Environment emergency monitoring information management system

Abstract

The invention discloses an environment emergency monitoring information management system, which comprises a data acquisition module, a data processing module, a report generation module, a quick query module, an information release module and an artificial intelligence consultation module, acquires environment data in real time through a sensor and a manual input unit, and analyzes and processes the data by using a multi-thread and big data technology. The report generation module automatically generates various emergency reports, supports template management and user-defined editing, and ensures timeliness and accuracy of the reports. And the rapid query module is combined with artificial intelligence and a decision tree model to provide pollutant feature recognition and emergency response suggestions. The information issuing module ensures data security through encryption transmission, and can quickly issue a monitoring report and early warning information through an APP. The system is further provided with data backup and asynchronous loading functions, and data integrity and user operation experience are guaranteed. By integrating a plurality of advanced technologies, environment monitoring data can be accurately processed, the intelligence and response speed of emergency management are improved, and the environmental safety is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of environmental monitoring, and in particular to an environmental emergency monitoring information management system. Background Art

[0002] With the increasing severity of environmental problems and the frequent occurrence of sudden environmental incidents, timely and accurate acquisition of environmental monitoring data and emergency response have become increasingly important. Traditional environmental monitoring systems mostly rely on manual data collection and analysis, resulting in cumbersome information transmission and inefficient data processing and report generation. In modern environmental emergency management, the real-time and accuracy of data, as well as the ability to rapidly respond to sudden environmental incidents, are key to system design and implementation. Therefore, it is crucial to develop an efficient, intelligent, and automated environmental emergency monitoring information management system to address complex and ever-changing environmental pollution issues and improve the efficiency and effectiveness of emergency management.

[0003] Most existing environmental monitoring technologies rely on traditional data collection and processing methods, typically using sensors to collect environmental data in real time. However, these technologies still have many limitations in data analysis and processing. Traditional monitoring systems can typically only provide basic pollutant concentration data and are slow to respond to environmental changes. They are unable to quickly identify pollution sources, determine emergency levels, or propose effective response measures. Furthermore, the manual process of report generation and data querying is cumbersome, prone to human error, and lacks intelligent analysis and prediction capabilities. The data processing modules in traditional systems are generally unable to perform efficient large-scale data processing and in-depth analysis, resulting in a failure to maximize the value of monitoring data.

[0004] Furthermore, many existing environmental monitoring systems suffer from relatively weak security and data protection measures, making them particularly vulnerable to data leakage and tampering during information transmission. As environmental monitoring reports often involve sensitive data, ensuring the security of reports and data is crucial. In traditional systems, report generation and publication lack encryption technology protection, and data backup mechanisms are inadequate, potentially leading to the loss or improper access of monitoring data. Furthermore, existing environmental monitoring systems often neglect user experience. Traditional systems feature a single interface, cumbersome operational processes, and an inability to provide real-time feedback and interaction, placing a significant burden on users. Summary of the Invention

[0005] In order to overcome the shortcomings and deficiencies of the prior art, the present invention provides an environmental emergency monitoring information management system.

[0006] An environmental emergency monitoring information management system includes a data acquisition module, a data processing module, a report generation module, a quick query module, an information release module, and an artificial intelligence consulting module:

[0007] The data acquisition module is used to collect environmental monitoring data and information in real time, and its output end is connected to the input end of the data processing module;

[0008] The data processing module receives data from the data acquisition module, organizes and classifies the data, and transmits the processed data to the report generation module, the quick query module, and the artificial intelligence consulting module respectively;

[0009] The report generation module automatically generates various emergency monitoring reports, including preliminary investigation reports, incident bulletins, and special analysis reports, based on the monitoring data and on-site environmental information provided by the data processing module;

[0010] The quick query module combines artificial intelligence technology and big data analysis, using a preset classification model to analyze the data provided by the data processing module to identify pollutant characteristics, determine the emergency level of events, query the best response measures, and query the expert database. The classification model adopts a decision tree model with a decision tree depth of 5 and a minimum sample split number of 10.

[0011] The information publishing module receives the monitoring report and warning information generated by the report generation module and publishes them directly through the APP;

[0012] The artificial intelligence consulting module receives data provided by the data processing module, uses the natural language processing model to analyze problems in the monitoring work, and provides constructive suggestions for the difficulties encountered in emergency monitoring work; the natural language processing model adopts the BERT model with 12 hidden layers and 12 attention heads.

[0013] Furthermore, the data acquisition module includes a sensor acquisition unit and a manual input unit:

[0014] The sensor acquisition unit is set at multiple monitoring points to automatically collect various data in the environment, including pollutant concentrations, meteorological parameters, and hydrological parameters. The sensor uses a high-precision electrochemical sensor, and the detection accuracy of common pollutants reaches the ppb level.

[0015] The manual entry unit is used by monitoring personnel to manually enter data that cannot be collected by sensors, descriptions of special on-site conditions, and preliminary investigation information; the manual entry unit is provided with a format verification function to perform real-time verification on the format of the entered data to ensure that the data format meets system requirements.

[0016] Furthermore, the data processing module adopts multi-threaded processing technology, specifically including:

[0017] Use the Microsoft.Office.Interop.Excel dynamic link library to perform multi-threaded operations on Excel files; the number of multi-threads is set to 4, and the thread priorities are divided into three levels: high, medium, and low;

[0018] Ability to read and write Excel cell data simultaneously in the background, and process multiple worksheets in parallel; when processing worksheets, the processing time interval between each worksheet does not exceed 50 milliseconds;

[0019] Operate Excel internal objects and insert or delete pictures in Excel; the position of picture insertion is automatically determined based on the data processing results, and the size of the inserted picture is 800 pixels × 600 pixels.

[0020] Furthermore, the report generation module also includes a template management submodule:

[0021] The template management submodule stores a variety of emergency monitoring report templates, including report templates for different types of sudden environmental events; the number of templates is no less than 10;

[0022] It can automatically match and call the corresponding report template according to the event type and monitoring data; the matching algorithm adopts a keyword-based similarity matching algorithm, and the keyword matching threshold is set to 0.8;

[0023] Supports customized editing of report templates, and monitoring personnel can modify the template content and format according to actual needs; when the edited template is saved, version management is automatically performed to record the template modification history.

[0024] Furthermore, the pollutant feature recognition function in the quick query module adopts a convolutional neural network model:

[0025] The convolutional neural network model consists of 3 convolutional layers, 2 pooling layers, and 1 fully connected layer. The convolution kernel sizes of the convolutional layers are 3×3, 5×5, and 3×3, respectively, and the stride length is 1.

[0026] The pooling layer uses maximum pooling, the pooling kernel size is 2×2, and the stride is 2;

[0027] The number of neurons in the fully connected layer is 128; this model is used to extract and identify features of pollutant data to determine the types and characteristics of pollutants.

[0028] Furthermore, the information publishing module is provided with an encrypted transmission submodule:

[0029] The encrypted transmission submodule adopts AES encryption algorithm, and the encryption key length is 256 bits;

[0030] The monitoring report and warning information are encrypted and then transmitted through the APP; at the receiving end of the APP, they are decrypted using the corresponding decryption key to ensure the security of information transmission.

[0031] Furthermore, the artificial intelligence consulting module also combines a reinforcement learning model:

[0032] The reinforcement learning model uses a deep Q network, an experience replay buffer size of 10,000, and a discount factor of 0.99;

[0033] By interacting with environmental emergency monitoring scenarios, we continuously learn and optimize strategies to provide monitoring personnel with more accurate and effective advice; after each monitoring task is completed, we update and train the model based on the actual results.

[0034] Furthermore, the system also includes a data backup module:

[0035] The data backup module uses a combination of off-site backup and local backup; the off-site backup server is set up in an area 500 kilometers away from the local server;

[0036] A full data backup is automatically performed every 24 hours, and an incremental data backup is performed every hour. The backup data is stored in the tape library and cloud storage. The tape library stores the backup data for the preset period of 30 days, and the cloud storage stores the backup data for the preset period of 1 year.

[0037] Furthermore, the system adopts a multi-window interactive design, including the original record interactive window, the report generation interactive window, the information quick query window and the information release window:

[0038] The original record interaction form is used to display and manage the original data collected by the data acquisition module. It is connected to the data acquisition module in real time. The data display interface of the original record interaction form adopts a table format, with 10 data records displayed per row.

[0039] The report generation interactive window is connected to the report generation module and displays the report generation process and results. The report generation interactive window provides a report preview function, and the font size of the preview page is 12 points.

[0040] The information quick query form interacts with the quick query module to perform various query functions; the information quick query form is equipped with a search box, and the default prompt message of the search box is "Enter query keywords";

[0041] The information release form is connected to the information release module and is used to release monitoring reports and early warning information; the information release form is equipped with a release record viewing function, which is used to view 100 release records in a preset time period.

[0042] Furthermore, the system uses asynchronous data loading technology, including:

[0043] Using asynchronous programming technology, the data is loaded into the computer memory in batches; the amount of data loaded in each batch is 1000 records;

[0044] During the data loading process, the loading progress bar is displayed and the progress bar is updated twice per second;

[0045] Improve the responsiveness of the system user interface and prevent UI thread blocking through asynchronous data loading.

[0046] Beneficial effects:

[0047] This invention proposes an environmental emergency monitoring information management system that integrates multiple modules to achieve efficient and intelligent environmental monitoring and emergency management. First, the system collects environmental monitoring data in real time and utilizes a data processing module to organize and analyze the data. Using a quick query module, the system leverages artificial intelligence and big data technologies to rapidly identify pollutant characteristics, determine the emergency event level, and provide optimal response measures, providing precise support for decision-making. The report generation module automatically generates various emergency reports, reducing manual operations and improving work efficiency. The system also incorporates multiple data processing and transmission security measures to ensure the accuracy and security of monitoring data and reports. Using convolutional neural networks and the BERT natural language processing model, the system can deeply analyze pollutant characteristics and provide constructive recommendations, enhancing the intelligence of emergency response. Furthermore, an encrypted transmission submodule ensures data security during transmission. Regarding data backup, the system utilizes a combination of remote and local backup to ensure data integrity and recoverability. Furthermore, the system optimizes the user experience through asynchronous data loading and multi-window interactive design, improving system response speed and interaction efficiency. Overall, the system not only improves the accuracy and response speed of environmental monitoring, but also greatly enhances the intelligence level of emergency management through artificial intelligence and data analysis technologies, and enhances emergency handling capabilities and information security. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a system module diagram of the present invention;

[0049] Figure 2 The present invention is a flowchart of the system implementation steps. DETAILED DESCRIPTION

[0050] It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of this application can be combined with each other. The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0051] like Figure 1 As shown, an environmental emergency monitoring information management system includes: a data acquisition module, a data processing module, a report generation module, a quick query module, an information release module and an artificial intelligence consulting module:

[0052] The data acquisition module is used to collect environmental monitoring data and information in real time, and its output end is connected to the input end of the data processing module;

[0053] Specifically, the data acquisition module is the foundation of the environmental emergency monitoring information management system. Its main task is to collect environmental monitoring data and information in real time and accurately. In practical applications, it covers a variety of sensor types and data collection methods. For environmental monitoring data, it collects key indicators such as the concentration of pollutants in the atmosphere (such as PM2.5, PM10, sulfur dioxide, nitrogen oxides, etc.), the pH value of water bodies, dissolved oxygen, chemical oxygen demand, etc. At the same time, it also collects meteorological information such as temperature, humidity, wind speed, and wind direction, as these meteorological conditions have a significant impact on the diffusion and distribution of pollutants.

[0054] In terms of technical parameters, the sensors used are highly precise and stable. For example, atmospheric pollutant sensors can achieve accuracy down to the microgram or even nanogram level, enabling them to sensitively detect subtle changes in environmental pollutants. The frequency of data collection can be adjusted based on actual needs. In emergency monitoring scenarios, the frequency may be increased, perhaps to every minute or even less frequently, to ensure timely monitoring of dynamic environmental changes.

[0055] The significance of this module lies in providing original, reliable data support for the entire system. Subsequent data processing, report generation, and decision-making all rely on this accurate data. It is widely used in various environmental emergencies, such as chemical plant leaks and air pollution caused by fires. It can quickly obtain on-site environmental data, providing first-hand information for emergency response.

[0056] The data processing module receives data from the data acquisition module, organizes and classifies the data, and transmits the processed data to the report generation module, the quick query module, and the artificial intelligence consulting module respectively;

[0057] Specifically, the data processing module plays a key role in connecting the upper and lower levels of the system. It receives large amounts of raw data from the data acquisition module and organizes and categorizes it. During data organization, the collected raw data is cleaned to remove noise and errors, such as outliers caused by sensor failures or communication interference. Furthermore, the data is standardized, unifying the format and units for subsequent analysis and processing.

[0058] In terms of classification and processing, data is categorized based on type and source, such as air monitoring data, water quality monitoring data, and meteorological data. Specific algorithms and models are then applied to further analyze and mine these different types of data. For example, for atmospheric pollutant data, concentration trends and spatial distribution characteristics can be analyzed.

[0059] From a technical perspective, the data processing module's processing power and efficiency are crucial. It needs to be able to process large amounts of data in a short period of time, for example, thousands or even tens of thousands of data records per hour. Furthermore, data processing accuracy must be high, with minimal error rates.

[0060] The significance of this module is to transform raw, disorganized data into valuable information, providing a high-quality data foundation for subsequent report generation, query analysis, etc. In the application scenario, when an environmental emergency occurs, it can quickly process large amounts of monitoring data and provide timely and accurate information support for emergency decision-making.

[0061] The report generation module automatically generates various emergency monitoring reports, including preliminary investigation reports, incident bulletins, and special analysis reports, based on the monitoring data and on-site environmental information provided by the data processing module;

[0062] Specifically, the report generation module automatically generates various emergency monitoring reports based on the monitoring data and on-site environmental information provided by the data processing module. These reports include preliminary investigation reports, incident reports, special analysis reports, etc. Different types of reports have different focuses and uses.

[0063] Preliminary investigation reports are typically generated shortly after an emergency occurs. They primarily provide basic information about the incident and preliminary results from on-site monitoring, enabling emergency response personnel to quickly understand the situation. Incident bulletins provide real-time updates on the latest developments and monitoring data, allowing relevant personnel to stay informed. Special analysis reports provide in-depth analysis of the incident, such as the source of pollutants, their diffusion pathways, and their impacts on the environment and human health, providing a scientific basis for developing long-term response measures.

[0064] In terms of technical parameters, the report generation module offers a high degree of automation and customization. It can quickly generate reports with standardized formats and accurate content based on pre-set templates and rules. It also supports user customization of report templates based on actual needs, such as adjusting the report format and content structure.

[0065] The module's significance lies in transforming complex monitoring data into intuitive and easy-to-understand reports, providing strong support for emergency decision-making. It plays a vital role in both the initial rapid response phase of an emergency and the subsequent in-depth investigation and assessment phases.

[0066] The quick query module combines artificial intelligence technology and big data analysis, using a preset classification model to analyze the data provided by the data processing module to identify pollutant characteristics, determine the emergency level of events, query the best response measures, and query the expert database. The classification model adopts a decision tree model with a decision tree depth of 5 and a minimum sample split number of 10.

[0067] Specifically, the Quick Query Module combines artificial intelligence technology with big data analysis, utilizing a pre-set decision tree classification model to analyze data provided by the Data Processing Module, enabling multiple query functions. Regarding pollutant feature identification, the decision tree model classifies and identifies pollutants based on various indicators, such as concentration, chemical properties, and source, accurately determining their type and characteristics.

[0068] When determining the emergency response level of an incident, the model comprehensively considers factors such as the pollutant's hazard level, scope of impact, and duration to determine the incident's emergency response level (general, major, serious, or extremely serious), providing clear grading standards for emergency response. The optimal response measure query function selects the most appropriate solution from a pre-set response measure library based on the incident type and emergency level, providing guidance to emergency response personnel. The expert database query function allows users to query expert information in related fields, ensuring timely access to professional advice and support when encountering complex issues.

[0069] The depth of the decision tree model is 5, and the minimum number of sample splits is 10. These parameters are set to improve the generalization ability and processing efficiency of the model while ensuring the accuracy of the model.

[0070] The module is designed to improve the efficiency of emergency monitoring and the scientific nature of decision-making. In practical scenarios, when emergency response personnel need to quickly understand the situation and determine response strategies, the module can provide timely and accurate information support.

[0071] The information publishing module receives the monitoring report and warning information generated by the report generation module and publishes them directly through the APP;

[0072] Specifically, the information release module receives monitoring reports and warning information generated by the report generation module and quickly publishes them directly through the app. In practical applications, it can promptly convey important environmental monitoring information to relevant personnel, including emergency commanders, monitoring personnel, and the public.

[0073] Emergency commanders can access the latest incident developments and monitoring reports in real time through the app, enabling them to make timely decisions and direct emergency response operations. Monitoring personnel can use the app to understand the environmental conditions in their respective areas and adjust monitoring plans and frequencies. For the public, the information released by the app allows them to stay informed about the safety of their surroundings and take appropriate protective measures.

[0074] In terms of technical parameters, the information release module is highly timely and stable. It can push information to a large number of users in a short period of time, ensuring timely delivery of information. Furthermore, to ensure the security and reliability of information, technical means such as encrypted transmission and identity verification are employed.

[0075] The module aims to enhance the flow and sharing of information, improve the coordination of emergency response, and enhance public participation. In practical scenarios, when an environmental emergency occurs, information can be quickly transmitted to all parties, reducing the risks associated with information asymmetry.

[0076] The artificial intelligence consulting module receives data provided by the data processing module, uses the natural language processing model to analyze problems in the monitoring work, and provides constructive suggestions for the difficulties encountered in emergency monitoring work; the natural language processing model adopts the BERT model with 12 hidden layers and 12 attention heads.

[0077] Specifically, the AI ​​consulting module receives data from the data processing module and uses the BERT natural language processing model to analyze monitoring issues and provide constructive suggestions for addressing difficulties encountered in emergency monitoring. The BERT model has powerful language understanding and semantic analysis capabilities, enabling it to accurately understand and analyze natural language questions entered by users.

[0078] During emergency monitoring, monitors may encounter a variety of complex issues, such as determining monitoring locations, selecting appropriate monitoring methods, and assessing the hazards of pollutants. The AI ​​consulting module can answer these questions and provide recommendations based on historical data and relevant knowledge. For example, when a monitor asks how to determine monitoring locations in a specific area, the module can provide reasonable recommendations based on factors such as the area's topography, meteorological conditions, and the distribution of pollution sources.

[0079] The BERT model has 12 hidden layers and 12 attention heads. These parameter settings enable the model to better capture the semantic information and contextual relationships in the language, improving the accuracy of question analysis and suggestion provision.

[0080] The module's purpose is to provide intelligent support for emergency monitoring, helping monitoring personnel solve problems and improving the quality and efficiency of monitoring work. It plays a vital role in both daily monitoring and emergency response.

[0081] Preferably, the data acquisition module includes a sensor acquisition unit and a manual input unit:

[0082] The sensor acquisition unit is set at multiple monitoring points to automatically collect various data in the environment, such as pollutant concentrations, meteorological parameters, hydrological parameters, etc. The sensor uses a high-precision electrochemical sensor, and the detection accuracy of common pollutants reaches ppb level;

[0083] The manual entry unit is used by monitoring personnel to manually enter data that cannot be collected by sensors, such as descriptions of special on-site conditions, preliminary investigation information, etc.; the manual entry unit is provided with a format verification function to perform real-time verification on the format of the entered data to ensure that the data format meets the system requirements.

[0084] Specifically, the data acquisition module is the foundational component of the environmental emergency monitoring information management system's data acquisition. It consists of a sensor acquisition unit and a manual entry unit. These sensor acquisition units, distributed across multiple monitoring locations, automatically collect various environmental data. High-precision electrochemical sensors play a key role, detecting common pollutants with accuracy reaching the part-per-billion level. This enables the system to discern even the most subtle changes in environmental pollutants. For example, they can accurately detect trace leaks of hazardous gases around chemical parks. The manual entry unit complements sensor acquisition, allowing monitoring personnel to manually enter data such as descriptions of special on-site circumstances and preliminary investigation information that cannot be captured by sensors. Furthermore, this unit's format verification ensures that the entered data is standardized and complies with system requirements. For example, when entering the time of an event, the system verifies in real time that the format is "YYYY-MM-DDHH:MM:SS." This module provides a comprehensive and accurate data foundation for subsequent data processing and analysis, making it indispensable in all environmental emergency monitoring scenarios, from sudden pollution incidents to routine environmental quality monitoring.

[0085] Preferably, the data processing module adopts multi-threaded processing technology, specifically including:

[0086] Use the Microsoft.Office.Interop.Excel dynamic link library to perform multi-threaded operations on Excel files; the number of multi-threads is set to 4, and the thread priorities are divided into three levels: high, medium, and low;

[0087] Ability to read and write Excel cell data simultaneously in the background, and process multiple worksheets in parallel; when processing worksheets, the processing time interval between each worksheet does not exceed 50 milliseconds;

[0088] You can operate Excel internal objects, such as inserting or deleting pictures in Excel; the position of the picture insertion is automatically determined according to the data processing results, and the size of the inserted picture is 800 pixels × 600 pixels.

[0089] Specifically, the data processing module utilizes multi-threaded processing technology, leveraging the Microsoft.Office.Interop.Excel dynamic link library to operate Excel files. Four threads are configured, with thread priorities divided into high, medium, and low levels, effectively improving processing efficiency. The module can simultaneously process multiple worksheets in parallel in the background, with each worksheet processed within 50 milliseconds, significantly reducing data processing time. For example, when faced with large amounts of monitoring data, it can quickly complete data reading and writing operations. Furthermore, it can operate on internal Excel objects, such as automatically determining the image insertion position based on data processing results and inserting an 800 x 600 pixel image. This feature makes data processing results more intuitive and easier to analyze. The module's significance lies in efficiently processing and integrating large amounts of collected data, providing accurate and organized data support for subsequent report generation and query analysis. This significantly improves system performance and responsiveness when processing large-scale environmental monitoring data.

[0090] Preferably, the report generation module further includes a template management submodule:

[0091] The template management submodule stores a variety of emergency monitoring report templates, including report templates for different types of sudden environmental events; the number of templates is no less than 10;

[0092] It can automatically match and call the corresponding report template according to the event type and monitoring data; the matching algorithm adopts a keyword-based similarity matching algorithm, and the keyword matching threshold is set to 0.8;

[0093] Supports customized editing of report templates. Monitoring personnel can modify the template content and format according to actual needs. When the edited template is saved, version management is automatically performed to record the template modification history.

[0094] Specifically, the template management submodule in the report generation module is the core of the system's generation of emergency monitoring reports. It stores report templates for no less than 10 different types of sudden environmental events, covering a variety of scenarios such as fires and leaks. Through a keyword-based similarity matching algorithm, it can automatically match and call the corresponding report template according to the event type and monitoring data. The keyword matching threshold is set to 0.8 to ensure the accuracy of template matching. At the same time, this submodule supports monitoring personnel to customize the report template to meet different actual needs. When the edited template is saved, version management will be automatically performed to record the template's modification history. This function facilitates the maintenance and traceability of report templates. For example, when dealing with pollution incidents in different industries, the format and content of the report can be adjusted according to the specific circumstances. The significance of this module is to quickly and accurately generate standardized emergency monitoring reports, providing strong support for emergency decision-making.

[0095] Preferably, the pollutant feature recognition function in the quick query module adopts a convolutional neural network model:

[0096] The convolutional neural network model consists of 3 convolutional layers, 2 pooling layers, and 1 fully connected layer. The convolution kernel sizes of the convolutional layers are 3×3, 5×5, and 3×3, respectively, and the stride length is 1.

[0097] The pooling layer uses maximum pooling, the pooling kernel size is 2×2, and the stride is 2;

[0098] The number of neurons in the fully connected layer is 128; this model is used to extract and identify features of pollutant data to determine the types and characteristics of pollutants.

[0099] Specifically, the pollutant feature identification function in the quick query module uses a convolutional neural network model. This model consists of three convolutional layers, two pooling layers, and one fully connected layer. The convolution kernel sizes of the convolution layers are 3×3, 5×5, and 3×3, respectively, with a step size of 1, which effectively extracts features from pollutant data. The pooling layer uses max pooling with a pooling kernel size of 2×2 and a step size of 2, which reduces data dimensionality and computational complexity. The fully connected layer has 128 neurons, which are used to classify and identify extracted features. Using this model, the system can accurately determine the type and characteristics of pollutants. For example, when analyzing complex mixed pollutants, it can quickly identify the various pollutant components contained within. The significance of this module is to provide emergency monitoring personnel with rapid and accurate pollutant information so that effective response measures can be taken in a timely manner. In the event of an environmental emergency, it can help monitoring personnel quickly understand the pollutant situation and make correct decisions.

[0100] Preferably, the information publishing module is provided with an encrypted transmission submodule:

[0101] The encrypted transmission submodule adopts AES encryption algorithm, and the encryption key length is 256 bits;

[0102] The monitoring report and warning information are encrypted and then transmitted through the APP; at the receiving end of the APP, they are decrypted using the corresponding decryption key to ensure the security of information transmission.

[0103] Specifically, the encrypted transmission submodule of the information release module adopts the AES encryption algorithm, and the encryption key length is 256 bits. When transmitting monitoring reports and early warning information, they are first encrypted and then transmitted through the APP. At the receiving end of the APP, decryption is performed using the corresponding decryption key to ensure the security of information transmission. In today's environment where information security is of paramount importance, this function can effectively prevent monitoring information from being stolen or tampered with. For example, when it comes to sensitive environmental monitoring data, encrypted transmission can protect the privacy and integrity of the data. The significance of this module is to convey accurate and secure monitoring information to relevant personnel in a timely manner, including emergency commanders, the public, etc. In environmental emergencies, it can enable all parties to understand the situation of the incident in a timely manner and take appropriate measures.

[0104] Preferably, the artificial intelligence consulting module is further combined with a reinforcement learning model:

[0105] The reinforcement learning model uses a deep Q network (DQN), with an experience replay buffer size of 10,000 and a discount factor of 0.99;

[0106] By interacting with environmental emergency monitoring scenarios, we continuously learn and optimize strategies to provide monitoring personnel with more accurate and effective advice; after each monitoring task is completed, we update and train the model based on the actual results.

[0107] Specifically, the artificial intelligence consulting module combines a reinforcement learning model and adopts a deep Q network (DQN). The experience replay buffer size is 10,000, and the discount factor is 0.99. By interacting with environmental emergency monitoring scenarios, the model continuously learns and optimizes strategies. After each monitoring task is completed, the model will be updated and trained based on the actual results. For example, when a new environmental problem is detected, the model will adjust its strategy based on the actual treatment effect. This enables the module to provide more accurate and effective advice to monitoring personnel. In complex environmental emergency monitoring scenarios, monitoring personnel may encounter various difficulties. The module can use the knowledge and experience it has learned to provide monitoring personnel with a scientific basis for decision-making. Its significance lies in improving the intelligence level of emergency monitoring work, improving monitoring efficiency and the scientific nature of decision-making.

[0108] Preferably, the system further includes a data backup module:

[0109] The data backup module uses a combination of off-site backup and local backup; the off-site backup server is set up in an area 500 kilometers away from the local server;

[0110] A full data backup is automatically performed every 24 hours, and an incremental data backup is performed every hour. The backup data is stored in the tape library and cloud storage. The tape library stores the backup data for the preset period of 30 days, and the cloud storage stores the backup data for the preset period of 1 year.

[0111] Specifically, the data backup module uses a combination of offsite and local backup to ensure data security and reliability. The offsite backup server is located 500 kilometers away from the local server to prevent simultaneous loss of local and offsite data due to natural disasters, human damage, and other factors. A full data backup is automatically performed every 24 hours, and an incremental data backup is performed every hour, ensuring data timeliness and integrity. Backup data is stored in a tape library and cloud storage, with the tape library retaining 30 days of backup data and the cloud storage retaining one year of backup data. This allows data to be restored and queried at different timescales. For example, recent monitoring data can be retrieved from the tape library; historical data can be retrieved from cloud storage. The significance of this module is to ensure the security and recoverability of system data, enabling rapid recovery in the event of data loss or corruption, ensuring the normal operation of the system.

[0112] Preferably, the system adopts a multi-window interactive design, including an original record interactive window, a report generation interactive window, an information quick query window, and an information release window:

[0113] The original record interaction form is used to display and manage the original data collected by the data acquisition module. It is connected to the data acquisition module in real time. The data display interface of the original record interaction form adopts a table format, with 10 data records displayed per row.

[0114] The report generation interactive window is connected to the report generation module and displays the report generation process and results. The report generation interactive window provides a report preview function, and the font size of the preview page is 12 points.

[0115] The information quick query form interacts with the quick query module to perform various query functions; the information quick query form is equipped with a search box, and the default prompt message of the search box is "Enter query keywords";

[0116] The information release form is connected to the information release module and is used to release monitoring reports and early warning information; the information release form is equipped with a release record viewing function, which can view 100 release records in a preset time period.

[0117] Specifically, the system's multi-window interactive design includes a raw record interactive window, a report generation interactive window, an information quick query window, and an information release window. The raw record interactive window is connected to the data collection module in real time, displaying and managing raw data in a tabular format with 10 data records per row, making it easy for monitoring personnel to review and manage collected data. The report generation interactive window is connected to the report generation module, displaying the report generation process and results, and providing a report preview function with a 12-point font size, allowing monitoring personnel to preview the report's style and content. The information quick query window interacts with the quick query module and features a search box with a default prompt of "Enter search keywords," allowing users to quickly locate the information they need. The information release window is connected to the information release module and is used to publish monitoring reports and warning information. It also features a function for viewing published records, with a maximum of 100 published records. The significance of this multi-window interactive design lies in providing an intuitive and convenient user interface, improving user efficiency and experience. In actual use, monitoring personnel can quickly switch between different windows to complete corresponding operations according to their needs.

[0118] Preferably, the system adopts asynchronous data loading technology, specifically including:

[0119] Using asynchronous programming technology, the data is loaded into the computer memory in batches; the amount of data loaded in each batch is 1000 records;

[0120] During the data loading process, the loading progress bar is displayed and the progress bar is updated twice per second;

[0121] Improve the responsiveness of the system user interface and prevent UI thread blocking through asynchronous data loading.

[0122] Specifically, the asynchronous data loading technology adopted by the system uses asynchronous programming technology to load data into computer memory in batches, with each batch loading 1,000 records. During the data loading process, a loading progress bar is displayed with an update frequency of 2 times per second, allowing users to understand the loading progress in real time. Through asynchronous data loading, the blocking of the UI thread due to large amounts of data loading is avoided, and the response speed of the system user interface is improved. For example, when the user needs to view a large amount of monitoring data, there will be no interface freezes, and the operation can be carried out smoothly. The significance of this technology is to improve the performance of the system and the user experience, especially when processing large-scale data, allowing users to use the system more efficiently. In environmental emergency monitoring scenarios, fast data loading and response speed are crucial for timely acquisition and processing of information.

[0123] like Figure 2 As shown, the operating steps of an environmental emergency monitoring information management system include:

[0124] S1. Data Collection and Input: The system first acquires real-time environmental monitoring data through the data acquisition module. This module consists of a sensor acquisition unit and a manual input unit. The sensor acquisition unit automatically collects data such as pollutant concentrations and meteorological parameters in the environment, while the manual input unit allows monitoring personnel to manually input data that cannot be collected by sensors, such as descriptions of special site conditions and preliminary investigation information.

[0125] S2. Data Processing and Classification: Collected data is organized and classified by the Data Processing Module. This module performs multi-threaded data processing, utilizing the Microsoft.Office.Interop.Excel dynamic link library to operate Excel files, including parallel processing of multiple worksheets. The processed data is then transmitted to the Report Generation Module, the Quick Query Module, and the Artificial Intelligence Consulting Module.

[0126] S3. Report Generation and Template Management: The report generation module automatically generates various emergency monitoring reports, such as preliminary investigation reports and incident reports, based on monitoring data and on-site environmental information provided by the data processing module. Furthermore, the template management submodule stores multiple emergency report templates and automatically matches the corresponding template based on the incident type, supporting custom editing and version management of report templates.

[0127] S4. Rapid Query and Intelligent Analysis: Through the Rapid Query module, the system uses pre-defined classification models (such as decision trees) and convolutional neural network models to identify pollutant characteristics, determine event emergency levels, and query expert databases. This module, combined with artificial intelligence technology, supports in-depth analysis of pollutant data and emergency response recommendations.

[0128] S5. Information Release and Encrypted Transmission: The information release module is responsible for encrypting and transmitting monitoring reports and warning information through the app. The AES encryption algorithm is used to protect data security during transmission, ensuring the integrity and confidentiality of information transmission.

[0129] S6. AI Consulting and Reinforcement Learning: The AI ​​Consulting module uses natural language processing models (such as BERT) to analyze monitoring issues and, combined with a deep Q-network reinforcement learning model, provides constructive advice to monitors, improving the efficiency and accuracy of emergency monitoring. By continuously interacting with environmental emergency monitoring scenarios, the system continuously optimizes strategies and provides precise recommendations.

[0130] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and variations may be made to these 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 emergency monitoring information management system, characterized in that: It includes data collection module, data processing module, report generation module, quick query module, information release module and artificial intelligence consulting module: The data acquisition module is used to collect environmental monitoring data and information in real time, and its output end is connected to the input end of the data processing module; The data processing module receives data from the data acquisition module, organizes and classifies the data, and transmits the processed data to the report generation module, the quick query module, and the artificial intelligence consulting module respectively; The report generation module automatically generates various emergency monitoring reports, including preliminary investigation reports, incident bulletins, and special analysis reports, based on the monitoring data and on-site environmental information provided by the data processing module; The quick query module combines artificial intelligence technology and big data analysis, using a preset classification model to analyze the data provided by the data processing module to identify pollutant characteristics, determine the emergency level of events, query the best response measures, and query the expert database. The classification model adopts a decision tree model with a decision tree depth of 5 and a minimum sample split number of 10. The information publishing module receives the monitoring report and warning information generated by the report generation module and publishes them directly through the APP; The artificial intelligence consulting module receives data provided by the data processing module, uses the natural language processing model to analyze problems in the monitoring work, and provides constructive suggestions for the difficulties encountered in emergency monitoring work; the natural language processing model adopts the BERT model with 12 hidden layers and 12 attention heads.

2. An environmental emergency monitoring information management system according to claim 1, characterized in that: The data acquisition module includes a sensor acquisition unit and a manual input unit: The sensor acquisition unit is set at multiple monitoring points to automatically collect various data in the environment, including pollutant concentrations, meteorological parameters, and hydrological parameters. The sensor uses a high-precision electrochemical sensor, and the detection accuracy of common pollutants reaches the ppb level. The manual input unit is used for monitoring personnel to manually input data that cannot be collected by sensors, descriptions of special on-site conditions, and preliminary investigation information; The manual input unit is equipped with a format verification function to perform real-time verification on the format of the input data to ensure that the data format meets the system requirements.

3. An environmental emergency monitoring information management system according to claim 1, characterized in that: The data processing module adopts multi-thread processing technology, specifically including: Use the Microsoft.Office.Interop.Excel dynamic link library to perform multi-threaded operations on Excel files; the number of multi-threads is set to 4, and the thread priorities are divided into three levels: high, medium, and low; Ability to read and write Excel cell data simultaneously in the background, and process multiple worksheets in parallel; when processing worksheets, the processing time interval between each worksheet does not exceed 50 milliseconds; Operate Excel internal objects and insert or delete pictures in Excel; the position of picture insertion is automatically determined based on the data processing results, and the size of the inserted picture is 800 pixels × 600 pixels.

4. An environmental emergency monitoring information management system according to claim 1, characterized in that: The report generation module also includes a template management submodule: The template management submodule stores a variety of emergency monitoring report templates, including report templates for different types of sudden environmental events; The number of templates is no less than 10; Ability to automatically match and call corresponding report templates based on event type and monitoring data; The matching algorithm uses a keyword-based similarity matching algorithm, and the keyword matching threshold is set to 0.8; Supports customized editing of report templates, and monitoring personnel can modify the template content and format according to actual needs; when the edited template is saved, version management is automatically performed to record the template modification history.

5. An environmental emergency monitoring information management system according to claim 1, characterized in that: The pollutant feature recognition function in the quick query module adopts a convolutional neural network model: The convolutional neural network model consists of 3 convolutional layers, 2 pooling layers, and 1 fully connected layer. The convolution kernel sizes of the convolutional layers are 3×3, 5×5, and 3×3, respectively, and the stride length is 1. The pooling layer uses maximum pooling, the pooling kernel size is 2×2, and the stride is 2; The number of neurons in the fully connected layer is 128; this model is used to extract and identify features of pollutant data to determine the types and characteristics of pollutants.

6. An environmental emergency monitoring information management system according to claim 1, characterized in that: The information publishing module is provided with an encryption transmission submodule: The encrypted transmission submodule adopts AES encryption algorithm, and the encryption key length is 256 bits; The monitoring report and warning information are encrypted and then transmitted through the APP; at the receiving end of the APP, they are decrypted using the corresponding decryption key to ensure the security of information transmission.

7. An environmental emergency monitoring information management system according to claim 1, characterized in that: The AI ​​consulting module also incorporates a reinforcement learning model: The reinforcement learning model uses a deep Q network, an experience replay buffer size of 10,000, and a discount factor of 0.99; By interacting with environmental emergency monitoring scenarios, we continuously learn and optimize strategies to provide monitoring personnel with more accurate and effective advice; after each monitoring task is completed, we update and train the model based on the actual results.

8. An environmental emergency monitoring information management system according to claim 1, characterized in that: The system also includes a data backup module: The data backup module uses a combination of off-site backup and local backup; the off-site backup server is set up in an area 500 kilometers away from the local server; Automatically perform a full data backup every 24 hours and an incremental data backup every hour; The backup data is stored in the tape library and cloud storage. The tape library stores the backup data for a preset period of 30 days, and the cloud storage stores the backup data for a preset period of 1 year.

9. An environmental emergency monitoring information management system according to claim 1, characterized in that: The system adopts a multi-window interactive design, including the original record interactive window, report generation interactive window, information quick query window and information release window: The original record interaction form is used to display and manage the original data collected by the data acquisition module. It is connected to the data acquisition module in real time. The data display interface of the original record interaction form adopts a table format, with 10 data records displayed per row. The report generation interactive window is connected to the report generation module and displays the report generation process and results. The report generation interactive window provides a report preview function, and the font size of the preview page is 12 points. The information quick query form interacts with the quick query module to perform various query functions; the information quick query form is equipped with a search box, and the default prompt message of the search box is "Enter query keywords"; The information release form is connected to the information release module and is used to release monitoring reports and early warning information; the information release form is equipped with a release record viewing function, which is used to view 100 release records in a preset time period.

10. An environmental emergency monitoring information management system according to claim 1, characterized in that: The system uses asynchronous data loading technology, including: Using asynchronous programming technology, the data is loaded into the computer memory in batches; the amount of data loaded in each batch is 1000 records; During the data loading process, the loading progress bar is displayed and the progress bar is updated twice per second; Improve the responsiveness of the system user interface and prevent UI thread blocking through asynchronous data loading.

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