility distribution of each Pareto solution belonging to different levels:

[0124]

[0125] In the above formula, H represents the number of levels; m=1,2,...,H, L represents the number of targets, and L=2 in the present application.

[0126] Step4. Let the utility value of each level be U(r), and calculate the comprehensive utility value of each Pareto solution: The scheme with the maximum comprehensive utility value and the credibility distribution acceptable to the decision maker is the optimal scheme.

[0127] Step 5: After the above optimization and allocation, the remaining or insufficient earthwork in the field is treated by discarding soil to a designated soil discarding field or borrowing soil from outside the field, so as to finally realize the balance of the filling and excavation amount in the slope construction area.

[0128] Take a new district construction project of a hospital in Enshi, Hubei as an example. The project covers an area of 57469 square meters, and the construction content includes one each of internal medicine building, surgery building, infectious disease building, outpatient medical building, and emergency center building. The project construction site has complex terrain, with a large number of slopes and valleys. The maximum natural ground difference is about 55m. After calculation, the total amount of excavation is 273213.2m 3 , the maximum excavation depth is 24m, the total amount of filling is 110396.2m 3 , and the maximum filling height is about 10m. The main earthwork construction machinery to be invested on site is 8 units of 350 excavators, 2 units of SD52 bulldozers, and 12 units of 15T muck trucks.

[0129] Step 1: According to the planar position of each proposed building on site, the distribution of filling and excavation areas, and the number of dominant construction machinery (excavators and bulldozers) to be invested on site, the entire earthwork construction area is divided into 7 earthwork allocation blocks.

[0130] Step 2: The 7 earthwork allocation blocks are expressed in the form of information binary tuple, together with the average earthmoving distance between blocks, as shown in the table below.

[0131]

[0132] Step 3: Establish a slope earthwork allocation model under low carbon constraints

[0133] min{Emd,F cost}

[0134]

[0135] In the formula,

[0136] S fc = 0.22 kg / kwh, C w = 14000 kg, L d = 1680 kg / m 3 , T hc = 25 m 3 , R s = 6%, S av = 30 km / h, C hp = 0.735, cc = 273.75, E mcf = 2.659 kg / L, p fuel = 0.85 kg / L, T pr = 49.822 m 3 / h.

[0137] Step 4: Model solution

[0138] The population size N = 100, the maximum number of iterations g max = 1000, mutation factor F = 0.1, crossover probability factor CR = 0.9, and the utility values of the five different levels of optimal, good, general, poor and worst are 10, 7.5, 5, 2.5 and 0 respectively. The optimal allocation scheme obtained according to the model solving process is shown in the following table.

[0139]

[0140] The credibility distribution of the optimal scheme belonging to different levels is {(optimal, 0.851), (good, 0.142), (general, 0.007), (poor, 0), (worst, 0)}, and the comprehensive utility value is 9.61.

[0141] Step 5: After the above optimization and allocation, the remaining 162817m 3 The earthwork is transported to the designated spoil ground, and thus the earthwork allocation work in the construction area of the project is completed.

[0142] Example 2

[0143] Unlike the above example, considering that the engineering construction personnel may not have the ability to solve multi-objective optimization problems by computer programming, the step 4 can also be solved by a simpler method, which only requires the construction technical personnel to be familiar with the EXCEL "planning solution" tool, which includes the following sub-steps:

[0144] Step 4.1': The target function, decision variable and constraint condition obtained in step 3 are respectively input into the corresponding cells of the EXCEL spreadsheet; only the target function 1 (minimization of carbon emissions) is used as the planning solution target, the EXCEL spreadsheet "data" - "planning solution" command is called, and the decision variable value when the target function 1 is optimal Em * is obtained, denoted as

[0145] Step 4.2': only the target function 2 (minimization of transportation cost) is used as the planning solution target, the EXCEL spreadsheet "data" - "planning solution" command is called again, and the decision variable value when the target function 2 is optimal Em is obtained, denoted as

[0146] Step 4.3': the common allocation quantity of and is taken, denoted as The earthwork allocation quantity when the two target functions are simultaneously optimal is represented by When the two objective function values ​​Emd 0 ,

[0147] Step 4.4': Let the total allocation quantity be... Add on the basis Afterwards, the value of objective function 2 is... That is, due to the increase Increase the value of objective function 2 Obviously, due to the increase Increase the value of objective function 1 by Emd * -Emd 0 Furthermore, it is said for The contribution to objective 1 is called for Contribution to objective 2;

[0148] Step 4.5': Similarly, let the total allocation quantity be... Add on the basis Afterwards, the value of objective function 1 is Emd. 1 That is, due to the increase Increase the value of target 1 by Emd 1 -Emd 0 Obviously, due to the increase Increase the value of objective 2 Furthermore, it is said for The contribution to objective 1 is called for Contribution to objective 2;

[0149] Step 4.6': Rank according to contribution level and When redistributing, it's worth noting that since both objective functions seek to minimize, the greater the contribution, the smaller the corresponding allocation value should be. Taking the allocation as an example, the method is as follows;

[0150] make The weight assigned to objective 1 is Weighting of Objective 2 Then The earthwork allocation volume assigned to Target 1 is The earthwork allocation amount assigned to Target 2 is... .

[0151] Similarly, we can obtain The earthwork allocation volumes assigned to the two targets are respectively and

[0152] Step 4.7': the final earthwork deployment scheme is target 1; Target 2:

[0153] Embodiment 3

[0154] The embodiment provides a slope earthwork deployment system under low-carbon constraint, comprising:

[0155] A block division module is configured to divide the entire earthwork construction area into n earthwork deployment blocks;

[0156] An information pair representation module is configured to represent each earthwork deployment block i by an information pair T i (Q i ,H i ), i = 1, 2, …, n; wherein Q i represents the filling amount or the digging amount of the earthwork deployment block i, Q i <0 represents the filling amount, and Q i ≥0 represents the digging amount; and H i represents the average elevation of the earthwork deployment block i.

[0157] A target model construction module is configured to take the minimum carbon emission and the lowest transportation cost as the optimization target of the earthwork deployment scheme, to establish a slope earthwork deployment model under low-carbon constraint and to determine constraint conditions;

[0158] A model solution module is configured to solve the slope earthwork deployment model under low-carbon constraint in the target model construction module based on a multi-objective optimization algorithm or a programming solution method;

[0159] After the above optimization and deployment, the remaining or insufficient earthwork in the field is treated by means of dumping to a designated spoil field or borrowing earthwork from outside the field, so as to finally realize the balance of the filling and digging amounts in the slope construction area.

[0160] Although the preferred embodiments of the present application have been described, those skilled in the art who have the basic inventive concept can make further changes and modifications to the embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0161] Obviously, those skilled in the art can make various modifications and variations to the embodiments of the present application without departing from the spirit and scope of the embodiments of the present application. Thus, if these modifications and variations of the embodiments of the present application fall within the scope of the claims of the present application and their equivalents, the present application also intends to include these modifications and variations.

[0162] Other parts not described in detail are prior art.

Claims

1. A method for slope earthwork deployment under low carbon constraint, characterized in that: The method comprises the following steps: Step 1: dividing the entire earthwork construction area into several earthwork allocation blocks; Step 2: divide each earthwork allocation block with an information binary tuple , ; wherein, is the amount of fill or cut of the earthwork allocation block , is the amount of fill, is the amount of cut; denotes the average elevation of the earthwork allocation block ; Step 3: Taking the minimum carbon emission and the lowest transportation cost as the targets of the optimization of the earthwork allocation scheme, establishing a slope earthwork allocation model under low carbon constraint and determining the constraint conditions; the slope earthwork allocation model under low carbon constraint is: ; Wherein, the carbon emission target function : Cost objective function : wherein, is the fuel consumption rate, kg / kwh; is the vehicle self-weight, kg; is the density of earthwork in loose state, kg / m 3 ; is the maximum volume of the vehicle compartment, m 3 ; is the road condition factor reflecting the road firmness, %; is the slope between the earthwork allocation block , , , denotes the downhill from the earthwork allocation block to the earthwork allocation block ; denotes the uphill from the earthwork allocation block to the earthwork allocation block ; denotes the flat section from the earthwork allocation block to the earthwork allocation block ; denotes the average vehicle driving speed, km / h; denotes the horsepower conversion coefficient; denotes the experience constant; denotes the energy conversion factor of fuel, kg / L; denotes the fuel density, kg / L; denotes the vehicle transportation efficiency, m 3 / h; is the amount of earthwork transported from the earthwork allocation block to the block ; is the average transportation distance of earthwork from the earthwork allocation block to the block ; The constraint condition is: wherein, respectively represent the total filling amount and the total excavation amount of earthwork construction area; Step 4: solving the slope earthwork allocation model under the low-carbon constraint in step 3 based on a multi-objective optimization algorithm or a programming solving method; Step 5: after the above optimization and allocation, the remaining or insufficient earthwork in the field is treated by dumping to a designated spoil field or borrowing earthwork from outside the field, so as to finally realize the balance of the fill and cut earthwork in the slope construction area.

2. The method according to claim 1, wherein, The step 1 divides the whole earthwork construction area into several earthwork allocation blocks according to the planned position of the building on the slope site and the type and quantity of the main construction machines to be put into the site. The step 1 divides the whole earthwork construction area into several earthwork allocation blocks according to the planned position of the building on the slope site and the type and quantity of the main construction machines to be put into the site. The step 1 divides the whole earthwork construction area into several earthwork allocation blocks according to the planned position of the building on the slope site and the type and quantity of the 3. The method according to claim 1, wherein, The multi-objective optimization algorithm in step 4 comprises the following sub-steps: Step 4.1: seeking a Pareto solution through a multi-objective optimization algorithm; Step 4.2: according to the Pareto solution obtained in step 4.1, an entropy weight method is used to assign weights to each target; Step 4.3: an evidence reasoning algorithm is used for optimal scheme decision-making.

4. The method according to claim 3, wherein, The step 4.1 comprises: Step 1. Model initialization, randomly generate an initial population consisting of individuals, calculate the fitness value of all individuals of the carbon emission target function and the cost target function , ; and set the initial iteration step ; Each of the above individuals are two-dimensional arrays, i.e. Step 2. Mutation operation, according to mutation strategy The mutation operation is performed on the fitness values of all individuals in the population: wherein, is the adjusted fitness value, is the iteration number, , , respectively represent three individuals randomly selected from the population at iteration step , i.e. the adjusted fitness value, is the mutation factor and , , and , , N is the population size; Step3. Cross operation, cross operation of the individual after mutation and the individual of the last generation population to generate candidate individual The operation strategy is as follows: wherein is a cross-over probability factor, ; is a randomly generated dimension index number, which ensures that at least one of the elements in the candidate individual is contributed by a variable value; Step4. Selection, combine the candidate individuals with the last generation population individuals into a population with a size of , calculate the objective function fitness value of each individual in the population, and sort the resulting objective function fitness values using the Pareto non-dominated level hierarchical and crowding distance sorting criteria, and select the top individuals to form a new population; Step5. Determine whether the loop end condition is met. If yes, output the result and stop the loop; otherwise, iterate the step number , go back to Step 2 for iteration until the loop end condition is met.

5. The method according to claim 4, wherein, The step 4.2 comprises: Step 1. According to the strategy The target fitness value of each individual under each target is normalized, where, , ; Step 2. Calculate the target entropy value : Step 3. Calculate the normalized weight of the target wherein, is the target normalization weight.

6. The method according to claim 5, wherein, The step 4.3 comprises: Step1. Calculate the fitness value of each Pareto solution under each objective Step2. Rank the fitness value of each Pareto solution under each objective in ascending order Step3. Further divide each objective series into Step4. Divide each objective series into five different levels, i.e. the best, the better, the general, the worse and the worst, and take the fitness value of the target ranked at the 1st, the 2nd, the 3rd, the 4th and the 5th as the level standard value of the five levels respectively ​​​​ Step 2. Express all target fitness values with a belief structure according to the following method: wherein denotes a fitness value belonging to a class of a confidence level, denotes a target series under a class and a class standard value; Step 3. Take the two target fitness values of each Pareto solution as evidence supporting the solution to belong to different levels, and use the following evidence reasoning algorithm for evidence fusion, so as to obtain the credibility distribution of each Pareto solution belonging to different levels: In the above formula, H represents the number of grades; , L represents the number of targets; Step4. Let each level utility value be , the comprehensive utility value of each Pareto solution is calculated: , the solution with the maximum comprehensive utility value and the credibility distribution that can be accepted by the decision maker is the optimal solution.

7. The method according to claim 1, wherein, The programming solving method in step 4 comprises the following sub-steps: Step 4.1': Solve the programming problem separately with the minimum carbon emission as the objective function 1, and get the optimal value of the objective function 1 The decision variable value at this time is denoted as , , ; Step 4.2': Solve the planning problem separately for objective function 2 with the objective function 2 minimized, denoted as the decision variable values at the time of the decision, denoted as , , ; Step 4.3': Take and as the common allocation quantity, denoted as , , which represents the earthwork allocation quantity when both objective functions are optimal, and calculate the values of the two objective functions when the decision variable value is , ;​ Step 4.4': After increasing the common allocation quantity by , the value of the objective function 2 is , i.e. the value of the objective function 2 increases by due to the increase of , the value of the objective function 1 increases by due to the increase of , the contribution degree of to the objective function 1 is set as , and the contribution degree of to the objective function 2 is set as ; Step 4.5': Let the total allocation quantity be... Add on the basis Afterwards, the value of objective function 1 is... That is, due to the increase Increase the value of target 1 Due to the increase Increase the value of objective function 2 ,set up for Contribution to Objective 1, set for Contribution to objective 2; Step 4.6': The contributions are redistributed according to the size of the contributions as follows; and are redistributed as follows; Let the assigned weight for objective function 1 is the assigned weight for objective function 2 is , the earthwork allocation amount assigned to objective function 1 is , and the earthwork allocation amount assigned to objective function 2 is ; Then The amount of earthwork allocated to the two targets is and ; Step 4.7': The final earthwork allocation scheme is the objective function 1; ; objective function 2: .

8. A slope earthwork deployment system under low carbon constraint, characterized in that: comprise: The block division module is configured to divide the whole earthwork construction region into earthwork allocation blocks. information pair representation module: which is used to represent each earthwork allocation block with an information pair , ; wherein, is the amount of fill or the amount of cut of the earthwork allocation block , is the amount of fill, is the amount of cut; represents the average elevation of the earthwork allocation block ; A target model construction module is used to take the minimum carbon emission and the lowest transportation cost as the optimization target of the earthwork allocation scheme, establish a slope earthwork allocation model under the low-carbon constraint, and determine the constraint condition; A model solving module is used to solve the slope earthwork allocation model under the low-carbon constraint in the target model construction module based on a multi-objective optimization algorithm or a programming solving method; After the above optimization and allocation, the remaining or insufficient earthwork in the field is treated by dumping to a designated spoil field or borrowing earthwork from outside the field, so as to finally realize the balance of the fill and cut earthwork in the slope construction area; The slope earthwork allocation system under the low-carbon constraint is used to execute the steps in the slope earthwork allocation method under the low-carbon constraint as claimed in any one of claims 1-7.

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