Biodegradable mulching film degradation monitoring method and system based on artificial intelligence

Abstract

The invention belongs to the technical field of biodegradable mulching films, particularly relates to a biodegradable mulching film degradation monitoring method and system based on artificial intelligence, and aims to solve the problems that in the existing biodegradable mulching film degradation monitoring process, the monitoring mode is single, factor data in the degradation process is inconvenient to comprehensively collect, and the monitoring efficiency is high. According to the technical scheme, the method comprises the following steps that S1, a mulching film is laid, and laying information is recorded; s2, setting sampling points according to the laying information; s3, regularly collecting mulching film samples according to the sampling points; s4, the collected mulching film sample is detected, and the mulching film environment is monitored; s5, recording and analyzing the monitoring data; s6, a report and an adjustment strategy are generated according to the monitoring result, through arrangement of the environment monitoring module and the mulching film monitoring module, factor data in the degradation process can be conveniently and comprehensively collected, and therefore the monitoring effect can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of biodegradable mulch films, and in particular to a biodegradable mulch film degradation monitoring method and system based on artificial intelligence. Background Art

[0002] Biodegradable mulch degradation monitoring refers to the activities of monitoring and controlling the degradation process of biodegradable mulch in the natural environment. This includes observing the degradation of mulch in soil, compost and other conditions to ensure that it decomposes according to the expected schedule and method, and eventually transforms into harmless substances such as carbon dioxide and water. The purpose of monitoring is to ensure that the performance of biodegradable mulch meets expectations and does not leave harmful residues in the environment, thereby protecting environmental safety and sustainable agricultural development. Through scientific and effective degradation monitoring, the degradation efficiency of mulch can be evaluated, product design and use strategies can be optimized, and the promotion and application of biodegradable mulch technology can be promoted.

[0003] In the existing technology, the monitoring method of biodegradable mulch degradation is relatively simple, and it is not convenient to comprehensively collect data on factors in the degradation process, which reduces the monitoring effect. For this reason, we propose an artificial intelligence-based biodegradable mulch degradation monitoring method and system to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art in the degradation monitoring process of biodegradable mulch, that is, the monitoring method is relatively single and it is not convenient to comprehensively collect the data of factors in the degradation process, thereby reducing the monitoring effect, and to propose a biodegradable mulch degradation monitoring method and system based on artificial intelligence.

[0005] The artificial intelligence-based biodegradable mulch degradation monitoring method and system provided in this application adopts the following technical solutions:

[0006] The biodegradable mulch degradation monitoring method based on artificial intelligence includes the following steps:

[0007] S1: Laying the ground film and recording the laying information;

[0008] S2: Set sampling points according to laying information;

[0009] S3: Collect mulch samples regularly according to sampling points;

[0010] S4: Test the collected ground film samples and monitor the ground film environment;

[0011] S5: Record and analyze the monitoring data;

[0012] S6: Generate reports and adjust strategies based on monitoring results.

[0013] The present invention also proposes an artificial intelligence-based biodegradable mulch degradation monitoring system, comprising: an input module, the input module is connected to a parameter setting module, the parameter setting module is connected to a mulch monitoring module, the mulch monitoring module is connected to a ring monitoring module, a mulch sampling module and a data acquisition module, the mulch sampling module is connected to a detection module, the data acquisition module is connected to a data analysis module, the data analysis module is connected to a data storage module, a production report module and a recording module, the data storage module is connected to a backup module, the recording module is connected to a comparison module, the production report module is connected to an evaluation module, the evaluation module is connected to a decision module and an abnormal warning module, and the abnormal warning module is connected to a remote control module.

[0014] Furthermore, the environmental monitoring module includes a temperature and humidity monitoring unit, a light monitoring unit and a soil monitoring unit, the temperature and humidity monitoring unit is connected to the light monitoring unit, and the light monitoring unit is connected to the soil monitoring unit.

[0015] Furthermore, the ground film sampling module includes a selection unit, a control unit and a sampling unit, the selection unit is connected to the control unit, and the control unit is connected to the sampling unit.

[0016] Furthermore, the ground film monitoring module includes a color monitoring unit, a rupture degree monitoring unit and a pollution condition monitoring unit, the color monitoring unit is connected to the rupture degree monitoring unit, and the rupture degree monitoring unit is connected to the pollution condition monitoring unit.

[0017] Furthermore, the input module includes a laying information input unit, a ground film information input unit and an environmental information input unit, the laying information input unit is connected to the ground film information input unit, and the ground film information input unit is connected to the environmental information input unit.

[0018] Furthermore, the input module is used to input the ground film laying time, location information, ground film information and annular soil information, and the parameter setting module is used to set the parameters of the environment monitoring module, the ground film monitoring module and the ground film sampling module.

[0019] Furthermore, the data acquisition module is used to collect monitoring data and detection data, and transmit them to the data analysis module, and the data analysis module is used to analyze and process the collected information.

[0020] Furthermore, the recording module is used to record the data analysis results and transmit them to the comparison module, and the comparison module is used to compare and analyze the collected data analysis results.

[0021] Furthermore, the data storage module is used to store the data analysis results and the collected data, and transmit them to the backup module, and the backup module is used to back up the data.

[0022] In summary, the present application includes at least one of the following beneficial technical effects:

[0023] 1. This solution can collect monitoring data and detection data through the data acquisition module and transmit them to the data analysis module. The data analysis module processes and analyzes the collected data and generates corresponding monitoring reports through the report generation module. The evaluation module and decision-making module evaluate the ground film according to the monitoring report and generate decision strategies;

[0024] 2. This solution can monitor environmental data and ground film data in real time through the environmental monitoring module and the ground film monitoring module, select and sample the laid ground film through the ground film sampling module, and detect and analyze the samples through the detection module;

[0025] 3. This solution uses the input module to input the ground film laying time, location information, ground film information and circular soil information, and the parameter setting module sets the parameters of the environmental monitoring module, ground film monitoring module and ground film sampling module.

[0026] The present invention can facilitate comprehensive collection of factor data during the degradation process by providing an environmental monitoring module and a ground film monitoring module, thereby effectively improving the monitoring effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flow chart of the artificial intelligence-based biodegradable mulch degradation monitoring method proposed by the present invention;

[0028] Figure 2 This is a structural block diagram of the artificial intelligence-based biodegradable mulch degradation monitoring system proposed by the present invention;

[0029] Figure 3 This is a structural block diagram of the environmental monitoring module of the artificial intelligence-based biodegradable mulch degradation monitoring system proposed by the present invention;

[0030] Figure 4 It is a structural block diagram of a mulch film sampling module of the artificial intelligence-based biodegradable mulch film degradation monitoring system proposed by the present invention;

[0031] Figure 5 This is a structural block diagram of a mulch film monitoring module of a biodegradable mulch film degradation monitoring system based on artificial intelligence proposed by the present invention;

[0032] Figure 6 This is a structural block diagram of the input module of the artificial intelligence-based biodegradable mulch degradation monitoring system proposed in the present invention. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0034] Embodiment 1

[0035] Reference Figure 1 , the biodegradable mulch degradation monitoring method based on artificial intelligence includes the following steps:

[0036] S1: Laying the ground film and recording the laying information;

[0037] S2: Set sampling points according to laying information;

[0038] S3: Collect mulch samples regularly according to sampling points;

[0039] S4: Test the collected ground film samples and monitor the ground film environment;

[0040] S5: Record and analyze the monitoring data;

[0041] S6: Generate reports and adjust strategies based on monitoring results.

[0042] Reference Figure 2 , this embodiment also proposes a biodegradable mulch degradation monitoring system based on artificial intelligence, including: an input module, the input module is connected to a parameter setting module, the parameter setting module is connected to a mulch monitoring module, the mulch monitoring module is connected to a ring monitoring module, a mulch sampling module and a data acquisition module, the mulch sampling module is connected to a detection module, the data acquisition module is connected to a data analysis module, the data analysis module is connected to a data storage module, a production report module and a recording module, the data storage module is connected to a backup module, the recording module is connected to a comparison module, the production report module is connected to an evaluation module, the evaluation module is connected to a decision module and an abnormal warning module, the abnormal warning module is connected to a remote control module, and the detection module uses chemical analysis, microbial activity detection and spectral analysis;

[0043] Chemical analysis

[0044] 1. Detection of Hazardous Substances

[0045] Heavy metal content detection: Atomic absorption spectroscopy or inductively coupled plasma mass spectrometry is used to detect the content of heavy metals (such as lead, cadmium, mercury, etc.) in the film to ensure that it meets environmental protection requirements and avoids pollution to the soil and crops.

[0046] 2. Analysis of degradation products

[0047] Analysis of biodegradation products: Through chromatography, mass spectrometry and other analytical techniques, the products after film degradation are analyzed in detail, which helps to determine whether the degradation products are harmless small molecules such as carbon dioxide and water, thereby evaluating the biodegradability and environmental friendliness of the mulch film.

[0048] 3. Testing additives and ingredients

[0049] For additive-based biodegradable mulch, it is necessary to test the added natural or synthetic polymers with biodegradable properties, biodegradation accelerators, processing aids and other ingredients to ensure that they meet the relevant standards and requirements;

[0050] Microbial activity detection

[0051] 1. Purpose of testing

[0052] Assess whether mulch materials have harmful effects on soil microorganisms;

[0053] Understand the role of microorganisms in the degradation of mulch films and the impact of mulch film degradation on microbial activity;

[0054] 2. Test content

[0055] Microbial quantity and types: Detect the quantity and types of microorganisms in soil or compost by culture or molecular biology methods to evaluate the effect of mulch degradation on the microbial community structure;

[0056] Microbial activity index: Determine the respiration rate, enzyme activity and other indicators of microorganisms in soil or compost to reflect the activity status of microorganisms;

[0057] Spectral analysis

[0058] Spectroscopic analysis is a method of identifying substances and determining their chemical composition and relative content based on their spectra. It is mainly based on the principles of molecular and atomic spectroscopy and includes the following key processes:

[0059] Energy source provides energy: First, there needs to be an energy source to provide energy to the substance being measured so that it produces a spectrum;

[0060] Energy interacts with the substance being measured: The energy provided interacts with the substance being measured, causing the substance to absorb or emit light of a specific wavelength;

[0061] Producing a detectable signal: This interaction produces a detectable signal, i.e. a spectrum;

[0062] The evaluation module uses linear regression and nonlinear regression algorithms;

[0063] Linear Regression

[0064] Linear regression is a statistical method used to describe the linear relationship between two or more variables. Its basic form is:

[0065] (y=\beta_0+\beta_1x_1+\beta_2x_2+\cdots+\beta_nx_n+\eps il on);

[0066] in:

[0067] (y) is the dependent variable (or response variable);

[0068] (\beta_0) is the intercept term;

[0069] (\beta_1,\beta_2,\ dots,\beta_n) are regression coefficients, which indicate the degree of influence of the independent variable on the dependent variable;

[0070] (x_1,x_2,\l dots,x_n) are independent variables (or explanatory variables);

[0071] (\eps il on) is the error term, which represents the part that the model cannot explain;

[0072] In simple linear regression, there is only one independent variable involved and the formula is simplified to: (y = \beta_0 + \beta_1x + \eps il on);

[0073] Nonlinear regression

[0074] Nonlinear regression is a statistical method used to describe the nonlinear relationship between variables. Unlike linear regression, the model form of nonlinear regression is not a straight line, but can be any form of curve. Its general form can be expressed as: (y = f (x_1, x_2, \l dots, x_n) + \eps il on);

[0075] in:

[0076] (y) is the dependent variable;

[0077] (f(x_1,x_2,\l dots,x_n)) is a nonlinear function, which indicates the nonlinear relationship between the independent variable and the dependent variable;

[0078] (x_1,x_2,\l dots,x_n) are independent variables;

[0079] (\eps il on) is the error term;

[0080] The specific form of nonlinear regression depends on the background of the actual problem and the characteristics of the data. It can be a combination of polynomial, exponential, logarithmic, power function, etc. For example, a simple nonlinear regression model may be:

[0081] (y=\beta_0+\beta_1x+\beta_2x2+\eps il on);

[0082] Or a more complex model like:

[0083] (y=\frac{\beta_0}{1+\exp(-\beta_1(x-\beta_2))}+\eps il on).

[0084] Reference Figure 3-Figure 6 The environmental monitoring module includes a temperature and humidity monitoring unit, a light monitoring unit and a soil monitoring unit. The temperature and humidity monitoring unit is connected to the light monitoring unit, and the light monitoring unit is connected to the soil monitoring unit. The ground film sampling module includes a selection unit, a control unit and a sampling unit. The selection unit is connected to the control unit, and the control unit is connected to the sampling unit. The ground film monitoring module includes a color monitoring unit, a crack degree monitoring unit and a pollution monitoring unit. The color monitoring unit is connected to the crack degree monitoring unit, and the crack degree monitoring unit is connected to the pollution monitoring unit. The input module includes a laying information input unit, a ground film information input unit and an environmental information input unit. The laying information input unit is connected to the ground film information input unit. The ground film information input unit The element is connected to the environmental information entry unit. The entry module is used to enter the ground film laying time, location information, ground film information and annular soil information. The parameter setting module is used to set the parameters of the environmental monitoring module, the ground film monitoring module and the ground film sampling module. The data acquisition module is used to collect monitoring data and detection data, and transmit them to the data analysis module. The data analysis module is used to analyze and process the collected information. The recording module is used to record the data analysis results and transmit them to the comparison module. The comparison module is used to compare and analyze the collected data analysis results. The data storage module is used to store the data analysis results and the collected data, and transmit them to the backup module. The backup module is used to back up the data.

[0085] The implementation principle in this embodiment is: when in use, the ground film laying time, location information, ground film information and annular soil information are entered through the entry module, the parameter setting module sets the parameters of the environmental monitoring module, the ground film monitoring module and the ground film sampling module, the environmental monitoring module and the ground film monitoring module can monitor the environmental data and the ground film data in real time, the ground film sampling module can select and sample the laid ground film, and the detection module can detect and analyze the samples, the data acquisition module can collect the monitoring data and the detection data, and transmit them to the data analysis module, the data analysis module processes and analyzes the collected data, and generates a corresponding monitoring report through the report generation module, the evaluation module and the decision module evaluate the ground film according to the monitoring report, and generate a decision strategy, the abnormal alarm module sends the ground film abnormality information according to the evaluation results, the user can access the monitoring system through the remote control module, view the real-time data, historical records and reports, and realize comprehensive monitoring and management of the degradation process.

[0086] Embodiment 2

[0087] The difference between this embodiment and embodiment one is that both the environmental monitoring module and the ground film monitoring module are connected to an automatic adjustment module, which can adjust the monitoring range or angle of the environmental monitoring module and the ground film monitoring module, thereby achieving comprehensive monitoring without blind spots and improving the accuracy of monitoring.

[0088] Embodiment 3

[0089] The difference between this embodiment and the first embodiment is that the data acquisition module is connected to a wireless transmission module, and the wireless transmission module adopts advanced wireless transmission technology to ensure that the monitoring system can transmit and update data in real time, so that users can grasp the degradation of the mulch film at any time.

[0090] Embodiment 4

[0091] The difference between this embodiment and the first embodiment is that the data acquisition module is connected to a security protection module, and the security protection module ensures the security of data transmission by setting a firewall and encryption.

[0092] Embodiment 5

[0093] The difference between this embodiment and the first embodiment is that the comparison module is connected to a standard library module, and the standard library module is used to establish a unified and complete evaluation method and index to ensure the accuracy of data comparison.

[0094] Experimental example

[0095] By comparing the biodegradable mulch degradation monitoring schemes proposed in Examples 1 to 5 with the conventional biodegradable mulch degradation monitoring schemes, the experimental data are as follows:

[0096]

[0097] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A biodegradable mulch degradation monitoring method based on artificial intelligence, characterized in that: The following steps are involved: S1: Laying the ground film and recording the laying information; S2: Set sampling points according to laying information; S3: Collect mulch samples regularly according to sampling points; S4: Test the collected ground film samples and monitor the ground film environment; S5: Record and analyze the monitoring data; S6: Generate reports and adjust strategies based on monitoring results.

2. The biodegradable mulch film degradation monitoring system based on artificial intelligence is characterized by: include: An input module, the input module is connected to a parameter setting module, the parameter setting module is connected to a ground film monitoring module, the ground film monitoring module is connected to a ring monitoring module, a ground film sampling module and a data acquisition module, the ground film sampling module is connected to a detection module, the data acquisition module is connected to a data analysis module, the data analysis module is connected to a data storage module, a production report module and a recording module, the data storage module is connected to a backup module, the recording module is connected to a comparison module, the production report module is connected to an evaluation module, the evaluation module is connected to a decision module and an abnormal warning module, and the abnormal warning module is connected to a remote control module.

3. The artificial intelligence-based biodegradable mulch degradation monitoring system according to claim 2 is characterized in that: The environmental monitoring module includes a temperature and humidity monitoring unit, a light monitoring unit and a soil monitoring unit. The temperature and humidity monitoring unit is connected to the light monitoring unit, and the light monitoring unit is connected to the soil monitoring unit.

4. The artificial intelligence-based biodegradable mulch degradation monitoring system according to claim 3 is characterized by: The ground film sampling module comprises a selection unit, a control unit and a sampling unit. The selection unit is connected to the control unit, and the control unit is connected to the sampling unit.

5. The artificial intelligence-based biodegradable mulch degradation monitoring system according to claim 4 is characterized in that: The ground film monitoring module comprises a color monitoring unit, a rupture degree monitoring unit and a pollution condition monitoring unit. The color monitoring unit is connected to the rupture degree monitoring unit, and the rupture degree monitoring unit is connected to the pollution condition monitoring unit.

6. The artificial intelligence-based biodegradable mulch degradation monitoring system according to claim 5 is characterized by: The input module comprises a laying information input unit, a ground film information input unit and an environmental information input unit. The laying information input unit is connected to the ground film information input unit, and the ground film information input unit is connected to the environmental information input unit.

7. The artificial intelligence-based biodegradable mulch degradation monitoring system according to claim 6 is characterized by: The input module is used to input the ground film laying time, location information, ground film information and annular soil information, and the parameter setting module is used to set the parameters of the environment monitoring module, the ground film monitoring module and the ground film sampling module.

8. The artificial intelligence-based biodegradable mulch degradation monitoring system according to claim 7 is characterized in that: The data acquisition module is used to collect monitoring data and detection data, and transmit them to the data analysis module, and the data analysis module is used to analyze and process the collected information.

9. The artificial intelligence-based biodegradable mulch degradation monitoring system according to claim 8 is characterized by: The recording module is used to record the data analysis results and transmit them to the comparison module, and the comparison module is used to compare and analyze the collected data analysis results.

10. The artificial intelligence-based biodegradable mulch degradation monitoring system according to claim 9 is characterized in that: The data storage module is used to store the data analysis results and the collected data, and transmit them to the backup module, and the backup module is used to back up the data.

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