A method and system for analyzing the disposal rate of waste agricultural film

Abstract

This application discloses a method and system for analyzing the disposal rate of waste agricultural film, relating to the field of waste agricultural film treatment. The method includes: selecting typical measurement areas in the target region, acquiring remote sensing time-series monitoring data, and extracting agricultural film change characteristics; then acquiring data from the Internet of Things and manually reported data. Based on production and sales data, the usage change characteristics are calibrated to obtain the calibrated usage amount of agricultural film; based on recycling change characteristics, the declared secondary utilization and disposal amounts are calibrated to obtain the calibrated secondary utilization and calibrated disposal amounts. A multi-source random forest network model is trained using data from typical measurement areas, and then the model is applied to the entire target region to predict the actual data in the target region and calculate the waste agricultural film disposal rate, thereby optimizing and adjusting the industrial structure. This application solves the problems of data overlap, missing data, and high error rates in traditional multi-source data statistics, improving the efficiency of waste agricultural film disposal rate calculation and enhancing the accuracy, objectivity, and representativeness of the calculation.

Description

Technical Field

[0001] This application relates to the field of waste agricultural film treatment, and in particular to a method and system for analyzing the disposal rate of waste agricultural film. Background Technology

[0002] With the modernization of agriculture, the use of agricultural film has been increasing year by year. However, problems such as low recycling rate and improper disposal of waste agricultural film are becoming increasingly prominent, causing a certain degree of residual pollution. Accurately calculating the disposal rate of waste agricultural film is a key basis for evaluating the effectiveness of pollution control and formulating policies and measures.

[0003] Currently, the calculation of waste agricultural film disposal rates mainly relies on traditional methods such as manual statistics and sampling surveys. These methods suffer from drawbacks such as data omissions, data lag, narrow coverage, and poor accuracy, failing to meet the needs of refined management. In particular, data collection from farmers often suffers from omissions, errors, or duplicates, rendering the data's reference value very limited. Furthermore, some waste agricultural film recycling companies have unclear and incomplete records. The statistical methods used by network models often differ significantly from the local waste agricultural film recycling and disposal environment, resulting in insufficient representativeness and consequently, low efficiency, poor accuracy, and a lack of representativeness in the calculation of waste agricultural film disposal rates. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for analyzing the disposal rate of waste agricultural film, which can solve the problems of data overlap, missing data, and high error rate in traditional multi-source data statistics, improve the efficiency of waste agricultural film disposal rate calculation, and enhance the accuracy, objectivity and representativeness of the calculation.

[0005] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for analyzing the disposal rate of waste agricultural film. The method includes: screening typical measurement areas within a target region; the typical measurement areas are used to characterize the overall recycling and disposal operation environment of waste agricultural film in the target region; the agricultural film includes: fully biodegradable mulch film, polyethylene mulch film, and greenhouse film; monitoring changes in agricultural film within the typical measurement areas in different time periods using satellite remote sensing images, and extracting agricultural film usage change characteristics and agricultural film recycling change characteristics using the obtained remote sensing time-series monitoring data; acquiring production monitoring data, sales monitoring data, declared secondary utilization volume, and declared disposal volume of agricultural film in the controlled network of agricultural film within the typical measurement areas using the Internet of Things; calibrating the agricultural film usage change characteristics based on the production monitoring data and sales monitoring data, and summarizing the calibrated agricultural film usage volume of the typical measurement areas; and further analyzing the declared secondary utilization volume and declared disposal volume of agricultural film based on the agricultural film recycling change characteristics. The process involves: calibrating the agricultural film to obtain the calibrated secondary utilization and disposal volumes in typical measurement areas; manually summarizing the actual usage, secondary utilization, and disposal volumes of agricultural film in these areas; constructing a multi-source random forest network model linking these typical measurement areas, using the calibrated usage, secondary utilization, and disposal volumes of agricultural film obtained at different times as inputs and the corresponding actual usage, secondary utilization, and disposal volumes as outputs; training the model to obtain a trained multi-source random forest network model; applying the trained random forest network model to the entire target area to predict the actual usage, secondary utilization, and disposal volumes of agricultural film, and calculating the waste agricultural film disposal rate in the target area; and optimizing the industrial structure of the agricultural film controlled network in the target area based on the waste agricultural film disposal rate.

[0006] Secondly, this application also provides a computer system, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the waste agricultural film disposal rate analysis method described in the first aspect.

[0007] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application achieves an accurate assessment of the use and recycling of agricultural film over a large scale by first measuring typical areas, then calibrating multi-source data, and finally using random forest extrapolation.

[0008] First, typical measurement areas representing the overall recycling and disposal operation environment are selected within the target region. The main purpose is to narrow down the measurement scope to easily obtain accurate data on the entire process of agricultural film circulation, providing the most accurate training samples for the random forest model. Then, this application utilizes the ease with which agricultural film can be captured by remote sensing. It monitors changes in agricultural film within typical areas using satellite remote sensing imagery at different time intervals, extracting dynamic characteristics of usage and recycling. These characteristics are used as the primary data source for usage volume. A multi-source data cross-validation mechanism is employed, using production and sales data for calibration, resulting in a calibrated usage volume of agricultural film. Simultaneously, this application leverages the IoT deployment capabilities of the sites, enabling more convenient and accurate data acquisition, to obtain secondary utilization and disposal volumes. These are used as the primary data source for recycling volume, with remote sensing-detected agricultural film reduction serving as auxiliary calibration data. The calibrated secondary utilization and disposal volumes of agricultural film are then summarized. Finally, a multi-source random forest network model is constructed, trained using calibration data as input and manually reported actual data as output. The trained model is then applied to the entire target region to predict the actual usage, secondary utilization, and disposal of agricultural film, thereby calculating the waste agricultural film disposal rate. This provides accurate data support for optimizing and adjusting the industrial structure of the controlled agricultural film network. In summary, this application solves the problems of data overlap, missing data, and high error rates in traditional multi-source data statistics, improves the efficiency of waste agricultural film disposal rate calculation, and avoids subjective bias caused by traditional multi-source data weight fusion calculation methods through data mutual calibration, enhancing the accuracy, objectivity, and representativeness of the calculation. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart illustrating the waste agricultural film disposal rate analysis method provided in the embodiments of this application.

[0011] Figure 2 This is a schematic diagram of the data processing flow in the waste agricultural film disposal rate analysis method provided in the embodiments of this application.

[0012] Figure 3 This is an internal structure diagram of a computer system provided in an embodiment of this application. Detailed Implementation

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0015] Example 1, such as Figures 1-2 As shown in the figure, this embodiment provides a method for analyzing the disposal rate of waste agricultural film, the method comprising: S1. Select typical measurement areas within the target region; the typical measurement areas are used to characterize the overall recycling and disposal operation environment of waste agricultural film in the target region; the agricultural film includes: fully biodegradable mulch film, polyethylene mulch film and greenhouse film.

[0016] In practical applications, the typical measurement area mainly refers to the area that can represent the entire target area, such as farmland type, each node of the agricultural film controlled network and its industrial capacity, which is equivalent to a miniature target area.

[0017] S2. Monitor changes in agricultural film in typical measurement areas by time period using satellite remote sensing images, and extract the characteristics of changes in agricultural film use and recycling using the obtained remote sensing time-series monitoring data.

[0018] Furthermore, during the time-segmented monitoring of the satellite remote sensing images, the time period ranges from 3 to 7 days.

[0019] In practical applications, the time frame is generally within one week to avoid inaccurate satellite remote sensing data detection within this interval due to farmers changing agricultural film themselves.

[0020] Optionally, to avoid inaccurate satellite remote sensing data due to farmers replacing agricultural film themselves, satellite remote sensing also includes a process for analyzing and calibrating the old and new agricultural film. The process for analyzing and calibrating the old and new agricultural film specifically includes: when the monitored area of ​​agricultural film in the farmland has not changed significantly and the agricultural film has changed from old to new (indicating that the farmers replaced the agricultural film themselves during the interval between the acquisition of two satellite remote sensing images), the remote sensing time-series monitoring data should be calibrated.

[0021] Further step S2 specifically includes: S21. Over the typical monitoring area, satellite remote sensing was used to collect images of fully biodegradable mulch film, polyethylene mulch film and greenhouse film in different time periods according to the corresponding spectra, and the remote sensing time series monitoring data were obtained by summarizing them.

[0022] S22. By comparing the changes of the same farmland in different time periods in remote sensing time series monitoring data, farmland with incremental changes in agricultural film is marked as farmland using agricultural film. The thickness of agricultural film, the projected area of ​​agricultural film, and the estimated edge area of ​​agricultural film that generated the increment are extracted, and the agricultural film usage change characteristics are obtained by mapping the feature raster layer. Farmland with reduced changes in agricultural film is marked as farmland recycling agricultural film. The thickness of agricultural film, the projected area of ​​agricultural film, and the estimated edge area of ​​agricultural film that generated the reduction are deduced, and the agricultural film recycling change characteristics are obtained by mapping the feature raster layer.

[0023] S3. Utilize the Internet of Things to obtain production monitoring data, sales monitoring data, agricultural film declarations for secondary use (reuse), and agricultural film declarations for disposal in the controlled network of agricultural film within a typical measurement area.

[0024] Furthermore, the controlled network of agricultural film includes at least: production enterprises, sales outlets, secondary utilization sites, and disposal sites; the disposal sites are used to carry out harmless disposal of agricultural film; harmless disposal includes: incorporating it into the rural waste treatment system, incinerating it for power generation, etc.

[0025] S4. Based on production monitoring data and sales monitoring data, the characteristics of agricultural film usage changes are calibrated, and the calibrated usage of agricultural film in typical measurement areas is summarized.

[0026] Furthermore, step S4 specifically includes: S41. Perform spatiotemporal registration of production monitoring data and sales monitoring data in different time periods to construct a dynamic database of agricultural film production and sales.

[0027] S42. For each characteristic of agricultural film usage change, the corresponding basic information is labeled; the basic information includes: characteristic time period, farmland and farmer.

[0028] S43. Based on basic information, subtract the sales volume of agricultural film from the production volume of agricultural film in the dynamic database of agricultural film production and sales, and estimate the inventory of agricultural film and the flow of agricultural film across typical measurement areas during characteristic periods.

[0029] S44. Based on the relationship between agricultural film production, sales and use, linear regression analysis was performed on the agricultural film inventory and flow across typical measurement areas during characteristic periods to initially calibrate the characteristics of agricultural film use changes.

[0030] S45. Based on the basic information corresponding to the usage change characteristics of each agricultural film, trace the thickness and sales volume of agricultural film, and use the thickness and sales volume of agricultural film to perform secondary cross-validation calibration on the usage change characteristics of agricultural film after the initial calibration, and summarize all the usage change characteristics of agricultural film after secondary cross-validation calibration to obtain the calibrated usage of agricultural film in a typical measurement area.

[0031] S5. Based on the characteristics of agricultural film recycling changes, the declared secondary utilization amount and the declared disposal amount of agricultural film are calibrated to obtain the calibrated secondary utilization amount and calibrated disposal amount of agricultural film in typical measurement areas.

[0032] Furthermore, step S5 specifically includes: S51. Use the YOLO model to assess the recycling value of agricultural film recycling change characteristics. When the assessment result is higher than the assessment threshold, mark the agricultural film corresponding to the current agricultural film recycling change characteristics as agricultural film that should be reused; when the assessment result is lower than the assessment threshold, mark the agricultural film corresponding to the current agricultural film recycling change characteristics as agricultural film that should be disposed of.

[0033] S52. Calculate the amount of agricultural film that should be reused and the amount of agricultural film that should be disposed of, respectively, to obtain the amount of agricultural film that should be reused and the amount of agricultural film that should be disposed of.

[0034] S53. For each farmland corresponding to the change characteristics of agricultural film recycling, the amount of agricultural film to be reused and the amount of agricultural film to be disposed of are calibrated using the amount of agricultural film to be reused and the amount of agricultural film to be disposed of, respectively, to obtain the calibrated amount of agricultural film to be reused and the calibrated amount of agricultural film to be disposed of in typical measurement areas; wherein, in the process of calibrating the amount of agricultural film to be reused and the amount of agricultural film to be disposed of using the amount of agricultural film to be reused and the amount of agricultural film to be disposed of, the amount of fully biodegradable mulch film to be disposed of in the amount of agricultural film to be disposed of is added to the amount of agricultural film to be disposed of.

[0035] In practical applications, fully biodegradable mulch film can degrade on its own. Therefore, secondary utilization sites and disposal sites will not include it in the declared amount of agricultural film for secondary utilization and disposal. So, it is necessary to monitor it through satellite remote sensing and directly add the monitoring results of fully biodegradable mulch film to the declared amount of agricultural film for disposal.

[0036] S6. The actual usage, secondary utilization, and disposal of agricultural film in typical measurement areas are summarized by manual reporting.

[0037] Furthermore, the manual reporting involves interviewing each farmer in the farmland where changes in agricultural film have occurred.

[0038] Optional methods of inquiry include telephone inquiry, questionnaire, and face-to-face inquiry.

[0039] S7. Construct a multi-source random forest network model that associates typical measurement areas. Take the amount of agricultural film calibration usage, secondary utilization, and disposal obtained at different times as inputs, and take the corresponding actual usage, secondary utilization, and disposal of agricultural film as outputs. Train the model to obtain a trained multi-source random forest network model.

[0040] S8. Apply the trained random forest network model to the entire target area to predict the actual usage, actual secondary utilization, and actual disposal of agricultural film in the target area, and calculate the waste agricultural film disposal rate of the target area.

[0041] Furthermore, the formula for calculating the waste agricultural film disposal rate is as follows: .

[0042] In the formula, The disposal rate of waste agricultural film; This refers to the actual amount of agricultural film used. This represents the actual amount of agricultural film used in secondary applications. This represents the actual amount of agricultural film disposed of.

[0043] S9. Optimize and adjust the industrial structure of the agricultural film controlled network in the target area based on the waste agricultural film disposal rate.

[0044] Furthermore, the standard for the agricultural film disposal rate is 85%.

[0045] Optionally, step S9 specifically includes: S91. When the waste agricultural film disposal rate in the target area meets the standard, the supply quota of the production enterprise and the agricultural film allocation of the sales outlet will be dynamically adjusted based solely on the waste agricultural film disposal rate.

[0046] S92. When the waste agricultural film disposal rate in the target area does not meet the standard, the supply quota of production enterprises and the agricultural film allocation of sales outlets shall be dynamically adjusted based on the waste agricultural film disposal rate. The ratio of polyethylene mulch film supply to fully biodegradable mulch film supply in the target area shall also be adjusted. At the same time, the service radius and processing load of existing secondary utilization sites and disposal sites shall be assessed, and new sites shall be planned or existing sites shall be expanded in terms of capacity.

[0047] As an optional implementation method, this application uses City A as an example to describe in detail the steps of the waste agricultural film disposal rate analysis method: The first step is to select Township a in City A as a typical measurement area. Township a has a complete range of crops covered by film and has a complete and controlled network of waste agricultural film recycling stations and disposal companies, which can represent the overall operating environment of City A.

[0048] The second step involves using high-resolution satellite imagery to photograph township a every 5 days, and then using the obtained remote sensing time-series monitoring data to extract the characteristics of changes in agricultural film use and recycling.

[0049] The third step involves connecting five manufacturers, 20 sales outlets, and three recycling stations within Township A through an Internet of Things (IoT) system to capture real-time data on the output, sales volume, reported secondary utilization, and disposal volume of polyethylene mulch film, fully biodegradable mulch film, and greenhouse film.

[0050] The fourth step involves calibrating the area of ​​agricultural film used by remote sensing by combining it with the sales figures obtained from the Internet of Things (IoT) during the same period. This is done by analyzing store inventory and cross-regional sales data through questionnaires or inquiries to correct for errors in remote sensing monitoring, ultimately summarizing the calibrated usage of agricultural film in Township A.

[0051] Step 5: Use the YOLO model to evaluate the recycled film monitored by remote sensing. Film with good color, low breakage, and few impurities is marked as suitable for reuse, while film with severe damage, high fragmentation, and many impurities is marked as needing disposal. Compare this result with the data reported by farmers. In addition, the amount of fully biodegradable mulch film that needs disposal should be added to the reported disposal amount.

[0052] Step 6: The visiting personnel went deep into the fields of Township A and visited each farmer whose changes in plastic film were detected by remote sensing. They recorded and summarized in detail the purchase channels, actual purchase amount, secondary utilization amount, and waste film disposal amount for each farmer.

[0053] Step 7: Using the calibration usage, secondary utilization, and disposal amounts of township a at different times as inputs, and the corresponding actual manually reported data as outputs, train a multi-source random forest network model to analyze the mapping relationship between remote sensing IoT data and actual data.

[0054] Step 8: Input the remote sensing monitoring data and IoT production and sales data of all townships in City A into the trained model to predict the actual usage, secondary utilization and disposal of agricultural film in City A, and calculate the disposal rate of waste agricultural film in City A.

[0055] Step 9: It was found that the disposal rate in City A did not meet the standard, and the industrial structure of the agricultural film controlled network in the target area was optimized and adjusted.

[0056] The technical effects of this application are as follows: This application achieves an accurate assessment of the use and recycling of agricultural film over a large scale by first measuring typical areas, then calibrating multi-source data, and finally using random forest extrapolation.

[0057] First, typical measurement areas representing the overall recycling and disposal operation environment are selected within the target region. The main purpose is to narrow down the measurement scope to easily obtain accurate data on the entire process of agricultural film circulation, providing the most accurate training samples for the random forest model. Then, this application utilizes the ease with which agricultural film can be captured by remote sensing. It monitors changes in agricultural film within typical areas using satellite remote sensing imagery at different time intervals, extracting dynamic characteristics of usage and recycling. These characteristics are used as the primary data source for usage volume. A multi-source data cross-validation mechanism is employed, using production and sales data for calibration, resulting in a calibrated usage volume of agricultural film. Simultaneously, this application leverages the IoT deployment capabilities of the sites, enabling more convenient and accurate data acquisition, to obtain secondary utilization and disposal volumes. These are used as the primary data source for recycling volume, with remote sensing-detected agricultural film reduction serving as auxiliary calibration data. The calibrated secondary utilization and disposal volumes of agricultural film are then summarized. Finally, a multi-source random forest network model is constructed, trained using calibration data as input and manually reported actual data as output. The trained model is then applied to the entire target region to predict the actual usage, secondary utilization, and disposal of agricultural film, thereby calculating the waste agricultural film disposal rate. This provides accurate data support for optimizing and adjusting the industrial structure of the controlled agricultural film network. In summary, this application solves the problems of data overlap, missing data, and high error rates in traditional multi-source data statistics, improves the efficiency of waste agricultural film disposal rate calculation, and avoids subjective bias caused by traditional multi-source data weight fusion calculation methods through data mutual calibration, enhancing the accuracy, objectivity, and representativeness of the calculation.

[0058] Example 2: This example provides a computer system, which can be a server or a terminal, and its internal structure diagram can be as follows. Figure 3 As shown, the computer system includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores forced oscillation samples and sub / supersynchronous oscillation samples. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the aforementioned method for rapid prediction and identification of the dominant frequency of sub / supersynchronous oscillations in new energy power systems based on transfer learning.

[0059] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer system to which the present application is applied. A specific computer system may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0060] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0061] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0062] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0063] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for analyzing the disposal rate of waste agricultural film, characterized in that, The method includes: Typical measurement areas are selected within the target region; these typical measurement areas are used to characterize the overall recycling and disposal operation environment of waste agricultural film in the target region; the agricultural film includes: fully biodegradable mulch film, polyethylene mulch film, and greenhouse film; By monitoring changes in agricultural film in typical measurement areas through satellite remote sensing images in different time periods, and using the obtained remote sensing time-series monitoring data, the characteristics of changes in agricultural film use and recycling were extracted. The Internet of Things is used to acquire production monitoring data, sales monitoring data, agricultural film declarations for secondary use, and agricultural film declarations for disposal in the controlled network of agricultural film in a typical measurement area. Based on production and sales monitoring data, the characteristics of agricultural film usage changes are calibrated, and the calibrated usage of agricultural film in typical measurement areas is summarized. Specifically, this includes: spatiotemporal registration of production and sales monitoring data by time period to construct a dynamic database of agricultural film production and sales; for each agricultural film usage change characteristic, corresponding basic information is defined, including the characteristic time period, farmland, and farmers; based on the basic information, the agricultural film production volume is subtracted from the agricultural film sales volume in the dynamic database to estimate the agricultural film inventory and cross-regional flow during the characteristic time period; based on the correlation between agricultural film production, sales, and usage, linear regression analysis is performed on the agricultural film inventory and cross-regional flow during the characteristic time period to initially calibrate each agricultural film usage change characteristic; based on the basic information corresponding to each agricultural film usage change characteristic, the agricultural film thickness and sales volume are traced, and the agricultural film thickness and sales volume are used to perform a secondary cross-validation calibration on the initially calibrated agricultural film usage change characteristics, and all secondary cross-validation calibrated agricultural film usage change characteristics are summarized to obtain the calibrated usage of agricultural film in typical measurement areas. Based on the changing characteristics of agricultural film recycling, the declared secondary utilization and disposal volumes of agricultural film were calibrated to obtain the calibrated secondary utilization and disposal volumes for typical measurement areas. Specifically, this included: using the YOLO model to assess the recycling value of agricultural film recycling changes; when the assessment result was higher than the assessment threshold, the agricultural film corresponding to the current recycling change characteristics was marked as agricultural film that should be reused; when the assessment result was lower than the assessment threshold, the agricultural film corresponding to the current recycling change characteristics was marked as agricultural film that should be disposed of; and the quantities of agricultural film that should be reused and agricultural film that should be disposed of were statistically analyzed separately. The quantities of agricultural film to be reused and agricultural film to be disposed of were obtained. For each type of farmland corresponding to the change characteristics of agricultural film recycling, the quantities of agricultural film to be reused and agricultural film to be disposed of were calibrated using the quantities of agricultural film to be reused and agricultural film to be disposed of, respectively, to obtain the calibrated quantities of agricultural film to be reused and agricultural film to be disposed of in typical measurement areas. In the process of calibrating the quantities of agricultural film to be reused and agricultural film to be disposed of using the quantities of agricultural film to be reused and agricultural film to be disposed of, the amount of fully biodegradable agricultural film to be disposed of was added to the amount of agricultural film to be disposed of. The actual usage, secondary utilization, and disposal of agricultural film in typical measurement areas were summarized by manual data entry. A multi-source random forest network model is constructed to associate typical measurement areas. The amount of agricultural film calibration usage, secondary utilization, and disposal obtained at different times are used as inputs, and the corresponding actual usage, secondary utilization, and disposal of agricultural film are used as outputs. The model is trained to obtain a well-trained multi-source random forest network model. The trained random forest network model is applied to the entire target area to predict the actual amount of agricultural film used, the actual amount of agricultural film reused, and the actual amount of agricultural film disposed of in the target area, and to calculate the waste agricultural film disposal rate in the target area. Based on the waste agricultural film disposal rate, the industrial structure of the controlled agricultural film network in the target area is optimized and adjusted.

2. The method for analyzing the disposal rate of waste agricultural film according to claim 1, characterized in that, By monitoring changes in agricultural film in typical measurement areas through satellite remote sensing imagery at different time intervals, and using the obtained remote sensing time-series monitoring data, the characteristics of changes in agricultural film use and recycling were extracted, specifically including: Over the typical monitoring area, satellite remote sensing was used to collect images of fully biodegradable mulch film, polyethylene mulch film and greenhouse film at different time periods according to the corresponding spectra, and the remote sensing time-series monitoring data were obtained by summarizing them. By comparing the changes of the same farmland in different time periods in remote sensing time-series monitoring data, farmland with incremental changes in agricultural film is marked as farmland using agricultural film. The thickness, projected area, and estimated edge area of ​​the agricultural film that generated the incremental changes are extracted, and the characteristics of agricultural film usage changes are obtained by mapping a feature raster layer. Farmland with reduced changes in agricultural film is marked as farmland recycling agricultural film. The thickness, projected area, and estimated edge area of ​​the agricultural film that generated the reduced changes are deduced, and the characteristics of agricultural film recycling changes are obtained by mapping a feature raster layer.

3. The method for analyzing the disposal rate of waste agricultural film according to claim 1, characterized in that, The controlled network of agricultural film includes at least: production enterprises, sales outlets, secondary utilization sites, and disposal sites; the disposal sites are used to carry out harmless disposal of agricultural film.

4. The method for analyzing the disposal rate of waste agricultural film according to claim 1, characterized in that, The standard for the agricultural film disposal rate is 85%.

5. The method for analyzing the disposal rate of waste agricultural film according to claim 1, characterized in that, During the time-segmented monitoring of satellite remote sensing images, the time period ranges from 3 to 7 days.

6. The method for analyzing the disposal rate of waste agricultural film according to claim 1, characterized in that, The manual reporting method involves interviewing each farmer in the farmland where changes in agricultural film have occurred.

7. The method for analyzing the disposal rate of waste agricultural film according to claim 1, characterized in that, The formula for calculating the waste agricultural film disposal rate is as follows: ； In the formula, The disposal rate of waste agricultural film; This refers to the actual amount of agricultural film used. This represents the actual amount of agricultural film used in secondary applications. This represents the actual amount of agricultural film disposed of.

8. A computer system, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the waste agricultural film disposal rate analysis method according to any one of claims 1-7.

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