Soil ARGs distribution analysis method, system and device based on clustering analysis and storage medium

Abstract

The invention discloses a soil ARGs distribution analysis method, system and device based on clustering analysis and a storage medium, and belongs to the technical field of soil resistance gene analysis. The method comprises the following steps: acquiring resistance gene concentration information and geographical location information of a target soil area; determining an initial center of clustering analysis according to the resistance gene concentration information and the geographic position information of the target soil area; based on the initial center of clustering analysis, clustering each resistance gene of the target soil area to obtain a resistance gene distribution result of the target soil area; and evaluating a resistance gene distribution result of the target soil area by adopting the contour coefficient, and dividing to obtain a resistance gene distribution grade of the target soil area. According to the invention, clustering parameters can be adjusted in real time according to data distribution characteristics, and the adaptability and flexibility of clustering analysis are improved; a hot spot area with high resistance gene concentration can be accurately identified, and scientific support is provided for monitoring and treatment of farmland antibiotic pollution.

Description

Technical Field

[0001] The present invention belongs to the technical field of soil resistance gene analysis, and specifically relates to a soil ARGs distribution analysis method, system, device and storage medium based on cluster analysis. Background Art

[0002] It is well known that the application of organic fertilizers can effectively improve soil fertility, soil physical, chemical and biological properties, and alleviate soil degradation, but it can also introduce antibiotic resistance genes (ARGs) and drug-resistant pathogens, posing a potential threat to soil health. However, research on the problem of ARGs pollution in farmland soil has not yet attracted attention. Applying cluster analysis to the distribution analysis of ARGs in farmland soil is a new research idea.

[0003] However, traditional clustering methods have the problem of randomness in the selection of initial cluster centers, which may lead to slow convergence and fall into local optimal solutions. At the same time, when dealing with data sets with significant spatial geographic features, it is often difficult to take into account the multi-feature distribution of the data at the same time, resulting in unstable clustering results. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and to provide a soil ARGs distribution analysis method, system, device and storage medium based on cluster analysis, which can adjust clustering parameters in real time according to data distribution characteristics and improve the adaptability and flexibility of cluster analysis.

[0005] The present invention provides the following technical solutions:

[0006] In a first aspect, a soil ARGs distribution analysis method based on cluster analysis is provided, comprising: obtaining resistance gene concentration information and geographical location information of a target soil area; determining an initial center of cluster analysis based on the resistance gene concentration information and geographical location information of the target soil area; clustering each resistance gene in the target soil area based on the initial center of cluster analysis to obtain a resistance gene distribution result of the target soil area; and evaluating the resistance gene distribution result of the target soil area using a silhouette coefficient to obtain a resistance gene distribution level of the target soil area.

[0007] As an optional technical solution of the present invention, after obtaining the resistance gene concentration information and geographical location information of the target soil area, the resistance gene concentration information and geographical location information of the target soil area are normalized, and expressed as:

[0008] ;

[0009] in, Indicates resistance gene concentration information or geographical location information, Represents the normalized resistance gene concentration information or geographical location information, Indicates the maximum value of resistance gene concentration information or geographical location information. Indicates the minimum value of resistance gene concentration information or geographical location information.

[0010] As an optional technical solution of the present invention, the initial center of the cluster analysis is determined according to the resistance gene concentration information and geographical location information of the target soil area, including:

[0011] A set of initial center points is randomly selected in the target soil area according to a preset random number of times, wherein each set of initial center points includes k points, and k represents the target cluster number of cluster analysis;

[0012] The sum of squared errors between each data point and the nearest initial center point in the target soil area after each random selection is calculated and expressed as:

[0013] ;

[0014] in, represents the sum of squared errors of each data point in the target soil region, n represents the total number of data points, represents the i-th data point, Representation and The nearest initial center point;

[0015] The sum of squared errors of each data point in the target soil area The smallest set of initial center points is used as the initial center for cluster analysis.

[0016] As an optional technical solution of the present invention, the initial center based on the cluster analysis is used to cluster the resistance genes in the target soil area to obtain the distribution results of the resistance genes in the target soil area, including:

[0017] The weight values ​​of resistance gene concentration and geographical location are introduced to calculate the weighted distance between resistance gene concentration and geographical location, which is expressed as:

[0018] ;

[0019] in, represents the weighted distance, represents the weight value of the resistance gene concentration, Indicates the weight value of the geographical location. represents the difference in resistance gene concentration between data points x and y, Represents the distance between data points x and y.

[0020] As an optional technical solution of the present invention, adjusting the weights of the resistance gene concentration information and the geographical location information during the clustering process includes:

[0021] Multiple iterations are performed during the clustering process, and the weight values ​​of resistance gene concentration and geographical location are recalculated after each iteration, which are expressed as:

[0022] ;

[0023] ;

[0024] in, represents the adjusted resistance gene concentration weight value, Represents the adjusted anti-geolocation weight value, represents the variance of the resistance gene concentration, represents the variance of geographical location, and C represents the total weight control factor.

[0025] As an optional technical solution of the present invention, the use of the silhouette coefficient to evaluate the distribution results of the resistance genes in the target soil area includes:

[0026] The silhouette coefficient is expressed as:

[0027] ;

[0028] Among them, S represents the silhouette coefficient, a represents the average distance from any sample point to other sample points in the cluster, and b represents the average distance from any sample point to the nearest other cluster.

[0029] As an optional technical solution of the present invention, the resistance gene distribution levels in the target soil area include:

[0030] When S < 0, it means that the distribution of resistance gene concentration is extremely unreasonable;

[0031] When 0≤S<0.2, it means that the distribution of resistance gene concentration is unreasonable;

[0032] When 0.2≤S<0.5, it means that the distribution of resistance gene concentration is generally reasonable;

[0033] When 0.5≤S<1, it means that the distribution of resistance gene concentration is reasonable.

[0034] In a second aspect, a soil ARGs distribution analysis system based on cluster analysis is provided, comprising: a data acquisition module for acquiring resistance gene concentration information and geographical location information of a target soil area;

[0035] An initial center determination module, used to determine the initial center of cluster analysis based on the resistance gene concentration information and geographical location information of the target soil area;

[0036] A clustering module is used to cluster the resistance genes in the target soil area based on the initial center of the cluster analysis to obtain the distribution results of the resistance genes in the target soil area;

[0037] The evaluation module is used to evaluate the resistance gene distribution results of the target soil area using the silhouette coefficient, and to divide the resistance gene distribution level of the target soil area.

[0038] In a third aspect, a soil ARGs distribution analysis device based on cluster analysis is provided, comprising a processor and a storage medium;

[0039] The storage medium is used to store instructions;

[0040] The processor is used to operate according to the instructions to execute the steps of the soil ARGs distribution analysis method based on cluster analysis described in the first aspect.

[0041] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that when the program is executed by a processor, the steps of the soil ARGs distribution analysis method based on cluster analysis described in the first aspect are implemented.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The soil ARGs distribution analysis method based on cluster analysis provided by the present invention can select the initial center more accurately based on the geographical location and the resistance gene concentration, and continuously adjust the weights of the geographical location and the resistance gene concentration during the clustering process, thereby improving the adaptability and flexibility of the cluster analysis; at the same time, it can accurately identify hotspot areas with high resistance gene concentrations, providing scientific support for the monitoring and control of antibiotic pollution in farmland. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flow chart of a soil ARGs distribution analysis method based on cluster analysis in an embodiment of the present invention;

[0045] Figure 2 is a distribution diagram of resistance gene concentrations at soil sampling points in an embodiment of the present invention;

[0046] Figure 3 is a distribution diagram of the initial center in an embodiment of the present invention;

[0047] Figure 4 is a distribution diagram of the resistance gene distribution results in the embodiment of the present invention;

[0048] Figure 5 is a dynamic adjustment diagram of weight values ​​in an embodiment of the present invention;

[0049] Figure 6 It is a distribution diagram of hot spot areas in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0051] Example 1

[0052] This embodiment provides a soil ARGs distribution analysis method based on cluster analysis. Figure 1 As shown, the method comprises the following steps:

[0053] Step 1: Obtain resistance gene concentration information and geographic location information of the target soil area.

[0054] In order to ensure the dimensional consistency between different data information, after obtaining the resistance gene concentration information and geographical location information of the target soil area, the resistance gene concentration information and geographical location information of the target soil area are normalized and expressed as:

[0055] ;

[0056] in, Indicates resistance gene concentration information or geographical location information, Represents the normalized resistance gene concentration information or geographical location information, Indicates the maximum value of resistance gene concentration information or geographical location information. Indicates the minimum value of resistance gene concentration information or geographical location information.

[0057] Normalization processing can make different data information in the same numerical range, thereby avoiding a certain data information dominating the clustering process.

[0058] like Figure 2 Figure 2 shows the concentration of antibiotic resistance genes (ARGs) at 50 soil sampling sites. The bubble size indicates the concentration value, providing an initial distribution of ARG concentrations in the studied soil area.

[0059] Step 2: Determine the initial center of cluster analysis based on the resistance gene concentration information and geographical location information of the target soil area.

[0060] 2.1. According to the preset random number of times, a group of initial center points are randomly selected in the target soil area, where each group of initial center points includes k points, and k represents the target cluster number of cluster analysis.

[0061] 2.2. Calculate the sum of squared errors between each data point and the nearest initial center point in the target soil area after each random selection, expressed as:

[0062] ;

[0063] in, represents the sum of squared errors of each data point in the target soil region, n represents the total number of data points, represents the i-th data point, Representation and The nearest initial center point.

[0064] 2.3. Sum the squared error of each data point in the target soil area The smallest set of initial center points is used as the initial center of cluster analysis. This is because the smaller the SSE, the better the current set of initial center points can describe the distribution of data points and has better clustering performance.

[0065] In this embodiment, Figure 3 As shown, the red mark in the figure indicates the initial center.

[0066] Step 3: Based on the initial center of cluster analysis, cluster the resistance genes in the target soil area to obtain the resistance gene distribution results of the target soil area.

[0067] The weight values ​​of resistance gene concentration and geographical location are introduced to calculate the weighted distance between resistance gene concentration and geographical location, which is expressed as:

[0068] ;

[0069] in, represents the weighted distance, represents the weight value of the resistance gene concentration, Indicates the weight value of the geographical location. represents the difference in resistance gene concentration between data points x and y, Represents the distance between data points x and y. By adjusting the weight value of the resistance gene concentration and geographic location weights , the effects of resistance gene concentration and geographic information on initial center selection can be flexibly controlled.

[0070] Therefore, the weights of the resistance gene concentration information and the geographical location information are adjusted during the clustering process. Specifically, multiple iterations are performed during the clustering process, and the weight values ​​of the resistance gene concentration and the geographical location are recalculated after each iteration, which are expressed as:

[0071] ;

[0072] ;

[0073] in, represents the adjusted resistance gene concentration weight value, Represents the adjusted anti-geolocation weight value, represents the variance of the resistance gene concentration, represents the variance of geographical location, and C represents the total weight control factor.

[0074] Dynamic weighted adjustment ensures flexible weight allocation under different data distributions, so that both high-concentration resistance areas and areas with significant spatial distribution can be reasonably allocated clustering results.

[0075] like Figure 4 As shown in the figure, the distribution results of resistance genes after clustering are displayed. Different colors represent different clusters. The distribution of each resistance gene concentration area can be clearly seen in the figure. Figure 5 As shown in the figure, the weight adjustment during the clustering process is demonstrated. As the number of iterations increases, the weights of resistance gene concentration and geographical location gradually tend to be balanced.

[0076] Step 4: Use the silhouette coefficient to evaluate the resistance gene distribution results of the target soil area and divide the resistance gene distribution level of the target soil area.

[0077] The silhouette coefficient is expressed as:

[0078] ;

[0079] Among them, S represents the silhouette coefficient, a represents the average distance from any sample point to other sample points in the cluster, and b represents the average distance from any sample point to the nearest other cluster.

[0080] The value range of the silhouette coefficient is [-1, 1]. The closer it is to 1, the better the clustering effect is. The closer it is to -1, the more overlapping the clustering results are or the poorer the clustering effect is. The distribution levels of resistance genes in the target soil area include:

[0081] When S < 0, it means that the distribution of resistance gene concentration is extremely unreasonable, the samples may be misclassified, and the cluster structure cannot accurately reflect the data distribution. At this time, it is necessary to reselect the cluster center and re-cluster.

[0082] When 0≤S<0.2, it means that the distribution of resistance gene concentration is unreasonable, the compactness between data within the cluster is low, the separation between clusters is not obvious, and the results may contain more misclassifications.

[0083] When 0.2≤S<0.5, it means that the distribution of resistance gene concentration is generally reasonable, the data within the cluster have a certain degree of compactness, and the separation between clusters is also obvious, but there may be overlaps on some boundaries.

[0084] When 0.5≤S<1, it means that the distribution of resistance gene concentration is reasonable, the data compactness within the cluster is high, and the separation between clusters is good.

[0085] According to the resistance gene distribution level and resistance gene distribution results, clusters with higher resistance gene concentrations are selected as high-concentration hotspot areas. Hotspot areas will serve as key areas for soil antibiotic resistance gene pollution control and provide decision-making support for management departments. Figure 6 Red areas mark hotspots with the highest resistance gene concentrations, helping to identify high-concentration areas that require focused management.

[0086] Example 2

[0087] This embodiment provides a soil ARGs distribution analysis system based on cluster analysis, including:

[0088] The data acquisition module is used to obtain the resistance gene concentration information and geographical location information of the target soil area.

[0089] The initial center determination module is used to determine the initial center of the cluster analysis based on the resistance gene concentration information and geographical location information of the target soil area.

[0090] The clustering module is used to cluster the resistance genes in the target soil area based on the initial center of the cluster analysis to obtain the distribution results of the resistance genes in the target soil area.

[0091] The evaluation module is used to evaluate the resistance gene distribution results of the target soil area using the silhouette coefficient, and to divide the resistance gene distribution level of the target soil area.

[0092] Example 3

[0093] This embodiment provides a soil ARGs distribution analysis device based on cluster analysis, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the soil ARGs distribution analysis method based on cluster analysis described in Example 1.

[0094] Example 4

[0095] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the soil ARGs distribution analysis method based on cluster analysis described in Embodiment 1 are implemented.

[0096] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0097] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0098] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0099] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0100] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A soil ARGs distribution analysis method based on cluster analysis, characterized in that: include: Obtaining resistance gene concentration information and geographic location information of target soil areas; Determining the initial center of cluster analysis according to the resistance gene concentration information and geographical location information of the target soil area; Based on the initial center of cluster analysis, each resistance gene in the target soil area is clustered to obtain the distribution results of resistance genes in the target soil area; The silhouette coefficient was used to evaluate the distribution results of resistance genes in the target soil area and divide the resistance gene distribution levels of the target soil area.

2. The soil ARGs distribution analysis method based on cluster analysis according to claim 1, characterized in that: After obtaining the resistance gene concentration information and geographical location information of the target soil area, the resistance gene concentration information and geographical location information of the target soil area are normalized and expressed as: ； in, Indicates resistance gene concentration information or geographical location information, Represents the normalized resistance gene concentration information or geographical location information, Indicates the maximum value of resistance gene concentration information or geographical location information. Indicates the minimum value of resistance gene concentration information or geographical location information.

3. The soil ARGs distribution analysis method based on cluster analysis according to claim 1, characterized in that: The method of determining the initial center of the cluster analysis according to the resistance gene concentration information and the geographical location information of the target soil area includes: A set of initial center points is randomly selected in the target soil area according to a preset random number of times, wherein each set of initial center points includes k points, and k represents the target cluster number of cluster analysis; The sum of squared errors between each data point and the nearest initial center point in the target soil area after each random selection is calculated and expressed as: ； in, represents the sum of squared errors of each data point in the target soil region, n represents the total number of data points, represents the i-th data point, Representation and The nearest initial center point; The sum of squared errors of each data point in the target soil area The smallest set of initial center points is used as the initial center for cluster analysis.

4. The soil ARGs distribution analysis method based on cluster analysis according to claim 1, characterized in that: The initial center based on the cluster analysis is used to cluster the resistance genes in the target soil area to obtain the distribution results of the resistance genes in the target soil area, including: The weight values ​​of resistance gene concentration and geographical location are introduced to calculate the weighted distance between resistance gene concentration and geographical location, which is expressed as: ； in, represents the weighted distance, represents the weight value of the resistance gene concentration, Indicates the weight value of the geographical location. represents the difference in resistance gene concentration between data points x and y, Represents the distance between data points x and y.

5. The soil ARGs distribution analysis method based on cluster analysis according to claim 4, characterized in that: Adjusting the weights of the resistance gene concentration information and the geographical location information during the clustering process includes: Multiple iterations are performed during the clustering process, and the weight values ​​of resistance gene concentration and geographical location are recalculated after each iteration, which are expressed as: ； ； in, represents the adjusted resistance gene concentration weight value, Indicates the adjusted anti-geolocation weight value, represents the variance of the resistance gene concentration, represents the variance of geographical location, and C represents the total weight control factor.

6. The soil ARGs distribution analysis method based on cluster analysis according to claim 1, characterized in that: The method of using the silhouette coefficient to evaluate the distribution results of resistance genes in the target soil area includes: The silhouette coefficient is expressed as: ； Among them, S represents the silhouette coefficient, a represents the average distance from any sample point to other sample points in the cluster, and b represents the average distance from any sample point to the nearest other cluster.

7. The soil ARGs distribution analysis method based on cluster analysis according to claim 6, characterized in that: The resistance gene distribution levels in the target soil area include: When S < 0, it means that the distribution of resistance gene concentration is extremely unreasonable; When 0≤S<0.2, it means that the distribution of resistance gene concentration is unreasonable; When 0.2≤S<0.5, it means that the distribution of resistance gene concentration is generally reasonable; When 0.5≤S<1, it means that the distribution of resistance gene concentration is reasonable.

8. A soil ARGs distribution analysis system based on cluster analysis, characterized in that: include: A data acquisition module, used to obtain resistance gene concentration information and geographical location information of a target soil area; An initial center determination module, used to determine the initial center of cluster analysis based on the resistance gene concentration information and geographical location information of the target soil area; A clustering module is used to cluster the resistance genes in the target soil area based on the initial center of the cluster analysis to obtain the distribution results of the resistance genes in the target soil area; The evaluation module is used to evaluate the resistance gene distribution results of the target soil area using the silhouette coefficient, and to divide the resistance gene distribution level of the target soil area.

9. A soil ARGs distribution analysis device based on cluster analysis, characterized in that: including processor and storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the soil ARGs distribution analysis method based on cluster analysis according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the soil ARGs distribution analysis method based on cluster analysis as described in any one of claims 1 to 7 are implemented.

Images