Solid waste resource utilization method and system

Abstract

The invention provides a solid waste resource utilization method and system, and the method comprises the steps: constructing an evaluation index of historical solid waste data of each region, and carrying out the data evaluation through employing the evaluation index, and obtaining a correlation sequence corresponding to the historical solid waste data of each region; constructing a data graph of each region based on the correlation coefficient of each correlation sequence; inputting the data atlas of each region into a preset prediction model for data calculation to obtain a corresponding data processing result; performing model optimization on the prediction model according to the optimal order and the data processing result, and importing a resource utilization strategy into the optimized prediction model to construct an optimal processing model; and obtaining current solid waste data of each region, inputting the current solid waste data into the optimal processing model, generating a resource utilization task of each region, and processing the current solid waste data of each region according to the resource utilization task.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for resource utilization of solid waste. Background Art

[0002] With the rapid development of science and technology and the improvement of people's living standards, the emission of solid waste generated by industrialization is gradually increasing, leading to environmental pollution problems.

[0003] The resource utilization of solid waste is to transform it into new usable resources through a series of processing, production and other methods. However, the current resource utilization of solid waste is usually achieved by manually collecting the corresponding waste and judging whether the collected waste meets the requirements of resource utilization based on experience. However, as the amount of solid waste continues to increase, it is impossible to achieve accurate resource utilization requirements simply through experience, and there is a possibility that the effective reuse of waste will not meet expectations. Summary of the invention

[0004] The embodiments of the present application provide a method and system for resource utilization of solid waste to at least solve the deficiencies in the above-mentioned related technologies.

[0005] In a first aspect, an embodiment of the present application provides a method for resource utilization of solid waste, comprising the following steps: Step 1: Obtain historical solid waste data of each region, and construct evaluation indicators for each of the historical solid waste data, and use the evaluation indicators to perform data evaluation on the historical solid waste data of each of the regions to obtain a correlation sequence corresponding to the historical solid waste data of each of the regions; Step 2: Calculate the correlation coefficient of each correlation sequence, and construct a data map of each region based on each correlation coefficient; Step 3: Inputting the data map of each of the regions into a preset prediction model for data calculation to obtain corresponding data processing results; Step 4: Optimizing the prediction model according to the optimal order and the data processing result, and introducing a resource utilization strategy into the optimized prediction model to construct an optimal processing model; Step 5: Obtain the current solid waste data of each of the areas, and input the current solid waste data into the optimal treatment model, generate resource utilization tasks for each of the areas, and process the current solid waste data of each of the areas according to the resource utilization tasks.

[0006] Furthermore, the step 1 includes: Acquire data dimensions of each of the historical solid waste data, and construct evaluation indicators of each of the historical solid waste data according to the data dimensions; The evaluation index of each of the historical solid waste data is processed with explanatory variables to obtain a correlation sequence between the historical solid waste data of each of the regions.

[0007] Furthermore, the step 2 includes: Defining an availability coefficient of solid waste data, and performing data processing on each of the correlation sequences according to the availability coefficient and the correlation algorithm to calculate the correlation coefficient of each of the correlation sequences; The correlation coefficients of the correlation sequences are plotted to obtain the resource utilization degree of the solid waste in each of the regions, and a data map of each of the regions is constructed according to the resource utilization degree of the solid waste in each of the regions.

[0008] Furthermore, the step three includes: Inputting the data map into a preset prediction model to perform data calculation, so that the prediction model can optimize performance parameters to obtain the optimal order; The particle swarm optimization algorithm is used to process the data of each correlation sequence to obtain the cumulative generation sequence corresponding to each correlation sequence, and the corresponding model parameters are constructed according to each cumulative generation sequence.

[0009] Furthermore, the step 4 includes: Optimizing the prediction model according to the optimal order and the data processing result to obtain a corresponding optimized prediction model; Construct resource utilization indicators for solid waste, fuse the resource utilization indicators with the optimization prediction model, perform model error verification on the model after the indicator fusion, and output the model after the indicator fusion if the model error verification passes.

[0010] In a second aspect, the embodiment of the present application further provides a solid waste resource utilization system, including: A data evaluation module, used to obtain the historical solid waste data of each region, and construct evaluation indicators for each of the historical solid waste data, and use the evaluation indicators to perform data evaluation on the historical solid waste data of each of the regions to obtain a correlation sequence corresponding to the historical solid waste data of each of the regions; A graph construction module, used to calculate the correlation coefficient of each correlation sequence, and construct a data graph of each region based on each correlation coefficient; A data processing module, used for inputting the data map of each of the regions into a preset prediction model to perform data calculation to obtain corresponding data processing results; A model optimization module, used to optimize the prediction model according to the optimal order and the data processing result, and introduce a resource utilization strategy into the optimized prediction model to construct an optimal processing model; The resource utilization module is used to obtain the current solid waste data of each of the areas, input the current solid waste data into the optimal treatment model, generate resource utilization tasks for each of the areas, and process the current solid waste data of each of the areas according to the resource utilization tasks.

[0011] Furthermore, the data evaluation module includes: An indicator construction unit, used to obtain data dimensions of each of the historical solid waste data, and construct evaluation indicators of each of the historical solid waste data according to the data dimensions; The sequence construction unit is used to perform explanatory variable processing on the evaluation indicators of each of the historical solid waste data to obtain a correlation sequence between the historical solid waste data of each of the regions.

[0012] Furthermore, the graph construction module includes: A correlation coefficient calculation unit, used for defining an available coefficient of solid waste data, and performing data processing on each of the correlation sequences according to the available coefficient and the correlation algorithm, so as to calculate the correlation coefficient of each of the correlation sequences; The map construction unit is used to map the correlation coefficients of each of the correlation sequences to obtain the resource utilization degree of the solid waste in each of the regions, and to construct a data map of each of the regions according to the resource utilization degree of the solid waste in each of the regions.

[0013] Furthermore, the data processing module includes: A data calculation unit, used for inputting the data map into a preset prediction model to perform data calculation, so that the prediction model can optimize performance parameters to obtain the optimal order; The parameter construction unit is used to perform data processing on each of the correlation sequences using a particle swarm optimization algorithm to obtain a cumulative generation sequence corresponding to each of the correlation sequences, and to construct a corresponding model parameter according to each of the cumulative generation sequences.

[0014] Furthermore, the model optimization module includes: A model optimization unit, used for optimizing the prediction model according to the optimal order and the data processing result to obtain a corresponding optimized prediction model; The indicator fusion unit is used to construct resource utilization indicators of solid waste, and fuse the resource utilization indicators with the optimization prediction model, and perform model error verification on the model after the indicator fusion. If the model error verification passes, the model after the indicator fusion is output.

[0015] Compared with the related art, the embodiment of the present application provides a method and system for resource utilization of solid waste. Evaluation indicators are constructed for the historical solid waste data of each region, and the evaluation indicators are used to evaluate the data to obtain a correlation sequence, and the correlation coefficient is calculated using the correlation sequence. A data map of each region is constructed based on the correlation coefficient to determine the degree of resource utilization in each region, thereby improving the accuracy of resource utilization processing; a prediction model is used to perform data calculation on the data map to simulate and predict the corresponding data processing results, the data processing results and the optimal order of the model are optimized, and a resource utilization strategy is added to the model optimization, so that the solid waste data of each region can be effectively processed as a resource, further improving the processing efficiency and processing accuracy.

[0016] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 It is a flow chart of the solid waste resource utilization method in the first embodiment of the present invention; Figure 2 for Figure 1 Detailed flow chart of step S101; Figure 3 for Figure 1 Detailed flow chart of step S102; Figure 4 for Figure 1 Detailed flow chart of step S103; Figure 5 for Figure 1 Detailed flow chart of step S104; Figure 6 It is a structural block diagram of a solid waste resource utilization system in the second embodiment of the present invention.

[0018] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0020] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0021] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0022] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. Embodiment 1

[0023] See also Figure 1, which shows a method for resource utilization of solid waste in the first embodiment of the present invention, and the method specifically includes steps S101 to S105: S101, obtaining historical solid waste data of each region, and constructing evaluation indicators for each of the historical solid waste data, and using the evaluation indicators to perform data evaluation on the historical solid waste data of each of the regions to obtain a correlation sequence corresponding to the historical solid waste data of each of the regions; For further information, see Figure 2 , the step S101 specifically includes steps S1011~S1012: S1011, obtaining data dimensions of each of the historical solid waste data, and constructing evaluation indicators of each of the historical solid waste data according to the data dimensions; S1012, performing explanatory variable processing on the evaluation index of each of the historical solid waste data to obtain a correlation sequence between the historical solid waste data of each of the regions.

[0024] In the specific implementation, the data dimension of the historical solid waste data of each region is obtained, wherein the data dimension includes statistical indicators related to the solid waste data, that is, the relevant benefits that can be generated when the solid waste is used as a resource, such as: the economic benefits, environmental benefits and social benefits generated by the resource utilization; Specifically, evaluation indicators of each historical solid waste data are constructed according to the obtained data dimensions, the evaluation indicators are initialized, and the grayscale correlation analysis algorithm is used to perform explanatory variable processing on the evaluation indicators after the initialization. The discretized data are converted into a continuous segmented geometric broken line form through linear interpolation, so as to construct the correlation sequence between the historical solid waste data of each region. The correlation sequence can reflect the development trend and corresponding development degree of the historical solid waste data of each region when utilizing resources.

[0025] S102, calculating the correlation coefficient of each correlation sequence, and constructing a data map of each region based on each correlation coefficient; For further information, see Figure 3 , the step S102 specifically includes steps S1021~S1022: S1021, defining an availability coefficient of solid waste data, and performing data processing on each of the correlation sequences according to the availability coefficient and the correlation algorithm to calculate a correlation coefficient of each of the correlation sequences; S1022, mapping the correlation coefficients of the correlation sequences to obtain the resource utilization degree of the solid waste in each of the regions, and constructing a data map of each of the regions according to the resource utilization degree of the solid waste in each of the regions.

[0026] In the specific implementation, the availability coefficient of solid waste data is defined, and the correlation sequence above is analyzed using the correlation algorithm and the availability coefficient. Data processing is performed to obtain the correlation coefficient of each correlation series: ; In the formula, Indicates the month, represents the availability coefficient, represents the minimum value in the correlation sequence, Represents the maximum value in the correlation series.

[0027] Furthermore, the correlation coefficient obtained above is plotted into a corresponding correlation coefficient line graph to obtain the resource utilization degree of solid waste in each region, and the obtained resource utilization degree is constructed into a corresponding data map, wherein in the data map, the larger the correlation coefficient coefficient, the higher the resource utilization degree of solid waste in the corresponding region.

[0028] S103, inputting the data map of each of the regions into a preset prediction model for data calculation to obtain corresponding data processing results; For further information, see Figure 4 , the step S103 specifically includes steps S1031~S1032: S1031, inputting the data map into a preset prediction model to perform data calculation, so that the prediction model can optimize performance parameters to obtain the optimal order; S1032, using a particle swarm optimization algorithm to perform data processing on each of the correlation sequences to obtain a cumulative generation sequence corresponding to each of the correlation sequences, and constructing a corresponding model parameter according to each of the cumulative generation sequences.

[0029] In the specific implementation, the data map obtained above is input into the pre-built prediction model for data calculation, so that the prediction model can optimize the performance parameters to obtain the optimal order, and the particle swarm optimization algorithm is used to process the data of each correlation sequence to obtain the cumulative generation sequence corresponding to each correlation sequence; Furthermore, the accumulated generated sequence is estimated by least square method to obtain corresponding basic model parameters, wherein the basic model parameters include model performance parameters and model structure parameters.

[0030] S104, optimizing the prediction model according to the optimal order and the data processing result, and introducing a resource utilization strategy into the optimized prediction model to construct an optimal processing model; For further information, see Figure 5 , the step S104 specifically includes steps S1041-S1042: S1041, optimizing the prediction model according to the optimal order and the data processing result to obtain a corresponding optimized prediction model; S1042, construct resource utilization indicators for solid waste, and fuse the resource utilization indicators with the optimization prediction model, perform model error verification on the model after the indicator fusion, and output the model after the indicator fusion if the model error verification passes.

[0031] In the specific implementation, the prediction model is optimized by using the optimal order and model parameters obtained above to calculate the intermediate variables of the prediction model, and the intermediate variables are calculated using a preset time response algorithm to obtain the corresponding optimized prediction model; Furthermore, a resource utilization index of solid waste is constructed, which is the index information corresponding to the resource utilization of solid waste. The resource utilization index is fused with the optimization prediction model to integrate the resource utilization index into the optimization prediction model, and the model error verification is performed on the model after the indicator fusion to ensure that the error of the model after the indicator fusion meets the preset error threshold. If the error of the model after the indicator fusion meets the preset error threshold, it means that the model error verification is passed, and the model after the indicator fusion is output.

[0032] S105, obtaining current solid waste data of each of the areas, and inputting the current solid waste data into the optimal processing model, generating resource utilization tasks for each of the areas, and processing the current solid waste data of each of the areas according to the resource utilization tasks.

[0033] During the specific implementation, the current solid waste data of each area is obtained, and the current solid waste data is input into the above-mentioned optimal treatment model, so that the optimal treatment model generates tasks according to the type of solid waste and data dimension, thereby obtaining the resource utilization tasks of each area, and using the resource utilization tasks to treat the current solid waste areas in each area.

[0034] In summary, the solid waste resource utilization method in the above-mentioned embodiment of the present invention constructs evaluation indicators for the historical solid waste data of each region, and uses the evaluation indicators to evaluate the data to obtain a correlation sequence, uses the correlation sequence to calculate the correlation coefficient, and constructs a data map of each region based on the correlation coefficient to determine the degree of resource utilization in each region, thereby improving the accuracy of resource utilization processing; uses the prediction model to perform data calculation on the data map to simulate and predict the corresponding data processing results, optimizes the data processing results and the optimal order of the model, and adds a resource utilization strategy to the model optimization, so that the solid waste data of each region can be effectively processed as a resource, and the processing efficiency and processing accuracy can be further improved. Embodiment 2

[0035] Another aspect of the present invention is to provide a solid waste resource utilization system. Figure 6 , shown is a solid waste resource utilization system in the second embodiment of the present invention, comprising: The data evaluation module 11 is used to obtain the historical solid waste data of each region, and construct evaluation indicators for each of the historical solid waste data, and use the evaluation indicators to perform data evaluation on the historical solid waste data of each region to obtain a correlation sequence corresponding to the historical solid waste data of each region; Furthermore, the data evaluation module 11 includes: An indicator construction unit, used to obtain data dimensions of each of the historical solid waste data, and construct evaluation indicators of each of the historical solid waste data according to the data dimensions; The sequence construction unit is used to perform explanatory variable processing on the evaluation indicators of each of the historical solid waste data to obtain a correlation sequence between the historical solid waste data of each of the regions.

[0036] A graph construction module 12, used to calculate the correlation coefficient of each correlation sequence, and construct a data graph of each region based on each correlation coefficient; Furthermore, the graph construction module 12 includes: A correlation coefficient calculation unit, used for defining an available coefficient of solid waste data, and performing data processing on each of the correlation sequences according to the available coefficient and the correlation algorithm, so as to calculate the correlation coefficient of each of the correlation sequences; The map construction unit is used to map the correlation coefficients of each of the correlation sequences to obtain the resource utilization degree of the solid waste in each of the regions, and to construct a data map of each of the regions according to the resource utilization degree of the solid waste in each of the regions.

[0037] The data processing module 13 is used to input the data map of each region into a preset prediction model to perform data calculation to obtain corresponding data processing results; Furthermore, the data processing module 13 includes: A data calculation unit, used for inputting the data map into a preset prediction model to perform data calculation, so that the prediction model can optimize performance parameters to obtain the optimal order; The parameter construction unit is used to perform data processing on each of the correlation sequences using a particle swarm optimization algorithm to obtain a cumulative generation sequence corresponding to each of the correlation sequences, and to construct a corresponding model parameter according to each of the cumulative generation sequences.

[0038] A model optimization module 14 is used to optimize the prediction model according to the optimal order and the data processing result, and introduce a resource utilization strategy into the optimized prediction model to construct an optimal processing model; Furthermore, the model optimization module 14 includes: A model optimization unit, used for optimizing the prediction model according to the optimal order and the data processing result to obtain a corresponding optimized prediction model; The indicator fusion unit is used to construct resource utilization indicators of solid waste, and fuse the resource utilization indicators with the optimization prediction model, and perform model error verification on the model after the indicator fusion. If the model error verification passes, the model after the indicator fusion is output.

[0039] The resource utilization module 15 is used to obtain the current solid waste data of each of the areas, input the current solid waste data into the optimal treatment model, generate resource utilization tasks for each of the areas, and process the current solid waste data of each of the areas according to the resource utilization tasks.

[0040] The functions or operation steps implemented when the above modules are executed are substantially the same as those in the above method embodiments, and will not be repeated here.

[0041] The solid waste resource utilization system provided in the embodiment of the present invention has the same implementation principle and technical effects as those in the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the system embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0042] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0043] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for resource utilization of solid waste, characterized in that: The following steps are involved: Step 1: Obtain historical solid waste data of each region, and construct evaluation indicators for each of the historical solid waste data, and use the evaluation indicators to perform data evaluation on the historical solid waste data of each region to obtain a correlation sequence corresponding to the historical solid waste data of each region; Step 2: Calculate the correlation coefficient of each correlation sequence, and construct a data map of each region based on each correlation coefficient; Step 3: Inputting the data map of each of the regions into a preset prediction model for data calculation to obtain corresponding data processing results; Step 4: Optimizing the prediction model according to the optimal order and the data processing result, and introducing a resource utilization strategy into the optimized prediction model to construct an optimal processing model; Step 5: Obtain the current solid waste data of each of the areas, and input the current solid waste data into the optimal treatment model, generate resource utilization tasks for each of the areas, and process the current solid waste data of each of the areas according to the resource utilization tasks.

2. The method for recycling solid waste according to claim 1, characterized in that: The step one comprises: Acquire data dimensions of each of the historical solid waste data, and construct evaluation indicators of each of the historical solid waste data according to the data dimensions; The evaluation index of each of the historical solid waste data is processed with explanatory variables to obtain a correlation sequence between the historical solid waste data of each of the regions.

3. The method for recycling solid waste according to claim 1, characterized in that: The second step comprises: Defining an availability coefficient of solid waste data, and performing data processing on each of the correlation sequences according to the availability coefficient and the correlation algorithm to calculate the correlation coefficient of each of the correlation sequences; The correlation coefficients of the correlation sequences are plotted to obtain the resource utilization degree of the solid waste in each of the regions, and a data map of each of the regions is constructed according to the resource utilization degree of the solid waste in each of the regions.

4. The method for recycling solid waste according to claim 1, characterized in that: The step three comprises: Inputting the data map into a preset prediction model to perform data calculation, so that the prediction model can optimize performance parameters to obtain the optimal order; The particle swarm optimization algorithm is used to process the data of each correlation sequence to obtain the cumulative generation sequence corresponding to each correlation sequence, and the corresponding model parameters are constructed according to each cumulative generation sequence.

5. The method for recycling solid waste according to claim 1, characterized in that: The fourth step comprises: Optimizing the prediction model according to the optimal order and the data processing result to obtain a corresponding optimized prediction model; Construct resource utilization indicators for solid waste, fuse the resource utilization indicators with the optimization prediction model, perform model error verification on the model after the indicator fusion, and output the model after the indicator fusion if the model error verification passes.

6. A solid waste resource utilization system, characterized in that: include: A data evaluation module, used to obtain the historical solid waste data of each region, and construct evaluation indicators for each of the historical solid waste data, and use the evaluation indicators to perform data evaluation on the historical solid waste data of each of the regions to obtain a correlation sequence corresponding to the historical solid waste data of each of the regions; A graph construction module, used to calculate the correlation coefficient of each correlation sequence, and construct a data graph of each region based on each correlation coefficient; A data processing module, used for inputting the data map of each of the regions into a preset prediction model for data calculation to obtain corresponding data processing results; A model optimization module, used to optimize the prediction model according to the optimal order and the data processing result, and introduce a resource utilization strategy into the optimized prediction model to construct an optimal processing model; The resource utilization module is used to obtain the current solid waste data of each of the areas, input the current solid waste data into the optimal treatment model, generate resource utilization tasks for each of the areas, and process the current solid waste data of each of the areas according to the resource utilization tasks.

7. The solid waste resource utilization system according to claim 6, characterized in that: The data evaluation module comprises: An indicator construction unit, used to obtain data dimensions of each of the historical solid waste data, and construct evaluation indicators of each of the historical solid waste data according to the data dimensions; The sequence construction unit is used to perform explanatory variable processing on the evaluation indicators of each of the historical solid waste data to obtain a correlation sequence between the historical solid waste data of each of the regions.

8. The solid waste resource utilization system according to claim 6, characterized in that: The graph construction module includes: A correlation coefficient calculation unit, used for defining an available coefficient of solid waste data, and performing data processing on each of the correlation sequences according to the available coefficient and the correlation algorithm, so as to calculate the correlation coefficient of each of the correlation sequences; The map construction unit is used to map the correlation coefficients of each of the correlation sequences to obtain the resource utilization degree of the solid waste in each of the regions, and to construct a data map of each of the regions according to the resource utilization degree of the solid waste in each of the regions.

9. The solid waste resource utilization system according to claim 6, characterized in that: The data processing module comprises: A data calculation unit, used for inputting the data map into a preset prediction model to perform data calculation, so that the prediction model can optimize performance parameters to obtain the optimal order; The parameter construction unit is used to perform data processing on each of the correlation sequences using a particle swarm optimization algorithm to obtain a cumulative generation sequence corresponding to each of the correlation sequences, and to construct a corresponding model parameter according to each of the cumulative generation sequences.

10. The solid waste resource utilization system according to claim 6, characterized in that: The model optimization module includes: A model optimization unit, used for optimizing the prediction model according to the optimal order and the data processing result to obtain a corresponding optimized prediction model; The indicator fusion unit is used to construct resource utilization indicators of solid waste, and fuse the resource utilization indicators with the optimization prediction model, and perform model error verification on the model after the indicator fusion. If the model error verification passes, the model after the indicator fusion is output.

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