Distributed commanding and dispatching device based on environment monitoring platform

Abstract

The invention relates to the technical field of environment monitoring, in particular to a distributed commanding and dispatching device based on an environment monitoring platform, which comprises a pollution source information acquisition module, a pollution judgment module, an alarm sending module, a processing mode matching module and a personnel distribution module, the monitoring information acquisition module is used for acquiring monitoring information of a plurality of regions in the electric power pipe gallery in real time; the pollution judgment module is used for judging a pollution source based on the monitoring information and extracting pollution source information; the alarm sending module is used for sending an alarm to personnel in a corresponding region based on the pollution source information; the processing mode matching module is used for matching a corresponding pollution processing mode according to the pollution source information; and the personnel distribution module is used for distributing corresponding professionals for treatment according to the requirements of the pollution treatment modes. Therefore, the environmental pollution events can be quickly responded and effectively managed, and the safety and the efficiency of electric power pipe gallery operating personnel are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a distributed command and dispatch device based on an environmental monitoring platform. Background Art

[0002] An environmental monitoring platform is a system that integrates data collection, analysis, and management, and can monitor and evaluate the environmental quality and abnormal states in a power cable gallery in real time. The platform uses sensor network technology to comprehensively monitor various environmental elements such as air, soil, and noise.

[0003] Existing environmental monitoring platforms mainly monitor the data in the power cable gallery, which is not convenient for analyzing the pollution sources based on the data and notifying the corresponding personnel for processing, thus reducing the processing efficiency. Summary of the Invention

[0004] The purpose of the present invention is to provide a distributed command and dispatch device based on an environmental monitoring platform, aiming at enabling rapid response and effective management of environmental pollution incidents, and greatly improving the safety and efficiency of the operating personnel in the power cable gallery.

[0005] To achieve the above purpose, the present invention provides a distributed command and dispatch device based on an environmental monitoring platform, including a pollution source information acquisition module, a pollution judgment module, an alarm sending module, a treatment method matching module, and a personnel allocation module;

[0006] The pollution source information acquisition module is used to acquire the monitoring information of multiple areas in the monitored power cable gallery in real time;

[0007] The pollution judgment module is used to judge the pollution source based on the monitoring information and extract the pollution source information;

[0008] The alarm sending module is used to send an alarm to the personnel in the corresponding area based on the pollution source information;

[0009] The treatment method matching module is used to match the corresponding pollution treatment method according to the pollution source information;

[0010] The personnel allocation module is used to allocate the corresponding professional personnel for treatment according to the requirements of the pollution treatment method.

[0011] Among them, the pollution source information acquisition module includes a monitoring point setting unit, a monitoring data acquisition unit, a wireless transmission unit, and a data preprocessing unit;

[0012] The monitoring point setting unit is used to set multiple monitoring stations in the power cable gallery according to geographical features, population density, and industrial distribution locations;

[0013] The monitoring data acquisition unit is used to acquire the sensing data obtained from multiple monitoring stations;

[0014] The wireless transmission unit is used to send the acquired sensing data to the data center;

[0015] The data preprocessing unit is used to clean the acquired sensing data to obtain the monitoring information in the monitored power pipe gallery.

[0016] Among them, the monitoring point setting unit includes a natural feature acquisition subunit, a population feature acquisition subunit, an industrial feature acquisition subunit, a map subunit, and an arrangement subunit;

[0017] The natural feature acquisition subunit is used to collect and analyze the geographical feature data of the target power pipe gallery;

[0018] The population feature acquisition subunit is used to obtain the population density distribution map in the power pipe gallery;

[0019] The industrial feature acquisition subunit is used to obtain the distribution map of industrial facilities in the power pipe gallery;

[0020] The map subunit is used to make an environmental risk map based on the natural geographical feature data, the population density distribution map, and the industrial facility distribution map, and mark the potential pollution sources and their influence ranges;

[0021] The arrangement subunit is used to deploy monitoring points inside each pollution source power pipe gallery.

[0022] Among them, the pollution judgment module includes a monitoring information acquisition unit, a screening unit, and a clustering analysis unit;

[0023] The monitoring information acquisition unit is used to collect the monitoring information of all relevant monitoring points from the pollution source information acquisition module;

[0024] The screening unit is used to preliminarily screen the monitoring information and identify the data points that exceed the preset environmental quality standards;

[0025] The clustering analysis unit is used to use clustering analysis to find the pollution events with similar characteristics to the known pollution events among the data points that exceed the preset environmental quality standards.

[0026] Among them, the screening unit includes a quality standard setting subunit, a comparison subunit, and a marking subunit;

[0027] The quality standard setting subunit is used to obtain the corresponding quality standard data;

[0028] The comparison subunit is used to compare the monitoring information with the quality standard data to obtain the difference data;

[0029] The annotation subunit is used for visual annotation based on the difference data.

[0030] Among them, the clustering analysis unit includes a feature extraction subunit, a clustering setting subunit, and a clustering calculation subunit;

[0031] The feature extraction subunit is used for extracting pollution features based on the monitoring information, and the pollution features include pollutant types, concentrations, occurrence times, and locations;

[0032] The clustering setting subunit is used for using the K-means clustering algorithm and specifying the number of clusters;

[0033] The clustering calculation subunit is used for inputting the pollution features into the K-means clustering algorithm to generate a clustering result.

[0034] Among them, the alarm sending module includes a pollution diffusion path calculation unit, an alarm unit, and a notification unit. The pollution diffusion path calculation unit is used for simulating the pollutant diffusion path by combining meteorological data and geographical information;

[0035] The alarm unit is used for formulating corresponding alarm information according to the severity of the pollutant diffusion path;

[0036] The notification unit is used for selecting a corresponding notification channel to send the alarm information.

[0037] Among them, the processing method matching module includes a pollution feature information acquisition unit, a solution matching unit, and a solution sorting unit;

[0038] The pollution feature information acquisition unit is used for acquiring pollution feature information based on the pollution source information;

[0039] The solution matching unit is used for matching a group of treatment solutions from the existing pollution treatment technology library based on the pollution feature information;

[0040] The solution sorting unit is used for sorting and displaying the group of treatment solutions.

[0041] A distributed command and dispatch device based on an environmental monitoring platform according to the present invention, wherein the pollution source information acquisition module collects environmental data from multiple regions in the monitored power pipe gallery in real time. These data are sourced from a sensor network spread across various locations and cover air pollutant indicators. Through communication technologies (such as GPRS, 3G / 4G, LoRaWAN, etc.), the collected data is transmitted to a central database for storage and analysis.

[0042] After obtaining sufficient monitoring information, the pollution judgment module conducts a preliminary screening of the collected data to identify data points or power cable galleries that exceed the preset environmental quality standards. Then, statistical analysis methods and machine learning algorithms are used to deeply analyze these abnormal data and simulate the pollutant diffusion path, so as to accurately determine the pollution source and its possible influence range. Once the pollution source is determined, the module will automatically extract relevant information to provide a basis for subsequent actions.

[0043] Based on the pollution source information provided by the pollution judgment module, the alarm sending module quickly activates the early warning mechanism. According to the type and severity of the pollution, personalized alarm information is formulated, and the most appropriate communication method (such as text messages, emails, social media announcements, etc.) is selected to send warnings to the people in the affected areas.

[0044] Once the pollution source and its characteristics are identified, the treatment method matching module recommends corresponding pollution treatment plans according to the specific situation. This includes, but is not limited to, the selection and combined application of physical methods, chemical methods, and bioremediation technologies. By comprehensively considering factors such as the cost-benefit ratio, implementation difficulty, and environmental protection effect of each treatment method, one or more optimal solutions are finally selected. When necessary, the successful experiences of previous similar cases can also be used for reference to further optimize the treatment strategy.

[0045] The personnel allocation module reasonably deploys professional and technical personnel to participate in the pollution control work according to the specific requirements of the selected treatment method. First, the professional skill requirements for each task need to be clarified; then, candidates meeting the conditions are searched in the established professional talent pool, and personnel closer in geographical location are preferentially arranged to carry out the tasks considering geographical location factors. At the same time, in order to ensure smooth and efficient teamwork, a detailed division of labor plan and emergency plan are also required to deal with emergencies. This distributed command and dispatch device based on the environmental monitoring platform realizes the rapid response and effective management of environmental pollution incidents, greatly improving the safety and efficiency of power cable gallery operators. Description of the Drawings

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 is the structural diagram of a distributed command and dispatch device based on the environmental monitoring platform of the present invention.

[0048] Figure 2 is the structural diagram of the pollution source information acquisition module of the present invention.

[0049] Figure 3 It is the structural diagram of the monitoring point setting unit of the present invention.

[0050] Figure 4 It is the structural diagram of the pollution judgment module of the present invention.

[0051] Figure 5 It is the structural diagram of the screening unit of the present invention.

[0052] Figure 6 It is the structural diagram of the cluster analysis unit of the present invention.

[0053] Figure 7 It is the structural diagram of the alarm sending module of the present invention.

[0054] Figure 8 It is the structural diagram of the processing method matching module of the present invention.

[0055] Pollution source information acquisition module 101, pollution judgment module 102, alarm sending module 103, processing method matching module 104, personnel allocation module 105, monitoring point setting unit 106, monitoring data acquisition unit 107, wireless transmission unit 108, data preprocessing unit 109, natural feature acquisition sub-unit 110, population feature acquisition sub-unit 111, industrial feature acquisition sub-unit 112, map sub-unit 113, layout sub-unit 114, monitoring information acquisition unit 115, screening unit 116, cluster analysis unit 117, quality standard setting sub-unit 118, comparison sub-unit 119, annotation sub-unit 120, feature extraction sub-unit 121, cluster setting sub-unit 122, cluster calculation sub-unit 123, pollution diffusion path calculation unit 124, alarm unit 125, notification unit 126, pollution feature information acquisition unit 127, solution matching unit 128, solution sorting unit 129. Detailed implementation manners

[0056] The embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0057] Please refer to Figures 1 to 8, the present invention provides a distributed command and dispatch device based on an environmental monitoring platform, including a pollution source information acquisition module 101, a pollution judgment module 102, an alarm sending module 103, a treatment method matching module 104, and a personnel allocation module 105; the pollution source information acquisition module 101 is used to acquire in real time the monitoring information of multiple regions in the monitored power cable gallery; the pollution judgment module 102 is used to judge the pollution source based on the monitoring information and extract the pollution source information; the alarm sending module 103 is used to send an alarm to the personnel in the corresponding region based on the pollution source information; the treatment method matching module 104 is used to match the corresponding pollution treatment method according to the pollution source information; the personnel allocation module 105 is used to allocate the corresponding professional personnel for treatment according to the requirements of the pollution treatment method.

[0058] In this embodiment, the pollution source information acquisition module 101 collects in real time the environmental data from multiple regions in the monitored power cable gallery. These data are sourced from a sensor network spread across various locations and cover air pollutant indicators. Through communication technologies (such as GPRS, 3G / 4G, LoRaWAN, etc.), the collected data are transmitted to a central database for storage and analysis.

[0059] After obtaining sufficient monitoring information, the pollution judgment module 102 conducts a preliminary screening of the collected data to identify data points or power cable galleries that exceed the preset environmental quality standards. Then, statistical analysis methods and machine learning algorithms are used to deeply analyze these abnormal data, and the pollutant diffusion path is simulated in combination with meteorological conditions, so as to accurately determine the pollution source and its possible influence range. Once the pollution source is determined, this module will automatically extract relevant information to provide a basis for subsequent actions.

[0060] Based on the pollution source information provided by the pollution judgment module 102, the alarm sending module 103 quickly activates the early warning mechanism. According to the type and severity of the pollution, personalized alarm information is formulated, and the most appropriate communication method (such as text message, email, social media announcement, etc.) is selected to send a warning to the personnel in the affected area.

[0061] Once the pollution source and its characteristics are clarified, the treatment method matching module 104 recommends the corresponding pollution treatment plan according to the specific situation. This includes, but is not limited to, the selection and combined application of physical methods, chemical methods, and bioremediation technologies. By comprehensively considering factors such as the cost-benefit ratio, implementation difficulty, and environmental protection effect of each treatment method, one or more sets of optimal solutions are finally selected. When necessary, the successful experience of previous similar cases can also be used to further optimize the treatment strategy.

[0062] The personnel allocation module 105 reasonably deploys professional and technical personnel to participate in pollution control work according to the specific requirements of the selected processing method. First, the professional skill requirements for each task need to be clarified; then, search for eligible candidates in the established professional talent pool and give priority to arranging personnel with shorter distances to perform tasks considering geographical location factors. At the same time, to ensure smooth and efficient teamwork, a detailed division of labor plan and emergency plan also need to be formulated to deal with emergencies.

[0063] In summary, this distributed command and dispatch device based on the environmental monitoring platform realizes the rapid response and effective management of environmental pollution incidents, greatly improving the safety and efficiency of the operation personnel in the power cable gallery.

[0064] The pollution source information acquisition module 101 includes a monitoring point setting unit 106, a monitoring data acquisition unit 107, a wireless transmission unit 108, and a data preprocessing unit 109; the monitoring point setting unit 106 is used to set multiple monitoring stations within the power cable gallery according to geographical features, population density, and industrial distribution locations; the monitoring data acquisition unit 107 is used to acquire the sensing data obtained from multiple monitoring stations; the wireless transmission unit 108 is used to send the collected sensing data to the data center; the data preprocessing unit 109 is used to perform data cleaning on the acquired sensing data to obtain the monitoring information within the power cable gallery.

[0065] The monitoring point setting unit 106 reasonably arranges multiple monitoring stations within the monitored power cable gallery according to factors such as geographical features, population density, and industrial distribution. This process not only needs to consider the impact of terrain and landform on pollutant diffusion but also pay special attention to high-risk areas such as densely populated areas or heavy industry concentration areas. By scientifically planning the positions of monitoring points, the representativeness and comprehensiveness of data collection can be ensured.

[0066] The monitoring data acquisition unit 107 collects sensing data from each of the established monitoring stations. These data include but are not limited to air quality indicators (such as PM2.5, PM10), water quality parameters (such as pH value, dissolved oxygen), noise levels, etc., reflecting the actual state of the local environment. Specifically, a high-precision sensor network is used to automatically record and upload the monitored information. To ensure the continuity and accuracy of the data, the sensors need to be calibrated regularly, and a redundancy mechanism is established to prevent data loss caused by single-point failures.

[0067] The wireless transmission unit 108 securely and efficiently sends the sensing data collected from each monitoring site to the data center. Considering the large amount of data and the requirement for real-time performance, it is crucial to select an appropriate communication technology. Wireless communication methods such as GPRS, 3G / 4G, LoRaWAN, and NB-IoT are selected according to actual needs. At the same time, to ensure the security of data transmission, encryption measures need to be implemented and verified at the receiving end to ensure the integrity of the data.

[0068] The data preprocessing unit 109 is a core link for preliminary processing of the original sensing data, aiming to remove noise and error values and improve data quality. The data after cleaning can more accurately reflect the real situation of the monitored power pipe gallery, providing a reliable basis for subsequent analysis.

[0069] The specific operation is to execute the data cleaning process using algorithms and technical means, such as removing outliers that significantly deviate from the normal range, filling in missing values, and standardizing data formats from different sources. In addition, statistical methods can also be applied to detect trend changes to assist in identifying potential problems.

[0070] In summary, through the close cooperation among its internal units, the pollution source information acquisition module 101 realizes a complete set of processes from the selection and layout of monitoring points to the collection, transmission, and preliminary processing of data. This not only improves the efficiency and accuracy of environmental monitoring work but also provides solid data support for further pollution judgment, alarm sending, and response measures.

[0071] The monitoring point setting unit 106 includes a natural feature acquisition subunit 110, a population feature acquisition subunit 111, an industrial feature acquisition subunit 112, a map subunit 113, and an arrangement subunit 114; the natural feature acquisition subunit 110 is used to collect and analyze natural geographical feature data such as the location and orientation of the target power pipe gallery; the population feature acquisition subunit 111 is used to obtain the population density distribution map inside the power pipe gallery; the industrial feature acquisition subunit 112 is used to obtain the industrial facility distribution map inside the power pipe gallery; the map subunit 113 is used to create an environmental risk map based on the natural geographical feature data, population density distribution map, and industrial facility distribution map, marking potential pollution sources and their influence ranges; the arrangement subunit 114 is used to deploy monitoring points inside each pollution source power pipe gallery.

[0072] The natural feature acquisition subunit 110 collects and analyzes the topographic and geomorphic natural geographical feature data inside the target power pipe gallery. This information is crucial for understanding how pollutants spread in the environment.

[0073] The specific operation is to generate detailed geographical feature layers through satellite remote sensing images, digital elevation models (DEMs), and other geospatial data sources. This includes, but is not limited to, topographic elements such as mountains, rivers, lakes, and their influence patterns on air flow and water flow. Based on this information, the possible diffusion paths of pollutants and their impact ranges can be predicted.

[0074] The population characteristics acquisition subunit 111 acquires and analyzes the population density distribution map within the power cable gallery. Understanding the crowd aggregation situation helps determine which areas need to be monitored preferentially to protect public health. The specific operation is to integrate population statistics data from government statistical departments or third-party service providers to create a population density distribution map.

[0075] The industrial characteristics acquisition subunit 112 draws a distribution map of industrial facilities within the power cable gallery and identifies the locations of major pollution sources. This is of great significance for deploying monitoring resources targeted. Obtain the latest list of industrial enterprises and their location information from the environmental supervision department, especially those involved in heavy chemical production and high energy consumption. At the same time, historical emission records and violation reports also need to be considered to more accurately assess potential risks. Combining the production process flow and technical level of enterprises, further refine the classification of pollution sources.

[0076] Based on the natural geographical feature data, population density distribution map, and industrial facility distribution map provided by the first three subunits, the map subunit 113 creates an environmental risk map, marking the potential pollution sources and their impact ranges.

[0077] The specific operation is to perform overlay analysis on data from different sources to form a comprehensive environmental risk map. This map not only shows the specific locations of various pollution sources but also visually presents the relative risk levels of each region through color coding or other visualization methods. For example, red represents the extremely high-risk area, and yellow indicates the medium-risk area, etc. In addition, important infrastructure such as water supply systems and sewage treatment plants can also be marked to help decision-makers better plan emergency response measures.

[0078] According to the results of the environmental risk map, the deployment subunit 114 deploys monitoring points inside each power cable gallery of the pollution source. This subunit ensures that the monitoring network can cover all key power cable galleries, guaranteeing the effectiveness and comprehensiveness of data collection.

[0079] The specific operation is to select the best monitoring points based on risk levels and geographical conditions. In high-risk areas, increase the number of monitoring stations to improve the monitoring frequency; while in low-risk areas, the layout density can be appropriately reduced. Considering the influence of meteorological factors (such as wind direction and wind speed), the directions and heights of some monitoring points also need to be adjusted. Finally, formulate a detailed installation plan, specifying the technical specifications, maintenance cycles, and other requirements for each monitoring point.

[0080] In summary, the monitoring point setting unit 106 realizes the whole process management from basic data collection to the final monitoring point layout through the coordinated work of the above five sub-units. This not only improves the accuracy and reliability of the environmental monitoring system, but also provides a solid foundation for subsequent pollution judgment, alarm sending and response measures.

[0081] The pollution judgment module 102 includes a monitoring information acquisition unit 115, a screening unit 116, and a cluster analysis unit 117; the monitoring information acquisition unit 115 is used to collect monitoring information of all relevant monitoring points from the pollution source information acquisition module 101; the screening unit 116 is used to perform preliminary screening of the monitoring information and identify data points that exceed the preset environmental quality standards; the cluster analysis unit 117 is used to use cluster analysis to find pollution events with similar characteristics to known pollution events among the data points that exceed the preset environmental quality standards.

[0082] The monitoring information acquisition unit 115 collects real-time monitoring information of all relevant monitoring points from the pollution source information acquisition module 101. Such data include but are not limited to air pollutant concentrations (such as PM2.5, PM10, SO2, etc.), water quality indicators (such as pH value, dissolved oxygen), noise levels, and other factors that may affect environmental quality.

[0083] The specific operation is to establish a stable data interface with the pollution source information acquisition module 101, and synchronize the latest monitoring data regularly or on demand. In order to ensure the integrity and accuracy of the data, a data verification mechanism needs to be implemented, such as checking whether the timestamp of the data is reasonable, whether there are abnormal values, etc. In addition, considering that the data formats from different sources may be different, it is necessary to unify the data format for subsequent processing.

[0084] The screening unit 116 performs a preliminary screening of the collected monitoring information to identify data points that exceed the preset environmental quality standards. This process helps focus on the power pipeline corridors that actually have problems and avoids wasting resources on data that fluctuates within the normal range.

[0085] According to the relevant regulations and guidelines issued by the national or local environmental protection departments, set clear quality standard thresholds for each monitoring indicator. Then develop or select appropriate algorithms to automate the screening process. By comparing the actual measured values ​​of each indicator with the corresponding threshold, mark the data points that exceed the standard. Remove obvious errors or outliers caused by equipment failure or other abnormal factors to improve the reliability of the screening results. Organize the screened out-of-standard data and related information to generate a detailed out-of-standard report for further analysis.

[0086] The clustering analysis unit 117 uses advanced data analysis techniques to find pollution events with similar characteristics among the data points that exceed the preset environmental quality standards, and compares them with known pollution events to identify patterns and trends.

[0087] Select a suitable clustering algorithm according to the data characteristics and analysis objectives, such as K-means, Hierarchical clustering, or DBSCAN, etc. Different algorithms are suitable for different types of data sets and problem scenarios. Determine the key variables for clustering, such as pollutant types, concentration levels, occurrence time and location, etc. Standardize the data if necessary to eliminate the influence of dimensional differences. Input the selected out-of-standard data into the selected clustering algorithm and run the model to generate clustering results. Use visualization tools to display the characteristics of the members within each cluster to help understand the relevance between different pollution events. Compare the newly discovered clusters with the known pollution events in the historical database to find common features. This step helps predict potential future problems and provides a basis for formulating targeted treatment strategies. As new data accumulates, regularly update the model parameters and optimize the analysis process to improve the clustering accuracy and practicality.

[0088] In summary, through the close cooperation of each internal unit of the pollution judgment module 102, it realizes the accurate positioning of out-of-standard events from a large amount of monitoring data and deeply explores the hidden laws and patterns behind them. This not only improves the efficiency and accuracy of environmental monitoring work, but also provides strong support for timely and effective pollution control.

[0089] The screening unit 116 includes a quality standard setting subunit 118, a comparison subunit 119, and a marking subunit 120; the quality standard setting subunit 118 is used to obtain the corresponding quality standard data; the comparison subunit 119 is used to compare the monitoring information with the quality standard data to obtain difference data; the marking subunit 120 is used to perform visual marking based on the difference data.

[0090] The quality standard setting subunit 118 obtains and maintains the corresponding environmental quality standard data, which are usually formulated by national or local environmental protection departments according to scientific research and public health needs.

[0091] Specifically, download the latest environmental quality standards for air quality, water quality, etc. from official channels (such as the websites of environmental protection departments) and store them in the system database. This includes but is not limited to air pollutant concentration limits, chemical oxygen demand (COD) in water bodies, pH value ranges, etc. Due to laws, regulations, and technological progress, environmental quality standards may change. Therefore, a mechanism for regular inspection and update needs to be established to ensure that the standards used are always the latest. Classify and organize the standard data according to different types of pollutants and their monitoring indicators for quick search and application during subsequent processing.

[0092] The comparison subunit 119 compares the monitoring information with the corresponding environmental quality standard data one by one, calculates the difference data to determine which monitoring point data exceeds the specified threshold.

[0093] First, match the real-time monitoring data from the monitoring information acquisition unit 115 with the standard data provided by the quality standard setting subunit 118 to ensure that a corresponding environmental quality standard can be found for each monitoring indicator. For each indicator of each monitoring point, calculate the difference between the measured value and the standard value. If the difference is positive, it means that a certain indicator at this point exceeds the standard; otherwise, it means it meets the standard requirements. Record all comparison results in detail to form a report containing the over-standard situation and specific values. This report will become the basic data for subsequent analysis.

[0094] Based on the difference data generated by the comparison subunit 119, the annotation subunit 120 visually annotates the over-standard power pipe corridors or monitoring points, enabling managers to intuitively understand the current environmental situation.

[0095] The specific operation is to select a suitable geographic information system (GIS) or other visualization platform as the display tool. Highlight the location and degree of over-standard power pipe corridors through map annotation, color coding, etc.

[0096] Define clear annotation rules, such as using different colors to represent different degrees of over-standard situations (red represents severe over-standard, yellow represents mild over-standard). Text descriptions can also be added to provide more detailed over-standard information, such as the specific pollutant name and over-standard multiple. To improve the user experience, an interactive user interface can be developed to allow users to click on any annotation point to view detailed over-standard data and even trace historical records. In addition, an export function should be supported to facilitate generating reports or sharing data with other systems. Establish an effective feedback channel to enable relevant stakeholders to timely understand the over-standard situation and adjust the annotation strategy or put forward improvement suggestions according to the actual situation.

[0097] The clustering analysis unit 117 includes a feature extraction subunit 121, a clustering setting subunit 122, and a clustering calculation subunit 123. The feature extraction subunit 121 is used to extract pollution features based on monitoring information, and the pollution features include pollutant types, concentrations, occurrence times, and locations. The clustering setting subunit 122 is used to use the K-means clustering algorithm and specify the number of clusters. The clustering calculation subunit 123 is used to input the pollution features into the K-means clustering algorithm to generate a clustering result.

[0098] The feature extraction subunit 121 extracts key pollution features based on monitoring information, including but not limited to pollutant types, concentrations, occurrence times, and locations, etc., providing basic data for subsequent clustering analysis.

[0099] The specific operation is to clean the original monitoring data, remove noise and outliers, and ensure the data quality. This step is crucial for improving the accuracy of clustering analysis. Then, select the feature variables that can best reflect the essence of pollution events according to the research purpose. For example, for air pollution, the concentrations of pollutants such as PM2.5, PM10, and SO2 may be focused on; for water pollution, indicators such as pH value and chemical oxygen demand (COD) are concerned.

[0100] Considering the variation laws of pollutants over time and space, extract the timestamp information of each feature to help understand the development trend and dynamic changes of pollution events. Convert the location information of all monitoring points into a unified geographic coordinate format (such as longitude and latitude) for easy visualization display and spatial analysis on the map.

[0101] The clustering setting subunit 122 configures the specific parameters of the clustering algorithm, especially selects a suitable clustering algorithm and specifies the number of clusters. This step directly affects the effectiveness and interpretability of the final clustering result. In this case, the K-means clustering algorithm is selected. This is an unsupervised learning method widely used in the field of data mining and is particularly suitable for discovering groups of data points with similar features. The selection of the K value is a key step and usually requires combining domain knowledge and experimental verification to determine the optimal value. Statistical methods such as the Elbow Method or the Silhouette Coefficient can be used to evaluate the clustering effects under different K values, and select the K value that can minimize the within-cluster distance and maximize the between-cluster distance. Randomly select or select the initial clustering center points according to experience to start the iterative process of the K-means algorithm. Reasonable initial center points help to accelerate the convergence speed and avoid falling into local optimal solutions.

[0102] The clustering calculation subunit 123 inputs the pollution features obtained from the feature extraction subunit 121 into the configured K-means clustering algorithm, performs clustering calculations, and generates the final clustering results. Since there may be significant differences in the dimensions of different feature variables, it is necessary to standardize the input data (such as Z-score standardization) to bring each variable within the same scale range, thus ensuring the fairness of the clustering results. The standardized pollution feature data is input into the K-means clustering algorithm, and iterative calculations start according to the set K value until the predetermined convergence conditions are met (such as the cluster centers no longer change or the maximum number of iterations is reached). A detailed report containing the members and features of each clustering cluster is generated, and visualization tools (such as scatter plots, heat maps, etc.) are used to display the spatial distribution of the clustering results. In addition, an interactive interface can be provided for users to explore the similarities and differences between different clusters, and gain an in-depth understanding of the patterns and trends of pollution events. As new data accumulates, the model parameters are updated regularly and the clustering strategy is optimized to improve the clustering accuracy and practicality.

[0103] The alarm sending module 103 includes a pollution diffusion path calculation unit 124, an alarm unit 125, and a notification unit 126. The pollution diffusion path calculation unit 124 is used to simulate the pollution diffusion path by combining meteorological data and geographical information.

[0104] The alarm unit 125 is used to formulate corresponding alarm information according to the severity of the pollution diffusion path. The notification unit 126 is used to select the corresponding notification channel to send the alarm information. The processing method matching module 104 includes a pollution feature information acquisition unit, a solution matching unit, and a solution sorting unit. The pollution feature information acquisition unit is used to obtain pollution feature information based on the pollution source information. The solution matching unit is used to match a set of treatment solutions from the existing pollution treatment technology library based on the pollution feature information. The solution sorting unit is used to sort and display the set of treatment solutions.

[0105] The pollution diffusion path calculation unit 124 simulates the diffusion path of pollutants in the atmosphere or water body by combining meteorological data (such as wind speed, wind direction, etc.).

[0106] Real-time meteorological data is obtained from weather stations or satellite remote sensing and combined with topographic and geomorphic geographical information. A suitable diffusion model (such as the Gaussian plume model, CALPUFF, etc.) is selected to predict the diffusion pattern of pollutants based on the input data. As new data is continuously updated, the simulation parameters are adjusted in real time to reflect the latest meteorological conditions and environmental changes, ensuring that the prediction results are as close to the actual situation as possible. A map of the pollutant concentration distribution is generated to visually display the diffusion of pollutants in a specific power pipe gallery over time, helping decision-makers quickly understand the scope of pollution impact.

[0107] Formulate corresponding alarm information according to the severity of the pollutant diffusion path to ensure that residents and relevant departments in the affected areas can take necessary protective measures in a timely manner.

[0108] The specific operation is to evaluate the degree of pollution impact on different regions based on the data provided by the pollution diffusion path calculation unit 124, and identify high-risk power pipe corridors. Personalized alarm information is formulated according to different types of pollution events and their severity. For example, for air pollution events, provide the Air Quality Index (AQI) and recommended protective measures; for water pollution, specify the affected water area and recommended water usage precautions. Different warning levels (such as yellow warning, orange warning, red warning) are set according to the urgency and potential hazards of the pollution event, and corresponding response strategies are formulated accordingly.

[0109] The notification unit 126 selects the most suitable channel to send the alarm information to ensure that the information can be quickly and accurately conveyed to the target audience.

[0110] Select appropriate communication methods according to the characteristics of the affected population, such as text messages, emails, social media announcements, push messages from dedicated applications, etc. For particularly serious pollution events, emergency notifications can also be issued through traditional media such as radio and television. To ensure that the information covers as wide an audience as possible, especially those who may not have received the initial notification, establish a multi-round notification mechanism. Collect feedback from the recipients, evaluate the effect of this notification, and continuously optimize the notification process and technical means based on the experience and lessons in actual operations.

[0111] The processing method matching module 104 aims to screen out the most suitable treatment plan from the existing pollution control technology library according to the specific pollution source information, and sort and display it for subsequent implementation. This module includes three core units: the pollution characteristic information acquisition unit, the plan matching unit, and the plan sorting unit.

[0112] The pollution characteristic information acquisition unit extracts key pollution characteristic information based on the pollution source information, providing a basis for subsequent matching of treatment plans.

[0113] Extract detailed information such as pollutant types, concentrations, emission rates, and geographical locations from the pollution source information acquisition module 101. Deeply analyze the extracted data to determine the specific values and change trends of each pollution characteristic, laying a foundation for further matching of treatment plans.

[0114] The plan matching unit matches a set of feasible treatment plans from the existing pollution control technology library based on the pollution characteristic information.

[0115] The specific operation is to maintain a technology library containing various pollution control technologies, covering a variety of methods such as physical methods, chemical methods, and bioremediation. Select a suitable algorithm to implement the automated scheme matching process. By comparing the pollution characteristics with the requirements of each scheme in the technology library, a technology combination suitable for the current pollution situation is screened out. Draw on the successful treatment experience of previous similar pollution incidents to further optimize the selected scheme and improve its practicability and effectiveness.

[0116] The scheme sorting unit sorts and displays the matched treatment scheme groups according to certain criteria, facilitating the decision maker to quickly select the optimal scheme.

[0117] The specific operation is to set a set of evaluation criteria based on factors such as cost-benefit ratio, implementation difficulty, and environmental protection effect to measure the advantages and disadvantages of each scheme. Score each scheme according to the set criteria, and obtain the total score after comprehensively considering various indicators. Sort all candidate schemes from high to low according to the total score and clearly display them through the user interface for the decision maker to refer to.

[0118] In summary, through the close cooperation of each internal subunit of the alarm sending module 103 and the treatment method matching module 104, it realizes the precise simulation of the pollution diffusion path to the efficient notification sending, as well as the precise extraction of pollution characteristics to the selection and sorting of the best treatment scheme. This not only improves the speed and accuracy of environmental emergency response, but also provides strong support for the scientific treatment of environmental pollution.

[0119] The above-disclosed is only a preferred embodiment of the present invention. Of course, it cannot be used to limit the scope of the rights of the present invention. Those of ordinary skill in the art can understand the entire or partial processes of implementing the above embodiments, and the equivalent changes made according to the claims of the present invention still fall within the scope covered by the invention.

Claims

1. A distributed command and dispatch device based on an environmental monitoring platform, characterized in that: It includes pollution source information acquisition module, pollution judgment module, alarm sending module, processing method matching module and personnel allocation module; The pollution source information acquisition module is used to acquire monitoring information of multiple areas in the power pipeline corridor in real time; The pollution judgment module is used to judge the pollution source based on the monitoring information and extract the pollution source information; The alarm sending module is used to send an alarm to people in the corresponding area based on the pollution source information; The processing method matching module is used to match the corresponding pollution processing method according to the pollution source information; The personnel allocation module is used to allocate corresponding professionals to handle the pollution according to the requirements of the pollution treatment method.

2. A distributed command and dispatch device based on an environmental monitoring platform as claimed in claim 1, characterized in that: The pollution source information acquisition module includes a monitoring point setting unit, a monitoring data acquisition unit, a wireless transmission unit and a data preprocessing unit; The monitoring point setting unit is used to set up multiple monitoring sites in the power pipeline corridor according to geographical features, population density and industrial distribution location; The monitoring data acquisition unit is used to acquire sensor data obtained from multiple monitoring sites; The wireless transmission unit is used to send the collected sensor data to the data center; The data preprocessing unit is used to clean the acquired sensor data to obtain monitoring information for monitoring the power pipeline corridor.

3. A distributed command and dispatch device based on an environmental monitoring platform as claimed in claim 2, characterized in that: The monitoring point setting unit includes a natural feature acquisition subunit, a population feature acquisition subunit, an industrial feature acquisition subunit, a map subunit and a layout subunit; The natural feature acquisition subunit is used to collect and analyze the geographical feature data of the target power pipeline corridor; The population characteristic acquisition subunit is used to obtain a population density distribution map within the power pipeline corridor; The industrial feature acquisition subunit is used to obtain a distribution map of industrial facilities within the power pipeline corridor; The map subunit is used to produce an environmental risk map based on natural geographical feature data, population density distribution map and industrial facility distribution map, marking potential pollution sources and their impact range; The deployment subunit is used to deploy monitoring points in each pollution source power pipeline corridor.

4. A distributed command and dispatch device based on an environmental monitoring platform as claimed in claim 3, characterized in that: The pollution judgment module includes a monitoring information acquisition unit, a screening unit, and a cluster analysis unit; The monitoring information acquisition unit is used to collect monitoring information of all relevant monitoring points from the pollution source information acquisition module; The screening unit is used to perform preliminary screening of the monitoring information and identify data points that exceed preset environmental quality standards; The cluster analysis unit is used to use cluster analysis to find pollution events with similar characteristics to known pollution events among data points that exceed preset environmental quality standards.

5. A distributed command and dispatch device based on an environmental monitoring platform as claimed in claim 4, characterized in that: The screening unit includes a quality standard setting subunit, a comparison subunit and a marking subunit; The quality standard setting subunit is used to obtain corresponding quality standard data; The comparison subunit is used to compare the monitoring information with the quality standard data to obtain difference data; The annotation subunit is used to perform visual annotation based on the difference data.

6. A distributed command and dispatch device based on an environmental monitoring platform as claimed in claim 5, characterized in that: The cluster analysis unit includes a feature extraction subunit, a cluster setting subunit, and a cluster calculation subunit; The feature extraction subunit is used to extract pollution features based on monitoring information, wherein the pollution features include pollutant types, concentrations, time and location of occurrence; The cluster setting subunit is used to use the K-means clustering algorithm and specify the number of clusters; The clustering calculation subunit is used to input the pollution characteristics into the K-means clustering algorithm to generate a clustering result.

7. A distributed command and dispatch device based on an environmental monitoring platform as claimed in claim 6, characterized in that: The alarm sending module includes a pollution diffusion path calculation unit, an alarm unit and a notification unit, wherein the pollution diffusion path calculation unit is used to simulate the pollutant diffusion path by combining meteorological data and geographic information; The alarm unit is used to formulate corresponding alarm information according to the severity of the pollutant diffusion path; The notification unit is used to select a corresponding notification channel to send the alarm information.

8. A distributed command and dispatch device based on an environmental monitoring platform as claimed in claim 7, characterized in that: The processing mode matching module includes a pollution characteristic information acquisition unit, a scheme matching unit and a scheme sorting unit; The pollution characteristic information acquisition unit is used to acquire pollution characteristic information based on pollution source information; The scheme matching unit is used to match a treatment scheme group from an existing pollution control technology library based on pollution characteristic information; The scheme sorting unit is used to sort and display the processing scheme groups.

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