Carbon emission CEMS remote monitoring module based on cloud server

Abstract

The invention relates to the technical field of carbon emission, in particular to a carbon emission CEMS (Continuous Emission Monitoring System) remote monitoring module based on a cloud server, which comprises front-end monitoring equipment, a cloud server platform and a user side, wherein the front-end monitoring equipment comprises a flue gas analyzer, a flow meter, a temperature sensor and a pressure sensor, and is responsible for acquiring carbon emission data in real time and uploading the data to the cloud server platform through a network module; wherein the cloud server platform stores and analyzes data uploaded by the front-end monitoring equipment by using big data processing and cloud computing technologies, and provides real-time alarm, historical data query and trend analysis functions. According to the carbon emission CEMS remote monitoring system, more efficient data transmission, more accurate carbon emission prediction and more reliable equipment maintenance can be realized, and self-learning and optimization of the system are realized through an adaptive algorithm, so that the performance and stability of the system are continuously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission, and in particular to a carbon emission CEMS remote monitoring module based on a cloud server. Background Art

[0002] The Continuous Emission Monitoring System (CEMS) is used to monitor the emission of greenhouse gases such as carbon dioxide (CO2) from industrial facilities in real time. By combining CEMS with cloud servers, remote monitoring, data storage, analysis and management can be achieved, thereby providing timely and accurate carbon emission data for environmental regulatory authorities and enterprises.

[0003] In actual use, relevant data collection mostly relies on sensors for data collection and transmission. However, in the data transmission process, the relevant collected data transmission often has data transmission delays and losses, thereby improving the accuracy of relevant system data analysis. In the subsequent use process, the system cannot be prepared to make predictions based on the data content and assist users in judging the status of the equipment and related data information content in advance.

[0004] Therefore, a carbon emission CEMS remote monitoring module based on cloud server is proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a carbon emission CEMS remote monitoring module based on a cloud server to solve the problems raised in the above background technology.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a carbon emission CEMS remote monitoring module based on a cloud server, comprising a front-end monitoring device, a cloud server platform and a user terminal;

[0007] The front-end monitoring equipment includes flue gas analyzers, flow meters, temperature sensors, and pressure sensors, which are responsible for collecting carbon emission data in real time and uploading the data to the cloud server platform through the network module;

[0008] The cloud server platform uses big data processing and cloud computing technologies to store and analyze data uploaded by front-end monitoring devices, and provides real-time alarm, historical data query, and trend analysis functions;

[0009] The user side accesses the cloud server platform through PC and mobile devices to monitor carbon emissions in real time, generate reports, and take corresponding measures based on system warnings.

[0010] Preferably, the cloud server platform includes real-time data collection and uploading, data storage and management, real-time monitoring and early warning, data analysis and report generation, remote equipment diagnosis and maintenance, and multi-level authority management.

[0011] Preferably, the real-time data collection and uploading obtains carbon dioxide concentration, flue gas flow, temperature, and pressure data through front-end monitoring equipment, and transmits the collected data to the cloud server in real time through the Internet of Things module;

[0012] The data storage and management, the cloud server classifies and stores the uploaded monitoring data;

[0013] The real-time monitoring and early warning, the user end can view various monitoring parameters in real time, the system automatically generates monitoring charts according to the set thresholds, and when the monitoring data exceeds the set thresholds, the system automatically sends an alarm notification to the user end;

[0014] The data analysis and report generation system provides carbon emission trend forecasts and automatically generates emission reports by analyzing historical data;

[0015] The remote equipment diagnosis and maintenance monitors the operating status of CEMS equipment in real time, predicts failures, and prompts users to perform maintenance. Through remote access, technicians can diagnose equipment problems online;

[0016] The hierarchical authority management allocates different system authorities according to user roles and adopts multiple security measures such as data encryption and user authentication to protect data privacy.

[0017] Preferably, a data compression algorithm is used in the real-time data collection and uploading, and Huffman coding and LZ77 are selected to compress the data;

[0018] This includes the calculation of compression ratio: Let the size of uncompressed data be S u , the compressed data size is S c , the compression ratio is:

[0019]

[0020] Compressed transmission time: Assume the transmission rate is R, then the transmission time of uncompressed data is The transmission time of compressed data is The optimization goal is to minimize T c ;

[0021] The real-time data collection and upload adopts data sharding and parallel transmission, which includes sharding processing: sharding a large data set into small fragments, each of which is of size, and transmitting through multiple parallel channels.

[0022] Transmission time calculation: Assume the number of parallel channels is M, then the transmission time is:

[0023]

[0024] By adjusting the value of M, T parallel Minimum.

[0025] Preferably, the data analysis and report generation includes intelligent analysis optimization, specifically including data preprocessing: standardizing the input data, assuming that the data set is X = {x1, x2, ..., x n}, the standardized data is:

[0026]

[0027] Among them, μ is the mean and σ is the standard deviation;

[0028] The PCA method is used to select the main features, and the data after dimensionality reduction is:

[0029] Z=XW

[0030] Where W is the feature matrix;

[0031] Prediction model optimization: Use multi-layer perceptron or long short-term memory network machine learning models for trend forecasting;

[0032] Minimize the loss function: Let the true value be y and the predicted value be Then the loss function is:

[0033]

[0034] The model parameters are continuously adjusted through the back-propagation algorithm to minimize the loss function L.

[0035] Preferably, the remote equipment diagnosis and maintenance adopts equipment maintenance prediction optimization to predict equipment maintenance needs in advance by analyzing equipment operation data, thereby reducing downtime;

[0036] Specifically, it includes data collection and feature extraction: collecting equipment operation status data: including temperature, vibration, and operation time; extracting features: assuming that the feature vector is f = {f1, f2, ..., f m}, select the features related to the fault through correlation analysis,

[0037] Fault prediction model: Model training: Use historical fault data to train the prediction model. Let the model output be P (fault), which represents the probability of a fault.

[0038] Threshold setting: Set the fault alarm threshold T. When P(fault)>T, the system will alarm and recommend maintenance.

[0039] Maintenance optimization: Maintenance timing selection: Calculate the optimal maintenance timing based on the predicted failure probability and the importance of equipment operation optimal , to minimize maintenance costs and failure losses:

[0040] C total =C maintenance (t optimal )+C failure ×P(fault)

[0041] Optimization goal: Select t optimal Make C total Minimum.

[0042] Preferably, the remote equipment diagnosis and maintenance also includes a feedback loop mechanism and adaptive optimization: Collect fault processing data: The data after each fault processing is stored in the cloud server, and the data after each fault processing is fed back to the model to update the model parameters. The model parameters θ are updated using the gradient descent method or other optimization algorithms through the feedback data after each fault processing:

[0043]

[0044] Among them, α is the learning rate, is the gradient of the loss function,

[0045] Processing step optimization: Analyze the steps and results of each fault handling, find the most efficient steps, and record the optimized processing steps in the system.

[0046] Preferably, the real-time monitoring and early warning adopts WebSocket or HTTP long connection technology to obtain the latest monitoring data in real time. The data refresh frequency is dynamically adjusted according to network conditions and user needs. The default setting is to refresh once per second. A data cache layer is set on the user side to store the monitoring data of the most recent period.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. The carbon emission CEMS remote monitoring system will be able to achieve more efficient data transmission, more accurate carbon emission prediction, more reliable equipment maintenance, and self-learning and optimization of the system through adaptive algorithms, thereby continuously improving the performance and stability of the system;

[0049] 2. The system combines traditional carbon emission monitoring with modern cloud computing and big data technologies, greatly improving monitoring efficiency and data analysis capabilities, and providing more advanced technical support for corporate environmental management. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 This is a system block diagram of the carbon emission CEMS remote monitoring module of the cloud server of the present invention;

[0052] Figure 2 This is a block diagram of the cloud server platform module of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] See also Figure 1 to Figure 2 , the present invention provides a technical solution:

[0055] A carbon emission CEMS remote monitoring module based on a cloud server, comprising a front-end monitoring device, a cloud server platform and a user terminal;

[0056] The front-end monitoring equipment includes flue gas analyzers, flow meters, temperature sensors, and pressure sensors, which are responsible for collecting carbon emission data in real time and uploading the data to the cloud server platform through the network module;

[0057] The cloud server platform uses big data processing and cloud computing technologies to store and analyze data uploaded by front-end monitoring devices, and provides real-time alarm, historical data query, and trend analysis functions;

[0058] The user side accesses the cloud server platform through PC and mobile devices to monitor carbon emissions in real time, generate reports, and take corresponding measures based on system warnings.

[0059] Specifically, the cloud server platform includes real-time data collection and upload, data storage and management, real-time monitoring and early warning, data analysis and report generation, remote equipment diagnosis and maintenance, and multi-level authority management.

[0060] Specifically, the real-time data collection and uploading obtains carbon dioxide concentration, flue gas flow, temperature, and pressure data through front-end monitoring equipment, and transmits the collected data to the cloud server in real time through the Internet of Things module;

[0061] The data storage and management, the cloud server classifies and stores the uploaded monitoring data;

[0062] The real-time monitoring and early warning, the user end can view various monitoring parameters in real time, the system automatically generates monitoring charts according to the set thresholds, and when the monitoring data exceeds the set thresholds, the system automatically sends an alarm notification to the user end;

[0063] The data analysis and report generation system provides carbon emission trend forecasts and automatically generates emission reports by analyzing historical data;

[0064] The remote equipment diagnosis and maintenance monitors the operating status of CEMS equipment in real time, predicts failures, and prompts users to perform maintenance. Through remote access, technicians can diagnose equipment problems online;

[0065] The hierarchical authority management allocates different system authorities according to user roles and adopts multiple security measures such as data encryption and user authentication to protect data privacy.

[0066] Specifically, the real-time data collection and uploading adopts a data compression algorithm, and Huffman coding and LZ77 are selected to compress the data;

[0067] This includes the calculation of compression ratio: Let the size of uncompressed data be S u , the compressed data size is S c , the compression ratio is:

[0068]

[0069] Compressed transmission time: Assume the transmission rate is R, then the transmission time of uncompressed data is The transmission time of compressed data is The optimization goal is to minimize T c ;

[0070] The real-time data collection and upload adopts data sharding and parallel transmission, which includes sharding processing: sharding a large data set into small fragments, each of which is of size, and transmitting through multiple parallel channels.

[0071] Transmission time calculation: Assume the number of parallel channels is M, then the transmission time is:

[0072]

[0073] By adjusting the value of M, Tparallel Minimum.

[0074] Specifically, the data analysis and report generation includes intelligent analysis optimization, including data preprocessing: standardizing the input data, assuming that the data set is X = {x1, x2, ..., x n}, the standardized data is:

[0075]

[0076] Among them, μ is the mean and σ is the standard deviation;

[0077] The PCA method is used to select the main features, and the data after dimensionality reduction is:

[0078] Z=XW

[0079] Where W is the feature matrix;

[0080] Prediction model optimization: Use multi-layer perceptron or long short-term memory network machine learning models for trend forecasting;

[0081] Minimize the loss function: Let the true value be y and the predicted value be Then the loss function is:

[0082]

[0083] The model parameters are continuously adjusted through the back-propagation algorithm to minimize the loss function L.

[0084] Specifically, the remote equipment diagnosis and maintenance adopts equipment maintenance prediction optimization to predict equipment maintenance needs in advance by analyzing equipment operation data, thereby reducing downtime;

[0085] Specifically, it includes data collection and feature extraction: collecting equipment operation status data: including temperature, vibration, and operation time; extracting features: assuming that the feature vector is f = {f1, f2, ..., f m}, select the features related to the fault through correlation analysis,

[0086] Fault prediction model: Model training: Use historical fault data to train the prediction model. Let the model output be P (fault), which represents the probability of a fault.

[0087] Threshold setting: Set the fault alarm threshold T. When P(fault)>T, the system will alarm and recommend maintenance.

[0088] Maintenance optimization: Maintenance timing selection: Calculate the optimal maintenance timing based on the predicted failure probability and the importance of equipment operation optimal , to minimize maintenance costs and failure losses:

[0089] C total =C maintenance (t optimal )+C failure ×P(fault)

[0090] Optimization goal: Select t optimal Make C total Minimum.

[0091] Specifically, the remote equipment diagnosis and maintenance also includes a feedback loop mechanism and adaptive optimization: Collect fault processing data: The data after each fault processing is stored in the cloud server, and the data after each fault processing is fed back to the model to update the model parameters. The model parameters θ are updated using the gradient descent method or other optimization algorithms through the feedback data after each fault processing:

[0092] θ new =θ old -α▽L(θ);

[0093] Among them, α is the learning rate, ▽L(θ) is the gradient of the loss function,

[0094] Processing step optimization: Analyze the steps and results of each fault handling, find the most efficient steps, and record the optimized processing steps in the system.

[0095] Specifically, the real-time monitoring and early warning optimizes the viewing of real-time monitoring parameters on the user side, provides real-time and accurate monitoring parameters, and ensures that users can obtain information intuitively and conveniently.

[0096] 1. Data refresh and push mechanism:

[0097] Real-time data update: WebSocket or HTTP long connection technology is used to ensure that the user end can obtain the latest monitoring data in real time. The data refresh frequency can be dynamically adjusted according to network conditions and user needs, and the default setting is to refresh once per second.

[0098] Data caching mechanism: A data caching layer is set up on the user side to store the monitoring data of the recent period to reduce the frequency of network transmission and improve the data response speed.

[0099] 2. User interface optimization:

[0100] Dynamic dashboard design: Use responsive design to make the dashboard adapt to the screen size of different devices. Use high-contrast color schemes to ensure that users can clearly see the parameters in different lighting environments.

[0101] Personalized data view: allows users to customize the displayed monitoring parameters according to their needs, and can set different chart types (such as line charts, bar charts, pie charts, etc.) for display.

[0102] 3. Parameter reminder and alarm:

[0103] Visual alarm prompt: When the monitoring parameters exceed the set threshold, the system automatically triggers the alarm mechanism and issues an alarm with highlighted color and pop-up prompts in the user interface.

[0104] Sound and vibration alerts: Optional sound and vibration alerts (for mobile devices) to enhance user attention to abnormal situations.

[0105] Historical data playback: Users can playback historical data to view monitoring conditions and trends over a specific time period.

[0106] 2. The system automatically generates monitoring charts with optimized content. The system automatically generates monitoring charts based on the set thresholds, allowing users to intuitively understand the changes and trends in monitoring data.

[0107] 1. Automatically generate chart logic:

[0108] Threshold setting: Users can set upper and lower thresholds for each monitoring parameter. When the parameters reach or exceed these thresholds, the system automatically generates corresponding charts and marks abnormal data points.

[0109] Chart type selection: Automatically select the most suitable chart type based on the nature of the monitoring parameters and the user's needs. For example, for time series data, select a line chart or an area chart; for proportional data, select a pie chart or a stacked bar chart.

[0110] 2. Dynamic chart generation:

[0111] Real-time data streaming charts: Using real-time streaming chart technology, the chart can be automatically updated and reflect the changes in parameters in real time. For example, libraries such as D3.js and Chart.js can be used to create dynamic visualization charts.

[0112] Intelligent chart annotation: The system automatically generates annotations based on data changes, marking important trends, peaks or abnormal points. For example, when a parameter suddenly rises or falls, the system will add annotations to the chart to explain the possible reasons or suggest users to pay attention.

[0113] 3. Data analysis and prediction:

[0114] Trend analysis charts: The system generates trend charts based on historical data to predict parameter changes over a period of time. Short-term forecasts are made using methods such as moving average or exponential smoothing.

[0115] Anomaly detection chart: Combine machine learning algorithms (such as KMeans clustering or Isolation Forest) to detect outliers in the data and generate corresponding anomaly detection charts to help users identify potential problems.

[0116] 4. Chart export and sharing:

[0117] Multi-format export: Allows users to export generated charts into multiple formats (such as PNG, PDF, Excel, etc.) to facilitate report writing and data sharing.

[0118] One-click sharing: Integrated social sharing function, users can share charts with others via email, instant messaging tools or social media.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A carbon emission CEMS remote monitoring module based on cloud server, It is characterized in that Including front-end monitoring equipment, cloud server platform and user end; The front-end monitoring equipment includes flue gas analyzers, flow meters, temperature sensors, and pressure sensors, which are responsible for collecting carbon emission data in real time and uploading the data to the cloud server platform through the network module; The cloud server platform uses big data processing and cloud computing technologies to store and analyze data uploaded by front-end monitoring devices, and provides real-time alarm, historical data query, and trend analysis functions; The user side accesses the cloud server platform through PC and mobile devices to monitor carbon emissions in real time, generate reports, and take corresponding measures based on system warnings.

2. According to claim 1, a carbon emission CEMS remote monitoring module based on a cloud server, Features: The cloud server platform includes real-time data collection and uploading, data storage and management, real-time monitoring and early warning, data analysis and report generation, remote equipment diagnosis and maintenance, and multi-level authority management.

3. According to claim 2, a carbon emission CEMS remote monitoring module based on a cloud server, Features: The real-time data collection and uploading obtains the carbon dioxide concentration, flue gas flow, temperature and pressure data through the front-end monitoring equipment, and transmits the collected data to the cloud server in real time through the Internet of Things module; The data storage and management, the cloud server classifies and stores the uploaded monitoring data; The real-time monitoring and early warning, the user end can view various monitoring parameters in real time, the system automatically generates monitoring charts according to the set thresholds, and when the monitoring data exceeds the set thresholds, the system automatically sends an alarm notification to the user end; The data analysis and report generation system provides carbon emission trend forecasts and automatically generates emission reports by analyzing historical data; The remote equipment diagnosis and maintenance monitors the operating status of CEMS equipment in real time, predicts failures, and prompts users to perform maintenance. Through remote access, technicians can diagnose equipment problems online; The hierarchical authority management allocates different system authorities according to user roles and adopts multiple security measures such as data encryption and user authentication to protect data privacy.

4. According to claim 3, a carbon emission CEMS remote monitoring module based on a cloud server, Features: The real-time data collection and uploading adopts data compression algorithm, and Huffman coding and LZ77 are selected to compress the data; This includes the calculation of compression ratio: Let the size of uncompressed data be S u , the compressed data size is S c , the compression ratio is: Compressed transmission time: Assume the transmission rate is R, then the transmission time of uncompressed data is The transmission time of compressed data is The optimization goal is to minimize T c ; The real-time data collection and upload adopts data sharding and parallel transmission, which includes sharding processing: sharding a large data set into small fragments, each of which is of size, and transmitting through multiple parallel channels. Transmission time calculation: Assume the number of parallel channels is M, then the transmission time is: By adjusting the value of M, T parallel Minimum.

5. According to claim 4, a carbon emission CEMS remote monitoring module based on a cloud server, Features: The data analysis and report generation includes intelligent analysis optimization, specifically including data preprocessing: standardizing the input data, assuming that the data set is X={x 1 ,x 2 ,...,x n }, the standardized data is: Among them, μ is the mean and σ is the standard deviation; The PCA method is used to select the main features, and the data after dimensionality reduction is: Z=XW Where W is the feature matrix; Prediction model optimization: Use multi-layer perceptron or long short-term memory network machine learning models for trend forecasting; Minimize the loss function: Let the true value be y and the predicted value be Then the loss function is: The model parameters are continuously adjusted through the back-propagation algorithm to minimize the loss function L.

6. A carbon emission CEMS remote monitoring module based on a cloud server according to claim 5, Features: The remote equipment diagnosis and maintenance adopts equipment maintenance prediction optimization to predict equipment maintenance needs in advance by analyzing equipment operation data, thereby reducing downtime; Specifically include data collection and feature extraction: collecting equipment operating status data: including temperature, vibration, operating time, extracting features: setting The eigenvector is f = {f 1 ,f 2 ,...,f m }, select the features related to the fault through correlation analysis, Fault prediction model: Model training: Use historical fault data to train the prediction model. Let the model output be P (fault), which represents the probability of a fault. Threshold setting: Set the fault alarm threshold T. When P(fault)>T, the system will alarm and recommend maintenance. Maintenance optimization: Maintenance timing selection: Calculate the optimal maintenance timing based on the predicted failure probability and the importance of equipment operation optimal , to minimize maintenance costs and failure losses: C total =C maintenance (t optimal )+C failure ×P(fault) Optimization goal: Select t optimal Make C total Minimum.

7. A carbon emission CEMS remote monitoring module based on a cloud server according to claim 6, Features: The remote equipment diagnosis and maintenance also includes a feedback loop mechanism and adaptive optimization: Collect fault processing data: The data after each fault processing is stored in the cloud server, and the data after each fault processing is fed back to the model to update the model parameters. The model parameters θ are updated using the gradient descent method or other optimization algorithms through the feedback data after each fault processing: i new =θ old -α▽L(θ); Among them, α is the learning rate, ▽L(θ) is the gradient of the loss function, Processing step optimization: Analyze the steps and results of each fault handling, find the most efficient steps, and record the optimized processing steps in the system.

8. According to claim 3, a carbon emission CEMS remote monitoring module based on a cloud server, Features: The real-time monitoring and early warning adopts WebSocket or HTTP long connection technology to obtain the latest monitoring data in real time. The data refresh frequency is dynamically adjusted according to network conditions and user needs. The default setting is to refresh once per second. A data cache layer is set on the user side to store the monitoring data of the most recent period.

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