Composting device control method and system for saline-alkali soil composting

Abstract

The invention relates to the technical field of composting equipment control, in particular to a composting device control method and system for saline-alkali soil composting. Determining a preset composting environment parameter set of the composting device in a preset time period according to a preset composting process; collecting real-time composting environment parameters in the composting device at a plurality of preset time nodes to obtain a real-time composting environment parameter set of the composting device in a preset time period; calculating the similarity between each real-time composting environment parameter set and a preset composting environment parameter set, and determining abnormal composting parameters in the composting device according to the similarity between each real-time composting environment parameter set and the preset composting environment parameter set; the state of the composting device is analyzed according to the abnormal composting parameters, the composting device is regulated and controlled according to the state analysis result of the composting device, and according to the method, the composting environment parameters are precisely regulated and controlled, so that the composting efficiency is effectively improved, and the composting quality is ensured.

Description

Technical Field

[0001] The invention relates to the technical field of composting equipment control, in particular to a composting device control method and system for composting in saline-alkali land. Background Art

[0002] As a special land resource, saline-alkali land has become a major challenge in agricultural production because of its high content of soil salt and alkaline substances, which limits the growth of crops. The application of traditional agricultural methods on saline-alkali land is often limited, and composting, as a biological soil improvement technology, is of great significance for improving the soil quality and crop yield of saline-alkali land. In the process of composting through composting equipment, key environmental parameters such as temperature, humidity, and oxygen supply need to be precisely controlled to promote the activity of microorganisms and the decomposition of organic matter. Traditional methods rely on manual observation and adjustment, with low efficiency and poor stability. It is difficult to create a microenvironment suitable for saline-alkali composting, which has a great impact on composting efficiency and product quality. Summary of the invention

[0003] The invention overcomes the shortcomings of the prior art and provides a composting device control method and system for composting in saline-alkali land.

[0004] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is:

[0005] The first aspect of the present invention discloses a composting device control method for composting in saline-alkali land, comprising the following steps:

[0006] Obtaining a preset composting process for a composting device, and determining a preset composting environment parameter set for the composting device within a preset time period according to the preset composting process;

[0007] Collecting real-time composting environment parameters inside the composting device at a number of preset time nodes to obtain a set of real-time composting environment parameters of the composting device within a preset time period;

[0008] Calculating the similarity between each real-time composting environment parameter set and a preset composting environment parameter set, and determining abnormal composting parameters in the composting device according to the similarity between each real-time composting environment parameter set and the preset composting environment parameter set;

[0009] Performing a status analysis on the composting device according to the abnormal composting parameters, and regulating the composting device according to the status analysis result of the composting device;

[0010] Wherein, the composting environmental parameters include temperature, humidity, oxygen concentration, pH value and carbon-nitrogen ratio.

[0011] More specifically, the similarity between each real-time composting environment parameter set and the preset composting environment parameter set is calculated as follows:

[0012] Using the time point as the horizontal axis scale and the composting parameter as the vertical axis scale, construct several plane rectangular coordinate systems; obtain the real-time composting environment parameter set and the preset composting environment parameter set of various same type of composting parameters;

[0013] Drawing a real-time composting parameter line graph and a preset composting parameter line graph respectively in a corresponding plane rectangular coordinate system according to a real-time composting environment parameter set and a preset composting environment parameter set of the same type of composting parameters;

[0014] Obtaining coordinate information corresponding to the real-time composting parameter line graph and the preset composting parameter line graph at the same horizontal coordinate node in a plane rectangular coordinate system;

[0015] Calculating the Mahalanobis distance between the real-time composting parameter line graph and the preset composting parameter line graph in the same horizontal coordinate node according to the coordinate information, and obtaining the Mahalanobis distance between the real-time composting parameter line graph and the preset composting parameter line graph in each horizontal coordinate node;

[0016] The Mahalanobis distances between the real-time composting parameter line graph and the preset composting parameter line graph in each horizontal axis node are summed and then averaged to obtain an average Mahalanobis distance, and the similarity between the corresponding real-time composting environment parameter set and the preset composting environment parameter set is determined according to the obtained average Mahalanobis distance;

[0017] Repeat the above steps until the similarity between the real-time composting environment parameter set and the preset composting environment parameter set is calculated.

[0018] More specifically, the abnormal composting parameters in the composting device are determined according to the similarity between each real-time composting environment parameter set and the preset composting environment parameter set, specifically:

[0019] Compare the similarity between each real-time composting environment parameter set and the preset composting environment parameter set with a preset similarity threshold;

[0020] If the degree of similarity between a certain real-time composting environment parameter set and a preset composting environment parameter set is not greater than a preset similarity threshold, the corresponding composting parameter in the composting device is calibrated as an abnormal composting parameter;

[0021] If the similarity between a certain real-time composting environment parameter set and a preset composting environment parameter set is greater than a preset similarity threshold, the corresponding composting parameters in the composting device are calibrated as normal composting parameters.

[0022] More specifically, the state of the composting device is analyzed according to the abnormal composting parameters, and the composting device is regulated according to the state analysis result of the composting device, specifically:

[0023] Acquire product structure information of the composting device, determine each control module in the composting device according to the product structure information, and acquire control function characteristic information of each control module;

[0024] A multi-head attention mechanism is introduced, and based on the multi-head attention mechanism and the control function characteristic information of each control module, the attention score between each control module and each composting environmental parameter is analyzed; according to the attention score between each control module and each composting environmental parameter, the influence degree of each control module on each composting environmental parameter is determined;

[0025] If the influence degree of a certain control module on a certain composting environmental parameter is greater than a preset influence degree threshold, the control module is marked as an influencing control module of the composting environmental parameter;

[0026] If the influence degree of a certain control module on a certain composting environmental parameter is not greater than a preset influence degree threshold, the control module is marked as a non-influencing control module for the composting environmental parameter;

[0027] Repeat the above steps to analyze and obtain the influencing control modules of each composting parameter; construct a knowledge archive based on the influencing control modules of each composting parameter; wherein the knowledge archive stores the knowledge information of the influencing control modules of each composting parameter.

[0028] The following steps are also included:

[0029] Acquire abnormal composting parameters in the composting device, import the abnormal composting parameters into the knowledge archive for matching, and obtain the influencing control module of the abnormal composting parameters through matching; mark the matched influencing control module as a target monitoring control module;

[0030] Acquire the operation record of the composting device, and acquire the corresponding fault record data when various operation fault conditions occur in the target monitoring and control module according to the operation record;

[0031] Acquire the real-time operation data of the target monitoring and control module, introduce the Jaccard similarity algorithm, calculate the Jaccard similarity between the real-time operation data and the fault record data corresponding to various operation fault conditions by the Jaccard similarity algorithm, and obtain a plurality of Jaccard similarities;

[0032] Compare the calculated Jaccard similarities with the preset similarity threshold; if at least one Jaccard similarity is greater than the preset similarity threshold, it means that the target monitoring control module has failed, and the composting device is controlled to stop working;

[0033] And obtain the fault record data corresponding to the Jaccard similarity greater than the preset similarity threshold, further determine the fault type of the composting device according to the fault record data corresponding to the Jaccard similarity greater than the preset similarity threshold, generate a fault report according to the determined fault type, and send the fault report to the preset platform for display.

[0034] The following steps are also included:

[0035] If each Jaccard similarity is not greater than the preset similarity threshold, then the preset operation data range of the target monitoring and control module working at the current time node is obtained according to the preset composting process of the composting device;

[0036] Determine whether the real-time operation data is within a preset operation data range; if so, it indicates that the operation state of the target monitoring and control module is normal, and the target monitoring and control module is not regulated;

[0037] If it is not, it means that the operating state of the target monitoring and control module is abnormal, and the real-time operating data is adjusted to within the preset operating data range.

[0038] The second aspect of the present invention discloses a composting device control system for saline-alkali land composting, the control system comprising a memory and a processor, the memory storing a composting device control method program, when the composting device control method program is executed by the processor, the following steps are implemented:

[0039] Obtaining a preset composting process for a composting device, and determining a preset composting environment parameter set for the composting device within a preset time period according to the preset composting process;

[0040] Collecting real-time composting environment parameters inside the composting device at a number of preset time nodes to obtain a set of real-time composting environment parameters of the composting device within a preset time period;

[0041] Calculating the similarity between each real-time composting environment parameter set and a preset composting environment parameter set, and determining abnormal composting parameters in the composting device according to the similarity between each real-time composting environment parameter set and the preset composting environment parameter set;

[0042] Performing a status analysis on the composting device according to the abnormal composting parameters, and regulating the composting device according to the status analysis result of the composting device;

[0043] Wherein, the composting environmental parameters include temperature, humidity, oxygen concentration, pH value and carbon-nitrogen ratio.

[0044] The present invention solves the technical defects existing in the background technology, and has the following beneficial effects: obtaining a preset composting process of a composting device, and determining a preset composting environment parameter set of the composting device within a preset time period according to the preset composting process; collecting real-time composting environment parameters inside the composting device at several preset time nodes, and obtaining a real-time composting environment parameter set of the composting device within a preset time period; calculating the similarity between each real-time composting environment parameter set and the preset composting environment parameter set, and determining abnormal composting parameters in the composting device according to the similarity between each real-time composting environment parameter set and the preset composting environment parameter set; performing state analysis on the composting device according to the abnormal composting parameters, and regulating and processing the composting device according to the state analysis results of the composting device. The method effectively improves composting efficiency and ensures composting quality by accurately regulating composting environment parameters; reduces the intensity of manual operation, improves the automation and intelligence level of the composting process, and timely adjusts the strategy according to real-time monitoring data to achieve continuous optimization of the process, thereby improving the reliability and stability of the entire composting system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0046] Figure 1 A first method flow chart of a composting device control method for composting in saline-alkali land;

[0047] Figure 2 A second method flow chart of a composting device control method for composting in saline-alkali land;

[0048] Figure 3 The present invention is a system block diagram of a control system for a composting device used for composting in saline-alkali land. DETAILED DESCRIPTION

[0049] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0050] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0051] like Figure 1 As shown, the first aspect of the present invention discloses a composting device control method for composting in saline-alkali land, comprising the following steps:

[0052] S102: Obtaining a preset composting process of the composting device, and determining a preset composting environment parameter set of the composting device within a preset time period according to the preset composting process;

[0053] S104: collecting real-time composting environment parameters inside the composting device at a number of preset time points to obtain a set of real-time composting environment parameters of the composting device within a preset time period;

[0054] S106: calculating the similarity between each real-time composting environment parameter set and a preset composting environment parameter set, and determining the abnormal composting parameter in the composting device according to the similarity between each real-time composting environment parameter set and the preset composting environment parameter set;

[0055] S108: Analyzing the state of the composting device according to the abnormal composting parameters, and regulating the composting device according to the state analysis result of the composting device;

[0056] Wherein, the composting environmental parameters include temperature, humidity, oxygen concentration, pH value and carbon-nitrogen ratio.

[0057] Among them, the preset composting process of the composting device refers to a set of detailed operating procedures, control parameter settings and operation strategies formulated by relevant technical personnel before the start of composting production based on factors such as specific raw material characteristics, required compost product properties, environmental conditions and equipment capabilities.

[0058] More specifically, if Figure 2 As shown, the similarity between each real-time composting environment parameter set and the preset composting environment parameter set is calculated, specifically:

[0059] S202: constructing a plurality of plane rectangular coordinate systems with time points as horizontal coordinate scales and composting parameters as vertical coordinate scales; obtaining real-time composting environment parameter sets and preset composting environment parameter sets of various same type of composting parameters;

[0060] S204: drawing a real-time composting parameter line graph and a preset composting parameter line graph in a corresponding plane rectangular coordinate system according to the real-time composting environment parameter set and the preset composting environment parameter set of the same type of composting parameters;

[0061] S206: Obtaining coordinate information corresponding to the real-time composting parameter line graph and the preset composting parameter line graph at the same horizontal coordinate node in a plane rectangular coordinate system;

[0062] S208: calculating the Mahalanobis distance between the real-time composting parameter line graph and the preset composting parameter line graph in the same horizontal coordinate node according to the coordinate information, and obtaining the Mahalanobis distance between the real-time composting parameter line graph and the preset composting parameter line graph in each horizontal coordinate node;

[0063] S210: summing up the Mahalanobis distances between the real-time composting parameter line graph and the preset composting parameter line graph at each horizontal axis node and taking the average value to obtain an average Mahalanobis distance, and determining the degree of similarity between the corresponding real-time composting environment parameter set and the preset composting environment parameter set according to the obtained average Mahalanobis distance;

[0064] S212: Repeat the above steps until the similarity between the real-time composting environment parameter set and the preset composting environment parameter set is calculated.

[0065] It should be noted that, first, multiple plane rectangular coordinate systems with time points as abscissas and different composting parameters as ordinates are constructed. Then, the real-time collected composting environmental parameters and the preset ideal parameters are plotted into line graphs respectively. By comparing the coordinate information of the two line graphs at the same time node, the Mahalanobis distance at each node is calculated, and then summed and averaged to obtain the average Mahalanobis distance during the entire monitoring period. This average value reflects the degree of similarity between the real-time environmental parameters and the preset parameters. By repeating this process, the similarity between the real-time parameter set and the preset parameter set can be continuously evaluated. By drawing real-time and preset composting parameter line graphs in the coordinate system, intuitive monitoring and visual display of the composting process are achieved. Using the Mahalanobis distance as a measure of similarity, the difference between the real-time composting environmental parameters and the preset parameters can be quantitatively evaluated. By comparing the average Mahalanobis distance, abnormal conditions in the composting process can be quickly identified, providing a basis for timely adjustment and optimization of the composting conditions. It can consider multiple composting parameters at the same time to achieve a comprehensive evaluation of the composting environment and improve the accuracy and reliability of the evaluation. In summary, this composting environmental parameter monitoring and similarity evaluation method based on Mahalanobis distance provides an effective technical means for the optimization and management of the composting process through real-time monitoring, visualization and data analysis.

[0066] More specifically, the abnormal composting parameters in the composting device are determined according to the similarity between each real-time composting environment parameter set and the preset composting environment parameter set, specifically:

[0067] Compare the similarity between each real-time composting environment parameter set and the preset composting environment parameter set with a preset similarity threshold;

[0068] If the degree of similarity between a certain real-time composting environment parameter set and a preset composting environment parameter set is not greater than a preset similarity threshold, the corresponding composting parameter in the composting device is calibrated as an abnormal composting parameter;

[0069] If the similarity between a certain real-time composting environment parameter set and a preset composting environment parameter set is greater than a preset similarity threshold, the corresponding composting parameters in the composting device are calibrated as normal composting parameters.

[0070] It should be noted that each real-time composting environment parameter set is compared with the preset composting environment parameter set to calculate the similarity between them. If the similarity is not greater than the preset similarity threshold, the corresponding composting parameter is calibrated as an abnormal composting parameter; if the similarity is greater than the preset similarity threshold, the corresponding composting parameter is calibrated as a normal composting parameter. This can quickly and accurately identify abnormal conditions in the composting process, so that timely measures can be taken to make adjustments to ensure the quality and efficiency of the composting. By comparing the real-time composting environment parameter set with the preset composting environment parameter set, deviations in the composting process can be discovered in time, and corresponding measures can be taken to make adjustments, thereby avoiding quality problems and inefficiency caused by abnormal composting processes. At the same time, the degree of automation of the composting process can be improved, manual intervention can be reduced, and production efficiency can be improved.

[0071] More specifically, the state of the composting device is analyzed according to the abnormal composting parameters, and the composting device is regulated according to the state analysis result of the composting device, specifically:

[0072] Acquire product structure information of the composting device, determine each control module in the composting device according to the product structure information, and acquire control function characteristic information of each control module;

[0073] A multi-head attention mechanism is introduced, and based on the multi-head attention mechanism and the control function characteristic information of each control module, the attention score between each control module and each composting environmental parameter is analyzed; according to the attention score between each control module and each composting environmental parameter, the influence degree of each control module on each composting environmental parameter is determined;

[0074] If the influence degree of a certain control module on a certain composting environmental parameter is greater than a preset influence degree threshold, the control module is marked as an influencing control module of the composting environmental parameter;

[0075] If the influence degree of a certain control module on a certain composting environmental parameter is not greater than a preset influence degree threshold, the control module is marked as a non-influencing control module for the composting environmental parameter;

[0076] Repeat the above steps to analyze and obtain the influencing control modules of each composting parameter; construct a knowledge archive based on the influencing control modules of each composting parameter; wherein the knowledge archive stores the knowledge information of the influencing control modules of each composting parameter.

[0077] Among them, the control modules in the composting device include but are not limited to temperature control module, humidity control module, ventilation control module, carbon-nitrogen ratio control module, material conveying module, data recording and analysis module, and alarm and fault diagnosis module. The control function characteristic information of the control module refers to the detailed information used to describe and define the characteristics and behaviors that a specific control module needs to have when performing its tasks in the automation system. This information usually includes key elements such as the module's input and output interface definition, control algorithm, working mode, parameter setting, response time and accuracy requirements, interaction method with other modules, and fault handling strategy.

[0078] It should be noted that by obtaining the product structure information of the composting device, the various control modules in the device and their control function feature information are first determined. Then, the multi-head attention mechanism is introduced, which is a model structure widely used in the fields of natural language processing and deep learning, which can help the model better capture the dependencies between different positions when processing sequence data. The multi-head attention mechanism is used to analyze the relationship between each control module and the composting environmental parameters, and the degree of influence of each control module on the composting environmental parameters is determined by calculating the attention score. Specifically, if the degree of influence of a control module on a composting environmental parameter exceeds a preset threshold, then the control module is marked as a control module that has a significant influence on the composting environmental parameter; otherwise, it is marked as a non-influencing control module. Through such analysis, it is possible to systematically identify which control modules play a key role in maintaining or adjusting specific composting environmental parameters. Furthermore, based on these analysis results, a knowledge archive is constructed to store the relevant knowledge information of each composting parameter and its influencing control module. Such a knowledge archive can not only provide a theoretical basis for the optimization and maintenance of composting equipment, but also provide guidance for the design of control strategies for new composting environmental parameters in the future, thereby improving the efficiency and quality of the composting process, reducing resource waste, and promoting sustainable development. In general, this AI-based composting device control method, through intelligent analysis and knowledge accumulation, can not only achieve precise control of the composting process and improve composting efficiency, but also provide important technical support and decision-making basis for the further development of composting technology.

[0079] The following steps are also included:

[0080] Acquire abnormal composting parameters in the composting device, import the abnormal composting parameters into the knowledge archive for matching, and obtain the influencing control module of the abnormal composting parameters through matching; mark the matched influencing control module as a target monitoring control module;

[0081] Acquire the operation record of the composting device, and acquire the corresponding fault record data when various operation fault conditions occur in the target monitoring and control module according to the operation record;

[0082] Acquire the real-time operation data of the target monitoring and control module, introduce the Jaccard similarity algorithm, calculate the Jaccard similarity between the real-time operation data and the fault record data corresponding to various operation fault conditions by the Jaccard similarity algorithm, and obtain a plurality of Jaccard similarities;

[0083] Compare the calculated Jaccard similarities with the preset similarity threshold; if at least one Jaccard similarity is greater than the preset similarity threshold, it means that the target monitoring control module has failed, and the composting device is controlled to stop working;

[0084] And obtain the fault record data corresponding to the Jaccard similarity greater than the preset similarity threshold, further determine the fault type of the composting device according to the fault record data corresponding to the Jaccard similarity greater than the preset similarity threshold, generate a fault report according to the determined fault type, and send the fault report to the preset platform for display.

[0085] The operation data includes the current, voltage, response speed, signal stability, etc. of each control module. The operation record of the composting device is an electronic document or database used to record the operation of the composting device, including electronic record information such as operation time, environmental parameters, material addition, failure and maintenance, and operation.

[0086] It should be noted that after the abnormal composting parameters in the composting device are obtained through the above steps, they are imported into the knowledge archive for matching, so as to find the control module related to the abnormal parameters and with influence, and mark it as the target monitoring control module. Then, the system analyzes the historical operation records of the composting device to extract the fault record data of the target monitoring control module when various operation faults occur. Further, the Jaccard similarity algorithm is introduced to compare the real-time operation data of the target monitoring control module with the historical fault record data, and calculate the similarity between the two. By comparing with the preset similarity threshold, when it is found that the similarity between the real-time operation data and the historical fault record data exceeds the threshold, the system will determine that the target monitoring control module has failed, and immediately control the composting device to stop working, so as to avoid irreversible effects on composting due to equipment failure and ensure the quality of composting. In the event of a fault, the system will also obtain fault record data corresponding to high similarity, further determine the specific fault type by analyzing these data, and generate a detailed fault report. Finally, the fault report will be sent to the preset platform for display, so that relevant personnel can review and take subsequent maintenance or repair actions. In summary, intelligent fault prediction and management not only improves the operating efficiency and stability of the composting device, but also provides a scientific basis for equipment maintenance and management, which can effectively ensure the quality of composting.

[0087] The following steps are also included:

[0088] If each Jaccard similarity is not greater than the preset similarity threshold, then the preset operation data range of the target monitoring and control module working at the current time node is obtained according to the preset composting process of the composting device;

[0089] Determine whether the real-time operation data is within a preset operation data range; if so, it indicates that the operation state of the target monitoring and control module is normal, and the target monitoring and control module is not regulated;

[0090] If it is not, it means that the operating state of the target monitoring and control module is abnormal, and the real-time operating data is adjusted to within the preset operating data range.

[0091] It should be noted that, first, by calculating the Jaccard similarity between the real-time operation data and the historical fault record data, it is identified whether there is a potential operation anomaly in the target monitoring and control module. If all Jaccard similarities do not exceed the preset threshold, it indicates that the operation data of the current target monitoring and control module is consistent with the historical normal operation data, that is, the operation state is normal and no regulation is required. On the contrary, if there is at least one Jaccard similarity exceeding the preset threshold, it means that the real-time operation data of the target monitoring and control module is highly similar to the historical fault data, which may imply an abnormal operation state. At this time, the system will call the preset composting process of the composting device to obtain the preset operation data range that the module should follow at the current time node. Next, the system will check whether the real-time operation data falls within this preset range. If the real-time operation data does exceed the preset range, the system will automatically adjust it to return to the preset operation data range to ensure the stability and efficiency of the composting process. In summary, this control mechanism effectively improves the operation efficiency, stability and environmental protection of the composting device through intelligent data analysis and automatic regulation, and provides strong technical support for the optimization and control of the composting process.

[0092] In addition, the method further comprises the following steps:

[0093] Acquire image information of the actual state of compost in the composting device at a preset time node, and acquire standard characteristic image information of mature compost;

[0094] Performing feature transformation processing on the actual state image information to obtain an LBP matrix of the actual state image information, thereby obtaining a first LBP matrix; similarly, performing feature transformation processing on the standard feature image information to obtain an LBP matrix of the standard feature image information, thereby obtaining a second LBP matrix;

[0095] Calculating the Pearson correlation coefficient between the first LBP matrix and the second LBP matrix. If the Pearson correlation coefficient is greater than a preset coefficient value, it means that the composting is completed, and the composting device is controlled to stop working;

[0096] If the Pearson correlation coefficient is not greater than the preset coefficient value, traversing the matrix values ​​at the same position in the first LBP matrix and the second LBP matrix to determine whether the matrix values ​​at the same position in the first LBP matrix and the second LBP matrix are the same;

[0097] If they are different, the corresponding matrix position is marked as a compost singular position, and so on, the compost singular position in the composting device is obtained, and the compost singular position is sent to a preset terminal for display.

[0098] It should be noted that, first, the system collects real-time image information in the composting device at a preset time node, and simultaneously obtains the ideal state image of mature compost as a reference standard. This real-time monitoring mechanism ensures immediate feedback on the composting state, which helps to adjust and optimize the composting process in a timely manner. Next, the system uses the local binary pattern (LBP) algorithm to extract features from the real-time image and the reference standard image. The LBP algorithm is a powerful texture analysis tool that can effectively capture local texture characteristics in an image. By converting the original image into an LBP matrix (local binary matrix), the system can simplify complex image information into a form that is easy to understand and compare. Subsequently, the system calculates the Pearson correlation coefficient between the real-time LBP matrix and the standard LBP matrix. The Pearson correlation coefficient measures the strength of the linear relationship between two variables, and is used here to evaluate the similarity between the real-time composting state and the ideal state. If the correlation coefficient exceeds the preset threshold, it means that the compost has reached a mature state, and the system will instruct the composting device to stop working to avoid excessive composting and waste of resources. If the correlation coefficient does not reach the preset threshold, the system will compare the matrix values ​​of the corresponding positions in the real-time LBP matrix and the standard LBP matrix. When it is found that the matrix values ​​of the two do not match in certain positions, the system marks these positions as "compost odd positions", which means that there may be problems or abnormalities in this part of the compost. This process helps to locate the problem area so that targeted measures can be taken to make adjustments. Finally, the system summarizes all the identified compost odd positions and displays them visually through the preset terminal. This intuitive feedback mechanism enables operators to quickly understand the problems in the composting process, so that they can intervene in time to ensure the quality of the compost. Overall, through automated image processing and data analysis, accurate monitoring and intelligent decision-making of the composting process are achieved, which not only improves composting efficiency, but also reduces labor costs, thereby improving efficiency and resource utilization.

[0099] like Figure 3 As shown, the second aspect of the present invention discloses a composting device control system for saline-alkali land composting, the control system comprises a memory 41 and a processor 62, the memory 41 stores a composting device control method program, when the composting device control method program is executed by the processor 62, the following steps are implemented:

[0100] Obtaining a preset composting process for a composting device, and determining a preset composting environment parameter set for the composting device within a preset time period according to the preset composting process;

[0101] Collecting real-time composting environment parameters inside the composting device at a number of preset time nodes to obtain a set of real-time composting environment parameters of the composting device within a preset time period;

[0102] Calculating the similarity between each real-time composting environment parameter set and a preset composting environment parameter set, and determining abnormal composting parameters in the composting device according to the similarity between each real-time composting environment parameter set and the preset composting environment parameter set;

[0103] Performing a status analysis on the composting device according to the abnormal composting parameters, and regulating the composting device according to the status analysis result of the composting device;

[0104] Wherein, the composting environmental parameters include temperature, humidity, oxygen concentration, pH value and carbon-nitrogen ratio.

[0105] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0106] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0107] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0108] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0109] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0110] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A composting device control method for saline-alkali land composting, characterized in that: The following steps are involved: Obtaining a preset composting process for a composting device, and determining a preset composting environment parameter set for the composting device within a preset time period according to the preset composting process; Collecting real-time composting environment parameters inside the composting device at a number of preset time nodes to obtain a set of real-time composting environment parameters of the composting device within a preset time period; Calculating the similarity between each real-time composting environment parameter set and a preset composting environment parameter set, and determining abnormal composting parameters in the composting device according to the similarity between each real-time composting environment parameter set and the preset composting environment parameter set; Performing a status analysis on the composting device according to the abnormal composting parameters, and regulating the composting device according to the status analysis result of the composting device; Wherein, the composting environmental parameters include temperature, humidity, oxygen concentration, pH value and carbon-nitrogen ratio.

2. The composting device control method for saline-alkali land composting according to claim 1, characterized in that: The similarity between each real-time composting environment parameter set and the preset composting environment parameter set is calculated as follows: Using the time point as the horizontal axis scale and the composting parameter as the vertical axis scale, construct several plane rectangular coordinate systems; obtain the real-time composting environment parameter set and the preset composting environment parameter set of various same type of composting parameters; Drawing a real-time composting parameter line graph and a preset composting parameter line graph respectively in a corresponding plane rectangular coordinate system according to a real-time composting environment parameter set and a preset composting environment parameter set of the same type of composting parameters; Obtaining coordinate information corresponding to the real-time composting parameter line graph and the preset composting parameter line graph at the same horizontal coordinate node in a plane rectangular coordinate system; Calculating the Mahalanobis distance between the real-time composting parameter line graph and the preset composting parameter line graph in the same horizontal coordinate node according to the coordinate information, and obtaining the Mahalanobis distance between the real-time composting parameter line graph and the preset composting parameter line graph in each horizontal coordinate node; The Mahalanobis distances between the real-time composting parameter line graph and the preset composting parameter line graph in each horizontal axis node are summed and then averaged to obtain an average Mahalanobis distance, and the similarity between the corresponding real-time composting environment parameter set and the preset composting environment parameter set is determined according to the obtained average Mahalanobis distance; Repeat the above steps until the similarity between the real-time composting environment parameter set and the preset composting environment parameter set is calculated.

3. The composting device control method for saline-alkali land composting according to claim 1, characterized in that: The abnormal composting parameters in the composting device are determined according to the similarity between each real-time composting environment parameter set and the preset composting environment parameter set, specifically: Compare the similarity between each real-time composting environment parameter set and the preset composting environment parameter set with a preset similarity threshold; If the degree of similarity between a certain real-time composting environment parameter set and a preset composting environment parameter set is not greater than a preset similarity threshold, the corresponding composting parameter in the composting device is calibrated as an abnormal composting parameter; If the degree of similarity between a certain real-time composting environment parameter set and a preset composting environment parameter set is greater than a preset similarity threshold, the corresponding composting parameters in the composting device are calibrated as normal composting parameters.

4. The composting device control method for saline-alkali land composting according to claim 1, characterized in that: The state of the composting device is analyzed according to the abnormal composting parameters, and the composting device is regulated according to the state analysis result of the composting device, specifically: Acquire product structure information of the composting device, determine each control module in the composting device according to the product structure information, and acquire control function characteristic information of each control module; A multi-head attention mechanism is introduced, and based on the multi-head attention mechanism and the control function characteristic information of each control module, the attention score between each control module and each composting environment parameter is analyzed; Determining the influence of each control module on each composting environmental parameter according to the attention score between each control module and each composting environmental parameter; If the influence degree of a certain control module on a certain composting environmental parameter is greater than a preset influence degree threshold, the control module is marked as an influencing control module of the composting environmental parameter; If the influence degree of a certain control module on a certain composting environmental parameter is not greater than a preset influence degree threshold, the control module is marked as a non-influencing control module for the composting environmental parameter; Repeat the above steps to analyze and obtain the influencing control modules of each composting parameter; construct a knowledge archive based on the influencing control modules of each composting parameter; wherein the knowledge archive stores the knowledge information of the influencing control modules of each composting parameter.

5. The method for controlling a composting device for composting in saline-alkali land according to claim 4, characterized in that: The following steps are also included: Acquire abnormal composting parameters in the composting device, import the abnormal composting parameters into the knowledge archive for matching, and obtain the influencing control module of the abnormal composting parameters through matching; Marking the matched influential control module as a target monitoring control module; Acquire the operation record of the composting device, and acquire the corresponding fault record data when various operation fault conditions occur in the target monitoring and control module according to the operation record; Acquire the real-time operation data of the target monitoring and control module, introduce the Jaccard similarity algorithm, calculate the Jaccard similarity between the real-time operation data and the fault record data corresponding to various operation fault conditions by the Jaccard similarity algorithm, and obtain a plurality of Jaccard similarities; Compare the calculated Jaccard similarities with the preset similarity threshold; if at least one Jaccard similarity is greater than the preset similarity threshold, it means that the target monitoring control module has failed, and the composting device is controlled to stop working; And obtain the fault record data corresponding to the Jaccard similarity greater than the preset similarity threshold, further determine the fault type of the composting device according to the fault record data corresponding to the Jaccard similarity greater than the preset similarity threshold, generate a fault report according to the determined fault type, and send the fault report to the preset platform for display.

6. The method for controlling a composting device for composting in saline-alkali land according to claim 5, characterized in that: The following steps are also included: If each Jaccard similarity is not greater than the preset similarity threshold, then the preset operation data range of the target monitoring and control module working at the current time node is obtained according to the preset composting process of the composting device; Determining whether the real-time operating data is within a preset operating data range; If it is, it means that the operation status of the target monitoring and control module is normal, and the target monitoring and control module is not regulated; If it is not, it means that the operating state of the target monitoring and control module is abnormal, and the real-time operating data is adjusted to within the preset operating data range.

7. A composting device control system for saline-alkali land composting, characterized in that: The control system includes a memory and a processor. The memory stores a composting device control method program. When the composting device control method program is executed by the processor, the following steps are implemented: Obtaining a preset composting process for a composting device, and determining a preset composting environment parameter set for the composting device within a preset time period according to the preset composting process; Collecting real-time composting environment parameters inside the composting device at a number of preset time nodes to obtain a set of real-time composting environment parameters of the composting device within a preset time period; Calculating the similarity between each real-time composting environment parameter set and a preset composting environment parameter set, and determining abnormal composting parameters in the composting device according to the similarity between each real-time composting environment parameter set and the preset composting environment parameter set; Performing a status analysis on the composting device according to the abnormal composting parameters, and regulating the composting device according to the status analysis result of the composting device; Wherein, the composting environmental parameters include temperature, humidity, oxygen concentration, pH value and carbon-nitrogen ratio.

8. A composting device control system for composting in saline-alkali land according to claim 7, characterized in that: The state of the composting device is analyzed according to the abnormal composting parameters, and the composting device is regulated according to the state analysis result of the composting device, specifically: Acquire product structure information of the composting device, determine each control module in the composting device according to the product structure information, and acquire control function characteristic information of each control module; A multi-head attention mechanism is introduced, and based on the multi-head attention mechanism and the control function characteristic information of each control module, the attention score between each control module and each composting environment parameter is analyzed; Determining the influence of each control module on each composting environmental parameter according to the attention score between each control module and each composting environmental parameter; If the influence degree of a certain control module on a certain composting environmental parameter is greater than a preset influence degree threshold, the control module is marked as an influencing control module of the composting environmental parameter; If the influence degree of a certain control module on a certain composting environmental parameter is not greater than a preset influence degree threshold, the control module is marked as a non-influencing control module for the composting environmental parameter; Repeat the above steps to analyze and obtain the influencing control modules of each composting parameter; construct a knowledge archive based on the influencing control modules of each composting parameter; wherein the knowledge archive stores the knowledge information of the influencing control modules of each composting parameter.

9. A composting device control system for composting in saline-alkali land according to claim 8, characterized in that: The following steps are also included: Acquire abnormal composting parameters in the composting device, import the abnormal composting parameters into the knowledge archive for matching, and obtain the influencing control module of the abnormal composting parameters through matching; Marking the matched influential control module as a target monitoring control module; Acquire the operation record of the composting device, and acquire the corresponding fault record data when various operation fault conditions occur in the target monitoring and control module according to the operation record; Acquire the real-time operation data of the target monitoring and control module, introduce the Jaccard similarity algorithm, calculate the Jaccard similarity between the real-time operation data and the fault record data corresponding to various operation fault conditions by the Jaccard similarity algorithm, and obtain a plurality of Jaccard similarities; Compare the calculated Jaccard similarities with the preset similarity threshold; if at least one Jaccard similarity is greater than the preset similarity threshold, it means that the target monitoring control module has failed, and the composting device is controlled to stop working; And obtain the fault record data corresponding to the Jaccard similarity greater than the preset similarity threshold, further determine the fault type of the composting device according to the fault record data corresponding to the Jaccard similarity greater than the preset similarity threshold, generate a fault report according to the determined fault type, and send the fault report to the preset platform for display.

10. A composting device control system for saline-alkali land composting according to claim 9, characterized in that: The following steps are also included: If each Jaccard similarity is not greater than the preset similarity threshold, then the preset operation data range of the target monitoring and control module working at the current time node is obtained according to the preset composting process of the composting device; Determining whether the real-time operating data is within a preset operating data range; If it is, it means that the operation status of the target monitoring and control module is normal, and the target monitoring and control module is not regulated; If it is not, it means that the operating state of the target monitoring and control module is abnormal, and the real-time operating data is adjusted to within the preset operating data range.

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