Blanking method and device for photovoltaic support profile material, medium and equipment

Abstract

The invention relates to the technical field of photovoltaic supports, in particular to a discharging method and device for photovoltaic support profile materials, a medium and equipment. According to the method, blanking is carried out by obtaining the demand parameters of the photovoltaic support profile materials, so that the blanking method can flexibly meet different design rules and construction demands of household photovoltaic power stations; meanwhile, an initial blanking scheme is generated in a circular queue mode, some possible splicing combinations can be prevented from being omitted, and rich and diversified initial solutions are provided for a subsequent genetic algorithm. Besides, the improved genetic algorithm is adopted to carry out utilization rate calculation, crossing and mutation operation on multiple initial blanking schemes, so that the obtained final blanking scheme can improve the utilization rate of profiles, reduce the generation of waste materials, effectively reduce the production cost and improve the construction efficiency of the photovoltaic support.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic brackets, and specifically relates to a cutting method, device, medium and equipment for photovoltaic bracket profile materials. Background Art

[0002] The photovoltaic bracket system is a key component in the photovoltaic power generation system, and is usually spliced by profiles of various different lengths. In practical applications, in order to reduce material waste and improve construction efficiency, it is often necessary to cut and splice profiles of standard lengths. However, in traditional material cutting and processing, it mainly relies on technicians' personal experience or simple rules, and there are no clear requirements for controlling the material utilization rate. Such a rough processing method will cause a large amount of profile waste and it is difficult to achieve a high material utilization rate and splicing efficiency.

[0003] In the household photovoltaic field, due to different geographical locations of power station construction, different installed capacities, different construction heights, different construction investors, etc., the materials required between individual power stations vary greatly. Therefore, there will be certain differences in the material splicing rules for each power station. For example, in the design and construction of a certain photovoltaic power station, at most 1 splicing is allowed for the main beam or purlin profiles in the same span, the edge column profiles cannot be spliced, the cantilever beam cannot be spliced, etc. At present, the household photovoltaic construction cutting method basically still refers to the personal experience of construction workers, resulting in a high rework rate, which in turn increases material waste. Summary of the Invention

[0004] In view of this, the present invention provides a cutting method and device for photovoltaic bracket profile materials to solve the problem of high rework rate and easy material waste in the existing household photovoltaic construction cutting method.

[0005] In a first aspect, the present invention provides a cutting method for photovoltaic bracket profile materials, the method comprising: obtaining demand parameters for photovoltaic bracket profile materials; generating a plurality of initial cutting schemes in a circular queue manner according to the demand parameters, each initial cutting scheme including the types of raw materials, the target lengths of different functional materials, and splicing rules; calculating the utilization rate, performing crossover and mutation operations on the plurality of initial cutting schemes by using an improved genetic algorithm to obtain a final cutting scheme.

[0006] In the present invention, by obtaining the demand parameters of the photovoltaic support profile materials for cutting, the cutting method can flexibly meet different design rules and construction requirements of household photovoltaic power stations; meanwhile, the initial cutting plan is generated in the way of a circular queue, which can avoid missing some possible splicing combinations and provide a rich variety of initial solutions for the subsequent genetic algorithm. In addition, an improved genetic algorithm is used to calculate the utilization rate, perform crossover and mutation operations on a variety of initial cutting plans, so that the final cutting plan obtained can improve the utilization rate of the profiles, reduce the generation of waste materials, effectively reduce the production cost, and improve the construction efficiency of the photovoltaic support.

[0007] In an optional implementation manner, a variety of initial cutting plans are generated in the way of a circular queue according to the demand parameters, including: according to the demand parameters, constructing constraint conditions with the least waste, relatively the least number of splicing times in the cutting plan, and the least reusable surplus materials; generating a variety of initial cutting plans in the way of a circular queue according to the demand parameters and the constraint conditions.

[0008] In the present invention, by constructing constraint conditions with the least waste, relatively the least number of splicing times in the cutting plan, and the least reusable surplus materials, the formed initial cutting plan preliminarily meets the requirements and improves the efficiency of subsequent optimization.

[0009] In an optional implementation manner, generating a variety of initial cutting plans in the way of a circular queue according to the demand parameters further includes: screening spliceable material combinations and non-spliceable material combinations according to the demand parameters; generating a variety of initial cutting plans in the way of a circular queue based on the non-spliceable material combinations and the corresponding demand parameters; calculating the material utilization rate of the spliceable material combinations.

[0010] In the present invention, by screening spliceable and non-spliceable materials and forming an initial cutting plan based on the non-spliceable materials, the efficiency of optimization is further improved.

[0011] In an optional implementation manner, an improved genetic algorithm is used to calculate the utilization rate, perform crossover and mutation operations on a variety of initial cutting plans to obtain a final cutting plan, including: calculating the material utilization rate of the initial cutting plan by using an improved fitness function; selecting an initial cutting plan according to the material utilization rate and performing a crossover operation using a k-gene exchange scheme to generate a new cutting plan, where the gene includes the cutting parameters in the cutting plan; performing a mutation operation on the new cutting plan based on a mutation factor and generating a new cutting plan; repeating the process of recalculating the material utilization rate, crossover operation, and mutation operation until a preset condition is reached to obtain the final cutting plan.

[0012] In an alternative embodiment, a mutation operation is performed on the new cutting plan, and a new cutting plan is generated, including: randomly selecting gene positions in the cutting plan after crossover, deleting the genes behind the gene positions, where the genes include the cutting parameters in the cutting plan; regenerating the genes behind the gene positions according to the demand parameters to obtain a new cutting plan.

[0013] In an alternative embodiment, in the mutation operation, when the material utilization rate increases after performing the crossover mutation operation, the mutation factor is set to a preset value; when the material utilization rate remains unchanged or decreases after performing the crossover mutation operation, the mutation factor is increased.

[0014] In the present invention, by dynamically adjusting the mutation factor, the final cutting plan can be determined faster.

[0015] In an alternative embodiment, the improved fitness function is represented by the following formula:

[0016]

[0017] In the formula, L N represents the total length of N types of raw materials used, N represents the number of types of raw materials, D represents the target length after splicing, and C N represents the remaining material of the last raw material.

[0018] In the present invention, by using this formula as the improved fitness function, the calculation of the material utilization rate is realized, and the final obtained cutting plan has a high material utilization rate.

[0019] In a second aspect, the present invention provides a cutting device for photovoltaic support profile materials, the device includes: a parameter acquisition module for acquiring demand parameters for photovoltaic support profile materials; an initial plan generation module for generating a plurality of initial cutting plans in a circular queue manner according to the demand parameters, each initial cutting plan including the number of types of raw materials, the target lengths of different functional materials, and splicing rules; a final plan generation module for calculating the utilization rate, performing crossover and mutation operations on a plurality of initial cutting plans by using an improved genetic algorithm to obtain a final cutting plan.

[0020] In a third aspect, the present invention provides a computer device, including: a memory and a processor, which are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the cutting method for photovoltaic support profile materials in the first aspect or any corresponding embodiment thereof.

[0021] Fourthly, the present invention provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the blanking method for photovoltaic bracket profile materials according to the first aspect or any corresponding embodiment thereof.

[0022] Fifthly, the present invention provides a computer program product, including computer instructions, and the computer instructions are used to cause a computer to execute the blanking method for photovoltaic bracket profile materials according to the first aspect or any corresponding embodiment thereof. Description of the Drawings

[0023] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0024] Figure 1 is a schematic flowchart of the blanking method for photovoltaic bracket profile materials according to an embodiment of the present invention;

[0025] Figure 2 is a schematic flowchart of another blanking method for photovoltaic bracket profile materials according to an embodiment of the present invention;

[0026] Figure 3 is a schematic flowchart of the genetic algorithm adopted by the blanking method according to an embodiment of the present invention;

[0027] Figure 4 is a schematic diagram of the result of the blanking scheme according to an embodiment of the present invention;

[0028] Figure 5 is a structural block diagram of the blanking device for photovoltaic bracket profile materials according to an embodiment of the present invention;

[0029] Figure 6 is a schematic hardware structure diagram of the computer device according to an embodiment of the present invention. Detailed Embodiments

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0031] According to an embodiment of the present invention, an embodiment of a cutting method for photovoltaic support profile materials is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0032] In this embodiment, a cutting method for photovoltaic support profile materials is provided, which can be used in electronic devices such as computers, mobile phones, tablet computers, etc. Figure 1 It is a flowchart of a cutting method for photovoltaic support profile materials according to an embodiment of the present invention, as Figure 1 shown, and this process includes the following steps:

[0033] Step S101, obtain the demand parameters for photovoltaic support profile materials. Among them, the photovoltaic support is the main component of the photovoltaic power generation system. For the photovoltaic power generation system, it is divided into household photovoltaic systems and large-scale photovoltaic power stations. And the phenomenon of material waste in the household photovoltaic system is relatively obvious. Therefore, the photovoltaic support in this embodiment mainly refers to the photovoltaic support of the household photovoltaic system. In other embodiments, this photovoltaic support can also be the photovoltaic support applied to large-scale photovoltaic power stations.

[0034] Specifically, the photovoltaic support is used to support the photovoltaic modules in the photovoltaic power generation system, so that the photovoltaic modules can receive sunlight as much as possible, and at the same time enhance the overall stability of the photovoltaic power generation system. The profiles of the photovoltaic support can be steel structures or aluminum alloy structures. In the photovoltaic power generation system, the photovoltaic support mainly includes functional material structures such as side columns, middle columns, cantilever beams, main beams, purlins, etc. In order to reduce material waste, it is often necessary to splice profiles of the same specification to obtain the required functional materials.

[0035] In practical applications, in different application scenarios, such as when constructing different household photovoltaic power stations, based on different power station sizes and design requirements, the required functional materials are different. Therefore, for the construction of different household photovoltaic power stations, it is necessary to first obtain their demand parameters. The demand parameters specifically include the types of profiles required, the quantity of functional materials, the target length of the materials, and the splicing rules for different materials. For example, which specific specifications of profiles are needed to form which types of functional materials, and the lengths required for different functional materials, etc. The splicing rules specifically include whether the materials can be spliced, the upper and lower limits of the number of splicing segments, and so on. For example, the demand parameters include using C-shaped steel to form the middle column, the target length of the middle column is 3 meters, and at most three sections of materials can be used for splicing.

[0036] Step S102: Generate multiple initial cutting plans in the form of a circular queue according to the demand parameters. Each initial cutting plan includes the types of raw materials, the target lengths of different functional materials, and the splicing rules. Specifically, after obtaining the user's demand parameters, multiple initial cutting plans can be generated based on these parameters, and these multiple initial cutting plans can be used as the initial population for subsequent genetic algorithm optimization. In the genetic algorithm, if the population size is too small, although the calculation speed can be improved, the algorithm performance deteriorates due to insufficient sampling points; if the population size is too large, the computational complexity of the algorithm increases, resulting in slow convergence. Therefore, the population size should be determined according to the scale of the problem. The selection of the initial plan has a great impact on the genetic path, which in turn affects the genetic efficiency and the quality of the output results. Thus, in practice, the number of initial cutting plans can be limited according to the actual situation.

[0037] In this embodiment, the initial cutting plan is generated based on the demand parameters, that is, different splicing plans are generated according to the splicing rules for different materials in the demand parameters. For example, when the demand parameters include using C-shaped steel to form the middle column, the target length of the middle column is 3 meters, and at most three sections of materials can be used for splicing. Then, in different initial cutting plans, the splicing plans including using two sections of materials and using three sections of materials can be included respectively. In addition, the types and quantities of functional materials required in actual applications may be numerous, so the required functional materials can be generated in a certain order. In actual applications, functional materials include edge columns, middle columns, truss columns, cantilever beams, main beams, purlins, etc. Specifically, the edge column represents the outermost perimeter columns of the photovoltaic power station, and the edge column cannot be spliced; the middle column represents the middle-span columns except for the outermost columns of the power station, and the middle column can be spliced; the truss column represents the truss scheme columns of the photovoltaic power station.

[0038] Based on this, in this embodiment, a circular queue is used to generate multiple initial splicing plans. Specifically, according to the input splicing rules such as the minimum and maximum number of splicing segments for different functional materials, the circular queue starts to work. It traverses these rules in a cyclic manner and combines the relevant information of the materials, such as material length, quantity, etc., to combine different materials. For example, according to the splicing rules, for a raw material of a certain length, the circular queue may first try to intercept the material length required for an edge column from the raw material, and then consider whether the remaining part can meet the splicing requirements of other materials (such as purlins). By continuously cycling and trying different interception and combination methods in this way, a series of initial splicing plans are generated.

[0039] Thus, through cyclic traversal of the rules and material combination, the circular queue ensures that the generated initial plans not only conform to the rules but also have a certain degree of diversity, increasing the possibility of the algorithm finding the optimal solution.

[0040] Step S103: Use an improved genetic algorithm to calculate the utilization rate, perform crossover and mutation operations on multiple initial cutting plans, and obtain the final cutting plan. Specifically, after obtaining multiple initial cutting plans, this embodiment uses an improved genetic algorithm to optimize them to obtain a better cutting plan as the final cutting plan. Among them, in this improved genetic algorithm, the utilization rate of materials is calculated, and crossover and mutation operations are used to optimize and screen the initial cutting plans. That is, the final cutting plan determined by this genetic algorithm can achieve a better material utilization rate, thereby improving the utilization rate of profiles, reducing the generation of waste, effectively reducing production costs, and improving the construction efficiency of the photovoltaic support.

[0041] The cutting method for photovoltaic support profile materials provided by the embodiment of the present invention performs cutting by obtaining the demand parameters of the photovoltaic support profile materials, so that this cutting method can flexibly respond to different design rules and construction requirements of household photovoltaic power stations; at the same time, using the method of circular queue to generate the initial cutting plan can avoid missing some possible splicing combinations and provide a rich variety of initial solutions for the subsequent genetic algorithm. In addition, using an improved genetic algorithm to calculate the utilization rate, perform crossover and mutation operations on multiple initial cutting plans enables the obtained final cutting plan to improve the utilization rate of profiles, reduce the generation of waste, effectively reduce production costs, and improve the construction efficiency of the photovoltaic support.

[0042] In this embodiment, a cutting method for photovoltaic support profile materials is provided, and this method includes the following steps:

[0043] Step S201: Obtain the demand parameters of the photovoltaic support profile materials. For details, please refer to Figure 1 Step S101 of the illustrated embodiment, which will not be elaborated here.

[0044] Step S202: Generate multiple initial cutting plans in the way of circular queue according to the demand parameters. Each initial cutting plan includes the types of raw materials, the target lengths of different functional materials, and the splicing rules.

[0045] Specifically, the above step S202 includes:

[0046] Step S2021: According to the demand parameters, construct constraint conditions with the least waste, the relatively least number of splicing times in the cutting plan, and the least reusable remaining materials.

[0047] Step S2022: Generate multiple initial cutting plans in the way of circular queue according to the demand parameters and the constraint conditions.

[0048] In this embodiment, in order to make the initially generated blanking plan initially meet the requirements and reduce the time for subsequent genetic algorithm optimization, before generating the initial blanking plan, constraint conditions can be generated based on the requirements first, and then multiple initial blanking plans can be generated based on the relevant parameters after being constrained by the constraint conditions. Among them, the constraint conditions constructed in this embodiment include the least waste, the relatively least number of splicing times in the blanking plan, and the least amount of reusable remaining materials.

[0049] Specifically, when forming the functional materials of the photovoltaic support, due to the different lengths of the raw materials and the materials to be formed, the raw materials need to be cut, and the remaining raw materials after cutting are called remaining materials. For the formed remaining materials, it can be further determined whether they can form other functional materials alone or by splicing (the size of the remaining materials is relatively large) during this construction or subsequent constructions. If they can form, it means that the remaining materials can still be reused. If they cannot form, or there are still raw materials that cannot be reused after forming other functional materials, the remaining materials or the remaining raw materials are called waste materials. And if the remaining materials cannot be used in this construction and can only be used in the next construction, the remaining materials need to be transported back to the warehouse. Therefore, although the reuse of the remaining materials reduces the cost of raw materials, the recycling process of the remaining materials also increases the transportation and inventory costs accordingly. Therefore, controlling the quantity of recyclable remaining materials is also used as a constraint condition. For the number of splicing times in the blanking plan, if there are too many, it will consume a lot of time and energy and increase the cutting cost. Therefore, too many splicing times will increase the complexity of the operation during cutting, directly leading to an increase in the processing cost.

[0050] Based on the above analysis, the constraint conditions can be expressed as:

[0051]

[0052] and is an integer

[0053] and is an integer

[0054]

[0055] In the formula, N represents the number of types of raw materials, M represents the number of blanking plans; s i represents the length of the remaining material generated by cutting one profile according to the i-th cutting method; λ i represents whether the remaining material generated by the i-th blanking plan can be reused (if s i ≥ω, then λ i =1; otherwise λ i =0); ω represents the minimum length of reusable remaining materials; L represents the length of the raw material; g j represents the length of the j-th material, j = 1, 2,..., N; q j represents the required quantity of the j-th material; yi represents the number of materials used for cutting according to the i-th cutting method, where i = 1, 2,..., M; x ij represents the number of the j-th material cut in the i-th cutting method; Assume that the raw material inventory is sufficient.

[0056] Among them, in constraint condition (1), z1, z2, and z3 respectively represent the three objectives of the least waste, the least number of splicing times, and the least reusable remaining material. The formula of constraint condition (2) means that the sum of the lengths of all raw materials cut in the i-th cutting plan must be less than or equal to the length of the raw material. The formula of constraint condition (3) means that the cutting of the j-th material must meet the demand of this material.

[0057] After determining the constraint conditions, the constraint conditions can be used as the constraints of the initial cutting plan, that is, the multiple initial cutting plans generated need to meet the limitations of the constraint conditions.

[0058] In an alternative implementation manner, multiple initial cutting plans are generated in the form of a circular queue according to the demand parameters, and it further includes: screening the spliceable material combinations and non-spliceable material combinations according to the demand parameters; generating multiple initial cutting plans in the form of a circular queue based on the non-spliceable material combinations and the corresponding demand parameters; calculating the material utilization rate of the spliceable material combinations.

[0059] Specifically, among the obtained demand parameters, some materials can be spliced and some materials cannot be spliced. Therefore, before obtaining the demand parameters to generate the initial cutting plan, the spliceable materials and non-spliceable materials are screened first, and the spliceable material combinations and non-spliceable material combinations are formed respectively. For the non-spliceable material combinations, the cutting plan can be directly formed without the subsequent optimization process of the genetic algorithm. However, the material utilization rate can be calculated for them. When forming the initial cutting plan, it is determined based on the non-spliceable material combinations without considering the spliceable material combinations.

[0060] Step S203, using an improved genetic algorithm to perform utilization rate calculation, crossover, and mutation operations on multiple initial cutting plans to obtain the final cutting plan;

[0061] Specifically, the above step S203 includes:

[0062] Step S2031, using an improved fitness function to calculate the material utilization rate of the initial cutting plan; Specifically, in this embodiment, an improved fitness function is set based on the goal of the least total waste, that is, the highest material utilization rate. The fitness function is specifically expressed by the following formula:

[0063]

[0064] In the formula, LN represents the total length of N raw materials used, where N represents the number of types of raw materials, D represents the target length after splicing, and C N represents the remaining material of the last raw material. According to this formula, the closer the calculated F(x) is to 1, the higher the material utilization rate.

[0065] Step S2032, select an initial cutting plan according to the material utilization rate, and perform crossover operation using the K-gene exchange scheme to generate a new cutting plan. The gene includes the cutting parameters in the cutting plan. Specifically, after calculating the material utilization rates of different initial cutting plans using the above fitness function, select the initial cutting plan with a high material utilization rate for subsequent crossover and mutation operations. Among them, for each cutting plan, it can be regarded as a chromosome, and the relevant parameters in each plan are used as genes. Specifically, this gene represents how many times each profile (raw material) is cut, how many times the cut material segments are spliced to form the corresponding functional material, and the order of cutting to form the corresponding functional material, etc. That is, this gene includes all the parameters related to cutting.

[0066] For the crossover operation, it is to exchange the genes of two chromosomes. Specifically in this application, it is to exchange the cutting parameters in the two selected cutting plans. Since each initial cutting plan contains a large number of cutting parameters, the convergence efficiency of the single-point crossover method is relatively low. Therefore, this embodiment adopts the K-gene exchange scheme. Specifically, in the first few crossover processes of the genetic algorithm, K takes a relatively large value, such as 3, and as the optimization degree of the solution in the genetic algorithm increases, the value of K becomes smaller and smaller, such as 1 can be taken in the later stage.

[0067] In the K-gene exchange scheme, the value of K represents the number of genes in each crossover operation. For example, when K takes the value of 3, it means randomly selecting three genes in each initial cutting plan for exchange. Thus, new initial cutting plans can be generated through the K-gene exchange scheme in the crossover operation. In a specific embodiment, when using two-point crossover in multi-point crossover and the crossover probability is 0.8, the pseudo-code description of the crossover process is as follows:

[0068] Input: population; crossover probability;

[0069] 1: for i = 1: population size, the number of crossovers to be performed is the same as the population size, enter the crossover operation;

[0070] 2: Randomly select two individuals and use their chromosomes for crossover;

[0071] 3: Generate a random number within (0, 1) and compare it with the crossover probability;

[0072] If pick < crossover probability, terminate the crossover operation and return to the first step for the next loop;

[0073] If pick ≥ crossover probability, the crossover operation continues;

[0074] 4: Randomly generate two numbers a and b, with the generation range being [2, m + n - 1], and sort to ensure b > a.

[0075] [a, b] is the range of multi - point crossover, and m + n is the number of types of cutting methods for two specifications of raw materials.

[0076] 5: Swap the gene data in the corresponding [a, b] position range in the two chromosomes.

[0077] 6: end, terminate the loop;

[0078] Output: The population of material splicing schemes after crossover.

[0079] Step S2033, perform a mutation operation on the new blanking scheme based on the mutation factor and generate a new blanking scheme; specifically, when performing the mutation operation, the genes in the blanking scheme are mutated to form a new blanking scheme.

[0080] In an alternative embodiment, the above - mentioned step S2033 includes:

[0081] Step a1, randomly select a gene position in the blanking scheme after crossover and delete the genes after the gene position. Specifically, this process can be understood as randomly selecting the blanking parameter at any position in the blanking scheme, and then deleting the blanking parameters after this blanking parameter, or restoring it to the raw material data to be cut and matched. As can be seen from the above analysis, the basic process of blanking is to first select a profile, cut it to form the required target - length material, and then judge whether the remaining surplus material can be cut or spliced with other materials to form new materials. If it can form, then cut to form new materials, or continue to select the next profile for cutting, and splice the cut material segments with the surplus material to form new materials. Thus, the blanking parameters in the blanking scheme include the cutting and splicing parameters sorted in the blanking process. Therefore, deleting the genes after the randomly selected gene position can be understood as deleting the cutting and splicing methods for some profiles.

[0082] Step a2, regenerate the genes after the gene position according to the demand parameters to obtain a new blanking scheme. Specifically, for the restored raw material data to be cut and matched, the above - mentioned method of the initial blanking scheme can be continued to re - determine the new cutting and splicing scheme, and thus a new blanking scheme is obtained.

[0083] During the mutation operation, a mutation factor is usually used to control the mutation operation, such as controlling the probability and intensity of the mutation operation. In this embodiment, when the material utilization rate increases after the crossover and mutation operation, the mutation factor is set to a preset value; when the material utilization rate remains unchanged or decreases after the crossover and mutation operation, the mutation factor is increased.

[0084] Specifically, after each effective genetic operation (for example, after selection, crossover, and mutation, offspring individuals with higher fitness are produced), the mutation factor is set to 1. This is a relatively stable basic state, meaning that the mutation operation is at a relatively moderate occurrence probability and intensity level. When no better result is found after a round of genetic operations (i.e., the fitness of the newly produced individuals does not increase), the mutation factor will increase. As the mutation factor increases, the probability of the mutation operation occurring increases, and the variation range of the cutting plan genes may also increase, prompting the algorithm to perform more and more extensive mutation operations on individuals in order to jump out of the local optimal solution area that may be currently trapped and continue to search for a better cutting plan until offspring individuals with higher fitness are generated.

[0085] In a specific embodiment, the initial mutation rate is set to 0.1 and dynamically adjusted. The pseudo-code description of the crossover process is as follows:

[0086] Input: population; mutation probability;

[0087] 1: for i = 1: population size, the number of mutation operations to be performed is the same as the population size, enter the mutation operation;

[0088] 2: Randomly select an individual and mutate the genes inside its chromosome;

[0089] 3: Generate a random number within (0, 1) and compare it with the mutation probability;

[0090] if pick < mutation probability, terminate the crossover operation and return to the first step for the next loop;

[0091] if pick ≥ mutation probability, the mutation operation continues;

[0092] 4: Randomly generate a number a, generate a range of [1, m + n], and a is the gene position for mutation;

[0093] 5: Find the maximum value L corresponding to the gene position a, randomly generate a number b, generate a range [0, L], and replace the data at the original gene position a;

[0094] 6: end, end the loop;

[0095] Output: population of material splicing plans after mutation.

[0096] Step S2034: Recalculate the material utilization rate, crossover operation, and mutation operation processes until the preset conditions are met to obtain the final cutting plan. Specifically, after performing the crossover operation and mutation operation, it is necessary to recalculate the material utilization rate of the newly generated cutting plan. If the requirements cannot be met, it is necessary to continue with the selection, crossover, and mutation operations until the material utilization rate meets the preset requirements or the iteration count limit is reached, and the plan with the highest fitness during the iteration process is used as the final cutting plan. The final cutting plan includes the target length of the required materials, the number of splicing segments, and the cutting method for the raw materials, etc.

[0097] In view of the problem of complex cutting rules caused by the differentiation of splicing rules for materials with different functional attributes in household photovoltaic construction, the present invention uses a cutting and splicing algorithm for photovoltaic support profile materials based on the genetic algorithm to determine the cutting plan. By utilizing the self-adaptive and global optimization capabilities of the genetic algorithm, the profiles of the same specification are further subdivided according to their different functions, so as to achieve the optimal configuration of material splicing and cutting. This method can realize the splicing of multiple materials and support the splicing rules under the same specification but different functional attributes at the same time. Users can customize the required splicing rules according to different application scenarios. For example, the upper limit of the number of splicing segments and the maximum number of splicing segments of the materials can be set, and the spliceable attributes of the profiles at different functional positions can be set to flexibly respond to different design rules and construction requirements of household power stations.

[0098] The present invention simulates the natural selection process based on the genetic algorithm, generates new material splicing plans in each generation through crossover operations and mutation operations, evaluates the advantages and disadvantages of each plan globally through a fitness function, and sets and adjusts the fitness value to improve the global searchability. Finally, it assists in selecting the relatively optimal cutting times and splicing plans, thereby improving the utilization rate of profiles, reducing the generation of waste materials, effectively reducing production costs, and improving the construction efficiency of photovoltaic supports.

[0099] The present invention quickly generates an ordered cutting list, avoids the phenomenon of random cutting by construction personnel due to lack of dimensions, and the generated profile cutting plan consumes the least amount of whole steel, has the relatively smallest loss rate, and the relatively fewest cutting times for construction personnel.

[0100] As a specific application example of the embodiment of the present invention, as Figure 2 shown, the cutting method for photovoltaic support profile materials is implemented using the following process:

[0101] 1. Obtain the demand parameters of the cutting plan (including the target length of the materials, whether they can be spliced, requirements for the number of splicing segments, etc.). And determine the goals of the least waste, relatively fewest splicing times, and least reusable surplus materials as constraints.

[0102] 2. Determine the classification materials as spliceable and non-spliceable according to the demand parameters, and form the initial combination of non-spliceable materials and the initial combination of spliceable materials. Calculate the initial utilization rate for the initial combination of non-spliceable materials.

[0103] 3. For the initial combination of spliceable materials, generate the initial cutting plan in the way of a circular queue.

[0104] 4. Optimize the initial cutting plan by using the genetic algorithm. Specifically, calculate whether the fitness (i.e., material utilization rate) of the initial cutting plan meets the preset conditions. When it does not meet, use the crossover and mutation operations to regenerate a new cutting plan, calculate the material utilization rate of the new cutting plan. If the material utilization rate decreases, perform the crossover and mutation operations again. If the utilization rate increases, continue to judge whether it meets the conditions. If it meets, output directly. If it does not meet, continue the process of crossover, mutation, and calculation of the utilization rate until the material utilization rate reaches the preset requirements or reaches the iteration number limit, and obtain the plan with the highest fitness during the iteration process as the final cutting plan.

[0105] Among them, the specific optimization process of the genetic algorithm can be implemented as follows: Generate the initial population (initial cutting plan) based on the demand parameters (such as material attributes, whether spliceable, maximum number of splices, etc.). Calculate the fitness of the plan and judge whether it meets the preset conditions. If it does not meet, perform the crossover and mutation operations, and calculate the fitness again. If it does not meet, continue the process of crossover, mutation, and calculation of the utilization rate until the material utilization rate reaches the preset requirements or reaches the iteration number limit, and obtain the plan with the highest fitness during the iteration process as the final cutting plan. Figure 3 In a specific embodiment, if the demand parameters include: the length of the raw material is L = 6m, and the lengths and quantities of the profiles to be cut are shown in Table 1 as follows:

[0106] Table 1

[0107]

[0108] Length (mm) Quantity (pcs) Material functional attribute 1800 10 Edge column 2110 10 Edge column (4), middle column (6) 2430 10 Edge column (4), middle column (6) 2750 5 Edge column (2), middle column (3)

[0109] Figure 4 Then, by running the above cutting method for the profiles of the photovoltaic support in the software, the final cutting plan is shown in the interface display. It can be seen that the quantities of the 1800mm, 2150mm, 2500mm, and 2750mm column profiles are consistent with the demand. According to this method, the specific value of the final material utilization rate is 93.15%. The material utilization rate of the manual cutting method in the actual project is 80%, and all the remaining materials can be utilized without waste generation. Therefore, the present invention significantly improves the utilization rate of materials.

[0110] Figure 4 ​​​In the shown interface, whether splicing is available is indicated by a tick, meaning the material can be spliced, and no tick means it cannot be spliced; the maximum quantity obtained by splicing represents the available surplus quantity (in actual applications, it is generally required that the number of spliced columns does not exceed one-third of the total quantity and at most does not exceed 4); the minimum utilization length of the spliced material represents the minimum utilization length of the leftover material, and different material types have different requirements; the nesting list includes the generated cutting plan. The length represents the required length of this type of material; the quantity represents the required quantity of this type of material.

[0111] The method of the present invention has the advantages of efficient use of materials, flexible configuration of splicing rules, optimized construction efficiency, and adaptive optimization. Specifically, through the global search ability of the genetic algorithm, it controls the quality of the bill of materials from the source, reduces the loss of raw materials, and reduces rework and error rates. At the same time, it uses the leftover materials to maximize the material utilization rate; by supporting the splicing of multiple sections of materials and allowing users to customize the splicing rules, it adapts to the needs of photovoltaic construction projects under different installed capacities. By optimizing the strategies of profile cutting and splicing and optimizing the nesting of large materials, it reduces the on-site construction time and complexity. By adopting the genetic algorithm, it can adaptively adjust parameters and has better global optimization ability to adapt to the multi-cycle production environment, ensuring that the optimal cutting plan can still be obtained within a reasonable calculation time and under complex material requirements.

[0112] In this embodiment, a cutting device for photovoltaic support profile materials is also provided. This device is used to implement the above-mentioned embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0113] This embodiment provides a cutting device for photovoltaic support profile materials, as Figure 5 shown, including:

[0114] A parameter acquisition module 51, configured to acquire the demand parameters for photovoltaic support profile materials;

[0115] An initial plan generation module 52, configured to generate multiple initial cutting plans in a circular queue manner according to the demand parameters. Each initial cutting plan includes the types of raw materials, the target lengths of different functional materials, and the splicing rules;

[0116] A final plan generation module 53, configured to perform utilization rate calculation, crossover, and mutation operations on multiple initial cutting plans by using an improved genetic algorithm to obtain the final cutting plan.

[0117] The further functional descriptions of the above-mentioned various modules are the same as those in the corresponding above-mentioned embodiments, and will not be repeated here.

[0118] An embodiment of the present invention further provides a computer device having the above-mentioned Figure 5 shown cutting device for photovoltaic bracket profile materials.

[0119] Please refer to Figure 6 , Figure 6 which is a schematic structural diagram of a computer device provided by an alternative embodiment of the present invention. As Figure 6 shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (such as an array of servers, a set of blade servers, or a multi-processor system). Figure 6 In

[0120] FIG. 14, one processor 10 is taken as an example.

[0121] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above-mentioned hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above-mentioned programmable logic device can be a complex programmable logic device, a field programmable gate array, a general array logic, or any combination thereof.

[0122] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above-mentioned embodiments.

[0123] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, a hard disk, or a solid state drive; the memory 20 may further include a combination of the above types of memory.

[0124] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0125] Embodiments of the present invention also provide a computer-readable storage medium. The methods according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented by downloading through a network the original computer code stored in a remote storage medium or a non-transitory machine-readable storage medium and to be stored in a local storage medium, so that the methods described herein can be stored as such software processes on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid state drive, etc.; further, the storage medium can also include a combination of the above types of memory. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0126] A part of the present invention can be applied as a computer program product, such as computer program instructions, which when executed by a computer, can call or provide the methods and / or technical solutions according to the present invention through the operation of the computer. Those skilled in the art should be able to understand that the forms of existence of computer program instructions in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways for computer program instructions to be executed by a computer include, but are not limited to: the computer directly executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Herein, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible by the computer.

[0127] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A cutting method for photovoltaic support profile materials, characterized in that, The method includes: Obtaining the demand parameters for the photovoltaic support profile materials; Generating multiple initial cutting plans in a circular queue manner according to the demand parameters, where each initial cutting plan includes the types of raw materials, the target lengths of different functional materials, and the splicing rules; Performing utilization rate calculation, crossover, and mutation operations on multiple initial cutting plans using an improved genetic algorithm to obtain the final cutting plan.

2. The method according to claim 1, characterized in that, Generating multiple initial cutting plans in a circular queue manner according to the demand parameters, including: Constructing constraint conditions based on the demand parameters with the least waste, the relatively fewest splicing times in the cutting plan, and the least amount of reusable leftover materials; Generating multiple initial cutting plans in a circular queue manner according to the demand parameters and the constraint conditions.

3. The method according to claim 1, characterized in that Generating multiple initial cutting plans in a circular queue manner according to the demand parameters further includes: Screening the spliceable material combinations and non-spliceable material combinations according to the demand parameters; Generating multiple initial cutting plans in a circular queue manner based on the non-spliceable material combinations and the corresponding demand parameters; Calculating the material utilization rate for the spliceable material combinations.

4. The method according to claim 1, characterized in that, Performing utilization rate calculation, crossover, and mutation operations on multiple initial cutting plans using an improved genetic algorithm to obtain the final cutting plan, including: Calculating the material utilization rate of the initial cutting plan using an improved fitness function; Selecting the initial cutting plan according to the material utilization rate and performing crossover operations using the k-gene exchange scheme to generate a new cutting plan, where the gene includes the cutting parameters in the cutting plan; Performing mutation operations on the new cutting plan based on the mutation factor and generating a new cutting plan; Repeating the process of recalculating the material utilization rate, crossover operations, and mutation operations until a preset condition is reached to obtain the final cutting plan.

5. The method according to claim 4, wherein Performing mutation operations on the new cutting plan and generating a new cutting plan, including: Randomly selecting gene positions in the cutting plan after crossover, deleting the genes after the gene positions, where the gene includes the cutting parameters in the cutting plan; Regenerating the genes after the gene positions according to the demand parameters to obtain a new cutting plan.

6. The method according to claim 4, characterized in that, In the mutation operation, when the material utilization rate increases after the crossover mutation operation, the mutation factor is set to a preset value; when the material utilization rate remains unchanged or decreases after the crossover mutation operation, the mutation factor is increased.

7. The method according to claim 4, wherein The improved fitness function is expressed by the following formula: Wherein, L N represents the total length of N raw materials used, N represents the number of types of raw materials, D represents the target length after splicing, and C N represents the remaining material of the last raw material.

8. A cutting device for photovoltaic support profile materials, characterized in that, The device includes: A parameter acquisition module for obtaining the demand parameters for the photovoltaic support profile materials; An initial plan generation module for generating multiple initial cutting plans in a circular queue manner according to the demand parameters, where each initial cutting plan includes the types of raw materials, the target lengths of different functional materials, and the splicing rules; A final plan generation module for performing utilization rate calculation, crossover, and mutation operations on multiple initial cutting plans using an improved genetic algorithm to obtain the final cutting plan.

9. A computer device, characterized in that, Including: A memory and a processor, which are communicatively connected to each other. Computer instructions are stored in the memory, and the processor executes the computer instructions to execute the blanking method for photovoltaic support profile materials according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Computer instructions are stored on the computer-readable storage medium, and the computer instructions are used to cause a computer to execute the blanking method for photovoltaic support profile materials according to any one of claims 1 to 7.

Images