1. A method for predicting the quality of industrial products of cross-border renewable resources is characterized by comprising the following steps: the method comprises the following steps:

s1, firstly, determining cross-border use environment and use duration of renewable resource industrial products, and searching use data of similar products through big data;

s2, establishing a loss model through the use data of the industrial product, and evaluating the loss of the product in different time periods and different use environments;

s3, calculating and predicting the service life of the product in different environments;

s4, calculating carbon emission in the production, transportation and use processes of the cross-border renewable resource industrial product;

and S5, predicting the quality of the industrial product of the renewable resource by combining the use environment, the use duration and the data of the product.

2. The method of claim 1, wherein in S1, similar products comprise other renewable resource products in the same environment, the same renewable resource products in other environments, and the same renewable resource products in the same environment.

3. The method as claimed in claim 2, wherein the usage data includes usage duration, loss ratio and usage environment, and after the usage data of similar products is collected, the data is sorted according to usage data of same products in same environment, different environments of same products and sequence of different products in same environment.

4. The method according to claim 3, wherein in step S2, when the loss model is built, the data about the use of the same product in the same environment is used as the main data, and the data about different environments of the same product and the data about different products in the same environment are used as the reference data.

5. The method for predicting the quality of the cross-border renewable resource industrial product according to claim 4, wherein after the loss model is established, the corresponding product loss can be calculated by inputting the use environment and the use duration of the renewable resource industrial product;

and calculating and predicting the residual service life of the product in the environment, substituting the data into different environments, and calculating and predicting the residual service life of the renewable resource industrial product in different environments.

6. The method according to claim 1, wherein in S4, the carbon emission per unit time of the cross-border renewable resource industrial product is calculated according to the duration of the renewable resource industrial product usage during the production, transportation and usage process of the cross-border renewable resource industrial product;

and S5, combining the carbon emission data and the loss data to predict the quality of the renewable resource industrial product.

7. The system of any one of claims 1 to 6, wherein the system comprises a collection module for loss data collection, a model construction module for building a loss model, an assessment module for calculating loss, a carbon emission module for calculating carbon emission, and a prediction module for comprehensive quality prediction.

8. The system of claim 7, wherein the collection module and the carbon emission module collect data, collect actual recorded data first, collect big data through internet, and classify and screen the data after collecting the data.

9. The system of claim 7, wherein after the model is built by the model building module, the model building module selects actual usage data for verification, calculates loss data under the same usage environment and usage duration, compares the calculated data with the actual data, and completes the model building if the error is less than 5%.

10. The cross-border renewable resource industrial product quality prediction system of claim 7, wherein the prediction module predicts the quality of the renewable resource industrial product by both carbon emission and loss, the carbon emission being assessed by a carbon footprint of the industrial product;

the prediction result of the prediction module is the shortest use time and the environmental protection use time, and the starting time of the shortest use time and the environmental protection use time is the product quality prediction time;

and in the shortest use time, the industrial product is normal in performance and safety and can be normally used, after the shortest use time is exceeded, the performance of the industrial product is reduced, the safety is normal and the industrial product can be continuously used, and after the shortest use time is exceeded, the performance of the industrial product is reduced, the safety is reduced and the industrial product cannot be continuously used.

Background

With the continuous development of the current economic technology, people pay more and more attention to environmental protection, wherein renewable resources and carbon emission are the current hot environmental protection topics, renewable resources refer to natural resources that can maintain or increase the amount of deposits at a certain growth rate through natural forces, for renewable resources, the continuous utilization of the resources is realized mainly by reasonably regulating and controlling the utilization rate of the resources, the continuous utilization of renewable resources is mainly restricted by the natural growth rule, the renewable resources from animals and plants in the nature are inexhaustible resources, the carbon footprint refers to the set of greenhouse gas emission caused by enterprise organizations, activities, products or individuals through transportation, food production and consumption, various production processes and the like, the influence of energy consciousness and behaviors of one person on the nature is described, and people are called to do the method from self;

however, at present, no reasonable quality prediction method and system are established for cross-border renewable resource industrial products, the use duration of the renewable resource industrial products and the mastery of carbon emission data are not comprehensive enough, the quality of the renewable resource industrial products cannot be predicted, the industrial products cannot be predicted and utilized reasonably, and the products produced from renewable resources are not used reasonably and are wasted.

Disclosure of Invention

The invention provides a method and a system for predicting the quality of cross-border renewable resource industrial products, which can effectively solve the problems that no reasonable quality prediction method and system are established for the cross-border renewable resource industrial products, the use duration and carbon emission data of the renewable resource industrial products are mastered to be incomplete, the quality of the renewable resource industrial products cannot be predicted, the industrial products cannot be predicted and utilized reasonably, and the products produced from renewable resources are not used reasonably, so that waste is caused.

In order to achieve the purpose, the invention provides the following technical scheme: a method for predicting the quality of industrial products of cross-border renewable resources comprises the following steps:

s1, firstly, determining cross-border use environment and use duration of renewable resource industrial products, and searching use data of similar products through big data;

s2, establishing a loss model through the use data of the industrial product, and evaluating the loss of the product in different time periods and different use environments;

s3, calculating and predicting the service life of the product in different environments;

s4, calculating carbon emission in the production, transportation and use processes of the cross-border renewable resource industrial product;

and S5, predicting the quality of the industrial product of the renewable resource by combining the use environment, the use duration and the data of the product.

According to the above technical solution, in S1, the similar products include other renewable resource products in the same environment, the same renewable resource products in other environments, and the same renewable resource products in the same environment.

According to the technical scheme, the use data comprises use duration, a loss proportion and use environments, and after the use data of similar products are collected, the data are sequenced according to the use data of the same product in the same environment, different environments of the same product and the sequence of the different products in the same environment.

According to the above technical solution, in S2, when the loss model is established, the data of the same product in the same environment is used as the main data, and the data of different environments of the same product and the data of different products in the same environment are used as the reference data.

According to the technical scheme, after the loss model is established, the corresponding product loss can be calculated by inputting the use environment and the use duration of the renewable resource industrial product;

and calculating and predicting the residual service life of the product in the environment, substituting the data into different environments, and calculating and predicting the residual service life of the renewable resource industrial product in different environments.

According to the technical scheme, in the S4, the carbon emission per unit time of the cross-border renewable resource industrial product is calculated by combining the use duration of the renewable resource industrial product in the processes of production, transportation and use of the cross-border renewable resource industrial product;

and S5, combining the carbon emission data and the loss data to predict the quality of the renewable resource industrial product.

According to the technical scheme, the system comprises a collection module for collecting loss data, a model building module for building a loss model, an evaluation module for calculating loss, a carbon emission module for calculating carbon emission and a prediction module for predicting comprehensive quality.

According to the technical scheme, the collecting module and the carbon emission module collect data, firstly, actually recorded data are collected, then, big data are collected through the internet, and the data are classified and screened after the data are collected.

According to the technical scheme, after the model is established by the model establishing module, actual use data is selected for verification, loss data is calculated under the conditions of the same use environment and the same use duration, the calculated data is compared with the actual data, and the model establishment is completed when the error is lower than 5%.

According to the technical scheme, the quality prediction of renewable resource industrial products by the prediction module comprises two aspects of carbon emission and loss, wherein the carbon emission is carried out by evaluating the carbon footprint of the industrial products;

the prediction result of the prediction module is the shortest use time and the environmental protection use time, and the starting time of the shortest use time and the environmental protection use time is the product quality prediction time;

and in the shortest use time, the industrial product is normal in performance and safety and can be normally used, after the shortest use time is exceeded, the performance of the industrial product is reduced, the safety is normal and the industrial product can be continuously used, and after the shortest use time is exceeded, the performance of the industrial product is reduced, the safety is reduced and the industrial product cannot be continuously used.

Compared with the prior art, the invention has the beneficial effects that:

1. the loss data of the cross-border renewable resource industrial product is collected and evaluated by establishing the loss model, so that the loss conditions of the renewable resource industrial product in different use environments and in use duration are obtained, people can conveniently master the use condition of the renewable resource industrial product, the use space of the industrial product can be larger according to actual conditions, the renewable resource industrial product can be reasonably used, and the waste of the renewable resource industrial product is reduced.

2. The carbon emission data in the production, transportation and use processes of the renewable resource industrial products are collected, the carbon footprints are tracked, and the carbon emission of the cross-border renewable resource industrial products in unit time is calculated, so that the shortest use time and the environment-friendly use time of the cross-border renewable resource industrial products are calculated, and the renewable resource industrial products are more environment-friendly.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.

In the drawings:

FIG. 1 is a flow chart of the steps of the prediction method of the present invention;

fig. 2 is a block diagram of the system architecture of the present invention.

Detailed Description

The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.

Example (b): as shown in fig. 1, the present invention provides a technical solution, a method for predicting quality of industrial products from cross-border renewable resources, comprising the following steps:

s1, firstly, determining cross-border use environment and use duration of renewable resource industrial products, and searching use data of similar products through big data;

s2, establishing a loss model through the use data of the industrial product, and evaluating the loss of the product in different time periods and different use environments;

s3, calculating and predicting the service life of the product in different environments;

s4, calculating carbon emission in the production, transportation and use processes of the cross-border renewable resource industrial product;

and S5, predicting the quality of the industrial product of the renewable resource by combining the use environment, the use duration and the data of the product.

According to the above technical solution, in S1, the similar products include other renewable resource products in the same environment, the same renewable resource products in other environments, and the same renewable resource products in the same environment.

According to the technical scheme, the use data comprises use duration, a loss proportion and use environments, and after the use data of similar products are collected, the data are sequenced according to the use data of the same product in the same environment, different environments of the same product and the sequence of the different products in the same environment.

According to the above technical solution, in S2, when the loss model is established, the data of the same product in the same environment is used as the main data, and the data of different environments of the same product and the data of different products in the same environment are used as the reference data.

According to the technical scheme, after the loss model is established, the corresponding product loss can be calculated by inputting the use environment and the use duration of the renewable resource industrial product;

and calculating and predicting the residual service life of the product in the environment, substituting the data into different environments, and calculating and predicting the residual service life of the renewable resource industrial product in different environments.

According to the technical scheme, in S4, the carbon emission per unit time of the cross-border renewable resource industrial product is calculated by combining the use duration of the renewable resource industrial product in the processes of production, transportation and use of the cross-border renewable resource industrial product;

and S5, combining the carbon emission data and the loss data to predict the quality of the renewable resource industrial product.

As shown in fig. 2, according to the above technical solution, the system includes a collection module for collecting loss data, a model construction module for establishing a loss model, an evaluation module for calculating loss, a carbon emission module for calculating carbon emission, and a prediction module for predicting comprehensive quality.

According to the technical scheme, the collecting module and the carbon emission module collect data, the data recorded actually are collected firstly, then the data are collected through internet big data, and the data are classified and screened after the data are collected, so that the data are more organized.

According to the technical scheme, after the model is established by the model establishing module, actual use data is selected for verification, loss data is calculated under the conditions of the same use environment and the same use duration, the calculated data is compared with the actual data, and the model establishment is completed when the error is lower than 5%.

According to the technical scheme, the quality prediction of renewable resource industrial products by the prediction module comprises two aspects of carbon emission and loss, and the carbon emission is carried out by evaluating the carbon footprint of the industrial products;

the prediction result of the prediction module is the shortest use time and the environmental protection use time, and the starting time of the shortest use time and the environmental protection use time is the product quality prediction time;

and in the shortest use time, the industrial product is normal in performance and safety and can be normally used, after the shortest use time is exceeded, the performance of the industrial product is reduced, the safety is normal and the industrial product can be continuously used, and after the shortest use time is exceeded, the performance of the industrial product is reduced, the safety is reduced and the industrial product cannot be continuously used.

Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.