Digital precise metering system for industrial gas

Abstract

The invention discloses an industrial gas digital accurate metering system, and particularly relates to the field of industrial metering, the industrial gas digital accurate metering system comprises a data acquisition module, an analysis and judgment module, a classification and division module and a summary analysis module, and all the modules are connected through signals; the data acquisition module respectively acquires flow, pressure and temperature of industrial gas, generates a parameter acquisition signal and sends the parameter acquisition signal to the analysis and judgment module; the analysis and judgment module receives the parameter acquisition signals, analyzes and judges the parameter acquisition signals, and screens and marks abnormal data; the classification and division module classifies and divides normal data and classifies and divides abnormal data. Compared with a traditional metering system, the industrial gas metering system has the remarkable advantages of being high in metering precision, high in real-time performance, high in reliability, convenient to operate and the like, the metering management level of industrial gas can be effectively improved, the production cost is reduced, and a powerful guarantee is provided for safe and stable operation of industrial production.

Description

Technical Field

[0001] The present invention relates to the field of industrial measurement, and more specifically, to a digital precise measurement system for industrial gas. Background Art

[0002] Industrial gas is a flammable gas produced or used in the industrial production process, mainly used as fuel or chemical raw material.

[0003] In the industrial production process, gas is widely used as an important energy medium. Accurately measuring gas flow, pressure, temperature and other parameters is extremely critical for production cost accounting, energy management and production process optimization. However, traditional gas metering methods often have many problems, such as low measurement accuracy, great influence by environmental factors, untimely data collection and processing, etc., which makes it difficult to meet the needs of modern industry for accurate gas measurement and efficient management.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an industrial gas digital precision metering system to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An industrial gas digital precision metering system includes a data acquisition module, an analysis and judgment module, a classification module, and a summary and analysis module, and each module is connected through a signal;

[0008] The data acquisition module collects the flow, pressure and temperature of industrial gas respectively, and generates parameter acquisition signals and sends them to the analysis and judgment module;

[0009] The analysis and judgment module receives the parameter acquisition signal, analyzes and judges the parameter acquisition signal, and screens and marks abnormal data;

[0010] The classification module classifies and divides normal data and classifies and divides abnormal data;

[0011] The summary and analysis module summarizes and analyzes normal data and abnormal data.

[0012] In a preferred embodiment, the operation of the data acquisition module specifically includes the following:

[0013] The flow rate of industrial gas is collected through a flow sensor, the pressure of industrial gas is collected through a pressure sensor, and the temperature of industrial gas is collected through a temperature sensor;

[0014] Start the flow sensor, pressure sensor and temperature sensor, and perform calibration and self-test on the sensors;

[0015] Set the data collection frequency. The collection frequency of the flow sensor is set to 10-15 seconds, the collection frequency of the pressure sensor is set to 20-25 seconds, and the collection frequency of the temperature sensor is set to 5-10 seconds;

[0016] According to the set frequency, the flow value, pressure value and temperature value of industrial gas are collected simultaneously, and these data are integrated to generate parameter collection signals;

[0017] The generated parameter acquisition signal is transmitted wirelessly to the analysis and judgment module.

[0018] In a preferred embodiment, the analysis and judgment module operation specifically includes the following contents:

[0019] The normal threshold ranges of flow rate, pressure and temperature are pre-set, the flow rate range is set to 10-100 cubic meters per hour, the pressure range is set to 0.8-1.2 MPa, and the temperature range is set to 20-80 degrees Celsius;

[0020] Compare the received flow, pressure and temperature data with the corresponding normal threshold ranges respectively;

[0021] Perform abnormal screening and marking. If the data exceeds the normal threshold range, it is determined to be abnormal data, marked, and the type, abnormal value and collection time of the abnormal data are recorded;

[0022] The types of abnormal data include flow anomaly, pressure anomaly and temperature anomaly;

[0023] Data within the normal threshold range is judged as normal data and directly transmitted to the classification module.

[0024] In a preferred embodiment, the abnormal data is completely stored in a special abnormal database, and its detailed information is recorded;

[0025] Record abnormal events in the system log, indicating the type of abnormality and the time when the abnormality was first discovered;

[0026] Compare and analyze abnormal data with historical normal operation data and data of similar abnormal cases in the past to further confirm the severity and cause of the abnormality;

[0027] Generate corresponding emergency measures suggestions based on the preliminary diagnosis results and abnormality types.

[0028] In a preferred embodiment, the classification module operation includes the following specific contents:

[0029] Receive normal data and abnormal data from the analysis and judgment module;

[0030] Classify normal data according to their time sequence, flow range, pressure level or temperature range;

[0031] Classify abnormal data by abnormal type, and associate the time when the abnormality occurred and the duration of the abnormality.

[0032] In a preferred embodiment, the summary analysis module operation specifically includes the following contents:

[0033] Receive normal data classification results and abnormal data classification results from the classification module;

[0034] Conduct statistical analysis on normal data, calculate the average flow rate, pressure and temperature under different categories, draw trend charts of flow rate, pressure and temperature over time, and analyze the operating rules and stability of industrial gas under normal operating conditions;

[0035] Summarize and analyze abnormal data, count the number of occurrences, first occurrence time, most recent occurrence time and average duration of different abnormal types, analyze the distribution patterns and trends of abnormal data, and determine whether there are systemic failures or potential safety hazards.

[0036] In a preferred embodiment, a detailed metering analysis report is generated based on the summary analysis results of normal data and abnormal data, and the report content includes an overall operation status assessment of industrial gas, an abnormal situation description and improvement suggestions;

[0037] Based on the pressure change and flow fluctuation data of industrial gas during transportation, the energy loss of industrial gas during pipeline transmission is analyzed.

[0038] In a preferred embodiment, a trend prediction model of gas parameters is constructed based on historical normal data and abnormal data;

[0039] Predict the changing trend of gas flow, pressure and temperature in the future through time series analysis;

[0040] Based on the analysis results of abnormal data and the actual situation of industrial production, various abnormal warning thresholds are dynamically adjusted.

[0041] The technical effects and advantages of the digital accurate metering system for industrial gas of the present invention are as follows:

[0042] 1. Pre-set the normal threshold range of flow, pressure and temperature. By comparing the actual data received with the threshold, the abnormal data can be accurately screened out and marked, and the type, abnormal value and collection time of the abnormal data can be clearly identified. At the same time, the normal data can be transmitted in an orderly manner, realizing the classification management of data;

[0043] 2. Normal data is classified according to factors such as time sequence, flow size range, pressure level or temperature range, which is convenient for subsequent in-depth analysis of the normal operating status of industrial gas, such as observing the usage patterns in different time periods, the usage efficiency in different flow ranges, etc. Abnormal data is classified according to the abnormality type and associated with the occurrence time and duration. Targeted processing of abnormal situations can be achieved, the cause of the abnormality can be quickly located and its impact on production can be evaluated, making data processing more orderly and efficient, and improving the data management efficiency of the entire system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The figure is a structural schematic diagram of a digital accurate metering system for industrial gas according to the present invention. DETAILED DESCRIPTION

[0045] 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.

[0046] Example 1

[0047] Figure 1 The present invention provides an industrial gas digital precision metering system, which includes a data acquisition module, an analysis and judgment module, a classification module, and a summary and analysis module, and each module is connected by a signal;

[0048] The data acquisition module collects the flow, pressure and temperature of industrial gas respectively, and generates parameter acquisition signals and sends them to the analysis and judgment module;

[0049] The analysis and judgment module receives the parameter acquisition signal, analyzes and judges the parameter acquisition signal, and screens and marks abnormal data;

[0050] The classification module classifies and divides normal data and classifies and divides abnormal data;

[0051] The summary and analysis module summarizes and analyzes normal data and abnormal data.

[0052] The operation of the data acquisition module specifically includes the following:

[0053] The flow rate of industrial gas is collected through a flow sensor, the pressure of industrial gas is collected through a pressure sensor, and the temperature of industrial gas is collected through a temperature sensor;

[0054] Start the flow sensor, pressure sensor and temperature sensor, and perform calibration and self-test on the sensors;

[0055] Set the data collection frequency. The collection frequency of the flow sensor is set to 10 seconds, the collection frequency of the pressure sensor is set to 20 seconds, and the collection frequency of the temperature sensor is set to 5 seconds.

[0056] According to the set frequency, the flow value, pressure value and temperature value of industrial gas are collected simultaneously, and these data are integrated to generate parameter collection signals;

[0057] The generated parameter acquisition signal is transmitted wirelessly to the analysis and judgment module.

[0058] It should be noted that setting different acquisition frequencies for different parameters is an optimization strategy. Flow sensors can capture changes in gas flow in a relatively timely manner, which is very important for industrial processes that require precise control of gas flow (such as precise supply of gas according to the progress of the reaction during a chemical reaction). Pressure sensors can effectively monitor changes in pressure without excessively occupying system resources, because pressure usually changes more slowly than flow and temperature. Temperature sensors take into account that temperature may change rapidly, especially in some heat exchange processes or equipment start / stop stages. High-frequency acquisition can better track temperature dynamics and provide more refined data support for subsequent analysis and control.

[0059] The analysis and judgment module operation specifically includes the following contents:

[0060] The normal threshold ranges of flow rate, pressure and temperature are preset in advance, the flow rate range is set to 20 cubic meters per hour, the pressure range is set to 0.8 MPa, and the temperature range is set to 30 degrees Celsius;

[0061] Compare the received flow, pressure and temperature data with the corresponding normal threshold ranges respectively;

[0062] Perform abnormal screening and marking. If the data exceeds the normal threshold range, it is determined to be abnormal data, marked, and the type, abnormal value and collection time of the abnormal data are recorded;

[0063] The types of abnormal data include flow anomaly, pressure anomaly and temperature anomaly;

[0064] Data within the normal threshold range is judged as normal data and directly transmitted to the classification module.

[0065] The abnormal data is completely stored in a special abnormal database, recording its detailed information;

[0066] Record abnormal events in the system log, indicating the type of abnormality and the time when the abnormality was first discovered;

[0067] Compare and analyze abnormal data with historical normal operation data and data of similar abnormal cases in the past to further confirm the severity and cause of the abnormality;

[0068] Generate corresponding emergency measures suggestions based on the preliminary diagnosis results and abnormality types.

[0069] It should be noted that by presetting the normal threshold range of flow, pressure and temperature, the system can clearly distinguish between normal data and abnormal data. This threshold-based judgment method is simple and effective, and can promptly capture situations where industrial gas operating parameters exceed the normal range, such as sudden increase or decrease in flow, too high or too low pressure, abnormal temperature increase, etc., thereby achieving accurate monitoring of the operating status of the industrial gas system.

[0070] Filter and mark abnormal data, clarify the type of abnormal data (flow abnormality, pressure abnormality and temperature abnormality), abnormal value and collection time, and directly transmit normal data to the classification module, which is helpful for subsequent data classification and processing. This classification management can make the data more orderly in the subsequent analysis and processing process, facilitate the use of targeted processing methods for different types of data, and improve the efficiency of data processing.

[0071] Comparing and analyzing abnormal data with historical normal operation data and past similar abnormal case data can further confirm the severity and scope of the abnormality. By referring to historical data, it is possible to find out whether the current abnormality is an accidental event, whether there is a potential systemic problem, or whether it has a similar pattern to the previous abnormality, providing strong support for accurately judging the nature of the abnormality and possible causes.

[0072] The classification module operation includes the following specific contents:

[0073] Receive normal data and abnormal data from the analysis and judgment module;

[0074] Classify normal data according to their time sequence, flow range, pressure level or temperature range;

[0075] Classify abnormal data by abnormal type, and associate the time when the abnormality occurred and the duration of the abnormality.

[0076] It should be noted that classifying normal data according to factors such as time sequence, flow size range, pressure level or temperature range can facilitate the subsequent in-depth analysis of the normal operation status of industrial gas. For example, by classifying by time sequence, the usage pattern of industrial gas in different time periods can be observed; by classifying by flow size range, the gas usage efficiency and the operation status of related equipment in different flow ranges can be analyzed; by classifying by pressure level and temperature range, it is helpful to understand the performance of gas under different physical states, so as to better optimize the industrial production process.

[0077] Abnormal data is classified according to the abnormal type, and the time and duration of the abnormality are associated, so that targeted handling of abnormal situations can be achieved. Different types of abnormalities may correspond to different causes and handling methods. For example, flow abnormalities may involve pipeline leakage or gas equipment failure, and pressure abnormalities may be related to pipeline blockage or compressor failure. Through this classification method, the cause of the abnormality can be quickly located, and the impact on production can be evaluated based on the duration of the abnormality, so that more accurate measures can be taken to solve the problem.

[0078] The operation of the summary analysis module specifically includes the following contents:

[0079] Receive normal data classification results and abnormal data classification results from the classification module;

[0080] Conduct statistical analysis on normal data, calculate the average flow rate, pressure and temperature under different categories, draw trend charts of flow rate, pressure and temperature over time, and analyze the operating rules and stability of industrial gas under normal operating conditions;

[0081] Summarize and analyze abnormal data, count the number of occurrences, first occurrence time, most recent occurrence time and average duration of different abnormal types, analyze the distribution patterns and trends of abnormal data, and determine whether there are systemic failures or potential safety hazards.

[0082] It should be noted that summarizing and analyzing abnormal data and collecting information such as the number of occurrences, first occurrence time, most recent occurrence time, and average duration of different abnormal types can provide important clues for troubleshooting possible faults in industrial gas systems. For example, if the number of occurrences of a certain type of abnormality (such as flow abnormality) increases frequently and the average duration becomes longer, this may indicate that there are persistent problems with related equipment (such as flow meters, valves, etc.) that require further in-depth inspection and repair.

[0083] Based on the comprehensive analysis of abnormal data, it can be determined whether there is a systemic fault. When multiple types of abnormalities appear at the same time and show a certain correlation and regularity, it is likely that a core link of the entire industrial gas system (such as gas generators, pipeline networks, etc.) has a systemic problem. This judgment helps to organize comprehensive system inspections and repairs in a timely manner to prevent the fault from further expanding and affecting the normal progress of the entire industrial production.

[0084] By analyzing the distribution patterns and trends of abnormal data, it is possible to discover potential safety hazards in advance. For example, if the abnormal pressure data shows a trend of gradually increasing and the fluctuation range is getting larger, even if it has not yet caused obvious failures, it may indicate that there is a risk of pressure overload in the pipeline system. Preventive measures need to be taken in time, such as strengthening pipeline monitoring and adjusting pressure control strategies, to avoid possible safety accidents.

[0085] Generate a detailed measurement analysis report based on the summary and analysis results of normal and abnormal data. The report includes the overall operation status evaluation of industrial gas, abnormal situation description and improvement suggestions;

[0086] Based on the pressure change and flow fluctuation data of industrial gas during transportation, the energy loss of industrial gas during pipeline transmission is analyzed.

[0087] Based on historical normal data and abnormal data, a trend prediction model for gas parameters is constructed;

[0088] Predict the changing trend of gas flow, pressure and temperature in the future through time series analysis;

[0089] Based on the analysis results of abnormal data and the actual situation of industrial production, various abnormal warning thresholds are dynamically adjusted.

[0090] It should be noted that providing an overall operating status assessment of industrial gas enables enterprise managers to intuitively understand the performance and stability of the gas system. For example, it can be used to understand whether the gas supply is sufficient and whether the pressure is stable within an appropriate range, thereby providing a basis for adjusting the production plan. Descriptions of abnormal conditions can help to quickly locate the root cause of the problem. For example, abnormally low flow during a period of time may be due to upstream supply problems or pipeline leaks, making it convenient to take targeted repair measures in a timely manner. Improvement suggestions can guide enterprises to optimize the gas system, such as upgrading flow control equipment to improve metering accuracy or optimizing pipeline layout to reduce pressure loss according to the report recommendations, thereby promoting production efficiency and cost reduction.

[0091] The trend prediction model built based on historical data can predict the changing trends of gas flow, pressure, and temperature in advance. Enterprises can arrange production in advance based on this, such as reserving sufficient production materials or adjusting the operating parameters of gas-using equipment before the expected peak flow. In terms of equipment maintenance, maintenance personnel can be arranged in advance to inspect and maintain related equipment before abnormal changes in pressure or temperature may occur, prevent failures, and reduce production interruption losses caused by sudden equipment failures.

[0092] Example 2

[0093] Figure 1 The present invention provides an industrial gas digital precision metering system, which includes a data acquisition module, an analysis and judgment module, a classification module, and a summary and analysis module, and each module is connected by a signal;

[0094] The data acquisition module collects the flow, pressure and temperature of industrial gas respectively, and generates parameter acquisition signals and sends them to the analysis and judgment module;

[0095] The analysis and judgment module receives the parameter acquisition signal, analyzes and judges the parameter acquisition signal, and screens and marks abnormal data;

[0096] The classification module classifies and divides normal data and classifies and divides abnormal data;

[0097] The summary and analysis module summarizes and analyzes normal data and abnormal data.

[0098] The operation of the data acquisition module specifically includes the following:

[0099] The flow rate of industrial gas is collected through a flow sensor, the pressure of industrial gas is collected through a pressure sensor, and the temperature of industrial gas is collected through a temperature sensor;

[0100] Start the flow sensor, pressure sensor and temperature sensor, and perform calibration and self-test on the sensors;

[0101] Set the data collection frequency. The collection frequency of the flow sensor is set to 12 seconds, the collection frequency of the pressure sensor is set to 22 seconds, and the collection frequency of the temperature sensor is set to 8 seconds.

[0102] According to the set frequency, the flow value, pressure value and temperature value of industrial gas are collected simultaneously, and these data are integrated to generate parameter collection signals;

[0103] The generated parameter acquisition signal is transmitted wirelessly to the analysis and judgment module.

[0104] The analysis and judgment module operation specifically includes the following contents:

[0105] The normal threshold ranges of flow rate, pressure and temperature are preset in advance, the flow rate range is set to 60 cubic meters per hour, the pressure range is set to 1.0 MPa, and the temperature range is set to 50 degrees Celsius;

[0106] Compare the received flow, pressure and temperature data with the corresponding normal threshold ranges respectively;

[0107] Perform abnormal screening and marking. If the data exceeds the normal threshold range, it is determined to be abnormal data, marked, and the type, abnormal value and collection time of the abnormal data are recorded;

[0108] The types of abnormal data include flow anomaly, pressure anomaly and temperature anomaly;

[0109] Data within the normal threshold range is judged as normal data and directly transmitted to the classification module.

[0110] The abnormal data is completely stored in a special abnormal database, recording its detailed information;

[0111] Record abnormal events in the system log, indicating the type of abnormality and the time when the abnormality was first discovered;

[0112] Compare and analyze abnormal data with historical normal operation data and data of similar abnormal cases in the past to further confirm the severity and cause of the abnormality;

[0113] Generate corresponding emergency measures suggestions based on the preliminary diagnosis results and abnormality types.

[0114] The classification module operation includes the following specific contents:

[0115] Receive normal data and abnormal data from the analysis and judgment module;

[0116] Classify normal data according to their time sequence, flow range, pressure level or temperature range;

[0117] Classify abnormal data by abnormal type, and associate the time when the abnormality occurred and the duration of the abnormality.

[0118] The operation of the summary analysis module specifically includes the following contents:

[0119] Receive normal data classification results and abnormal data classification results from the classification module;

[0120] Conduct statistical analysis on normal data, calculate the average flow rate, pressure and temperature under different categories, draw trend charts of flow rate, pressure and temperature over time, and analyze the operating rules and stability of industrial gas under normal operating conditions;

[0121] Summarize and analyze abnormal data, count the number of occurrences, first occurrence time, most recent occurrence time and average duration of different abnormal types, analyze the distribution patterns and trends of abnormal data, and determine whether there are systemic failures or potential safety hazards.

[0122] Generate a detailed measurement analysis report based on the summary and analysis results of normal and abnormal data. The report includes the overall operation status evaluation of industrial gas, abnormal situation description and improvement suggestions;

[0123] Based on the pressure change and flow fluctuation data of industrial gas during transportation, the energy loss of industrial gas during pipeline transmission is analyzed.

[0124] Based on historical normal data and abnormal data, a trend prediction model for gas parameters is constructed;

[0125] Predict the changing trend of gas flow, pressure and temperature in the future through time series analysis;

[0126] Based on the analysis results of abnormal data and the actual situation of industrial production, various abnormal warning thresholds are dynamically adjusted.

[0127] Example 3

[0128] Figure 1 The present invention provides an industrial gas digital precision metering system, which includes a data acquisition module, an analysis and judgment module, a classification module, and a summary and analysis module, and each module is connected by a signal;

[0129] The data acquisition module collects the flow, pressure and temperature of industrial gas respectively, and generates parameter acquisition signals and sends them to the analysis and judgment module;

[0130] The analysis and judgment module receives the parameter acquisition signal, analyzes and judges the parameter acquisition signal, and screens and marks abnormal data;

[0131] The classification module classifies and divides normal data and classifies and divides abnormal data;

[0132] The summary and analysis module summarizes and analyzes normal data and abnormal data.

[0133] The operation of the data acquisition module specifically includes the following:

[0134] The flow rate of industrial gas is collected through a flow sensor, the pressure of industrial gas is collected through a pressure sensor, and the temperature of industrial gas is collected through a temperature sensor;

[0135] Start the flow sensor, pressure sensor and temperature sensor, and perform calibration and self-test on the sensors;

[0136] Set the data collection frequency. The collection frequency of the flow sensor is set to 15 seconds, the collection frequency of the pressure sensor is set to 25 seconds, and the collection frequency of the temperature sensor is set to 10 seconds.

[0137] According to the set frequency, the flow value, pressure value and temperature value of industrial gas are collected simultaneously, and these data are integrated to generate parameter collection signals;

[0138] The generated parameter acquisition signal is transmitted wirelessly to the analysis and judgment module.

[0139] The analysis and judgment module operation specifically includes the following contents:

[0140] The normal threshold ranges of flow rate, pressure and temperature are preset in advance, the flow rate range is set to 100 cubic meters per hour, the pressure range is set to 1.2 MPa, and the temperature range is set to 80 degrees Celsius;

[0141] Compare the received flow, pressure and temperature data with the corresponding normal threshold ranges respectively;

[0142] Perform abnormal screening and marking. If the data exceeds the normal threshold range, it is determined to be abnormal data, marked, and the type, abnormal value and collection time of the abnormal data are recorded;

[0143] The types of abnormal data include flow anomaly, pressure anomaly and temperature anomaly;

[0144] Data within the normal threshold range is judged as normal data and directly transmitted to the classification module.

[0145] The abnormal data is completely stored in a special abnormal database, recording its detailed information;

[0146] Record abnormal events in the system log, indicating the type of abnormality and the time when the abnormality was first discovered;

[0147] Compare and analyze abnormal data with historical normal operation data and data of similar abnormal cases in the past to further confirm the severity and cause of the abnormality;

[0148] Generate corresponding emergency measures suggestions based on the preliminary diagnosis results and abnormality types.

[0149] The classification module operation includes the following specific contents:

[0150] Receive normal data and abnormal data from the analysis and judgment module;

[0151] Classify normal data according to their time sequence, flow range, pressure level or temperature range;

[0152] Classify abnormal data by abnormal type, and associate the time when the abnormality occurred and the duration of the abnormality.

[0153] The operation of the summary analysis module specifically includes the following contents:

[0154] Receive normal data classification results and abnormal data classification results from the classification module;

[0155] Conduct statistical analysis on normal data, calculate the average flow rate, pressure and temperature under different categories, draw trend charts of flow rate, pressure and temperature over time, and analyze the operating rules and stability of industrial gas under normal operating conditions;

[0156] Summarize and analyze abnormal data, count the number of occurrences, first occurrence time, most recent occurrence time and average duration of different abnormal types, analyze the distribution patterns and trends of abnormal data, and determine whether there are systemic failures or potential safety hazards.

[0157] Generate a detailed measurement analysis report based on the summary and analysis results of normal and abnormal data. The report includes the overall operation status evaluation of industrial gas, abnormal situation description and improvement suggestions;

[0158] Based on the pressure change and flow fluctuation data of industrial gas during transportation, the energy loss of industrial gas during pipeline transmission is analyzed.

[0159] Based on historical normal data and abnormal data, a trend prediction model for gas parameters is constructed;

[0160] Predict the changing trend of gas flow, pressure and temperature in the future through time series analysis;

[0161] Based on the analysis results of abnormal data and the actual situation of industrial production, various abnormal warning thresholds are dynamically adjusted.

[0162] If it is a small fine chemical enterprise or a production process with extremely high requirements for gas parameter accuracy and stability, Example 1 may be the best choice. Although there may be certain false alarms, it can ensure that abnormal situations are discovered in time. For general-sized industrial enterprises, Example 2 is more appropriate, achieving a good balance between the timeliness of data collection and the accuracy of abnormality judgment. For large-scale, large-scale industrial scenarios with relatively complex working conditions but not very sensitive to subtle abnormalities, such as large steel joint ventures, Example 3 may be the best, which can effectively reduce the data processing burden and adapt to a larger range of parameter changes.

[0163] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0164] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An industrial gas digital precision metering system, characterized in that: It includes data collection module, analysis and judgment module, classification module, summary and analysis module, and each module is connected through signals; The data acquisition module collects the flow, pressure and temperature of industrial gas respectively, and generates parameter acquisition signals and sends them to the analysis and judgment module; The analysis and judgment module receives the parameter acquisition signal, analyzes and judges the parameter acquisition signal, and screens and marks abnormal data; The classification module classifies and divides normal data and classifies and divides abnormal data; The summary and analysis module summarizes and analyzes normal data and abnormal data.

2. The industrial gas digital precision metering system according to claim 1 is characterized by: The operation of the data acquisition module specifically includes the following: The flow rate of industrial gas is collected through a flow sensor, the pressure of industrial gas is collected through a pressure sensor, and the temperature of industrial gas is collected through a temperature sensor; Start the flow sensor, pressure sensor and temperature sensor, and perform calibration and self-test on the sensors; Set the data collection frequency. The collection frequency of the flow sensor is set to 10-15 seconds, the collection frequency of the pressure sensor is set to 20-25 seconds, and the collection frequency of the temperature sensor is set to 5-10 seconds; According to the set frequency, the flow value, pressure value and temperature value of industrial gas are collected simultaneously, and these data are integrated to generate parameter collection signals; The generated parameter acquisition signal is transmitted wirelessly to the analysis and judgment module.

3. The industrial gas digital precision metering system according to claim 2 is characterized by: The analysis and judgment module operation specifically includes the following contents: The normal threshold ranges of flow rate, pressure and temperature are pre-set, the flow rate range is set to 10-100 cubic meters per hour, the pressure range is set to 0.8-1.2 MPa, and the temperature range is set to 20-80 degrees Celsius; Compare the received flow, pressure and temperature data with the corresponding normal threshold ranges respectively; Perform abnormal screening and marking. If the data exceeds the normal threshold range, it is determined to be abnormal data, marked, and the type, abnormal value and collection time of the abnormal data are recorded; The types of abnormal data include flow anomaly, pressure anomaly and temperature anomaly; Data within the normal threshold range is judged as normal data and directly transmitted to the classification module.

4. The industrial gas digital precision metering system according to claim 3 is characterized by: The abnormal data is completely stored in a special abnormal database, recording its detailed information; Record abnormal events in the system log, indicating the type of abnormality and the time when the abnormality was first discovered; Compare and analyze abnormal data with historical normal operation data and data of similar abnormal cases in the past to further confirm the severity and cause of the abnormality; Generate corresponding emergency measures suggestions based on the preliminary diagnosis results and abnormality types.

5. The industrial gas digital precision metering system according to claim 4 is characterized by: The classification module operation includes the following specific contents: Receive normal data and abnormal data from the analysis and judgment module; Classify normal data according to their time sequence, flow range, pressure level or temperature range; Classify abnormal data by abnormal type, and associate the time when the abnormality occurred and the duration of the abnormality.

6. The digital accurate metering system for industrial gas according to claim 5 is characterized by: The operation of the summary analysis module specifically includes the following contents: Receive normal data classification results and abnormal data classification results from the classification module; Conduct statistical analysis on normal data, calculate the average flow rate, pressure and temperature under different categories, draw trend charts of flow rate, pressure and temperature over time, and analyze the operating rules and stability of industrial gas under normal operating conditions; Summarize and analyze abnormal data, count the number of occurrences, first occurrence time, most recent occurrence time and average duration of different abnormal types, analyze the distribution patterns and trends of abnormal data, and determine whether there are systemic failures or potential safety hazards.

7. The industrial gas digital precision metering system according to claim 6 is characterized by: Generate a detailed measurement analysis report based on the summary and analysis results of normal and abnormal data. The report includes the overall operation status evaluation of industrial gas, abnormal situation description and improvement suggestions; Based on the pressure change and flow fluctuation data of industrial gas during transportation, the energy loss of industrial gas during pipeline transmission is analyzed.

8. The industrial gas digital precision metering system according to claim 7 is characterized by: Based on historical normal data and abnormal data, a trend prediction model for gas parameters is constructed; Predict the changing trend of gas flow, pressure and temperature in the future through time series analysis; Based on the analysis results of abnormal data and the actual situation of industrial production, various abnormal warning thresholds are dynamically adjusted.

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