Artificial lawn grass planting process parameter regulation and control system and method based on big data

Abstract

The invention discloses an artificial lawn grass planting process parameter regulation and control system and method based on big data, and relates to the field of grass planting height and area measurement. The artificial lawn grass planting process is optimized from the two parameter angles of the initial height and the initial area of grass planting in an artificial lawn, so that the artificial endurance time is maximized; a height difference value mapping model and an area difference value mapping model are constructed, so that a mapping model is provided for subsequently obtaining the difference value of the grass planting height and the area of each sub-region in a region to be paved with the artificial lawn before and after the influence of natural factors and human factors; further, the final value of the grass planting height can be calculated through the initial value of the grass planting height, so that whether the initial values of the set grass planting height and area meet requirements or not is judged according to the final values of the grass planting height and area of the artificial lawn to be laid, and then optimization is carried out.

Description

Technical Field

[0001] The present invention belongs to the field of grass planting height and area measurement, and in particular, relates to an artificial turf grass planting process parameter control system and method based on big data. Background Art

[0002] When carrying out the process of planting artificial turf, the initial height of the grass and the initial planting area are two aspects that need to be focused on; if the initial height of the grass and the initial planting area are unreasonable, the grass on the artificial turf may be affected by more serious natural and human factors over time, causing the artificial turf to be severely damaged, with low durability, requiring more maintenance operations or even unable to continue to be used, thereby causing losses to the company. Summary of the invention

[0003] In view of the problems in the related technology, the present invention proposes an artificial turf planting process parameter control system and method based on big data to overcome the above-mentioned technical problems existing in the existing related technology.

[0004] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0005] The present invention is a method for controlling process parameters of artificial turf planting based on big data, comprising the following steps:

[0006] S1, collecting initial average grass height data, initial grass area data, durable natural influencing factor data and durable human influencing factor data of each sub-area in multiple existing artificial turf areas, and obtaining an existing grass initial height average data matrix, an existing grass initial area data matrix, a historical lawn durable natural influencing factor data matrix set and a historical lawn durable human influencing factor data matrix;

[0007] S2, using the existing grass initial height average data matrix, the existing grass initial area data matrix, the historical lawn durability natural influencing factor data matrix set, the historical lawn durability human influencing factor data matrix and the corresponding final grass height and area data to construct a height difference mapping model and an area difference mapping model;

[0008] S3, collecting the data of durable natural influencing factors and the data of durable human influencing factors of each sub-area in the area to be laid with artificial turf, and obtaining a data matrix of durable natural influencing factors and a data matrix set of durable human influencing factors to be laid;

[0009] S4, optimizing the initial grass height data and area data of each sub-area in the artificial turf area to be laid by coordinating the data matrix of the durable natural influencing factors to be laid, the data matrix set of the durable human influencing factors to be laid, the height difference mapping model and the area difference mapping model, and obtaining the final initial grass height data set and the final initial grass area data set;

[0010] The initial average grass height in an artificial lawn refers to the average height of the grass after the lawn is newly laid and before it is affected by any human or natural factors; similarly, the initial grass area data refers to the area of ​​the lawn after the lawn is newly laid and before it is affected by any human or natural factors; the height of the grass and the lawn area will change with the passage of time and under the influence of various natural and human factors; based on the above, this scheme collects the initial average grass height data, initial grass area data, durable natural influencing factor data, durable human influencing factor data, and the grass height after various influencing factors in each sub-area of ​​multiple existing artificial turf areas. The grass height data and area data provide data support for the subsequent construction of the height difference mapping model and the area difference mapping model; by constructing the height difference mapping model and the area difference mapping model, a mapping model is provided for the subsequent acquisition of the difference in grass height and area before and after the influence of natural factors and human factors in each sub-area in the area to be laid for artificial turf, and then the final value of the grass height can be calculated by the initial value of the grass height, so as to determine whether the initial values ​​of the set grass height and area meet the requirements according to the final values ​​of the grass height and area of ​​the artificial turf to be laid, and then optimize; among them, the distribution of the initial grass height and the initial grass area of ​​the artificial turf has a great influence on the durability of the artificial turf.

[0011] Preferably, the S1 comprises the following steps:

[0012] S11, setting a plurality of existing artificial turf areas to obtain an existing artificial turf area set; dividing each artificial turf area in the existing artificial turf area set to obtain an existing artificial turf sub-area matrix a; as follows,

[0013]

[0014] Among them, a ijrepresents the jth artificial lawn sub-area obtained by dividing the i-th artificial lawn area in the existing artificial lawn area set, a1′ represents the total number of artificial lawn sub-areas obtained by dividing each artificial lawn area in the existing artificial lawn area set, and a′2 represents the total number of existing artificial lawn areas set; the initial average grass height data and the initial grass area data of each artificial lawn sub-area in the existing artificial lawn sub-area matrix are measured to obtain the existing grass initial height average data matrix And the existing initial grass planting area data matrix They are as follows:

[0015]

[0016] in, Respectively represent the initial average grass height data and the initial grass area data of the j-th sub-area of ​​the i-th artificial lawn area in the existing artificial lawn area set;

[0017] S12. Set the type set of natural factors affecting lawn durability And the type set of factors affecting lawn durability represents the set i-th type of natural factors affecting lawn durability, b1 represents the total number of set natural factors affecting lawn durability; represents the set i-th type of artificial factors affecting lawn durability, b2 represents the total number of set artificial factors affecting lawn durability; multiple historical data collection time points are set to obtain a set of historical collection time points b1′ i Indicates the set i-th historical collection time point, Indicates the total number of set historical collection time points;

[0018] According to the lawn durability natural influencing factor type set and the historical collection time point set, data of each lawn durability natural influencing factor type in each existing artificial lawn area in the existing artificial lawn area set at each historical collection time point is collected to obtain a historical lawn durability natural influencing factor data matrix set The data matrix of natural factors affecting the durability of historical turf in the i-th existing artificial turf area in the existing artificial turf area set is as follows:

[0019]

[0020] in, Represents data of natural factors affecting lawn durability of the kth type in the i-th existing artificial lawn area in the existing artificial lawn area set at the j-th historical collection time point;

[0021] According to the lawn durable human impact factor type set and the historical collection time point set, data of each lawn durable human impact factor type in each existing artificial lawn sub-area in the existing artificial lawn sub-area matrix at each historical collection time point is collected to obtain a historical lawn durable human impact factor data matrix as follows,

[0022]

[0023] in, The data matrix of the artificial factors affecting the durability of the lawn in the jth artificial lawn sub-area of ​​the i-th artificial lawn area in the existing artificial lawn area set is represented. express The data of natural factors affecting lawn durability of the k′th type at the kth historical collection time point;

[0024] S13, at the last historical collection time point of the historical collection time point set, collect the average grass height data and grass area data of each sub-region in the existing artificial turf sub-region matrix, and obtain the existing grass final height average data matrix And the final area data matrix of existing grass planting They are as follows:

[0025]

[0026] in, Respectively represent the average grass height data and grass area data of the j-th sub-region of the i-th artificial lawn region in the existing artificial lawn region set at the last historical collection time point of the historical collection time point set;

[0027] Each existing grass planting final height average data in the existing grass planting final height average data matrix is ​​subtracted from the corresponding existing grass planting initial height average data in the existing grass planting initial height average data matrix and the absolute value is taken to obtain the existing grass planting average height difference data matrix. Each existing final grass planting area data in the existing final grass planting area data matrix is ​​subtracted from the corresponding existing initial grass planting area data in the existing initial grass planting area data matrix and the absolute value is taken to obtain an existing grass planting area difference data matrix. They are as follows:

[0028]

[0029] in, They respectively represent the existing grass average height difference data and the existing grass area difference data of the jth sub-area of ​​the i-th artificial lawn area in the existing artificial lawn area set; the calculation formulas are as follows:

[0030]

[0031] The natural factors affecting lawn durability mainly include ultraviolet radiation, temperature changes, rainfall and humidity. Ultraviolet rays can damage grass fibers, causing their colors to fade and their strength to decrease. Temperature changes: extreme temperature changes may cause expansion and contraction of artificial turf materials, thus affecting their stability and lifespan. Rainfall and humidity: excessive rainfall can cause excessive soil moisture, affecting root growth, and high humidity environments can accelerate the degradation of artificial turf. The artificial factors affecting lawn durability mainly include maintenance frequency, frequency and intensity of use, usage methods, frequency of contact with chemicals, frequency of use of sharp objects, and frequency of proximity to heat sources. Since people are not evenly distributed in various parts of artificial turf at the same time, the division of artificial turf into regions makes the process of analyzing the reasons for the differences between artificial turf planting data before and after more accurate.

[0032] Preferably, S2 comprises the following steps:

[0033] S21, constructing a height difference mapping model using the existing grass initial height average data matrix, the historical lawn durability natural influencing factor data matrix set, the historical lawn durability human influencing factor data matrix and the existing grass average height difference data matrix;

[0034] S22, constructing an area difference mapping model using the existing grass planting initial area data matrix, the historical lawn durability natural influencing factor data matrix set, the historical lawn durability human influencing factor data matrix and the existing grass planting area difference data matrix;

[0035] A height difference mapping model is constructed by using the existing average data matrix of initial grass planting height, the data matrix set of historical lawn durability natural influencing factors, the data matrix set of historical lawn durability human influencing factors, and the existing average grass planting height difference data matrix, so that the obtained height difference mapping model has a good mapping ability from the initial grass planting height, lawn durability natural influencing factors data, and lawn durability human influencing factors data to the grass planting height difference data; then an area difference mapping model is constructed by using the existing initial grass planting area data matrix, the historical lawn durability natural influencing factors data matrix set, the historical lawn durability human influencing factors data matrix, and the existing grass planting area difference data matrix, so that the obtained area difference mapping model has a good mapping ability from the initial grass planting area data, lawn durability natural influencing factors data, and lawn durability human influencing factors data to the grass planting area difference data.

[0036] Preferably, S3 comprises the following steps:

[0037] S31, setting an area to be laid with artificial turf; dividing the area to be laid with artificial turf to obtain a set of sub-areas to be laid with artificial turf; setting multiple current data collection time points to obtain a set of current time points; predicting the durable natural influencing factor data and the durable human influencing factor data of each sub-area in the set of sub-areas to be laid with artificial turf according to the set of current time points, the set of types of durable natural influencing factors of turf and the set of types of durable human influencing factors of turf, and obtaining a data matrix c1 of durable natural influencing factors to be laid and a data matrix set c2 of durable human influencing factors to be laid = {c 21 ,c 22 ,...,c 2i ,...,c 2c′},c 2i represents the durable human influence factor data matrix of the ith artificial turf sub-region to be laid in the artificial turf sub-region set, c′ represents the total number of sub-regions in the artificial turf sub-region set; c1, c 2i They are as follows:

[0038]

[0039] Among them, c 1ij represents the data of natural factors affecting the durability of the jth type of lawn at the i-th current data collection time point in the area to be laid artificial lawn; c 1ijk Indicates the k-th type of turf durability human influencing factor data at the j-th current data collection time point in the i-th artificial turf sub-area to be laid;

[0040] S32: Preset the initial height data of artificial turf planting in each artificial turf sub-region to be laid in the artificial turf sub-region set to obtain the initial height data set of artificial turf planting to be laid Representing the preset initial height data of artificial turf in the ith sub-region of the artificial turf sub-region set to be laid; measuring the initial area data of each sub-region in the artificial turf sub-region set to be laid, and obtaining the initial area data set of the artificial turf to be laid Represents the initial area data of the i-th sub-area in the set of sub-areas to be laid with artificial turf;

[0041] By predicting the durable natural influencing factor data and human influencing factor data of the area to be laid artificial turf and each of its sub-areas, and presetting the initial grass planting height and initial grass planting area data, data support is provided for the subsequent use of height difference mapping model and area difference mapping model to obtain the corresponding grass planting height data and grass planting area data after the influence of natural factors and human factors at multiple time points and the difference data between the current grass planting height data and grass planting area data.

[0042] Preferably, S4 comprises the following steps:

[0043] S41, inputting the initial height data set of the grass to be laid, the data matrix of the durable natural influencing factors to be laid, and the data matrix set of the durable human influencing factors to be laid into the height difference mapping model for mapping, so as to obtain the initial height difference data set to be laid; inputting the initial area data set of the grass to be laid, the data matrix of the durable natural influencing factors to be laid, and the data matrix set of the durable human influencing factors to be laid into the area difference mapping model for mapping, so as to obtain the initial area difference data set to be laid;

[0044] Subtract the data corresponding to the initial height data set of the grass to be laid and the data corresponding to the initial height difference data set to be laid to obtain the final height data set of the grass to be laid represents the final height data of the grass planted in the ith sub-area of ​​the artificial turf sub-area set to be laid; then the data corresponding to the initial area data set of grass planted to be laid and the data corresponding to the initial area difference data set to be laid are subtracted to obtain the final area data set of grass planted to be laid Indicates the final grass planting area data of the i-th sub-area in the set of sub-areas to be laid with artificial turf;

[0045] S42, adjusting the initial height dataset of the grass to be laid and the initial area dataset of the grass to be laid according to the final height dataset of the grass to be laid and the final area dataset of the grass to be laid, to obtain a final initial height dataset of the grass to be laid and a final initial area dataset of the grass to be laid;

[0046] Since the obtained final height dataset of the grass to be laid and the final area dataset of the grass to be laid may contain data that does not meet the requirements, it means that within a certain time range, the damage to the artificial turf exceeds the predetermined level, so the initial height and initial area of ​​the grass need to be adjusted.

[0047] Preferably, the S42 comprises the following steps:

[0048] S421, setting a corresponding final grass planting height threshold and a final grass planting area threshold according to the initial grass planting height data set and the initial grass planting area data set to be laid, to obtain a final grass planting height threshold set and a final grass planting area threshold set;

[0049] S422, when there is grass planting final height data less than the final grass planting height threshold value corresponding to the final grass planting height threshold value set in the final grass planting height data set or there is grass planting final area data less than the final grass planting area threshold value set in the final grass planting area data set, the grass planting initial height data set to be laid and the grass planting initial area data set to be laid are adjusted until there is no grass planting final height data less than the final grass planting height threshold value corresponding to the final grass planting height threshold value set in the final grass planting height data set and there is no grass planting final area data less than the final grass planting area threshold value set in the final grass planting area data set, so as to obtain the final grass planting initial height data set to be laid and the final grass planting initial area data set to be laid;

[0050] By setting a final grass height threshold set and a final grass area threshold set, a quantitative judgment standard is provided for judging whether the grass height data and grass area data of each area in the artificial turf meet the requirements.

[0051] Preferably, the adjusting of the initial height dataset of the grass to be laid and the initial area dataset of the grass to be laid in S422 comprises the following steps:

[0052] S4221, constructing grass planting height area to optimize tuna population represents the i-th tuna in the grass height and area optimization tuna population, d represents the size of the grass height and area optimization tuna population; the maximum number of iterations of the grass height and area optimization tuna population is set to d1′ and the current number of iterations is set to d2′; the search space dimension of the grass height and area optimization tuna population is 2·c′;

[0053] S4222: setting a grass planting height range for laying artificial turf in the artificial turf area to be laid and an area range for each sub-area in the artificial turf area to be laid, to obtain a grass planting height range. And the sub-region area interval set respectively represent a lower limit and an upper limit of the grass planting height for laying the artificial turf in the area to be laid; as follows,

[0054]

[0055] in, respectively represent the lower limit and the upper limit of the area of ​​the i-th sub-region in the area to be laid with artificial turf;

[0056] The grass height and area are set according to the grass height interval and the sub-area area interval set to optimize the initial position of each tuna in the tuna population, and an initial position matrix set is obtained. represents the initial position matrix of the i-th tuna in the tuna population optimized by the grass planting height area; as follows,

[0057]

[0058] in, represents the position component of the initial position of the i-th tuna in the tuna population with optimized grass planting height and area in the dimension of initial grass planting height in the j-th sub-area of ​​the area to be laid with artificial turf; represents the component of the initial position of the i-th tuna in the tuna population with optimized grass planting height and area in the j-th sub-area area to be laid with artificial turf; The calculation formulas are as follows:

[0059]

[0060] In the formula, rand i1j 、rand i2j Respectively for Generate a random number between 0 and 1;

[0061] S4223, according to the final height data set of the grass to be laid And the final area dataset to be planted Construct the fitness function e of tuna population optimized by grass height and area as follows:

[0062]

[0063] S4224, start iteration, and set the current iteration number d2′ to 1 before iteration; in the first round of iteration, use the grass height area to optimize the fitness function e of the tuna population, and cooperate with the height difference mapping model and the area difference mapping model to calculate the fitness value of the initial position matrix of each tuna in the initial position matrix set to obtain a first fitness value set; use the maximum fitness in the first fitness value set and the corresponding initial position matrix of the tuna as the first global optimal fitness and the first global optimal position; update the initial position matrix of each tuna in the initial position matrix set according to the first global optimal fitness and the first global optimal position; after the update is completed, add 1 to the current iteration number d2′ and enter the next round of iteration;

[0064] In each other round of iteration, the fitness function e of the grass height and area optimization tuna population is used, and the height difference mapping model and the area difference mapping model are used to calculate the fitness value of the position matrix of each tuna in the grass height and area optimization tuna population updated in the previous round of iteration, to obtain a second fitness value set; the maximum fitness in the second fitness value set and the corresponding tuna position matrix are used as the second global optimal fitness and the second global optimal position; the position matrix of each tuna in the grass height and area optimization tuna population updated in the previous round of iteration is updated again according to the second global optimal fitness and the second global optimal position; after the update is completed, the current iteration number d2′ is increased by 1 and the next iteration is entered;

[0065] S4225. When d2′≥d1′, stop iteration and obtain the final global optimal position matrix; otherwise, continue iteration until d2′≥d1′; divide the data in the final global optimal position to obtain an optimized initial height data set for grass planting to be laid and an optimized initial area data set for grass planting to be laid; input the optimized initial height data set for grass planting to be laid, the data matrix of durable natural influencing factors to be laid and the data matrix of durable human influencing factors to be laid into the height difference mapping model for mapping to obtain an optimized height difference data set for grass planting to be laid; The initial area data set of the grass to be laid, the data matrix of the durable natural influencing factors to be laid, and the data matrix set of the durable human influencing factors to be laid are input into the area difference mapping model for mapping to obtain the optimized area difference data set to be laid; the data corresponding to the initial height data set of the grass to be laid after optimization and the data corresponding to the height difference data set to be laid after optimization are subtracted, and the data corresponding to the initial area data set of the grass to be laid after optimization and the data corresponding to the area difference data set to be laid after optimization are subtracted to obtain the final height data set of the grass to be laid after optimization and the final area data set of the grass to be laid after optimization;

[0066] When there is no final grass height data less than the final grass height threshold corresponding to the final grass height threshold set in the optimized final height data set of the grass planted to be laid, and there is no final grass area data less than the final grass area threshold corresponding to the final grass area threshold set in the optimized final area data set of the grass planted to be laid, the optimized initial grass height data set to be laid and the optimized initial grass area data set to be laid are used as the final initial grass height data set to be laid and the final initial grass area data set to be laid respectively; otherwise, return to S4224 to continue iterating until there is no final grass height data less than the final grass height threshold corresponding to the final grass height threshold set in the optimized final height data set of the grass planted to be laid, and there is no final grass area data less than the final grass area threshold corresponding to the final grass area threshold set in the optimized final area data set of the grass planted to be laid;

[0067] The tuna optimization algorithm can effectively search the entire solution space in the exploration stage to avoid falling into the local optimal solution too early; it can quickly converge to the optimal solution in the pursuit stage and the foraging stage, and has a high search efficiency; and only a small number of parameters need to be set, making the algorithm relatively simple to implement; based on the above advantages, the tuna optimization algorithm is used in this scheme to perform multiple iterative optimizations on the grass height data and area data of each sub-area in the area to be laid with artificial turf, and the minimum sum of the final grass height and final area of ​​each sub-area in the area to be laid with artificial turf is used as the fitness function; therefore, as the iteration proceeds, the final grass height of each sub-area in the area to be laid with artificial turf becomes higher and higher, and the final area becomes larger and larger, so that the surface wears less and less on the artificial turf within the corresponding time range, and finally meets the requirements.

[0068] The artificial turf grass planting process parameter control system based on big data includes an existing artificial turf area division module, an existing grass planting data collection module, a first durability influencing factor data collection module, a grass planting data difference calculation acquisition module, a mapping model construction module, a second durability influencing factor data collection module, a final data calculation module for the artificial turf to be laid, and a grass planting initial parameter optimization module.

[0069] The present invention has the following beneficial effects:

[0070] 1. The present invention optimizes the process of artificial turf from two parameter perspectives, namely, the initial height and initial area of ​​the artificial turf, so as to maximize the durability of the artificial turf; wherein, by constructing a height difference mapping model and an area difference mapping model, a mapping model is provided for the subsequent acquisition of the difference in grass height and area of ​​each sub-area in the area to be laid in the artificial turf before and after the influence of natural factors and human factors, and then the final value of the grass height can be calculated by the initial value of the grass height, so as to determine whether the initial values ​​of the grass height and area meet the requirements according to the final values ​​of the grass height and area of ​​the artificial turf to be laid, and then optimize the method.

[0071] 2. The final height dataset of the grass to be laid and the final area dataset of the grass to be laid obtained in the present invention may contain data that does not meet the requirements, which means that the damage to the artificial turf exceeds the predetermined level, so the initial height and initial area of ​​the grass need to be adjusted.

[0072] 3. In the present invention, the grass height data and area data of each sub-area in the area to be laid artificial turf are iteratively optimized multiple times by using the tuna optimization algorithm, and the minimum sum of the final grass height and final area of ​​each sub-area in the area to be laid artificial turf is used as the fitness function; therefore, as the iteration proceeds, the final grass height of each sub-area in the area to be laid artificial turf becomes higher and higher, and the final area becomes larger and larger, so that the surface wears less and less on the artificial turf within a corresponding time range, and finally meets the requirements.

[0073] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying creative work.

[0075] Figure 1 The figure is a flow chart of the method for controlling process parameters of artificial turf planting based on big data of the present invention. DETAILED DESCRIPTION

[0076] The following will be combined with the drawings in the embodiments of the invention to clearly and completely describe the technical solutions in the embodiments of the invention. Obviously, the described embodiments are only part of the embodiments of the invention, not all of the embodiments. Based on the embodiments in the invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the invention.

[0077] In the description of the present invention, it is necessary to understand that the terms "opening", "upper", "lower", "top", "middle", "inside" and the like indicating orientation or positional relationship are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the invention.

[0078] Embodiment 1

[0079] See also Figure 1 This embodiment is a method for controlling parameters of artificial turf planting process based on big data, comprising the following steps:

[0080] S1, collecting initial average grass height data, initial grass area data, durable natural influencing factor data and durable human influencing factor data of each sub-area in multiple existing artificial turf areas, and obtaining an existing grass initial height average data matrix, an existing grass initial area data matrix, a historical lawn durable natural influencing factor data matrix set and a historical lawn durable human influencing factor data matrix;

[0081] The S1 comprises the following steps:

[0082] S11, setting a plurality of existing artificial turf areas to obtain an existing artificial turf area set; dividing each artificial turf area in the existing artificial turf area set to obtain an existing artificial turf sub-area matrix a; as follows,

[0083]

[0084] Among them, a ij represents the jth artificial lawn sub-area obtained by dividing the i-th artificial lawn area in the existing artificial lawn area set, a1′ represents the total number of artificial lawn sub-areas obtained by dividing each artificial lawn area in the existing artificial lawn area set, and a′2 represents the total number of existing artificial lawn areas set; the initial average grass height data and the initial grass area data of each artificial lawn sub-area in the existing artificial lawn sub-area matrix are measured to obtain the existing grass initial height average data matrix And the existing initial grass planting area data matrix They are as follows:

[0085]

[0086] in, Respectively represent the initial average grass height data and the initial grass area data of the j-th sub-area of ​​the i-th artificial lawn area in the existing artificial lawn area set;

[0087] S12. Set the type set of natural factors affecting lawn durability And the type set of factors affecting lawn durability represents the set i-th type of natural factors affecting lawn durability, b1 represents the total number of set natural factors affecting lawn durability; represents the set i-th type of artificial factors affecting lawn durability, b2 represents the total number of set artificial factors affecting lawn durability; multiple historical data collection time points are set to obtain a set of historical collection time points b1′ i Indicates the set i-th historical collection time point, Indicates the total number of set historical collection time points;

[0088] According to the lawn durability natural influencing factor type set and the historical collection time point set, data of each lawn durability natural influencing factor type in each existing artificial lawn area in the existing artificial lawn area set at each historical collection time point is collected to obtain a historical lawn durability natural influencing factor data matrix set The data matrix of natural factors affecting the durability of historical turf in the i-th existing artificial turf area in the existing artificial turf area set is as follows:

[0089]

[0090] in, Represents data of natural factors affecting lawn durability of the kth type in the i-th existing artificial lawn area in the existing artificial lawn area set at the j-th historical collection time point;

[0091] According to the lawn durable human impact factor type set and the historical collection time point set, data of each lawn durable human impact factor type in each existing artificial lawn sub-area in the existing artificial lawn sub-area matrix at each historical collection time point is collected to obtain a historical lawn durable human impact factor data matrix as follows,

[0092]

[0093] in, The data matrix of the artificial factors affecting the durability of the lawn in the jth artificial lawn sub-area of ​​the i-th artificial lawn area in the existing artificial lawn area set is represented. express The data of natural factors affecting lawn durability of the k′th type at the kth historical collection time point;

[0094] S13, at the last historical collection time point of the historical collection time point set, collect the average grass height data and grass area data of each sub-region in the existing artificial turf sub-region matrix, and obtain the existing grass final height average data matrix And the final area data matrix of existing grass planting They are as follows:

[0095]

[0096] in, Respectively represent the average grass height data and grass area data of the j-th sub-region of the i-th artificial lawn region in the existing artificial lawn region set at the last historical collection time point of the historical collection time point set;

[0097] Each existing grass planting final height average data in the existing grass planting final height average data matrix is ​​subtracted from the corresponding existing grass planting initial height average data in the existing grass planting initial height average data matrix and the absolute value is taken to obtain the existing grass planting average height difference data matrix. Each existing final grass planting area data in the existing final grass planting area data matrix is ​​subtracted from the corresponding existing initial grass planting area data in the existing initial grass planting area data matrix and the absolute value is taken to obtain an existing grass planting area difference data matrix. They are as follows:

[0098]

[0099] in, They respectively represent the existing grass average height difference data and the existing grass area difference data of the jth sub-area of ​​the i-th artificial lawn area in the existing artificial lawn area set; the calculation formulas are as follows:

[0100]

[0101] S2, using the existing grass initial height average data matrix, the existing grass initial area data matrix, the historical lawn durability natural influencing factor data matrix set, the historical lawn durability human influencing factor data matrix and the corresponding final grass height and area data to construct a height difference mapping model and an area difference mapping model;

[0102] The S2 comprises the following steps:

[0103] S21, constructing a height difference mapping model using the existing grass initial height average data matrix, the historical lawn durability natural influencing factor data matrix set, the historical lawn durability human influencing factor data matrix and the existing grass average height difference data matrix;

[0104] The S21 comprises the following steps:

[0105] S211, constructing a first initial SVM mapping model and setting a first training data ratio; dividing the existing grass planting initial height average data matrix, the historical lawn durability natural influencing factor data matrix set, the historical lawn durability human influencing factor data matrix and the existing grass planting average height difference data matrix according to the first training data ratio, to obtain the existing grass planting initial height average training data matrix, the first historical lawn durability natural influencing factor training data matrix set, the first historical lawn durability human influencing factor training data matrix, the existing grass planting average height difference training data matrix, the existing grass planting initial height average test data matrix, the first historical lawn durability natural influencing factor test data matrix set, the first historical lawn durability human influencing factor test data matrix and the existing grass planting average height difference test data matrix;

[0106] S212, setting a first training error threshold; inputting the existing grass initial height average training data matrix, the first historical lawn durability natural influencing factor training data matrix set, the first historical lawn durability human influencing factor training data matrix as training data, and the existing grass average height difference training data matrix as training labels into the first initial SVM mapping model for training; during the training process, when the training error is less than the first training error threshold, stop the training and obtain the first trained SVM mapping model; otherwise, continue the training until the training error is less than the first training error threshold;

[0107] S213, setting a first test accuracy threshold; inputting the existing grass initial height average test data matrix, the first historical lawn durability natural influencing factor test data matrix set, the first historical lawn durability human influencing factor test data matrix as test data and the existing grass average height difference test data matrix as test labels into the first trained SVM mapping model for testing; after the test is completed, the first test accuracy is obtained; when the first test accuracy is greater than or equal to the first test accuracy threshold, the first trained SVM mapping model is used as the height difference mapping model; otherwise, returning to S212 to continue iterating until the first test accuracy is greater than or equal to the first test accuracy threshold, and the height difference mapping model is obtained;

[0108] S22, constructing an area difference mapping model using the existing grass planting initial area data matrix, the historical lawn durability natural influencing factor data matrix set, the historical lawn durability human influencing factor data matrix and the existing grass planting area difference data matrix;

[0109] The S22 comprises the following steps:

[0110] S221, construct a second initial SVM mapping model and set a second training data ratio; divide the existing grass planting initial area data matrix, the historical lawn durability natural influencing factor data matrix set, the historical lawn durability human influencing factor data matrix and the existing grass planting area difference data matrix according to the second training data ratio to obtain the existing grass planting initial area training data matrix, the second historical lawn durability natural influencing factor training data matrix set, the second historical lawn durability human influencing factor training data matrix, the existing grass planting area difference training data matrix, the existing grass planting initial area test data matrix, the second historical lawn durability natural influencing factor test data matrix set, the second historical lawn durability human influencing factor test data matrix and the existing grass planting area difference test data matrix;

[0111] S222, setting a second training error threshold; inputting the existing initial grass planting area training data matrix, the second historical lawn durability natural influencing factor training data matrix set, the second historical lawn durability human influencing factor training data matrix as training data, and the existing grass planting area difference training data matrix as training labels into the second initial SVM mapping model for training; during the training process, when the training error is less than the second training error threshold, stop the training and obtain the second trained SVM mapping model; otherwise, continue the training until the training error is less than the second training error threshold;

[0112] S223, setting a second test accuracy threshold; inputting the existing grass initial area test data matrix, the second historical lawn durability natural influencing factor test data matrix set, the second historical lawn durability human influencing factor test data matrix as test data and the existing grass area difference test data matrix as test labels into the second trained SVM mapping model for testing; after the test is completed, the second test accuracy is obtained; when the second test accuracy is greater than or equal to the second test accuracy threshold, the second trained SVM mapping model is used as the height difference mapping model; otherwise, returning to S222 to continue iterating until the second test accuracy is greater than or equal to the second test accuracy threshold, and the height difference mapping model is obtained;

[0113] S3, collecting the data of durable natural influencing factors and the data of durable human influencing factors of each sub-area in the area to be laid with artificial turf, and obtaining a data matrix of durable natural influencing factors and a data matrix set of durable human influencing factors to be laid;

[0114] The S3 comprises the following steps:

[0115] S31, setting an area to be laid with artificial turf; dividing the area to be laid with artificial turf to obtain a set of sub-areas to be laid with artificial turf; setting multiple current data collection time points to obtain a set of current time points; predicting the durable natural influencing factor data and the durable human influencing factor data of each sub-area in the set of sub-areas to be laid with artificial turf according to the set of current time points, the set of types of durable natural influencing factors of turf and the set of types of durable human influencing factors of turf, and obtaining a data matrix c1 of durable natural influencing factors to be laid and a data matrix set c2 of durable human influencing factors to be laid = {c 21 ,c 22 ,...,c 2i ,...,c 2c′},c 2i represents the durable human influence factor data matrix of the ith artificial turf sub-region to be laid in the artificial turf sub-region set, c′ represents the total number of sub-regions in the artificial turf sub-region set; c1, c 2i They are as follows:

[0116]

[0117] Among them, c 1ij represents the data of natural factors affecting the durability of the jth type of lawn at the i-th current data collection time point in the area to be laid artificial lawn; c 1ijk Indicates the k-th type of turf durability human influencing factor data at the j-th current data collection time point in the i-th artificial turf sub-area to be laid;

[0118] S32: Preset the initial height data of artificial turf planting in each artificial turf sub-region to be laid in the artificial turf sub-region set to obtain the initial height data set of artificial turf planting to be laid Representing the preset initial height data of artificial turf in the ith sub-region of the artificial turf sub-region set to be laid; measuring the initial area data of each sub-region in the artificial turf sub-region set to be laid, and obtaining the initial area data set of the artificial turf to be laid Represents the initial area data of the i-th sub-area in the set of sub-areas to be laid with artificial turf;

[0119] S4, optimizing the initial grass height data and area data of each sub-area in the artificial turf area to be laid by coordinating the data matrix of the durable natural influencing factors to be laid, the data matrix set of the durable human influencing factors to be laid, the height difference mapping model and the area difference mapping model, and obtaining the final initial grass height data set and the final initial grass area data set;

[0120] The S4 comprises the following steps:

[0121] S41, inputting the initial height data set of the grass to be laid, the data matrix of the durable natural influencing factors to be laid, and the data matrix set of the durable human influencing factors to be laid into the height difference mapping model for mapping, so as to obtain the initial height difference data set to be laid; inputting the initial area data set of the grass to be laid, the data matrix of the durable natural influencing factors to be laid, and the data matrix set of the durable human influencing factors to be laid into the area difference mapping model for mapping, so as to obtain the initial area difference data set to be laid;

[0122] Subtract the data corresponding to the initial height data set of the grass to be laid and the data corresponding to the initial height difference data set to be laid to obtain the final height data set of the grass to be laid represents the final height data of the grass planted in the ith sub-area of ​​the artificial turf sub-area set to be laid; then the data corresponding to the initial area data set of grass planted to be laid and the data corresponding to the initial area difference data set to be laid are subtracted to obtain the final area data set of grass planted to be laid Indicates the final grass planting area data of the i-th sub-area in the set of sub-areas to be laid with artificial turf;

[0123] S42, adjusting the initial height dataset of the grass to be laid and the initial area dataset of the grass to be laid according to the final height dataset of the grass to be laid and the final area dataset of the grass to be laid, to obtain a final initial height dataset of the grass to be laid and a final initial area dataset of the grass to be laid;

[0124] The S42 comprises the following steps:

[0125] S421, setting a corresponding final grass planting height threshold and a final grass planting area threshold according to the initial grass planting height data set and the initial grass planting area data set to be laid, to obtain a final grass planting height threshold set and a final grass planting area threshold set;

[0126] S422, when there is grass planting final height data less than the final grass planting height threshold value corresponding to the final grass planting height threshold value set in the final grass planting height data set or there is grass planting final area data less than the final grass planting area threshold value set in the final grass planting area data set, the grass planting initial height data set to be laid and the grass planting initial area data set to be laid are adjusted until there is no grass planting final height data less than the final grass planting height threshold value corresponding to the final grass planting height threshold value set in the final grass planting height data set and there is no grass planting final area data less than the final grass planting area threshold value set in the final grass planting area data set, so as to obtain the final grass planting initial height data set to be laid and the final grass planting initial area data set to be laid;

[0127] The adjustment of the initial height dataset of the grass to be laid and the initial area dataset of the grass to be laid in S422 includes the following steps:

[0128] S4221, constructing grass planting height area to optimize tuna population represents the i-th tuna in the grass height and area optimization tuna population, d represents the size of the grass height and area optimization tuna population; the maximum number of iterations of the grass height and area optimization tuna population is set to d1′ and the current number of iterations is set to d2′; the search space dimension of the grass height and area optimization tuna population is 2·c′;

[0129] S4222: setting a grass planting height range for laying artificial turf in the artificial turf area to be laid and an area range for each sub-area in the artificial turf area to be laid, to obtain a grass planting height range. And the sub-region area interval set respectively represent a lower limit and an upper limit of the grass planting height for laying the artificial turf in the area to be laid; as follows,

[0130]

[0131] in, respectively represent the lower limit and the upper limit of the area of ​​the i-th sub-region in the area to be laid with artificial turf;

[0132] The grass height and area are set according to the grass height interval and the sub-area area interval set to optimize the initial position of each tuna in the tuna population, and an initial position matrix set is obtained. represents the initial position matrix of the i-th tuna in the tuna population optimized by the grass planting height area; as follows,

[0133]

[0134] in, represents the position component of the initial position of the i-th tuna in the tuna population with optimized grass planting height and area in the dimension of initial grass planting height in the j-th sub-area of ​​the area to be laid with artificial turf; represents the component of the initial position of the i-th tuna in the tuna population with optimized grass planting height and area in the j-th sub-area area to be laid with artificial turf; The calculation formulas are as follows:

[0135]

[0136] In the formula, rand i1j 、rand i2j Respectively for Generate a random number between 0 and 1;

[0137] S4223, according to the final height data set of the grass to be laid And the final area dataset to be planted Construct the fitness function e of tuna population optimized by grass height and area as follows:

[0138]

[0139] S4224, start iteration, and set the current iteration number d2′ to 1 before iteration; in the first round of iteration, use the grass height area to optimize the fitness function e of the tuna population, and cooperate with the height difference mapping model and the area difference mapping model to calculate the fitness value of the initial position matrix of each tuna in the initial position matrix set to obtain a first fitness value set; use the maximum fitness in the first fitness value set and the corresponding initial position matrix of the tuna as the first global optimal fitness and the first global optimal position; update the initial position matrix of each tuna in the initial position matrix set according to the first global optimal fitness and the first global optimal position; after the update is completed, add 1 to the current iteration number d2′ and enter the next round of iteration;

[0140] In each other round of iteration, the fitness function e of the grass height and area optimization tuna population is used, and the height difference mapping model and the area difference mapping model are used to calculate the fitness value of the position matrix of each tuna in the grass height and area optimization tuna population updated in the previous round of iteration, to obtain a second fitness value set; the maximum fitness in the second fitness value set and the corresponding tuna position matrix are used as the second global optimal fitness and the second global optimal position; the position matrix of each tuna in the grass height and area optimization tuna population updated in the previous round of iteration is updated again according to the second global optimal fitness and the second global optimal position; after the update is completed, the current iteration number d2′ is increased by 1 and the next iteration is entered;

[0141] S4225. When d2′≥d1′, stop iteration and obtain the final global optimal position matrix; otherwise, continue iteration until d2′≥d1′; divide the data in the final global optimal position to obtain an optimized initial height data set for grass planting to be laid and an optimized initial area data set for grass planting to be laid; input the optimized initial height data set for grass planting to be laid, the data matrix of durable natural influencing factors to be laid and the data matrix of durable human influencing factors to be laid into the height difference mapping model for mapping to obtain an optimized height difference data set for grass planting to be laid; The initial area data set of the grass to be laid, the data matrix of the durable natural influencing factors to be laid, and the data matrix set of the durable human influencing factors to be laid are input into the area difference mapping model for mapping to obtain the optimized area difference data set to be laid; the data corresponding to the initial height data set of the grass to be laid after optimization and the data corresponding to the height difference data set to be laid after optimization are subtracted, and the data corresponding to the initial area data set of the grass to be laid after optimization and the data corresponding to the area difference data set to be laid after optimization are subtracted to obtain the final height data set of the grass to be laid after optimization and the final area data set of the grass to be laid after optimization;

[0142] When there is no final grass height data less than the final grass height threshold corresponding to the final grass height threshold set in the optimized final height data set of grass planting to be laid, and there is no final grass area data less than the final grass area threshold corresponding to the final grass area threshold set in the optimized final area data set of grass planting to be laid, the optimized initial grass height data set to be laid and the optimized initial grass area data set to be laid are respectively used as the final initial grass height data set to be laid and the final initial grass area data set to be laid; otherwise, return to S4224 to continue iterating until there is no final grass height data less than the final grass height threshold corresponding to the final grass height threshold set in the optimized final height data set of grass planting to be laid, and there is no final grass area data less than the final grass area threshold corresponding to the final grass area threshold set in the optimized final area data set of grass planting to be laid.

[0143] Embodiment 2

[0144] This embodiment discloses an artificial turf grass planting process parameter control system based on big data, and the system can implement the method of the above embodiment, including an existing artificial turf area division module, an existing grass planting data collection module, a first durability influencing factor data collection module, a grass planting data difference calculation acquisition module, a mapping model construction module, a second durability influencing factor data collection module, a final data calculation module for artificial turf grass planting to be laid, and a grass planting initial parameter optimization module;

[0145] The existing artificial turf area division module is used to divide a plurality of existing artificial turf areas to obtain an existing artificial turf sub-area matrix;

[0146] The existing grass planting data collection module is used to collect the initial average grass planting height data and the initial grass planting area data of each sub-region in the existing artificial turf sub-region matrix, and obtain the existing grass planting initial height average data matrix and the existing grass planting initial area data matrix;

[0147] The first durable influencing factor data collection module is used to collect the durable natural influencing factor data and the durable human influencing factor data of each sub-area in the existing artificial turf sub-area matrix, and obtain the historical turf durable natural influencing factor data matrix set and the historical turf durable human influencing factor data matrix;

[0148] The grass planting data difference calculation and acquisition module is used to make a difference between the existing grass planting initial height average data matrix and the existing grass planting initial area data matrix and the corresponding final height average data and area data, and take the absolute value to obtain the existing grass planting average height difference data matrix and the existing grass planting area difference data matrix;

[0149] The mapping model construction module is used to construct a height difference mapping model and an area difference mapping model using an existing grass initial height average data matrix, an existing grass initial area data matrix, a historical lawn durability natural influencing factor data matrix set, a historical lawn durability human influencing factor data matrix, an existing grass average height difference data matrix, and an existing grass area difference data matrix;

[0150] The second durable influencing factor data collection module is used to collect the durable natural influencing factor data and the durable human influencing factor data of each sub-area in the artificial turf area to be laid, and obtain the durable natural influencing factor data matrix and the durable human influencing factor data matrix set to be laid;

[0151] The final data calculation module for artificial turf to be laid is used to use the initial grass planting height data, area data, the durable natural influencing factor data matrix to be laid and the durable human influencing factor data matrix set to be laid of each sub-area in the artificial turf area to be laid, and cooperate with the height difference mapping model and the area difference mapping model to map and calculate the final data of artificial turf to be laid, so as to obtain the final height data set of the grass to be laid and the final area data set of the grass to be laid;

[0152] The grass planting initial parameter optimization module is used to optimize the grass planting initial height data and area data of each sub-area in the artificial turf area to be laid according to the final grass planting height data set and the final grass planting area data set to be laid, so as to obtain the final grass planting initial height data set and the final grass planting initial area data set.

[0153] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0154] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the invention, so that those skilled in the art can understand and use the invention well.

Claims

1. A method for controlling parameters of artificial turf planting process based on big data, characterized in that: The following steps are involved: S1, collecting initial average grass height data, initial grass area data, durable natural influencing factor data and durable human influencing factor data of each sub-area in multiple existing artificial turf areas, and obtaining an existing grass initial height average data matrix, an existing grass initial area data matrix, a historical lawn durable natural influencing factor data matrix set and a historical lawn durable human influencing factor data matrix; S2, using the existing grass initial height average data matrix, the existing grass initial area data matrix, the historical lawn durability natural influencing factor data matrix set, the historical lawn durability human influencing factor data matrix and the corresponding final grass height and area data to construct a height difference mapping model and an area difference mapping model; S3, collecting the data of durable natural influencing factors and the data of durable human influencing factors of each sub-area in the area to be laid with artificial turf, and obtaining a data matrix of durable natural influencing factors and a data matrix set of durable human influencing factors to be laid; S4. Optimize the initial height data and area data of the grass in each sub-area in the artificial turf area to be laid by coordinating the data matrix of the durable natural influencing factors to be laid, the data matrix set of the durable human influencing factors to be laid, the height difference mapping model and the area difference mapping model to obtain the final initial height data set of the grass to be laid and the final initial area data set of the grass to be laid.

2. The method for controlling parameters of artificial turf planting process based on big data according to claim 1, characterized in that: The S1 comprises the following steps: S11, measuring the initial average grass height data and the initial grass area data in a plurality of artificial turf sub-areas to obtain an existing grass initial height average data matrix and an existing grass initial area data matrix; S12, setting a set of natural factors affecting lawn durability and a set of artificial factors affecting lawn durability; setting multiple historical data collection time points to obtain a set of historical collection time points; collecting data of each type of natural factors affecting lawn durability in each existing artificial lawn area at each historical collection time point according to the set of natural factors affecting lawn durability and the set of historical collection time points to obtain a set of historical natural factors affecting lawn durability data matrix; collecting data of each type of artificial factors affecting lawn durability in each existing artificial lawn sub-area at each historical collection time point according to the set of artificial factors affecting lawn durability and the set of historical collection time points to obtain a set of historical artificial factors affecting lawn durability data matrix; S13. Collect the average grass height data and grass area data of each sub-area at the last historical collection time point in the set of historical collection time points to obtain an existing grass final height average data matrix and an existing grass final area data matrix; subtract the existing grass final height average data matrix from the existing grass initial height average data matrix and take the absolute value operation to obtain an existing grass average height difference data matrix; subtract the existing grass final area data matrix from the existing grass initial area data matrix and take the absolute value operation to obtain an existing grass area difference data matrix.

3. The method for controlling parameters of artificial turf planting process based on big data according to claim 2 is characterized in that: The S2 comprises the following steps: S21, constructing a height difference mapping model using the existing grass initial height average data matrix, the historical lawn durability natural influencing factor data matrix set, the historical lawn durability human influencing factor data matrix and the existing grass average height difference data matrix; S22, constructing an area difference mapping model using the existing grass planting initial area data matrix, the historical lawn durability natural influencing factor data matrix set, the historical lawn durability human influencing factor data matrix and the existing grass planting area difference data matrix.

4. The method for controlling parameters of artificial turf planting process based on big data according to claim 3 is characterized in that: The height difference mapping model adopts the SVM model.

5. The method for controlling parameters of artificial turf planting process based on big data according to claim 3 is characterized in that: The area difference mapping model adopts the SVM model.

6. The method for controlling parameters of artificial turf planting process based on big data according to claim 5 is characterized in that: The S3 comprises the following steps: S31, setting an area to be laid with artificial turf; dividing the area to be laid with artificial turf to obtain a set of sub-areas to be laid with artificial turf; setting multiple current data collection time points to obtain a set of current time points; predicting the durable natural influencing factor data and the durable human influencing factor data of each sub-area in the set of sub-areas to be laid with artificial turf according to the set of current time points, the set of types of durable natural influencing factors of turf and the set of types of durable human influencing factors of turf, to obtain a data matrix of durable natural influencing factors to be laid and a data matrix set of durable human influencing factors to be laid; S32, presetting the initial height data for planting artificial turf in each sub-region to be laid in the set of sub-regions to be laid, to obtain an initial height data set for planting grass to be laid; measuring the initial area data of each sub-region in the set of sub-regions to be laid, to obtain an initial area data set for planting grass to be laid.

7. The method for controlling parameters of artificial turf planting process based on big data according to claim 6, characterized in that: The S4 comprises the following steps: S41, inputting the initial height data set of the grass to be laid, the data matrix of the durable natural influencing factors to be laid, and the data matrix set of the durable human influencing factors to be laid into the height difference mapping model for mapping, and obtaining the initial height difference data set to be laid; inputting the initial area data set of the grass to be laid, the data matrix of the durable natural influencing factors to be laid, and the data matrix set of the durable human influencing factors to be laid into the area difference mapping model for mapping, and obtaining the initial area difference data set to be laid; subtracting the data corresponding to the initial height data set of the grass to be laid from the data corresponding to the initial height difference data set to be laid, and obtaining the final height data set of the grass to be laid; and then subtracting the data corresponding to the initial area data set of the grass to be laid from the data corresponding to the initial area difference data set to be laid, and obtaining the final area data set of the grass to be laid; S42, adjusting the initial height dataset of the grass to be laid and the initial area dataset of the grass to be laid according to the final height dataset of the grass to be laid and the final area dataset of the grass to be laid to obtain a final initial height dataset of the grass to be laid and a final initial area dataset of the grass to be laid.

8. The method for controlling parameters of artificial turf planting process based on big data according to claim 7, characterized in that: The S42 comprises the following steps: S421, setting a corresponding final grass planting height threshold and a final grass planting area threshold according to the initial grass planting height data set and the initial grass planting area data set to be laid, to obtain a final grass planting height threshold set and a final grass planting area threshold set; S422. When the final height data set of the grass to be laid contains grass final height data that is less than the final grass height threshold corresponding to the final grass height threshold set, or when the final area data set of the grass to be laid contains grass final area data that is less than the final grass area threshold corresponding to the final grass area threshold set, the initial height data set of the grass to be laid and the initial area data set of the grass to be laid are adjusted to obtain the final initial height data set of the grass to be laid and the final initial area data set of the grass to be laid.

9. The method for controlling parameters of artificial turf planting process based on big data according to claim 8, characterized in that: The adjustment of the initial height dataset of the grass to be laid and the initial area dataset of the grass to be laid in S422 includes the following steps: S4221, constructing a tuna population optimized for grass planting height and area; setting the maximum number of iterations of the tuna population optimized for grass planting height and area to d1′ and the current number of iterations to d2′; S4222, setting a grass planting height value interval for laying artificial turf in the artificial turf area to be laid and an area value interval for each sub-area in the artificial turf area to be laid, to obtain a grass planting height interval and a sub-area area interval set; setting the initial position of each tuna in the tuna population to optimize the grass planting height and area according to the grass planting height interval and the sub-area area interval set, to obtain an initial position matrix set; S4223, constructing a fitness function for optimizing the tuna population by the height and area of ​​the grass to be laid according to the final height data set of the grass to be laid and the final area data set of the grass to be laid; S4224, start iteration; in each round of iteration, the fitness function of the grass height and area optimization tuna population is used, and the height difference mapping model and the area difference mapping model are used to calculate the fitness value of the position matrix of each tuna in the grass height and area optimization tuna population updated in the previous round of iteration, and the position matrix of each tuna in the grass height and area optimization tuna population updated in the previous round of iteration is updated; S4225. When d2′≥d1′, stop iteration and obtain the final global optimal position matrix; otherwise, continue iteration until d2′≥d1′; obtain the final initial height data set of the grass to be laid and the final initial area data set of the grass to be laid according to the final global optimal position.

10. A system for implementing the method for controlling parameters of artificial turf planting process based on big data as described in any one of claims 1 to 9.

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