Method for generating environmental protection solution based on environmental protection technical data
By introducing equipment such as quantum magnetometers and spectrometers, combined with multi-dimensional data sources and a federated learning framework, the problem of sensors being susceptible to interference has been solved, high-precision environmental data collection and prediction has been achieved, and the ability to respond quickly to environmental pollution has been improved.
Patent Information
- Application Number
- CN202510619716.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-14
- Publication Date
- 2025-09-12
AI Technical Summary
Existing environmental protection solutions rely on sensors installed at sewage outlets. The monitoring data is susceptible to human intervention, resulting in inaccurate monitoring and failure to meet actual needs.
Quantum magnetometers and spectrometers are introduced to improve the sensitivity of trace pollutant detection. Remote sensing satellites, meteorological data and socio-economic data are combined to build a multi-dimensional data information source. ETL tools are used to integrate data, and a distributed storage and federated learning framework is established. Through association rule mining and genetic algorithm optimization models, high-precision predictions and emergency plans are generated.
It achieves accurate collection and prediction of multi-dimensional pollution data, improves the ability to quickly respond to environmental pollution, ensures data privacy and improves prediction accuracy.
Smart Images
Figure SMS_1
Abstract
Description
Technical Field
[0001] The present invention relates to the field of environmental protection technology, and in particular to a method for generating environmental protection solutions based on environmental protection technology data. Background Art
[0002] Environmental protection, also known as environmental protection, refers to the various actions humans take to address current or potential environmental problems, coordinate the relationship between humans and the environment, and ensure sustainable economic and social development. The methods and means include engineering technology, administrative management, innovation and research and development, as well as legal, economic, and educational approaches.
[0003] With the development of science and technology, the requirements for environmental protection in modern society are becoming increasingly stringent. In order to improve the rapid response capabilities of environmental protection, it is necessary to combine various types of environmental data to generate environmental protection solutions. Existing environmental protection solutions rely solely on environmental protection data from various types of sensors pre-installed at sewage outlets. Moreover, these sensors are also subject to human intervention during actual operation, resulting in inaccurate monitoring data and the resulting environmental protection solutions failing to meet actual needs. To address the above technical issues, we have designed a method for generating environmental protection solutions based on environmental protection technology data. Summary of the Invention
[0004] The purpose of the present invention is to provide a method for generating environmental protection solutions based on environmental protection technology data, which has the advantage of obtaining environmental pollution data in multiple dimensions, and solves the problem that environmental protection data is only obtained by monitoring various types of sensors installed in advance at the sewage outlet, and these sensors are also affected by human intervention during actual operation, making the monitoring data inaccurate, resulting in the generated environmental protection solutions being unable to meet actual needs.
[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for generating an environmental protection solution based on environmental protection technical data, comprising the following steps: Step 1: Utilize various sensors to collect air, water quality, and noise parameters in real time. In addition to traditional sensors, quantum magnetometers and spectrometers are introduced to increase the sensitivity of trace pollutant detection to the ppt level. Combined with remote sensing satellite data, meteorological data, and socioeconomic statistics, this ultimately creates a multi-dimensional data source. Step 2: Use ETL tools to integrate monitoring data from different sources, formats, and structures, as well as unstructured reports and image information, to build a unified environmental database. This data is sharded and stored on multiple independent nodes to achieve distributed storage and efficient access to petabyte-level data. Step 3: Use association rule mining technology to identify pollutant diffusion patterns; simultaneously introduce genetic algorithms to optimize solid waste treatment process parameters; establish a high-precision three-dimensional dynamic model of key areas, and use multi-physics field coupling simulation to predict the impact range of sudden leaks and generate emergency evacuation routes and plans in advance; Step 4: Build a GIS map with real-time pollution heat maps to support pollutant concentration threshold warnings; integrate IoT devices to enable remote start and stop control; further integrate satellite imagery, drone inspection videos, and sewage outlet voiceprint data to train a large environmental protection data model; Step 5: Connect multiple systems, including DCS and EMS, through the OPC-UA protocol to build an emissions reduction data lake. Use the knowledge graph to align semantic differences, then employ the analytic hierarchy process to determine indicator weights. Use components to construct a three-dimensional radar chart: time axis, space axis, and indicator axis. Finally, introduce the entropy method to dynamically adjust weights to avoid subjective bias. Ultimately, establish a KPI matrix to quantify emissions reduction results. Step 6: The central server initializes the global model architecture and distributes encrypted initial parameters to each participating node. Each participating node performs training using local data, updates parameters, and processes gradient information through an encryption algorithm. The server integrates the encrypted gradients using a secure aggregation protocol and updates the global model. The updated global model is returned to each node, where it is simultaneously evaluated on a local validation set. Anomalous nodes trigger a federated elimination mechanism, enabling the federated learning framework to update the prediction model. Furthermore, each monitoring node uploads only parameter gradients, rather than raw data, to protect privacy and improve prediction accuracy.
[0006] Step 7: Build an alliance-based emission rights trading platform to automatically execute emission quota cancellation and carbon credit settlement through smart contracts.
[0007] Preferably, in step three, the association rules and the atmospheric diffusion model include a Gaussian plume model to verify the scientific nature of the rules; further, the association rules are combined with a graph neural network (GNN) to enhance the modeling capability of complex diffusion networks.
[0008] Preferably, in step four, training the large environmental protection data model includes deploying a lightweight AI chip in the PLC controller of the sewage treatment plant to realize localized water quality prediction model training.
[0009] Preferably, in step five, the semantic differences are aligned using the knowledge graph, including the calibration of the definition of "emissions" in different systems; the time axis is the annual progress, the space axis is the regional comparison, and the indicator axis is the KPI completion.
[0010] Preferably, in step one, the air data collection method includes using atmospheric monitoring stations to collect indicators such as PM2.5, PM10, sulfides and nitrogen compounds every hour, while supplemented by drone inspections to expand data coverage.
[0011] Preferably, in step one, the quantum magnetometers are arranged three-dimensionally in a "sky-air-ground-well" manner, and multiple quantum nodes form sub-meter pollution tracing capabilities.
[0012] Preferably, in step 2, multiple independent nodes synchronize data status via an inter-node P2P protocol.
[0013] Preferably, in step seven, the transaction data of the trading platform is uploaded to the cloud platform.
[0014] Compared with the prior art, the present invention has the following beneficial effects: Through the use of the above method, the present invention has the advantage of obtaining environmental pollution data in multiple dimensions. It can collect the range and concentration of pollution on a large scale through remote sensing satellites and drones, and reversely calculate the location of the pollution source by superimposing the atmospheric diffusion model; further combining meteorological data with real-time emission parameters, using the Gaussian plume model to warn of future pollution coverage areas; based on the above, it can generate accurate environmental protection solutions, thereby improving the ability to respond quickly when environmental pollution occurs. DETAILED DESCRIPTION
[0015] Please refer to a method for generating an environmental protection solution based on environmental protection technical data, comprising the following steps: Step 1: Utilize various sensors to collect air, water quality, and noise parameters in real time. In addition to traditional sensors, quantum magnetometers and spectrometers are introduced to increase the sensitivity of trace pollutant detection to the ppt level. Combined with remote sensing satellite data, meteorological data, and socioeconomic statistics, this ultimately creates a multi-dimensional data source. Step 2: Use ETL tools to integrate monitoring data from different sources, formats, and structures, as well as unstructured reports and image information, to build a unified environmental database. This data is sharded and stored on multiple independent nodes to achieve distributed storage and efficient access to petabyte-level data. Step 3: Use association rule mining technology to identify pollutant diffusion patterns; simultaneously introduce genetic algorithms to optimize solid waste treatment process parameters; establish a high-precision three-dimensional dynamic model of key areas, and use multi-physics field coupling simulation to predict the impact range of sudden leaks and generate emergency evacuation routes and plans in advance; Step 4: Build a GIS map with real-time pollution heat maps to support pollutant concentration threshold warnings; integrate IoT devices to enable remote start and stop control; further integrate satellite imagery, drone inspection videos, and sewage outlet voiceprint data to train a large environmental protection data model; Step 5: Connect multiple systems, including DCS and EMS, through the OPC-UA protocol to build an emissions reduction data lake. Use the knowledge graph to align semantic differences, then employ the analytic hierarchy process to determine indicator weights. Use components to construct a three-dimensional radar chart: time axis, space axis, and indicator axis. Finally, introduce the entropy method to dynamically adjust weights to avoid subjective bias. Ultimately, establish a KPI matrix to quantify emissions reduction results. Step 6: The central server initializes the global model architecture and sends encrypted initial parameters to each participating node. Each participating node performs training using local data, updates parameters, and processes gradient information through an encryption algorithm. The server integrates the encrypted gradients using a secure aggregation protocol and updates the global model. The updated global model is returned to each node, and a local validation set evaluation is performed simultaneously. Abnormal nodes trigger a federated elimination mechanism, enabling the federated learning framework to update the prediction model. Furthermore, each monitoring node only uploads parameter gradients rather than raw data, which both protects privacy and improves prediction accuracy. Step 7: Build an alliance-based emission rights trading platform to automatically execute emission quota cancellation and carbon credit settlement through smart contracts.
[0016] In step three, association rules and atmospheric diffusion models, including the Gaussian plume model, are used to verify the scientific nature of the rules; further association rules are combined with graph neural networks (GNNs) to enhance the modeling capabilities of complex diffusion networks.
[0017] In step 4, training the large environmental protection data model includes deploying lightweight AI chips in the PLC controller of the sewage treatment plant to realize localized water quality prediction model training.
[0018] In step five, the knowledge graph is used to align semantic differences, including the calibration of the definition of "emissions" in different systems; the time axis is the annual progress, the spatial axis is the regional comparison, and the indicator axis is the KPI completion.
[0019] In step one, air data is collected by using atmospheric monitoring stations to collect indicators such as PM2.5, PM10, sulfides, and nitrogen oxides every hour, while using drone inspections to expand data coverage.
[0020] In step one, quantum magnetometers are arranged in a three-dimensional manner using a "sky-air-ground-well" approach, and multiple quantum nodes form sub-meter pollution tracing capabilities.
[0021] In step 2, multiple independent nodes synchronize data status through the inter-node P2P protocol.
[0022] In step seven, the transaction data on the trading platform is uploaded to the cloud platform, which helps the audit department to trace the pollution control history of any enterprise.
[0023]
[0024] To sum up: This method of generating environmental protection solutions based on environmental protection technology data, through the use of the above method, solves the problem that environmental protection data is obtained only by monitoring various types of sensors installed in advance at the sewage outlet, and these sensors are also affected by human intervention during actual operation, making the monitoring data inaccurate, resulting in the generated environmental protection solutions being unable to meet actual needs.
Claims
1. A method for generating environmental protection solutions based on environmental protection technical data, characterized by: The steps include: Step 1: Utilize various sensors to collect air, water quality, and noise parameters in real time. In addition to traditional sensors, quantum magnetometers and spectrometers are introduced to increase the sensitivity of trace pollutant detection to the ppt level. Combined with remote sensing satellite data, meteorological data, and socioeconomic statistics, this ultimately creates a multi-dimensional data source. Step 2: Use ETL tools to integrate monitoring data from different sources, formats, and structures, as well as unstructured reports and image information, to build a unified environmental database. This data is sharded and stored on multiple independent nodes to achieve distributed storage and efficient access to petabyte-level data. Step 3: Use association rule mining technology to identify pollutant diffusion patterns; simultaneously introduce genetic algorithms to optimize solid waste treatment process parameters; establish a high-precision three-dimensional dynamic model of key areas, and use multi-physics field coupling simulation to predict the impact range of sudden leaks and generate emergency evacuation routes and plans in advance; Step 4: Build a GIS map with real-time pollution heat maps to support pollutant concentration threshold warnings; integrate IoT devices to enable remote start and stop control; further integrate satellite imagery, drone inspection videos, and sewage outlet voiceprint data to train a large environmental protection data model; Step 5: Connect to multiple source systems such as DCS and EMS through the OPC-UA protocol to build an emission reduction data lake; We used knowledge graphs to align semantic differences, then employed the analytic hierarchy process to determine indicator weights. We then constructed a three-dimensional radar chart using components: time axis, space axis, and indicator axis. Finally, we introduced the entropy method to dynamically adjust weights to avoid subjective bias, ultimately establishing a KPI matrix to quantify emission reduction effectiveness. Step 6: The central server initializes the global model architecture and sends the encrypted initial parameters to each participating node; Each participating node uses local data to perform training, updates parameters, and processes gradient information using an encryption algorithm. The server uses a secure aggregation protocol to integrate encrypted gradients and update the global model. The updated global model is then returned to each node for simultaneous local validation set evaluation. Abnormal nodes trigger a federated elimination mechanism, enabling the federated learning framework to update the prediction model. Furthermore, each monitoring node only uploads parameter gradients, rather than raw data, to protect privacy and improve prediction accuracy. Step 7: Build an alliance-based pollution rights trading platform to automatically execute emission quota cancellation and carbon credit settlement through smart contracts.
2. The method for generating an environmental protection solution based on environmental protection technical data according to claim 1, characterized in that: In step three, the association rules and the atmospheric diffusion model include a Gaussian plume model to verify the scientific nature of the rules; the association rules are further combined with a graph neural network (GNN) to enhance the modeling capability of complex diffusion networks.
3. The method for generating an environmental protection solution based on environmental protection technical data according to claim 1, characterized in that: In step 4, training the large environmental protection data model includes deploying a lightweight AI chip in the PLC controller of the sewage treatment plant to realize localized water quality prediction model training.
4. The method for generating an environmental protection solution based on environmental protection technical data according to claim 1, characterized in that: In step five, the semantic differences are aligned using the knowledge graph, including the definition calibration of "emissions" in different systems; the time axis is the annual progress, the spatial axis is the regional comparison, and the indicator axis is the KPI completion.
5. The method for generating an environmental protection solution based on environmental protection technical data according to claim 1, characterized in that: In step 1, the air data is collected by using atmospheric monitoring stations to collect indicators such as PM2.5, PM10, sulfides and nitrogen compounds every hour, while supplemented by drone inspections to expand data coverage.
6. The method for generating an environmental protection solution based on environmental protection technical data according to claim 1, characterized in that: In step 1, the quantum magnetometer is arranged in a three-dimensional manner using a "sky-air-ground-well" approach, and multiple quantum nodes form a sub-meter pollution tracing capability.
7. The method for generating an environmental protection solution based on environmental protection technical data according to claim 1, characterized in that: In the step 2, multiple independent nodes synchronize data status through an inter-node P2P protocol.
8. The method for generating an environmental protection solution based on environmental protection technical data according to claim 1, characterized in that: In step seven, the transaction data of the trading platform is uploaded to the cloud platform.