Method, device and storage device for maintaining aggregate discharge and batching optimization of a stockpile

Abstract

The application provides a method, equipment and storage equipment for maintaining the conical shape of aggregate discharge and batching optimization, comprising: setting a quality factor for each discharge port; obtaining aggregate pile height data; updating the quality factor formula; judging the aggregate size of the pile according to the pile height data; calculating the smoothness coefficient of each discharge port; calculating the quality factor of each discharge port; taking the sum of the quality factors of all discharge ports as the fitness function, performing genetic algorithm optimization on the following discharge port as the chromosome, obtaining the optimal discharge port switch state, and performing discharge port switch control; and performing the above steps again at a certain period to obtain the optimal result. The application has the beneficial effect of: ensuring that the aggregate pile maintains a conical shape during discharge, avoiding the problems of aggregate landslides or collapses caused by traditional discharge methods, and combining the two work links of aggregate discharge and batching, thereby reducing the cost of construction sand and gravel aggregate.

Description

Technical Field

[0001] This invention relates to the field of sand and gravel aggregates for construction, and in particular to a method, equipment, and storage device for maintaining aggregate discharge and batching optimization while maintaining stockpile shape. Background Technology

[0002] With the increasing number of construction projects, the amount of sand and gravel aggregate used in construction is showing a year-on-year increasing trend. Sand and gravel aggregate is a material used to produce concrete, and it is generally irregular in shape and has different particle sizes. Materials are usually stockpiled according to different particle sizes to facilitate screening and mixing during the production process.

[0003] Aggregate batching is the process of mixing different types and sizes of aggregates in a specific ratio for use in the preparation of materials such as concrete and asphalt concrete in construction and civil engineering. The purpose of aggregate batching is to adjust the composition and proportion of aggregates according to the specific needs and performance requirements of a project to obtain ideal mixture properties. Aggregate discharge refers to the process of removing aggregates from aggregate storage areas or equipment and supplying them to the construction site or other locations where they are needed. Aggregate discharge requires ensuring an accurate supply and appropriate quantity of aggregates to meet the needs and requirements of the project.

[0004] For a stockpile silo that feeds from the top and discharges from the bottom, a cone-shaped aggregate pile will form around the feed inlet. The aggregates at the feed inlet will vary in size and be distributed in different areas of the pile.

[0005] Traditional discharge methods often disregard the impact on the stockpile shape, arbitrarily discharging material based solely on the discharge volume. In cases with multiple discharge points, discharging from only one point can significantly alter the original stockpile shape. For example, some areas might have all the aggregate removed, while others remain intact. This leads to changes in the stockpile shape during subsequent feeds, altering the slope and affecting stability, potentially causing landslides or collapses in certain areas. These factors significantly alter the aggregate size distribution in the silo, impacting the aggregate mix ratio. Therefore, it is necessary to maintain a roughly conical stock shape while ensuring stable discharge and meeting specific mix ratios. Summary of the Invention

[0006] To address the aforementioned problems, this invention provides a method, apparatus, and storage device for maintaining aggregate pile shape and optimizing aggregate discharge and batching. The method for maintaining aggregate pile shape and optimizing aggregate discharge and batching mainly includes:

[0007] S1: Set a quality factor for each feed port;

[0008] S2: Obtain aggregate stockpile height data;

[0009] S3: Update the quality factor formula using the stockpile height data;

[0010] S4: Determine the aggregate particle size of the stockpile based on the stockpile height data;

[0011] S5: Calculate the stability coefficient of each discharge port based on the aggregate particle size of the stockpile;

[0012] S6: Calculate the quality factor for each feed port according to steps S1-S5;

[0013] S7: Use the sum of the quality factors of all feed ports as the fitness function, perform a genetic algorithm with the feed ports as chromosomes to find the optimal solution, obtain the optimal feed port switching state, and perform feed port switching control.

[0014] S8: Repeat steps S2 to S7 at certain intervals to continuously maintain aggregate output and batching optimization.

[0015] Furthermore, in step S1, a quality factor is set for each feeding port. The larger the value of the quality factor, the worse the quality. The basic calculation method is as follows:

[0016] P(g k (h s )x k )+Q(g k (h r )x k )+R(m k x k )

[0017]

[0018] Where: P is the weighted average of the difference between the aggregate height at the discharge port and the height of the surrounding aggregate; Q is the weighted average of the difference between the aggregate height at the discharge port and the heights of other discharge ports at the same distance from the feed port; R is the weighted average of the aggregate size at the discharge port and the batching ratio; h s h is the difference between the height of the aggregate at the discharge port and the height of the surrounding aggregate. r x is the difference between the aggregate height at the discharge port and the height at other discharge ports at the same distance from the feed port; k This indicates the opening / closing status of the feed inlet; its value can only be 0 or 1; m k The stationarity coefficient is expressed as follows: Among them, l k The particle size distribution at the current feed inlet is given by n, where n is the total number of feed inlets; h lim h is the maximum allowable height difference of the stockpile. low The penalty weight is h; h is the height of the aggregate above the discharge port. p The material feeding height within one cycle, when h < h pAt this time, the feeding port does not participate in feeding.

[0019] Furthermore, in step S2, the process of obtaining the aggregate stockpile height data is as follows:

[0020] The height of the aggregate pile is sampled by LiDAR, and a 3D model is created to obtain the height data of the aggregate pile.

[0021] Furthermore, in step S3, to enable the system to adapt to different working conditions, the three weighting coefficients are dynamically updated. The process of updating the quality factor formula using the stockpile height data is as follows:

[0022] If significant subsidence occurs in the aggregate at the current feed inlet, the weight P of the current feed inlet is increased. The update formula is as follows:

[0023] Where h high This represents the upper limit of a significant height difference;

[0024] If there is a significant height difference between the current feed inlet's aggregate height and the height of other feed inlets at the same distance from the feed inlet, then the weight Q of this feed inlet is increased. The update formula is as follows:

[0025] Where h high This represents the upper limit of a significant height difference;

[0026] Since the material is transported to the downstream production line via conveyor belt after exiting the discharge port, when the number of discharge ports open on the same conveyor belt exceeds a certain threshold of the average number of discharge ports, the R value is gradually reduced. The update formula for weight R is:

[0027]

[0028] Where, x open x represents the number of feed ports opened in the current column. average x represents the average number of feed ports in the current column. high x is the upper limit threshold of the current column's feed port. low This is the lower limit threshold of the current column's feed port.

[0029] Further, in step S4, the aggregate particle size of the stockpile is determined based on the stockpile height data, specifically including the following steps:

[0030] S11: Record historical data of material level height at each discharge port;

[0031] S12: Determine the height h of the increased feed height of the material pile by observing the change in material height around the feed inlet. add : Where h suriLet i be the change in material height around the discharge port of the i-th selected point, where i is the number of selected points.

[0032] S13: Determine the reduction in discharge port height h based on the opening time of the discharge port. sub :h sub =v sub t sub , where v sub t is the material feeding speed at the feeding port. sub The time the feed inlet is open;

[0033] S14: Determine the distribution of aggregate size at different material levels at the discharge port.

[0034] Furthermore, in step S14, the particle size distribution of aggregates at different material levels at the discharge port is determined using the following rules:

[0035] Rule 1: If the current stockpile height is at a low level, and the portion of the stockpile that increases in height is also at a low level, then the portion of the stockpile that increases in height represents large-diameter particles.

[0036] Rule 2: If the current stockpile height is at the low level, and the portion of the stockpile that increases in height is at the middle level, then the portion of the stockpile that increases in height represents medium-sized particles.

[0037] Rule 3: If the current stockpile height is at a low level, and the portion of the stockpile that increases in height is at a high level, then the portion of the stockpile that increases in height represents small-diameter particles.

[0038] Rule 4: If the current height of the stockpile is at the middle level, and the portion of the stockpile that increases in height is at the low level, then the portion of the stockpile that increases in height represents large-diameter particles.

[0039] Rule 5: If the current height of the stockpile is at the middle level, and the increased height of the stockpile due to feeding is also at the middle level, then the increased height of the stockpile is for medium-sized particles.

[0040] Rule 6: If the current height of the stockpile is at the middle level, and the increased height of the stockpile due to feeding is at the high level, then the increased height of the stockpile represents small-diameter particles.

[0041] Rule 7: If the current stockpile height is at the high level, and the portion of the stockpile that increases in height is at the low level, then the portion of the stockpile that increases in height represents large-diameter particles.

[0042] Rule 8: If the current stockpile height is at the high level, and the increased height of the stockpile is at the middle level, then the increased height of the stockpile is for medium-sized particles.

[0043] Rule 9: If the current height of the material pile is at the high level, and the portion of the material pile that increases in height is also at the high level, then the portion of the material pile that increases in height is for small-diameter particles.

[0044] Furthermore, in step S7, the process of using a genetic algorithm with the following feed inlet as the chromosome to find the optimal solution is as follows:

[0045] S21: Initialize the population: Based on the requirements of the problem, an initial population is randomly generated. Each individual in the population represents a solution to the problem, represented in binary form.

[0046] S22: Assess fitness:

[0047] The fitness of each individual, i.e., the quality of the solution, is evaluated by a fitness function, which is the sum of the quality factors of all feed ports in step S1:

[0048] minf(x1, ..., x) n )=P(g1(h s )x1+…+g n (h s )x n )+Q(g1(h r )x1+…+g n (h r )x n )+R(m1x1+…+m n x n )

[0049]

[0050] x1+…+x n <x max

[0051] Where x max This represents the maximum number of feed inlets that the system allows to open.

[0052] S23: Selection operation: The selection operation is performed using the roulette wheel method; based on the fitness of individuals, a certain number of individuals are selected as parents with a certain probability to produce the next generation of individuals;

[0053] S24: Crossover operation: Select a pair of individuals from the parent generation and generate new individuals through crossover operation; crossover operation simulates gene hybridization, and produces offspring with new combination characteristics by exchanging gene segments of two individuals;

[0054] S25: Mutation operation:

[0055] Mutation operations are performed on new individuals to introduce new gene variants. Mutation operations simulate gene mutations by randomly changing certain genes in an individual to introduce new gene combinations, thereby increasing the diversity of the search space. Mutation operations are performed probabilistically.

[0056] Introducing a self-updating mechanism into the traditional genetic algorithm, with the following rule: when the current optimal solution of the population is close to the global optimal solution x... a At that time, the current optimal solution x of the population is... g The adaptive update is performed using the following formula:

[0057]

[0058] Among them, X g Let x be the global probability of mutation. limit The minimum gradient threshold between the current solution and the optimal solution;

[0059] S26: Replacement operation: Replace some individuals in the original population with newly generated individuals to form a new generation of population;

[0060] S27: Determine if the termination condition is met. If yes, the algorithm ends and returns the found optimal solution. If no, return to step S22 and continue execution. Through multiple generations of evolution and selection, the genetic algorithm gradually optimizes the solutions in the population, making them gradually approach the optimal solution. The termination condition must satisfy at least one of the following conditions: (1) the maximum number of iterations is reached, and (2) a solution that is close enough to the optimal solution is found.

[0061] A storage device that stores instructions and data for implementing a method for optimizing aggregate discharge and batching while maintaining a stockpile shape.

[0062] An aggregate discharge and batching optimization device for maintaining the stockpile shape includes: a processor and the storage device; the processor loads and executes instructions and data in the storage device to implement an aggregate discharge and batching optimization method for maintaining the stockpile shape.

[0063] The beneficial effects of the technical solution provided by this invention are as follows: This invention sets a quality factor for each discharge port; acquires aggregate pile height data, updates the quality factor formula based on the pile height data, determines the aggregate particle size based on the pile height data, calculates the stability coefficient of each discharge port based on the aggregate particle size, calculates the quality factor of each discharge port, uses the sum of the quality factors of all discharge ports as the fitness function, and performs optimal solution using a genetic algorithm with the discharge ports as chromosomes to obtain the optimal discharge port opening and closing state, controls the discharge port opening and closing, and repeats the above operations at a certain period to continuously maintain the aggregate discharge and batching optimization. Ensuring that the aggregate pile maintains a conical shape during discharge avoids the problems of aggregate landslides or collapses caused by traditional discharge methods. Aggregate batching is performed simultaneously with aggregate discharge, merging two work stages and reducing the cost of construction sand and gravel aggregates. Attached Figure Description

[0064] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings:

[0065] Figure 1 This is a flowchart of a method for optimizing aggregate discharge and batching while maintaining the pile shape, according to an embodiment of the present invention.

[0066] Figure 2 This is a schematic diagram showing the particle size distribution of aggregates at different material levels in an embodiment of the present invention.

[0067] Figure 3 This is a schematic diagram of the hardware device working in an embodiment of the present invention. Detailed Implementation

[0068] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0069] Embodiments of the present invention provide a method, apparatus, and storage device for maintaining aggregate discharge and batching optimization while maintaining the pile shape.

[0070] Please refer to Figure 1 , Figure 1 This is a flowchart of a method for optimizing aggregate discharge and batching while maintaining the stockpile shape, as described in an embodiment of the present invention. Specifically, it includes:

[0071] S1: Set a quality factor for each feed port.

[0072] The quality factor, the higher the value, the worse the quality. The basic calculation method is as follows:

[0073] P(g k (h s )x k )+Q(g k (h r )x k )+R(m k x k )

[0074]

[0075] Where: P is the weighted average of the difference between the aggregate height at the discharge port and the height of the surrounding aggregate; Q is the weighted average of the difference between the aggregate height at the discharge port and the heights of other discharge ports at the same distance from the feed port; R is the weighted average of the aggregate size at the discharge port and the batching ratio; n is the total number of discharge ports; x k This indicates the opening / closing status of the feed inlet; its value can only be 0 or 1; h s h is the difference between the height of the aggregate at the discharge port and the height of the surrounding aggregate. r h is the difference between the aggregate height at the discharge port and the height at other discharge ports at the same distance from the feed port. limh is the maximum allowable height difference of the stockpile. low The penalty weight is a large positive number used for positive correction; m k The stationarity coefficient is expressed as follows: Among them l k (k = 1, ..., n) represents the current particle size distribution at the feed inlet; h represents the height of the aggregate above the feed inlet, 0 ≤ h ≤ 50 m. p The feeding height within one cycle (the specific height depends on the size of the feeding opening), when h < h p At that time, the feeding port does not participate in feeding; g k () indicates the addition of a soft constraint.

[0076] S2: Obtain aggregate stockpile height data.

[0077] The height of the aggregate pile is sampled by LiDAR, and a 3D model is created using existing technology to obtain the height data of the aggregate pile.

[0078] S3: Update the quality factor formula using the stockpile height data.

[0079] Due to changes in operating conditions, the weighting coefficients P, Q, and R in the above quality factor formula may not be the desired values. Therefore, the weighting coefficients need to be dynamically adjusted.

[0080] A larger P indicates a greater need to ensure a stable aggregate pile shape under different operating conditions; a larger Q indicates a greater need to ensure a uniform pile shape; and a larger R indicates a greater need to ensure more uniform and stable aggregate distribution. Therefore, to enable the system to adapt to different operating conditions, the three weighting coefficients are dynamically updated:

[0081] If significant subsidence occurs in the aggregate at the current feed inlet, the weight P of the current feed inlet is increased. The update formula is as follows:

[0082] Where h high This represents the upper limit of a significant height difference;

[0083] If there is a significant height difference between the current feed inlet's aggregate height and the height of other feed inlets at the same distance from the feed inlet, then the weight Q of this feed inlet is increased. The update formula is as follows:

[0084] Where h high This represents the upper limit of a significant height difference.

[0085] Since the material is transported to the downstream production line via conveyor belt after exiting the discharge port, when the number of discharge ports open on the same conveyor belt exceeds a certain threshold of the average number of discharge ports, the R value is gradually reduced. The update formula is as follows:

[0086]

[0087] Where x open x represents the number of feed ports opened in the current column. average x represents the average number of feed ports in the current column. high x is the upper limit threshold of the current column's feed port. low This is the lower limit threshold of the current column's feed port.

[0088] S4: Determine the aggregate particle size of the stockpile based on the stockpile height data;

[0089] For a stockpile with feed from the top, larger aggregates tend to be distributed more readily on the periphery of the stockpile, while smaller aggregates tend to be distributed more readily in the center. Therefore, to determine the particle size distribution at the current discharge port, the following steps are defined:

[0090] S11: Record historical data of material level height at each discharge port;

[0091] S12: Determine the height h of the increased feed height of the material pile by observing the change in material height around the feed inlet. add Its general expression is: Where h suri Let i be the change in material height around the discharge port of the i-th selected point, where i is the number of selected points.

[0092] S13: Determine the reduction in discharge port height h based on the opening time of the discharge port. sub Its general expression is: h sub =v sub t sub , where v sub The material feeding speed at the discharge port is determined based on different working conditions, t sub The time the feed inlet is open;

[0093] S14: Determine the distribution of aggregate size at different material levels at the discharge port.

[0094] like Figure 2 As shown, the particle size distribution of aggregates at different material levels at the discharge port is determined by the following rules:

[0095] Rule 1: If the current stockpile height is at a low level, and the portion of the stockpile that increases in height is also at a low level, then the portion of the stockpile that increases in height represents large-diameter particles.

[0096] Rule 2: If the current stockpile height is at the low level, and the portion of the stockpile that increases in height is at the middle level, then the portion of the stockpile that increases in height represents medium-sized particles.

[0097] Rule 3: If the current stockpile height is at a low level, and the portion of the stockpile that increases in height is at a high level, then the portion of the stockpile that increases in height represents small-diameter particles.

[0098] Rule 4: If the current height of the stockpile is at the middle level, and the portion of the stockpile that increases in height is at the low level, then the portion of the stockpile that increases in height represents large-diameter particles.

[0099] Rule 5: If the current height of the stockpile is at the middle level, and the increased height of the stockpile due to feeding is also at the middle level, then the increased height of the stockpile is for medium-sized particles.

[0100] Rule 6: If the current height of the stockpile is at the middle level, and the increased height of the stockpile due to feeding is at the high level, then the increased height of the stockpile represents small-diameter particles.

[0101] Rule 7: If the current stockpile height is at the high level, and the portion of the stockpile that increases in height is at the low level, then the portion of the stockpile that increases in height represents large-diameter particles.

[0102] Rule 8: If the current stockpile height is at the high level, and the increased height of the stockpile is at the middle level, then the increased height of the stockpile is for medium-sized particles.

[0103] Rule 9: If the current height of the material pile is at the high level, and the portion of the material pile that increases in height is also at the high level, then the portion of the material pile that increases in height is for small-diameter particles.

[0104] S5: Calculate the stability coefficient of each discharge port based on the aggregate particle size of the stockpile.

[0105] S6: Calculate the quality factor for each feed port according to steps S1-S5.

[0106] S7: Using the sum of the quality factors of all feed ports as the fitness function, a genetic algorithm with the feed ports as chromosomes is used to find the optimal feed port switching state, and then the feed port switching is controlled. The basic process is as follows:

[0107] S21: Initialize the population: Based on the requirements of the problem, randomly generate an initial population. Each individual in the population represents a solution to the problem, represented in binary form.

[0108] S22: Evaluate Fitness: Evaluate the fitness of each individual, i.e., the quality of the solution, using a fitness function. The fitness function is the sum of the quality factors of all feed ports from step S1:

[0109] minf(x1, ..., x) n )=P(g1(h s )x1+…+g n (h s )x n )+Q(g1(h r )x1+…+g n (h r )x n )+R(m1x1+…+mn x n )

[0110]

[0111] x1+…+x n <x max

[0112] Where x max This represents the maximum number of feed ports that the system allows to open.

[0113] S23: Selection Operation: The selection operation is performed using a roulette wheel selection method. Based on the fitness of individuals, a subset of individuals are selected as parents with a certain probability to produce the next generation. Generally, individuals with higher fitness have a greater probability of being selected to increase the transmission of superior genes.

[0114] S24: Crossover Operation: Select a pair of individuals from the parent generation and generate new individuals through a crossover operation. The crossover operation simulates gene hybridization, producing offspring with new combined characteristics by exchanging gene segments between two individuals. Here, a two-point crossover is chosen; the paired chromosomes are randomly assigned two or more crossover points, and then the crossover operation is performed to change the chromosome gene sequence.

[0115] S25: Mutation Operation: Performing a mutation operation on a new individual to introduce new gene variants. The mutation operation simulates gene mutation, introducing new gene combinations by randomly altering certain genes within the individual, thus increasing the diversity of the search space. Mutation operations are performed probabilistically.

[0116] Since genetic algorithms tend to fluctuate around the optimal solution, a self-updating mechanism is introduced to address this issue, with the following rule: when the current optimal solution of the population is close to the global optimal solution x... a At that time, the current optimal solution x of the population is... g The adaptive update is performed using the following formula:

[0117]

[0118] Where X g Let x be the global probability of mutation. limit This is the minimum gradient threshold between the current solution and the optimal solution.

[0119] S26: Replacement operation: Replace some individuals in the original population with newly generated individuals to form a new generation of population.

[0120] S27: Determine if the termination condition is met, such as reaching the maximum number of iterations or finding a solution sufficiently close to the optimal solution. If the termination condition is met, the algorithm terminates and returns the found optimal solution; otherwise, return to S22 to continue execution. Through multiple generations of evolution and selection, the genetic algorithm gradually optimizes the solutions in the population, making them progressively closer to the optimal solution.

[0121] S8: Based on actual production needs, repeat steps S2 to S7 at certain intervals to continuously maintain aggregate output and batching optimization.

[0122] Please see Figure 3 , Figure 3 This is a schematic diagram of the hardware device in an embodiment of the present invention. The hardware device specifically includes: an aggregate discharge and batching optimization device 401 for maintaining the pile shape, a processor 402, and a storage device 403.

[0123] An aggregate discharge and batching optimization device 401 for maintaining pile shape: The aggregate discharge and batching optimization device 401 for maintaining pile shape implements the aggregate discharge and batching optimization method for maintaining pile shape.

[0124] Processor 402: The processor 402 loads and executes the instructions and data in the storage device 403 to implement the aggregate discharge and batching optimization method for maintaining the pile shape.

[0125] Storage device 403: The storage device 403 stores instructions and data; the storage device 403 is used to implement the aggregate discharge and batching optimization method for maintaining the pile shape.

[0126] The beneficial effects of this invention are as follows: This invention sets a quality factor for each discharge port; acquires aggregate pile height data, updates the quality factor formula based on the pile height data, determines the aggregate particle size based on the pile height data, calculates the stability coefficient of each discharge port based on the aggregate particle size, calculates the quality factor of each discharge port, and uses the sum of the quality factors of all discharge ports as the fitness function. A genetic algorithm with the discharge ports as chromosomes is then used to find the optimal solution, obtaining the optimal discharge port opening and closing state. Discharge port opening and closing control is then implemented, and the above operations are repeated periodically to continuously maintain the aggregate discharge and batching optimization. Ensuring the aggregate pile maintains a near-conical shape during discharge avoids the problems of aggregate landslides or collapses caused by traditional discharge methods. Aggregate batching is performed simultaneously with aggregate discharge, merging two work stages and reducing the cost of construction sand and gravel aggregates.

[0127] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing aggregate discharge and batching while maintaining stockpile shape, characterized in that: Includes the following steps: S1: Set a quality factor for each feeding port. The larger the quality factor value, the worse the quality. The basic calculation method is as follows: in: P The weighted average is the difference between the height of the aggregate at the discharge port and the height of the surrounding aggregate. Q The weighted average is the difference between the aggregate height at the discharge port and the heights at other discharge ports at the same distance from the feed port. R The weighting of the particle size at the feed inlet and the batching ratio is calculated. This is the difference between the height of the aggregate at the discharge port and the height of the surrounding aggregate. It is the difference between the height of the aggregate at the discharge port and the height of other discharge ports at the same distance from the feed port; This indicates the opening or closing status of the feed inlet; its value can only be 0 or 1. The stationarity coefficient is expressed as follows: ,in, This refers to the particle size classification at the current feed inlet. This represents the total number of feed inlets; This is the maximum allowable height difference of the stockpile in the system; For the purpose of imposing penalties; The height of the aggregate above the discharge port. The material feeding height within one cycle, when At this time, the feeding port does not participate in feeding; S2: Obtain aggregate stockpile height data; S3: Update the quality factor formula using the stockpile height data, as follows: If significant subsidence occurs in the aggregate at the current feed inlet, the weight P of the current feed inlet is increased. The update formula is as follows: P= , in This represents the upper limit of a significant height difference; If there is a significant height difference between the current feed inlet's aggregate height and the heights of other feed inlets at the same distance from the feed inlet, then the weight Q of this feed inlet is increased. The update formula is as follows: Q= , in This represents the upper limit of a significant height difference; Since the material is transported to the downstream production line via conveyor belt after exiting the discharge port, when the number of discharge ports open on the same conveyor belt exceeds a certain threshold of the average number of discharge ports, the R value is gradually reduced. The update formula for weight R is: in, This represents the number of feed ports open in the current column. This represents the average number of feed ports in the current column. This is the upper threshold of the current column's feed inlet. This is the lower limit threshold of the current column's feed port; S4: Determine the aggregate particle size of the stockpile based on the stockpile height data; S5: Calculate the stability coefficient of each discharge port based on the aggregate particle size of the stockpile; S6: Calculate the quality factor for each feed port according to steps S1-S5; S7: Use the sum of the quality factors of all feed ports as the fitness function, perform a genetic algorithm with the feed ports as chromosomes to find the optimal solution, obtain the optimal feed port switching state, and perform feed port switching control. S8: Repeat steps S2 to S7 at certain intervals to continuously maintain aggregate output and batching optimization.

2. The method for optimizing aggregate discharge and batching while maintaining stockpile shape as described in claim 1, characterized in that: In step S2, the process of obtaining the aggregate stockpile height data is as follows: The height of the aggregate pile is sampled by LiDAR, and a 3D model is created to obtain the height data of the aggregate pile.

3. The method for optimizing aggregate discharge and batching while maintaining stockpile shape as described in claim 1, characterized in that: In step S4, the aggregate particle size of the stockpile is determined based on the stockpile height data, which specifically includes the following steps: S11: Record historical data of material level height at each discharge port; S12: Determine the height of the feed pile increase by observing the change in material height around the feed inlet. : ,in Let i be the change in material height around the discharge port of the i-th selected point, where i is the number of selected points. S13: Determine the reduction in discharge port height based on the discharge port opening time. : ,in The feeding speed at the feeding port, The time the feed inlet is open; S14: Determine the distribution of aggregate size at different material levels at the discharge port.

4. The method for optimizing aggregate discharge and batching while maintaining stockpile shape as described in claim 3, characterized in that: In step S14, the particle size distribution of aggregates at different material levels at the discharge port is determined according to the following rules: Rule 1: If the current stockpile height is at a low level, and the portion of the stockpile that increases in height is also at a low level, then the portion of the stockpile that increases in height represents large-diameter particles. Rule 2: If the current stockpile height is at the low level, and the portion of the stockpile that increases in height is at the middle level, then the portion of the stockpile that increases in height represents medium-sized particles. Rule 3: If the current stockpile height is at a low level, and the portion of the stockpile that increases in height is at a high level, then the portion of the stockpile that increases in height represents small-diameter particles. Rule 4: If the current height of the stockpile is at the middle level, and the portion of the stockpile that increases in height is at the low level, then the portion of the stockpile that increases in height represents large-diameter particles. Rule 5: If the current stockpile height is at the middle level, and the increased height of the stockpile due to feeding is also at the middle level, then the increased height of the stockpile represents medium-sized particles. Rule 6: If the current height of the stockpile is at the middle level, and the increased height of the stockpile due to feeding is at the high level, then the increased height of the stockpile represents small-diameter particles. Rule 7: If the current stockpile height is at the high level, and the portion of the stockpile that increases in height is at the low level, then the portion of the stockpile that increases in height represents large-diameter particles. Rule 8: If the current stockpile height is at the high level, and the increased height of the stockpile is at the middle level, then the increased height of the stockpile is for medium-sized particles. Rule 9: If the current height of the material pile is at the high level, and the portion of the material pile that increases in height is also at the high level, then the portion of the material pile that increases in height is for small-diameter particles.

5. The method for optimizing aggregate discharge and batching while maintaining stockpile shape as described in claim 4, characterized in that: In step S7, the process of using a genetic algorithm with the following feed inlet as the chromosome to find the optimal solution is as follows: S21: Initialize the population: Based on the requirements of the problem, an initial population is randomly generated. Each individual in the population represents a solution to the problem, represented in binary form. S22: Assess fitness: The fitness of each individual, i.e., the quality of the solution, is evaluated by a fitness function, which is the sum of the quality factors of all feed ports in step S1: in This represents the maximum number of feed inlets that the system allows to open. S23: Selection operation: The selection operation is performed using the roulette wheel method; based on the fitness of individuals, a certain number of individuals are selected as parents to produce the next generation of individuals; S24: Crossover operation: Select a pair of individuals from the parent generation and generate new individuals through crossover operation; crossover operation simulates gene hybridization, and produces offspring with new combination characteristics by exchanging gene segments of two individuals; S25: Mutation operation: Mutation operations are performed on new individuals to introduce new gene variants. Mutation operations simulate gene mutations by randomly changing certain genes in an individual to introduce new gene combinations, thereby increasing the diversity of the search space. Mutation operations are performed probabilistically. Introducing a self-updating mechanism into the traditional genetic algorithm, with the following rule: when the current optimal solution of the population is close to the global optimal solution... At that time, the current optimal solution of the population is... The adaptive update formula is as follows: in, The global probability of mutation. The minimum gradient threshold between the current solution and the optimal solution; S26: Replacement operation: Replace some individuals in the original population with newly generated individuals to form a new generation of population; S27: Determine if the termination condition is met. If yes, the algorithm ends and returns the found optimal solution. If no, return to step S22 and continue execution. Through multiple generations of evolution and selection, the genetic algorithm gradually optimizes the solutions in the population, making them gradually approach the optimal solution. The termination condition must satisfy at least one of the following conditions: (1) the maximum number of iterations is reached, and (2) a solution that is close enough to the optimal solution is found.

6. A storage device, characterized in that: The storage device stores instructions and data for implementing the aggregate discharge and batching optimization method for maintaining the pile shape as described in any one of claims 1 to 4.

7. An aggregate discharge and batching optimization device for maintaining pile shape, characterized in that: include: A processor and a storage device; the processor loads and executes instructions and data in the storage device to implement the aggregate discharge and batching optimization method for maintaining the pile shape as described in any one of claims 1 to 4.

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