Solid waste resource extraction process index prediction method and device, equipment and storage medium

Abstract

The invention discloses a product process index prediction method, device and equipment for solid waste resource extraction and a storage medium, and the prediction method comprises the steps: setting process control data of solid waste resource treatment in solid waste resource data as first preset data; the step of preprocessing the solid waste resource data in the preset data database comprises the substeps of updating first preset data; various data of the solid waste resource data are used as feature data, and a prediction model with the purpose of predicting product process indexes is established; the method comprises the following steps: performing multi-dimensional analysis on raw material data of solid waste resources, process route data of solid waste resource treatment, process control data of solid waste resource treatment and product process index data, and preprocessing the solid waste resource data; the relevance between each data of the solid waste resource data and the product process indexes is determined, and a high-accuracy prediction model is established, so that efficient extraction of material resources and energy resources of solid wastes is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of solid waste resource extraction, and in particular to a method, device, equipment and storage medium for predicting process indicators of solid waste resource extraction. Background Art

[0002] Solid waste refers to solid waste that pollutes the environment and is discarded because it cannot be used at a certain time and place, which is generated by human production and construction, daily life and other activities. Solid waste resource utilization refers to the technical method of taking management and process measures to recover materials and energy from solid waste, accelerate the circulation of materials and energy, and create a wide range of economic value. Solid waste resource utilization is the main destination of solid waste.

[0003] After certain treatment or processing, useful substances contained in solid waste can be extracted and continue to play a role in industrial and agricultural production processes. Some solid waste can also be changed into new energy or resources. Due to the wide sources of solid waste in large urban agglomerations, complex types and components, it is difficult to directly compare and reference existing technologies. This leads to a lot of time for researchers to make scientific decisions and formulate the best feasible process routes. The understanding of the process routes of solid waste and solid waste resource extraction products is not comprehensive enough, which easily leads to high trial and error costs.

[0004] The inventors have found through research that the methods for extracting solid waste resources in the prior art still have at least the following defects:

[0005] It is impossible to efficiently extract the material and energy resources of solid waste.

[0006] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgment or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the invention

[0007] The purpose of the present invention is to be able to efficiently extract material resources and energy resources of solid waste.

[0008] The present invention provides a method for predicting product process indicators for solid waste resource extraction, comprising the steps of:

[0009] S11, setting the process control data of solid waste resource treatment in the solid waste resource data as the first preset data; the process control data includes one or more data of temperature, pressure and processing volume;

[0010] S12, pre-processing the solid waste resource data in the preset data database, including:

[0011] Updating the first preset data includes:

[0012] respectively calculating the difference between each item of data in the first preset data and the corresponding standard data, and updating each item of data in the first preset data with the difference; determining the standard data includes determining the standard data corresponding to each item of data in the first preset data based on the set product process index;

[0013] S13. Using various data of solid waste resource data as characteristic data, a prediction model is established for the purpose of predicting product process indicators.

[0014] Preferably, in an embodiment of the present invention, the preprocessing further includes:

[0015] Updating the second preset data includes:

[0016] By re-encoding, each item of data in the second preset data is converted into binary data; the second preset data is the solid waste resource source data, solid waste resource spatiotemporal information data and solid waste resource processing process route data in the solid waste resource data.

[0017] Preferably, in an embodiment of the present invention, the preprocessing further includes:

[0018] A first dimensionality reduction process is performed on the third preset data and the first preset data; the third preset data is the physical and chemical property data of the solid waste resources in the solid waste resource data.

[0019] Preferably, in an embodiment of the present invention, the preprocessing further includes:

[0020] The fourth preset data is updated, including:

[0021] According to the preset level classification rules, the levels corresponding to the various data of the product process indicator are determined respectively; the fourth preset data is the product process indicator data.

[0022] Preferably, in an embodiment of the present invention, the preprocessing further includes:

[0023] The second preset data, the first preset data after the first dimensionality reduction processing, and the third preset data are subjected to a second dimensionality reduction processing, and each item of data is updated to be new feature data.

[0024] In another aspect of the present invention, there is also provided a product process index prediction device for solid waste resource extraction, comprising:

[0025] A data classification unit, used to set the process control data of solid waste resource treatment in the solid waste resource data as the first preset data; the process control data includes one or more data of temperature, pressure and processing volume;

[0026] A preprocessing unit, used for preprocessing the solid waste resource data in the preset data database, including a first updating module;

[0027] The first updating module is used to update the first preset data, including:

[0028] respectively calculating the difference between each item of data in the first preset data and the corresponding standard data, and updating each item of data in the first preset data with the difference; determining the standard data includes determining the standard data corresponding to each item of data in the first preset data based on the set product process index;

[0029] The modeling unit is used to establish a prediction model for the purpose of predicting product process indicators by taking various data of solid waste resource data as feature data.

[0030] Preferably, in the embodiment of the present invention, the pre-processing unit further includes:

[0031] The second updating module is used to update the second preset data, including:

[0032] By re-encoding, each item of data in the second preset data is converted into binary data; the second preset data is the solid waste resource source data, solid waste resource spatiotemporal information data and solid waste resource processing process route data in the solid waste resource data.

[0033] Preferably, in the embodiment of the present invention, the pre-processing unit further includes:

[0034] The third updating module is used to perform a first dimensionality reduction process on the third preset data and the first preset data; the third preset data is the physical and chemical property data of the solid waste resources in the solid waste resource data.

[0035] Preferably, in the embodiment of the present invention, the pre-processing unit further includes:

[0036] The fourth updating module is used to update the fourth preset data, including:

[0037] According to the preset level classification rules, the levels corresponding to the various data of the product process indicator are determined respectively; the fourth preset data is the product process indicator data.

[0038] Preferably, in the embodiment of the present invention, the pre-processing unit further includes:

[0039] The fifth updating module is used to perform a second dimensionality reduction process on the second preset data, the first preset data after the first dimensionality reduction process, and the third preset data, and update each data into new feature data.

[0040] On the other hand, there is provided a device for predicting product process indicators for solid waste resource extraction, which includes a computer program stored on a medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the methods described in the above aspects and achieves the same technical effects.

[0041] On the other hand, a storage medium is provided on which a computer program is stored. When the computer program is executed by a processor, each step of the product process indicator prediction method for solid waste resource extraction as described in any one of the above items is implemented.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The present invention provides a method for predicting product process indicators for solid waste resource extraction, firstly, setting the process control data of solid waste resource treatment in the solid waste resource data as the first preset data; then preprocessing the solid waste resource data, including updating the first preset data; respectively calculating the difference between each data in the first preset data and the corresponding standard data, and updating each data in the first preset data with the difference; determining the standard data, including determining the standard data corresponding to each data in the first preset data based on the set product process indicator; finally, using each data of the solid waste resource data as feature data, establishing a prediction model for the purpose of predicting the product process indicator.

[0044] In the present invention, the correspondence between the difference between the direct data of the first preset data and the standard data and the product process index is clearer. Therefore, the prediction accuracy of the prediction model can be effectively improved by preprocessing the first preset data.

[0045] Furthermore, the present invention conducts multi-dimensional analysis on raw material data of solid waste resources, process route data of solid waste resource treatment, process control data of solid waste resource treatment and product process index data, and pre-processes the solid waste resource data; determines the correlation between various data of solid waste resource data and product process index, and establishes a high-accuracy prediction model; in this way, the user can first collect data on the solid waste resources to be processed and generate input data for the prediction model; and then, according to actual needs, determine the optimal process route and process control strategy for solid waste resource treatment through the prediction model's prediction of product process indexes, so that the prediction model can provide effective guidance for efficient extraction of solid waste resources.

[0046] In addition, the present invention is mainly based on feature data identification and prediction of recycled materials, and establishes a technology that can quickly identify feature data and extract solid waste resource raw materials, forming an intelligent decision-making method that integrates solid waste resource property analysis, optimal route selection, product process indicator prediction and economic cost accounting.

[0047] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, and to make the above and other purposes, technical features and advantages of the present invention easier to understand, one or more preferred embodiments are listed below and described in detail with the accompanying drawings as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 It is a step diagram of the product process index prediction method for solid waste resource extraction in the present invention;

[0050] Figure 2 It is a structural schematic diagram of a product process index prediction device for solid waste resource extraction in the present invention;

[0051] Figure 3 It is a structural schematic diagram of a product process index prediction device for solid waste resource extraction in the present invention. DETAILED DESCRIPTION

[0052] The specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings, but it should be understood that the protection scope of the present invention is not limited by the specific implementation modes.

[0053] Unless explicitly stated otherwise, throughout the specification and claims, the term “comprise” or variations such as “include” or “comprising”, etc., will be understood to include the stated elements or components but not to exclude other elements or components.

[0054] In this document, the terms "first", "second", etc. are used to distinguish two different elements or parts, and are not used to limit a specific position or relative relationship. In other words, in some embodiments, the terms "first", "second", etc. can also be interchangeable.

[0055] Embodiment 1

[0056] In order to efficiently extract the material and energy resources of solid waste, such as Figure 1 As shown, in an embodiment of the present invention, a method for predicting product process indicators for solid waste resource extraction is provided, comprising the steps of:

[0057] S11, setting the process control data of solid waste resource treatment in the solid waste resource data as the first preset data; the process control data includes one or more data of temperature, pressure and processing volume;

[0058] The inventive ideas of the present invention include: since the raw materials of solid waste resources come from a wide range of sources and are complex in types and composition, most of the existing prediction methods use single-factor analysis methods, which are not comprehensive enough in analyzing solid waste resources, resulting in poor guidance performance for solid waste resource extraction products, thereby bringing higher trial and error costs.

[0059] In order to solve the above problems, the present invention conducts multi-dimensional analysis on solid waste resource source data, solid waste resource spatiotemporal information data, solid waste resource treatment process route data, solid waste resource physical and chemical property data, solid waste resource treatment process control data and product process indicator data, and pre-processes the solid waste resource data; determines the correlation between various data of solid waste resource data and product process indicators, and establishes a high-accuracy prediction model.

[0060] In an embodiment of the present invention, solid waste resource data is classified, and process control data for solid waste resource treatment in the solid waste resource data is set as first preset data; wherein the process control data for solid waste resource treatment may specifically include temperature, pressure, residence time and processing volume corresponding to each process route.

[0061] S12, pre-processing the solid waste resource data in the preset data database, including:

[0062] Updating the first preset data includes:

[0063] respectively calculating the difference between each item of data in the first preset data and the corresponding standard data, and updating each item of data in the first preset data with the difference; determining the standard data includes determining the standard data corresponding to each item of data in the first preset data based on the set product process index;

[0064] For solid waste resource extraction products, since the solid waste resource data includes a large number of data items, if the original values ​​of these data items are used as feature data for model training and verification during modeling, the generated prediction model will have poor prediction accuracy.

[0065] After research, the inventors found that some data items in the solid waste resource data, such as the process control data of solid waste resource treatment, can determine the optimal value of the data item for solid waste resource extraction products based on the product process indicators; for example, under a certain product process indicator standard, the optimal value of each data item in the corresponding process control data.

[0066] Next, in the embodiment of the present invention, for the set product process index, the optimal value corresponding to the process control data item in the first preset data is predetermined, and the optimal value of the process control data item is used as the standard data.

[0067] After the above settings, the specific process of preprocessing the first preset data may include:

[0068] For a certain data item, the difference between the data of the data item and the standard data is calculated according to the corresponding standard data, and the data of the corresponding data item is updated with the difference result, thereby replacing the original data; for example: the standard data for determining the temperature value in the process control data is A, and the original temperature value obtained from the solid waste resource data is B, and the difference between B and A is updated to the original temperature value.

[0069] For the first preset data, compared with the direct data itself, the correspondence between the difference between the direct data and the standard data and the product process index will be clearer. Therefore, after the above preprocessing, the prediction accuracy of the prediction model can be effectively improved.

[0070] Furthermore, in an embodiment of the present invention, updating the first preset data may also include: normalizing the covariance of the first preset data, eliminating data with high correlation, performing a first dimensionality reduction process, performing a correlation coefficient analysis on each data in the newly obtained process control data, the correlation coefficient analysis is the normalized covariance between each data, eliminating data with high correlation, and performing preliminary dimensionality reduction processing.

[0071] Furthermore, in order to further improve the accuracy of the prediction model, the preprocessing process in the embodiment of the present invention may also include: updating the second preset data, including: converting each item of data in the second preset data into binary data by re-encoding; the second preset data is the solid waste resource source data, solid waste resource spatiotemporal information data and process route data of solid waste resource treatment in the solid waste resource data.

[0072] In the implementation of the present invention, solid waste resource source data, solid waste resource spatiotemporal information data and solid waste resource treatment process route data (i.e., second preset data), wherein the solid waste resource source data may include: industrial solid waste resources, waste materials, urban and rural garbage and hazardous waste and other data items; the solid waste resource spatiotemporal information data may include location and time and other data items; the solid waste resource treatment process route data may include combustion, pyrolysis, gasification, composting, regeneration and other data items.

[0073] Since the data conditions of solid waste resource sources, spatiotemporal information data of solid waste resources and process route data of solid waste resource treatment are relatively fixed and are all non-numerical data items, they are re-encoded according to one-hot coding and converted into binary data.

[0074] Furthermore, in order to further improve the accuracy of the prediction model, in an embodiment of the present invention, the preprocessing process may also include: performing a first dimensionality reduction processing on the third preset data and the first preset data; the third preset data is the physical and chemical properties data of the solid waste resources in the solid waste resource data; wherein the physical and chemical properties data of the solid waste resources may include data items such as elemental composition, density, carbon-hydrogen ratio and volatile matter.

[0075] Furthermore, in an embodiment of the present invention, preprocessing may also include: updating fourth preset data, including: determining the levels corresponding to various data of product process indicators respectively according to preset level division rules; the fourth preset data is the product process indicator data.

[0076] Since whether the product process index meets the preset standard is judged based on whether each index is within the set range, in order to reduce the difficulty of correspondence between the independent variable data and the dependent variable data in the prediction model and reduce the difficulty of modeling the prediction model, in the embodiment of the present invention, a method of characterizing the data of each index (i.e., each data) of the product process index by setting a grade prediction value is also adopted, specifically:

[0077] For each data item of the product process indicator, a corresponding classification interval is set. For example, for the data item of gas yield, it can be divided into multiple classifications. In this way, the corresponding level can be determined according to the specific values ​​of the product process indicator data. Similarly, other data items such as gas calorific value and ton power generation can be divided into multiple classifications.

[0078] In practical applications, the data items of product process indicators may specifically include: gas yield, gas calorific value, power generation per ton, gas hydrogen-carbon ratio, exhaust gas pollutant concentration, recycled material blending ratio, etc.

[0079] It should be noted that, in the embodiments of the present invention, the number of classifications of each data item of the product process indicator can be determined by those skilled in the art according to needs and experience, and is not specifically limited here.

[0080] Furthermore, in an embodiment of the present invention, the preprocessing may also include: performing a second dimensionality reduction processing on the second preset data, as well as the first preset data after the first dimensionality reduction processing and the third preset data, and updating each item of data to new feature data; in actual applications, the principal component analysis (PCA) method may be used to further reduce the dimensionality of the above three types of preset data to obtain new feature data for each item after reduction.

[0081] S13. Using various data of solid waste resource data as characteristic data, a prediction model is established for the purpose of predicting product process indicators.

[0082] In the embodiment of the present invention, the prediction model may select a neural network model or an integrated learning model according to the actual application; the neural network model or the integrated learning model is established according to the preprocessed data.

[0083] The prediction model is trained by the constructed training set, and the predicted values ​​of the product process indicators are output; the accuracy of the prediction model is verified by the constructed test set; in the embodiment of the present invention, the data set is composed of the new feature data and the predicted values ​​of various data of the product process indicators; and the data set is reasonably divided into a training set and a test set.

[0084] The prediction model is machine-learned through the training set, and the prediction model outputs the predicted values ​​of each data item of the product process indicators extracted from solid waste resources in each process route. The accuracy of the prediction model is then continuously cross-validated through the test set to obtain a prediction model with high accuracy.

[0085] In practical applications, users can first collect data on the solid waste resources currently to be processed to generate input data for the prediction model (such as solid waste resource source data and solid waste resource spatiotemporal information data, etc.); then, based on actual needs, the prediction model can be used to predict product process indicators to determine the optimal process route and process control strategy for solid waste resource treatment.

[0086] The following is a specific example to illustrate the optimization effect of the prediction model on the efficiency of solid waste resource extraction. For example, the forest waste generated in a certain area of ​​Liaoning Province was analyzed and tested by the experimenters. The solid waste resource source data, solid waste resource spatiotemporal information data, solid waste resource physical and chemical properties data, solid waste resource market processing costs and target recycled material output and cost data were submitted to the prediction system including the prediction model. The prediction system can query that such solid waste resource raw materials can be used to produce synthesis gas. The prediction model is used to predict and score this type of forest waste. The result is that the best process route is to produce hydrogen-rich synthesis gas with a quality of 7 points by gasification method. According to this process route, the actual result is synthesis gas with a hydrogen concentration of 60%, which provides raw materials for further synthesis of jet fuel, thereby verifying the effectiveness of the prediction model in optimizing the process route when extracting solid waste resources.

[0087] In practical applications, the database in the embodiment of the present invention includes an operation interface (such as user access, data security and background maintenance, etc.), an underlying database mainly based on various solid waste resources, a reserved interface for statistical commercial software, a data import and analysis visualization toolkit and a client program.

[0088] The database data includes the sources of solid waste resources generated by an urban agglomeration in a certain area of ​​Liaoning (such as industrial solid waste, waste materials, urban and rural garbage and hazardous waste, etc.), the spatiotemporal information of solid waste resources (such as the location and time of solid waste resources, etc.), the market price of solid waste resource treatment costs, the output and transportation cost of solid waste resources, the physical and chemical properties of solid waste resources (such as elemental composition, density, carbon-hydrogen ratio and volatile matter, etc.), the process routes for treating solid waste resources in the solid waste resource recycling park in the region (such as combustion, pyrolysis, gasification, composting and recycling, etc.), process conditions (such as temperature, pressure and processing volume, etc.) and product process indicators (such as gas yield, gas calorific value, ton power generation, gas hydrogen-carbon ratio, tail gas pollutant concentration and recycled material blending ratio, etc.); the abnormal data are eliminated through statistical calculation methods, the useful information is classified and labeled according to the above variables, and stored in different target files of the database, the elements are extracted through the element extraction model, and the query model is trained.

[0089] When users conduct forward queries based on data items such as the source of solid waste resources and the physical and chemical properties of solid waste resources to obtain different process routes and product process indicators; or conduct reverse searches for different solid resources based on process routes and product process indicators, the query model calculates the similarity of various types of tags in the database based on the query request, and determines the tag with the highest similarity, determines the tag to be queried, and outputs the target file containing this type of tag as the result, which is convenient for users to choose; in addition, the data has a built-in mathematical operation toolkit to facilitate users to calculate numerical data.

[0090] Furthermore, when implementing data type classification, the present invention can perform the following analysis and matching on the original data of solid waste resources, specifically:

[0091] The data set to be processed for new solid waste resources: the sources of solid waste resources (such as industrial solid waste, waste materials, urban and rural garbage and hazardous waste, etc.), the spatiotemporal information of solid waste resources (such as the location and time of solid waste resources, etc.), the physical and chemical properties of solid waste resources (such as elemental composition, density, carbon-hydrogen ratio and volatile matter, etc.) and other data are input into the database, and the abnormal data are eliminated through statistical calculation methods, and the useful information is classified and labeled and stored in the new target file of the database; according to one or more tags selected by the user, the target file containing these tags is queried and output, and the similarity of each solid waste resource, extraction device tag information and each information item in the new target file selected in the output target file is calculated, and the target file with higher similarity is output again; the user can make scientific decisions based on the process routes and process indicators of existing solid waste resources in the database that have high similarity with the new solid waste resources.

[0092] In summary, the present invention provides a method for predicting product process indicators for solid waste resource extraction, firstly, the process control data of solid waste resource treatment in the solid waste resource data is set as the first preset data; then the solid waste resource data is preprocessed, including updating the first preset data; the difference between each data in the first preset data and the corresponding standard data is calculated respectively, and each data in the first preset data is updated with the difference; determining the standard data includes, based on the set product process indicators, determining the standard data corresponding to each data in the first preset data; finally, using each data of the solid waste resource data as feature data, a prediction model for the purpose of predicting product process indicators is established.

[0093] In the present invention, the correspondence between the difference between the direct data of the first preset data and the standard data and the product process index is clearer. Therefore, the prediction accuracy of the prediction model can be effectively improved by preprocessing the first preset data.

[0094] Furthermore, the present invention conducts multi-dimensional analysis on raw material data of solid waste resources, process route data of solid waste resource treatment, process control data of solid waste resource treatment and product process index data, and pre-processes the solid waste resource data; determines the correlation between various data of solid waste resource data and product process index, and establishes a high-accuracy prediction model; in this way, the user can first collect data on the solid waste resources to be processed and generate input data for the prediction model; and then, according to actual needs, determine the optimal process route and process control strategy for solid waste resource treatment through the prediction model's prediction of product process indexes, so that the prediction model can provide effective guidance for efficient extraction of solid waste resources.

[0095] In addition, the present invention is mainly based on feature data identification and prediction of recycled materials, and establishes a technology that can quickly identify feature data and extract solid waste resource raw materials, forming an intelligent decision-making method that integrates solid waste resource property analysis, optimal route selection, product process indicator prediction and economic cost accounting.

[0096] Embodiment 2

[0097] Corresponding to the method embodiment, in another aspect of the embodiment of the present invention, a product process index prediction device for solid waste resource extraction is also provided. Figure 2 The schematic diagram of the structure of the product process index prediction device for solid waste resource extraction provided by the embodiment of the present invention is shown. The product process index prediction device for solid waste resource extraction is Figure 1 The device corresponding to the method for predicting product process indicators for solid waste resource extraction described in the corresponding embodiment, that is, implemented by means of a virtual device Figure 1 The product process index prediction method for solid waste resource extraction in the corresponding embodiment, each virtual module constituting the product process index prediction device for solid waste resource extraction can be executed by an electronic device, such as a network device, a terminal device or a server. Specifically, the product process index prediction device for solid waste resource extraction in the embodiment of the present invention includes:

[0098] The data classification unit 01 is used to set the process control data of solid waste resource treatment in the solid waste resource data as the first preset data; the process control data includes one or more data of temperature, pressure and treatment volume;

[0099] The preprocessing unit 02 is used to preprocess the solid waste resource data in the preset data database, including a first updating module;

[0100] The first updating module is used to update the first preset data, including:

[0101] respectively calculating the difference between each item of data in the first preset data and the corresponding standard data, and updating each item of data in the first preset data with the difference; determining the standard data includes determining the standard data corresponding to each item of data in the first preset data based on the set product process index;

[0102] In the embodiment of the present invention, for the set product process index, the optimal value corresponding to the process control data item in the first preset data is predetermined, and the optimal value of the process control data item is used as the standard data.

[0103] After the above settings, the specific process of preprocessing the first preset data may include:

[0104] For a certain data item, the difference between the data of the data item and the standard data is calculated according to the corresponding standard data, and the data of the corresponding data item is updated with the difference result, thereby replacing the original data; for example: the standard data for determining the temperature value in the process control data is A, and the original temperature value obtained from the solid waste resource data is B, and the difference between B and A is updated to the original temperature value.

[0105] For the first preset data, compared with the direct data itself, the correspondence between the difference between the direct data and the standard data and the product process index will be clearer. Therefore, after the above preprocessing, the prediction accuracy of the prediction model can be effectively improved.

[0106] Furthermore, in an embodiment of the present invention, the preprocessing unit 02 may also include: a second update module, used to update the second preset data, including: converting each item of data in the second preset data into binary data by re-encoding; the second preset data is the solid waste resource source data, solid waste resource spatiotemporal information data and process route data of solid waste resource treatment in the solid waste resource data.

[0107] Solid waste resource source data, solid waste resource spatiotemporal information data and solid waste resource treatment process route data (i.e., the second preset data), wherein the solid waste resource source data may include: industrial solid waste resources, waste materials, urban and rural garbage, and hazardous wastes and other data items; solid waste resource spatiotemporal information data items may include location and time and other data items; solid waste resource treatment process route data may include combustion, pyrolysis, gasification, composting, regeneration and other data items.

[0108] Since the data conditions of solid waste resource sources, spatiotemporal information data of solid waste resources and process route data of solid waste resource treatment are relatively fixed and are all non-numerical data items, they are re-encoded according to one-hot coding and converted into binary data.

[0109] Furthermore, in an embodiment of the present invention, the preprocessing unit 02 may also include: a third updating module, used to perform a first dimensionality reduction processing on third preset data and the first preset data; the third preset data is the physical and chemical properties data of solid waste resources in the solid waste resource data; wherein, the physical and chemical properties data of solid waste resources may include data items such as elemental composition, density, carbon-hydrogen ratio and volatile matter.

[0110] Furthermore, in the embodiment of the present invention, the preprocessing unit 02 may further include: a fourth updating module, configured to update the fourth preset data, including:

[0111] According to the preset level classification rules, the levels corresponding to the various data of the product process indicator are determined respectively; the fourth preset data is the product process indicator data.

[0112] Since whether the product process index meets the preset standard is judged based on whether each index is within the set range, in order to reduce the difficulty of correspondence between the independent variable data and the dependent variable data in the prediction model and reduce the difficulty of modeling the prediction model, in the embodiment of the present invention, a method of characterizing the data of each index (i.e., each data) of the product process index by setting a grade prediction value is also adopted, specifically:

[0113] For each data item of the product process indicator, a corresponding classification interval is set. For example, for the data item of gas yield, it can be divided into multiple classifications. In this way, the corresponding level can be determined according to the specific values ​​of the product process indicator data. Similarly, other data items such as gas calorific value and ton power generation can be divided into multiple classifications.

[0114] Furthermore, in the embodiment of the present invention, the preprocessing unit 02 may further include:

[0115] The fifth updating module is used to perform a second dimensionality reduction processing on the second preset data, as well as the first preset data after the first dimensionality reduction processing and the third preset data, and update each data into a new feature data; in actual applications, the principal component analysis PCA method can be used to further reduce the dimensionality of the above three types of preset data to obtain new feature data for each item after reduction.

[0116] The modeling unit 03 is used to establish a prediction model for the purpose of predicting product process indicators by taking various data of solid waste resource data as feature data.

[0117] In the embodiment of the present invention, the prediction model may select a neural network model or an integrated learning model according to the actual application; the neural network model or the integrated learning model is established according to the preprocessed data.

[0118] The prediction model is trained by the constructed training set, and the predicted values ​​of the product process indicators are output; the accuracy of the prediction model is verified by the constructed test set; in the embodiment of the present invention, the data set is composed of the new feature data and the predicted values ​​of various data of the product process indicators; and the data set is reasonably divided into a training set and a test set.

[0119] The prediction model is machine-learned through the training set, and the prediction model outputs the predicted values ​​of each data item of the product process indicators extracted from solid waste resources in each process route. The accuracy of the prediction model is then continuously cross-validated through the test set to obtain a prediction model with high accuracy.

[0120] It should be noted that the specific implementation and technical effects of the product process index prediction device for solid waste resource extraction in the embodiment of the present invention can be referred to Figure 1The corresponding product process index prediction method for solid waste resource extraction will not be repeated here.

[0121] Embodiment 3

[0122] Corresponding to the method embodiment, in the embodiment of the present invention, there is also provided a product process index prediction device for solid waste resource extraction, such as a terminal, a server, etc. Among them, the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto.

[0123] An example diagram of a hardware structure block diagram of a product process index prediction device for solid waste resource extraction provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, this may include:

[0124] Processor 1, communication interface 2, memory 3 and communication bus 4;

[0125] The processor 1, the communication interface 2, and the memory 3 communicate with each other via the communication bus 4;

[0126] Optionally, the communication interface 2 may be an interface of a communication module, such as an interface of a GSM module;

[0127] The processor 1 may be a central processing unit CPU, or an application-specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0128] The memory 3 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0129] The processor 1 is specifically used to execute the computer program stored in the memory 3 to perform the following steps:

[0130] S11, setting the process control data of solid waste resource treatment in the solid waste resource data as the first preset data; the process control data includes one or more data of temperature, pressure and processing volume;

[0131] S12, pre-processing the solid waste resource data in the preset data database, including:

[0132] Updating the first preset data includes:

[0133] respectively calculating the difference between each item of data in the first preset data and the corresponding standard data, and updating each item of data in the first preset data with the difference; determining the standard data includes determining the standard data corresponding to each item of data in the first preset data based on the set product process index;

[0134] S13. Using various data of solid waste resource data as characteristic data, a prediction model is established for the purpose of predicting product process indicators.

[0135] The above product can execute the method provided in the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the product process index prediction method for solid waste resource extraction provided in the embodiment of the present invention.

[0136] Embodiment 4

[0137] In an embodiment of the present invention, a storage medium is further provided, which may store a program suitable for execution by a processor, wherein the program is used to:

[0138] S11, setting the process control data of solid waste resource treatment in the solid waste resource data as the first preset data; the process control data includes one or more data of temperature, pressure and processing volume;

[0139] S12, pre-processing the solid waste resource data in the preset data database, including:

[0140] Updating the first preset data includes:

[0141] respectively calculating the difference between each item of data in the first preset data and the corresponding standard data, and updating each item of data in the first preset data with the difference; determining the standard data includes determining the standard data corresponding to each item of data in the first preset data based on the set product process index;

[0142] S13. Using various data of solid waste resource data as characteristic data, a prediction model is established for the purpose of predicting product process indicators.

[0143] Optionally, the detailed functions and extended functions of the program may refer to the above description.

[0144] The above product can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the methods provided by other embodiments of the present invention.

[0145] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0146] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0147] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0148] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0149] It should be understood that in the embodiments of the present application, the various embodiments and features can be combined with each other to solve the aforementioned technical problems.

[0150] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0151] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting product process indicators for solid waste resource extraction, characterized in that: Includes steps: S11, setting the process control data of solid waste resource treatment in the solid waste resource data as the first preset data; the process control data includes one or more data of temperature, pressure and processing volume; S12, pre-processing the solid waste resource data in the preset data database, including: Updating the first preset data includes: respectively calculating the difference between each item of data in the first preset data and the corresponding standard data, and updating each item of data in the first preset data with the difference; determining the standard data includes determining the standard data corresponding to each item of data in the first preset data based on the set product process index; S13. Using various data of solid waste resource data as characteristic data, a prediction model is established for the purpose of predicting product process indicators.

2. The method for predicting product process indicators for solid waste resource extraction according to claim 1, characterized in that: The preprocessing further comprises: Updating the second preset data includes: By re-encoding, each item of data in the second preset data is converted into binary data; the second preset data is the solid waste resource source data, solid waste resource spatiotemporal information data and solid waste resource processing process route data in the solid waste data.

3. The method for predicting product process indicators for solid waste resource extraction according to claim 2, characterized in that: The preprocessing further comprises: A first dimensionality reduction process is performed on the third preset data and the first preset data; the third preset data is the physical and chemical property data of the solid waste resources in the solid waste resource data.

4. The method for predicting product process indicators for solid waste resource extraction according to claim 3, characterized in that: The preprocessing further comprises: The fourth preset data is updated, including: According to the preset level classification rules, the levels corresponding to the various data of the product process indicator are determined respectively; the fourth preset data is the product process indicator data.

5. The method for predicting product process indicators for solid waste resource extraction according to claim 4, characterized in that: The preprocessing further comprises: The second preset data, the first preset data after the first dimensionality reduction processing, and the third preset data are subjected to a second dimensionality reduction processing, and each item of data is updated to be new feature data.

6. A product process index prediction device for solid waste resource extraction, characterized in that: include: A data classification unit, used for setting the process control data of solid waste resource treatment in the solid waste resource data as the first preset data; The process control data includes one or more data of temperature, pressure and processing volume; The preprocessing unit is used to preprocess the solid waste resource data in the preset data database, including: Updating the first preset data includes: respectively calculating the difference between each item of data in the first preset data and the corresponding standard data, and updating each item of data in the first preset data with the difference; determining the standard data includes determining the standard data corresponding to each item of data in the first preset data based on the set product process index; The modeling unit is used to establish a prediction model for the purpose of predicting product process indicators by taking various data of solid waste resource data as feature data.

7. The product process index prediction device for solid waste resource extraction according to claim 6 is characterized in that: The pre-processing unit further includes: The second updating module is used to update the second preset data, including: By re-encoding, each item of data in the second preset data is converted into binary data; the second preset data is the solid waste resource source data, solid waste resource spatiotemporal information data and solid waste resource processing process route data in the solid waste resource data.

8. The product process index prediction device for solid waste resource extraction according to claim 7 is characterized in that: The pre-processing unit further includes: The third updating module is used to perform a first dimensionality reduction process on the third preset data and the first preset data; the third preset data is the physical and chemical property data of the solid waste resources in the solid waste resource data.

9. The product process index prediction device for solid waste resource extraction according to claim 8, characterized in that: The pre-processing unit further includes: The fourth updating module is used to update the fourth preset data, including: According to the preset level classification rules, the levels corresponding to the various data of the product process indicator are determined respectively; the fourth preset data is the product process indicator data.

10. The product process index prediction device for solid waste resource extraction according to claim 9, characterized in that: The pre-processing unit further includes: The fifth updating module is used to perform a second dimensionality reduction process on the second preset data, the first preset data after the first dimensionality reduction process, and the third preset data, and update each data into new feature data.

11. A product process index prediction device for solid waste resource extraction, characterized in that: include: Memory for storing computer programs; A processor is used to call and execute the computer program to implement the steps of the product process index prediction method for solid waste resource extraction as described in any one of claims 1-5.

12. A storage medium, characterized in that: It includes a software program, which is suitable for a processor to execute the steps of the product process indicator prediction method for solid waste resource extraction as described in any one of claims 1-5.

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