Method, device and equipment for generating ore blending transportation scheme through genetic algorithm and medium

Abstract

The invention discloses a method, device and equipment for generating an ore blending transportation scheme through a genetic algorithm and a medium. The method comprises the following steps: obtaining a to-be-operated ore blending transportation task; randomly generating at least one initial ore blending transportation scheme according to the ore blending transportation task; after the initial ore blending transportation schemes serve as initial population individuals in the initial population, target population individuals meeting fitness scoring conditions are determined through multiple iterations based on a genetic algorithm and a preset fitness calculation function; and determining the target population individuals as a target ore blending transportation scheme matched with the ore blending transportation task. According to the embodiment of the invention, the whole implementation process is based on the transportation times of different load-carrying transport vehicles between the working face and the crushing opening, a scheduling scheme in which the planned yield and the ore grade meet the requirements is generated, the scheduling precision is ensured, the production and transportation cost is reduced, the ore blending working efficiency is improved, and the ore blending quality is improved. And the sustainable mining capacity of the mine can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering machinery, and in particular to a method, device, equipment and medium for generating an ore distribution and transportation plan using a genetic algorithm. Background Art

[0002] Open-pit mines usually have multiple working faces in production at the same time. The ore grades of different working faces are different. The ore needs to be transported to the crushing port by a transport vehicle for crushing. There may be multiple crushing ports, and each crushing port may have different requirements for the grade and output of the ore produced. In the actual mine transportation scenario, transport vehicles with different loads may be mixed, and the number of transport vehicles with different loads is different. In addition, the working time varies due to the different distances from the working face to the crushing port. Mine scheduling is difficult and the ore allocation plan often cannot be flexibly adjusted.

[0003] Existing technology builds a case library on the relationship between ore grade and output by collecting historical data, and uses a multi-objective optimization algorithm to calculate the ideal ore output of each working face. However, it cannot control the quality of the final ore produced, nor does it consider the relationship between working time and output, resulting in inaccurate intelligent scheduling. Summary of the invention

[0004] Based on this, the present invention provides a method, device, equipment and medium for generating ore distribution and transportation plans using a genetic algorithm to solve the problem that existing ore distribution and transportation plans are not comprehensive enough, resulting in inaccurate scheduling.

[0005] In a first aspect, an embodiment of the present invention provides a method for generating an ore distribution and transportation plan using a genetic algorithm, the method comprising:

[0006] Obtaining the ore distribution and transportation tasks to be carried out, wherein the ore distribution and transportation tasks are tasks of transporting the ore in a set number of mine working faces in the target mine to a set number of crushing ports for operation through transport vehicles with various loads; each mine working face produces ore of a set grade;

[0007] According to the ore distribution and transportation task, at least one initial ore distribution and transportation plan is randomly generated, wherein the initial ore distribution and transportation plan includes selecting a transport vehicle with a set load to transport the ore in each mine working face to a set crushing port in a set number of transport trips;

[0008] After taking each initial ore distribution and transportation plan as the initial population individual in the initial population, based on the genetic algorithm and the preset fitness calculation function, multiple iterations are performed to determine the target population individual that meets the fitness score condition;

[0009] The target population individuals are determined as the target ore distribution and transportation plan that matches the ore distribution and transportation task.

[0010] In a second aspect, an embodiment of the present invention further provides a device for generating a ore distribution and transportation plan using a genetic algorithm, the device comprising:

[0011] The task acquisition module is used to acquire the ore distribution and transportation tasks to be carried out, wherein the ore distribution and transportation tasks are tasks of transporting the ore in a set number of mine working faces in the target mine to a set number of crushing ports for operation through transport vehicles with various loads; each mine working face produces ore of a set grade;

[0012] An initial plan generation module is used to randomly generate at least one initial ore distribution and transportation plan according to the ore distribution and transportation task, wherein the initial ore distribution and transportation plan includes selecting a transport vehicle with a set load to transport the ore in each mine working face to a set crushing port in a set number of transport trips;

[0013] The target population individual determination module is used to determine the target population individuals that meet the fitness score conditions through multiple iterations based on the genetic algorithm and the preset fitness calculation function after taking each initial ore distribution and transportation plan as the initial population individuals in the initial population;

[0014] The target scheme determination module is used to determine the target population individuals as the target ore distribution and transportation scheme that matches the ore distribution and transportation task.

[0015] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method of generating a mineral distribution and transportation plan using a genetic algorithm as described in any embodiment of the present invention.

[0019] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement a method for generating a mineral distribution and transportation plan using a genetic algorithm as described in any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present invention constructs a ore distribution and transportation plan based on a genetic algorithm, and uses the ore distribution and transportation plan as a population individual for crossover and mutation operations, and continuously iterates, calculates the fitness score of each generation of population individuals during the iteration process, and obtains the optimal ore distribution and transportation plan when the fitness score conditions are met. The entire process is based on the transportation trips between the working face and the crushing port by different load-carrying transport vehicles, and generates a scheduling plan that meets the requirements for both planned output and ore grade. While ensuring scheduling accuracy, it further reduces production and transportation costs, improves ore distribution work efficiency, and helps to improve the sustainable mining capacity of the mine.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 This is a flow chart of a method for generating an ore distribution and transportation plan using a genetic algorithm according to Embodiment 1 of the present invention;

[0024] Figure 2 is a flow chart of another method for generating an ore distribution and transportation plan using a genetic algorithm according to the second embodiment of the present invention;

[0025] Figure 3 It is a schematic diagram of the structure of a device for generating an ore distribution and transportation plan using a genetic algorithm according to a third embodiment of the present invention;

[0026] Figure 4 The invention is a schematic diagram of the structure of an electronic device for implementing a method for generating an ore distribution and transportation plan using a genetic algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment 1

[0030] Figure 1 This is a flow chart of a method for generating a ore distribution and transportation plan using a genetic algorithm provided in the first embodiment of the present invention. This embodiment can be applied to the automatic dispatching of transport vehicles performing mining tasks. The method can be executed by a device for generating an ore distribution and transportation plan using a genetic algorithm. The device can be implemented in the form of hardware and / or software and can be configured in a mine dispatching system. Figure 1 As shown, the method includes:

[0031] S110. Obtain the ore distribution and transportation tasks to be carried out, wherein the ore distribution and transportation tasks are the tasks of transporting the ores in a set number of mine working faces in the target mine to a set number of crushing ports for operation by transport vehicles with various loads; each mine working face produces ore of a set grade.

[0032] In the actual ore distribution and transportation task, transport vehicles with different loads are responsible for transporting ore from different working faces of the mine to different crushing ports for ore crushing. There are multiple working faces involved in the task. The grade can refer to the content of a certain element in the ore, expressed as a percentage. Since the ore grades produced by different working faces are different, when the output of each working face is different, the grade of the mixed ore finally transported to a certain crushing port for crushing is also different.

[0033] S120. According to the ore distribution and transportation task, at least one initial ore distribution and transportation plan is randomly generated, wherein the initial ore distribution and transportation plan includes selecting a transport vehicle with a set load to transport the ore in each mine working face to a set crushing port in a set number of transport trips.

[0034] In an ore distribution and transportation plan, the number of transport trips for each load-carrying vehicle on different transport routes is included, and different transport routes refer to the routes from any working face to any crushing port, and different transport routes require different times. At the same time, the ore distribution and transportation task includes restrictions on the number of crushing ports and the number of working faces. Therefore, the randomly generated initial ore distribution and transportation plan gives the number of transport trips for different load-carrying vehicles on each transport route. According to the load and transport trips of each load-carrying vehicle, multiple parameters used for subsequent fitness calculation can be calculated.

[0035] S130, after taking each initial ore distribution and transportation plan as an initial population individual in the initial population, based on a genetic algorithm and a preset fitness calculation function, multiple iterations are performed to determine a target population individual that meets a fitness score condition.

[0036] Each initial ore distribution scheme is regarded as an initial population individual, and each initial population individual will be regarded as an initial solution for the ore distribution and transportation task, and each initial solution can obtain a different fitness function value, and different fitness function values ​​determine the iteration result of the genetic algorithm. Among them, satisfying the fitness score condition refers to whether the fitness score or the number of iterations reaches the specified threshold, and the specific scoring conditions are described in detail in the subsequent embodiments.

[0037] Optionally, before iterating multiple times based on the genetic algorithm and the preset fitness calculation function to determine the target population individuals that meet the fitness score condition, the following may also be included:

[0038] Reading the gene sequence of the individuals of the initial population, wherein the gene sequence is composed of a set number of crushing port sets, each crushing port set is composed of a set number of working surface sets, and each working surface set is composed of transport trips of different load-carrying transport vehicles;

[0039] Extract the transport trips corresponding to the current load transport vehicle in each work surface set, and sum them up to obtain the planned total trips of the current load transport vehicle;

[0040] In each working face set of the current crushing port set, the transport trips corresponding to each load-carrying vehicle are multiplied by its load to obtain the transport weight of each load-carrying vehicle. The transport weights of each load-carrying vehicle are summed to obtain the planned output of each working face for the current crushing port. The planned outputs of the same working faces in all crushing port sets are summed to obtain the planned total output of each working face.

[0041] The planned output of all working faces in the current crushing opening set is calculated by weighted average to obtain the planned grade of each crushing opening;

[0042] In the current crushing port set, the planned total output of each working face is summed up to obtain the planned output of the current crushing port;

[0043] Based on the gene sequence, an M×N time-consuming matrix is ​​constructed for each load-carrying vehicle, where M is the number of crushing openings and N is the number of working faces, denoted as The total working time of the current heavy-duty transport vehicle is calculated according to the transport time matrix, and the ratio of the total working time to the maximum parallel number of the current heavy-duty transport vehicle is calculated to obtain the planned maximum working time of the current heavy-duty transport vehicle, and the maximum working time and minimum working time of the current heavy-duty transport vehicle are extracted through the time matrix.

[0044] Specifically, in this embodiment, before calculating the fitness, it is necessary to solve the parameters in the fitness function according to the gene sequences in the individuals in the initial population. In a specific example, if the number of crushing openings is 2, the number of working faces is 4, and the types of transport vehicles with different loads are 3, the gene sequence of an individual in the initial population can be: [4,17,5,0,0,0,18,6,19,1,0,2][7,3,7,0,5,4,0,2,5,5,0,5]. From the gene sequence, it can be seen that the crushing opening sets are A1{4,17,5,0,0,0,18,6,19,1,0,2][7,3,7,0,5,4,0,2,5,5,0,5]. ,19,1,0,2} and B1{7,3,7,0,5,4,0,2,5,5,0,5}; while the working face sets in A1 are m1{4,17,5},m2{0,0,0},m3{18,6,19},m4{1,0,2}, and the working face sets in B1 are m1{7,3,7},m2{0,5,4},m3{0,2,5},m4{1,0,2}. Then, according to the crushing port set and the working face set, the number of transport trips of each type of transport vehicle on a certain transport route can be obtained, such as the number of transport trips of transport vehicle c1 from working face m1 to crushing port A1 is 4, the number of transport trips of transport vehicle c2 from working face m1 to crushing port A1 is 17, and the number of transport trips of transport vehicle c3 from working face m1 to crushing port A1 is 5.

[0045] The planned total number of trips for each type of load-carrying transport vehicle is obtained by selecting the transport trips corresponding to the type of load-carrying transport vehicle in each working face set and summing them up. The planned total number of trips for transport vehicle c1 is 4+0+18+1+7+0+0+1=31, that is, the planned number of trips for each crushing port can be obtained.

[0046] The planned output is calculated based on the number of transport trips and the load capacity of each load-carrying transport vehicle. Continuing with the above example, if the load capacity of transport vehicle c1 is X, the load capacity of transport vehicle c2 is Y, and the load capacity of transport vehicle c3 is Z, then the planned output from working face m1 to crushing port A1 is 4X+17Y+5Z; similarly, the planned output from working face m1 to crushing port A2 is 7X+3Y+7Z, and the planned total output of working face m1 is 11X+20Y+12Z.

[0047] Since the ore grade produced by each working face is different, assuming that the grade of working face m1 is a%, the grade of working face m2 is b%, the grade of working face m3 is c%, and the grade of working face m4 is d%, then the ore grade of each crushing port is the weighted average of the planned output of all working faces under the current crushing port and their corresponding grades.

[0048] For the current crushing port A1, its planned output is the sum of the planned outputs of working faces m1, m2, m3 and m4. The algorithm for the planned output of each working face at the current crushing port has been explained above.

[0049] Finally, since the distances of different transportation routes are different, the working time of the transport vehicle is affected by its transportation trips and becomes different. Continuing with the above example, the time consumption matrix of the transport vehicle c1 can be expressed as For example, T 11 Refers to the working time of the transport vehicle c1 from the working surface m1 to the crushing port A1, T 24 It refers to the working time of the transport vehicle c1 from the working surface m4 to the crushing port A2. Since the distance and transportation trips of each transportation route are known, the maximum working time, minimum working time and total working time of the transport vehicle c1 can be obtained in the time matrix. It should be noted that due to the influence of the working environment, the number of vehicles of each load-bearing transport vehicle working at the same time will change. Therefore, by presetting the maximum parallel rate, the maximum parallel number of each load-bearing transport vehicle is obtained. The planned maximum working time of the transport vehicle c1 is the ratio of the total working time to the maximum parallel number of the transport vehicle c1.

[0050] S140: Determine the target population individuals as the target ore distribution and transportation plan that matches the ore distribution and transportation task.

[0051] Since the target population individual is the optimal individual that meets the fitness score condition, the solution corresponding to the target population individual is used as the final ore distribution and transportation plan. The final ore distribution and transportation plan is based on the planned output, planned grade, working time, etc., and the optimal scheduling plan for the transportation trips of various load-carrying vehicles is obtained.

[0052] The technical solution of the embodiment of the present invention constructs a ore distribution and transportation plan based on a genetic algorithm, and uses the ore distribution and transportation plan as a population individual for crossover and mutation operations, and continuously iterates, calculates the fitness score of each generation of population individuals during the iteration process, and obtains the optimal ore distribution and transportation plan when the fitness score conditions are met. The entire process is based on the transportation trips between the working face and the crushing port by different load-carrying transport vehicles, and generates a scheduling plan that meets the requirements for both planned output and ore grade. While ensuring scheduling accuracy, it further reduces production and transportation costs, improves ore distribution work efficiency, and helps to improve the sustainable mining capacity of the mine.

[0053] Embodiment 2

[0054] Figure 2 This is a flow chart of another method for generating a ore distribution and transportation plan using a genetic algorithm provided in the second embodiment of the present invention. This embodiment is based on the above embodiment and is refined. Figure 2 As shown, the method includes:

[0055] S210. Obtain the ore distribution and transportation tasks to be carried out, wherein the ore distribution and transportation tasks are the tasks of transporting the ore in a set number of mine working faces in the target mine to a set number of crushing ports for operation by transport vehicles with various loads; each mine working face produces ore of a set grade.

[0056] S220. According to the ore distribution and transportation task, at least one initial ore distribution and transportation plan is randomly generated, wherein the initial ore distribution and transportation plan includes selecting a transport vehicle with a set load to transport the ore in each mine working face to a set crushing port in a set number of transport trips.

[0057] S230. According to the required production restriction conditions matching each crushing port, at least one qualified population individual is identified from each of the initial population individuals to obtain a qualified population.

[0058] In at least one randomly generated initial ore distribution and transportation plan, it is necessary to determine the required production conditions before confirming it as a qualified population individual, that is, according to the initial ore distribution and transportation plan, according to the method described in the above embodiment, the initial production of each crushing port is calculated according to the number of transportation trips and the load capacity of each load-carrying vehicle, and the initial planned production is compared with the preset target production of each crushing port. Each initial population individual that meets the required production constraint conditions is regarded as a qualified population individual, and multiple qualified population individuals constitute a qualified population, that is, each qualified population individual corresponds to a solution of the ore distribution and transportation plan.

[0059] Optionally, according to the required production restriction conditions matching each crushing port, identifying at least one qualified population individual from each of the initial population individuals to obtain the qualified population may include:

[0060] Determine the number of trips of each load-carrying transport vehicle in transporting ore from different working faces of the mine to the current crushing port in each initial population individual; each crushing port needs to receive ore from multiple working faces, and each load-carrying transport vehicle corresponds to a different transport load;

[0061] Multiply the number of trips of each load-carrying vehicle from each working face to the current crushing port by its corresponding transport load to obtain the total transport load of all load-carrying vehicles at the current crushing port, which is also the initial output of the current crushing port.

[0062] The error rate is calculated using the initial production and the preset target production. If the error rate is lower than the error rate threshold, the initial population individuals meet the required production restriction conditions that match the current crushing port, and the initial population individuals that meet the required production restriction conditions are identified as qualified population individuals;

[0063] Otherwise, the initial population individuals do not meet the demand production restriction conditions matching the current crushing port, and the initial population individuals are adjusted until the error rate is lower than the error rate threshold, and the adjusted initial population individuals are identified as qualified population individuals;

[0064] The individuals of the initial population are merged with the individuals of the adjusted initial population to construct a qualified population.

[0065] Continuing with the above example, if the gene sequence of the initial population individuals is [4,17,5,0,0,0,18,6,19,1,0,2][7,3,7,0,5,4,0,2,5,5,0,5], the broken mouth sets are A1{4,17,5,0,0,0,18,6,19,1,0,2} and B1{7,3,7,0,5,4,0,2,5,5,0,5}, and the working surface sets in A1 are m1{4,17,5},m2{0,0,0},m3{18,6,19},m4{1,0,2}, and the working surface sets in B1 are m1{7,3,7},m2{0,5,4},m3{0,2,5},m4{1,0,2}. The initial output of crushing port A1 is the sum of the planned outputs of the four working faces, and the target output refers to the pre-set required output value for each crushing port. The initial error rate is calculated based on the initial output and the target output. If the initial error rate does not exceed the preset error threshold, the required output restriction condition is met, otherwise it is not met. The initial population individuals that do not meet the required output restriction condition are adjusted, and the above-mentioned determination of the required output restriction condition is repeated until the initial error rate is lower than the error rate threshold, and they are taken as qualified populations together with the original qualified population individuals.

[0066] S240, taking the qualified population as the iteration starting point, generating new populations generation by generation through crossover mutation, until the individual with the highest fitness score in the newly generated new population meets the fitness score condition.

[0067] Furthermore, taking the qualified population as the iterative starting point, a new population is generated generation by generation through crossover mutation until the population individual with the highest fitness score in the latest generated new population meets the fitness score condition, which may include:

[0068] In the qualified population, a crossover operation is performed on the qualified population individuals by a roulette wheel method to generate a first population, a mutation operation is performed on the first population to obtain a second population, and the second population is added to the first population to obtain a new population with an increased number of individuals;

[0069] Compare the number of individuals in the new population with the first threshold. When the number of individuals in the new population does not reach the first threshold, repeat the above operation until the number of individuals in the newly generated new population reaches the first threshold, at which time the crossover and mutation operations are stopped.

[0070] Iterate the newly generated new population, and after each iteration, use the pre-built fitness function to calculate the fitness score of the newly generated new population corresponding to the current iteration, and select the highest fitness score to compare with the second threshold. If the highest fitness score reaches the second threshold, the fitness score condition is met;

[0071] If the highest fitness score does not reach the second threshold, determine whether the number of iterations corresponding to the latest generated new population reaches the third threshold. If the number of iterations reaches the third threshold, the fitness score condition is met.

[0072] If the highest fitness score does not reach the second threshold and the number of iterations does not reach the third threshold, the operation of iterating the latest generated new population is repeatedly performed until the fitness score condition is met.

[0073] The gene exchange point and exchange method of the crossover operation are determined according to the characteristics of the population, and a new population is generated for the first time by exchanging some genes, that is, the first population. The form of gene mutation is determined according to the characteristics of the first population, and gene mutation is performed for the second time to generate a new population, that is, the second population. After the second population is added to the first population, the number of individuals in the new population increases, and crossover mutation is performed in the above manner until the number of individuals in the new population reaches the set value, that is, the first threshold.

[0074] Iterate the new population that meets the first threshold requirement, and calculate the fitness score of the new population after each iteration, compare the calculated highest fitness score with the preset fitness threshold (second threshold), if the highest fitness score is greater than the second threshold, the fitness score condition is met, and the current new population is returned as the best result; if the highest fitness score is less than the second threshold, the number of iterations is determined, if the number of iterations of the current new population reaches the preset iteration threshold (third threshold), the fitness score condition is also met, and the current new population can be returned as the best result; if the highest fitness score does not reach the second threshold and the number of iterations does not reach the third threshold, the fitness score condition is not met, and the next iteration is performed on the current new population until the fitness score condition is met and the iteration is stopped.

[0075] Optionally, while using a pre-built fitness function to calculate the fitness score of the latest generated new population corresponding to the current iteration, the following may also be included:

[0076] Select the individual with the highest fitness score from the fitness scores of the newly generated new population corresponding to the current iteration, add it to the elite pool of fixed capacity, and repeat the above operation until the elite pool is full;

[0077] When the elite pool is full, the highest fitness score in the newly generated new population corresponding to each iteration is compared with the lowest fitness score in the elite pool. If the highest fitness score is greater than the lowest fitness score in the elite pool, the individual corresponding to the lowest fitness score in the elite pool is replaced by the individual corresponding to the highest fitness score to obtain a new elite pool corresponding to the current iteration;

[0078] If the number of iterations of the current iteration reaches the elite regression threshold, all elite individuals in the new elite pool corresponding to the current iteration are added to the latest generated new population corresponding to the current iteration to obtain the new population updated by the individuals, and the new population updated by the individuals is used as the latest generated new population corresponding to the next iteration for fitness score calculation.

[0079] An elite retention strategy is introduced in the embodiment of the present invention, which is specifically manifested in that the individual with the highest fitness score in each iteration is saved as an elite individual in the elite pool. Since the capacity of the elite pool is fixed, the elite pool is full, that is, the number of elite individuals that can be accommodated in the elite pool reaches the maximum. In each iteration after the elite pool is full, it is necessary to compare the highest fitness score in the latest generated new population corresponding to the current iteration with the lowest fitness in the elite pool, that is, the last-in-first-out elimination strategy. If the highest fitness score is higher than the lowest fitness score in the elite pool, the individual corresponding to the highest fitness score is replaced with the elite individual corresponding to the lowest fitness score to generate a new elite pool.

[0080] Among them, the elite regression threshold refers to the trigger condition for adding elite individuals in the elite pool to the new population. Specifically, the elite regression threshold is accumulated with the increase of the number of iterations. When it is accumulated to the elite regression threshold, all elite individuals in the current elite pool are added to the new population corresponding to the current iteration. At this time, the accumulated value is cleared and re-accumulated as the number of iterations increases. The above operation is repeated until the population stops iterating.

[0081] Furthermore, before using the pre-built fitness function to calculate the fitness score of the latest generated new population corresponding to the current iteration, the following steps may also be included:

[0082] Based on the planned output of each crushing port, the planned grade of each crushing port, the planned total output of each working face, the planned total trips of each load-carrying vehicle, the planned maximum working time of each load-carrying vehicle, and the maximum and minimum working time of each load-carrying vehicle, a fitness function is constructed. The formula is as follows:

[0083]

[0084] Z is the lowest constant that can be set in the system;

[0085] Among them, f(o) is the planned production fraction of each crushing port, expressed as:

[0086]

[0087] Gap1 refers to the actual output difference rate. The target output difference rate, target output and confirmed output are fixed values. o is the weight corresponding to the planned production fraction of each crushing opening;

[0088] f(c) is the planned grade fraction for each crushing opening, expressed as:

[0089]

[0090] The target grade of each crushing port is a fixed value, the threshold range is a preset fixed interval, and the average grade is obtained by weighted average of the planned grades of all crushing ports, W c is the weight corresponding to the planned grade score of each crushing opening;

[0091] f(ω) is the planned capacity fraction of each working face, expressed as:

[0092]

[0093] The total working time of each working face is a fixed value, and the average capacity of the working face is obtained by taking the weighted average of the planned capacities of all working faces, W ω is the weight corresponding to the planned capacity of each working face;

[0094] f(b) is the planned capacity fraction of each crushing port, expressed as:

[0095]

[0096] The total working time of each crushing port is a fixed value, and the average capacity of the crushing port is obtained by taking the weighted average of the planned capacities of all crushing ports, W b is the weight corresponding to the planned capacity score of each crushing port;

[0097] f(t) is the planned total number of trips for each load transport vehicle, expressed as:

[0098]

[0099] gap2=|Total planned trips for each load transport vehicle - average trips

[0100] Gap2 refers to the actual trip difference value. The average load is obtained by taking the weighted average of the loads of all load transport vehicles. t is the weight corresponding to the total planned trips of each load-carrying vehicle;

[0101] f(co) is the planned maximum working time fraction of each load transport vehicle, expressed as:

[0102]

[0103] The expected maximum working time of each load transport vehicle is a fixed value, W co is the weight corresponding to the planned maximum working time score of each load transport vehicle;

[0104] f(ba) is the planned average working time fraction of each load transport vehicle, expressed as:

[0105]

[0106] When f(ba)<0, f(ba)=0, W ba is the weight corresponding to the planned average working time score of each load transport vehicle.

[0107] In an embodiment of the present invention, the fitness function constructed includes seven types of parameters and weight values ​​corresponding to each type of parameters, and a calculation formula is given for the score value of each type of parameter. Specifically, in the calculation of the planned production score f(o), the value of gap1 can be calculated by obtaining the planned production of each crushing port, and the value of the planned production score f(o) is determined according to the value of gap1; it can be seen from the above embodiment that the ore grade of each crushing port is the weighted average of the planned production of all working faces under the current crushing port and the corresponding grade, and the ore grade of each crushing port is also the planned grade. By obtaining the planned grade of each crushing port, the value of the planned grade score f(c) of each crushing port can be determined; in the calculation of the planned capacity score f(ω) of each working face, the planned capacity is the weighted average of the planned production of each working face and the weighted average of the planned production of each working face and the weighted average of the planned production of each working face. Similarly, in the calculation of the planned capacity score f(b) of each crushing port, the planned capacity is the ratio of the planned output of each crushing port to the working time of each crushing port; in the calculation of the planned total trips f(t) of each load-carrying vehicle, the value of gap2 can be calculated by obtaining the planned total trips of each load-carrying vehicle, and the value of the planned total trips f(t) of each load-carrying vehicle can be determined according to the value of gap2; in the calculation of the planned maximum working time f(co) of each load-carrying vehicle, it should be noted that when the planned maximum working time ≥ the expected maximum working time, a penalty mechanism is introduced, that is, through After calculating the value of f(co), subtract it from 50 to reduce its score value. If f(co)-50<0, the score value of f(co) is 0. In the calculation of the planned average working time f(ba) of each heavy-duty transport vehicle, the maximum working time and the minimum working time of each heavy-duty transport vehicle are obtained through the pre-constructed time-consuming matrix.

[0108] S250, determining the population individual with the highest fitness score as the target population individual.

[0109] S260: Determine the target population individuals as the target ore distribution and transportation plan that matches the ore distribution and transportation task.

[0110] The technical solution of the embodiment of the present invention mainly explains the construction of the fitness function and the iteration of the population by refining the overall solution. Specifically, by constructing calculation formulas for seven types of parameters and preset weight values ​​corresponding to each type of parameters, the score of the fitness function is constrained from multiple dimensions, that is, the higher the score that meets the constraints, the lower the score that does not meet the constraints. By setting clear scoring rules, the fitness function can improve key indicators in a targeted manner during the optimization process, and to the greatest extent ensure that the planned output and planned grade requirements are met with the minimum number of transportation trips and working time. At the same time, the elite retention strategy introduced in the iterative process can provide a better starting point for subsequent iterations, avoid the loss of excellent genes in crossover and mutation operations, and accelerate the efficiency of finding the optimal solution. In actual work, it can not only shorten the calculation time, but also improve the reliability of the ore distribution and transportation plan.

[0111] Embodiment 3

[0112] Figure 3 The schematic diagram of the structure of a device for generating a ore distribution and transportation plan using a genetic algorithm provided in the third embodiment of the present invention. Figure 3 As shown, the device comprises:

[0113] The task acquisition module 310 is used to acquire the ore distribution and transportation tasks to be performed, wherein the ore distribution and transportation tasks are tasks of transporting the ore in a set number of mine working faces in the target mine to a set number of crushing ports for operation by transport vehicles with various loads; each mine working face produces ore of a set grade;

[0114] The initial plan generation module 320 is used to randomly generate at least one initial ore distribution and transportation plan according to the ore distribution and transportation task, wherein the initial ore distribution and transportation plan includes selecting a transport vehicle with a set load to transport the ore in each mine working face to a set crushing port in a set number of transport trips;

[0115] The target population individual determination module 330 is used to determine the target population individuals that meet the fitness score conditions through multiple iterations based on the genetic algorithm and the preset fitness calculation function after taking each initial ore distribution and transportation plan as the initial population individuals in the initial population;

[0116] The target solution determination module 340 is used to determine the target population individuals as the target ore distribution and transportation solution that matches the ore distribution and transportation task.

[0117] The technical solution of the embodiment of the present invention constructs a ore distribution and transportation plan based on a genetic algorithm, and uses the ore distribution and transportation plan as a population individual for crossover and mutation operations, and continuously iterates, calculates the fitness score of each generation of population individuals during the iteration process, and obtains the optimal ore distribution and transportation plan when the fitness score conditions are met. The entire process is based on the transportation trips between the working face and the crushing port by different load-carrying transport vehicles, and generates a scheduling plan that meets the requirements for both planned output and ore grade. While ensuring scheduling accuracy, it further reduces production and transportation costs, improves ore distribution work efficiency, and helps to improve the sustainable mining capacity of the mine.

[0118] Optionally, based on the above embodiments, the target population individual determination module 330 may include:

[0119] A qualified population construction unit is used to identify at least one qualified population individual from each of the initial population individuals according to the required production restriction conditions matching each crushing port, so as to obtain a qualified population;

[0120] The population updating unit is used to use the qualified population as the iteration starting point and generate a new population generation by generation through crossover mutation until the individual with the highest fitness score in the newly generated new population meets the fitness score condition;

[0121] The population determination unit is used to determine the population individual with the highest fitness score as the target population individual.

[0122] Optionally, based on the above embodiments, the qualified population construction unit may also be used to determine, in each initial population individual, the number of transport trips of each load-carrying transport vehicle in transporting ore from different working faces of the mine to the current crushing port; wherein each crushing port needs to receive ore from multiple working faces, and each load-carrying transport vehicle corresponds to a different transport load;

[0123] Multiply the number of trips of each load-carrying vehicle from each working face to the current crushing port by its corresponding transport load to obtain the total transport load of all load-carrying vehicles at the current crushing port, which is also the initial output of the current crushing port.

[0124] The error rate is calculated using the initial production and the preset target production. If the error rate is lower than the error rate threshold, the initial population individuals meet the required production restriction conditions that match the current crushing port, and the initial population individuals that meet the required production restriction conditions are identified as qualified population individuals;

[0125] Otherwise, the initial population individuals do not meet the demand production restriction conditions matching the current crushing port, and the initial population individuals are adjusted until the error rate is lower than the error rate threshold, and the adjusted initial population individuals are identified as qualified population individuals;

[0126] The individuals of the initial population are merged with the individuals of the adjusted initial population to construct a qualified population.

[0127] Optionally, based on the above embodiments, the population updating unit may also be used to perform a crossover operation on qualified population individuals in a roulette manner in a qualified population to generate a first population, perform a mutation operation on the first population to obtain a second population, and add the second population to the first population to obtain a new population with an increased number of individuals;

[0128] Compare the number of individuals in the new population with the first threshold. When the number of individuals in the new population does not reach the first threshold, repeat the above operation until the number of individuals in the newly generated new population reaches the first threshold, at which time the crossover and mutation operations are stopped.

[0129] Iterate the newly generated new population, and after each iteration, use the pre-built fitness function to calculate the fitness score of the newly generated new population corresponding to the current iteration, and select the highest fitness score to compare with the second threshold. If the highest fitness score reaches the second threshold, the fitness score condition is met;

[0130] If the highest fitness score does not reach the second threshold, determine whether the number of iterations corresponding to the latest generated new population reaches the third threshold. If the number of iterations reaches the third threshold, the fitness score condition is met.

[0131] If the highest fitness score does not reach the second threshold and the number of iterations does not reach the third threshold, the operation of iterating the latest generated new population is repeatedly performed until the fitness score condition is met.

[0132] Optionally, on the basis of the above embodiments, it further includes an elite retention unit, which is used to calculate the fitness score of the latest generated new population corresponding to the current iteration by using a pre-built fitness function after each iteration, and select the individual corresponding to the highest fitness score from the fitness scores of the latest generated new population corresponding to the current iteration, and add it to the elite pool of fixed capacity, and repeat the above operation until the elite pool is full;

[0133] When the elite pool is full, the highest fitness score in the newly generated new population corresponding to each iteration is compared with the lowest fitness score in the elite pool. If the highest fitness score is greater than the lowest fitness score in the elite pool, the individual corresponding to the lowest fitness score in the elite pool is replaced by the individual corresponding to the highest fitness score to obtain a new elite pool corresponding to the current iteration;

[0134] If the number of iterations of the current iteration reaches the elite regression threshold, all elite individuals in the new elite pool corresponding to the current iteration are added to the latest generated new population corresponding to the current iteration to obtain the new population updated by the individuals, and the new population updated by the individuals is used as the latest generated new population corresponding to the next iteration for fitness score calculation.

[0135] Optionally, on the basis of the above embodiments, it further includes a parameter calculation unit, which is used to read the gene sequence of the initial population individual before iterating multiple times to determine the target population individual that meets the fitness score condition based on the genetic algorithm and the preset fitness calculation function, wherein the gene sequence is composed of a set number of crushing port sets, each crushing port set is composed of a set number of working surface sets, and each working surface set is composed of transport trips of different load-carrying transport vehicles;

[0136] Extract the transport trips corresponding to the current load transport vehicle in each work surface set, and sum them up to obtain the planned total trips of the current load transport vehicle;

[0137] In each working face set of the current crushing port set, the transport trips corresponding to each load-carrying vehicle are multiplied by its load to obtain the transport weight of each load-carrying vehicle. The transport weights of each load-carrying vehicle are summed to obtain the planned output of each working face for the current crushing port. The planned outputs of the same working faces in all crushing port sets are summed to obtain the planned total output of each working face.

[0138] The planned output of all working faces in the current crushing opening set is calculated by weighted average to obtain the planned grade of each crushing opening;

[0139] In the current crushing port set, the planned total output of each working face is summed up to obtain the planned output of the current crushing port;

[0140] Based on the gene sequence, an M×N time-consuming matrix is ​​constructed for each load-carrying vehicle, where M is the number of crushing openings and N is the number of working faces, denoted as The total working time of the current heavy-duty transport vehicle is calculated according to the transport time matrix, and the ratio of the total working time to the maximum parallel number of the current heavy-duty transport vehicle is calculated to obtain the planned maximum working time of the current heavy-duty transport vehicle, and the maximum working time and minimum working time of the current heavy-duty transport vehicle are extracted through the time matrix.

[0141] Optionally, based on the above embodiments, the present invention further includes a fitness function construction unit, which is used to calculate the fitness score of the latest generated new population corresponding to the current iteration by using the pre-constructed fitness function.

[0142] Based on the planned output of each crushing port, the planned grade of each crushing port, the planned total output of each working face, the planned total trips of each load-carrying vehicle, the planned maximum working time of each load-carrying vehicle, and the maximum and minimum working time of each load-carrying vehicle, a fitness function is constructed. The formula is as follows:

[0143]

[0144] Z is the lowest constant that can be set in the system;

[0145] Among them, f(o) is the planned production fraction of each crushing port, expressed as:

[0146]

[0147] Gap1 refers to the actual output difference rate. The target output difference rate, target output and confirmed output are fixed values. o is the weight corresponding to the planned production fraction of each crushing opening;

[0148] f(c) is the planned grade fraction for each crushing opening, expressed as:

[0149]

[0150] The target grade of each crushing port is a fixed value, the threshold range is a preset fixed interval, and the average grade is obtained by weighted average of the planned grades of all crushing ports, W c is the weight corresponding to the planned grade score of each crushing opening;

[0151] f(ω) is the planned capacity fraction of each working face, expressed as:

[0152]

[0153] The total working time of each working face is a fixed value, and the average capacity of the working face is obtained by taking the weighted average of the planned capacities of all working faces, W ω is the weight corresponding to the planned capacity of each working face;

[0154] f(b) is the planned capacity fraction of each crushing port, expressed as:

[0155]

[0156] The total working time of each crushing port is a fixed value, and the average capacity of the crushing port is obtained by taking the weighted average of the planned capacities of all crushing ports, W b is the weight corresponding to the planned capacity score of each crushing port;

[0157] f(t) is the planned total number of trips for each load transport vehicle, expressed as:

[0158]

[0159] gap2=|Total planned trips for each load transport vehicle - average trips

[0160] Gap2 refers to the actual trip difference value. The average load is obtained by taking the weighted average of the loads of all load transport vehicles. t is the weight corresponding to the total planned trips of each load-carrying vehicle;

[0161] f(co) is the planned maximum working time fraction of each load transport vehicle, expressed as:

[0162]

[0163] The expected maximum working time of each load transport vehicle is a fixed value, W co is the weight corresponding to the planned maximum working time score of each load transport vehicle;

[0164] f(ba) is the planned average working time fraction of each load transport vehicle, expressed as:

[0165]

[0166] When f(ba)<0, f(ba)=0, W ba is the weight corresponding to the planned average working time score of each load transport vehicle.

[0167] The device for generating a ore distribution and transportation plan using a genetic algorithm provided in an embodiment of the present invention can execute the method for generating a ore distribution and transportation plan using a genetic algorithm provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0168] Embodiment 4

[0169] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0170] like Figure 4As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0171] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0172] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a method for generating a ore distribution and transportation plan using a genetic algorithm.

[0173] That is: obtaining the ore distribution and transportation tasks to be carried out, wherein the ore distribution and transportation tasks are the tasks of transporting the ore in a set number of mine working faces in the target mine to a set number of crushing ports for operation through transport vehicles with various loads; each mine working face produces ore of a set grade;

[0174] According to the ore distribution and transportation task, at least one initial ore distribution and transportation plan is randomly generated, wherein the initial ore distribution and transportation plan includes selecting a transport vehicle with a set load to transport the ore in each mine working face to a set crushing port in a set number of transport trips;

[0175] After taking each initial ore distribution and transportation plan as the initial population individual in the initial population, based on the genetic algorithm and the preset fitness calculation function, multiple iterations are performed to determine the target population individual that meets the fitness score condition;

[0176] The target population individuals are determined as the target ore distribution and transportation plan that matches the ore distribution and transportation task.

[0177] In some embodiments, a method for generating a ore distribution and transportation plan using a genetic algorithm may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for generating a ore distribution and transportation plan using a genetic algorithm described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute a method for generating a ore distribution and transportation plan using a genetic algorithm by any other appropriate means (e.g., by means of firmware).

[0178] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0179] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0180] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0181] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0182] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0183] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0184] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0185] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for generating a ore distribution and transportation plan using a genetic algorithm, characterized in that: include: Obtaining the ore distribution and transportation tasks to be carried out, wherein the ore distribution and transportation tasks are tasks of transporting the ore in a set number of mine working faces in the target mine to a set number of crushing ports for operation through transport vehicles with various loads; each mine working face produces ore of a set grade; According to the ore distribution and transportation task, at least one initial ore distribution and transportation plan is randomly generated, wherein the initial ore distribution and transportation plan includes selecting a transport vehicle with a set load to transport the ore in each mine working face to a set crushing port in a set number of transport trips; After taking each initial ore distribution and transportation plan as the initial population individual in the initial population, based on the genetic algorithm and the preset fitness calculation function, multiple iterations are performed to determine the target population individual that meets the fitness score condition; The target population individuals are determined as the target ore distribution and transportation plan that matches the ore distribution and transportation task.

2. The method according to claim 1, characterized in that After taking each initial ore distribution and transportation plan as the initial population individual in the initial population, based on the genetic algorithm and the preset fitness calculation function, multiple iterations are performed to determine the target population individuals that meet the fitness score conditions, including: According to the required production restriction conditions matching each crushing port, at least one qualified population individual is identified from each of the initial population individuals to obtain a qualified population; Taking the qualified population as the starting point of iteration, a new population is generated generation by generation through crossover mutation until the individual with the highest fitness score in the newly generated new population meets the fitness score condition; The population individual with the highest fitness score is determined as the target population individual.

3. The method according to claim 2, characterized in that According to the demand production restriction conditions matched with each crushing port, at least one qualified population individual is identified from each of the initial population individuals to obtain a qualified population, including: Determine the number of trips of each load-carrying transport vehicle in transporting ore from different working faces of the mine to the current crushing port in each initial population individual; each crushing port needs to receive ore from multiple working faces, and each load-carrying transport vehicle corresponds to a different transport load; Multiply the number of trips of each load-carrying vehicle from each working face to the current crushing port by its corresponding transport load to obtain the total transport load of all load-carrying vehicles at the current crushing port, which is also the initial output of the current crushing port. The error rate is calculated using the initial production and the preset target production. If the error rate is lower than the error rate threshold, the initial population individuals meet the required production restriction conditions that match the current crushing port, and the initial population individuals that meet the required production restriction conditions are identified as qualified population individuals; Otherwise, the initial population individuals do not meet the demand production restriction conditions matching the current crushing port, and the initial population individuals are adjusted until the error rate is lower than the error rate threshold, and the adjusted initial population individuals are identified as qualified population individuals; The initial population individuals are merged with the adjusted initial population individuals to construct a qualified population.

4. The method according to claim 3, characterized in that Taking the qualified population as the starting point of iteration, a new population is generated generation by generation through crossover mutation until the individual with the highest fitness score in the newly generated new population meets the fitness score conditions, including: In the qualified population, a crossover operation is performed on the qualified population individuals by a roulette wheel method to generate a first population, a mutation operation is performed on the first population to obtain a second population, and the second population is added to the first population to obtain a new population with an increased number of individuals; Compare the number of individuals in the new population with the first threshold. When the number of individuals in the new population does not reach the first threshold, repeat the above operation until the number of individuals in the newly generated new population reaches the first threshold, at which time the crossover and mutation operations are stopped. Iterate the newly generated new population, and after each iteration, use the pre-built fitness function to calculate the fitness score of the newly generated new population corresponding to the current iteration, and select the highest fitness score to compare with the second threshold. If the highest fitness score reaches the second threshold, the fitness score condition is met; If the highest fitness score does not reach the second threshold, determine whether the number of iterations corresponding to the latest generated new population reaches the third threshold. If the number of iterations reaches the third threshold, the fitness score condition is met. If the highest fitness score does not reach the second threshold and the number of iterations does not reach the third threshold, the operation of iterating the latest generated new population is repeatedly performed until the fitness score condition is met.

5. The method according to claim 4, characterized in that While using the pre-built fitness function to calculate the fitness score of the latest generated new population corresponding to the current iteration, it also includes: Select the individual with the highest fitness score from the fitness scores of the newly generated new population corresponding to the current iteration, add it to the elite pool of fixed capacity, and repeat the above operation until the elite pool is full; When the elite pool is full, the highest fitness score in the newly generated new population corresponding to each iteration is compared with the lowest fitness score in the elite pool. If the highest fitness score is greater than the lowest fitness score in the elite pool, the individual corresponding to the lowest fitness score in the elite pool is replaced by the individual corresponding to the highest fitness score to obtain a new elite pool corresponding to the current iteration; If the number of iterations of the current iteration reaches the elite regression threshold, all elite individuals in the new elite pool corresponding to the current iteration are added to the latest generated new population corresponding to the current iteration to obtain the new population updated by the individuals, and the new population updated by the individuals is used as the latest generated new population corresponding to the next iteration for fitness score calculation.

6. The method according to claim 1, characterized in that Before the target population individuals that meet the fitness score conditions are determined through multiple iterations based on the genetic algorithm and the preset fitness calculation function, the following steps are also included: Reading the gene sequence of the individuals of the initial population, wherein the gene sequence is composed of a set number of crushing port sets, each crushing port set is composed of a set number of working surface sets, and each working surface set is composed of transport trips of different load-carrying transport vehicles; Extract the transport trips corresponding to the current load transport vehicle in each work surface set, and sum them up to obtain the planned total trips of the current load transport vehicle; In each working face set of the current crushing port set, the transport trips corresponding to each load-carrying vehicle are multiplied by its load to obtain the transport weight of each load-carrying vehicle. The transport weights of each load-carrying vehicle are summed to obtain the planned output of each working face for the current crushing port. The planned outputs of the same working faces in all crushing port sets are summed to obtain the planned total output of each working face. The planned output of all working faces in the current crushing opening set is calculated by weighted average to obtain the planned grade of each crushing opening; In the current crushing port set, the planned total output of each working face is summed up to obtain the planned output of the current crushing port; Based on the gene sequence, an M×N time-consuming matrix is ​​constructed for each load-carrying vehicle, where M is the number of crushing openings and N is the number of working faces, denoted as The total working time of the current heavy-duty transport vehicle is calculated according to the transport time matrix, and the ratio of the total working time to the maximum parallel number of the current heavy-duty transport vehicle is calculated to obtain the planned maximum working time of the current heavy-duty transport vehicle, and the maximum working time and minimum working time of the current heavy-duty transport vehicle are extracted through the time matrix.

7. The method according to claim 6, characterized in that Before using the pre-built fitness function to calculate the fitness score of the latest generated new population corresponding to the current iteration, it also includes: Based on the planned output of each crushing port, the planned grade of each crushing port, the planned total output of each working face, the planned total trips of each load-carrying vehicle, the planned maximum working time of each load-carrying vehicle, and the maximum and minimum working time of each load-carrying vehicle, a fitness function is constructed. The formula is as follows: Z is the lowest constant that can be set in the system; Among them, f(o) is the planned production fraction of each crushing port, expressed as: Gap1 refers to the actual output difference rate. The target output difference rate, target output and confirmed output are fixed values. o is the weight corresponding to the planned production fraction of each crushing opening; f(c) is the planned grade fraction for each crushing opening, expressed as: The target grade of each crushing port is a fixed value, the threshold range is a preset fixed interval, and the average grade is obtained by weighted average of the planned grades of all crushing ports, W c is the weight corresponding to the planned grade score of each crushing opening; f(ω) is the planned capacity fraction of each working face, expressed as: The total working time of each working face is a fixed value, and the average capacity of the working face is obtained by taking the weighted average of the planned capacities of all working faces, W ω is the weight corresponding to the planned capacity of each working face; f(b) is the planned capacity fraction of each crushing port, expressed as: The total working time of each crushing port is a fixed value, and the average capacity of the crushing port is obtained by taking the weighted average of the planned capacities of all crushing ports, W b is the weight corresponding to the planned capacity score of each crushing port; f(t) is the planned total number of trips for each load transport vehicle, expressed as: gap2=|Total planned trips for each load transport vehicle - average trips Gap2 refers to the actual trip difference value. The average load is obtained by taking the weighted average of the loads of all load transport vehicles. t is the weight corresponding to the total planned trips of each load-carrying vehicle; f(co) is the planned maximum working time fraction of each load transport vehicle, expressed as: The expected maximum working time of each load transport vehicle is a fixed value, W co is the weight corresponding to the planned maximum working time score of each load transport vehicle; f(ba) is the planned average working time fraction of each load transport vehicle, expressed as: When f(ba)<0, f(ba)=0, W ba is the weight corresponding to the planned average working time score of each load transport vehicle.

8. A device for generating ore distribution and transportation plan using a genetic algorithm, characterized in that: include: The task acquisition module is used to acquire the ore distribution and transportation tasks to be carried out, wherein the ore distribution and transportation tasks are tasks of transporting the ore in a set number of mine working faces in the target mine to a set number of crushing ports for operation through transport vehicles with various loads; each mine working face produces ore of a set grade; An initial plan generation module is used to randomly generate at least one initial ore distribution and transportation plan according to the ore distribution and transportation task, wherein the initial ore distribution and transportation plan includes selecting a transport vehicle with a set load to transport the ore in each mine working face to a set crushing port in a set number of transport trips; The target population individual determination module is used to determine the target population individuals that meet the fitness score conditions through multiple iterations based on the genetic algorithm and the preset fitness calculation function after taking each initial ore distribution and transportation plan as the initial population individuals in the initial population; The target scheme determination module is used to determine the target population individuals as the target ore distribution and transportation scheme that matches the ore distribution and transportation task.

9. An electronic device, characterized in that: The electronic device comprises: 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, and the computer program is executed by the at least one processor so that the at least one processor can execute a method for generating a mineral distribution and transportation plan using a genetic algorithm as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement a method for generating a ore distribution and transportation plan using a genetic algorithm according to any one of claims 1 to 7 when executed.

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