Soil trace element measurement method based on online elimination of polyatomic ion interference

Abstract

The invention relates to the field of soil trace element measurement, in particular to a soil trace element measurement method based on online elimination of polyatomic ion interference, which comprises the following steps: establishing historical soil trace element measurement data characteristics by using real-time soil trace element measurement data; establishing a soil trace element measurement data multistage classification model based on online elimination of polyatomic ion interference by using the historical soil trace element measurement data characteristics; acquiring a comprehensive comparison result of the soil trace element measurement data according to the real-time soil trace element measurement data and a soil trace element measurement data multi-stage classification model; the soil trace element measurement result is obtained by integrating the comparison result of the soil trace element measurement data, logic optimization in the implementation scheme and data exception output outside the scheme are synchronously carried out, and under the conditions of an exceptional state and the like, quick self-adjustment is carried out to adapt to a scheme implementation scene while stable operation of optimization scheduling is ensured.

Description

Technical Field

[0001] The invention relates to the field of soil trace element measurement, and in particular to a soil trace element measurement method based on online elimination of polyatomic ion interference. Background Art

[0002] Polyatomic ions (or molecular ions) are the main source of interference in ICPMS. In the implementation process, in the processing of its lower-level segmented data, soil trace element measurement data has a wide range of sources and too many environmental interference factors. When used in important scenarios, it is necessary to ensure the high credibility of data output. Therefore, a reasonable and reliable solution is needed to coordinate and optimize the solution inside and outside. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention provides a soil trace element measurement method based on online elimination of polyatomic ion interference, through the establishment of a binary classification model, step-by-step verification and analysis, and finally outputting liquid level measurement data and corresponding processing results.

[0004] To achieve the above object, the present invention provides a soil trace element measurement method based on online elimination of polyatomic ion interference, comprising:

[0005] S1. Establish historical soil trace element measurement data characteristics using real-time soil trace element measurement data;

[0006] S2. Establishing a multi-level classification model for soil trace element measurement data based on online elimination of polyatomic ion interference using the historical soil trace element measurement data characteristics;

[0007] S3. Obtaining a comprehensive comparison result of the soil trace element measurement data according to the real-time soil trace element measurement data and the multi-level classification model of the soil trace element measurement data;

[0008] S4. Obtain soil trace element measurement results by using the soil trace element measurement data to comprehensively compare the results.

[0009] Preferably, the method of establishing historical soil trace element measurement data features using real-time soil trace element measurement data includes:

[0010] S1-1, obtaining real-time soil trace element measurement data and environmental data associated with the real-time soil trace element measurement data;

[0011] S1-2. Acquire a corresponding historical soil trace element measurement data set according to the real-time soil trace element measurement data;

[0012] S1-3, using the associated environmental data to screen the historical soil trace element measurement data set to obtain a screening result of the historical soil trace element measurement data set;

[0013] S1-4. Utilize the screening and processing results as historical soil trace element measurement data features.

[0014] Furthermore, the historical soil trace element measurement data set is screened and processed using the associated environmental data to obtain the screening and processing results of the historical soil trace element measurement data set, including:

[0015] S1-3-1. Obtaining associated environmental data of historical soil trace element measurement data according to the historical soil trace element measurement data set;

[0016] S1-3-2, sequentially determine whether the associated environmental data corresponding to each subset in the historical soil trace element measurement data set is consistent with the associated environmental data of the real-time soil trace element measurement data, if so, obtain the corresponding subset of the historical soil trace element measurement data set to establish a primary data set, otherwise, use the non-corresponding subset of the historical soil trace element measurement data set to establish a secondary data set;

[0017] S1-3-3. Utilize the primary data set and the secondary data set as the screening processing result of the historical soil trace element measurement data set.

[0018] Furthermore, using the historical soil trace element measurement data characteristics to establish a multi-level classification model for soil trace element measurement data based on online elimination of polyatomic ion interference includes:

[0019] S2-1, using the historical soil trace element measurement data characteristics corresponding to the primary data set to divide the positive data training set and the positive data verification set;

[0020] S2-2, using the positive data training set and the positive data verification set to respectively establish a positive environment data training set and a positive environment data verification set;

[0021] S2-3, using the positive data training set and the positive environment data training set as input, taking the normal state as output, establishing the number of hidden layers in the positive environment data training set corresponding to the type of environment data, and training based on the neural network algorithm to obtain a basic positive output model;

[0022] S2-4, using the forward data verification set and the forward environment data verification set to input the basic forward output model to obtain a verification result of the basic forward output model;

[0023] S2-5. Determine whether all the verification results of the basic forward output model are normal. If so, retain the current basic forward output model. Otherwise, obtain the forward data verification set and the forward environment data training set whose verification results are not normal and add them to the forward data training set and the forward environment data training set respectively, and return to S2-3.

[0024] S2-6, using the historical soil trace element measurement data characteristics corresponding to the secondary data set to divide the negative data training set and the negative data verification set;

[0025] S2-7, using the negative data training set and the negative data verification set to respectively establish a negative environment data training set and a negative environment data verification set;

[0026] S2-8, using the negative data training set and the negative environment data training set as input, taking the abnormal state as output, establishing the number of hidden layers corresponding to the type of environment data in the negative environment data training set, and training based on the neural network algorithm to obtain a basic negative output model;

[0027] S2-9, using the negative data verification set and the negative environment data verification set to input the basic negative output model to obtain a verification result of the basic negative output model;

[0028] S2-10, determine whether all the verification results of the basic negative output model are abnormal. If so, retain the current basic negative output model. Otherwise, obtain the negative data verification set and the negative environment data verification set whose verification results are not abnormal, add them to the negative data training set and the negative environment data training set respectively, and return to S2-8;

[0029] S2-11, combining the basic positive output model and the basic negative output model as a multi-level classification model for soil trace element measurement data;

[0030] Among them, the positive data training set is an 80% subset of the primary data set, the positive data verification set is a 20% subset of the primary data set, the negative data training set is an 80% subset of the secondary data set, and the negative data verification set is a 20% subset of the secondary data set.

[0031] Further, obtaining a comprehensive comparison result of the soil trace element measurement data according to the real-time soil trace element measurement data and the soil trace element measurement data multi-level classification model includes:

[0032] S3-1, using the real-time soil trace element measurement data to input the soil trace element measurement data multi-level classification model to obtain the soil trace element measurement data multi-level classification result of the real-time soil trace element measurement data;

[0033] S3-2, using the multi-level classification result of the soil trace element measurement data, performing feature comparison processing on the real-time soil trace element measurement data to obtain a feature comparison processing result of the real-time soil trace element measurement data;

[0034] S3-3, using the multi-level classification result of the soil trace element measurement data to perform an overfitting verification process on the multi-level classification model of the soil trace element measurement data to obtain an overfitting verification process result of the multi-level classification model of the soil trace element measurement data;

[0035] S3-4. Using the feature comparison processing result and the overfitting verification processing result as the comprehensive comparison result of soil trace element measurement data.

[0036] Furthermore, the feature comparison processing of the real-time soil trace element measurement data is performed using the multi-level classification result of the soil trace element measurement data to obtain the feature comparison processing result of the real-time soil trace element measurement data, including:

[0037] Obtaining environmental data associated with the real-time soil trace element measurement data;

[0038] Establishing a historical soil trace element measurement data set of the same associated environmental data based on the associated environmental data;

[0039] Determine whether the corresponding states of each subset in the historical soil trace element measurement data set of the same associated environmental data are all consistent, if so, use the corresponding states of each subset in the historical soil trace element measurement data set of the same associated environmental data as independent comparison states, otherwise, obtain a relatively high proportion type of the corresponding state of each subset in the historical soil trace element measurement data set of the same associated environmental data as an independent comparison state;

[0040] Determine whether the independent comparison state is consistent with the multi-level classification result of the soil trace element measurement data. If so, output the multi-level classification result of the current soil trace element measurement data as the feature comparison processing result of the real-time soil trace element measurement data. Otherwise, use the real-time soil trace element measurement data corresponding to the independent comparison state and the associated environmental data of the real-time soil trace element measurement data to add the corresponding training set respectively, and return to S2-1;

[0041] The independent comparison status includes whether the real-time soil trace element measurement data is normal or abnormal.

[0042] Furthermore, the multi-level classification result of the soil trace element measurement data is used to perform overfitting verification processing on the multi-level classification model of the soil trace element measurement data, and the overfitting verification processing result of the multi-level classification model of the soil trace element measurement data includes:

[0043] S3-3-1, determine whether the number of repeated training of the multi-level classification model of soil trace element measurement data is greater than the number of data types of the associated environmental data corresponding to the real-time soil trace element measurement data, if so, the overfitting verification processing result of the multi-level classification model of soil trace element measurement data is that overfitting exists, otherwise, execute S3-3-2;

[0044] S3-3-2. Determine whether the relative ratio of the number of subsets of the positive data training set corresponding to the multi-level classification result of the soil trace element measurement data to the number of subsets of the negative data training set is greater than 1.5. If so, the overfitting verification processing result of the multi-level classification model of the soil trace element measurement data is that overfitting exists; otherwise, the overfitting verification processing result of the multi-level classification model of the soil trace element measurement data is that there is no overfitting.

[0045] Furthermore, the soil trace element measurement results obtained by using the soil trace element measurement data comprehensive comparison result include:

[0046] S4-1, performing soil trace element measurement data scheduling processing according to the characteristic comparison processing result of the soil trace element measurement data comprehensive comparison result to obtain the soil trace element measurement data scheduling processing result;

[0047] S4-2, performing optimization processing on the soil trace element measurement data according to the overfitting verification processing result of the comprehensive comparison result of the soil trace element measurement data to obtain the soil trace element measurement data optimization processing result;

[0048] S4-3. Utilize the soil trace element measurement data scheduling processing result and the soil trace element measurement data optimization processing result as the soil trace element measurement result.

[0049] Further, the soil trace element measurement data scheduling processing is performed according to the characteristic comparison processing result of the soil trace element measurement data comprehensive comparison result to obtain the soil trace element measurement data scheduling processing result, including:

[0050] S4-1-1, determine whether the characteristic comparison processing result of the comprehensive comparison result of the soil trace element measurement data is normal, if so, the soil trace element measurement data scheduling processing result is passed, and the corresponding output of the soil trace element measurement data scheduling processing result is empty, and the characteristic comparison processing result of the current soil trace element measurement data comprehensive comparison result is retained, otherwise, execute S4-1-2;

[0051] S4-1-2. Determine whether the characteristic comparison processing result of the comprehensive comparison result of the current soil trace element measurement data corresponds to the real-time soil trace element measurement data and the associated environmental data of the real-time soil trace element measurement data, and whether there is any historical soil trace element measurement data or any associated environmental data of the historical soil trace element measurement data that does not correspond. If so, the scheduling processing result of the soil trace element measurement data fails, and the current real-time soil trace element measurement data and the associated environmental data of the real-time soil trace element measurement data are output. Otherwise, return to S1-3-1.

[0052] Further, the soil trace element measurement data optimization processing is performed according to the overfitting verification processing result of the comprehensive comparison result of the soil trace element measurement data to obtain the soil trace element measurement data optimization processing result including:

[0053] S4-2-1, determine whether the overfitting verification processing result of the comprehensive comparison result of the soil trace element measurement data is overfitted, if so, obtain a training set with a relatively low number of subsets of the positive data training set and the negative data training set corresponding to the multi-level classification result of the soil trace element measurement data as the optimization basic template, and execute S4-2-2, otherwise, the optimization processing result of the soil trace element measurement data passes, and the overfitting verification processing result of the current comprehensive comparison result of the soil trace element measurement data is retained, and the optimization processing result of the soil trace element measurement data is empty;

[0054] S4-2-2, obtaining a positive data training set and a negative data training set with the same number of subsets according to the optimized basic template;

[0055] S4-2-3. Use the current positive data training set and negative data training set as updated positive data training set and negative data training set, and return to S2.

[0056] Compared with the closest prior art, the present invention has the following beneficial effects:

[0057] It improves the accuracy and efficiency of the output of soil trace element measurement data. Through intelligent processing of measurement data from a wide range of sources, it has strong environmental adaptability. The logic optimization within the implementation plan and the output of abnormal data outside the plan are carried out simultaneously. In abnormal conditions, it ensures stable operation of optimized scheduling while quickly self-adjusting to adapt to the implementation scenario of the plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 The present invention provides a flow chart of a soil trace element measurement method based on online elimination of polyatomic ion interference. DETAILED DESCRIPTION

[0059] The specific implementation modes of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0060] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] Embodiment 1:

[0062] The present invention provides a soil trace element measurement method based on online elimination of polyatomic ion interference, such as Figure 1 As shown, including:

[0063] S1. Establish historical soil trace element measurement data characteristics using real-time soil trace element measurement data;

[0064] S2. Establishing a multi-level classification model for soil trace element measurement data based on online elimination of polyatomic ion interference using the historical soil trace element measurement data characteristics;

[0065] S3. Obtaining a comprehensive comparison result of the soil trace element measurement data according to the real-time soil trace element measurement data and the multi-level classification model of the soil trace element measurement data;

[0066] S4. Obtain soil trace element measurement results by using the soil trace element measurement data to comprehensively compare the results.

[0067] S1 specifically includes:

[0068] S1-1, obtaining real-time soil trace element measurement data and environmental data associated with the real-time soil trace element measurement data;

[0069] S1-2. Acquire a corresponding historical soil trace element measurement data set according to the real-time soil trace element measurement data;

[0070] S1-3, using the associated environmental data to screen the historical soil trace element measurement data set to obtain a screening result of the historical soil trace element measurement data set;

[0071] S1-4. Utilize the screening and processing results as historical soil trace element measurement data features.

[0072] S1-3 specifically includes:

[0073] S1-3-1. Obtaining associated environmental data of historical soil trace element measurement data according to the historical soil trace element measurement data set;

[0074] S1-3-2, sequentially determine whether the associated environmental data corresponding to each subset in the historical soil trace element measurement data set is consistent with the associated environmental data of the real-time soil trace element measurement data, if so, obtain the corresponding subset of the historical soil trace element measurement data set to establish a primary data set, otherwise, use the non-corresponding subset of the historical soil trace element measurement data set to establish a secondary data set;

[0075] S1-3-3. Utilize the primary data set and the secondary data set as the screening processing result of the historical soil trace element measurement data set.

[0076] S2 specifically includes:

[0077] S2-1, using the historical soil trace element measurement data characteristics corresponding to the primary data set to divide the positive data training set and the positive data verification set;

[0078] S2-2, using the positive data training set and the positive data verification set to respectively establish a positive environment data training set and a positive environment data verification set;

[0079] S2-3, using the positive data training set and the positive environment data training set as input, taking the normal state as output, establishing the number of hidden layers in the positive environment data training set corresponding to the type of environment data, and training based on the neural network algorithm to obtain a basic positive output model;

[0080] S2-4, using the forward data verification set and the forward environment data verification set to input the basic forward output model to obtain a verification result of the basic forward output model;

[0081] S2-5. Determine whether all the verification results of the basic forward output model are normal. If so, retain the current basic forward output model. Otherwise, obtain the forward data verification set and the forward environment data training set whose verification results are not normal and add them to the forward data training set and the forward environment data training set respectively, and return to S2-3.

[0082] S2-6, using the historical soil trace element measurement data characteristics corresponding to the secondary data set to divide the negative data training set and the negative data verification set;

[0083] S2-7, using the negative data training set and the negative data verification set to respectively establish a negative environment data training set and a negative environment data verification set;

[0084] S2-8, using the negative data training set and the negative environment data training set as input, taking the abnormal state as output, establishing the number of hidden layers corresponding to the type of environment data in the negative environment data training set, and training based on the neural network algorithm to obtain a basic negative output model;

[0085] S2-9, using the negative data verification set and the negative environment data verification set to input the basic negative output model to obtain a verification result of the basic negative output model;

[0086] S2-10, determine whether all the verification results of the basic negative output model are abnormal. If so, retain the current basic negative output model. Otherwise, obtain the negative data verification set and the negative environment data verification set whose verification results are not abnormal, add them to the negative data training set and the negative environment data training set respectively, and return to S2-8;

[0087] S2-11, combining the basic positive output model and the basic negative output model as a multi-level classification model for soil trace element measurement data;

[0088] Among them, the positive data training set is an 80% subset of the primary data set, the positive data verification set is a 20% subset of the primary data set, the negative data training set is an 80% subset of the secondary data set, and the negative data verification set is a 20% subset of the secondary data set.

[0089] S3 specifically includes:

[0090] S3-1, using the real-time soil trace element measurement data to input the soil trace element measurement data multi-level classification model to obtain the soil trace element measurement data multi-level classification result of the real-time soil trace element measurement data;

[0091] S3-2, using the multi-level classification result of the soil trace element measurement data, performing feature comparison processing on the real-time soil trace element measurement data to obtain a feature comparison processing result of the real-time soil trace element measurement data;

[0092] S3-3, using the multi-level classification result of the soil trace element measurement data to perform an overfitting verification process on the multi-level classification model of the soil trace element measurement data to obtain an overfitting verification process result of the multi-level classification model of the soil trace element measurement data;

[0093] S3-4. Using the feature comparison processing result and the overfitting verification processing result as the comprehensive comparison result of soil trace element measurement data.

[0094] S3-2 specifically includes:

[0095] S3-2-1, obtaining the associated environmental data of the real-time soil trace element measurement data;

[0096] S3-2-2, establishing a historical soil trace element measurement data set of the same associated environmental data based on the associated environmental data;

[0097] S3-2-3, determine whether the corresponding states of each subset in the historical soil trace element measurement data set of the same associated environmental data are all consistent, if so, use the corresponding states of each subset in the historical soil trace element measurement data set of the same associated environmental data as independent comparison states, otherwise, obtain a relatively high proportion type of the corresponding state of each subset in the historical soil trace element measurement data set of the same associated environmental data as an independent comparison state;

[0098] S3-2-4, judging whether the independent comparison state is consistent with the multi-level classification result of the soil trace element measurement data, if so, outputting the multi-level classification result of the current soil trace element measurement data as the feature comparison processing result of the real-time soil trace element measurement data, otherwise, using the real-time soil trace element measurement data corresponding to the independent comparison state and the associated environmental data of the real-time soil trace element measurement data to add to the corresponding training set respectively, and returning to S2-1;

[0099] The independent comparison status includes whether the real-time soil trace element measurement data is normal or abnormal.

[0100] S3-3 specifically includes:

[0101] S3-3-1, determine whether the number of repeated training of the multi-level classification model of soil trace element measurement data is greater than the number of data types of the associated environmental data corresponding to the real-time soil trace element measurement data, if so, the overfitting verification processing result of the multi-level classification model of soil trace element measurement data is that overfitting exists, otherwise, execute S3-3-2;

[0102] S3-3-2. Determine whether the relative ratio of the number of subsets of the positive data training set corresponding to the multi-level classification result of the soil trace element measurement data to the number of subsets of the negative data training set is greater than 1.5. If so, the overfitting verification processing result of the multi-level classification model of the soil trace element measurement data is that overfitting exists; otherwise, the overfitting verification processing result of the multi-level classification model of the soil trace element measurement data is that there is no overfitting.

[0103] S4 specifically includes:

[0104] S4-1, performing soil trace element measurement data scheduling processing according to the characteristic comparison processing result of the soil trace element measurement data comprehensive comparison result to obtain the soil trace element measurement data scheduling processing result;

[0105] S4-2, performing optimization processing on the soil trace element measurement data according to the overfitting verification processing result of the comprehensive comparison result of the soil trace element measurement data to obtain the soil trace element measurement data optimization processing result;

[0106] S4-3. Utilize the soil trace element measurement data scheduling processing result and the soil trace element measurement data optimization processing result as the soil trace element measurement result.

[0107] S4-1 specifically includes:

[0108] S4-1-1, determine whether the characteristic comparison processing result of the comprehensive comparison result of the soil trace element measurement data is normal, if so, the soil trace element measurement data scheduling processing result is passed, and the corresponding output of the soil trace element measurement data scheduling processing result is empty, and the characteristic comparison processing result of the current soil trace element measurement data comprehensive comparison result is retained, otherwise, execute S4-1-2;

[0109] S4-1-2. Determine whether the characteristic comparison processing result of the comprehensive comparison result of the current soil trace element measurement data corresponds to the real-time soil trace element measurement data and the associated environmental data of the real-time soil trace element measurement data, and whether there is any historical soil trace element measurement data or any associated environmental data of the historical soil trace element measurement data that does not correspond. If so, the scheduling processing result of the soil trace element measurement data fails, and the current real-time soil trace element measurement data and the associated environmental data of the real-time soil trace element measurement data are output. Otherwise, return to S1-3-1.

[0110] S4-2 specifically includes:

[0111] S4-2-1, determine whether the overfitting verification processing result of the comprehensive comparison result of the soil trace element measurement data is overfitted, if so, obtain a training set with a relatively low number of subsets of the positive data training set and the negative data training set corresponding to the multi-level classification result of the soil trace element measurement data as the optimization basic template, and execute S4-2-2, otherwise, the optimization processing result of the soil trace element measurement data passes, and the overfitting verification processing result of the current comprehensive comparison result of the soil trace element measurement data is retained, and the optimization processing result of the soil trace element measurement data is empty;

[0112] S4-2-2, obtaining a positive data training set and a negative data training set with the same number of subsets according to the optimized basic template;

[0113] S4-2-3. Use the current positive data training set and negative data training set as updated positive data training set and negative data training set, and return to S2.

[0114] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention 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 code.

[0115] The present invention 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 block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks 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.

[0116] 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.

[0117] 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. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A soil trace element measurement method based on online elimination of polyatomic ion interference, characterized in that: include: S1. Establish historical soil trace element measurement data characteristics using real-time soil trace element measurement data; S2. Establishing a multi-level classification model for soil trace element measurement data based on online elimination of polyatomic ion interference using the historical soil trace element measurement data characteristics; S3. Obtaining a comprehensive comparison result of the soil trace element measurement data according to the real-time soil trace element measurement data and the multi-level classification model of the soil trace element measurement data; S4. Obtain soil trace element measurement results by using the soil trace element measurement data to comprehensively compare the results.

2. The soil trace element measurement method based on online elimination of polyatomic ion interference as claimed in claim 1, characterized in that: The method of establishing historical soil trace element measurement data using real-time soil trace element measurement data includes: S1-1, obtaining real-time soil trace element measurement data and environmental data associated with the real-time soil trace element measurement data; S1-2. Acquire a corresponding historical soil trace element measurement data set according to the real-time soil trace element measurement data; S1-3, using the associated environmental data to screen the historical soil trace element measurement data set to obtain a screening result of the historical soil trace element measurement data set; S1-4. Utilize the screening and processing results as historical soil trace element measurement data features.

3. The soil trace element measurement method based on online elimination of polyatomic ion interference as claimed in claim 2, characterized in that: The screening processing result of the historical soil trace element measurement data set obtained by screening the historical soil trace element measurement data set using the associated environmental data includes: S1-3-1. Obtaining associated environmental data of historical soil trace element measurement data according to the historical soil trace element measurement data set; S1-3-2, sequentially determine whether the associated environmental data corresponding to each subset in the historical soil trace element measurement data set is consistent with the associated environmental data of the real-time soil trace element measurement data, if so, obtain the corresponding subset of the historical soil trace element measurement data set to establish a primary data set, otherwise, use the non-corresponding subset of the historical soil trace element measurement data set to establish a secondary data set; S1-3-3. Utilize the primary data set and the secondary data set as the screening processing result of the historical soil trace element measurement data set.

4. The soil trace element measurement method based on online elimination of polyatomic ion interference as claimed in claim 2, characterized in that: Using the historical soil trace element measurement data characteristics to establish a multi-level classification model for soil trace element measurement data based on online elimination of polyatomic ion interference includes: S2-1, using the historical soil trace element measurement data characteristics corresponding to the primary data set to divide the positive data training set and the positive data verification set; S2-2, using the positive data training set and the positive data verification set to respectively establish a positive environment data training set and a positive environment data verification set; S2-3, using the positive data training set and the positive environment data training set as input, taking the normal state as output, establishing the number of hidden layers in the positive environment data training set corresponding to the type of environment data, and training based on the neural network algorithm to obtain a basic positive output model; S2-4, using the forward data verification set and the forward environment data verification set to input the basic forward output model to obtain a verification result of the basic forward output model; S2-5. Determine whether all the verification results of the basic forward output model are normal. If so, retain the current basic forward output model. Otherwise, obtain the forward data verification set and the forward environment data training set whose verification results are not normal and add them to the forward data training set and the forward environment data training set respectively, and return to S2-3. S2-6, using the historical soil trace element measurement data characteristics corresponding to the secondary data set to divide the negative data training set and the negative data verification set; S2-7, using the negative data training set and the negative data verification set to respectively establish a negative environment data training set and a negative environment data verification set; S2-8, using the negative data training set and the negative environment data training set as input, taking the abnormal state as output, establishing the number of hidden layers corresponding to the type of environment data in the negative environment data training set, and training based on the neural network algorithm to obtain a basic negative output model; S2-9, using the negative data verification set and the negative environment data verification set to input the basic negative output model to obtain a verification result of the basic negative output model; S2-10, determine whether all the verification results of the basic negative output model are abnormal. If so, retain the current basic negative output model. Otherwise, obtain the negative data verification set and the negative environment data verification set whose verification results are not abnormal, add them to the negative data training set and the negative environment data training set respectively, and return to S2-8; S2-11, combining the basic positive output model and the basic negative output model as a multi-level classification model for soil trace element measurement data; Among them, the positive data training set is an 80% subset of the primary data set, the positive data verification set is a 20% subset of the primary data set, the negative data training set is an 80% subset of the secondary data set, and the negative data verification set is a 20% subset of the secondary data set.

5. The soil trace element measurement method based on online elimination of polyatomic ion interference as claimed in claim 3, characterized in that: Obtaining a comprehensive comparison result of the soil trace element measurement data according to the real-time soil trace element measurement data and the soil trace element measurement data multi-level classification model includes: S3-1, using the real-time soil trace element measurement data to input the soil trace element measurement data multi-level classification model to obtain the soil trace element measurement data multi-level classification result of the real-time soil trace element measurement data; S3-2, using the multi-level classification result of the soil trace element measurement data, performing feature comparison processing on the real-time soil trace element measurement data to obtain a feature comparison processing result of the real-time soil trace element measurement data; S3-3, using the multi-level classification result of the soil trace element measurement data to perform an overfitting verification process on the multi-level classification model of the soil trace element measurement data to obtain an overfitting verification process result of the multi-level classification model of the soil trace element measurement data; S3-4. Using the feature comparison processing result and the overfitting verification processing result as the comprehensive comparison result of soil trace element measurement data.

6. The soil trace element measurement method based on online elimination of polyatomic ion interference as claimed in claim 5, characterized in that: The feature comparison processing result of the real-time soil trace element measurement data is obtained by performing feature comparison processing on the real-time soil trace element measurement data using the multi-level classification result of the soil trace element measurement data, and the feature comparison processing result of the real-time soil trace element measurement data includes: Obtaining environmental data associated with the real-time soil trace element measurement data; Establishing a historical soil trace element measurement data set of the same associated environmental data based on the associated environmental data; Determine whether the corresponding states of each subset in the historical soil trace element measurement data set of the same associated environmental data are all consistent, if so, use the corresponding states of each subset in the historical soil trace element measurement data set of the same associated environmental data as independent comparison states, otherwise, obtain a relatively high proportion type of the corresponding state of each subset in the historical soil trace element measurement data set of the same associated environmental data as an independent comparison state; Determine whether the independent comparison state is consistent with the multi-level classification result of the soil trace element measurement data. If so, output the multi-level classification result of the current soil trace element measurement data as the feature comparison processing result of the real-time soil trace element measurement data. Otherwise, use the real-time soil trace element measurement data corresponding to the independent comparison state and the associated environmental data of the real-time soil trace element measurement data to add the corresponding training set respectively, and return to S2-1; The independent comparison status includes whether the real-time soil trace element measurement data is normal or abnormal.

7. The soil trace element measurement method based on online elimination of polyatomic ion interference as claimed in claim 5, characterized in that: The multi-level classification result of soil trace element measurement data is used to perform overfitting verification processing on the multi-level classification model of soil trace element measurement data to obtain the overfitting verification processing result of the multi-level classification model of soil trace element measurement data, including: S3-3-1, determine whether the number of repeated training of the multi-level classification model of soil trace element measurement data is greater than the number of data types of the associated environmental data corresponding to the real-time soil trace element measurement data, if so, the overfitting verification processing result of the multi-level classification model of soil trace element measurement data is that overfitting exists, otherwise, execute S3-3-2; S3-3-2. Determine whether the relative ratio of the number of subsets of the positive data training set corresponding to the multi-level classification result of the soil trace element measurement data to the number of subsets of the negative data training set is greater than 1.

5. If so, the overfitting verification processing result of the multi-level classification model of the soil trace element measurement data is that overfitting exists; otherwise, the overfitting verification processing result of the multi-level classification model of the soil trace element measurement data is that there is no overfitting.

8. The soil trace element measurement method based on online elimination of polyatomic ion interference as claimed in claim 5, characterized in that: The soil trace element measurement results obtained by using the soil trace element measurement data to comprehensively compare the results include: S4-1, performing soil trace element measurement data scheduling processing according to the characteristic comparison processing result of the soil trace element measurement data comprehensive comparison result to obtain the soil trace element measurement data scheduling processing result; S4-2, performing optimization processing on the soil trace element measurement data according to the overfitting verification processing result of the comprehensive comparison result of the soil trace element measurement data to obtain the soil trace element measurement data optimization processing result; S4-3. Utilize the soil trace element measurement data scheduling processing result and the soil trace element measurement data optimization processing result as the soil trace element measurement result.

9. The soil trace element measurement method based on online elimination of polyatomic ion interference as claimed in claim 8, characterized in that: The soil trace element measurement data scheduling processing is performed according to the characteristic comparison processing result of the soil trace element measurement data comprehensive comparison result to obtain the soil trace element measurement data scheduling processing result, which includes: S4-1-1, determine whether the characteristic comparison processing result of the comprehensive comparison result of the soil trace element measurement data is normal, if so, the soil trace element measurement data scheduling processing result is passed, and the corresponding output of the soil trace element measurement data scheduling processing result is empty, and the characteristic comparison processing result of the current soil trace element measurement data comprehensive comparison result is retained, otherwise, execute S4-1-2; S4-1-2. Determine whether the characteristic comparison processing result of the comprehensive comparison result of the current soil trace element measurement data corresponds to the real-time soil trace element measurement data and the associated environmental data of the real-time soil trace element measurement data, and whether there is any historical soil trace element measurement data or any associated environmental data of the historical soil trace element measurement data that does not correspond. If so, the scheduling processing result of the soil trace element measurement data fails, and the current real-time soil trace element measurement data and the associated environmental data of the real-time soil trace element measurement data are output. Otherwise, return to S1-3-1.

10. The soil trace element measurement method based on online elimination of polyatomic ion interference according to claim 9, characterized in that: According to the overfitting verification processing result of the comprehensive comparison result of the soil trace element measurement data, the soil trace element measurement data optimization processing is performed to obtain the soil trace element measurement data optimization processing result, including: S4-2-1, determine whether the overfitting verification processing result of the comprehensive comparison result of the soil trace element measurement data is overfitted, if so, obtain a training set with a relatively low number of subsets of the positive data training set and the negative data training set corresponding to the multi-level classification result of the soil trace element measurement data as the optimization basic template, and execute S4-2-2, otherwise, the optimization processing result of the soil trace element measurement data passes, and the overfitting verification processing result of the current comprehensive comparison result of the soil trace element measurement data is retained, and the optimization processing result of the soil trace element measurement data is empty; S4-2-2, obtaining a positive data training set and a negative data training set with the same number of subsets according to the optimized basic template; S4-2-3. Use the current positive data training set and negative data training set as updated positive data training set and negative data training set, and return to S2.

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