Soil environment quality monitoring method and related device

Abstract

The invention discloses a soil environment quality monitoring method, a soil environment quality monitoring device, soil environment quality monitoring equipment and a computer readable storage medium. The method comprises the following steps: setting a plurality of physical property sensors and a plurality of chemical property sensors in a to-be-monitored area based on planning data; performing data acquisition through a physical property sensor and a chemical property sensor to obtain physical property data and chemical property data; performing preprocessing and label processing on the physical property data and the chemical property data to obtain training data; training the classification model through the training data to obtain a soil quality monitoring model; wherein the classification model is a model constructed based on soil quality classification characteristic data; and reasoning the acquired sensor data based on the soil quality monitoring model to obtain target soil quality data. Therefore, the soil environment can be monitored quickly, accurately, comprehensively and in real time, and the increasingly severe soil environment problem can be solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of environmental monitoring, and more specifically, relates to a soil environmental quality monitoring method, a soil environmental quality monitoring device, a soil environmental quality monitoring equipment, and a computer-readable storage medium. Background Art

[0002] With the acceleration of industrialization and urbanization and the increasing intensity of agricultural production activities, the soil environment is facing unprecedented pressure and challenges. As an important part of the ecosystem, the quality of soil is directly related to the quality and safety of agricultural products, the balance of the ecosystem and human health.

[0003] In the relevant technologies, soil environmental quality monitoring mainly relies on manual sampling and laboratory chemical analysis methods. This method has many limitations: on the one hand, manual sampling is difficult to ensure the comprehensiveness and representativeness of the sampling points, and it is easy to miss some key areas, resulting in inaccurate assessment of the overall status of the soil environment; on the other hand, the laboratory analysis process is cumbersome and time-consuming. It often takes several days or even weeks from sample collection to result acquisition, which cannot meet the needs of real-time monitoring of the soil environment. Moreover, traditional methods are greatly affected by human factors, and the technical level and degree of operation standardization of different operators may cause deviations in the analysis results.

[0004] In addition, as people pay more and more attention to soil environmental quality, the number of monitored parameters is increasing, including various physical, chemical and biological indicators. Traditional monitoring methods are difficult to process such large amounts of complex data efficiently and accurately.

[0005] Therefore, there is an urgent need for a new and intelligent soil environmental quality monitoring method that can achieve rapid, accurate, comprehensive and real-time monitoring of the soil environment to cope with increasingly severe soil environmental problems. Summary of the invention

[0006] The purpose of this application is to provide a soil environment quality monitoring method, a soil environment quality monitoring device, a soil environment quality monitoring equipment, and a computer-readable storage medium to quickly, accurately, comprehensively, and in real time monitor the soil environment to cope with increasingly severe soil environmental problems.

[0007] In view of the above defects or improvement needs of the prior art, the present invention provides a soil environmental quality monitoring method, comprising:

[0008] Based on the planning data, multiple physical property sensors and multiple chemical property sensors are set in the area to be monitored;

[0009] Collecting data through the physical property sensor and the chemical property sensor to obtain physical property data and chemical property data;

[0010] Preprocessing and labeling the physical property data and the chemical property data to obtain training data;

[0011] The classification model is trained by the training data to obtain a soil quality monitoring model; wherein the classification model is a model constructed based on the soil quality classification characteristic data;

[0012] The acquired sensor data is inferred based on the soil quality monitoring model to obtain target soil quality data.

[0013] Optionally, multiple physical property sensors and multiple chemical property sensors are set in the area to be monitored based on the planning data, including:

[0014] Inferring the geographic information of the area to be monitored based on a geographic classification model to obtain a plurality of sub-areas to be monitored; wherein the geographic classification model is a model trained based on geographic classification training data;

[0015] A corresponding physical property sensor and chemical property sensor are set for each of the sub-areas to be monitored.

[0016] Optionally, collecting data through the physical property sensor and the chemical property sensor to obtain physical property data and chemical property data includes:

[0017] The physical property sensor and the chemical property sensor collect data within a preset time period to obtain physical property data and chemical property data.

[0018] Optionally, the classification model is trained using the training data to obtain a soil quality monitoring model, including:

[0019] The input layer, hidden layer and output layer of the classification model are set based on the soil quality classification characteristic data; wherein the soil quality classification characteristic data includes: soil moisture, temperature, pH, nutrient content and heavy metal content;

[0020] The classification model is trained using the training data to obtain the soil quality monitoring model.

[0021] The present application also provides a soil environmental quality monitoring device, comprising:

[0022] A sensor setting module, used to set a plurality of physical property sensors and a plurality of chemical property sensors in the area to be monitored based on the planning data;

[0023] A data acquisition module, used to acquire data through the physical property sensor and the chemical property sensor to obtain physical property data and chemical property data;

[0024] A data processing module, used for preprocessing and labeling the physical property data and the chemical property data to obtain training data;

[0025] A model training module, used to train the classification model through the training data to obtain a soil quality monitoring model; wherein the classification model is a model constructed based on the soil quality classification characteristic data;

[0026] The quality monitoring module is used to infer the acquired sensor data based on the soil quality monitoring model to obtain target soil quality data.

[0027] Optionally, the sensor setting module is specifically used to infer the geographic information of the area to be monitored based on a geographic classification model to obtain multiple sub-areas to be monitored; wherein the geographic classification model is a model trained based on geographic classification training data; and corresponding physical property sensors and chemical property sensors are set for each sub-area to be monitored.

[0028] Optionally, the data acquisition module is specifically used to acquire data through the physical property sensor and the chemical property sensor within a preset time period to obtain physical property data and chemical property data.

[0029] Optionally, the model training module is specifically used to set the input layer, hidden layer, and output layer of the classification model based on the soil quality classification characteristic data; wherein the soil quality classification characteristic data include: soil moisture, temperature, pH, nutrient content, and heavy metal content; the classification model is trained using the training data to obtain the soil quality monitoring model.

[0030] The present application also provides a soil environmental quality monitoring device, comprising:

[0031] Memory for storing computer programs;

[0032] A processor is used to implement the steps of the soil environmental quality monitoring method as described above when executing the computer program.

[0033] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the soil environmental quality monitoring method as described above are implemented.

[0034] A soil environmental quality monitoring method provided by the present application includes: setting up multiple physical property sensors and multiple chemical property sensors in a to-be-monitored area based on planning data; collecting data through the physical property sensors and the chemical property sensors to obtain physical property data and chemical property data; preprocessing and labeling the physical property data and the chemical property data to obtain training data; training a classification model through the training data to obtain a soil quality monitoring model; wherein the classification model is a model constructed based on soil quality classification characteristic data; and reasoning the acquired sensor data based on the soil quality monitoring model to obtain target soil quality data.

[0035] It has the following beneficial effects:

[0036] By rationally setting up sensors, accurately collecting and processing data, and training and optimizing classification models, efficient, accurate, comprehensive, and real-time monitoring of soil environmental quality can be achieved. This can effectively solve the problems of incomplete sampling, time-consuming analysis, large human errors, and difficult data processing in traditional methods, provide a scientific basis for agricultural production and ecological protection, and effectively promote the development of soil environmental monitoring technology, improve soil quality management, and help environmental protection and sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present application 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 embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0038] Figure 1 A flow chart of a soil environmental quality monitoring method provided in an embodiment of the present application;

[0039] Figure 2 A schematic diagram of the structure of a soil environment quality monitoring device provided in an embodiment of the present application;

[0040] Figure 3 This is a schematic diagram of the structure of the soil environmental quality monitoring equipment provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] The purpose of this application is to provide a soil environment quality monitoring method, a soil environment quality monitoring device, a soil environment quality monitoring equipment, and a computer-readable storage medium to quickly, accurately, comprehensively, and in real time monitor the soil environment to cope with increasingly severe soil environmental problems.

[0042] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0043] The following is an example of a soil environmental quality monitoring method provided by the present application.

[0044] Please refer to Figure 1 , Figure 1 A flow chart of a soil environmental quality monitoring method provided in an embodiment of the present application.

[0045] In this embodiment, the method may include:

[0046] S101, setting a plurality of physical property sensors and a plurality of chemical property sensors in the area to be monitored based on the planning data;

[0047] This step ensures that the physical and chemical characteristics of the soil environment in the monitored area can be fully and accurately obtained by rationally planning the layout of the sensors. The soil environment in different locations may be different. Setting up the sensors according to the planned data can enable the sensors to cover representative areas, thus providing basic data for subsequent accurate monitoring of soil quality.

[0048] First, it is necessary to collect relevant planning data of the area to be monitored, such as topographic data, land use type distribution data, historical soil pollution data (if any), etc. These data can help determine the key areas and reasonable layout of sensor settings.

[0049] The collected geographic information of the area to be monitored is inferred based on the geographic classification model. The geographic classification model is pre-trained based on a large amount of geographic classification training data. The model can divide the area to be monitored into multiple sub-areas to be monitored with similar geographic characteristics according to the input geographic information characteristics (such as altitude, slope, soil type distribution, etc.). For example, if a sub-area is low-lying and has a history of sewage discharge, then the area may be a key area for soil pollution monitoring and requires more densely installed sensors.

[0050] For each sub-area to be monitored, corresponding physical property sensors and chemical property sensors are set. Physical property sensors are used to measure physical parameters such as soil moisture, temperature, and compactness, while chemical property sensors are used to detect chemical indicators such as soil pH, nutrient content, and heavy metal content. For example, in farmland areas, sensors for detecting nutrient content such as nitrogen, phosphorus, and potassium may be more focused, while sensors for detecting heavy metal content may be more focused around industrial plants.

[0051] It can be seen that this step can more accurately capture the characteristic changes of soil environment in different regions by dividing sub-regions through geographic information reasoning and setting sensors in a targeted manner, avoiding the blindness of sensor setting, so that the collected data can better reflect the real situation of the soil environment. The sensors are reasonably allocated according to the needs of different regions, avoiding the waste of resources caused by excessive setting of sensors in unnecessary areas, and ensuring that there are enough monitoring points in key areas, thereby improving the overall efficiency and effectiveness of the monitoring system.

[0052] S102, collecting data through a physical property sensor and a chemical property sensor to obtain physical property data and chemical property data;

[0053] Based on S101, this step uses the physical or chemical sensing principle of the sensor to convert various physical and chemical properties in the soil environment into measurable electrical signals or digital signals, thereby realizing data collection. Different types of sensors target different soil characteristics and obtain corresponding data through specific working principles, providing multi-dimensional information for comprehensive assessment of soil quality.

[0054] Among them, the physical property sensors and chemical property sensors automatically collect data within the preset time period. The preset time period can be set according to the monitoring needs and the characteristics of soil environmental changes. For example, during the critical period of crop growth or the rainy season, which may affect the soil environmental changes, the collection interval can be shortened and the data collection frequency can be increased; while during the period when the soil environment is relatively stable, the collection interval can be appropriately extended to balance the monitoring accuracy and equipment energy consumption.

[0055] The working principles of physical property sensors vary. For example, a sensor that measures soil moisture may be based on the principle of capacitance. Changes in soil moisture will cause changes in the sensor's capacitance value, and the soil moisture data is determined by measuring the change in capacitance value. A temperature sensor uses the characteristics of a thermistor, whose resistance value changes with soil temperature, and temperature data is obtained by detecting the resistance value. A compactness sensor measures soil compactness by sensing the pressure of the soil on the sensor probe. There is a certain correspondence between the pressure and compactness, and compactness data is obtained.

[0056] Chemical property sensors also work according to different chemical principles. For example, pH sensors are based on the principle of ion-selective electrodes and determine the pH of the soil by measuring the concentration of hydrogen ions. Nutrient content sensors (such as nitrogen, phosphorus, potassium, etc.) may use the principle of spectrophotometry to detect nutrient content by using the absorbance changes caused by the chemical reaction between light of a specific wavelength and nutrients in the soil. Heavy metal content sensors often use anodic stripping voltammetry to precipitate heavy metal ions from the soil solution through electrochemical reactions and measure their current signals to determine the heavy metal content.

[0057] This step can continuously collect data within the preset time period, timely reflect the changes in the soil environment at different time points, provide data support for the dynamic assessment of soil quality, and help to timely discover abnormal changes in the soil environment. Collecting physical and chemical property data at the same time covers multiple aspects of the soil environment, which can provide a more comprehensive understanding of the health status of the soil, provide a rich information basis for accurate assessment of soil quality, and avoid the one-sidedness of assessment caused by single parameter monitoring.

[0058] S103, preprocessing and labeling the physical property data and the chemical property data to obtain training data;

[0059] Based on S102, the preprocessing in this step aims to remove interference factors such as noise and outliers in the data, improve data quality, and make the data more suitable for subsequent model training; label processing is to assign corresponding category labels to the data so that the data can be used for supervised model training, so that the model can learn the relationship between data characteristics and soil quality categories.

[0060] The data preprocessing process includes multiple steps. First, data cleaning is performed, and an outlier detection algorithm based on statistical methods is used. For example, the mean and standard deviation of the data are calculated, and a reasonable threshold range (such as mean ± 3 times the standard deviation) is set. Data outside this range is considered as outliers and removed to remove abnormal data caused by sensor failure, measurement error, etc. For noisy data, a filtering algorithm (such as Kalman filtering) is used for smoothing. By performing weighted averaging and other operations on the data sequence, the impact of random noise on the data is reduced, and the stability and accuracy of the data are improved. Then the cleaned data is normalized, and the data collected by different sensors are mapped to the same numerical range (such as [0,1]). Common normalization methods include minimum-maximum scaling, which helps to speed up model training and improve model performance.

[0061] Label processing requires adding labels to the data based on existing soil quality standards or accurate soil quality classification results obtained through professional laboratory analysis. For example, if it is known that the soil samples corresponding to a certain set of soil data are determined to be "good" quality soil after laboratory analysis, then the data set is labeled with the category label "good". These labeled data will be used as training data for subsequent classification model training.

[0062] It can be seen that the data quality after preprocessing in this step is higher. The reduction of noise and outliers can prevent the model from learning wrong feature patterns, making the model training more stable and accurate; the features after normalization are comparable in numerical value, which helps to improve the efficiency and generalization ability of model training. Through label processing, a clear learning goal is provided for the model, so that the model can learn the mapping relationship between different soil environmental data features and corresponding soil quality categories, so that it can accurately predict the quality category of unknown soil data based on the input, and improve the accuracy and reliability of soil quality monitoring.

[0063] S104, training the classification model through the training data to obtain a soil quality monitoring model; wherein the classification model is a model constructed based on the soil quality classification characteristic data;

[0064] Based on S103, the classification model in this step is constructed based on the soil quality classification characteristic data, and its purpose is to learn the characteristic patterns in the data and establish a mapping relationship between the input data (physical and chemical property data) and the output results (soil quality categories). Through training with a large amount of training data, the model continuously adjusts its own parameters to minimize the error between the prediction results and the true labels, thereby achieving accurate classification prediction of soil quality.

[0065] The input layer, hidden layer, and output layer of the classification model are set based on the soil quality classification characteristic data (including soil moisture, temperature, pH, nutrient content, heavy metal content, etc.). The number of nodes in the input layer is determined according to the characteristic dimension of the input data, that is, it corresponds to the number of parameters of the preprocessed physical and chemical property data, and each node receives a characteristic value (such as soil moisture value corresponds to one input node). The hidden layer is constructed by combining the fully connected layer and the convolutional layer. The fully connected layer is used to extract global features and nonlinear transformation of the input data. Through the fully connected relationship between neurons, the model can learn the complex interactive relationship between different features; the convolutional layer uses the convolution kernel to slide on the data for local feature extraction, which can automatically capture the local patterns and feature combinations in the data. For example, in the two-dimensional matrix representation of soil moisture and nutrient content data, the convolutional layer can detect the correlation pattern between moisture and nutrient content in different regions. The number of hidden layers and the number of nodes are determined through experiments and optimization to balance the complexity and generalization ability of the model and prevent the model from overfitting or underfitting. The number of nodes in the output layer is set according to the number of soil quality classification categories of the monitoring target. For example, if the soil quality is divided into four categories: "excellent", "good", "medium" and "poor", the output layer is set with 4 nodes, and the output value of each node represents the probability of the corresponding category.

[0066] The classification model is trained using the training data. The pre-processed and labeled training data is input into the model, and the back-propagation algorithm is used to calculate the error between the model prediction result and the true label. The weight and bias of the model are adjusted according to the error through the stochastic gradient descent optimizer. In each iterative training, the model calculates the prediction result based on the input data forward propagation, and then back-propagates the error from the output layer to the input layer through back-propagation. The weights and biases of each layer are adjusted according to the error, so that the model gradually learns the characteristic patterns in the data and continuously optimizes its own parameters to improve the accuracy of the prediction. During the training process, some techniques to prevent overfitting are also used, such as L2 regularization, which penalizes the model complexity by adding the square sum of weights to the loss function to prevent the model from overfitting the training data; using the Dropout layer, some neurons are randomly inactivated during the training process to increase the randomness and generalization ability of the model and avoid the model's excessive dependence on the training data.

[0067] It can be seen that the soil quality monitoring model trained in this step can accurately predict the category of soil quality based on the input soil physical and chemical property data, providing an efficient and accurate tool for soil environmental quality assessment, which helps to timely discover soil quality problems and take corresponding measures. Through the structural design of the hidden layer, the model can automatically learn the complex relationships and potential patterns between various characteristics in soil environmental data, has good adaptability to different regions and different types of soil environments, and can provide reliable monitoring results under complex and changeable soil environmental conditions.

[0068] S105, inferring the acquired sensor data based on the soil quality monitoring model to obtain target soil quality data.

[0069] Based on S104, in this step, the trained soil quality monitoring model is used to process the sensor data collected in real time. The model analyzes and judges the input sensor data according to the previously learned feature patterns and classification rules, thereby obtaining the predicted result of soil quality, that is, the target soil quality data.

[0070] When the physical property sensors and chemical property sensors collect new soil environmental data, these data are first preprocessed with the same steps as the training data, including data cleaning, normalization and other operations, to ensure that the data format and quality meet the model input requirements.

[0071] The preprocessed sensor data is input into the soil quality monitoring model. After the input layer of the model receives the data, it performs feature extraction and pattern recognition through the hidden layer. The weights and bias parameters learned in the previous training process are used to perform a series of mathematical operations and transformations on the input data, and finally the prediction results are obtained in the output layer. For example, if the output layer nodes represent the four categories of "excellent", "good", "medium" and "poor", the model may output a probability vector similar to [0.1, 0.3, 0.4, 0.2], indicating the probability of the soil sample belonging to each category. The category with the highest probability (in this case, "medium") is the soil quality category predicted by the model. At the same time, detailed probability information of the model's judgment on each category can also be obtained according to specific needs, as a reference for further analysis of the soil quality status.

[0072] It can be seen that this step can quickly process the newly collected sensor data, provide soil quality monitoring results in a timely manner, realize real-time dynamic monitoring of soil environmental quality, help to timely grasp the changes in soil environment, and provide timely and effective decision support for soil protection and management. Reasoning based on the trained model avoids the complex manual analysis process in traditional methods and improves the evaluation efficiency; at the same time, the accuracy of the model ensures the reliability of the evaluation results, which can more accurately reflect the soil quality status and provide a scientific basis for agricultural production, ecological environment protection, etc.

[0073] In summary, this embodiment can achieve efficient, accurate, comprehensive and real-time monitoring of soil environmental quality by reasonably setting sensors, accurately collecting and processing data, and training and optimizing classification models. It can effectively solve the problems of incomplete sampling, time-consuming analysis, large human errors and difficult data processing in traditional methods, provide a scientific basis for agricultural production, ecological protection, etc., effectively promote the development of soil environmental monitoring technology, improve the level of soil quality management, and help environmental protection and sustainable development.

[0074] Optionally, multiple physical property sensors and multiple chemical property sensors are set in the area to be monitored based on the planning data, including:

[0075] Inferring the geographic information of the monitored area based on the geographic classification model to obtain multiple sub-areas to be monitored; wherein the geographic classification model is a model trained based on geographic classification training data;

[0076] A corresponding physical property sensor and chemical property sensor are set for each sub-area to be monitored.

[0077] Optionally, data is collected through a physical property sensor and a chemical property sensor to obtain physical property data and chemical property data, including:

[0078] The physical property sensor and the chemical property sensor collect data within a preset time period to obtain physical property data and chemical property data.

[0079] Optionally, the classification model is trained using training data to obtain a soil quality monitoring model, including:

[0080] The input layer, hidden layer and output layer of the classification model are set based on the soil quality classification characteristic data; wherein the soil quality classification characteristic data includes: soil moisture, temperature, pH, nutrient content and heavy metal content;

[0081] The classification model is trained using training data to obtain a soil quality monitoring model.

[0082] A soil environment quality monitoring device provided in an embodiment of the present application is introduced below. The soil environment quality monitoring device and the soil environment quality monitoring method described below can refer to each other.

[0083] Please refer to Figure 2 , Figure 2 A schematic diagram of the structure of a soil environmental quality monitoring device provided in an embodiment of the present application.

[0084] In this embodiment, the device may include:

[0085] A sensor setting module 100, for setting a plurality of physical property sensors and a plurality of chemical property sensors in a to-be-monitored area based on planning data;

[0086] The data acquisition module 200 is used to acquire data through a physical property sensor and a chemical property sensor to obtain physical property data and chemical property data;

[0087] The data processing module 300 is used to pre-process and label the physical property data and the chemical property data to obtain training data;

[0088] The model training module 400 is used to train the classification model through training data to obtain a soil quality monitoring model; wherein the classification model is a model constructed based on soil quality classification characteristic data;

[0089] The quality monitoring module 500 is used to infer the acquired sensor data based on the soil quality monitoring model to obtain target soil quality data.

[0090] Optionally, a sensor setting module is specifically used to infer the geographic information of the monitored area based on a geographic classification model to obtain multiple sub-areas to be monitored; wherein the geographic classification model is a model trained based on geographic classification training data; and corresponding physical property sensors and chemical property sensors are set for each sub-area to be monitored.

[0091] Optionally, the data acquisition module is specifically used to collect data within a preset time period through a physical property sensor and a chemical property sensor to obtain physical property data and chemical property data.

[0092] Optionally, a model training module is specifically used to set the input layer, hidden layer, and output layer of the classification model based on soil quality classification characteristic data; wherein the soil quality classification characteristic data includes: soil moisture, temperature, pH, nutrient content, and heavy metal content; the classification model is trained through the training data to obtain a soil quality monitoring model.

[0093] This application also provides soil environmental quality monitoring equipment, please refer to Figure 3 , Figure 3 This is a schematic diagram of the structure of a soil environment quality monitoring device provided in an embodiment of the present application. The soil environment quality monitoring device may include:

[0094] Memory for storing computer programs;

[0095] The processor is used to implement the steps of any one of the soil environmental quality monitoring methods described above when executing a computer program.

[0096] like Figure 3As shown, it is a schematic diagram of the composition structure of the soil environment quality monitoring device, which may include: a processor 10, a memory 11, a communication interface 12 and a communication bus 13. The processor 10, the memory 11, and the communication interface 12 all communicate with each other through the communication bus 13.

[0097] In the embodiment of the present application, the processor 10 may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array or other programmable logic devices.

[0098] The processor 10 may call a program stored in the memory 11. Specifically, the processor 10 may execute operations in an embodiment of the abnormal IP identification method.

[0099] The memory 11 is used to store one or more programs, which may include program codes, and the program codes include computer operation instructions. In the embodiment of the present application, the memory 11 at least stores programs for implementing the following functions:

[0100] Based on the planning data, multiple physical property sensors and multiple chemical property sensors are set in the area to be monitored;

[0101] Data is collected through physical property sensors and chemical property sensors to obtain physical property data and chemical property data;

[0102] Preprocess and label the physical property data and chemical property data to obtain training data;

[0103] The classification model is trained by the training data to obtain a soil quality monitoring model; wherein the classification model is a model constructed based on the soil quality classification characteristic data;

[0104] The acquired sensor data is inferred based on the soil quality monitoring model to obtain the target soil quality data.

[0105] In a possible implementation, the memory 11 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function, etc.; the data storage area may store data created during use.

[0106] In addition, the memory 11 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0107] The communication interface 12 may be an interface of a communication module, and is used to connect to other devices or systems.

[0108] Of course, it should be noted that Figure 3 The structure shown does not constitute a limitation on the soil environment quality monitoring device in the embodiment of the present application. In actual applications, the soil environment quality monitoring device may include Figure 3 More or fewer components than shown, or combinations of certain components.

[0109] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned soil environmental quality monitoring methods can be implemented.

[0110] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0111] For an introduction to the computer-readable storage medium provided in this application, please refer to the above method embodiment, and this application will not go into details here.

[0112] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0113] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0114] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0115] The above is a detailed introduction to a soil environment quality monitoring method, a soil environment quality monitoring device, a soil environment quality monitoring equipment, and a computer-readable storage medium provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A soil environmental quality monitoring method, characterized in that: include: Based on the planning data, multiple physical property sensors and multiple chemical property sensors are set in the area to be monitored; Collecting data through the physical property sensor and the chemical property sensor to obtain physical property data and chemical property data; Preprocessing and labeling the physical property data and the chemical property data to obtain training data; The classification model is trained by the training data to obtain a soil quality monitoring model; wherein the classification model is a model constructed based on the soil quality classification characteristic data; The acquired sensor data is inferred based on the soil quality monitoring model to obtain target soil quality data.

2. The soil environmental quality monitoring method according to claim 1, characterized in that: Based on the planning data, multiple physical property sensors and multiple chemical property sensors are set in the area to be monitored, including: Inferring the geographic information of the area to be monitored based on a geographic classification model to obtain a plurality of sub-areas to be monitored; wherein the geographic classification model is a model trained based on geographic classification training data; A corresponding physical property sensor and chemical property sensor are set for each of the sub-areas to be monitored.

3. The soil environmental quality monitoring method according to claim 1, characterized in that: The physical property sensor and the chemical property sensor are used to collect data to obtain physical property data and chemical property data, including: The physical property sensor and the chemical property sensor collect data within a preset time period to obtain physical property data and chemical property data.

4. The soil environmental quality monitoring method according to claim 1, characterized in that: The classification model is trained by the training data to obtain a soil quality monitoring model, including: The input layer, hidden layer and output layer of the classification model are set based on the soil quality classification characteristic data; wherein the soil quality classification characteristic data includes: soil moisture, temperature, pH, nutrient content and heavy metal content; The classification model is trained using the training data to obtain the soil quality monitoring model.

5. A soil environmental quality monitoring device, characterized in that: include: A sensor setting module, used to set a plurality of physical property sensors and a plurality of chemical property sensors in the area to be monitored based on the planning data; A data acquisition module, used to acquire data through the physical property sensor and the chemical property sensor to obtain physical property data and chemical property data; A data processing module, used for preprocessing and labeling the physical property data and the chemical property data to obtain training data; A model training module, used to train the classification model through the training data to obtain a soil quality monitoring model; wherein the classification model is a model constructed based on the soil quality classification characteristic data; The quality monitoring module is used to infer the acquired sensor data based on the soil quality monitoring model to obtain target soil quality data.

6. The soil environment quality monitoring device according to claim 5, characterized in that: The sensor setting module is specifically used to infer the geographic information of the area to be monitored based on the geographic classification model to obtain multiple sub-areas to be monitored; wherein the geographic classification model is a model trained based on geographic classification training data; and corresponding physical property sensors and chemical property sensors are set for each sub-area to be monitored.

7. The soil environment quality monitoring device according to claim 6, characterized in that: The data acquisition module is specifically used to acquire data through the physical property sensor and the chemical property sensor within a preset time period to obtain physical property data and chemical property data.

8. The soil environment quality monitoring device according to claim 7, characterized in that: The model training module is specifically used to set the input layer, hidden layer and output layer of the classification model based on the soil quality classification characteristic data; wherein the soil quality classification characteristic data includes: soil moisture, temperature, pH, nutrient content, and heavy metal content; the classification model is trained by the training data to obtain the soil quality monitoring model.

9. A soil environmental quality monitoring device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the soil environmental quality monitoring method as described in any one of claims 1 to 4 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the soil environmental quality monitoring method according to any one of claims 1 to 4 are implemented.

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