CN114997733A - Method and system for evaluating yield of waste textiles - Google Patents

Abstract

The invention discloses a waste textile yield evaluation method and a waste textile yield evaluation system, which comprises the steps of gridding a region to be evaluated to obtain a grid subregion G; calculating to obtain a population coefficient P of the grid subregion G according to population data of the grid subregion G; calculating to obtain an economic coefficient E of the grid sub-region G according to economic data of the grid sub-region G; calculating to obtain a competition coefficient C of the grid subarea G according to the reserved quantity of the textile recovery sites of the grid subarea G; and calculating to obtain a waste textile yield coefficient O of the grid subregion G by integrating the population coefficient P, the economic coefficient E and the competition coefficient C. The invention mainly solves the problem of how to evaluate the yield of the waste textile; after population data, economic data and the quantity of the textile fabric recycling sites are substituted, objective, accurate and quantifiable evaluation can be carried out on the yield of the waste textile fabrics in the area, and therefore reference is provided for selection of distribution points of the textile fabric recycling sites.

Description

Evaluation method and evaluation system of waste textile production

technical field

The invention relates to the technical field of renewable resource recovery and data application, in particular to an evaluation method and an evaluation system for the output of waste textiles.

Background technique

Renewable resource recycling is an economic model in which materials are continuously recycled. It is becoming a global trend. The implementation of renewable resource recycling can achieve the purpose of saving resources and reducing pollution. Turn waste textiles into renewable resources.

In the life of residents, common textiles include clothing, bedding, curtains, bags, etc. The production locations of waste textiles are usually residential areas, settlements and commercial areas, and the generation of waste textiles is random. and dispersion, that is, waste textiles are randomly generated at any location in the residential area; therefore, it is necessary to set up waste textile recycling sites in order to provide convenient waste textile delivery points for residents in a certain area, and to facilitate waste textiles collection and transport of goods.

For waste textile recycling stations in a certain area, if the number is too large, so that the output of waste textiles is far less than the recycling quantity of waste textile recycling stations, it is easy to cause problems of large transfer workload and waste of location; Too little, so that the output of waste textiles is far greater than the number of waste textile recycling sites, which will overload the waste textile recycling sites, and it is not convenient for residents to put waste textiles; in addition, if the waste textile recycling sites are set In sparsely populated areas or areas with relatively backward economy, areas where residents live together and areas with relatively developed economy lack waste textile recycling sites, it is also inconvenient for residents to put waste textiles.

In the prior art, the selection of distribution points for textile recycling sites often relies on on-the-spot investigation and empirical judgment. There are many subjective factors, and it is difficult to select the distribution points for textile recycling sites in an objective, quantifiable, and fast batch manner.

In summary, how to objectively, accurately and quantify the output of waste textiles in any area, so as to provide a reference for the selection of distribution points for textile recycling sites, has become an urgent problem to be solved.

SUMMARY OF THE INVENTION

One of the objectives of the present invention is to provide a method for evaluating the output of waste textiles, which can objectively, accurately and quantify the output of waste textiles in any area.

Another object of the present invention is to provide an evaluation system for the production of waste textiles, which can objectively evaluate the production of waste textiles in any area after inputting quantifiable parameters.

For achieving the above object, the present invention provides the following technical solutions: a method for evaluating the output of waste and used textiles, which comprises the following steps:

S1, grid the area to be evaluated to obtain a number of grid sub-areas G;

S2a, according to the population data of the grid sub-region G, calculate and obtain the population coefficient P of the grid sub-region G;

S2b, calculating and obtaining the economic coefficient E of the grid sub-region G according to the economic data of the grid sub-region G;

S2c, calculate and obtain the competition coefficient C of the grid sub-area G according to the number of textile recycling sites in the grid sub-area G;

S3 , synthesizing the population coefficient P, the economic coefficient E, and the competition coefficient C, to calculate and obtain the waste textile production coefficient O of the grid sub-region G.

In the above technical solution, the step S2a specifically includes:

S2.1a, in the to-be-evaluated area, determine the lower-level administrative division to which the grid sub-area G belongs;

S2.2a. In the lower-level administrative division, count the total number G_Sum of the grid sub-regions G belonging to residential areas;

S2.3a. Calculate and obtain the unit grid population of the grid sub-region G

S2.4a. Calculate and obtain the population coefficient of the grid sub-region G

Wherein, P_Density _min is the unit grid population of the grid sub-region G with the smallest unit grid population, and P_Density _max is the unit grid population of the grid sub-region G with the largest unit grid population.

In the above technical solution, the step S2b specifically includes:

S2.1b, in the to-be-evaluated area, determine the lower-level administrative division and residential area to which the grid sub-area G belongs;

S2.2b, obtaining the normalized gross regional product E_Base1 of the lower-level administrative division to which the grid sub-region G belongs;

S2.3b, obtaining the normalized average price E_Base2 of residential units in the residential area to which the grid sub-area G belongs;

S2.4b. Calculate and obtain the economic coefficient E=E_Base1×E_Base2 of the grid sub-region G.

In the above technical solution, the step S2c specifically includes:

S2.1c, in the to-be-evaluated area, determine the lower-level administrative division to which the grid sub-area G belongs;

S2.2c. Obtain the number B of textile recycling sites in the lower-level administrative division to which the grid sub-area G belongs;

S2.3c. Calculate and obtain the competition coefficient C=1/B of the grid sub-region G.

In the above-mentioned technical scheme, in the described step S3, the waste textile production coefficient O is specifically:

O=(P ^k1 )*(E ^k2 )*(C ^k3 )*k4;

Among them, k1, k2, k3 and k4 are all weight coefficients.

In the above technical solution, the initial values of the weight coefficient k1 , the weight coefficient k2 , the weight coefficient k3 and the weight coefficient k4 are all 1.

In the above technical solution, after the step S3, it further includes:

S4. Perform cyclic and iterative optimization on the yield coefficient O of the waste textiles.

In the above technical solution, the step S4 specifically includes:

S4.1. Define hyperparameters: learning rate V1, learning rate V2, learning rate V3, learning rate V4, convergence threshold thrd, and maximum number of iterations R;

S4.2, obtain several groups of learning samples, any one of the learning samples is n _i =(O _i , P _i , E _i , C _i );

S4.3. Substitute a group of the learning samples into the waste textile yield coefficient O to obtain O _ni =(P _i ^k1 )*(E _i ^k2 )*(C _i ^k3 )*k4;

S4.4, calculate and obtain the deviation dO _i =O _ni -O _i of the theoretical calculation result of the waste textile yield coefficient O and the group of the learning samples;

S4.5. Calculate and obtain the partial derivatives of the waste textile production coefficient O to the weight coefficient k1, the weight coefficient k2, the weight coefficient k3 and the weight coefficient k4 respectively:

S4.6. Calculate and obtain the update amount of the weight coefficient k1, the weight coefficient k2, the weight coefficient k3 and the weight coefficient k4 respectively:

S4.7. Combining the learning rate V1, the learning rate V2, the learning rate V3 and the learning rate V4, respectively obtain the updated weight coefficient k1, the weight coefficient k2, the The weight coefficient k3 and the weight coefficient k4:

S4.8. Repeat steps S4.3-S4.7 until all the learning samples are used;

S4.9. Calculate the comprehensive error of this round of iterative tuning process

S4.10. Repeat steps S4.2-S4.9 until (O _loss <thrd||r≥R) is satisfied, then end the cyclic iterative tuning of the waste textile yield coefficient O;

Among them, r is the actual number of iteration rounds.

In the above technical solution, after the step S4, it further includes:

S5. Steps S2-S4 are repeated until the yield coefficient O of waste textiles in all the grid sub-regions G is obtained.

An evaluation system for the output of waste textiles, applied to any grid sub-region G in the region to be evaluated, comprising:

The population coefficient P, which is calculated and obtained according to the population data of the grid sub-region G;

The economic coefficient E, which is calculated and obtained according to the economic data of the grid sub-region G;

The competition coefficient C, which is calculated and obtained according to the number of textile recycling stations in the grid sub-region G;

And, the waste textile production coefficient O, which is obtained by comprehensively calculating the population coefficient P, the economic coefficient E and the competition coefficient C.

Compared with the prior art, the beneficial effects of the present invention are:

1. The method for evaluating the output of waste and used textiles of the present invention, a mathematical model for prediction can be established by applying this method, namely, the output coefficient O of waste and used textiles calculated by comprehensive population coefficient P, economic coefficient E and competition coefficient C, is substituted into the net. After the population data, economic data and the number of textile recycling stations in the grid area G, an objective, accurate and quantifiable assessment of the waste textile production in this area can be made, thus providing a reference for the selection of distribution points of textile recycling stations.

2. The evaluation system for the output of waste textiles of the present invention provides a waste textile output coefficient O calculated by the comprehensive population coefficient P, economic coefficient E and competition coefficient C, and is substituted into the grid sub-region G through the mathematical model for prediction. After the population data, economic data and the number of textile recycling stations, an objective, accurate and quantifiable assessment of the waste textile production in the area can be made, which can provide a reference for the selection of distribution points of textile recycling stations.

Description of drawings

FIG. 1 is a flow chart of steps in Embodiment 1 of the present invention.

FIG. 2 is a system structure diagram of Embodiment 3 of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Example 1:

This embodiment provides a method for evaluating the output of waste textiles, which is used to evaluate the output of waste textiles in any geographical area, and further provides a reference for the selection of distribution points of textile recycling sites.

Among them, waste textiles are waste clothes, bedding, curtains, bags, etc. produced in the process of residents' life or industrial production; textile recycling sites can be recycling bins composed of metal plates, recycling stations composed of fixed buildings, and appear at regular and fixed points. Mobile recycling vehicles and recycling sites that are also used by existing stores, in fact, any entity that can recycle waste textiles can be used as a textile recycling site.

Referring to Fig. 1, the method for evaluating the output of waste textiles of the present embodiment includes the following steps:

S1. Gridize the area to be evaluated to obtain several grid sub-areas G.

In this step, the area to be assessed is an administrative unit, such as a municipal administrative unit, a county-level administrative unit, a district/town-level administrative unit, and a special administrative region. The grid can also be divided according to the longitude and latitude lines, and the grid can also be divided according to the natural and cultural boundaries such as residential areas, water networks and roads; several grid sub-areas G are included in the area to be assessed, and they are mutually exclusive. do not overlap.

S2a: Calculate and obtain the population coefficient P of the grid sub-area G according to the population data of the grid sub-area G.

S2b, according to the economic data of the grid sub-area G, calculate and obtain the economic coefficient E of the grid sub-area G.

S2c: Calculate the competition coefficient C of the grid sub-area G according to the number of textile recycling stations in the grid sub-area G.

S3. Integrate the population coefficient P, the economic coefficient E and the competition coefficient C, and calculate and obtain the waste textile production coefficient O of the grid sub-region G.

The above-mentioned output of waste textiles refers to the total amount of waste textiles per unit time that the residents in the grid sub-area G may put into the newly set textile recycling station.

Specifically, step S2a specifically includes:

S2.1a, in the area to be assessed, determine the lower-level administrative division to which the grid sub-area G belongs.

In this step, the lower-level administrative division refers to the lower-level administrative division in the area to be evaluated. For example, if the area to be evaluated is a prefecture-level city, the lower-level administrative division is a district or town; according to the geographic coordinates of the center point of the grid sub-area G, To determine the subordinate administrative division to which the grid sub-area G belongs, the subordinate administrative division to which the grid sub-area G belongs can also be directly obtained from the Beidou spatial grid data system.

S2.2a. In lower-level administrative divisions, count the total number G_Sum of grid sub-regions G belonging to residential areas.

In this step, residential areas are usually residential areas, inhabited villages and commercial areas. You can determine whether a grid sub-area G belongs to a residential area by identifying the number, density and proportion of buildings in the map. Whether the grid sub-area G belongs to a residential area is directly obtained in the data system, and the statistical results can be corrected manually on the basis of the above method.

S2.3a. Calculate and obtain the unit grid population of the grid sub-area G

Among them, the population of subordinate administrative divisions can be obtained from the latest population census data, or from the population data published by the local government office; through the unit grid population P_Density, the grid sub-region can be objectively and quantifiably reflected The population density of G, under the same conditions, the greater the population density, the higher the output of waste textiles. On the contrary, the smaller the population density, the lower the output of waste textiles.

S2.4a, calculate and obtain the population coefficient of the grid sub-region G

Among them, P_Density _min is the unit grid population of a grid sub-region G with the smallest unit grid population, and P_Density _max is the unit grid population of a grid sub-region G with the largest unit grid population.

By using the population coefficient P as one of the parameters of the waste textile production coefficient O, the influence of the absolute population and population density of the grid sub-region G on the waste textile production can be objectively and quantifiably reflected.

Specifically, step S2b specifically includes:

S2.1b, in the to-be-evaluated area, determine the lower-level administrative division and residential area to which the grid sub-area G belongs.

In this step, the lower-level administrative division refers to the lower-level administrative division in the area to be evaluated. For example, if the area to be evaluated is a prefecture-level city, the lower-level administrative division is a district or town; according to the geographic coordinates of the center point of the grid sub-area G, To determine the lower-level administrative division to which the grid sub-area G belongs, it is also possible to directly obtain the lower-level administrative division to which the grid sub-area G belongs from the Beidou spatial grid data system; residential areas refer to commercial residential quarters and self-built housing clusters in the area to be assessed. As well as habitable building clusters such as unit residences, the residential area to which the grid sub-area G belongs can be determined according to the geographic coordinates of the center point of the grid sub-area G.

S2.2b: Obtain the normalized gross regional product E_Base1 of the lower-level administrative division to which the grid sub-region G belongs.

Among them, the original data of the regional GDP can be obtained from the latest economic census data, or from the economic data published by the local government office; the normalization of the regional GDP refers to the The original data is converted into the gross regional product data expressed in the same pricing unit within the same time period; the normalized gross regional product E_Base1 can objectively reflect the economic aggregate of the grid sub-region G. Under the same conditions, the regional The larger the gross production value E_Base1 is, the larger the output of waste textiles must be. On the contrary, the smaller the regional gross domestic product E_Base1 is, the smaller the output of waste textiles is.

S2.3b, obtain the normalized average price E_Base2 of residential units in the residential area to which the grid sub-area G belongs.

Among them, the original data of the average price of residential units can be obtained from the residential transaction data published by the local government office or the banking industry; the normalization of the average price of residential units refers to converting the original data of the average price of residential units into the same time period. The average price data of residential units represented by the same pricing unit; the normalized average price of residential units E_Base2 can objectively reflect the economic development level of grid sub-region G. Under the same conditions, the larger the average price of residential units E_Base2, the more wasteful The output of textiles is bound to increase relatively. On the contrary, the smaller the average price of residential unit E_Base2 is, the output of waste textiles is bound to be relatively low.

S2.4b, calculate and obtain the economic coefficient E=E_Base1×E_Base2 of the grid sub-region G.

By using the economic coefficient E as one of the parameters of the waste textile production coefficient O, it can objectively and quantify the influence of the economic aggregate and economic development level of the grid sub-region G on the waste textile production.

Specifically, step S2c specifically includes:

S2.1c, in the to-be-evaluated area, determine the lower-level administrative division to which the grid sub-area G belongs.

In this step, the lower-level administrative division refers to the lower-level administrative division in the area to be evaluated. For example, if the area to be evaluated is a prefecture-level city, the lower-level administrative division is a district or town; according to the geographic coordinates of the center point of the grid sub-area G, To determine the subordinate administrative division to which the grid sub-area G belongs, the subordinate administrative division to which the grid sub-area G belongs can also be directly obtained from the Beidou spatial grid data system.

S2.2c. Obtain the number B of textile recycling stations in the lower-level administrative division to which the grid sub-area G belongs.

The number of textile recycling sites B is the total number of textile recycling sites that have been set up or will be set up in the foreseeable future, which can be obtained through on-the-spot investigation and inquiries, or can be obtained from the local public service map of textiles. Obtained from the data of recycling sites; under the same conditions, the more textile recycling sites there are B, the less waste textiles residents in the grid sub-area G may put into the newly set textile recycling sites, that is, waste textiles The smaller the material output, on the contrary, the smaller the number B of textile recycling stations, the more waste textiles residents in the grid sub-area G may put into the newly set textile recycling station, that is, the more waste textiles the output is. many.

S2.3c, the competition coefficient C=1/B of the grid sub-region G is obtained by calculation.

By using the competition coefficient C as one of the parameters of the waste textile production coefficient O, it can objectively and quantify the influence of the number B of textile recycling stations in the grid sub-region G on the waste textile production.

As mentioned above, step S2a, step S2b and step S2c may be performed simultaneously, or may be performed in sequence, respectively. For the step of judging the lower-level administrative division to which the grid sub-region G belongs, the judgment result may be shared.

Specifically, in step S3, the waste textile production coefficient O is specifically:

O=(P ^k1 )*(E ^k2 )*(C ^k3 )*k4;

Among them, k1, k2, k3 and k4 are all weight coefficients; that is, k1 represents the weight of the influence of the population coefficient P on the output coefficient O of waste textiles, and k2 represents the weight of the influence of the economic coefficient E on the output coefficient O of waste textiles , k3 represents the influence weight of the competition coefficient C on the waste textile production coefficient O, k4 represents the overall ratio of the waste textile production coefficient O to the population coefficient P, economic coefficient E and competition coefficient C.

Specifically, the initial values of the weight coefficient k1 , the weight coefficient k2 , the weight coefficient k3 and the weight coefficient k4 are all 1.

So far, the initial mathematical model between population coefficient P, economic coefficient E, competition coefficient C and waste textile production coefficient O has been established, namely O=(P ^k1 )*(E ^k2 )*(C ^k3 )*k4, And the initial values of k1, k2, k3 and k4 are all 1.

For the above initial mathematical model, the population coefficient P, the economic coefficient E and the competition coefficient C have the same weights on the influence of the waste textile production coefficient O, and the product of the population coefficient P, the economic coefficient E and the competition coefficient C and The yield coefficient O of waste textiles is directly equal; however, in practice, the absolute number of population, population density, economic aggregate, economic development level, and the number of textile recycling stations in a certain area will affect the output of waste textiles. Often have different importance, that is, have different weights, in addition, the ratio of waste textile production coefficient O and the product of population coefficient P, economic coefficient E and competition coefficient C also needs to be adjusted.

Therefore, it is necessary to optimize the mathematical model of the output coefficient O of waste textiles. In this embodiment, an optimization method for the mathematical model of the output coefficient O of waste textiles is also provided, and the goal is to adjust the weight coefficient k1, The values of weight coefficient k2, weight coefficient k3 and weight coefficient k4 make the predicted value of the mathematical model of waste textile production coefficient O closer to the actual value, so that it can be used in actual prediction.

Specifically, the tuning method for the yield coefficient O of waste textiles is, after step S3, further comprising:

S4. Iteratively optimizes the yield coefficient O of waste textiles.

Further specifically, step S4 specifically includes:

S4.1. Define hyperparameters: learning rate V1, learning rate V2, learning rate V3, learning rate V4, convergence threshold thrd, and maximum number of iteration rounds R.

S4.2. Obtain several groups of learning samples, and any learning sample is n _i =(O _i , P _i , E _i , C _i ).

The above-mentioned learning samples are samples obtained from actual sampling, and are used to calculate the model error of the yield coefficient O of waste textiles to complete the process of machine learning; among them, P _i is the population coefficient obtained by calculating the actual population data, E _i That is, the economic coefficient calculated from the actual economic data, C _i is the competition coefficient calculated from the actual number of textile recycling stations retained, and O _i is the waste textile production coefficient calculated in the actual operation process.

S4.3. Substitute a set of learning samples into the yield coefficient O of waste textiles to obtain O _ni =(P _i ^k1 )*(E _i ^k2 )*(C _i ^k3 )*k4.

S4.4. Calculate the deviation dO _i \O _ni -O _i between the theoretical calculation result of the yield coefficient O of waste textiles and the group of learning samples.

That is, dO _i is the deviation between the predicted result of the waste textile yield coefficient O and the actual data.

S4.5. Calculate and obtain the partial derivatives of the waste textile production coefficient O to the weight coefficient k1, the weight coefficient k2, the weight coefficient k3 and the weight coefficient k4:

S4.6, calculate and obtain the update amount of the weight coefficient k1, the weight coefficient k2, the weight coefficient k3 and the weight coefficient k4 respectively:

In this step, dk1 is the update amount of the weight coefficient k1, which is used to update the weight coefficient k1 in combination with the learning rate V1, and dk2 is the update amount of the weight coefficient k2, which is used to update the weight coefficient k2 in combination with the learning rate V2, dk3 is the update amount of the weight coefficient k3, which is used to update the weight coefficient k3 in combination with the learning rate V3, and dk4 is the update amount of the weight coefficient k4, which is used to update the weight coefficient k4 in combination with the learning rate V4.

S4.7. Combine the learning rate V1, learning rate V2, learning rate V3 and learning rate V4 to obtain the updated weight coefficient k1, weight coefficient k2, weight coefficient k3 and weight coefficient k4 respectively:

So far, the weight coefficient k1, the weight coefficient k2, the weight coefficient k3, and the weight coefficient k4 have all been updated once, and the tuning process of the waste textile production coefficient O for a learning sample has been completed.

S4.8. Repeat steps S4.3-S4.7 until all learning samples are used.

So far, the waste textile yield coefficient O has completed a round of iterative tuning.

S4.9. Calculate the comprehensive error of this round of iterative tuning process

Through the comprehensive error O _loss , it is possible to quantitatively evaluate the deviation between the predicted result of the yield coefficient O of waste textiles and the actual data, so as to evaluate the prediction performance of the yield coefficient O of waste textiles.

S4.10. Repeat steps S4.2-S4.9 until (O _loss <thrd||r≥R) is satisfied, and end the cyclic iterative tuning of the yield coefficient O of waste textiles.

Among them, r is the actual number of iteration rounds.

Of course, if the condition of (O _loss <thrd||r≥R) is satisfied after one iterative tuning, the iterative tuning of the yield coefficient O of waste textiles can also be ended directly.

The adjusted waste textile production coefficient O provides a mathematical model for prediction. After substituting population data, economic data, and the number of textile recycling stations, it can predict the waste textile production in a specific area, so as to provide information for textile recycling. The site's distribution point selection provides a reference.

The method for evaluating the output of waste textiles in this embodiment provides a mathematical model for prediction. After substituting the population data, economic data and the number of textile recycling stations in the grid sub-area G, the output of waste textiles in this area can be evaluated. An objective, accurate and quantifiable assessment that informs the selection of distribution points for textile recycling sites.

Embodiment 2:

This embodiment provides a method for evaluating the yield of waste textiles. On the basis of the method for evaluating the output of waste textiles provided in the first embodiment, the waste textile yield coefficients of all grid sub-regions G in the region to be evaluated can also be obtained respectively. O.

In this embodiment, after step S4, it further includes:

S5. Repeat steps S2-S4 until the yield coefficient O of waste textiles in all grid sub-regions G is obtained.

In this way, the waste textile production coefficient O of all grid sub-regions G can be obtained respectively, which provides a reference for the selection of distribution points of textile recycling stations in the entire region to be evaluated.

Embodiment three:

Referring to FIG. 2 , the present embodiment provides an evaluation system for the production of waste textiles, which is used to evaluate the production of waste textiles in any geographical area, thereby providing a reference for the selection of distribution points of textile recycling sites.

Among them, waste textiles are waste clothes, bedding, curtains, bags, etc. produced in the process of residents' life or industrial production; textile recycling sites can be recycling bins composed of metal plates, recycling stations composed of fixed buildings, and appear at regular and fixed points. Mobile recycling vehicles and recycling sites that are also used by existing stores, in fact, any entity that can recycle waste textiles can be used as a textile recycling site.

The evaluation system for the production of waste textiles is applied to any grid sub-region G in the region to be evaluated, which includes:

The population coefficient P, which is calculated according to the population data of the grid sub-region G;

The economic coefficient E, which is calculated according to the economic data of the grid sub-region G;

The competition coefficient C, which is calculated according to the number of textile recycling stations in the grid sub-area G;

And, the waste textile production coefficient O, which is calculated from the comprehensive population coefficient P, economic coefficient E and competition coefficient C.

Among them, the areas to be assessed are administrative units, such as municipal administrative units, county-level administrative units, district and town-level administrative units, and special administrative regions. The grid can also be divided according to the longitude and latitude lines, and the grid can also be divided according to natural and cultural boundaries such as residential areas, water networks and roads; several grid sub-areas G are included in the area to be assessed and do not overlap each other. .

The specific methods of obtaining the population coefficient P include:

S1a, in the to-be-evaluated area, determine the lower-level administrative division to which the grid sub-area G belongs.

In this step, the lower-level administrative division refers to the lower-level administrative division in the area to be evaluated. For example, if the area to be evaluated is a prefecture-level city, the lower-level administrative division is a district or town; according to the geographic coordinates of the center point of the grid sub-area G, To determine the subordinate administrative division to which the grid sub-area G belongs, the subordinate administrative division to which the grid sub-area G belongs can also be directly obtained from the Beidou spatial grid data system.

S2a. In the lower-level administrative division, count the total number G_Sum of the grid sub-regions G belonging to the residential area.

In this step, residential areas are usually residential areas, inhabited villages and commercial areas. You can determine whether a grid sub-area G belongs to a residential area by identifying the number, density and proportion of buildings in the map. Whether the grid sub-area G belongs to a residential area is directly obtained in the data system, and the statistical results can be corrected manually on the basis of the above method.

S3a. Calculate and obtain the unit grid population of the grid sub-region G

Among them, the population of subordinate administrative divisions can be obtained from the latest population census data, or from the population data published by the local government office; through the unit grid population P_Density, the grid sub-region can be objectively and quantifiably reflected The population density of G, under the same conditions, the greater the population density, the higher the output of waste textiles. On the contrary, the smaller the population density, the lower the output of waste textiles.

S4a, calculate and obtain the population coefficient of the grid sub-region G

Among them, P_Density _min is the unit grid population of a grid sub-region G with the smallest unit grid population, and P_Density _max is the unit grid population of a grid sub-region G with the largest unit grid population.

By using the population coefficient P as one of the parameters of the waste textile production coefficient O, the influence of the absolute population and population density of the grid sub-region G on the waste textile production can be objectively and quantifiably reflected.

The specific methods of obtaining the economic coefficient E include:

S1b, in the to-be-evaluated area, determine the lower-level administrative division and residential area to which the grid sub-area G belongs.

In this step, the lower-level administrative division refers to the lower-level administrative division in the area to be evaluated. For example, if the area to be evaluated is a prefecture-level city, the lower-level administrative division is a district or town; according to the geographic coordinates of the center point of the grid sub-area G, To determine the lower-level administrative division to which the grid sub-area G belongs, it is also possible to directly obtain the lower-level administrative division to which the grid sub-area G belongs from the Beidou spatial grid data system; residential areas refer to commercial residential quarters and self-built housing clusters in the area to be assessed. As well as habitable building clusters such as unit residences, the residential area to which the grid sub-area G belongs can be determined according to the geographic coordinates of the center point of the grid sub-area G.

S2b: Obtain the normalized gross regional product E_Base1 of the subordinate administrative division to which the grid sub-region G belongs.

Among them, the original data of the regional GDP can be obtained from the latest economic census data, or from the economic data published by the local government office; the normalization of the regional GDP refers to the The original data is converted into the gross regional product data expressed in the same pricing unit within the same time period; the normalized gross regional product E_Base1 can objectively reflect the economic aggregate of the grid sub-region G. Under the same conditions, the regional The larger the gross production value E_Base1 is, the larger the output of waste textiles must be. On the contrary, the smaller the regional gross domestic product E_Base1 is, the smaller the output of waste textiles is.

S3b: Obtain the normalized average price E_Base2 of residential units in the residential area to which the grid sub-area G belongs.

Among them, the original data of the average price of residential units can be obtained from the residential transaction data published by the local government office or the banking industry; the normalization of the average price of residential units refers to converting the original data of the average price of residential units into the same time period. The average price data of residential units represented by the same pricing unit; the normalized average price of residential units E_Base2 can objectively reflect the economic development level of grid sub-region G. Under the same conditions, the larger the average price of residential units E_Base2, the more wasteful The output of textiles is bound to increase relatively. On the contrary, the smaller the average price of residential unit E_Base2 is, the output of waste textiles is bound to be relatively low.

S4b, calculating and obtaining the economic coefficient E=E_Base1×E_Base2 of the grid sub-region G.

By using the economic coefficient E as one of the parameters of the waste textile production coefficient O, it can objectively and quantify the influence of the economic aggregate and economic development level of the grid sub-region G on the waste textile production.

The specific method for obtaining the competition coefficient C includes:

S1c, in the to-be-evaluated area, determine the lower-level administrative division to which the grid sub-area G belongs.

In this step, the lower-level administrative division refers to the lower-level administrative division in the area to be evaluated. For example, if the area to be evaluated is a prefecture-level city, the lower-level administrative division is a district or town; according to the geographic coordinates of the center point of the grid sub-area G, To determine the subordinate administrative division to which the grid sub-area G belongs, the subordinate administrative division to which the grid sub-area G belongs can also be directly obtained from the Beidou spatial grid data system.

S2c: Obtain the number B of textile recycling stations in the lower-level administrative division to which the grid sub-region G belongs.

The number of textile recycling sites B is the total number of textile recycling sites that have been set up or will be set up in the foreseeable future, which can be obtained through on-the-spot investigation and inquiries, or can be obtained from the local public service map of textiles. Obtained from the data of recycling sites; under the same conditions, the more textile recycling sites there are B, the less waste textiles residents in the grid sub-area G may put into the newly set textile recycling sites, that is, waste textiles The smaller the material output, on the contrary, the smaller the number B of textile recycling stations, the more waste textiles residents in the grid sub-area G may put into the newly set textile recycling station, that is, the more waste textiles the output is. many.

S3c, calculating and obtaining the competition coefficient C=1/B of the grid sub-region G.

By using the competition coefficient C as one of the parameters of the waste textile production coefficient O, it can objectively and quantify the influence of the number B of textile recycling stations in the grid sub-region G on the waste textile production.

Specifically, the waste textile production coefficient O is specifically:

O=(P ^k1 )*(E ^k2 )*(C ^k3 )*k4;

Among them, k1, k2, k3 and k4 are all weight coefficients; that is, k1 represents the weight of the influence of the population coefficient P on the output coefficient O of waste textiles, and k2 represents the weight of the influence of the economic coefficient E on the output coefficient O of waste textiles , k3 represents the influence weight of the competition coefficient C on the waste textile production coefficient O, k4 represents the overall ratio of the waste textile production coefficient O to the population coefficient P, economic coefficient E and competition coefficient C.

Specifically, the initial values of the weight coefficient k1 , the weight coefficient k2 , the weight coefficient k3 and the weight coefficient k4 are all 1.

So far, the initial mathematical model between population coefficient P, economic coefficient E, competition coefficient C and waste textile production coefficient O has been established, namely O=(P ^k1 )*(E ^k2 )*(C ^k3 )*k4, And the initial values of k1, k2, k3 and k4 are all 1.

For the above initial mathematical model, the population coefficient P, the economic coefficient E and the competition coefficient C have the same weights on the influence of the waste textile production coefficient O, and the product of the population coefficient P, the economic coefficient E and the competition coefficient C and The yield coefficient O of waste textiles is directly equal; however, in practice, the absolute number of population, population density, economic aggregate, economic development level, and the number of textile recycling stations in a certain area will affect the output of waste textiles. Often have different importance, that is, have different weights, in addition, the ratio of waste textile production coefficient O and the product of population coefficient P, economic coefficient E and competition coefficient C also needs to be adjusted.

Therefore, it is necessary to optimize the mathematical model of the output coefficient O of waste textiles. In this embodiment, an optimization method for the mathematical model of the output coefficient O of waste textiles is also provided, and the goal is to adjust the weight coefficient k1, The values of weight coefficient k2, weight coefficient k3 and weight coefficient k4 make the predicted value of the mathematical model of waste textile production coefficient O closer to the actual value, so that it can be used in actual prediction.

The specific tuning method for the yield coefficient O of waste textiles is as follows: cyclically and iteratively tuning the yield coefficient O of waste textiles.

More specifically, the cyclic and iterative optimization of the yield coefficient O of waste textiles includes:

S1. Define hyperparameters: learning rate V1, learning rate V2, learning rate V3, learning rate V4, convergence threshold thrd, and maximum number of iterations R.

S2. Acquire several groups of learning samples, and any learning sample is n _i =(O _i , P _i , E _i , C _i ).

The above-mentioned learning samples are samples obtained from actual sampling, and are used to calculate the model error of the yield coefficient O of waste textiles to complete the process of machine learning; among them, P _i is the population coefficient obtained by calculating the actual population data, E _i That is, the economic coefficient calculated from the actual economic data, C _i is the competition coefficient calculated from the actual number of textile recycling stations retained, and O _i is the waste textile production coefficient calculated in the actual operation process.

S3. Substitute a group of learning samples into the yield coefficient O of waste textiles to obtain O _ni =(P _i ^k1 )*(E _i ^k2 )*(C _i ^k3 )*k4.

S4. Calculate the deviation dO _i =O _ni -O _i between the theoretical calculation result of the yield coefficient O of waste textiles and the group of learning samples.

That is, dO _i is the deviation between the predicted result of the waste textile yield coefficient O and the actual data.

S5. Calculate and obtain the partial derivatives of the waste textile production coefficient O to the weight coefficient k1, the weight coefficient k2, the weight coefficient k3 and the weight coefficient k4 respectively:

S6, calculate and obtain the update amount of the weight coefficient k1, the weight coefficient k2, the weight coefficient k3 and the weight coefficient k4 respectively:

In this step, dk1 is the update amount of the weight coefficient k1, which is used to update the weight coefficient k1 in combination with the learning rate V1, and dk2 is the update amount of the weight coefficient k2, which is used to update the weight coefficient k2 in combination with the learning rate V2, dk3 is the update amount of the weight coefficient k3, which is used to update the weight coefficient k3 in combination with the learning rate V3, and dk4 is the update amount of the weight coefficient k4, which is used to update the weight coefficient k4 in combination with the learning rate V4.

S7. Combine the learning rate V1, learning rate V2, learning rate V3 and learning rate V4 to obtain the updated weight coefficient k1, weight coefficient k2, weight coefficient k3 and weight coefficient k4 respectively:

So far, the weight coefficient k1, the weight coefficient k2, the weight coefficient k3, and the weight coefficient k4 have all been updated once, and the tuning process of the waste textile production coefficient O for a learning sample has been completed.

S8. Repeat steps S3-S7 until all learning samples are used.

So far, the waste textile yield coefficient O has completed a round of iterative tuning.

S9. Calculate the comprehensive error of this round of iterative tuning process

Through the comprehensive error O _loss , it is possible to quantitatively evaluate the deviation between the predicted result of the yield coefficient O of waste textiles and the actual data, so as to evaluate the prediction performance of the yield coefficient O of waste textiles.

S10, repeating steps S2-S9 until (O _loss <thrd||r≥R) is satisfied, and ending the cyclic iterative optimization of the yield coefficient O of waste textiles.

Among them, r is the actual number of iteration rounds.

Of course, if the condition of (O _loss <thrd||r≥R) is satisfied after one iterative tuning, the iterative tuning of the yield coefficient O of waste textiles can also be ended directly.

The adjusted waste textile production coefficient O provides a mathematical model for prediction. After substituting population data, economic data, and the number of textile recycling stations, it can predict the waste textile production in a specific area, so as to provide information for textile recycling. The site's distribution point selection provides a reference.

The evaluation system for the output of waste textiles in this embodiment provides a mathematical model for prediction. After substituting the population data, economic data and the number of textile recycling stations in the grid sub-area G, the output of waste textiles in this area can be evaluated. An objective, accurate and quantifiable assessment that informs the selection of distribution points for textile recycling sites.

Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, and substitutions can be made in these embodiments without departing from the principle and spirit of the invention and modifications, the scope of the present invention is defined by the appended claims and their equivalents.

Claims (10)

1. an assessment method of waste textile output, is characterized in that, comprises the following steps: S1, grid the area to be evaluated to obtain a number of grid sub-areas G; S2a, according to the population data of the grid sub-region G, calculate and obtain the population coefficient P of the grid sub-region G; S2b, calculating and obtaining the economic coefficient E of the grid sub-region G according to the economic data of the grid sub-region G; S2c, calculate and obtain the competition coefficient C of the grid sub-area G according to the number of textile recycling sites in the grid sub-area G; S3 , synthesizing the population coefficient P, the economic coefficient E, and the competition coefficient C, to calculate and obtain the waste textile production coefficient O of the grid sub-region G. 2. the evaluation method of waste textile output according to claim 1, is characterized in that, described step S2a specifically comprises: S2.1a, in the to-be-evaluated area, determine the lower-level administrative division to which the grid sub-area G belongs; S2.2a. In the lower-level administrative division, count the total number G_Sum of the grid sub-regions G belonging to residential areas; S2.3a. Calculate and obtain the unit grid population of the grid sub-region G

S2.4a. Calculate and obtain the population coefficient of the grid sub-region G

Wherein, P_Density _min is the unit grid population of the grid sub-region G with the smallest unit grid population, and P_Density _max is the unit grid population of the grid sub-region G with the largest unit grid population. 3. the evaluation method of waste textile output according to claim 1, is characterized in that, described step S2b specifically comprises: S2.1b, in the to-be-evaluated area, determine the lower-level administrative division and residential area to which the grid sub-area G belongs; S2.2b, obtaining the normalized gross regional product E_Base1 of the lower-level administrative division to which the grid sub-region G belongs; S2.3b, obtaining the normalized average price E_Base2 of residential units in the residential area to which the grid sub-area G belongs; S2.4b. Calculate and obtain the economic coefficient E=E_Base1×E_Base2 of the grid sub-region G. 4. the evaluation method of waste textile output according to claim 1, is characterized in that, described step S2c specifically comprises: S2.1c, in the to-be-evaluated area, determine the lower-level administrative division to which the grid sub-area G belongs; S2.2c. Obtain the number B of textile recycling sites in the lower-level administrative division to which the grid sub-area G belongs; S2.3c. Calculate and obtain the competition coefficient C=1/B of the grid sub-region G. 5. the evaluation method of waste and old textile yield according to claim 1, is characterized in that, in described step S3, waste and old textile yield coefficient O is specially: O=(P ^k1 )*(E ^k2 )*(C ^k3 )*k4; Among them, k1, k2, k3 and k4 are all weight coefficients. 6 . The method for evaluating the output of waste textiles according to claim 5 , wherein the initial value of the weight coefficient k1 , the weight coefficient k2 , the weight coefficient k3 and the weight coefficient k4 . The value is both 1. 7. The method for evaluating the output of waste textiles according to claim 5 or 6, characterized in that, after the step S3, further comprising: S4. Perform cyclic and iterative optimization on the yield coefficient O of the waste textiles. 8. the evaluation method of waste textile output according to claim 7, is characterized in that, described step S4 specifically comprises: S4.1. Define hyperparameters: learning rate V1, learning rate V2, learning rate V3, learning rate V4, convergence threshold thrd, and maximum number of iterations R; S4.2, obtain several groups of learning samples, any one of the learning samples is n _i =(O _i , P _i , E _i , C _i ); S4.3. Substitute a group of the learning samples into the waste textile yield coefficient O to obtain O _ni =(P _i ^k1 )*(E _i ^k2 )*(C _i ^k3 )*k4; S4.4, calculate and obtain the deviation dO _i =O _ni -O _i of the theoretical calculation result of the waste textile yield coefficient O and the group of the learning samples; S4.5. Calculate and obtain the partial derivatives of the waste textile production coefficient O to the weight coefficient k1, the weight coefficient k2, the weight coefficient k3 and the weight coefficient k4 respectively:

S4.6. Calculate and obtain the update amount of the weight coefficient k1, the weight coefficient k2, the weight coefficient k3 and the weight coefficient k4 respectively:

S4.7. Combining the learning rate V1, the learning rate V2, the learning rate V3 and the learning rate V4, respectively obtain the updated weight coefficient k1, the weight coefficient k2, the The weight coefficient k3 and the weight coefficient k4:

S4.8. Repeat steps S4.3-S4.7 until all the learning samples are used; S4.9. Calculate the comprehensive error of this round of iterative tuning process

S4.10. Repeat steps S4.2-S4.9 until (O _loss <thrd||r≥R) is satisfied, then end the cyclic iterative tuning of the waste textile yield coefficient O; Among them, r is the actual number of iteration rounds. 9. The method for evaluating the output of waste textiles according to claim 8, characterized in that, after the step S4, further comprising: S5. Repeat steps S2-S4 until the yield coefficient O of waste textiles in all the grid sub-regions G is obtained. 10. An evaluation system for the output of waste textiles, applied to any grid sub-region G in the region to be evaluated, characterized in that, comprising: The population coefficient P, which is calculated and obtained according to the population data of the grid sub-region G; The economic coefficient E, which is calculated and obtained according to the economic data of the grid sub-region G; The competition coefficient C, which is calculated and obtained according to the number of textile recycling stations in the grid sub-region G; And, the waste textile production coefficient O, which is obtained by comprehensively calculating the population coefficient P, the economic coefficient E and the competition coefficient C.

Images