CN120181443A - Gas data collection analysis system - Google Patents

Gas data collection analysis system Download PDF

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CN120181443A
CN120181443A CN202510212007.8A CN202510212007A CN120181443A CN 120181443 A CN120181443 A CN 120181443A CN 202510212007 A CN202510212007 A CN 202510212007A CN 120181443 A CN120181443 A CN 120181443A
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曹欣
谭建鑫
陆阳
宋志勇
解帅
杨美红
沙治金
张泽翔
李佳旭
许嘉怡
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Abstract

本发明提供了一种燃气数据收集分析系统，包括数据采集模块、数据传输模块、数据存储模块、数据分析模块以及报警与可视化模块。系统通过流量、压力、温度和气体浓度传感器实时采集燃气运行参数，利用校正算法提升数据准确性，基于无线通信技术将数据传输至云端或本地服务器存储，并采用分布式架构保证数据安全与高效存储。本发明的燃气数据收集分析系统利用时间序列预测模型和聚类算法实现燃气需求预测与异常检测，精确识别泄漏或异常运行情况，当检测到燃气泄漏、流量异常或设备故障时，自动触发报警并远程通知相关人员，同时提供数据可视化界面，支持实时监控与趋势分析。

The present invention provides a gas data collection and analysis system, including a data acquisition module, a data transmission module, a data storage module, a data analysis module, and an alarm and visualization module. The system collects gas operation parameters in real time through flow, pressure, temperature and gas concentration sensors, uses correction algorithms to improve data accuracy, transmits data to the cloud or local server storage based on wireless communication technology, and adopts a distributed architecture to ensure data security and efficient storage. The gas data collection and analysis system of the present invention uses time series prediction models and clustering algorithms to achieve gas demand prediction and anomaly detection, accurately identify leaks or abnormal operation conditions, and automatically triggers an alarm and remotely notifies relevant personnel when gas leaks, flow anomalies or equipment failures are detected. At the same time, it provides a data visualization interface to support real-time monitoring and trend analysis.

Description

Gas data collection analysis system
Technical Field
The invention relates to the technical field of gas data processing, in particular to a gas data collecting and analyzing system.
Background
With the acceleration of the urban process, the fuel gas is widely applied to industrial production, commercial operation and resident life as a clean and efficient energy source. The operation efficiency and the safety of the gas system are directly related to the reasonable utilization of energy and social public safety. All local gas enterprises and management parts are forced to develop automatic monitoring and control systems so as to monitor the transmission and use of the gas, ensure the safety of the gas and improve the efficiency of the gas.
However, because the gas system has a complex operating environment, a large number of pipelines and users are involved, and various leakage problems, insufficient consumption problems, low allocation efficiency and the like often exist. Specifically, the gas conveying system and the monitoring system in the prior art have the following main problems to be solved (1) the multi-dimensional data acquisition is insufficient, the current monitoring means of the gas system mainly depend on a single type of sensor (such as a pressure or flow sensor), the acquired data has limited dimension, and the comprehensive operation condition of the gas system cannot be accurately reflected. (2) The frequency of data acquisition is low, and the dynamic change gas data cannot be captured in real time, so that monitoring response is lagged. (3) Data transmission instability, traditional gas systems mostly adopt wired communication modes, such as RS485 or optical fiber communication. Although the wired transmission has high reliability, it is susceptible to physical damage, line aging, or external environmental interference, resulting in transmission interruption. (4) The data processing and analysis capability is limited, that is, the existing gas monitoring system can only perform simple threshold comparison or statistical analysis, lacks deep data mining capability, and is difficult to perform pattern recognition and trend prediction on mass data. (5) The alarm and early warning mechanism is weak, namely the alarm mechanism of the existing system is usually set based on a single data threshold value, the relevance between multi-dimensional data cannot be fully considered, and false alarm or missing alarm is easy to occur. For this reason, a gas data collection and analysis system is urgently designed to solve the above problems.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a fuel gas data collecting and analyzing system. The gas data collection and analysis system provided by the invention can monitor parameters such as flow, pressure, temperature, concentration and the like of a gas pipeline in real time, identify potential risks in time, and rapidly inform the processing, so that potential safety hazards are effectively reduced, and the reliability and operation safety of the gas system are improved.
In order to achieve the above object, the present invention provides the following technical solutions:
the invention provides a fuel gas data collection and analysis system, which comprises a data acquisition module, a data transmission module, a data storage module, a data analysis module, an early warning and decision module and a user interface module, wherein:
The data acquisition module is used for acquiring the gas flow (Q i), the pressure (P i), the temperature (T i) and the gas concentration (C i) in real time through the sensor and correcting the flow data according to the following formula:
Wherein Q i is the corrected flow of the ith gas pipeline, K is a correction coefficient related to the gas type, the pipeline diameter and the like, P i is the gas pressure in the pipeline, V i is the uncorrected instantaneous flow, and T i is the gas temperature;
The data transmission module is used for transmitting the acquired data to the data storage module in a wireless communication mode, and the data transmission rate R meets the following formula:
Wherein R is the data transmission rate, B is the channel bandwidth, P signal is the signal power (unit: watts), P noise is the noise power (unit: watts);
The data storage module is used for storing the acquired fuel gas data, and comprises real-time storage and history archiving;
the data analysis module comprises time sequence analysis, cluster analysis and classification analysis and is used for predicting gas requirements and detecting gas anomalies;
The early warning and decision module generates gas abnormality early warning signals A leak and A flow and a resource optimization scheduling scheme D supply based on the output of the data analysis module;
And the user interface module is used for displaying the gas running state, the historical trend and the alarm information through the visual interface.
As a preferred technical solution of the present invention, the correction coefficient K is calculated according to the following formula:
wherein R gas is the molar gas constant of fuel gas, and D is the diameter of the pipeline.
As a preferable technical scheme of the invention, the data analysis module predicts the gas demand based on a time sequence model, and the prediction formula is as follows:
Wherein: q (t) is the actual measurement flow at the time t; And predicting flow at the moment t, wherein alpha is a smoothing coefficient and satisfies 0< alpha <1.
As a preferable technical scheme of the invention, the data analysis module detects abnormal gas through a clustering algorithm, and the objective function is as follows:
Wherein J is a clustering objective function value, n is the number of data points, and k is the number of clustering centers;
x i is a data point, c j is a cluster center, z ij indicates whether data point x i belongs to the center c j,zij =1, z ij =0 does not belong.
As a preferable technical scheme of the invention, the early warning module classifies abnormal gas based on a Support Vector Machine (SVM), and the classification function is as follows:
The method comprises the steps of (a) taking f (x) as a classification result, alpha i and b as support vector machine model parameters, y i as a sample label, and K (x, x i) as a kernel function, wherein a Gaussian kernel function form is adopted:
as a preferable technical scheme of the invention, the gas leakage alarm in the early warning and decision module is judged by the following triggering conditions:
Wherein Δp=p in-Pout is the differential pressure, γ is the safety differential pressure threshold, C is the gas concentration, and C threshold is the safety concentration threshold.
As a preferred technical scheme of the invention, the user interface module calculates a trend value of the fuel gas consumption by the following formula and generates a trend chart:
Wherein T t is the average gas consumption at time T, m is the number of users, and Q i (T) is the gas flow of the ith user at time T.
As a preferable technical scheme of the invention, the early warning module supports gas resource scheduling optimization, and the objective function is as follows:
Where C is the total cost of the gas supply, a, b, C are weight coefficients, Q i is the gas flow, and P i and ΔP i are the pressure and pressure variation, respectively.
As a preferable technical scheme of the invention, the gas leakage probability (P leak) is calculated based on multi-source data in a joint way, and the formula is as follows:
Wherein P leak is the total leakage probability, and P i is the detection probability of the ith data source.
As a preferable technical scheme of the invention, the data analysis module adopts a comprehensive evaluation model of gas abnormality, the model is combined with the results of time sequence prediction, cluster analysis and support vector machine to evaluate the overall risk level of gas operation, and the risk function is as follows:
R=w1·J+w2·Pleak+w3·ΔQ
Wherein R is a comprehensive risk, J is a clustering target value, and P leak is a leakage probability; Is the flow rate change, and w 1、w2、w3 is the weight coefficient.
Based on the technical scheme, the gas data collection and analysis system provided by the invention has the following beneficial effects through practical application:
1. The gas data collection and analysis system can monitor parameters such as flow, pressure, temperature, concentration and the like of a gas pipeline in real time through multidimensional data collection and intelligent analysis technology, timely identify potential risks such as gas leakage, abnormal flow or equipment failure and the like, and rapidly inform related personnel to process through an intelligent alarm mechanism. The potential safety hazard is effectively reduced, and the reliability and the operation safety of the gas system are obviously improved.
2. The gas data collection and analysis system utilizes machine learning and big data analysis technology to deeply mine historical data and real-time data, provides a gas demand prediction, energy consumption analysis and resource scheduling optimization scheme, helps gas operation enterprises to reasonably allocate resources, and reduces energy waste. Meanwhile, through the modularized design, the system can be quickly adapted to different scene requirements, and support is provided for intelligent management of the gas system.
3. According to the gas data collection and analysis system, unmanned and automatic gas monitoring is realized through a wireless communication technology and a remote monitoring platform, and the workload and the labor cost of traditional manual inspection are reduced. The alarm and visualization functions support cross-platform operation (such as PC end, mobile end APP and the like), a user can check the gas running state in real time, management efficiency is improved, and operation cost is remarkably reduced.
Drawings
FIG. 1 is a schematic diagram of an organization architecture of a gas data collection analysis system of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
A detailed description of specific embodiments of the present invention will be given below with reference to a plurality of examples and fig. 1.
The invention relates to a fuel gas data collecting and analyzing system which comprises a data acquisition module, a data transmission module, a data storage module, a data analysis module, an early warning and decision module and a user interface module. Wherein,
The data acquisition module is used for acquiring the gas flow (Q i), the pressure (P i), the temperature (T i) and the gas concentration (C i) in real time through the sensor and correcting the flow data according to the following formula:
Wherein Q i is the corrected flow of the ith gas pipeline, K is a correction coefficient related to the gas type, the pipeline diameter and the like, P i is the gas pressure in the pipeline, V i is the uncorrected instantaneous flow, T i is the gas temperature, and the correction coefficient K is calculated according to the following formula:
wherein R gas is the molar gas constant of fuel gas, and D is the diameter of the pipeline.
The data transmission module is used for transmitting the acquired data to the data storage module in a wireless communication mode, and the data transmission rate R meets the following formula:
Wherein R is the data transmission rate, B is the channel bandwidth, P signal is the signal power (unit: watts), P noise is the noise power (unit: watts);
The data storage module is used for storing the acquired fuel gas data, and comprises real-time storage and history archiving;
the data analysis module comprises time sequence analysis, cluster analysis and classification analysis and is used for predicting gas requirements and detecting gas anomalies;
the data analysis module predicts the gas demand based on the time sequence model, and the prediction formula is as follows:
Wherein: q (t) is the actual measurement flow at the time t; The flow is predicted for the last moment of the moment t, alpha is a smooth coefficient, and 0< alpha <1 is satisfied;
The data analysis module detects abnormal gas through a clustering algorithm, and the objective function is as follows:
Wherein J is a clustering objective function value, n is the number of data points, and k is the number of clustering centers;
x i is a data point, c j is a cluster center, z ij indicates whether data point x i belongs to the center c j,zij =1, z ij =0 does not belong.
The early warning and decision module generates gas abnormality early warning signals A leak and A flow and a resource optimization scheduling scheme D supply based on the output of the data analysis module;
The early warning module supports gas resource scheduling optimization, and the objective function is as follows:
Where C is the total cost of the gas supply, a, b, C are weight coefficients, Q i is the gas flow, and P i and ΔP i are the pressure and pressure variation, respectively.
The early warning module classifies abnormal gas based on a Support Vector Machine (SVM), and the classification function is as follows:
The method comprises the steps of (a) taking f (x) as a classification result, alpha i and b as support vector machine model parameters, y i as a sample label, and K (x, x i) as a kernel function, wherein a Gaussian kernel function form is adopted:
The gas leakage alarm in the early warning and decision module is judged by the following triggering conditions:
Wherein Δp=p in-Pout is the differential pressure, γ is the safety differential pressure threshold, C is the gas concentration, and C threshold is the safety concentration threshold.
And the user interface module is used for displaying the gas running state, the historical trend and the alarm information through the visual interface.
The user interface module calculates a trend value of the fuel gas consumption through the following formula and generates a trend chart:
Wherein T t is the average gas consumption at time T, m is the number of users, and Q i (T) is the gas flow of the ith user at time T.
The gas leakage probability (P leak) is calculated based on the multi-source data in a joint way, and the formula is as follows:
Wherein P leak is the total leakage probability, and P i is the detection probability of the ith data source.
The data analysis module adopts a comprehensive evaluation model of gas abnormality, the model is combined with the results of time sequence prediction, cluster analysis and support vector machine to evaluate the overall risk level of gas operation, and the risk function is as follows:
R=w1·J+w2·Pleak+w3·ΔQ
Wherein R is a comprehensive risk, J is a clustering target value, and P leak is a leakage probability; Is the flow rate change, and w 1、w2、w3 is the weight coefficient.
Further description of the embodiments follows;
Example 1
The embodiment is a gas data acquisition and correction link in the operation of a gas data acquisition and analysis system:
The gas data acquisition module comprises the following sensors, namely a flow sensor, a pressure sensor, a temperature sensor, a concentration sensor and a concentration sensor, wherein the flow sensor is used for measuring instantaneous flow V i in real time, the unit is m 3/h, the pressure sensor is used for measuring gas pressure P i in a pipeline, the unit is MPa, the temperature sensor is used for measuring gas temperature T i, the unit is K, and the concentration sensor is used for measuring the concentration of combustible substances C i in the gas. The sensor of each node is connected to a data acquisition terminal (controller) through an RS485 bus, and the controller is responsible for preliminary processing and data storage.
The flow correction algorithm is implemented by correcting the gas flow Q i by the following formula:
K is a correction coefficient, and a calculation formula is as follows, depending on the type of fuel gas, the diameter of a pipeline and the like:
Wherein R gas is the molar gas constant of the fuel gas, the value is 8.314J/(mol k), and D is the diameter of the pipeline, and the unit is m.
The practical deployment case is an experimental environment, namely a gas pipe network (diameter D=0.1m, gas is natural gas) of a residential area of a certain city.
Data acquisition, namely, flow sensor reading, namely, V i＝12m3/h, pressure sensor reading, namely, P i =0.4 MPa, and temperature sensor reading, namely, T i =298K.
Calculating a correction coefficient K:
correction flow Q i:
example 2
The embodiment is a design and realization link of a data transmission module in the operation of the gas data collection and analysis system;
The data transmission architecture comprises a data acquisition terminal uploading data to a cloud server transmission interval in a wireless communication mode (such as LoRa or NB-IoT), wherein the data is uploaded once every 1 minute, and each uploaded data comprises flow Q i, pressure P i, temperature T i and concentration C io
Transmission rate optimization-transmission rate is calculated by the following formula:
In the experimental environment, it is assumed that the channel bandwidth b=125 kHz, the signal power P signal = -70dBm, and the noise power P noise = -100dBm.
Conversion power unit:
Psignal＝10-7W,Pnoise＝10-10W
Calculating the signal-to-noise ratio:
Data transmission rate:
R=125·103·log2(1+103)≈830kbit/s
And the transmission mechanism is that the data uploaded each time is subjected to AES encryption to ensure the safety, and CRC check is adopted in the data transmission process to ensure the integrity of the data.
Example 3
The embodiment is a link of gas demand prediction and time sequence analysis in the operation of a gas data collection and analysis system;
the time sequence prediction model is used for predicting the fuel gas demand based on a sliding weighted average model, and the prediction formula is as follows:
wherein Q (t) is the actual flow at the current moment t; And alpha is a smoothing coefficient, and the value range is 0< alpha <1.
In an actual application scene, obvious fluctuation exists in the daily gas use rule of certain industrial users. Future usage is predicted by the model, ensuring stability of the supply chain.
Example data of current actual flow Q (1) =100 m 3/h, predicted value at last timeSmoothing coefficient α=0.8. The predicted values are:
Example 4
The embodiment is that the abnormal gas detection and alarm are carried out in the operation of the gas data collection and analysis system;
anomaly detection, namely detecting gas flow anomalies by using a K-Means clustering algorithm:
the clustering objective function is:
classifying and alarming, namely classifying abnormal points by using a Support Vector Machine (SVM), wherein the classification function is as follows:
The kernel function is:
The output classification result includes "normal", "leak", "abnormally high flow", and the like.
In the fifth embodiment, the gas leakage alarm and the comprehensive evaluation are performed;
Leak alarm determination, gas leak alarm is determined by the following conditions:
Example parameters are differential pressure Δp=0.07 MPa, concentration c=15 ppm, safety threshold γ=0.05 MPa, C threshold =10 ppm.
Comprehensive risk assessment, wherein a risk assessment formula is as follows:
R=w1·J+w2·Pleak+w3·ΔQ
Wherein:
risk weight w 1＝0.4,w2＝0.3,w3 = 0.3.
It should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention, and not for limiting the same, and although the present invention has been described in detail with reference to the above-mentioned embodiments, it should be understood by those skilled in the art that the technical solution described in the above-mentioned embodiments may be modified or some technical features may be equivalently replaced, and these modifications or substitutions do not make the essence of the corresponding technical solution deviate from the spirit and scope of the technical solution of the embodiments of the present invention.

Claims (8)

1. The gas data collecting and analyzing system is characterized by comprising a data acquisition module, a data transmission module, a data storage module, a data analysis module, an early warning and decision module and a user interface module:
The data acquisition module is used for acquiring gas flow (Q i), pressure (P i), temperature (T i) and gas concentration (C i) in real time through the sensor, and correcting flow data according to the following formula:
Wherein Q i is the corrected flow of the ith gas pipeline, K is a correction coefficient related to the gas type, the pipeline diameter and the like, P i is the gas pressure in the pipeline, V i is the uncorrected instantaneous flow, and T i is the gas temperature;
the data transmission module is used for transmitting the acquired data to the data storage module in a wireless communication mode, and the data transmission rate R meets the following formula:
Wherein R is the data transmission rate, B is the channel bandwidth, P signal is the signal power (unit: watts), P noise is the noise power (unit: watts);
The data storage module is used for storing the acquired fuel gas data, and comprises real-time storage and history archiving;
The data analysis module is used for predicting the gas demand and detecting gas abnormality and comprises time sequence analysis, cluster analysis and classification analysis, and the data analysis module predicts the gas demand based on a time sequence model, wherein the prediction formula is as follows:
Wherein: q (t) is the actual measurement flow at the time t; The flow is predicted at the last moment of the moment t, alpha is a smooth coefficient and satisfies 0< alpha <1, the data analysis module detects abnormal gas through a clustering algorithm, and the objective function is as follows:
Wherein J is a clustering objective function value, n is the number of data points, and k is the number of clustering centers;
x i is a data point, c j is a cluster center, z ij indicates whether the data point x i belongs to the center c j,zij =1, z ij =0 does not belong;
The early warning and decision module generates gas abnormality early warning signals A leak and A flow and a resource optimization scheduling scheme D supply based on the output of the data analysis module;
The user interface module is used for displaying the gas running state, the historical trend and the alarm information through the visual interface.
2. The gas data collection analysis system of claim 1, wherein the correction factor K is calculated according to the following formula:
wherein R gas is the molar gas constant of fuel gas, and D is the diameter of the pipeline.
3. The gas data collection analysis system of claim 1, wherein the early warning and decision module classifies gas anomalies based on a Support Vector Machine (SVM) with a classification function of:
The method comprises the steps of (a) taking f (x) as a classification result, alpha i and b as support vector machine model parameters, y i as a sample label, and K (x, x i) as a kernel function, wherein a Gaussian kernel function form is adopted:
4. The gas data collection analysis system of claim 3, wherein the gas leak alarm in the early warning and decision module is determined by the following trigger conditions:
Wherein Δp=p in-Pout is the differential pressure, γ is the safety differential pressure threshold, C is the gas concentration, and C threshold is the safety concentration threshold.
5. The gas data collection analysis system of claim 4, wherein the gas leakage probability P leak is calculated based on a combination of multi-source data, and the formula is:
Wherein P leak is the total leakage probability, and P i is the detection probability of the ith data source.
6. The gas data collection analysis system of claim 1, wherein the user interface module calculates a trend value for the gas usage and generates a trend graph by:
Wherein T t is the average gas consumption at time T, m is the number of users, and Q i (T) is the gas flow of the ith user at time T.
7. The gas data collection analysis system of claim 1, wherein the early warning and decision module supports gas resource scheduling optimization with an objective function of:
Where C is the total cost of the gas supply, a, b, C are weight coefficients, Q i is the gas flow, and P i and ΔP i are the pressure and pressure variation, respectively.
8. The gas data collection and analysis system according to claim 1, wherein the data analysis module adopts a comprehensive assessment model of gas anomalies, and the model combines the results of time series prediction, cluster analysis and support vector machine to assess the overall risk level of gas operation, and the risk function is:
R=w1·J+w2·Pleak+w3·ΔQ
Wherein R is a comprehensive risk, J is a clustering target value, and P leak is a leakage probability; Is the flow rate change, and w 1、w2、w3 is the weight coefficient.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN120368232A (en) * 2025-06-26 2025-07-25 东北石油大学三亚海洋油气研究院 Natural gas pipeline leakage monitoring system based on NB-IoT technology
CN120954178A (en) * 2025-10-15 2025-11-14 西安莱德燃气设备有限公司 A method and system for intelligent gas monitoring

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN120368232A (en) * 2025-06-26 2025-07-25 东北石油大学三亚海洋油气研究院 Natural gas pipeline leakage monitoring system based on NB-IoT technology
CN120954178A (en) * 2025-10-15 2025-11-14 西安莱德燃气设备有限公司 A method and system for intelligent gas monitoring

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