Soil ecological risk assessment method and system based on multi-source data analysis

Abstract

The invention belongs to the technical field of soil ecological risk monitoring, and discloses a soil ecological risk assessment method and system based on multi-source data analysis. The system comprises a data acquisition module, a data preprocessing module, a comprehensive data analysis module, a database module, a risk dynamic early warning module and a decision report output module, the data preprocessing module is used for preprocessing a chemical data set to obtain a second chemical data set; the comprehensive data analysis module is used for processing the second chemical data set to obtain a risk prediction value; the risk dynamic early warning module is used for analyzing the risk prediction value and outputting a risk decision report; the decision report output module is used for processing and outputting the risk decision report; in general, the method has the remarkable advantages of being high in data prediction accuracy, good in auxiliary evaluation capability and high in risk decision-making practicability.

Description

Technical Field

[0001] The present invention relates to the technical field of soil ecological risk monitoring, and more specifically, to a soil ecological risk assessment method and system based on multi-source data analysis. Background Art

[0002] Ecological risk assessment refers to the evaluation of adverse ecological impacts caused by one or more internal or external factors, that is, the assessment of the likelihood and intensity of adverse impacts caused by chemical emissions, human activities, or biological activities, and the conduct of qualitative and targeted research. Its purpose is to help environmental management departments, enterprises, or scientific research institutions understand and predict the relationship between ecological influencing factors and the resulting ecological consequences, which is conducive to the formulation of soil use and environmental protection decisions. With the rapid development of my country's economy in recent years, people's awareness of soil protection has also greatly increased. Due to the needs of the government, enterprises, or scientific research institutions, soil ecological risk assessment activities have also increased. However, traditional soil ecological risk assessment often requires a large amount of manpower, material resources, and time costs. With the rapid development of the science and technology industry, people are increasingly inclined to use tools to assess soil ecological risks. However, the existing soil ecological assessment system is faced with a relatively large amount of data and only calculates the direct impact of one parameter on another, which is prone to inaccurate assessment reports. Therefore, how to improve the soil ecological risk assessment system based on multi-source data analysis has become a difficult problem that the soil ecological risk assessment industry needs to face.

[0003] The patent application publication number CN118822082A discloses a method and system for ecological risk assessment of heavy metal contaminated soil based on multi-source analysis. By making full use of the combination of field sampling data and remote sensing data, the method makes up for the lack of spatial coverage mismatch between the two sets of data. Through analysis and comparison of the data, the spatial distribution density of field sampling points and the accuracy of the data are evaluated, thereby improving the consistency and accuracy of the assessment results. By dividing the assessment area into high-interference and low-interference assessment areas, and taking timely early warning measures for the high-interference assessment areas, the risks caused by data inconsistency and deviation can be effectively reduced, and the safety of the environment and human health can be guaranteed. By utilizing multi-source data, comprehensive analysis and early warning mechanisms, the accuracy and timeliness of ecological risk assessment of heavy metal contaminated soil can be improved. Through timely early warning and effective management, the risks to the environment and human health can be reduced, providing strong support for the governance of soil pollution problems.

[0004] However, the above-mentioned heavy metal contaminated soil ecological risk assessment method and system based on multi-source analysis, although improving the accuracy of the assessment results by comparing field sampling data with remote sensing data, is still an assessment method that requires a large amount of manpower, material resources and time costs in soil ecological risk assessment. Therefore, how to reduce the amount of data required for assessment and improve the accuracy of risk assessment directly affects the development of the soil ecological risk assessment industry. When faced with multi-source data analysis, it is often easy for the final assessment results to be inaccurate due to the large number of basic parameters, the mutual influence of various parameters and the high amount of calculation, thereby resulting in a waste of assessment costs and inaccurate land use patterns in the designated area.

[0005] In view of this, the present invention proposes a soil ecological risk assessment method and system based on multi-source data analysis to solve the above problems. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned objectives, the present invention provides the following technical solutions: comprising:

[0007] The data acquisition module is used to collect chemical data sets, which include heavy metal data, organic pollution data, and soil physical and chemical data;

[0008] The data preprocessing module is used to preprocess the chemical data set to obtain a second chemical data set;

[0009] Furthermore, the chemical datasets are preprocessed in the following ways:

[0010] Obtain a set of chemical data sets;

[0011] By substituting into the calculation formula: Where Aai is the original value of the i-th parameter, is the mean value of the original values of all parameters, and σ is the standard deviation of the original values of all parameters;

[0012] Traverse the Aa values and obtain a second chemical data set based on the data set of the input data;

[0013] The comprehensive data analysis module is used to process the second chemical data set to obtain a risk prediction value;

[0014] Furthermore, the comprehensive data analysis module also includes a historical data scheduling module, a manual data input module, a model building module and a standard data scheduling module;

[0015] The historical data scheduling module is used to retrieve the second chemical data set and biological data set stored in the database module;

[0016] The manual data input module is used to support the data input of the half-maximal inhibition concentration in the model and the sensitivity of the sth parameter in the second chemical data set to the evaluation value;

[0017] The model building module is used to support the establishment of the model required by the system;

[0018] The standard data scheduling module is used to retrieve the standard evaluation value stored in the database module;

[0019] Furthermore, the comprehensive data analysis module also includes a module for analyzing the second chemical data set;

[0020] obtaining a second chemical data set;

[0021] By substituting into the calculation formula group: The microbial activity data Aa and enzyme activity data Ab were obtained respectively, where A0 is the standard activity in the absence of pollution, Bc50 is the half-maximal inhibition concentration, A1 is the coupling coefficient, A2 is the basic activity, and m is the slope coefficient;

[0022] Package the microbial activity data Aa and enzyme activity data Ab to obtain a biological data set;

[0023] Furthermore, the step of processing the second chemical data set includes:

[0024] Step 1: Based on the historical data scheduling module, a set of historical second chemical data sets and historical biological data sets are obtained and marked as A1, A2, A3...An based on the timestamps from small to large;

[0025] Step 2: Based on the model building module, an original prediction model is established according to the historical second chemical data set and the historical biological data set;

[0026] Step 3: Substitute into the calculation formula: The chemical impact value is obtained, where Baj0 is the standard value of the jth parameter in the biological data set, Bbs is the concentration of the sth parameter in the second chemical data set, Bc50,s is the half-maximal inhibition concentration of the second chemical data set s on the biological data set j, and ns is the slope parameter;

[0027] Step 4: Substitute into the calculation formula: The comprehensive reference value is obtained, where Cbj is the weight factor of the jth parameter in the biological data set;

[0028] Step 5: Based on the standard data scheduling module, obtain the standard evaluation value in the database module and substitute it into the calculation formula: ΔCa = |Ca-Car| to obtain the deviation reference value. If ΔCa>0.1, the model is modified, where Car is the standard evaluation value;

[0029] Step 6: Substitute the formula into the equation: The risk prediction value is obtained, where is the sensitivity of the sth parameter in the second chemical data set to the evaluation value, ΔBbs is the predicted concentration change of the parameter in the second chemical data set;

[0030] Step 7: Repeat steps 3 to 6 until the preset number of iterations is reached to obtain a risk prediction model;

[0031] Step 8: Input the second chemical data set into the risk prediction model and output a risk prediction value;

[0032] Furthermore, the methods for modifying the model include:

[0033] By substituting into the calculation formula: The modified weight factor is obtained, where Cs is the learning rate and ΔCaj is the contribution deviation of the jth parameter in the biological dataset;

[0034] The database module is used to store the second chemical data set, the biological data set, the standard assessment value and the risk prediction value;

[0035] The risk dynamic warning module is used to analyze the risk prediction value and output a risk decision report;

[0036] Furthermore, the risk prediction value can be analyzed by:

[0037] Obtain the risk prediction value. When the risk prediction value is less than R1, a low-risk report is generated. When the risk prediction value is greater than or equal to R1 and less than or equal to R2, a medium-risk report is generated. When the risk prediction value is greater than R2, a high-risk report is generated, where R1 and R2 are preset risk thresholds.

[0038] A low-risk report includes a statement that the soil ecological risk level is low and requires staff to conduct regular monitoring;

[0039] A medium-risk report includes a description of the moderate level of soil ecological risk, requiring staff to restrict land use and implement bioremediation;

[0040] A high-risk report includes a description of the high ecological risk level of the soil, requiring staff to immediately isolate the land and implement a combination of chemical and physical remediation;

[0041] Package low-risk reports, medium-risk reports, and high-risk reports to obtain a risk decision report;

[0042] The decision report output module is used to process and output the risk decision report;

[0043] Furthermore, the methods for processing and outputting the risk decision report include:

[0044] Output fixed templates based on user role selection;

[0045] Generate risk decision reports in a fixed format according to preset settings;

[0046] Integrate electronic signatures to generate legally binding risk decision reports;

[0047] Send to designated users via email, enterprise WeChat or API interface;

[0048] Furthermore, S1: collects chemical data sets, which include heavy metal data, organic pollution data, and soil physical and chemical data;

[0049] S2: preprocessing the chemical data set to obtain a second chemical data set;

[0050] S3: Processing the second chemical data set to obtain a risk prediction value;

[0051] S4: storing the second chemical data set, the biological data set, the standard assessment value, and the risk prediction value;

[0052] S5: Analyze the risk prediction value and output the risk decision report;

[0053] S6: Process and output the risk decision report.

[0054] The technical effects and advantages of the soil ecological risk assessment method and system based on multi-source data analysis of the present invention are as follows:

[0055] The present invention processes the second chemical data set to obtain a risk prediction value, which can effectively predict the future soil assessment status. Compared with traditional assessment methods, it greatly reduces the manpower, material and time costs required for the assessment, and the risk prediction model used during the processing can be corrected in real time, which greatly improves the accuracy of the risk prediction model. By analyzing the risk prediction value and outputting a risk decision report, it can effectively assist staff in taking corresponding measures based on the ecological assessment of the soil in the specified area, greatly reducing the labor cost and time cost required for decision-making. By processing and outputting the risk decision report, the risk decision report can be optimized in a targeted manner when facing different customer groups, greatly improving the practicality of the decision report. Overall, the present invention has the significant advantages of strong data prediction accuracy, good auxiliary assessment capabilities and high practicality of risk decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 Schematic diagram of the soil ecological risk assessment system based on multi-source data analysis of the present invention;

[0057] Figure 2Schematic diagram of the soil ecological risk assessment method based on multi-source data analysis of the present invention. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0059] The terms used in the embodiments of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The singular forms "a," "an," "the," and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, and unless the context clearly indicates otherwise, "a plurality" generally includes at least two.

[0060] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0061] In addition, the step sequence in the following method embodiments is only an example and not a strict limitation.

[0062] In practice, the server-side device deployed in the soil ecological risk assessment system based on multi-source data analysis may be composed of one or more devices. The aforementioned soil ecological risk assessment system based on multi-source data analysis can be implemented as a service instance, a virtual machine, or hardware devices. For example, the soil ecological risk assessment system based on multi-source data analysis can be implemented as a service instance deployed on one or more devices in a cloud node. Simply put, the soil ecological risk assessment system based on multi-source data analysis can be understood as software deployed on a cloud node, which provides the soil ecological risk assessment system based on multi-source data analysis to each client. Alternatively, the soil ecological risk assessment system based on multi-source data analysis can be implemented as a virtual machine deployed on one or more devices in a cloud node. Application software for managing each client can be installed in the virtual machine. Alternatively, the soil ecological risk assessment system based on multi-source data analysis can be implemented as a server-side device composed of multiple hardware devices of the same or different types, with one or more hardware devices configured to provide the soil ecological risk assessment system based on multi-source data analysis to each client.

[0063] In terms of implementation, the soil ecological risk assessment system based on multi-source data analysis and the user end are mutually compatible. Specifically, if the soil ecological risk assessment system based on multi-source data analysis is an application installed on a cloud service platform, the user end serves as a client that establishes a communication connection with the application. Alternatively, if the soil ecological risk assessment system based on multi-source data analysis is implemented as a website, the user end serves as a webpage. Alternatively, if the soil ecological risk assessment system based on multi-source data analysis is implemented as a cloud service platform, the user end serves as a mini-program within an instant messaging application.

[0064] like Figure 1 FIG. 1 is a system architecture diagram of a soil ecological risk assessment system based on multi-source data analysis provided by an embodiment of the present invention.

[0065] The soil ecological risk assessment system based on multi-source data analysis described in the present invention can be installed in a cloud server. In terms of implementation, it can be implemented as one or more service devices, as an application installed in the cloud (e.g., a mobile service operator's server, server cluster, etc.), or as a website. Depending on the functionality implemented, the soil ecological risk assessment system based on multi-source data analysis can include a data acquisition module, a data preprocessing module, a comprehensive data analysis module, a database module, a dynamic risk warning module, and a decision report output module. The modules described in the present invention, also referred to as units, refer to a series of computer program segments that can be executed by an electronic device processor and perform fixed functions, and are stored in the electronic device's memory.

[0066] In an embodiment of the present invention, in a soil ecological risk assessment system based on multi-source data analysis, each of the above modules can be implemented independently and called with other modules. The call here can be understood as a module being able to connect to multiple modules of another type and provide corresponding services to the multiple modules it is connected to. For example, the sharing and evaluation module can call the same information collection module to obtain the information collected by the information collection module. Based on the above characteristics, in the soil ecological risk assessment system based on multi-source data analysis provided by an embodiment of the present invention, the scope of application of the soil ecological risk assessment system architecture based on multi-source data analysis can be adjusted by adding modules and directly calling them without modifying the program code, thereby achieving cluster-based horizontal expansion, so as to achieve the purpose of quickly and flexibly expanding the soil ecological risk assessment system based on multi-source data analysis. In actual applications, the above modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in cloud servers.

[0067] Example 1

[0068] See also Figure 1As shown, the soil ecological risk assessment method and system based on multi-source data analysis described in this embodiment include:

[0069] The data acquisition module is used to collect chemical data sets, which include heavy metal data, organic pollution data, and soil physical and chemical data;

[0070] It should be explained that by using X-ray spectroscopy, soil samples in a designated area are collected and analyzed to obtain heavy metal data; by using high performance liquid chromatography, soil samples in a designated area are collected and analyzed to obtain organic pollution data; by using an organic matter meter, soil samples in a designated area are collected and measured to obtain soil physical and chemical data; by collecting soil samples in a designated area and inoculating them on a specific culture medium for cultivation, the microbial activity in the soil is measured to obtain microbial activity data; by using spectrophotometry, soil samples in a designated area are collected and analyzed to obtain enzyme activity data; by collecting soil samples in a designated area, the types and numbers of animals in the soil are obtained to obtain animal abundance data;

[0071] The data preprocessing module is used to preprocess the chemical data set to obtain a second chemical data set;

[0072] Further, the methods for preprocessing chemical datasets include:

[0073] Obtain a set of chemical data sets;

[0074] By substituting into the calculation formula: Where Aai is the original value of the i-th parameter, is the mean value of the original values of all parameters, and σ is the standard deviation of the original values of all parameters;

[0075] Traverse the Aa values and obtain the second chemical data set based on the data set of the input data;

[0076] The comprehensive data analysis module is used to process the second chemical data set to obtain a risk prediction value;

[0077] Furthermore, the comprehensive data analysis module also includes a historical data scheduling module, a manual data input module, a model building module and a standard data scheduling module;

[0078] The historical data scheduling module is used to retrieve the second chemical data set stored in the database module;

[0079] The manual data input module is used to support the data input of the half-maximal inhibition concentration in the model and the sensitivity of the sth parameter in the second chemical data set to the evaluation value;

[0080] The model building module is used to support the establishment of the model required by the system;

[0081] The standard data scheduling module is used to retrieve the standard evaluation value stored in the database module;

[0082] Furthermore, the comprehensive data analysis module also includes a module for analyzing the second chemical data set;

[0083] obtaining a second chemical data set;

[0084] By substituting into the calculation formula group: The microbial activity data Aa and enzyme activity data Ab were obtained respectively, where A0 is the standard activity in the absence of pollution, Bc50 is the half-maximal inhibition concentration, A1 is the coupling coefficient, A2 is the basic activity, and m is the slope coefficient;

[0085] Package the microbial activity data Aa and enzyme activity data Ab to obtain a biological data set;

[0086] Furthermore, the step of processing the second chemical data set includes:

[0087] Step 1: Based on the historical data scheduling module, a set of historical second chemical data sets and historical biological data sets are obtained and marked as A1, A2, A3...An based on the timestamps from small to large;

[0088] Step 2: Based on the model building module, an original prediction model is established according to the historical second chemical data set and the historical biological data set;

[0089] Step 3: Substitute into the calculation formula: The chemical impact value is obtained, where Baj0 is the standard value of the jth parameter in the biological data set, Bbs is the concentration of the sth parameter in the second chemical data set, Bc50,s is the half-maximal inhibition concentration of the second chemical data set s on the biological data set j, and ns is the slope parameter;

[0090] It should be explained that the half-maximal inhibitory concentration was obtained through experimental calibration and manual input; the slope parameter was used to characterize the toxicity sensitivity;

[0091] Step 4: Substitute into the calculation formula: The comprehensive reference value is obtained, where Cbj is the weight factor of the jth parameter in the biological data set;

[0092] It should be explained that the biological data set contains three parameters: microbial activity data, enzyme activity data, and animal abundance data. The value of the weight factor, for example, the microbial activity parameter is 0.3;

[0093] Step 5: Based on the standard data scheduling module, obtain the standard evaluation value in the database module and substitute it into the calculation formula: ΔCa = |Ca-Car| to obtain the deviation reference value. If ΔCa>0.1, the model is modified, where Car is the standard evaluation value;

[0094] Step 6: Substitute the formula into the equation: The risk prediction value is obtained, where is the sensitivity of the sth parameter in the second chemical data set to the evaluation value, ΔBbs is the predicted concentration change of the parameter in the second chemical data set;

[0095] It should be explained that the sensitivity of the sth parameter to the evaluation value in the second chemical data set is obtained by model derivation and obtained through manual input;

[0096] Step 7: Repeat steps 3 to 6 until the preset number of iterations is reached to obtain a risk prediction model;

[0097] Step 8: Input the second chemical data set into the risk prediction model and output a risk prediction value;

[0098] Further, the methods for modifying the model include:

[0099] By substituting into the calculation formula: The modified weight factor is obtained, where Cs is the learning rate and ΔCaj is the contribution deviation of the jth parameter in the biological dataset;

[0100] The database module is used to store the second chemical data set, the biological data set, the standard assessment value and the risk prediction value;

[0101] The risk dynamic warning module is used to analyze the risk prediction value and output a risk decision report;

[0102] Further, the methods for analyzing the risk prediction value include:

[0103] Obtain the risk prediction value. When the risk prediction value is less than R1, a low-risk report is generated. When the risk prediction value is greater than or equal to R1 and less than or equal to R2, a medium-risk report is generated. When the risk prediction value is greater than R2, a high-risk report is generated, where R1 and R2 are preset risk thresholds.

[0104] A low-risk report includes a statement that the soil ecological risk level is low and requires staff to conduct regular monitoring;

[0105] A medium-risk report includes a description of the moderate level of soil ecological risk, requiring staff to restrict land use and implement bioremediation;

[0106] A high-risk report includes a description of the high ecological risk level of the soil, requiring staff to immediately isolate the land and implement a combination of chemical and physical remediation;

[0107] Package low-risk reports, medium-risk reports, and high-risk reports to obtain a risk decision report;

[0108] The decision report output module is used to process and output the risk decision report;

[0109] Furthermore, the methods for processing and outputting the risk decision report include:

[0110] Output fixed templates based on user role selection;

[0111] It should be explained that the template includes different versions for government, business or scientific research. For example, the version for business outputs restoration costs, future benefits and land use recommendations;

[0112] Generate risk decision reports in a fixed format according to preset settings;

[0113] It should be explained that fixed formats include but are not limited to PDF format, Word format, HTML format and JSON format;

[0114] Integrate electronic signatures to generate legally binding risk decision reports;

[0115] Send to designated users via email, enterprise WeChat or API interface;

[0116] This embodiment has the beneficial effect that the risk prediction value obtained by processing the second chemical data set can effectively predict the future soil assessment status. Compared with the traditional assessment method, the manpower, material and time costs required for the assessment are greatly reduced, and the risk prediction model used in the processing can be corrected in real time for the model, which greatly improves the accuracy of the risk prediction model. By analyzing the risk prediction value and outputting the risk decision report, it can effectively assist the staff in taking corresponding measures for the ecological assessment of the soil in the specified area, which greatly reduces the labor cost and time cost required for decision-making. By processing and outputting the risk decision report, the risk decision report can be targetedly optimized when facing different customer groups, which greatly improves the practicability of the decision report. In general, the present invention has the significant advantages of strong data prediction accuracy, good auxiliary assessment capability and high practicability of risk decision-making.

[0117] Example 2

[0118] See also Figure 2As shown, for parts not described in detail in this embodiment, please refer to the description of Example 1. A soil ecological risk assessment method based on multi-source data analysis is provided, including: S1: collecting a chemical data set, the chemical data set including heavy metal data, organic pollution data and soil physical and chemical data;

[0119] S2: preprocessing the chemical data set to obtain a second chemical data set;

[0120] S3: Processing the second chemical data set to obtain a risk prediction value;

[0121] S4: storing the second chemical data set, the biological data set, the standard assessment value, and the risk prediction value;

[0122] S5: Analyze the risk prediction value and output the risk decision report;

[0123] S6: Process and output the risk decision report.

[0124] Example 3

[0125] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0126] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0127] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

[0128] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. Terms such as "first" and "second" are used to indicate names and do not imply any particular order.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A soil ecological risk assessment system based on multi-source data analysis, characterized by: The system includes a data acquisition module, a data preprocessing module, a comprehensive data analysis module, a database module, a risk dynamic warning module and a decision report output module, wherein: The data acquisition module is used to collect chemical data sets, which include heavy metal data, organic pollution data, and soil physical and chemical data; The data preprocessing module is used to preprocess the chemical data set to obtain a second chemical data set; The comprehensive data analysis module is used to process the second chemical data set to obtain a risk prediction value; The database module is used to store the second chemical data set, the biological data set, the standard assessment value and the risk prediction value; The risk dynamic warning module is used to analyze the risk prediction value and output a risk decision report; The decision report output module is used to process and output the risk decision report.

2. The soil ecological risk assessment system based on multi-source data analysis according to claim 1 is characterized in that: The comprehensive data analysis module also includes a historical data scheduling module, a manual data input module, a model building module and a standard data scheduling module, among which: The historical data scheduling module is used to retrieve the second chemical data set stored in the database module; The manual data input module is used to support the data input of the half-maximal inhibition concentration in the model and the sensitivity of the sth parameter in the second chemical data set to the evaluation value; The model building module is used to support the establishment of the model required by the system; The standard data scheduling module is used to retrieve the standard evaluation values stored in the database module.

3. The soil ecological risk assessment system based on multi-source data analysis according to claim 1 is characterized in that: The comprehensive data analysis module also includes a module for analyzing a second chemical data set; obtaining a second chemical data set; By substituting into the calculation formula group: The microbial activity data Ab and enzyme activity data Ac were obtained respectively, where A0 is the standard activity in the absence of pollution, Bc50 is the half-maximal inhibition concentration, A1 is the coupling coefficient, A2 is the basic activity, and m is the slope coefficient; The microbial activity data Ab and enzyme activity data Ac are packaged to obtain a biological data set.

4. The soil ecological risk assessment system based on multi-source data analysis according to claim 1 is characterized in that: The steps of processing the second chemical data set include: Step 1: Based on the historical data scheduling module, a set of historical second chemical data sets and historical biological data sets are obtained and marked as A1, A2, A3...An based on the timestamps from small to large; Step 2: Based on the model building module, an original prediction model is established according to the historical second chemical data set and the historical biological data set; Step 3: Substitute into the calculation formula: The chemical impact value is obtained, where Baj0 is the standard value of the jth parameter in the biological data set, Bbs is the concentration of the sth parameter in the second chemical data set, Bc50,s is the half-maximal inhibition concentration of the second chemical data set s on the biological data set j, and ns is the slope parameter; Step 4: Substitute into the calculation formula: The comprehensive reference value is obtained, where Cbj is the weight factor of the jth parameter in the biological data set; Step 5: Based on the standard data scheduling module, obtain the standard evaluation value in the database module and substitute it into the calculation formula: ΔCa = |Ca-Car| to obtain the deviation reference value. If ΔCa>0.1, the model is modified, where Car is the standard evaluation value; Step 6: Substitute the formula into the equation: The risk prediction value is obtained, where is the sensitivity of the sth parameter in the second chemical data set to the evaluation value, ΔBbs is the predicted concentration change of the parameter in the second chemical data set; Step 7: Repeat steps 3 to 6 until the preset number of iterations is reached to obtain a risk prediction model; Step 8: Input the second chemical data set into the risk prediction model and output a risk prediction value.

5. The soil ecological risk assessment system based on multi-source data analysis according to claim 4 is characterized in that: Ways to modify the model include: By substituting into the calculation formula: The modified weight factor is obtained, where Cs is the learning rate and ΔCaj is the contribution deviation of the jth parameter in the biological dataset.

6. The soil ecological risk assessment system based on multi-source data analysis according to claim 1 is characterized in that: Methods for analyzing risk prediction values include: Obtain the risk prediction value. When the risk prediction value is less than R1, a low-risk report is generated. When the risk prediction value is greater than or equal to R1 and less than or equal to R2, a medium-risk report is generated. When the risk prediction value is greater than R2, a high-risk report is generated, where R1 and R2 are preset risk thresholds. A low-risk report includes a statement that the soil ecological risk level is low and requires staff to conduct regular monitoring; A medium-risk report includes a description of the moderate level of soil ecological risk, requiring staff to restrict land use and implement bioremediation; A high-risk report includes a description of the high ecological risk level of the soil, requiring staff to immediately isolate the land and implement a combination of chemical and physical remediation; Package low-risk reports, medium-risk reports and high-risk reports to obtain a risk decision report.

7. The soil ecological risk assessment system based on multi-source data analysis according to claim 1 is characterized in that: The methods for processing and outputting the risk decision report include: Output fixed templates based on user role selection; Generate risk decision reports in a fixed format according to preset settings; Integrate electronic signatures to generate legally binding risk decision reports; Send to designated users via email, enterprise WeChat or API interface.

8. A soil ecological risk assessment method based on multi-source data analysis, implemented according to any one of claims 1 to 7, characterized in that: The following steps are included: S1: Collect chemical data sets, including heavy metal data, organic pollution data, and soil physical and chemical data; S2: preprocessing the chemical data set to obtain a second chemical data set; S3: Processing the second chemical data set to obtain a risk prediction value; S4: storing the second chemical data set, the biological data set, the standard assessment value, and the risk prediction value; S5: Analyze the risk prediction value and output the risk decision report; S6: Process and output the risk decision report.

Images