Compost maturity detection method

Abstract

The invention relates to the technical field of data processing, and discloses a compost maturity detection method, which comprises the following steps: firstly, collecting a PH value x1 on a compost pile through a PH sensor, collecting conductivity data x2 on the compost pile through a conductivity sensor, and collecting temperature data x3 on the compost pile through a temperature sensor; acquiring dissolved oxygen data x4 on the compost heap through a dissolved oxygen sensor, and acquiring ammonia nitrogen data x5 on the compost heap through an ammonia nitrogen sensor; sampling and measuring organic matters y1, nitrogen y2, phosphorus y3 and potash fertilizer y4 of the compost heap to obtain measured quantities; and carrying out range normalization on the sensor data x1, x2, x3, x4 and x5 and the biological indexes y1, y2 and y3. Whether the compost is mature or not is comprehensively judged according to recent historical monitoring data of the compost, and the inaccuracy of judging whether the compost maturity reaches the standard or not through single-index detection is far higher than that of judging whether the compost maturity reaches the standard or not.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, in particular to a method for detecting the maturity of compost. Background Art

[0002] Composting is a process in which microorganisms decompose organic waste and convert it into stable humus. Traditionally, the basis for judging compost maturity relies mainly on empirical observation and simple tests, specifically including temperature drop: if the temperature at the center of the pile remains below 40°C for more than three days, it indicates that the high temperature period has ended and the microbial activity has stabilized.

[0003] Color and Texture: The color changes from light yellow / brown to dark brown or black. The texture is loose, with no large chunks of undecomposed material. It forms a ball when squeezed by hand and falls apart when dropped to the ground. Odor: No pungent ammonia or rancid odor, but rather an earthy aroma.

[0004] Due to the continuous improvement of detection technology, such as the application with application number "202010042128.X", the method of using test paper is not only single, but also has no clear indicator data for various data in the compost. Since the composting process is relatively long, relying solely on experience and a single detection method cannot guarantee the quality of the compost after completion and whether the compost is fully mature. For compost without quantitative indicators, it is not only ineffective for crops in the later application object and unsafe, but also easy to cause problems such as crop burns. In addition, there is a single device on the market that detects ammonia nitrogen indicators for on-site testing to determine whether the compost is mature. The ammonia nitrogen content of the compost as well as the temperature, oxygen and other data are a roller coaster of data changes during the composting process. In the process, it is often easy to mistake the compost that is actually in the fermentation period or the lightly mature compost that still contains a large number of microorganisms as mature compost due to a single ammonia nitrogen test. In addition, the traditional judgment method, even if a single sensor is used for data detection, the compost is often due to factors such as ambient temperature and humidity. For example, the detection time is a rainy day or the rainy season, which often causes the moisture content in the detected compost to reach the compost maturity index, and it is easy to judge that the compost is mature. In fact, the compost is still in the early stage of composting. The traditional method cannot make a comprehensive judgment on whether the compost is mature based on the historical gradient data of the compost pile, which is easy to cause misjudgment. Therefore, the traditional method is based on a single data indicator, which often leads to a high misjudgment rate and inaccurate judgment.

[0005] Therefore, in order to solve the above problems, a method for detecting the maturity of compost is needed. Summary of the Invention

[0006] The object of the present invention is to provide a method for detecting compost maturity. The present invention uses multi-indicator quantitative fusion, combines sensor data (temperature, humidity, oxygen concentration) with biological indicators organic matter y1, nitrogen y2, phosphorus y3, and potash fertilizer y4 for sampling and measurement to obtain the measured amount, and uses a multi-indicator data fusion learning model. In addition, the present invention also makes a comprehensive judgment on whether the compost is mature based on the recent historical monitoring data of the compost, which far exceeds the inaccuracy of judging whether the compost maturity is mature and meets the standard by a single indicator detection. Through the deep fusion of sensors and biological indicators, the data of various fertilizer effects such as nitrogen and potassium in compost maturity detection is accurately determined.

[0007] The present invention is achieved in that:

[0008] The present invention provides a compost maturity detection method which is specifically performed by the following steps:

[0009] S1: The pH value x1 of the compost pile is collected through the pH sensor, the conductivity data x2 of the compost pile is collected through the conductivity sensor, the temperature data x3 of the compost pile is collected through the temperature sensor, the dissolved oxygen data x4 of the compost pile is collected through the dissolved oxygen sensor, and the ammonia nitrogen data x5 of the compost pile is collected through the ammonia nitrogen sensor; the pH value x1 collected by the pH sensor, the conductivity value x2 collected by the conductivity sensor, the temperature data x3 collected by the temperature sensor, the dissolved oxygen data x4 collected by the dissolved oxygen sensor, and the ammonia nitrogen data x5 collected by the ammonia nitrogen sensor are sampled once every 1 hour.

[0010] S2: sampling and measuring the organic matter y1, nitrogen y2, phosphorus y3, and potash fertilizer y4 on the compost pile to obtain the measured amounts; the interval period for sampling and measuring the organic matter y1, nitrogen y2, phosphorus y3, and potash fertilizer y4 on the compost pile to obtain the measured amounts is 24 hours.

[0011] S3: Perform range normalization on the sensor data x1, x2, x3, x4, x5 and the biological indicators y1, y2, y3; the range normalization of the sensor data and the biological indicator data is as follows:

[0012]

[0013] in, is the normalized value of the sensor data range, is the normalized data of the biological indicator range, x i,min 、x i,max is the historical extreme value of the sensor parameter, y j,min 、y j,max is the biological indicator threshold.

[0014] S4: Perform feature extraction and dynamic analysis on sensor data x1, x2, x3, x4, x5 and biological indicator data y1, y2, y3; specifically, perform the following steps:

[0015] S4.1: Calculate the parameter change rate for sensor data x1, x2, x3, x4, and x5 as follows:

[0016]

[0017] Wherein, Δt is the sampling interval time;

[0018] S4.2: Perform stability normalization scoring on sensor data x1, x2, x3, x4, and x5, as follows:

[0019]

[0020] Among them, S sensor,i (t) is the normalized score, ε i is the parameter stability threshold, |dx i / dt∣≤ε i When S sensor,i Close to 1, otherwise lower;

[0021] S4.3: Score the maturity of biological indicators y1, y2, and y3; first define the biological indicator threshold θ j , specifically as follows:

[0022]

[0023] Among them, S bio,j (t) is the maturity score of biological indicators.

[0024] S5: Establish a data detection model for the comprehensive maturity of compost, and detect and analyze the collected data based on the data model, and output the maturity of the compost.

[0025] Follow these steps:

[0026] S5.1: Perform weighted fusion based on the normalized sensor stability score and the biological indicator maturity score; first, the sensor stability weight is set to α i , the biological indicator weight is β j , weighted fusion is as follows:

[0027]

[0028] Among them, S sensor (t) is a weighted fusion model, satisfying ∑α i =0.6,∑β j =0.4;

[0029] S5.2: Dynamically adjust the time weight of the weighted fusion model, introduce the time decay factor γ(t), and give priority to trusting recent data, as shown in the following formula:

[0030] γ(t)=e -λt

[0031] Where λ is the attenuation coefficient;

[0032] S5.3: Calculate the comprehensive maturity index of the compost as follows:

[0033] IMS=γ(t)·[w1·S sensor (t)+w2·S bio (t)]

[0034] Among them, IMS is the integrated maturity index, where w1+w2=1.

[0035] The maturity of the compost is determined based on the IMS comprehensive maturity index. First, the IMS compost maturity thresholds T and C are set; as well as the threshold range of compost maturity. If IMS>T, the compost is determined to be fully mature. If T≥IMS≥C, the compost is determined to be basically mature. If C>IMS, the compost is determined to be a raw material pile.

[0036] Furthermore, the present invention provides a compost maturity detection system, comprising a data acquisition module for collecting raw data of chemical and biological indicators during the composting process;

[0037] The data preprocessing and fusion module cleans and integrates multi-source heterogeneous sensor data to extract key features, and performs multimodal fusion to integrate sensor data with biological indicators;

[0038] Maturity assessment algorithm module, which builds a model based on fused data to quantify compost maturity;

[0039] The decision-making and control module generates maturity warning and reminder strategies based on the assessment results, and issues warnings based on abnormalities in the sensor monitoring data;

[0040] In the user interaction visualization module, the sensor data x1, x2, x3, x4, and x5 are directly transmitted to the back-end server through the wireless network, and the biological indicators y1, y2, and y3 are connected to the back-end server through the smart terminal. The staff inputs the data into the back-end server through the user interaction visualization module through the smart terminal for data storage and analysis.

[0041] Furthermore, the present invention provides a computer-storable medium, wherein the storage medium stores a computer program. When the computer program in the storage medium is run, any one of the methods for detecting compost maturity described above is executed.

[0042] Furthermore, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the above-mentioned methods for detecting compost maturity when executing the computer program.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. Through the quantitative fusion of multiple indicators, the sensor data (temperature, humidity, oxygen concentration) is combined with the biological indicators of organic matter y1, nitrogen y2, phosphorus y3, and potassium fertilizer y4 for sampling and measurement to obtain the measurement quantity. Through the multi-indicator data fusion learning model, the accuracy of determining the maturity of the compost is greatly improved.

[0045] 2. This invention also uses recent historical monitoring data to comprehensively determine compost maturity, far surpassing the inaccuracy of single-indicator determinations. By integrating sensors with biological indicators, it achieves precise data on various fertilizer effects, such as nitrogen and potassium, for compost maturity testing.

[0046] 3. It can monitor abnormal data during the composting process. When abnormal indicator data appears, such as the temperature does not change for a long time, it means that the microbial content in the compost is low. The staff needs to replenish the compost materials in time to avoid the composting cycle becoming longer or even causing insufficient fermentation and low compost quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. It is understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0048] Figure 1 is a flow chart of the method of the present invention;

[0049] Figure 2 It is a system structure diagram of the present invention;

[0050] Figure 3 The data processing code of the present invention is Figure 1 ;

[0051] Figure 4 The data processing code of the present invention is Figure 2 ;

[0052] Figure 5 The data processing code of the present invention is Figure 3 ;. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention for which protection is sought, but is merely for selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0054] See also Figure 1-Figure 5 The present invention provides a method for detecting the maturity of compost, which is specifically performed by the following steps:

[0055] S1: The pH value x1 of the compost pile is collected through the pH sensor, the conductivity data x2 of the compost pile is collected through the conductivity sensor, the temperature data x3 of the compost pile is collected through the temperature sensor, the dissolved oxygen data x4 of the compost pile is collected through the dissolved oxygen sensor, and the ammonia nitrogen data x5 of the compost pile is collected through the ammonia nitrogen sensor; the pH value x1 collected by the pH sensor, the conductivity value x2 collected by the conductivity sensor, the temperature data x3 collected by the temperature sensor, the dissolved oxygen data x4 collected by the dissolved oxygen sensor, and the ammonia nitrogen data x5 collected by the ammonia nitrogen sensor are sampled once every 1 hour.

[0056] S2: sampling and measuring the organic matter y1, nitrogen y2, phosphorus y3, and potash fertilizer y4 on the compost pile to obtain the measured amounts; the interval period for sampling and measuring the organic matter y1, nitrogen y2, phosphorus y3, and potash fertilizer y4 on the compost pile to obtain the measured amounts is 24 hours.

[0057] S3: Perform range normalization on the sensor data x1, x2, x3, x4, x5 and the biological indicators y1, y2, y3; the range normalization of the sensor data and the biological indicator data is as follows:

[0058]

[0059] in, is the normalized value of the sensor data range, is the normalized data of the biological indicator range, x i,min 、xi,max is the historical extreme value of the sensor parameter, y j,min 、y j,max is the biological indicator threshold.

[0060] S4: Perform feature extraction and dynamic analysis on sensor data x1, x2, x3, x4, x5 and biological indicator data y1, y2, y3; specifically, perform the following steps:

[0061] S4.1: Calculate the parameter change rate for sensor data x1, x2, x3, x4, and x5 as follows:

[0062]

[0063] Wherein, Δt is the sampling interval time;

[0064] S4.2: Perform stability normalization scoring on sensor data x1, x2, x3, x4, and x5, as follows:

[0065]

[0066] Among them, S sensor,i (t) is the normalized score, ε i is the parameter stability threshold, |dx i / dt∣≤ε i When S sensor,i Close to 1, otherwise lower;

[0067] S4.3: Score the maturity of biological indicators y1, y2, and y3; first define the biological indicator threshold θ j , specifically as follows:

[0068]

[0069] Among them, S bio,j (t) is the maturity score of biological indicators.

[0070] S5: Establish a data detection model for the comprehensive maturity of compost, and detect and analyze the collected data based on the data model, and output the maturity of the compost.

[0071] Follow these steps:

[0072] S5.1: Perform weighted fusion based on the normalized sensor stability score and the biological indicator maturity score; first, the sensor stability weight is set to α i , the biological indicator weight is β j , weighted fusion is as follows:

[0073]

[0074] wherein S sensor (t) is a weighted fusion model, satisfying ∑α i = 0.6, ∑β j = 0.4;

[0075] S5.2: Dynamic time weight adjustment is performed on the weighted fusion model, a time decay factor γ(t) is introduced, and recent data is preferentially trusted, as follows:

[0076] γ(t) = e -λt

[0077] wherein λ is a decay coefficient;

[0078] S5.3: A compost comprehensive maturity index is calculated; the specific formula is as follows:

[0079] IMS = γ(t) · [w1·S sensor (t) + w2·S bio (t)]

[0080] wherein IMS is a comprehensive maturity index, and wherein w1 + w2 = 1.

[0081] According to the IMS comprehensive maturity index, it is determined whether the compost is mature, first, an IMS compost maturity threshold T and C are set; and a compost maturity threshold interval is set, if M(t) > T, it is determined that the compost is completely decomposed, if T ≥ M(t) ≥ C, it is determined that the compost is basically decomposed, and if C > M(t), it is determined that the compost is green material.

[0082] wherein T is set to 85, and C is 70;

[0083] Completely decomposed: IMS > 85;

[0084] Basically decomposed: 70 ≤ IMS ≤ 85;

[0085] Green material: IMS < 70.

[0086] Further, the present application provides a compost decomposition degree detection system, comprising a data acquisition module for acquiring original data of chemical and biological indicators in the composting process;

[0087] A data preprocessing and fusion module for cleaning, integrating sensor multi-source heterogeneous data, extracting key features, and performing multi-modal fusion to fuse sensor data and biological indicators;

[0088] A maturity evaluation algorithm module for constructing a model based on the fused data to quantify the compost maturity;

[0089] A decision and control module for generating maturity early warning and reminding strategies according to the evaluation results,

[0090] and issue early warnings based on abnormalities in sensor-monitored data;

[0091] User interaction visualization module, sensor data collected x1, x2, x3,

[0092] x4 and x5 are directly transmitted to the back-end server via the wireless network, and biological indicators y1, y2, and y3 are connected to the back-end server via the smart terminal. The staff uses the smart terminal through the user interactive visualization module to input the data into the back-end server for data storage and analysis.

[0093] In this embodiment, the present invention provides a computer-storable medium, wherein the storage medium stores a computer program. When the computer program in the storage medium is run, any one of the methods for detecting compost maturity described above is executed.

[0094] In this embodiment, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of any one of the above-described methods for detecting compost maturity are implemented.

[0095] In this embodiment, an actual data calculation process is given as an example, such as Table 1, a data monitoring example data table:

[0096] Table 1 Monitoring data table

[0097]

[0098]

[0099] In this embodiment, data processing and compost comprehensive maturity data detection are performed. During the data processing process, weights are assigned to various data, as shown in Table 2.

[0100] Table 2 Weight distribution table

[0101] index Weight Organic matter (y1) 0.2 Nitrogen (y2) 0.15 Phosphorus (y3) 0.1 Potassium (y4) 0.1 Temperature (℃)(x3) 0.05 Dissolved oxygen (mg / L) (x4) 0.05 Ammonia nitrogen (mg / kg) (x5) 0.05

[0102] In this embodiment, the comprehensive maturity index calculation is performed.

[0103] IMS=(0.0×0.3)+(1.0×0.2)+(1.0×0.15)+(1.0×0.1)+(1.0×0.1)+(0.45×0.05)+(0.2×0.05)+(0.0×0.05)

[0104] =0+0.2+0.15+0.1+0.1+0.0225+0.01+0

[0105] = 0.5825 (converted to percentage scale ≈ 58.25)

[0106] In this example, the maturity determination results for each sampling period are as follows:

[0107] Table 3 Maturity Results Table

[0108] Time point Integrated Maturity Score (IMS) Maturity Level 1 61.86 Raw pile 2 57.60 Raw pile 3 54.17 Raw pile 4 43.24 Raw pile 5 35.00 Raw pile

[0109] The above description is merely the preferred embodiment of this application, and is not intended to limit the scope of the application. Any modification, equivalent replacement or improvement made without departing from the spirit and principle of the application shall be included in the protection scope of the application.

Claims

1. A method for detecting compost maturity, characterized in that: Follow these steps: S1: collecting pH value of the compost pile x1 through the pH sensor, collecting conductivity data of the compost pile x2 through the conductivity sensor, collecting temperature data of the compost pile x3 through the temperature sensor, collecting dissolved oxygen data of the compost pile x4 through the dissolved oxygen sensor, and collecting ammonia nitrogen data of the compost pile x5 through the ammonia nitrogen sensor; S2: sampling and measuring the organic matter y1, nitrogen y2, phosphorus y3, and potash fertilizer y4 of the compost pile to obtain the measured amounts; S3: Perform range normalization on sensor data x1, x2, x3, x4, x5 and biological indicators y1, y2, y3; S4: Feature extraction and dynamic analysis of sensor data x1, x2, x3, x4, x5 and biological indicator data y1, y2, y3; S5: Establish a data detection model for the comprehensive maturity of compost, and detect and analyze the collected data based on the data model, and output the maturity of the compost.

2. A compost maturity detection method according to claim 1, characterized in that: In step S3, the sensor data and the bio-indicator data are normalized to their ranges as follows: in, is the normalized value of the sensor data range, is the normalized data of the biological indicator range, x i,min 、x i,max is the historical extreme value of the sensor parameter, y j,min 、y j,max is the biological indicator threshold.

3. A compost maturity detection method according to claim 1, characterized in that: In step S4, the following steps are specifically performed: S4.1: Calculate the parameter change rate for sensor data x1, x2, x3, x4, and x5 as follows: Wherein, Δt is the sampling interval time; S4.2: Perform stability normalization scoring on sensor data x1, x2, x3, x4, and x5, as follows: Among them, S sensor,i (t) is the normalized score, ε i is the parameter stability threshold, |dx i / dt∣≤ε i When S sensor,i Close to 1, otherwise lower; S4.3: Score the maturity of biological indicators y1, y2, and y3; first define the biological indicator threshold θ j , specifically as follows: Among them, S bio,j (t) is the maturity score of biological indicators.

4. A compost maturity detection method according to claim 1, characterized in that: In step S5, the following steps are specifically performed: S5.1: Perform weighted fusion based on the normalized sensor stability score and the biological indicator maturity score; first, the sensor stability weight is set to α i , the biological indicator weight is β j , weighted fusion is as follows: Among them, S sensor (t) is a weighted fusion model, satisfying ∑α i =0.6,∑β j =0.4; S5.2: Dynamically adjust the time weight of the weighted fusion model, introduce the time decay factor γ(t), and give priority to trusting recent data, as shown in the following formula: γ(t)=e -λt Where λ is the attenuation coefficient; S5.3: Calculate the comprehensive maturity index of the compost as follows: IMS=γ(t)·[w1·S sensor (t)+w2·S bio (t)] Among them, IMS is the integrated maturity index, where w1+w2=1.

5. A compost maturity detection method according to claim 4, characterized in that: The maturity of the compost is determined based on the IMS comprehensive maturity index. First, the IMS compost maturity thresholds T and C are set; as well as the threshold range of compost maturity. If IMS>T, the compost is determined to be fully mature. If T≥IMS≥C, the compost is determined to be basically mature. If C>IMS, the compost is determined to be a raw material pile.

6. A compost maturity detection method according to claim 1, characterized in that: In step S2, the organic matter y1, nitrogen y2, phosphorus y3, and potash fertilizer y4 are sampled and measured in the compost pile, and the measurement interval is 24 hours.

7. A compost maturity detection method according to claim 1, characterized in that: The sensor data including the pH value x1 collected by the pH sensor, the conductivity value x2 collected by the conductivity sensor, the temperature data x3 collected by the temperature sensor, the dissolved oxygen data x4 collected by the dissolved oxygen sensor, and the ammonia nitrogen data x5 collected by the ammonia nitrogen sensor are sampled once every one hour.

8. A compost maturity detection system, characterized by: Including data acquisition module to collect raw data of chemical and biological indicators during the composting process; The data preprocessing and fusion module cleans and integrates multi-source heterogeneous sensor data to extract key features, and performs multimodal fusion to integrate sensor data with biological indicators; Maturity assessment algorithm module, which builds a model based on fused data to quantify compost maturity; The decision-making and control module generates maturity warning and reminder strategies based on the assessment results, and issues warnings based on abnormalities in the sensor monitoring data; In the user interaction visualization module, the sensor data x1, x2, x3, x4, and x5 are directly transmitted to the back-end server through the wireless network, and the biological indicators y1, y2, and y3 are connected to the back-end server through the smart terminal. The staff inputs the data into the back-end server through the user interaction visualization module through the smart terminal for data storage and analysis.

9. A computer storable medium, characterized in that: The storage medium stores a computer program. When the computer program in the storage medium is run, the compost maturity detection method described in any one of claims 1 to 7 is executed.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of a compost maturity detection method according to any one of claims 1 to 7 are implemented.

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