Humic acid rapid analysis method, system and equipment based on cloud server and medium thereof

Abstract

The invention discloses a rapid humic acid analysis method, system and equipment based on a cloud server and a medium thereof.A spectrum sensor and an electrochemical sensor are used for rapidly collecting data of a humic acid sample, rapid analysis of humic acid is achieved by means of the strong computing power of the cloud server, the analysis time is greatly shortened, and the analysis efficiency is improved. The method is suitable for scenes requiring quick result acquisition; through use of the cloud server, a user does not need to deploy a complex analysis system locally, humic acid analysis can be carried out only through a network, operation is easy and convenient, popularization and application are easy, and the analysis cost is reduced; the analysis model is constructed by adopting a machine learning algorithm, the characteristic parameters can be automatically extracted from the spectrum data and the electrochemical data, and accurate analysis and prediction are carried out, so that the analysis accuracy and reliability are improved, and the interference of human factors is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of humic acid analysis, and in particular to a cloud server-based humic acid rapid analysis method, system, equipment and medium thereof. Background Art

[0002] Humic acid is widely present in natural environments such as soil, water, and coal. It is an organic substance formed by plant and animal residues through complex biochemical and geochemical processes. It has many important biological and environmental significances. In the agricultural field, humic acid can improve soil structure, increase soil fertility, and promote plant growth; in the environmental protection field, humic acid has the ability to adsorb harmful substances such as heavy metal ions and organic pollutants, and can be used for soil and water restoration; in the energy field, humic acid can be used as a potential biomass energy resource. Accurate and rapid analysis of the content and properties of humic acid is crucial to fully exert its role in agriculture, environmental protection, energy and other fields. However, traditional humic acid analysis methods have the following problems: 1. Traditional humic acid analysis requires the use of expensive instruments and equipment, as well as a large amount of chemical reagents and consumables. It is not only expensive, but also complicated to operate and requires professional technicians to operate and maintain. This is a huge economic burden for some small laboratories, enterprises and grassroots units. Second, traditional analysis methods require professional operators to have solid chemical analysis knowledge and rich experimental experience. They need to be familiar with the operating principles and methods of various instruments, master complex sample pre-treatment techniques, and be able to accurately process data and analyze results. This limits the popularity and application scope of humic acid analysis. 3. Traditional humic acid analysis methods usually require complex sample pre-treatment processes, such as extraction, purification, concentration, etc., and then use instruments and equipment for analysis and testing. The whole process often takes hours or even days, which is obviously not applicable for application scenarios that require rapid acquisition of analysis results, such as real-time monitoring, emergency treatment, etc.; Fourth, the humic acid analysis devices in the prior art often have the problems of complex operation, long time consumption, and need for professional operation. Moreover, the traditional humic acid analysis device can only analyze one humic acid sample at a time. When analyzing multiple types of humic acid samples, it often takes a long time to analyze, which cannot meet the modern high-efficiency and fast analysis requirements. To this end, a cloud server-based humic acid rapid analysis method, system, equipment and medium are proposed. Summary of the invention

[0003] The object of the present invention is to provide a method, system, device and medium for rapid analysis of humic acid based on a cloud server to solve one of the problems raised in the above background technology.

[0004] In order to solve the above technical problems, a technical solution adopted in this application is: a rapid analysis method of humic acid based on a cloud server, comprising the following steps: Step 1, obtaining spectral data and electrochemical data of the humic acid sample, wherein the spectral data is collected by a spectral sensor, and the spectral data includes near-infrared spectral data and ultraviolet-visible spectral data; the electrochemical data is collected by an electrochemical sensor, and the electrochemical data includes potential data and current data; Step 2: transmitting the spectral data and electrochemical data of the humic acid sample to the cloud server via a wireless network; Step 3: The cloud server preprocesses the spectral data and electrochemical data of the received humic acid sample; Step 4: Extract features from the preprocessed spectral data and electrochemical data based on the principal component analysis algorithm to extract characteristic parameters; Step 5: Based on the extracted characteristic parameters, a humic acid analysis model is constructed using a machine learning algorithm; Step 6: Input the parameter data of the humic acid sample to be analyzed into the trained humic acid analysis model for analysis to generate analysis results; Step 7: The cloud server monitors the analysis process in real time and records key parameters and abnormal information during the analysis process; Step 8: Transmit the analysis results to the user terminal via the network for the user to view and use.

[0005] As a further preferred embodiment of the present technical solution, in step three, the spectral data preprocessing includes data cleaning, baseline correction and standardization; the electrochemical data preprocessing includes data normalization, outlier removal and smoothing.

[0006] As a further preferred embodiment of the present technical solution, in step 4, the method for extracting characteristic parameters comprises the following steps: Step 1: Combine the preprocessed spectral data and electrochemical data into a comprehensive data set; Step 2: Organize the merged comprehensive data set into a data matrix, where rows represent samples and columns represent features; Step 3: Calculate the covariance matrix of the data matrix and perform eigendecomposition on the covariance matrix to obtain eigenvalues ​​and corresponding eigenvectors; Step 4: Select multiple principal components as the new coordinate system according to the size of the eigenvalues; Step 5: Project the data matrix into the new coordinate space. In the projected data matrix, each principal component is regarded as a characteristic parameter.

[0007] As a further preferred embodiment of the present technical solution, in step 5, the method for constructing the humic acid analysis model comprises the following steps: Step S1, according to the specific requirements of humic acid analysis, select a machine learning algorithm from linear regression, logistic regression, decision tree, random forest, support vector machine or neural network; Step S2, dividing the extracted feature parameters into training set data, validation set data and test set data; Step S3: training the selected machine learning algorithm using the training set data; Step S4, using the validation set data to validate and optimize the trained humic acid analysis model; Step S5, using the test set data to test the optimized humic acid analysis model; Step S6: deploy the trained humic acid analysis model to the cloud server.

[0008] As a further preferred embodiment of the present technical solution, the analysis results include humic acid content, humic acid type and composition, property parameters, quality assessment, anomaly detection and predicted trend.

[0009] As a further preferred embodiment of the present technical solution, the cloud server supports concurrent access by multiple users and also supports processing analysis requests of multiple humic acid samples at the same time. The cloud server has data encryption and access control functions.

[0010] To solve the above technical problems, another technical solution adopted in this application is: a humic acid rapid analysis system based on a cloud server, comprising: a data acquisition module, a data transmission module, a data processing module, a feature extraction module, a model building module, an analysis module, a monitoring module and a result output module; The data acquisition module is used to collect spectral data and electrochemical data of the humic acid sample using a spectral sensor and an electrochemical sensor; The data transmission module is used to transmit the spectral data and electrochemical data of the humic acid sample to the cloud server via a wireless network; The data preprocessing module is used for cloud preprocessing the spectral data and electrochemical data of the humic acid sample received by the cloud server; The feature extraction module is used to extract feature parameters from the preprocessed spectral data and electrochemical data using a principal component analysis algorithm; The model building module is used to build a humic acid analysis model based on the extracted characteristic parameters using a machine learning algorithm; The analysis module is used to input the parameter data of the humic acid sample to be analyzed into the trained humic acid analysis model for analysis to generate analysis results; The monitoring module is used to monitor the analysis process in real time and record key parameters and abnormal information in the analysis process; The result output module is used to output the analysis results and transmit them to the user terminal through the network for the user to view and use.

[0011] In order to solve the above technical problems, another technical solution adopted in this application is: a humic acid rapid analysis device based on a cloud server, comprising: The detection component comprises an operating table, a through groove, a pushing plate, a placement seat, a socket, a sample detection tube, a support tube, a lifting block, a support arm, a detection disk, a spectral sensor, an electrochemical sensor and an electric cylinder. A through groove is provided on one side of the upper surface of the operating table, a pushing plate is slidably connected to the inner side wall of the through groove, a plurality of placement seats are fixedly connected to the upper surface of the pushing plate, sockets are provided at the front and rear parts of the upper surface of the placement seat, a sample detection tube is fittedly connected to the inner side wall of the socket, a supporting tube is fixedly connected to the upper surface of the operating table near the rear part of the through groove, a lifting block is slidably connected to the inner side wall of the supporting tube, a supporting arm is fixedly connected to the center of the front surface of the lifting block, a detection disk is fixedly connected to the front part of the lower surface of the supporting arm, a plurality of spectral sensors are installed at the rear part of the lower surface of the detection disk, a plurality of electrochemical sensors are installed at the front part of the lower surface of the detection disk, an electric cylinder is fixedly connected to the top of the support tube, and the electric cylinder The output end is fixedly connected to the center of the upper surface of the lifting block; a threaded rod is penetrated on one side of the rear surface of the operating table near the through groove, a threaded hole is opened at the center of the front surface of the pushing plate, the outer wall of the threaded rod is threadedly connected to the inner wall of the threaded hole, a servo motor is fixedly connected to the outer side of the threaded rod on the rear surface of the operating table near the threaded rod, the rear surface of the threaded rod is fixedly connected to the output end of the servo motor, and a main machine is fixedly connected to the other side of the upper surface of the operating table, the output ends of the spectral sensor and the electrochemical sensor are electrically connected to the input end of the main machine, the input ends of the electric cylinder and the servo motor are electrically connected to the output end of the main machine, pressure sensors are installed on both sides of the inner rear wall of the through groove, and the output end of the pressure sensor is electrically connected to the input end of the main machine, an identification plate is provided on the upper surface of the placement seat near the front of the two jacks, a wireless transmission module is installed in the middle of the upper surface of the main machine, and a touch display screen is provided on the front surface of the main machine.

[0012] As a further preferred embodiment of the present technical solution, a limiting groove is provided on the front surface of the support cylinder, the rear portion of the outer side wall of the support arm is slidably connected to the inner side wall of the limiting groove, limiting slide grooves are provided on both sides of the inner side wall of the through groove, the middle portions of both sides of the push plate are fixedly connected to limiting slide bars, the outer side wall of the limiting slide bar is slidably connected to the inner side wall of the limiting slide groove, and support frames are fixedly connected to the four corners of the lower surface of the operating table.

[0013] To solve the above technical problems, another technical solution adopted in the present application is: an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a cloud server-based rapid analysis method for humic acid as described in any embodiment of the present invention.

[0014] To solve the above technical problems, another technical solution adopted in the present application is: the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a cloud server-based humic acid rapid analysis method described in any embodiment of the present invention.

[0015] Advantages of the present invention: 1. The present invention uses spectral sensors and electrochemical sensors to quickly collect data from humic acid samples, and with the help of the powerful computing power of cloud servers, it realizes rapid analysis of humic acid. Compared with traditional methods, it greatly shortens the analysis time and is suitable for scenarios that require rapid results, such as real-time monitoring and emergency treatment; 2. The present invention uses a cloud server, so that users do not need to deploy a complex analysis system locally, and can perform humic acid analysis through the Internet. It is simple to operate, easy to popularize and apply, and does not require expensive instruments and equipment and a large amount of chemical reagents, thus reducing the analysis cost; 3. The present invention uses a machine learning algorithm to construct an analysis model, which can automatically extract characteristic parameters from spectral data and electrochemical data and perform accurate analysis and prediction, which not only improves the accuracy and reliability of the analysis, but also reduces the interference of human factors and reduces the requirements for the professional skills of operators; 4. The present invention can rapidly analyze multiple types of humic acid samples by placing multiple sample detection tubes at the same time, greatly improving the analysis efficiency and meeting the modern high-efficiency analysis requirements. At the same time, the combination of spectral sensors and electrochemical sensors can detect humic acid from different angles, thus improving the accuracy and comprehensiveness of the detection. 5. The present invention realizes automatic pushing of samples and automatic lifting of the test plate by controlling the servo motor, electric cylinder and other components through the host, which reduces the complexity and error of manual operation and improves the reliability of analysis. At the same time, the test results are transmitted to the cloud server through the wireless transmission module, which is convenient for further data analysis and storage, and realizes intelligent management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 This is a schematic diagram of a process of a rapid analysis method of humic acid based on a cloud server according to Embodiment 1 of the present invention; Figure 2 Schematic diagram of a flow chart of a method for extracting characteristic parameters according to Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of a process for constructing a humic acid analysis model according to Embodiment 1 of the present invention; Figure 4 This is a functional module diagram of a cloud server-based humic acid rapid analysis system according to Embodiment 2 of the present invention; Figure 5 A schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention; Figure 6 This is a schematic diagram of a viewing angle structure of Embodiment 3 of the present invention; Figure 7 This is another perspective structural diagram of the third embodiment of the present invention; Figure 8 This is a schematic diagram of the push plate and the jack structure of the third embodiment of the present invention; Fig. 9 This is a schematic diagram of the support tube and detection disk structure of the third embodiment of the present invention.

[0018] In the figure: 1. Detection component; 11. Operating table; 12. Through slot; 13. Push plate; 14. Placement seat; 15. Socket; 16. Sample detection tube; 17. Support cylinder; 18. Lifting block; 19. Support arm; 20. Detection plate; 21. Spectral sensor; 22. Electrochemical sensor; 23. Electric cylinder; 24. Threaded rod; 25. Threaded hole; 26. Servo motor; 27. Main unit; 28. Pressure sensor; 29. ​​Limiting slot; 30. Limiting slide slot; 31. Limiting slide bar; 32. Wireless transmission module; 33. Touch screen; 34. Support frame; 35. Identification plate. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0020] Embodiment 1: Figure 1 1 is a flow chart of a method for rapid analysis of humic acid based on a cloud server according to an embodiment of the present invention. It should be noted that if there are substantially the same results, the method of the present application is not based on Figure 1 The process sequence shown is limited. Figure 1-Figure 3 As shown: A rapid analysis method of humic acid based on a cloud server, comprising the following steps: Step 1: Obtain spectral data and electrochemical data of the humic acid sample; wherein, for spectral data acquisition, a spectral sensor (such as a near-infrared spectrometer and a UV-visible spectrometer) is used to scan the humic acid sample to obtain near-infrared spectral data and UV-visible spectral data; for electrochemical data acquisition, an electrochemical sensor (such as a potentiometer and an ammeter) is used to perform electrochemical measurements on the humic acid sample to obtain potential data and current data.

[0021] Step 2: The spectral data and electrochemical data of the humic acid sample are transmitted to the cloud server via a wireless network (such as Wi-Fi or 4G / 5G network). The cloud server receives the data and stores it in a database for further processing. Step 3: The cloud server preprocesses the spectral data and electrochemical data of the received humic acid sample; wherein the spectral data preprocessing includes data cleaning, baseline correction and standardization; the electrochemical data preprocessing includes data normalization, outlier removal and smoothing; Step 4: Extract features from the preprocessed spectral data and electrochemical data based on the principal component analysis algorithm to extract characteristic parameters. The principal component analysis (PCA) algorithm transforms the original data into a set of linearly independent representations in each dimension through linear transformation. These new representations (i.e., principal components) can retain the information in the original data as much as possible while reducing the dimension of the data, which is convenient for subsequent data processing and modeling. Step 5. Based on the extracted characteristic parameters, use machine learning algorithms to build a humic acid analysis model; select an appropriate machine learning algorithm based on the nature of the problem and the characteristics of the data. For humic acid analysis, due to the complex relationship between spectral and electrochemical data, you can choose to use a regression algorithm (if the goal is to predict the concentration of humic acid) or a classification algorithm (if the goal is to distinguish different types of humic acid or identify its presence or absence); common regression algorithms include linear regression, polynomial regression, ridge regression, lasso regression, support vector machine regression (SVR), random forest regression, etc.; common classification algorithms include logistic regression, decision tree, random forest, gradient boosting tree (GBDT), support vector machine (SVM), neural network, etc.; Step 6: Input the parameter data of the humic acid sample to be analyzed into the trained humic acid analysis model for analysis to generate analysis results; specifically: first, the parameter data of the humic acid sample to be analyzed needs to be collected, and then the parameter data of the humic acid sample is passed to the trained humic acid analysis model. After the model receives the input data, it will calculate according to the rules learned internally. After the model calculation is completed, the corresponding analysis results will be generated; Step 7: The cloud server monitors the analysis process in real time and records key parameters and abnormal information during the analysis process; key parameters include the quality of model input data, model calculation time, stability of prediction results, etc.; abnormal information includes data anomalies, model performance degradation, resource usage abnormalities, etc.; when an abnormal situation is detected, the alarm mechanism is immediately triggered to notify relevant personnel through email, SMS or notification system; Step 8: The analysis results are transmitted to the user terminal via the network for the user to view and use, which provides users with a flexible and efficient access method and promotes the sharing and application of analysis results.

[0022] In the first embodiment, specifically, in step 1, spectral data is collected using a spectral sensor, and the spectral data includes near-infrared spectral data and ultraviolet-visible spectral data; Among them, the near-infrared spectrum data includes the absorbance value, reflectance value, peak position and intensity at a specific wavelength; Absorbance value at a specific wavelength: reflects the degree of absorption of humic acid to light of different wavelengths in the near-infrared band. Different combinations of absorbance values ​​can provide information about the molecular structure and chemical composition of humic acid; Reflectance value: indicates the reflectivity of humic acid samples to near-infrared light. Changes in reflectance can be associated with the physical properties and surface characteristics of humic acid. Peak position and intensity: The position and intensity of the absorption peak in the near-infrared spectrum can indicate the presence and content of specific chemical bonds or functional groups in humic acid. For example, the absorption peaks at certain wavelengths may be related to functional groups such as hydroxyl and carboxyl in humic acid.

[0023] Among them, the UV-visible spectrum data includes the absorbance values ​​at different wavelengths and the position and shape of the absorption band; Absorbance values ​​at different wavelengths: Similar to near-infrared spectroscopy, the absorbance values ​​in UV-visible spectroscopy can reflect the absorption of humic acid to light of a specific wavelength, thus providing information about the electronic structure and chemical properties of humic acid; Position and shape of absorption bands: The position and shape of absorption bands in UV-visible spectra can be used to infer the aromaticity, conjugated system and degree of oxidation of humic acid. For example, aromatic compounds usually have strong absorption bands in a specific wavelength range.

[0024] Electrochemical data is collected using electrochemical sensors, and the electrochemical data includes potential data and current data; Among them, the potential data includes electrode potential value and potential-time curve; Electrode potential value: Measuring the electrode potential of humic acid samples in an electrochemical system can reflect the redox properties of humic acid. Different electrode potentials can indicate the oxidation state or reduction state of humic acid, as well as its ability to participate in redox reactions. Potential-time curve: Recording the change of electrode potential over time can provide information about the dynamic behavior of humic acid in the electrochemical process. For example, the change of potential can reflect the redox reaction rate, stability and reversibility of humic acid.

[0025] Among them, the current data includes current intensity and current-potential curve; Current intensity: Measuring the current response of humic acid samples in the electrochemical system can be correlated with the concentration, reaction rate and electron transfer process of humic acid. The magnitude of the current intensity can reflect the redox activity and reaction degree of humic acid on the electrode surface. Current-potential curve (polarization curve): By changing the electrode potential and measuring the corresponding current, a current-potential curve can be obtained, which can provide information about the electrochemical behavior, redox mechanism and electrode reaction kinetics of humic acid. For example, the shape of the curve can reflect the redox reaction type and control step of humic acid.

[0026] In the first embodiment, specifically, in step three, the spectral data preprocessing includes data cleaning, baseline correction and standardization; wherein, Data cleaning: Use median filtering algorithm to remove noise interference; For the spectral data sequence {x1, x2, ..., xn}, the result y(i) after median filtering is: y(i)=median{x(ik),...,x(i),...,x(i+k)}; Among them, k is the filter window radius, and median represents the median operation; Baseline correction: The spectral data can be baseline corrected by using the polynomial fitting method. First, select the appropriate polynomial degree to fit the spectral data to obtain a fitting curve. Then, subtract the fitting curve from the original spectral data to obtain the spectral data after baseline correction. Standardization: The Z-score standardization method is used to make the spectral data of different features have the same scale; For the spectral data sequence {x1,x2,...,xn}, the standardized result z(i) is: z(i)=(x(i)-μ) / σ; Among them, μ is the mean of the data series and σ is the standard deviation.

[0027] Electrochemical data preprocessing includes data normalization, outlier removal and smoothing. Data normalization processing: using the maximum and minimum normalization algorithm; For the electrochemical data sequence {z1,z2,...,zm}, the normalized result w(i) is: w(i)=(z(i)-min(z)) / (max(z)-min(z)); Among them, min(z) represents the minimum value in the sequence, and max(z) represents the maximum value in the sequence; Removing outliers: By calculating the mean and standard deviation of the data, data points outside a certain range can be considered as outliers and removed; For example, for a data point zi, if |zi-μ|>kσ (where μ is the mean, σ is the standard deviation, and k is a constant), then zi is considered an outlier and removed; Smoothing: The electrochemical data were smoothed using the moving average method; For the electrochemical data sequence {z1,z2,...,zm}, the smoothed result s(i) is: s(i)=(z(ik)+...+z(i)+...+z(i+k)) / (2k+1); Where k is the moving average window radius.

[0028] In the first embodiment, specifically, in step 4, the method for extracting characteristic parameters includes the following steps: Step 1: Combine the preprocessed spectral data (such as near-infrared spectral data and UV-visible spectral data) and electrochemical data (such as potential data and current data) into a comprehensive data set; this comprehensive data set will contain multiple features (variables), each feature corresponding to a measurement value in the spectral or electrochemical data; Step 2: Organize the combined comprehensive data set into a data matrix, where rows represent samples (i.e., different humic acid samples or measurements) and columns represent features (i.e., individual measurement points of spectral or electrochemical data); Step 3: Calculate the covariance matrix of the data matrix and perform eigendecomposition on the covariance matrix to obtain eigenvalues ​​and corresponding eigenvectors; each element of the covariance matrix represents the covariance between two features, reflecting the linear relationship between them; the eigenvalue represents the variance contribution of each principal component, and the eigenvector describes the direction of the corresponding principal component; Step 4: Select multiple principal components (i.e., corresponding eigenvectors) as the new coordinate system according to the size of the eigenvalues. Usually, the principal components whose cumulative contribution rate (i.e., the ratio of the sum of the eigenvalues ​​of the first few principal components to the sum of the total eigenvalues) reaches a certain threshold (e.g., 85% or 90%) are selected. These principal components will contain most of the information in the data set. Step 5: Project the data matrix into the new coordinate space. In the projected data matrix (usually achieved by calculating the product of the original data matrix and the eigenvector matrix to obtain a new data matrix whose columns correspond to the selected principal components), each principal component is regarded as a characteristic parameter. These characteristic parameters are low-dimensional representations of the original data, which retain most of the important information of the original data while reducing the complexity and redundancy of the data.

[0029] In the first embodiment, specifically, in step five, the method for constructing a humic acid analysis model includes the following steps: Step S1, according to the specific needs of humic acid analysis, select a machine learning algorithm from linear regression, logistic regression, decision tree, random forest, support vector machine or neural network; for complex nonlinear relationships, neural network or ensemble learning methods (such as random forest) may be more suitable; Step S2, dividing the extracted feature parameters into training set data, validation set data and test set data; wherein the training set is used to train the model, the validation set is used to adjust the model parameters to prevent overfitting, and the test set is used to evaluate the final performance of the model; Step S3, using the training set data to train the selected machine learning algorithm; during the training process, the algorithm will learn the mapping relationship between the characteristic parameters and the humic acid characteristics (such as content, type, etc.); Step S4: Use the validation set data to validate the trained humic acid analysis model and evaluate its performance (such as accuracy, recall rate, F1 score, etc.). According to the validation results, make necessary adjustments and optimizations to the model, such as adjusting model parameters, changing algorithms, adding features, etc., to improve its generalization ability and prediction accuracy. Step S5: Testing the optimized humic acid analysis model using the test set data to evaluate its performance in practical applications. If the model performs well, it can be considered that the model is ready for practical humic acid analysis tasks. Step S6: deploy the trained humic acid analysis model to the cloud server so that the humic acid sample data to be analyzed can be received through the network and the analysis results can be generated in real time. The user can send a request and receive the analysis results through a user terminal (such as a computer, mobile phone, etc.).

[0030] In the first embodiment, specifically, the analysis results include humic acid content, humic acid type and composition, property parameters, quality assessment, anomaly detection and prediction trend; wherein, Humic acid content: This is one of the most direct and common analytical results. The model can predict or estimate the humic acid content in the humic acid sample, usually expressed as mass percentage, concentration or other appropriate units; Humic acid type and composition: If the model is designed to distinguish different types of humic acid or analyze its composition, the analysis results will include information on the type of humic acid or the relative proportions of each component; Property parameters: In addition to content and type, the model may also analyze and report some key property parameters of humic acid, such as pH, acidity, exchange capacity, functional group content, etc. These parameters are very important for understanding the chemical properties and application potential of humic acid; Quality assessment: In some cases, the model may be used to assess the quality of humic acid samples, which can be based on a comprehensive evaluation of a series of property parameters to determine whether the sample meets specific quality standards or requirements; Anomaly detection: If the model is trained to identify outliers or patterns in the data, the analysis results may also include an indication of whether there are any anomalies in the sample, which helps users identify and address potential problems in a timely manner; Predicting trends: For time series data or data sets with trend characteristics, the model may also be able to predict the changing trend of humic acid content or properties at future time points, which is very useful for long-term planning or market forecasting.

[0031] In the first embodiment, specifically, the cloud server supports concurrent access by multiple users, and also supports processing analysis requests of multiple humic acid samples at the same time, thereby improving analysis efficiency and system throughput; the cloud server has data encryption and access control functions to ensure the security of humic acid sample data and analysis results, and prevent data leakage and illegal access.

[0032] Embodiment 2: Figure 4 : is a functional module diagram of a humic acid rapid analysis system based on a cloud server according to an embodiment of the present application, such as Figure 4 As shown, a humic acid rapid analysis system based on a cloud server includes: a data acquisition module, a data transmission module, a data processing module, a feature extraction module, a model building module, an analysis module, a monitoring module and a result output module; A data acquisition module is used to collect spectral data and electrochemical data of humic acid samples using spectral sensors and electrochemical sensors; wherein the spectral data includes near-infrared spectral data and ultraviolet-visible spectral data; and the electrochemical data includes potential data and current data; A data transmission module, used to transmit the spectral data and electrochemical data of the humic acid sample to the cloud server via a wireless network; wherein the wireless network includes Wi-Fi, Bluetooth, mobile network, etc.; The data preprocessing module is used for cloud preprocessing the spectral data and electrochemical data of the humic acid sample received by the cloud server; wherein the spectral data preprocessing includes data cleaning, baseline correction and standardization; the electrochemical data preprocessing includes data normalization, outlier removal and smoothing; A feature extraction module is used to extract feature parameters from the preprocessed spectral data and electrochemical data using a principal component analysis algorithm; A model building module is used to build a humic acid analysis model based on the extracted characteristic parameters using a machine learning algorithm; An analysis module is used to input parameter data of the humic acid sample to be analyzed into a trained humic acid analysis model for analysis to generate analysis results; wherein the analysis results include humic acid content, humic acid type and composition, property parameters, quality assessment, anomaly detection and prediction trend; The monitoring module is used to monitor the analysis process in real time and record key parameters and abnormal information during the analysis process for subsequent data tracing and troubleshooting; The result output module is used to output the analysis results and transmit them to the user terminal through the network for the user to view and use. The user terminal includes a mobile phone, tablet computer, computer, etc.

[0033] For other details of the technical solutions implemented by each module in the system of the above embodiment, please refer to the description of a rapid analysis method of humic acid based on a cloud server in the above embodiment 1, which will not be repeated here.

[0034] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0035] Embodiment three: See also Figure 6-Figure 9The present invention provides a technical solution: a humic acid rapid analysis device based on a cloud server, comprising a detection component 1, the detection component 1 comprising an operating table 11, a through slot 12, a push plate 13, a placement seat 14, a socket 15, a sample detection tube 16, a support tube 17, a lifting block 18, a support arm 19, a detection disk 20, a spectral sensor 21, an electrochemical sensor 22 and an electric cylinder 23, a through slot 12 is provided on one side of the upper surface of the operating table 11, a push plate 13 is slidably connected to the inner side wall of the through slot 12, a plurality of placement seats 14 are fixedly connected to the upper surface of the push plate 13, the front and rear parts of the upper surface of the placement seat 14 are provided with sockets 15, and the inner side wall of the socket 15 is fittedly connected to the sample detection tube 1 6. A support cylinder 17 is fixedly connected to the rear of the upper surface of the operating table 11 near the through groove 12, and a lifting block 18 is slidably connected to the inner wall of the support cylinder 17. A support arm 19 is fixedly connected to the center of the front surface of the lifting block 18, and a detection disk 20 is fixedly connected to the front of the lower surface of the support arm 19. A plurality of spectral sensors 21 are installed at the rear of the lower surface of the detection disk 20, and a plurality of electrochemical sensors 22 are installed at the front of the lower surface of the detection disk 20. An electric cylinder 23 is fixedly connected to the top of the support cylinder 17, and the output end of the electric cylinder 23 is fixedly connected to the center of the upper surface of the lifting block 18. The sample detection tube 16 is limited and fixed by inserting the sample detection tube 16 into the socket 15 on the placement seat 14.

[0036] In the third embodiment, specifically: a threaded rod 24 penetrates one side of the rear surface of the operating table 11 near the through groove 12, a threaded hole 25 is opened at the center of the front surface of the pushing plate 13, the outer wall of the threaded rod 24 is threadedly connected to the inner wall of the threaded hole 25, a servo motor 26 is fixedly connected to the outer side of the threaded rod 24 on the rear surface of the operating table 11, the rear surface of the threaded rod 24 is fixedly connected to the output end of the servo motor 26, and is threadedly connected to the threaded hole 25 through the threaded rod 24, so as to facilitate the pushing plate 13 to move.

[0037] In the third embodiment, specifically: a host 27 is fixedly connected to the other side of the upper surface of the operating table 11, the output ends of the spectral sensor 21 and the electrochemical sensor 22 are electrically connected to the input end of the host 27, and the input ends of the electric cylinder 23 and the servo motor 26 are electrically connected to the output end of the host 27, so that the start and stop of the electric cylinder 23 and the servo motor 26 can be easily controlled through the host 27.

[0038] In the third embodiment, specifically: pressure sensors 28 are installed on both sides of the inner rear wall of the through groove 12, the output end of the pressure sensor 28 is electrically connected to the input end of the host 27, and an identification plate 35 is provided on the upper surface of the placement seat 14 near the front of the two jacks 15, and the identification plate 35 is used to facilitate identification of different types of humic acid samples.

[0039] In the third embodiment, specifically: a limiting groove 29 is provided on the front surface of the support tube 17, and the rear portion of the outer wall of the support arm 19 is slidably connected to the inner wall of the limiting groove 29, so that the rear portion of the outer wall of the support arm 19 slides in the limiting groove 29 on the front surface of the support tube 17, thereby ensuring the vertical lifting of the support arm 19.

[0040] In the third embodiment, specifically: limiting grooves 30 are provided on both sides of the inner wall of the through groove 12, and limiting slide bars 31 are fixedly connected to the middle parts of both sides of the pushing plate 13, and the outer walls of the limiting slide bars 31 are slidably connected to the inner walls of the limiting grooves 30. The limiting slide bars 31 on both sides of the pushing plate 13 slide in the limiting grooves 30 on the inner walls of the through groove 12, thereby ensuring the stability of the movement of the pushing plate 13.

[0041] In the third embodiment, specifically: a wireless transmission module 32 is installed in the middle of the upper surface of the host 27, and a touch display screen 33 is provided on the front surface of the host 27. The wireless transmission module 32 facilitates uploading the detection data of the spectral sensor 21 and the electrochemical sensor 22 to the server for analysis and storage, and the touch display screen 33 facilitates controlling the entire device and displaying detection information.

[0042] In the third embodiment, specifically: the four corners of the lower surface of the operating table 11 are fixedly connected with support frames 34 , and the support of the operating table 11 by the support frames 34 increases the stability of the operating table 11 .

[0043] Specifically, a humic acid rapid analysis device based on a cloud server, when in use, first, different types of humic acid samples are respectively loaded into a plurality of sample test tubes 16, and each sample is divided into two sample test tubes 16, and the two sample test tubes 16 containing the same sample are inserted into the jack 15 on the same placement seat 14, and the identification plate 35 on each placement seat 14 is used to distinguish different samples, and the power of the host 27 is turned on, and the host 27 starts to start, and the touch display screen 33 displays the operation interface, and the servo motor 26 is started through the touch display screen 33 on the host 27, and the servo motor 26 drives the threaded rod 24 to rotate. Since the threaded rod 24 is threadedly connected to the threaded hole 25 on the push plate 13, the push plate 13 slides along the through groove 12 under the action of the threaded rod 24, and when the push plate 13 pushes the sample test tube 16 on the placement seat 14 to the detection position, the pressure sensors 28 on both sides of the rear wall of the through groove 12 detect the pressure signal of the push plate 13 and transmit the signal to the host 27, and the host 27 receives the pressure signal. After receiving the signal from the force sensor 28, the electric cylinder 23 is controlled to start, and the output end of the electric cylinder 23 pushes the lifting block 18 to slide downward in the support tube 17. The lifting block 18 drives the support arm 19 to descend, and the detection plate 20 at the front of the lower surface of the support arm 19 descends accordingly. The spectral sensor 21 at the rear of the lower surface of the detection plate 20 and the electrochemical sensor 22 at the front are inserted into the sample detection tube 16 for detection, and the detection data are transmitted to the host 27. The host 27 receives the detection data of the spectral sensor 21 and the electrochemical sensor 22, and analyzes and processes them. The processed results are displayed on the touch display screen 33. At the same time, the host 27 transmits the detection results to the cloud server through the wireless transmission module 32 in the middle of the upper surface for further data analysis and storage. After the detection is completed, the host 27 automatically controls the electric cylinder 23 to retract, drives the detection plate 20 to rise to the initial position, and then controls the servo motor 26 to reverse, and pulls the push plate 13 back to the initial position for the next round of detection or taking out the sample detection tube 16.

[0044] Embodiment 4: like Figure 5 A schematic diagram of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0045] like Figure 5As shown, the electronic device 40 includes at least one processor 41, and a memory connected to the at least one processor 41 in communication, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by at least one processor 41, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0046] A number of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0047] The processor 41 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 41 executes the various methods and processes described above, such as a rapid analysis method of humic acid based on a cloud server.

[0048] In some embodiments, a cloud server-based humic acid rapid analysis method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the cloud server-based humic acid rapid analysis method described above may be performed. Alternatively, in other embodiments, the processor 41 may be configured to perform a cloud server-based humic acid rapid analysis method by any other appropriate means (e.g., by means of firmware).

[0049] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0050] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0051] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0052] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0053] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0054] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS (Virtual Private Server) services.

[0055] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0056] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

Claims

1. A rapid analysis method of humic acid based on a cloud server, characterized in that: The following steps are involved: Step 1: Acquire spectral data and electrochemical data of the humic acid sample, wherein the spectral data is collected by a spectral sensor, and the spectral data includes near-infrared spectral data and ultraviolet-visible spectral data; the electrochemical data is collected by an electrochemical sensor, and the electrochemical data includes potential data and current data; Step 2: transmitting the spectral data and electrochemical data of the humic acid sample to the cloud server via a wireless network; Step 3: The cloud server preprocesses the spectral data and electrochemical data of the received humic acid sample; Step 4: Extract features from the preprocessed spectral data and electrochemical data based on the principal component analysis algorithm to extract characteristic parameters; Step 5: Based on the extracted characteristic parameters, a humic acid analysis model is constructed using a machine learning algorithm; Step 6: Input the parameter data of the humic acid sample to be analyzed into the trained humic acid analysis model for analysis to generate analysis results; Step 7: The cloud server monitors the analysis process in real time and records key parameters and abnormal information during the analysis process; Step 8: Transmit the analysis results to the user terminal via the network for the user to view and use.

2. A cloud server-based rapid analysis method for humic acid according to claim 1, characterized in that: In step 4, the method for extracting characteristic parameters includes the following steps: Step 1: Combine the preprocessed spectral data and electrochemical data into a comprehensive data set; Step 2: Organize the merged comprehensive data set into a data matrix, where rows represent samples and columns represent features; Step 3: Calculate the covariance matrix of the data matrix and perform eigendecomposition on the covariance matrix to obtain eigenvalues ​​and corresponding eigenvectors; Step 4: Select multiple principal components as the new coordinate system according to the size of the eigenvalues; Step 5: Project the data matrix into the new coordinate space. In the projected data matrix, each principal component is regarded as a characteristic parameter.

3. The method for rapid analysis of humic acid based on a cloud server according to claim 1, characterized in that: In step five, the method for constructing a humic acid analysis model comprises the following steps: Step S1, according to the specific requirements of humic acid analysis, select a machine learning algorithm from linear regression, logistic regression, decision tree, random forest, support vector machine or neural network; Step S2, dividing the extracted feature parameters into training set data, validation set data and test set data; Step S3: training the selected machine learning algorithm using the training set data; Step S4, using the validation set data to validate and optimize the trained humic acid analysis model; Step S5, using the test set data to test the optimized humic acid analysis model; Step S6: deploy the trained humic acid analysis model to the cloud server.

4. The method for rapid analysis of humic acid based on a cloud server according to claim 1, characterized in that: The analysis results include humic acid content, humic acid type and composition, property parameters, quality assessment, anomaly detection and predicted trends.

5. A cloud server-based rapid analysis method for humic acid according to any one of claims 1 to 4, characterized in that: The cloud server supports concurrent access by multiple users and also supports simultaneous processing of analysis requests for multiple humic acid samples. The cloud server has data encryption and access control functions.

6. A humic acid rapid analysis system based on a cloud server, characterized in that: include: Data acquisition module, data transmission module, data processing module, feature extraction module, model building module, analysis module, monitoring module and result output module; The data acquisition module is used to collect spectral data and electrochemical data of the humic acid sample using a spectral sensor and an electrochemical sensor; The data transmission module is used to transmit the spectral data and electrochemical data of the humic acid sample to the cloud server via a wireless network; The data preprocessing module is used for cloud preprocessing the spectral data and electrochemical data of the humic acid sample received by the cloud server; The feature extraction module is used to extract feature parameters from the preprocessed spectral data and electrochemical data using a principal component analysis algorithm; The model building module is used to build a humic acid analysis model based on the extracted characteristic parameters using a machine learning algorithm; The analysis module is used to input the parameter data of the humic acid sample to be analyzed into the trained humic acid analysis model for analysis to generate analysis results; The monitoring module is used to monitor the analysis process in real time and record key parameters and abnormal information in the analysis process; The result output module is used to output the analysis results and transmit them to the user terminal through the network for the user to view and use.

7. A humic acid rapid analysis device based on a cloud server, characterized in that: include: A detection component (1), the detection component (1) comprising an operating table (11), a through slot (12), a push plate (13), a placement seat (14), a socket (15), a sample detection tube (16), a support tube (17), a lifting block (18), a support arm (19), a detection plate (20), a spectral sensor (21), an electrochemical sensor (22) and an electric cylinder (23), wherein a through slot (12) is provided on one side of an upper surface of the operating table (11), a push plate (13) is slidably connected to an inner side wall of the through slot (12), a plurality of placement seats (14) are fixedly connected to an upper surface of the push plate (13), and sockets are provided at the front and rear of the upper surface of the placement seat (14). (15), the inner side wall of the jack (15) is fittedly connected to a sample detection tube (16), the upper surface of the operating table (11) is fixedly connected to a support tube (17) near the rear of the through groove (12), the inner side wall of the support tube (17) is slidably connected to a lifting block (18), the center of the front surface of the lifting block (18) is fixedly connected to a support arm (19), the front part of the lower surface of the support arm (19) is fixedly connected to a detection disk (20), the rear part of the lower surface of the detection disk (20) is installed with a plurality of spectral sensors (21), the front part of the lower surface of the detection disk (20) is installed with a plurality of electrochemical sensors (22), the top of the support tube (17) is fixedly connected to an electrochemical sensor (21), and the upper side wall of the support tube (17) is fixedly connected to a lifting block (18). A movable cylinder (23), the output end of the electric cylinder (23) is fixedly connected to the center of the upper surface of the lifting block (18); a threaded rod (24) penetrates through the side of the rear surface of the operating table (11) close to the through groove (12); a threaded hole (25) is opened at the center of the front surface of the pushing plate (13); the outer wall of the threaded rod (24) is threadedly connected to the inner wall of the threaded hole (25); a servo motor (26) is fixedly connected to the outer side of the rear surface of the operating table (11) close to the threaded rod (24); the rear surface of the threaded rod (24) is fixedly connected to the output end of the servo motor (26); and a main machine (27) is fixedly connected to the other side of the upper surface of the operating table (11). The output ends of the spectral sensor (21) and the electrochemical sensor (22) are both electrically connected to the input end of the host (27); the input ends of the electric cylinder (23) and the servo motor (26) are both electrically connected to the output end of the host (27); pressure sensors (28) are installed on both sides of the inner rear wall of the through groove (12); the output end of the pressure sensor (28) is electrically connected to the input end of the host (27); an identification plate (35) is provided on the upper surface of the placement seat (14) near the front of the two sockets (15); a wireless transmission module (32) is installed in the middle of the upper surface of the host (27); and a touch display screen (33) is provided on the front surface of the host (27).

8. The humic acid rapid analysis device based on a cloud server according to claim 7, characterized in that: A limiting groove (29) is provided on the front surface of the support tube (17); the rear portion of the outer side wall of the support arm (19) is slidably connected to the inner side wall of the limiting groove (29); limiting slide grooves (30) are provided on both sides of the inner side wall of the through groove (12); the middle portions of both sides of the push plate (13) are fixedly connected to limiting slide bars (31); the outer side wall of the limiting slide bar (31) is slidably connected to the inner side wall of the limiting slide groove (30); and the four corners of the lower surface of the operating table (11) are fixedly connected to support frames (34).

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a cloud server-based rapid analysis method for humic acid as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement a cloud server-based rapid analysis method for humic acid as described in any one of claims 1 to 5 when executed.

Images