Environment analysis rating system based on environment influence data

Abstract

The invention relates to the technical field of environmental protection, in particular to an environmental analysis rating system based on environmental influence data, which provides a visual and understandable environmental quality evaluation result for a user mainly by collecting, processing, analyzing and rating the environmental influence data. The system is composed of a data collection unit, a data processing unit, an analysis rating unit and a display output unit, the data collection unit can collect various environmental influence data in real time through a built-in high-precision sensor and a collection device, and the data processing unit adopts an advanced data analysis algorithm to carry out deep mining and analysis on the data and displays the data in real time. According to the method, key information and rules in the data can be extracted, and the analysis rating unit is used for rating the environment quality by adopting a preset rating model and algorithm based on the processed data, so that the scientificity and accuracy of rating are ensured. Meanwhile, the self-learning ability can continuously optimize the rating model and algorithm, and the rating reliability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental protection, and in particular to an environmental analysis and rating system based on environmental impact data. Background Art

[0002] Environmental protection has become a global focus. In order to effectively manage and improve environmental quality, it is particularly urgent to establish a scientific and efficient environmental analysis and rating system.

[0003] At present, although there are many environmental analysis and rating systems, most of them are based on qualitative analysis and lack the support of quantitative data. This analysis method often relies on factors such as expert experience and subjective judgment, resulting in inaccurate and objective evaluation results.

[0004] Specifically, the existing environmental analysis rating system has the following problems:

[0005] Difficulty in data collection: Due to the complexity and diversity of environmental pollution and ecological damage, it is difficult to obtain comprehensive and accurate environmental data. This makes it difficult for the system to conduct a comprehensive and objective assessment of environmental quality.

[0006] Single evaluation method: Existing environmental analysis and rating systems mostly use single evaluation methods, such as expert scoring method, hierarchical analysis method, etc. These methods are often affected by human factors, and the evaluation results are not stable and reliable.

[0007] Lack of dynamic monitoring: Existing environmental analysis and rating systems often lack the ability to dynamically monitor environmental quality. This makes it difficult for the system to detect and respond to environmental pollution and ecological damage in a timely manner. Summary of the invention

[0008] 1. Technical issues to be resolved

[0009] In view of the deficiencies of the prior art, the present invention provides an environmental analysis rating system based on environmental impact data.

[0010] (II) Technical solution

[0011] To achieve the above-mentioned purpose, the present invention provides the following technical solution: The environmental analysis and rating system based on environmental impact data of the present invention is characterized by comprising a data collection unit, a data processing unit, an analysis and rating unit and a display output unit:

[0012] Wherein, the data collection unit collects various environmental impact data in real time through built-in high-precision sensors and collection equipment;

[0013] Among them, the data processing unit cleans, integrates and standardizes the collected data, and uses advanced data analysis algorithms to conduct in-depth mining and analysis of the data;

[0014] Among them, the analysis and rating unit uses the preset rating model and algorithm to rate the environmental quality based on the processed data, and has the self-learning ability to continuously optimize the rating model and algorithm;

[0015] Among them, the display output unit outputs the results of the analysis and rating unit in a visual form and has interactive functions for users to view and use.

[0016] Preferably, the data collection unit is connected to the environmental monitoring equipment through the Internet of Things, and adopts real-time monitoring and historical data query methods to collect various environmental impact data, including but not limited to air quality, water quality, soil pollution, noise, and biodiversity data.

[0017] Further preferably, in the data processing unit, for the in-depth mining and analysis of various environmental impact data (such as air quality, water quality, soil pollution, noise, biodiversity), when using advanced data analysis algorithms, the key parameters of data processing include but are not limited to the following:

[0018] Data preprocessing parameters:

[0019] Data cleaning threshold: set the standard for removing outliers and missing values ​​during data cleaning. For example, records with missing values ​​exceeding a certain ratio will be removed, or outliers beyond the range of mean ±n times standard deviation will be considered invalid data.

[0020] Data standardization method: Select appropriate standardization methods (such as Z-score standardization, Min-Max standardization) to normalize the data to facilitate comparison and comprehensive analysis between different indicators;

[0021] Environmental impact analysis parameters:

[0022] Analysis algorithm selection: Select an appropriate algorithm based on the data type and analysis purpose, such as regression analysis, cluster analysis, and principal component analysis.

[0023] Weight setting: For different types of environmental impact data (such as air quality, water quality, etc.), different weight values ​​are set according to their importance to the overall impact on the environment;

[0024] Prediction model parameters: For models that use machine learning algorithms for prediction or classification, it is necessary to set model training parameters such as learning rate, number of iterations, and regularization coefficient;

[0025] Data mining and in-depth analysis parameters:

[0026] Feature selection method: Select an appropriate feature selection method (such as information gain-based, chi-square test-based, etc.) to select the most representative features from the original data set;

[0027] Association rule mining threshold: Set the minimum support and confidence thresholds for the correlation between various indicators when mining association rules;

[0028] Time series analysis parameters: For time series data, set parameters such as time window size and sliding step size to capture the time trend and periodicity of the data;

[0029] Results evaluation and visualization parameters:

[0030] Selection of evaluation indicators: Select appropriate evaluation indicators (such as accuracy, recall, F1 value) to quantitatively evaluate the analysis results.

[0031] Selection of visualization method: According to the characteristics of the analysis results and user needs, select appropriate visualization methods (such as charts, heat maps, animations) to display the analysis results.

[0032] Further preferably, the analysis and rating unit includes an environmental impact analysis unit, a rating model building unit and a rating result display unit:

[0033] Among them, the key parameters of the environmental impact analysis unit include but are not limited to the following:

[0034] Analytical methods:

[0035] Statistical analysis: including descriptive statistics (such as mean, median, standard deviation, etc.), correlation analysis, regression analysis;

[0036] Machine learning: Select applicable machine learning algorithms, such as decision trees, random forests, neural networks, etc., for pattern recognition and prediction;

[0037] Other methods: Depending on the specific environment and data type, cluster analysis, principal component analysis (PCA), and factor analysis may also be included;

[0038] Analysis parameters:

[0039] Threshold setting: critical values ​​used to identify outliers or determine contamination levels;

[0040] Time window: For time series data, determine the time range or window size for analysis;

[0041] Variable selection: Selecting key variables or features for analysis;

[0042] Data Source:

[0043] Real-time data interface: ensure stable data connection with real-time monitoring sites or sensors;

[0044] Historical database: provides interfaces for querying and calling historical data;

[0045] Model training parameters (if using machine learning):

[0046] Learning rate: used to optimize the learning speed of the algorithm;

[0047] Iteration number: the number of rounds of model training;

[0048] Loss function: A function that measures the difference between the model's predictions and the actual values.

[0049] Again preferably, the key parameters of the rating model building unit include but are not limited to the following:

[0050] Model Type:

[0051] Linear model: such as weighted sum model, scoring is based on the weights of various factors;

[0052] Nonlinear models: such as neural networks and support vector machines, used to process complex nonlinear relationships;

[0053] Weight setting:

[0054] Assign weights to various influencing factors based on the results of the environmental impact analysis;

[0055] Weights can be static or dynamically adjusted based on specific conditions;

[0056] Rating criteria:

[0057] Define the criteria or thresholds for different rating levels;

[0058] Rating criteria can be customized according to policy requirements, industry standards or user needs;

[0059] Model Validation:

[0060] Use the validation data set to validate the model to ensure its accuracy and reliability;

[0061] Model validation can be performed using methods such as cross validation and leave-out validation.

[0062] Preferably, the key parameters of the rating result display unit include but are not limited to the following:

[0063] Display method:

[0064] Graphical interface: including but not limited to bar charts, pie charts, heat maps, maps, etc., used to intuitively display rating results;

[0065] Report generation: Generate detailed rating reports, including data tables, charts, and text analysis;

[0066] Interactivity:

[0067] Provide user interaction functions, including filter condition setting, data zooming, and chart switching;

[0068] Allow users to customize display content and format;

[0069] Export function:

[0070] Support exporting rating results to Excel and PDF formats;

[0071] Provide API interface to allow data exchange with other systems;

[0072] Data update frequency:

[0073] Set the update frequency of rating results, such as daily, weekly or monthly updates;

[0074] The update frequency can be adjusted dynamically according to user needs or data changes;

[0075] Further preferably, the main operating parameters of the display output unit are related to how to display the rating results to the user in an intuitive and understandable manner. The following is a summary of the operating parameters of this module:

[0076] 1. Graphical interface display:

[0077] Chart type selection:

[0078] According to the characteristics of the rating results, select appropriate chart types, such as bar charts, line charts, pie charts, heat maps, and maps, to show the status, trend, or distribution of different environmental indicators;

[0079] Interactivity settings:

[0080] Provide users with the functions of interacting with the graphical interface, such as zooming, dragging, filtering, and highlighting, so that users can have a deeper understanding of the data;

[0081] Dynamic Updates:

[0082] Set the data update frequency of the graphical interface to ensure that users can view the latest rating results in real time or regularly;

[0083] Visual customization:

[0084] Allow users to customize the style, color, font, etc. of the chart to meet the personalized needs of different users;

[0085] 2. Report Generation:

[0086] Report template selection:

[0087] Provide a variety of report templates for users to choose from, including simple version, detailed version, and customized version to meet the needs of different users;

[0088] Content customization:

[0089] Allow users to customize the content of the report, including adding or deleting certain environmental indicators and adjusting the structure of the report;

[0090] Data export format:

[0091] Supports exporting reports into multiple formats, such as PDF, Word, Excel, etc., so that users can view and edit them on different devices or software;

[0092] Report sharing:

[0093] Provide report sharing function, allowing users to share reports with others through email and social media;

[0094] 3. Other display methods:

[0095] Dashboard display:

[0096] Design a dashboard interface to centrally display the status and rating results of key environmental indicators, so that users can quickly understand the overall situation;

[0097] Mobile application display:

[0098] Develop mobile applications to display rating results on mobile devices, so that users can view them anytime and anywhere;

[0099] Large screen display:

[0100] Set up large-screen displays in public places such as conference rooms and offices to display rating results in real time, so that team members can pay attention to and discuss together;

[0101] 4. General parameters:

[0102] Interface language selection:

[0103] Supports multiple language interfaces to facilitate users with different language backgrounds;

[0104] Data Security:

[0105] Ensure the security of rating result data during display, such as data encryption and access permission control;

[0106] Performance optimization:

[0107] Optimize the performance of the display module to ensure stable and smooth operation under large amounts of data or high concurrency.

[0108] Again preferably, a user authority management function is also included to control the access rights and data access scope of different users to the system. The operating parameters of the user authority management function mainly involve how to control the access rights of different users to system functions and data to ensure data security and stable operation of the system. The following are some key operating parameters:

[0109] User type: defines the user type in the system, such as super administrator, general administrator, and general user. Each user type has different permission levels and accessible resource ranges;

[0110] Role management: A role is a collection of permissions that is used to group a group of users and grant them the same permissions. Administrators can define and edit roles and assign specific permissions to each role;

[0111] Permission settings: define permissions for various operations and data access, such as read, write, delete, and execute. Administrators can assign these permissions to each user or role to control their access to system functions and data;

[0112] Permission Level: Defines the rank or priority of each permission type, usually related to the security level of the user or role, where a super administrator may have the highest level of all permissions, while a normal user may have limited data access permissions.

[0113] Data object: specifies the data stored in the system or application, including files, folders, and database tables. Administrators can control user access rights to specific data objects.

[0114] Authorization scope: specifies the scope of data or functions that a user or role is authorized to access, which can be the entire system or a specific data or function area;

[0115] Login and authentication: Users need to log in to the system using a username and password or other authentication methods (such as fingerprint recognition, facial recognition);

[0116] Permission auditing and logging: The system should record logs of all user access and operations so that administrators can track and audit user activities;

[0117] Permission change notification: When a user's permissions change, the system should notify the relevant user or administrator;

[0118] Permission inheritance: In some cases, a user's permissions may be inherited from the organization or group to which the user belongs.

[0119] Preferably, it also includes a data backup and recovery function, and the operating parameters of the data backup and recovery function are key factors in ensuring data security, recoverability and continuous and stable operation of the system.

[0120] (III) Beneficial effects

[0121] Compared with the prior art, the present invention provides an environmental analysis and rating system based on environmental impact data, which has the following beneficial effects:

[0122] Real-time and accuracy:

[0123] The data collection unit can collect various environmental impact data in real time through built-in high-precision sensors and acquisition equipment, ensuring the accuracy and real-time nature of the data.

[0124] Data cleaning and integration:

[0125] The data processing unit cleans, integrates and standardizes the collected data, effectively removing outliers and missing values, improving data quality and facilitating subsequent analysis.

[0126] In-depth data analysis:

[0127] Using advanced data analysis algorithms to conduct in-depth mining and analysis of data can extract key information and patterns from the data, providing strong support for environmental quality assessment.

[0128] Scientific Rating:

[0129] The analysis and rating unit uses the preset rating model and algorithm to rate the environmental quality based on the processed data, ensuring the scientificity and accuracy of the rating. At the same time, its self-learning ability can continuously optimize the rating model and algorithm to improve the reliability of the rating.

[0130] Visual output and interactive functions:

[0131] The display output unit outputs the results of the analysis and rating unit in a visual form, which is convenient for users to view and understand intuitively. At the same time, the interactive function provided allows users to customize the display content and format, enhancing the flexibility of the system and user experience.

[0132] IoT access and multi-source data fusion:

[0133] The data collection unit is connected to environmental monitoring equipment through the Internet of Things, and can collect data on many aspects including air quality, water quality, soil pollution, noise, biodiversity, etc., realizing the integration of multi-source data and improving the comprehensiveness and accuracy of the analysis.

[0134] Parameter configuration and flexibility:

[0135] During the process of data processing, analysis and rating, the system provides a wealth of parameter configuration options, such as data cleaning thresholds, standardization methods, analysis algorithm selection, weight setting, etc. Users can flexibly configure according to actual needs to meet the analysis needs of different environments and data types.

[0136] Results evaluation and update frequency:

[0137] The rating result display unit provides the function of result evaluation, and users can quantitatively evaluate the analysis results through evaluation indicators. At the same time, the system supports regular data updates and re-ratings to ensure the timeliness and accuracy of the rating results.

[0138] Scalability and adaptability:

[0139] The system has strong scalability and adaptability, and can be customized and optimized according to different application scenarios and data types to meet the needs of different users and environments.

[0140] In summary, the environmental analysis and rating system based on environmental impact data provides strong technical support and decision-making basis for environmental protection and sustainable development through functions such as real-time data collection, in-depth data analysis, scientific rating and visual output. DETAILED DESCRIPTION

[0141] The technical solutions in the embodiments of the present invention are described clearly and completely below. 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.

[0142] The environmental analysis and rating system based on environmental impact data of the present invention mainly provides users with intuitive and easy-to-understand environmental quality assessment results by collecting, processing, analyzing and rating environmental impact data. The system consists of a data collection unit, a data processing unit, an analysis and rating unit, and a display output unit.

[0143] 1. Data Collection Unit

[0144] By accessing environmental monitoring equipment through Internet of Things technology, real-time monitoring and historical data query are used to collect various environmental impact data including air quality, water quality, soil pollution, noise, biodiversity, etc.

[0145] 2. Data Processing Unit

[0146] Data preprocessing:

[0147] Data cleaning: Remove outliers and missing values ​​based on the set threshold.

[0148] Data normalization: Select an appropriate normalization method, such as Z-score or Min-Max, to normalize the data.

[0149] In-depth exploration and analysis:

[0150] Select appropriate data analysis algorithms, such as regression analysis, cluster analysis, and principal component analysis.

[0151] Set weights for different environmental factors and adjust them according to their importance to the overall environmental impact.

[0152] When training a machine learning algorithm, set parameters such as learning rate, number of iterations, and regularization coefficient.

[0153] When selecting features and mining association rules, set thresholds and time series analysis parameters.

[0154] 3. Analysis and Rating Unit

[0155] Environmental Impact Analysis Unit:

[0156] Use statistical analysis, machine learning, and other methods to conduct in-depth analysis of pre-processed data.

[0157] Set thresholds, time windows, and variable selection parameters to ensure the accuracy of your analysis.

[0158] Utilize real-time and historical database interfaces to obtain stable data sources.

[0159] For machine learning algorithms, set training parameters such as learning rate, number of iterations, and loss function.

[0160] Rating model building blocks:

[0161] Select a linear model or a nonlinear model to build a rating model based on the analysis results.

[0162] Set weights, rating criteria, and model validation parameters to ensure the accuracy and reliability of the model.

[0163] Rating result display unit:

[0164] Provide graphical interface displays, such as bar charts, pie charts, heat maps, maps, etc., to intuitively display the rating results.

[0165] Generate detailed rating reports including data tables, charts and text analysis.

[0166] Provides user interaction functions, such as filter condition setting, data zooming, chart switching, etc.

[0167] It supports exporting rating results to Excel and PDF formats, and provides an API interface to exchange data with other systems.

[0168] 4. Display Output Unit

[0169] Graphical interface display:

[0170] Choose the appropriate chart type based on the rating results to ensure they are intuitive and easy to understand.

[0171] Provide interactive settings such as zooming, dragging, filtering, highlighting, etc. to enhance the user experience.

[0172] The entire system realizes real-time data collection through Internet of Things technology, uses advanced data processing and analysis algorithms to conduct in-depth mining and rating, and intuitively displays the rating results to users through a graphical interface, providing strong technical support for environmental quality monitoring and management.

[0173] It also includes data backup and recovery functions. The operating parameters of the data backup and recovery functions are key factors to ensure data security, recoverability and continuous and stable operation of the system. The following are some key operating parameters:

[0174] Data backup parameters

[0175] Backup Type:

[0176] Full backup: Back up the entire data set.

[0177] Incremental backup: Backs up only the data that has changed since the last backup.

[0178] Differential backup: Backs up data that has changed since the last full backup.

[0179] Backup frequency:

[0180] Set up regular backup intervals, such as daily, weekly, or monthly.

[0181] Backup speed:

[0182] A measure of the time required to back up a data set. In a cloud computing environment, the advantages of distributed computing can be used to increase backup speed.

[0183] Compression ratio:

[0184] The compression ratio of the backup data. The compression algorithm is used to reduce the storage space occupied.

[0185] Data integrity check:

[0186] Verify data integrity during the backup process to ensure the accuracy and consistency of backup data.

[0187] Backup storage location:

[0188] The physical location where the backup data is stored, such as local storage, cloud storage, or other remote storage facilities.

[0189] Encryption settings:

[0190] Backup data is encrypted using encryption algorithms to protect data confidentiality and security.

[0191] Data recovery parameters

[0192] Recovery Point Objective (RPO):

[0193] After a disaster occurs, the disaster recovery system can restore data to the time point before the disaster. RPO measures the degree of data loss that an enterprise can tolerate after a disaster occurs.

[0194] Recovery Time Objective (RTO):

[0195] The time interval from the moment the system downtime causes business suspension to the time when the system is restored to support the operation of the business department and the business resumes operation. RTO measures the recovery time that the enterprise can tolerate after a disaster.

[0196] Recovery speed:

[0197] The length of time from the start of recovery operations until the system returns to normal service.

[0198] Disaster recovery performance:

[0199] The system's ability to recover from catastrophic failures. Disaster recovery performance is improved through distributed storage of backup data and redundant backup mechanisms.

[0200] Failure recovery time:

[0201] The time required for a system to recover to normal working state after a failure. Shorter failure recovery time can reduce business interruption time and improve reliability.

[0202] Data Validation:

[0203] Verify the integrity and accuracy of data during the recovery process to ensure that the recovered data is consistent with system requirements.

[0204] Recovery test:

[0205] Perform recovery tests regularly to verify the validity of backup data and the feasibility of the recovery process.

[0206] Please note that the specific settings of the above parameters should be adjusted and optimized according to the specific needs of the environmental analysis and rating system, the importance of the data, and the requirements of business continuity. At the same time, in order to ensure the security and recoverability of the data, it is recommended to evaluate and adjust these parameters regularly.

[0207] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An environmental analysis rating system based on environmental impact data, characterized by: It includes data collection unit, data processing unit, analysis and rating unit and display output unit: Wherein, the data collection unit collects various environmental impact data in real time through built-in high-precision sensors and collection equipment; Among them, the data processing unit cleans, integrates and standardizes the collected data, and uses advanced data analysis algorithms to conduct in-depth mining and analysis of the data; Among them, the analysis and rating unit uses the preset rating model and algorithm to rate the environmental quality based on the processed data, and has the self-learning ability to continuously optimize the rating model and algorithm; Among them, the display output unit outputs the results of the analysis and rating unit in a visual form and has interactive functions for users to view and use.

2. The environmental analysis and rating system based on environmental impact data according to claim 1, characterized in that: The data collection unit is connected to the environmental monitoring equipment through the Internet of Things, and collects various environmental impact data, including but not limited to data on air quality, water quality, soil pollution, noise, and biodiversity, by real-time monitoring and historical data query.

3. The environmental analysis and rating system based on environmental impact data according to claim 2 is characterized in that: In the data processing unit, for the in-depth mining and analysis of various environmental impact data (such as air quality, water quality, soil pollution, noise, and biodiversity), when using advanced data analysis algorithms, the key parameters of data processing include but are not limited to the following: Data preprocessing parameters: Data cleaning threshold: set the standard for removing outliers and missing values ​​during data cleaning. For example, records with missing values ​​exceeding a certain ratio will be removed, or outliers beyond the range of mean ±n times standard deviation will be considered invalid data. Data standardization method: Select appropriate standardization methods (such as Z-score standardization, Min-Max standardization) to normalize the data to facilitate comparison and comprehensive analysis between different indicators; Environmental impact analysis parameters: Analysis algorithm selection: Select an appropriate algorithm based on the data type and analysis purpose, such as regression analysis, cluster analysis, and principal component analysis. Weight setting: For different types of environmental impact data (such as air quality, water quality, etc.), different weight values ​​are set according to their importance to the overall impact on the environment; Prediction model parameters: For models that use machine learning algorithms for prediction or classification, it is necessary to set model training parameters such as learning rate, number of iterations, and regularization coefficient; Data mining and in-depth analysis parameters: Feature selection method: Select an appropriate feature selection method (such as information gain-based, chi-square test-based, etc.) to select the most representative features from the original data set; Association rule mining threshold: Set the minimum support and confidence thresholds for the correlation between various indicators when mining association rules; Time series analysis parameters: For time series data, set parameters such as time window size and sliding step size to capture the time trend and periodicity of the data; Results evaluation and visualization parameters: Selection of evaluation indicators: Select appropriate evaluation indicators (such as accuracy, recall, F1 value) to quantitatively evaluate the analysis results. Selection of visualization method: According to the characteristics of the analysis results and user needs, select appropriate visualization methods (such as charts, heat maps, animations) to display the analysis results.

4. The environmental analysis and rating system based on environmental impact data according to claim 3 is characterized in that: The analysis and rating unit includes an environmental impact analysis unit, a rating model building unit and a rating result display unit: Among them, the key parameters of the environmental impact analysis unit include but are not limited to the following: Analytical methods: Statistical analysis: including descriptive statistics (such as mean, median, standard deviation, etc.), correlation analysis, regression analysis; Machine learning: Select applicable machine learning algorithms, such as decision trees, random forests, neural networks, etc., for pattern recognition and prediction; Other methods: Depending on the specific environment and data type, cluster analysis, principal component analysis (PCA), and factor analysis may also be included; Analysis parameters: Threshold setting: critical values ​​used to identify outliers or determine contamination levels; Time window: For time series data, determine the time range or window size for analysis; Variable selection: Selecting key variables or features for analysis; Data Source: Real-time data interface: ensure stable data connection with real-time monitoring sites or sensors; Historical database: provides interfaces for querying and calling historical data; Model training parameters (if using machine learning): Learning rate: used to optimize the learning speed of the algorithm; Iteration number: the number of rounds of model training; Loss function: A function that measures the difference between the model's predictions and the actual values.

5. The environmental analysis and rating system based on environmental impact data according to claim 4 is characterized in that: The key parameters of the rating model building unit include but are not limited to the following: Model Type: Linear model: such as weighted sum model, scoring is based on the weights of various factors; Nonlinear models: such as neural networks and support vector machines, used to process complex nonlinear relationships; Weight setting: Assign weights to various influencing factors based on the results of the environmental impact analysis; Weights can be static or dynamically adjusted based on specific conditions; Rating criteria: Define the criteria or thresholds for different rating levels; Rating criteria can be customized according to policy requirements, industry standards or user needs; Model Validation: Use the validation data set to validate the model to ensure its accuracy and reliability; Model validation can be performed using methods such as cross validation and leave-out validation.

6. The environmental analysis and rating system based on environmental impact data according to claim 5, characterized in that: The key parameters of the rating result display unit include but are not limited to the following: Display method: Graphical interface: including but not limited to bar charts, pie charts, heat maps, maps, etc., used to intuitively display rating results; Report generation: Generate detailed rating reports, including data tables, charts, and text analysis; Interactivity: Provide user interaction functions, including filter condition setting, data zooming, and chart switching; Allow users to customize display content and format; Export function: Support exporting rating results to Excel and PDF formats; Provide API interface to allow data exchange with other systems; Data update frequency: Set the update frequency of rating results, such as daily, weekly or monthly updates; The update frequency can be adjusted dynamically according to user needs or data changes.

7. The environmental analysis and rating system based on environmental impact data according to claim 6, characterized in that: The main operating parameters of the display output unit are related to how to display the rating results to the user in an intuitive and easy-to-understand manner. The following is a summary of the operating parameters of this module:

1. Graphical interface display: Chart type selection: According to the characteristics of the rating results, select appropriate chart types, such as bar charts, line charts, pie charts, heat maps, and maps, to show the status, trend, or distribution of different environmental indicators; Interactivity settings: Provide users with the functions of interacting with the graphical interface, such as zooming, dragging, filtering, and highlighting, so that users can have a deeper understanding of the data; Dynamic Updates: Set the data update frequency of the graphical interface to ensure that users can view the latest rating results in real time or regularly; Visual customization: Allow users to customize the style, color, font, etc. of the chart to meet the personalized needs of different users; 2. Report Generation: Report template selection: Provide a variety of report templates for users to choose from, including simple version, detailed version, and customized version to meet the needs of different users; Content customization: Allow users to customize the content of the report, including adding or deleting certain environmental indicators and adjusting the structure of the report; Data export format: Supports exporting reports into multiple formats, such as PDF, Word, Excel, etc., so that users can view and edit them on different devices or software; Report sharing: Provide report sharing function, allowing users to share reports with others through email and social media; 3. Other display methods: Dashboard display: Design a dashboard interface to centrally display the status and rating results of key environmental indicators, so that users can quickly understand the overall situation; Mobile application display: Develop mobile applications to display rating results on mobile devices, so that users can view them anytime and anywhere; Large screen display: Set up large-screen displays in public places such as conference rooms and offices to display rating results in real time, so that team members can pay attention to and discuss together; 4. General parameters: Interface language selection: Supports multiple language interfaces to facilitate users with different language backgrounds; Data Security: Ensure the security of rating result data during display, such as data encryption and access permission control; Performance optimization: Optimize the performance of the display module to ensure stable and smooth operation under large amounts of data or high concurrency.

8. The environmental analysis and rating system based on environmental impact data according to claim 7 is characterized in that: It also includes a user rights management function, which is used to control different users' access rights to the system and the scope of data access. The operating parameters of the user rights management function mainly involve how to control different users' access rights to system functions and data to ensure data security and stable operation of the system. The following are some key operating parameters: User type: defines the user type in the system, such as super administrator, general administrator, and general user. Each user type has different permission levels and accessible resource ranges; Role management: A role is a collection of permissions that is used to group a group of users and grant them the same permissions. Administrators can define and edit roles and assign specific permissions to each role; Permission settings: define permissions for various operations and data access, such as read, write, delete, and execute. Administrators can assign these permissions to each user or role to control their access to system functions and data; Permission Level: Defines the rank or priority of each permission type, usually related to the security level of the user or role, where a super administrator may have the highest level of all permissions, while a normal user may have limited data access permissions. Data object: specifies the data stored in a system or application, including files, folders, and database tables. Administrators can control user access rights to specific data objects. Authorization scope: specifies the scope of data or functions that a user or role is authorized to access, which can be the entire system or a specific data or function area; Login and authentication: Users need to log in to the system using a username and password or other authentication methods (such as fingerprint recognition, facial recognition); Permission auditing and logging: The system should record logs of all user access and operations so that administrators can track and audit user activities; Permission change notification: When a user's permissions change, the system should notify the relevant user or administrator; Permission inheritance: In some cases, a user's permissions may be inherited from the organization or group to which the user belongs.

9. The environmental analysis and rating system based on environmental impact data according to claim 8, characterized in that: It also includes data backup and recovery functions. The operating parameters of the data backup and recovery functions are key factors to ensure data security, recoverability, and continuous and stable operation of the system. The following are some key operating parameters: Data backup parameters Backup Type: Full backup: back up the entire data set; Incremental backup: backs up only the data that has changed since the last backup; Differential backup: backs up data that has changed since the last full backup; Backup frequency: Set up regular backup intervals, such as daily, weekly, and monthly; Backup speed: Measure the time required to back up a data set. In a cloud computing environment, take advantage of distributed computing to improve backup speed. Compression ratio: The compression ratio of backup data reduces the storage space occupied by compression algorithms; Data integrity check: Verify data integrity during the backup process to ensure the accuracy and consistency of backup data; Backup storage location: The physical location where the backup data is stored, such as local storage, cloud storage, or other remote storage facilities; Encryption settings: Use encryption algorithms to encrypt backup data to protect data confidentiality and security; Data recovery parameters Recovery Point Objective (RPO): After a disaster occurs, the disaster recovery system can restore data to the time point before the disaster. RPO measures the degree of data loss that the enterprise can tolerate after a disaster occurs. Recovery Time Objective (RTO): The time interval from the moment the system downtime causes business suspension to the time when the system is restored to support the operation of the business department and the business resumes operation. RTO measures the recovery time that the enterprise can tolerate after a disaster. Recovery speed: The length of time from the start of recovery operations until the system returns to normal service; Disaster recovery performance: The system's ability to recover from catastrophic failures, with distributed storage of backup data and redundant backup mechanisms to ensure disaster recovery performance; Failure recovery time: The time it takes for a system to recover to normal working state after a failure. Shorter failure recovery time can reduce business interruption time. Data Validation: Verify the integrity and accuracy of data during the recovery process to ensure that the recovered data is consistent with system requirements; Recovery test: Perform recovery tests regularly to verify the validity of backup data and the feasibility of the recovery process.