Data analysis system for gas scene simulation

Abstract

The invention discloses a data analysis system for gas scene simulation, particularly relates to the field of gas scene data analysis, and comprises a data batch division module, a data acquisition module, a data analysis module, a comprehensive data evaluation module and a real-time feedback module. The data acquisition module comprises a gas physical data acquisition unit and a gas environment data acquisition unit and is used for acquiring target data in real time; the data analysis module comprises a gas physical data analysis unit and a gas environment data analysis unit; the data analysis module is used for analyzing the data transmitted by the data acquisition module and transmitting an analysis result to the comprehensive data evaluation module; related parameters of gas scene simulation data analysis are divided into different batches, so that the accuracy and timeliness of analysis are effectively enhanced; the credibility and the accuracy of an analysis result are greatly improved by carrying out comprehensive evaluation and deep fusion on a data processing result.

Description

Technical Field

[0001] The present invention relates to the technical field of gas scene data analysis, and more specifically, to a data analysis system for gas scene simulation. Background Art

[0002] With the rapid advancement of digital technology, new paths and directions have been opened up for data analysis systems for gas scene simulation. Relying on digital technology, core information can be extracted from massive amounts of gas-related data, which helps to quickly discover potential safety hazards, thereby improving the accuracy and timeliness of data analysis.

[0003] The existing gas scene simulation data analysis system mainly includes data collection module, data transfer module, data processing module and data reporting module. The data collection module collects data such as gas pipeline pressure, flow and gas composition; the data transfer module transmits the collected data to the data processing module, which identifies, classifies and integrates data from various channels to obtain valuable results, which are clearly displayed through the data reporting module, so as to detect abnormalities in time, reduce the possibility of safety accidents, and ensure the safety and stability of gas use.

[0004] However, the current analysis system still has some defects. For example, in the data collection stage, only limited operating data and environmental data are collected, which makes the scope of the collected data narrow, thereby destroying the comprehensiveness of the data basis, causing deviations in the data processing results, and weakening the credibility of the processing results, which is not conducive to the rational application and sustainable development of gas scenario simulation.

[0005] Therefore, there is an urgent need to build a data analysis system for gas scenario simulation to solve the problems of incomplete data collection and inaccurate data processing results in the existing analysis system. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a data analysis system for gas scene simulation, which solves the problems raised in the above-mentioned background technology through the following scheme.

[0007] To achieve the above object, the present invention provides the following technical solution: a data analysis system for gas scene simulation, comprising:

[0008] Data batch division module: used to determine the data to be collected as target data, divide the target data into different batches according to equal time division, and mark them as 1, 2, ..., n in sequence;

[0009] Data acquisition module: including a gas physical data acquisition unit and a gas environment data acquisition unit, which are used to acquire target data in real time and transmit the acquired data to the data analysis module; the gas physical data acquisition unit is used to acquire gas flow data, gas composition data and gas energy data; the gas environment data acquisition unit is used to acquire environmental thermal data, environmental mechanical data and environmental electrical data;

[0010] Data analysis module: including a gas physical data analysis unit and a gas environment data analysis unit; used to analyze the data transmitted by the data acquisition module and transmit the analysis results to the comprehensive data evaluation module; the gas physical data analysis unit includes a gas flow data analysis node, a gas composition data analysis node and a gas energy data analysis node; the gas environment data analysis unit includes an environmental thermal data analysis node, an environmental mechanical data analysis node and an environmental electrical data analysis node;

[0011] Comprehensive data evaluation module: including a gas scenario simulation data analysis unit, which is used to comprehensively analyze the data transmitted by the data analysis module and transmit the analysis results to the real-time feedback module;

[0012] Real-time feedback module: used to establish preset values ​​of the gas scenario simulation comprehensive evaluation index, judge the gas scenario simulation comprehensive evaluation index value according to the preset values ​​of the gas scenario simulation comprehensive evaluation index, and send corresponding signals according to the judgment results.

[0013] Preferably, the gas flow data includes the turbulence coefficient Tc, the swirl frequency Fv and the Reynolds number Nr; the gas composition data includes the impurity content ratio Pc, the combustible component fluctuation rate Cv and the combustion aid content gradient Ag; the gas energy data includes the energy density per unit volume Ed and the energy release rate fluctuation value Ef.

[0014] Preferably, the environmental thermal data include environmental thermal conductivity gradient Ac, thermal radiation absorption coefficient Ca and environmental temperature field non-uniformity At; the environmental mechanical data include environmental pressure gradient Ap, wind field shear intensity Sw and vibration acceleration spectral density As; the environmental electrical data include electrostatic field intensity gradient Eg, air ionization rate Ar and electromagnetic radiation intensity spectrum Sr.

[0015] Preferably, the gas flow data analysis node is used to establish a gas flow data calculation model, import the gas flow data transmitted by the data acquisition module into the gas flow data calculation model, and obtain the gas flow complexity coefficient value. The gas flow data calculation model is specifically expressed as:

[0016]

[0017] Among them, α iIndicates the gas flow complexity coefficient value calculated for the i-th time, Tc i represents the turbulence coefficient collected at the i-th time, Fv i represents the vortex frequency collected for the i-th time, Nr i represents the Reynolds number collected at the ith time.

[0018] Preferably, the gas composition data analysis node is used to establish a gas composition data calculation model, import the gas composition data transmitted by the data acquisition module into the gas composition data calculation model, and obtain the gas stability coefficient value. The gas composition data calculation model is specifically expressed as:

[0019]

[0020] Among them, β i Indicates the gas stability coefficient value calculated for the i-th time, Pc i Indicates the impurity content ratio and Cv of the i-th collection i represents the fluctuation rate of the combustible component collected for the i-th time, Ag i Represents the combustion aid content gradient collected for the i-th time.

[0021] Preferably, the gas energy data analysis node is used to establish a gas energy data calculation model, import the gas energy data transmitted by the data acquisition module into the gas energy data calculation model, and obtain the gas energy efficiency coefficient value. The gas energy data calculation model is specifically expressed as:

[0022]

[0023] Among them, γ i Indicates the gas energy efficiency coefficient value calculated for the i-th time, Ed i represents the energy density per unit volume collected for the i-th time, Ef i Represents the energy release rate fluctuation value collected for the i-th time.

[0024] Preferably, the environmental thermal data analysis node is used to establish an environmental thermal data calculation model, import the environmental thermal data transmitted by the data acquisition module into the environmental thermal data calculation model, and obtain the gas thermal environment coupling coefficient value. The environmental thermal data calculation model is specifically expressed as:

[0025]

[0026] in, Indicates the thermal-environment coupling coefficient value of the gas calculated for the i-th time, Ac i represents the ambient thermal conductivity gradient collected at the ith time, Ca i represents the thermal radiation absorption coefficient collected for the i-th time, σ represents the standard deviation of the thermal radiation absorption coefficient, μ represents the mean value of the thermal radiation absorption coefficient, Ati Represents the non-uniformity of the ambient temperature field collected for the i-th time.

[0027] Preferably, the environmental mechanics data analysis node is used to establish an environmental mechanics data calculation model, import the environmental mechanics data transmitted by the data acquisition module into the environmental mechanics data calculation model, and obtain the gas environment interference coefficient value. The environmental mechanics data calculation model is specifically expressed as:

[0028]

[0029] Among them, λ i Indicates the gas environment interference coefficient value calculated for the i-th time, Ap i represents the ambient pressure gradient collected for the i-th time, Sw i represents the wind shear intensity collected for the i-th time, As i represents the vibration acceleration spectral density collected for the i-th time.

[0030] Preferably, the environmental electrical data analysis node is used to establish an environmental electrical data calculation model, import the environmental electrical data transmitted by the data acquisition module into the environmental electrical data calculation model, and obtain the gas electrical environmental safety factor value. The environmental electrical data calculation model is specifically expressed as:

[0031]

[0032] Among them, χ i Indicates the value of the gas-electricity environmental safety factor calculated for the i-th time, Eg i represents the electrostatic field intensity gradient collected for the ith time, Ar i represents the air ionization rate collected for the i-th time, Sr i Represents the electromagnetic radiation intensity spectrum collected for the i-th time.

[0033] Preferably, the gas scenario simulation data analysis unit is used to establish a gas scenario simulation data calculation model, and import the gas flow complexity coefficient value, gas stability coefficient value, gas energy efficiency coefficient value, gas thermal environment coupling coefficient value, gas environment interference coefficient value and gas electrical environment safety coefficient value transmitted by the data analysis module into the gas scenario simulation data calculation model to obtain a gas scenario simulation comprehensive evaluation index value; the gas scenario simulation data calculation model is specifically expressed as:

[0034]

[0035] Among them, A represents the calculated comprehensive evaluation index value of gas scenario simulation, α i represents the gas flow complexity coefficient value calculated for the i-th time, β i represents the gas stability coefficient value calculated for the i-th time, γ irepresents the gas energy efficiency coefficient value calculated for the i-th time, represents the value of the gas thermal environment coupling coefficient calculated for the i-th time, Indicates the minimum value of the calculated gas thermal environment coupling coefficient, Indicates the maximum value of the calculated gas thermal environment coupling coefficient, λ i Indicates the gas environment interference coefficient value calculated for the i-th time, λ min Indicates the minimum value of the calculated gas environment interference coefficient, λ max Indicates the maximum value of the calculated gas environment interference coefficient, χ i represents the value of the gas-electricity environmental safety factor calculated for the i-th time, χ min represents the minimum value of the calculated gas-electricity environmental safety factor, χ max Indicates the calculated maximum value of the gas-electricity environmental safety factor, i indicates starting from the i-th number, and n indicates ending at the n-th number.

[0036] Technical effects and advantages of the present invention:

[0037] 1. The present invention effectively enhances the accuracy and timeliness of the analysis by dividing the relevant parameters of the gas scene simulation data analysis into different batches. By collecting gas flow data, gas composition data, gas energy data, environmental thermal data, environmental mechanical data and environmental electrical data through multiple channels, this multi-angle data collection mode breaks away from the limitations of traditional data collection and builds a more comprehensive and stable data foundation for subsequent data analysis;

[0038] 2. The present invention conducts in-depth analysis of the collected data through data analysis, and then calculates the gas flow complexity coefficient value, gas stability coefficient value, gas energy efficiency coefficient value, gas thermal environment coupling coefficient value, gas environment interference coefficient value and gas electrical environment safety coefficient value, accurately indicating the links where problems may occur. By comprehensively evaluating and deeply integrating the results of data processing, the credibility and accuracy of the analysis results are greatly improved;

[0039] 3. The present invention continuously tracks through real-time monitoring. Once an abnormal situation is detected, an alarm signal is immediately issued. Relevant technical personnel and managers can quickly grasp the real-time dynamics and potential hidden dangers of the gas scene, and adjust and optimize the gas supply plan or simulation scene settings in time, providing a solid guarantee for efficient and accurate gas scene simulation data analysis and scientific and reasonable decision-making and deployment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0041] 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.

[0042] As attached Figure 1 A data analysis system for gas scene simulation shown in FIG. 1 includes a system operation database, a system central processor and a user information terminal, and is characterized in that it also includes a data batch division module, a data acquisition module, a data analysis module, a comprehensive data evaluation module and a real-time feedback module.

[0043] The system operation database includes all data texts of gas scene simulation and collects information texts output by each module in real time. The system central processor is used to control the information text instructions output by each module. The user information terminal is an information output device for receiving the gas scene simulation system.

[0044] The data batch division module is used to determine the data to be collected as target data, divide the target data into different batches in an equal time division manner, and mark them as 1, 2, ..., n in sequence. In this embodiment, it should be specifically explained that: the equal time division manner is to divide the data according to the characteristics of each target data by running the database through the background system, and collect the data once every equal time interval and divide it into different collection times according to the real-time requirements of each target data.

[0045] The data acquisition module includes a gas physical data acquisition unit and a gas environment data acquisition unit, which are used to acquire target data in real time and transmit the acquired data to the data analysis module; the gas physical data acquisition unit is used to acquire gas flow data, gas composition data and gas energy data; the gas environment data acquisition unit is used to acquire environmental thermal data, environmental mechanical data and environmental electrical data.

[0046] In this embodiment, it is specifically necessary to explain that: the gas flow data includes the turbulence coefficient Tc, the vortex frequency Fv and the Reynolds number Nr; the gas composition data includes the impurity content ratio Pc, the combustible component fluctuation rate Cv and the combustion aid content gradient Ag; the gas energy data includes the energy density per unit volume Ed and the energy release rate fluctuation value Ef; the environmental thermal data includes the environmental thermal conductivity gradient Ac, the thermal radiation absorption coefficient Ca and the environmental temperature field non-uniformity At; the environmental mechanical data includes the environmental pressure gradient Ap, the wind field shear intensity Sw and the vibration acceleration spectrum density As; the environmental electrical data includes the electrostatic field intensity gradient Eg, the air ionization rate Ar and the electromagnetic radiation intensity spectrum Sr.

[0047] The turbulence coefficient is a key indicator for analyzing the complex flow behavior of gas in the pipeline. During the gas transportation process, the turbulent state will cause energy loss and pressure fluctuations. Through the turbulence coefficient, the pipeline resistance loss can be accurately calculated, and the pipeline design can be optimized to reduce unnecessary energy consumption. For example, in a long-distance gas transmission pipeline, a high turbulence coefficient may mean that an additional pump station is needed to maintain the transmission pressure. At the same time, it is also very important for studying the flow mixing of gas at pipeline branches and elbows, and can help predict the diffusion and mixing effects of gas components in turbulence, which is meaningful for ensuring the uniformity of gas quality. The collection method is: select multiple representative measurement sections in the pipeline, which cover the straight sections, bends and branches of the pipeline. On each section, high-precision flow sensors distributed in a matrix are installed, and these sensors must be able to accurately measure the flow velocity changes within a small time interval. By synchronously collecting data from each sensor, the flow velocity vectors at different points on the section are obtained. Then, based on the complex fluid mechanics turbulence theory and mathematical model, combined with the geometric shape and surface roughness of the pipeline, the turbulence coefficient is calculated.

[0048] The vortex frequency is crucial for evaluating the flow stability of gas in local areas. The formation of vortices can lead to drastic changes in local pressure, which may cause pipeline vibration and fatigue damage. In gas systems, especially in some equipment with complex structures (such as gas turbine inlet ducts), high vortex frequencies may indicate that the equipment faces a greater risk of wear. At the same time, changes in vortex frequency will also affect the mixing efficiency of gas and other substances (such as the mixing of air in the burner), thereby affecting the combustion effect. By monitoring the vortex frequency, the equipment operating parameters can be adjusted in time to ensure the safe and stable operation of the system. The collection method is: install micro vortex sensors at key locations where vortices may occur (such as pipeline reductions, near valves, etc.). These sensors are based on the principles of fluid mechanics and sense the presence of vortices by detecting physical quantities related to vortices such as pressure difference and flow rate changes in the fluid. The signal processing unit inside the sensor converts these physical quantities into electrical signals, and its frequency is directly related to the vortex frequency. The data acquisition system collects the electrical signals output by the sensor at a high frequency, and accurately obtains the vortex frequency value after filtering, amplification, and other processing. To improve the accuracy of the measurement, the sensor may need to be calibrated using a standard fluid flow setup with known vortex frequency to adjust the sensor's sensitivity and measurement range.

[0049] The Reynolds number is the core parameter for judging the flow state of gas. Under different flow states, the physical properties and transmission laws of gas are very different. In the laminar state, the flow of gas is relatively stable, and the transfer of matter and heat is mainly carried out through molecular diffusion; while in the turbulent state, the presence of vortices greatly enhances the transfer of matter and heat. Understanding the flow state of gas is of decisive significance for selecting a suitable mathematical model to describe the flow of gas, predicting pressure loss, designing the size of pipelines and equipment, and optimizing the operating parameters of the gas transmission system. For example, when designing a small gas supply pipeline, if the Reynolds number shows that the flow is laminar, a simpler laminar model can be used for calculation to reduce the calculation complexity and ensure the design accuracy. The acquisition method is as follows: First, the flow rate of gas is measured by a high-precision flow sensor installed in the pipeline. These flow sensors are usually based on principles such as ultrasound and electromagnetic induction, and can accurately measure the flow rate at different positions. At the same time, it is necessary to determine the inner diameter of the pipeline, which can be obtained through the design parameters of the pipeline or using professional pipeline measurement tools (such as laser rangefinders to measure the pipeline diameter). The viscosity of the gas is another key parameter. For gas with known composition, its viscosity value can be obtained by laboratory measurement or by consulting a professional fluid physics manual based on the temperature and composition of the gas. Finally, the Reynolds number is calculated according to the Reynolds number calculation formula (Re = ρvd / μ, where ρ is the gas density, v is the flow rate, d is the pipe diameter, and μ is the viscosity). In the actual acquisition process, it is necessary to ensure the accuracy and synchronization of the flow rate measurement, as well as the consistency of the units of each parameter.

[0050] The impurity content ratio is one of the important indicators for measuring gas quality. The presence of impurities in gas may cause a variety of problems. During the combustion process, impurities may be deposited on the surface of the combustion equipment, reducing the heat transfer efficiency and affecting the normal operation of the equipment. For example, some metal impurities may react chemically with other components in the gas at high temperatures and corrode equipment such as burners. For gas transmission pipelines, impurities may be adsorbed on the inner wall of the pipeline, increasing the roughness of the pipeline, thereby increasing the pipeline resistance and increasing the transmission pressure loss. In addition, some impurities may affect the combustion performance of the gas, causing incomplete combustion, producing harmful gases such as carbon monoxide, and endangering the environment and human health. Therefore, real-time monitoring of the impurity content ratio is crucial to ensuring the quality of gas and the safe and stable operation of the system. The collection method is: installing an online component analyzer in the gas pipeline. This analyzer usually uses spectral analysis technology. The principle is to use the absorption or emission characteristics of different substances for specific wavelengths of light to identify and quantitatively analyze the impurity components. When the gas passes through the detection cavity of the analyzer, the light emitted by the light source passes through the gas and is received by the detector. The detector measures the change in light intensity and determines the type and content of the impurity by comparing and analyzing it with the known pure gas spectrum and the impurity standard spectrum. In order to ensure the accuracy of the measurement, the analyzer needs to be calibrated regularly, using standard gas samples containing known impurity concentrations for calibration operations, and adjusting the analyzer's detection parameters and algorithms to ensure that the accuracy of impurity content measurement is at the ppm level.

[0051] The fluctuation rate of the combustible component is directly related to the stability of gas combustion and the uniformity of energy release. In the industrial combustion process, a stable concentration of combustible components is the key to ensuring efficient combustion. If the fluctuation rate of the combustible component is large, it may lead to unstable combustion flames, flashback, flameout and other dangerous phenomena, and also affect the thermal efficiency of the combustion equipment. For example, in a gas boiler, the fluctuation of the combustible component may make the thermal output of the boiler unstable and unable to meet the requirements of the production process. For equipment such as gas turbines that have extremely high requirements for gas quality, the monitoring of the fluctuation rate of the combustible component is an important means to ensure the safe operation and stable performance of the equipment. The collection method is: using real-time gas composition monitoring equipment, which is usually based on chromatographic analysis or electrochemical sensor principles. Monitoring points are set at multiple key locations in the pipeline to ensure that the changes in gas composition can be fully reflected. For equipment based on chromatographic analysis, the gas sample is introduced into the chromatographic column, and different combustible components are separated in the chromatographic column according to the differences in their physical and chemical properties, and then the concentration changes of each component are detected by the detector. Electrochemical sensors use the electrochemical reaction between the combustible component and the sensor electrode to measure its concentration. By continuously collecting and analyzing these data, the fluctuation rate of the concentration of combustible components within a certain period of time is calculated. In order to improve the reliability of the measurement, the monitoring equipment needs to be regularly maintained and calibrated to ensure the sensitivity and accuracy of the sensor.

[0052] The combustion-supporting agent content gradient is of great significance for understanding the mixing state of the gas before combustion and the efficiency of the combustion process. In some applications that require precise control of the combustion process, the full and uniform mixing of the combustion-supporting agent (such as oxygen) and the gas is the key to achieving complete combustion. The existence of the combustion-supporting agent content gradient means that the mixing of the gas and the combustion-supporting agent is uneven, which may lead to incomplete local combustion and produce pollutants such as carbon deposits and nitrogen oxides. By monitoring the combustion-supporting agent content gradient, the design and operation parameters of the burner can be optimized, and the supply method of the combustion-supporting agent can be adjusted to improve the combustion efficiency and reduce pollutant emissions. The collection method is: multi-point sampling chemical sensors are set at different locations of the gas system (such as the pipeline before the burner inlet, the inside of the combustion chamber, etc.). These sensors are specifically used to detect the concentration of the combustion-supporting agent (such as oxygen sensors to detect oxygen content). By arranging the sensor array in three-dimensional space, the concentration data of the combustion-supporting agent at different locations can be obtained. Then, the gradient change of the combustion-supporting agent content is calculated using the spatial interpolation algorithm and data analysis technology. In order to ensure the accuracy of the measurement, the sensor needs to be calibrated so that it can accurately measure the concentration of the combustion-supporting agent under different environmental conditions. At the same time, attention should be paid to the response time and measurement range of the sensor to ensure that the dynamic changes of the combustion aid content can be reflected in a timely and accurate manner.

[0053] The energy density per unit volume is a key parameter for evaluating the energy storage and supply capacity of gas. In the design of gas storage and delivery systems, it determines the size of the storage container and the delivery capacity of the delivery pipeline. For example, for the design of natural gas storage tanks, the volume of the tank needs to be determined based on the energy density of natural gas and the expected storage volume. In the gas supply network, understanding the energy density of gas in different regions can optimize the control of delivery pressure and flow rate and ensure that the user end obtains a stable energy supply. At the same time, in terms of energy management and cost accounting, the energy density per unit volume is an important basis for calculating the energy value of gas, which is of great significance for formulating reasonable pricing strategies and energy use plans. The collection method is as follows: First, the composition of the gas is determined by a gas composition analyzer installed in the pipeline. Different gas components (such as methane, ethane, etc.) have different calorific values. The calorific value of each component can be obtained by precise laboratory measurement or by consulting an authoritative energy data manual. Then, according to the proportion of each component in the gas, the average calorific value of the mixed gas is calculated by the weighted average method. Finally, combined with the density of the gas (which can be obtained by a density sensor or calculated based on the gas composition and state equation), the energy density per unit volume is calculated. During the entire collection process, the accuracy of gas composition analysis and the precision of density measurement must be ensured to ensure the reliability of the calculation results of energy density per unit volume.

[0054] The energy release rate fluctuation value is crucial for evaluating the working stability and performance of gas combustion equipment. During the combustion process, a stable energy release rate is the key to ensuring the normal operation of the equipment. For example, in a gas turbine, the fluctuation of the energy release rate may cause the rotor speed to be unstable, affecting the power generation efficiency and the life of the equipment. For industrial heating furnaces, the fluctuation of the energy release rate will cause the temperature in the furnace to be uneven, affecting the product quality. By monitoring the energy release rate fluctuation value, abnormal conditions in the combustion process, such as insufficient combustion, unstable gas supply, etc., can be discovered in time, and corresponding measures can be taken to adjust, such as adjusting the gas flow, checking the burner status, etc., and the collection method is: heat flow sensors are installed near the combustion equipment, and these sensors can convert the received thermal radiation or heat convection into electrical signals. At the same time, equipped with a high-speed data acquisition system, the output signal of the heat flow sensor can be collected at an extremely high frequency (for example, thousands of times per second). The energy release rate is obtained by calculating the time derivative of the collected heat flow data. Then, the energy release rate fluctuation value is obtained by calculating the second-order derivative of the energy release rate data or using statistical analysis methods (such as calculating variance, etc.). In order to ensure the accuracy of the measurement, the heat flux sensor needs to be calibrated. The sensor should be calibrated using a standard heat source with known heat flux density, while ensuring that the sampling frequency and resolution of the data acquisition system meet the measurement requirements.

[0055] The environmental thermal conductivity gradient has a key influence on the heat exchange process between the gas and the surrounding environment. In gas transmission pipelines and storage equipment, the thermal conductivity gradient determines the direction and rate of heat transfer. For example, in an underground gas pipeline, the thermal conductivity gradient of the surrounding soil will affect the change in gas temperature in the pipeline. If the thermal conductivity gradient is large, heat may be quickly dissipated or transmitted from the pipeline, which may cause the gas temperature to drop, affect the physical properties of the gas (such as density, viscosity, etc.), and thus affect the flow and transmission efficiency of the gas. In some high-temperature gas application scenarios, understanding the environmental thermal conductivity gradient helps to design appropriate insulation measures to prevent excessive heat dissipation or transmission into the surrounding environment, and ensure the safety and stable operation of the system. The collection method is: install a thermal conductivity sensor array at different locations around the gas (including the soil around the pipeline, the air around the equipment, etc.). These sensors are based on different thermal conductivity measurement principles, such as the hot wire method, the transient plane heat source method, etc. In the hot wire method, the thermal conductivity is calculated by measuring the temperature change of the heating wire in the sample. For the transient plane heat source method, the heating element in the sensor generates heat in a short time, and the thermal conductivity is determined by measuring the temperature response. At each measurement point, the data collected by the sensor is transmitted to the computer through the data acquisition system. Then, the thermal conductivity gradient is calculated by the numerical difference method based on the thermal conductivity data at different measurement points. In order to ensure the accuracy of the measurement, the sensor needs to be calibrated and calibrated using standard materials with known thermal conductivity. At the same time, the contact between the sensor and the surrounding environment should be considered to avoid measurement errors.

[0056] The thermal radiation absorption coefficient determines the ability of the gas to absorb and release thermal radiation, which is of great significance in scenarios involving high-temperature gas or thermal radiation sources. During the gas combustion process, the flame will generate a large amount of thermal radiation. The absorption of thermal radiation by the gas will affect its temperature distribution and energy transfer process. For example, in a gas turbine combustion chamber, the absorption coefficient of thermal radiation by the gas will affect the temperature distribution of the combustion chamber wall, thereby affecting the life and performance of the combustion chamber. For some high-temperature industrial furnaces, if the thermal radiation absorption coefficient of the gas changes, it may cause an uneven temperature field in the furnace, affecting product quality. By understanding the thermal radiation absorption coefficient, the design and operating parameters of the combustion system can be optimized, and the thermal efficiency and equipment performance can be improved. The collection method is: setting a thermal radiation source and a detector in the environment. The thermal radiation source can be an artificial radiation source that simulates the thermal radiation generated in the actual combustion process, such as an infrared heating lamp. The detector can be a sensor based on the photoelectric effect or the thermal effect, which is used to measure the change in the radiation intensity of the gas before and after being subjected to thermal radiation. First, the original radiation intensity emitted by the thermal radiation source is measured in the absence of gas. Then, the gas is introduced into the measurement area, and the radiation intensity after passing through the gas is measured again. The thermal radiation absorption coefficient of the gas is calculated based on the radiation transmission theory and Beer-Lambert law and other related formulas. During the measurement process, it is necessary to ensure that the radiation intensity of the thermal radiation source is stable, the accuracy and response time of the detector meet the measurement requirements, and at the same time reduce the interference of environmental factors (such as reflection, scattering, etc.) on the measurement results.

[0057] The non-uniformity of the ambient temperature field reflects the difference in the distribution of the ambient temperature around the gas, which has an important impact on the diffusion and heat transfer of the gas. In a gas leakage accident, the non-uniformity of the ambient temperature field will affect the diffusion direction and speed of the gas. For example, in areas with lower temperatures, the gas may condense or settle, while in areas with higher temperatures, the gas may diffuse faster. In industrial combustion systems, the non-uniformity of the ambient temperature field will lead to uneven mixing of gas and air, affecting the combustion efficiency. By monitoring the non-uniformity of the ambient temperature field, the behavior of the gas in the environment can be predicted, corresponding safety measures can be taken (such as setting up ventilation devices in low-temperature areas where gas may accumulate), and the design and operation parameters of the combustion system can be optimized. The acquisition method is: using a distributed temperature sensor network to measure the temperature of the surrounding environment of the gas. These sensors can be thermocouples, thermistors or optical fiber temperature sensors. A large number of temperature sensors are installed at different locations around the gas system (including horizontal and vertical directions) to form a three-dimensional temperature measurement network. The temperature data collected by the sensor is transmitted to the computer through the data acquisition system. Then, the ambient temperature field model is constructed by a spatial interpolation algorithm (such as Kriging interpolation method, inverse distance weighted interpolation method, etc.). Finally, the temperature gradient is calculated based on the temperature field model, and the non-uniformity of the ambient temperature field is determined by statistical analysis methods (such as calculating the standard deviation of the gradient, etc.). When installing sensors, ensure that the sensors are evenly distributed to avoid excessive deviations in local temperature measurements. At the same time, the sensors should be calibrated regularly to ensure the accuracy of temperature measurement.

[0058] The environmental pressure gradient is an important factor affecting the direction and speed of gas flow in the environment. After the gas leaks, the environmental pressure gradient determines the diffusion path and diffusion speed of the gas. For example, in the presence of a pressure difference, the gas will flow from the high-pressure area to the low-pressure area. In the atmospheric environment, the difference in air pressure at different heights will cause different diffusion behaviors of the gas in the vertical direction. In a closed space, such as a gas leak in a building, the pressure difference between indoor and outdoor and the pressure gradient in different areas of the room will affect the accumulation and diffusion direction of the gas. By monitoring the environmental pressure gradient, the diffusion range after the gas leak can be predicted, providing a basis for formulating a safe evacuation plan and taking emergency measures. At the same time, during the gas transportation process, the environmental pressure gradient will also affect the pressure balance of the pipeline, which needs to be considered in the design and operation of the pipeline. The acquisition method is: installing high-precision pressure sensors at different heights and positions. These sensors can be piezoresistive, capacitive or piezoelectric pressure sensors, etc. In outdoor environments, sensors can be installed at different heights such as the top of a building, the ground, etc.; in indoor environments, they can be installed in different rooms, different floors, and near doors and windows. The sensor converts the measured pressure signal into an electrical signal and transmits it to the data acquisition system through a data acquisition line. The acquisition system collects and records data in real time. In order to calculate the pressure gradient, it is necessary to arrange sensors in three-dimensional space to form a spatial measurement network. Then, according to the pressure values ​​measured by sensors at different positions, the rate of change of pressure in each direction, that is, the pressure gradient, is calculated by numerical difference method. When installing the sensor, ensure that the sensor is firmly installed and well balanced with the surrounding pressure to avoid external interference. At the same time, the sensor should be calibrated regularly, and the measurement accuracy of the sensor should be checked and adjusted using a standard pressure source.

[0059] The wind shear intensity has an important influence on the diffusion and dilution rate of gas in the atmospheric environment. When gas leaks into the atmosphere, wind shear will lead to intensified mixing of gas and surrounding air, accelerate the diffusion of gas, reduce local gas concentration, and thus reduce the probability of dangerous accidents such as explosions. For some open-air gas facilities, such as gas pressure regulating stations, understanding the wind shear intensity helps to assess the safety risks after gas leaks under different weather conditions. In addition, in some industrial emissions of gas-containing waste gas, wind shear intensity is also of key significance for predicting the diffusion range and environmental impact of waste gas. The collection method is: meteorological wind field measurement equipment, such as laser radar, ultrasonic anemometer, etc. can be used. Laser radar measures wind speed by emitting laser beams and detecting the scattering signal of laser by aerosols in the atmosphere. It can scan at different heights and angles to obtain wind field information over a large area. Ultrasonic anemometers measure wind speed based on the principle that the propagation speed of ultrasonic waves in the air is related to wind speed. In the open area around the gas system and within the range that may be affected by gas leakage, multiple wind field measurement equipment are installed according to a certain spatial layout. These devices collect wind speed data at a high time frequency (for example, several times per minute). Then, by analyzing the wind speed data at different heights and locations, the rate of change of wind speed in the vertical and horizontal directions, that is, the wind field shear intensity, is calculated. During the measurement process, it is necessary to ensure that the installation location of the measuring equipment is not interfered by surrounding buildings, terrain and other factors in order to obtain accurate wind field data. At the same time, the equipment must be regularly maintained and calibrated to ensure measurement accuracy.

[0060] The vibration acceleration spectral density is meaningful for analyzing the stability and safety of gas equipment in a vibration environment. During the gas transportation and use process, many equipment (such as compressors, pumps, gas turbines, etc.) will generate vibrations during operation. At the same time, external environmental factors (such as earthquakes, traffic vibrations, etc.) may also cause equipment vibrations. Excessive vibration may cause loosening, wear, or even damage to equipment components, affecting the normal operation of the equipment and increasing the risk of gas leakage. By monitoring the vibration acceleration spectral density, abnormal vibration conditions can be discovered in a timely manner, fault diagnosis and predictive maintenance of the equipment can be performed, and the safe and reliable operation of the gas system can be guaranteed. The collection method is: vibration sensors are installed on gas equipment and pipelines. These sensors are usually piezoelectric acceleration sensors. They can convert the vibration acceleration of the equipment or pipeline into electrical signals. When installing the sensor, a suitable installation location should be selected, usually at vibration-sensitive locations such as the bearing seat of the equipment and the support point of the pipeline. The sensor is connected to the data acquisition system through a signal line, and the acquisition system collects vibration signals at a higher sampling frequency (for example, several thousand hertz). Then, through signal processing methods such as fast Fourier transform (FFT), the vibration acceleration signal in the time domain is converted into a frequency domain signal to obtain the vibration acceleration spectral density. In order to ensure the accuracy of the measurement, the sensor needs to be calibrated and calibrated using a standard vibration table so that its measurement accuracy at different frequencies meets the requirements. At the same time, attention should be paid to the shielding of the signal line to prevent external electromagnetic interference from affecting the measurement results.

[0061] The electrostatic field intensity gradient is used to assess the threat level of environmental static electricity to gas safety. During the transportation and use of gas, static electricity may be generated when the gas flows in the pipeline and rubs against the equipment. If static electricity cannot be eliminated in time, when the static field intensity reaches a certain threshold, spark discharge may be triggered, thereby igniting the gas. By monitoring the electrostatic field intensity gradient, the area and trend of static electricity accumulation can be determined, and corresponding anti-static measures can be taken, such as installing static eliminators, adding grounding devices, etc., to ensure the safe operation of the gas system. The collection method is: using an electrostatic field measuring instrument to measure the electrostatic field intensity at different positions. The electrostatic field measuring instrument is usually based on the principle of electric field induction, and measures the electric field intensity by detecting the induced charge of the probe by the electric field. Measuring probes are arranged around the gas system, including key locations such as the pipeline surface, the equipment housing, and the surrounding space. The probe converts the measured electrostatic field intensity signal into an electrical signal and collects it through a data acquisition system. In order to calculate the electrostatic field intensity gradient, multiple measurement points need to be reasonably arranged in three-dimensional space. Then, the rate of change of the electrostatic field intensity in each direction is calculated by the numerical difference method. During the measurement process, it is necessary to ensure that the measuring probe is not interfered by the external electric field, such as avoiding proximity to a strong electromagnetic field source. At the same time, the measuring instrument should be calibrated regularly and calibrated using a standard electric field source with known electrostatic field strength.

[0062] The air ionization rate is related to the electrical stability of the gas, and air ionization may change the electrical properties of the gas. In some special environments, such as the presence of radioactive substances, high-intensity electromagnetic fields, etc., the air may be ionized to produce a large number of ions. These ions will affect the distribution and migration of charges in the gas, which may make the gas more easily ignited by static electricity. At the same time, air ionization may also affect the electrical performance of gas equipment, such as the measurement accuracy of sensors. By monitoring the air ionization rate, the impact of the environment on the electrical stability of the gas can be evaluated, and corresponding protective measures can be taken, such as shielding electromagnetic fields, controlling radioactive substances, etc. The collection method is: the air ionization rate is determined by detecting the number of ion pairs generated by air ionization through air ionization measuring equipment. These measuring devices usually use ion collection electrodes to collect ions in the air, and calculate the ion concentration by measuring the current generated by the ions on the electrodes. Set multiple measurement points at different locations around the gas system to ensure that the air ionization situation can be fully reflected. The measuring equipment collects ion concentration data at a certain time interval, and then calculates the air ionization rate based on the time interval and the measurement volume. During the measurement process, it is necessary to ensure that the electrodes of the measuring equipment are clean to avoid contamination affecting the ion collection efficiency. At the same time, the measuring equipment must be calibrated and the equipment must be calibrated using standard gas with known ionization rate.

[0063] The electromagnetic radiation may affect the electronic equipment and sensors of the gas system. High-intensity electromagnetic radiation may interfere with the normal operation of the sensor, resulting in inaccurate measurement data, which in turn affects the judgment of the gas system status. For some gas equipment that uses electronic control (such as electronic regulating valves, gas alarms, etc.), electromagnetic radiation may cause the control system to malfunction, affecting the normal operation of the equipment. By monitoring the electromagnetic radiation intensity spectrum, the electromagnetic interference source can be identified, and protective measures such as electromagnetic shielding and filtering can be taken to ensure the normal operation of the electronic equipment and sensors of the gas system. The acquisition method is: using an electromagnetic radiation spectrum analyzer to measure the electromagnetic radiation intensity at different frequencies around the gas system. The spectrum analyzer receives the electromagnetic radiation signal and uses the internal signal processing circuit and algorithm to decompose the signal into components of different frequencies, and measures the intensity of each frequency component. Measurement points are set in all directions and key locations around the gas system to ensure that the area that may be affected by electromagnetic radiation can be fully covered. The spectrum analyzer scans and measures the entire frequency range of interest at a certain scanning frequency to obtain the electromagnetic radiation intensity spectrum. During the measurement process, it is necessary to ensure that the antenna of the spectrum analyzer is well matched with the surrounding environment to avoid reflection and interference affecting the measurement results. At the same time, the spectrum analyzer should be calibrated regularly and calibrated using a standard electromagnetic radiation source to ensure measurement accuracy.

[0064] The data analysis module includes a gas physical data analysis unit and a gas environment data analysis unit; it is used to analyze the data transmitted by the data acquisition module and transmit the analysis results to the comprehensive data evaluation module; the gas physical data analysis unit includes a gas flow data analysis node, a gas composition data analysis node and a gas energy data analysis node; the gas environment data analysis unit includes an environmental thermal data analysis node, an environmental mechanical data analysis node and an environmental electrical data analysis node.

[0065] In this embodiment, it is specifically required to be explained that: the gas flow data analysis node is used to establish a gas flow data calculation model, and the gas flow data transmitted by the data acquisition module is imported into the gas flow data calculation model to obtain the gas flow complexity coefficient value. The gas flow data calculation model is specifically expressed as:

[0066]

[0067] Among them, α i Indicates the gas flow complexity coefficient value calculated for the i-th time, Tc i represents the turbulence coefficient collected at the i-th time, Fv i represents the vortex frequency collected for the i-th time, Nr i represents the Reynolds number collected at the ith time.

[0068] In this embodiment, it is specifically required to be explained that: the gas composition data analysis node is used to establish a gas composition data calculation model, and the gas composition data transmitted by the data acquisition module is imported into the gas composition data calculation model to obtain the gas stability coefficient value. The gas composition data calculation model is specifically expressed as:

[0069]

[0070] Among them, β i Indicates the gas stability coefficient value calculated for the i-th time, Pc i Indicates the impurity content ratio and Cv of the i-th collection i represents the fluctuation rate of the combustible component collected for the i-th time, Ag i Represents the combustion aid content gradient collected for the i-th time.

[0071] In this embodiment, it is specifically required to be explained that: the gas energy data analysis node is used to establish a gas energy data calculation model, and the gas energy data transmitted by the data acquisition module is imported into the gas energy data calculation model to obtain the gas energy efficiency coefficient value. The gas energy data calculation model is specifically expressed as:

[0072]

[0073] Among them, γi Indicates the gas energy efficiency coefficient value calculated for the i-th time, Ed i represents the energy density per unit volume collected for the i-th time, Ef i Represents the energy release rate fluctuation value collected for the i-th time.

[0074] In this embodiment, it is specifically required to be explained that: the environmental thermal data analysis node is used to establish an environmental thermal data calculation model, and the environmental thermal data transmitted by the data acquisition module is imported into the environmental thermal data calculation model to obtain the gas thermal environment coupling coefficient value. The environmental thermal data calculation model is specifically expressed as:

[0075]

[0076] in, Indicates the thermal-environment coupling coefficient value of the gas calculated for the i-th time, Ac i represents the ambient thermal conductivity gradient collected at the ith time, Ca i represents the thermal radiation absorption coefficient collected for the i-th time, σ represents the standard deviation of the thermal radiation absorption coefficient, μ represents the mean value of the thermal radiation absorption coefficient, At i Represents the non-uniformity of the ambient temperature field collected for the i-th time.

[0077] In this embodiment, it is specifically required to be explained that: the environmental mechanics data analysis node is used to establish an environmental mechanics data calculation model, and the environmental mechanics data transmitted by the data acquisition module is imported into the environmental mechanics data calculation model to obtain the gas environment interference coefficient value. The environmental mechanics data calculation model is specifically expressed as:

[0078]

[0079] Among them, λ i Indicates the gas environment interference coefficient value calculated for the i-th time, Ap i represents the ambient pressure gradient collected for the i-th time, Sw i represents the wind shear intensity collected for the i-th time, As i represents the vibration acceleration spectral density collected for the i-th time.

[0080] In this embodiment, it is specifically required to be explained that: the environmental electrical data analysis node is used to establish an environmental electrical data calculation model, and the environmental electrical data transmitted by the data acquisition module is imported into the environmental electrical data calculation model to obtain the gas electrical environmental safety factor value. The environmental electrical data calculation model is specifically expressed as:

[0081]

[0082] Among them, χ i Indicates the value of the gas-electricity environmental safety factor calculated for the i-th time, Egi represents the electrostatic field intensity gradient collected for the ith time, Ar i represents the air ionization rate collected for the i-th time, Sr i Represents the electromagnetic radiation intensity spectrum collected for the i-th time.

[0083] The comprehensive data evaluation module includes a gas scenario simulation data analysis unit, which is used to perform comprehensive analysis on the data transmitted by the data analysis module and transmit the analysis results to the real-time feedback module.

[0084] In this embodiment, it is specifically required to be explained that: the gas scene simulation data analysis unit is used to establish a gas scene simulation data calculation model, and the gas flow complexity coefficient value, gas stability coefficient value, gas energy efficiency coefficient value, gas thermal environment coupling coefficient value, gas environment interference coefficient value and gas electrical environment safety coefficient value transmitted by the data analysis module are imported into the gas scene simulation data calculation model to obtain a gas scene simulation comprehensive evaluation index value; the gas scene simulation data calculation model is specifically expressed as:

[0085]

[0086] Among them, A represents the calculated comprehensive evaluation index value of gas scenario simulation, α i represents the gas flow complexity coefficient value calculated for the i-th time, β i represents the gas stability coefficient value calculated for the i-th time, γ i represents the gas energy efficiency coefficient value calculated for the i-th time, represents the value of the gas thermal environment coupling coefficient calculated for the i-th time, Indicates the minimum value of the calculated gas thermal environment coupling coefficient, Indicates the maximum value of the calculated gas thermal environment coupling coefficient, λ i Indicates the gas environment interference coefficient value calculated for the i-th time, λ min Indicates the minimum value of the calculated gas environment interference coefficient, λ max Indicates the maximum value of the calculated gas environment interference coefficient, χ i represents the value of the gas-electricity environmental safety factor calculated for the i-th time, χ min represents the minimum value of the calculated gas-electricity environmental safety factor, χ max Indicates the calculated maximum value of the gas-electricity environmental safety factor, i indicates starting from the i-th number, and n indicates ending at the n-th number.

[0087] The real-time feedback module is used to establish a preset value of the gas scene simulation comprehensive evaluation index, judge the gas scene simulation comprehensive evaluation index value according to the preset value of the gas scene simulation comprehensive evaluation index, and send a corresponding signal according to the judgment result.

[0088] In this embodiment, it should be specifically noted that: the preset value of the comprehensive evaluation index for gas scenario simulation is denoted as A def , when A def < A, a normal signal is sent, which indicates that the preset value of the comprehensive evaluation index for gas scenario simulation is less than the value of the comprehensive evaluation index for gas scenario simulation, indicating that the comprehensive state of gas scenario simulation is good; when A def ≥ A, a warning signal is sent to relevant technical management personnel, which indicates that the preset value of the comprehensive evaluation index for gas scenario simulation is greater than or equal to the value of the comprehensive evaluation index for gas scenario simulation, indicating that the comprehensive state of gas scenario simulation is poor.

[0089] In the present invention, by dividing the relevant parameters of gas scenario simulation data analysis into different batches, the accuracy and timeliness of the analysis are effectively enhanced. By collecting gas flow data, gas composition data, gas energy data, environmental thermology data, environmental mechanics data, and environmental electricity data through multiple channels, this multi-angle data collection mode gets rid of the limitations of traditional data collection and constructs a more comprehensive and stable data foundation for subsequent data analysis. Through data analysis, the collected data is deeply analyzed, and then the gas flow complexity coefficient value, gas stability coefficient value, gas energy efficiency coefficient value, gas thermal environment coupling coefficient value, gas environmental interference degree coefficient value, and gas electrical environment safety coefficient value are calculated, accurately indicating the links where problems may occur. Through the comprehensive evaluation and in-depth integration of the results of data processing, the credibility and accuracy of the analysis results are greatly improved. Through real-time monitoring and continuous tracking, once an abnormal situation is detected, an alarm signal is immediately sent, and relevant technical personnel and management personnel can quickly master the real-time dynamics and potential hidden dangers of the gas scenario, and timely adjust and optimize the gas supply plan or simulation scenario settings, providing a solid guarantee for achieving efficient and accurate gas scenario simulation data analysis and scientific and reasonable decision-making deployment.

[0090] Secondly: In the accompanying drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments are involved, and other structures can refer to the general design. Without conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0091] Finally: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data analysis system for gas scene simulation, characterized in that: include: Data batch division module: used to determine the data to be collected as target data, divide the target data into different batches according to equal time division, and mark them as 1, 2, ..., n in sequence; Data acquisition module: including a gas physical data acquisition unit and a gas environment data acquisition unit, which are used to acquire target data in real time and transmit the acquired data to the data analysis module; the gas physical data acquisition unit is used to acquire gas flow data, gas composition data and gas energy data; the gas environment data acquisition unit is used to acquire environmental thermal data, environmental mechanical data and environmental electrical data; Data analysis module: including gas physical data analysis unit and gas environment data analysis unit; Used to analyze the data transmitted by the data acquisition module and transmit the analysis results to the comprehensive data evaluation module; the gas physical data analysis unit includes a gas flow data analysis node, a gas composition data analysis node and a gas energy data analysis node; the gas environment data analysis unit includes an environment thermal data analysis node, an environment mechanical data analysis node and an environment electrical data analysis node; Comprehensive data evaluation module: including a gas scenario simulation data analysis unit, which is used to comprehensively analyze the data transmitted by the data analysis module and transmit the analysis results to the real-time feedback module; Real-time feedback module: used to establish preset values ​​of the gas scenario simulation comprehensive evaluation index, judge the gas scenario simulation comprehensive evaluation index value according to the preset values ​​of the gas scenario simulation comprehensive evaluation index, and send corresponding signals according to the judgment results.

2. A data analysis system for gas scene simulation according to claim 1, characterized in that: The gas flow data includes the turbulence coefficient Tc, the vortex frequency Fv and the Reynolds number Nr; the gas composition data includes the impurity content ratio Pc, the combustible component fluctuation rate Cv and the combustion aid content gradient Ag; the gas energy data includes the energy density per unit volume Ed and the energy release rate fluctuation value Ef.

3. A data analysis system for gas scene simulation according to claim 1, characterized in that: The environmental thermal data include the environmental thermal conductivity gradient Ac, the thermal radiation absorption coefficient Ca and the environmental temperature field non-uniformity At; the environmental mechanical data include the environmental pressure gradient Ap, the wind field shear intensity Sw and the vibration acceleration spectrum density As; the environmental electrical data include the electrostatic field intensity gradient Eg, the air ionization rate Ar and the electromagnetic radiation intensity spectrum Sr.

4. A data analysis system for gas scene simulation according to claim 1, characterized in that: The gas flow data analysis node is used to establish a gas flow data calculation model, import the gas flow data transmitted by the data acquisition module into the gas flow data calculation model, and obtain the gas flow complexity coefficient value. The gas flow data calculation model is specifically expressed as: Among them, α i Indicates the gas flow complexity coefficient value calculated for the i-th time, Tc i represents the turbulence coefficient collected at the i-th time, Fv i represents the vortex frequency collected for the i-th time, Nr i represents the Reynolds number collected at the ith time.

5. A data analysis system for gas scene simulation according to claim 1, characterized in that: The gas composition data analysis node is used to establish a gas composition data calculation model, import the gas composition data transmitted by the data acquisition module into the gas composition data calculation model, and obtain the gas stability coefficient value. The gas composition data calculation model is specifically expressed as: Among them, β i Indicates the gas stability coefficient value calculated for the i-th time, Pc i Indicates the impurity content ratio and Cv of the i-th collection i represents the fluctuation rate of the combustible component collected for the i-th time, Ag i Represents the combustion aid content gradient collected for the i-th time.

6. A data analysis system for gas scene simulation according to claim 1, characterized in that: The gas energy data analysis node is used to establish a gas energy data calculation model, import the gas energy data transmitted by the data acquisition module into the gas energy data calculation model, and obtain the gas energy efficiency coefficient value. The gas energy data calculation model is specifically expressed as: Among them, γ i Indicates the gas energy efficiency coefficient value calculated for the i-th time, Ed i represents the energy density per unit volume collected for the i-th time, Ef i Represents the energy release rate fluctuation value collected for the i-th time.

7. A data analysis system for gas scene simulation according to claim 1, characterized in that: The environmental thermal data analysis node is used to establish an environmental thermal data calculation model, import the environmental thermal data transmitted by the data acquisition module into the environmental thermal data calculation model, and obtain the gas thermal environment coupling coefficient value. The environmental thermal data calculation model is specifically expressed as: in, Indicates the thermal-environment coupling coefficient value of the gas calculated for the i-th time, Ac i represents the ambient thermal conductivity gradient collected at the ith time, Ca i represents the thermal radiation absorption coefficient collected for the i-th time, σ represents the standard deviation of the thermal radiation absorption coefficient, μ represents the mean value of the thermal radiation absorption coefficient, At i Represents the non-uniformity of the ambient temperature field collected for the i-th time.

8. A data analysis system for gas scene simulation according to claim 1, characterized in that: The environmental mechanics data analysis node is used to establish an environmental mechanics data calculation model, import the environmental mechanics data transmitted by the data acquisition module into the environmental mechanics data calculation model, and obtain the gas environment interference coefficient value. The environmental mechanics data calculation model is specifically expressed as: Among them, λ i Indicates the gas environment interference coefficient value calculated for the i-th time, Ap i represents the ambient pressure gradient collected for the i-th time, Sw i represents the wind shear intensity collected for the i-th time, As i represents the vibration acceleration spectral density collected for the i-th time.

9. A data analysis system for gas scene simulation according to claim 1, characterized in that: The environmental electrical data analysis node is used to establish an environmental electrical data calculation model, import the environmental electrical data transmitted by the data acquisition module into the environmental electrical data calculation model, and obtain the gas electrical environmental safety factor value. The environmental electrical data calculation model is specifically expressed as: Among them, χ i Indicates the value of the gas-electricity environmental safety factor calculated for the i-th time, Eg i represents the electrostatic field intensity gradient collected for the ith time, Ar i represents the air ionization rate collected for the i-th time, Sr i Represents the electromagnetic radiation intensity spectrum collected for the i-th time.

10. A data analysis system for gas scene simulation according to claim 1, characterized in that: The gas scenario simulation data analysis unit is used to establish a gas scenario simulation data calculation model, and import the gas flow complexity coefficient value, gas stability coefficient value, gas energy efficiency coefficient value, gas thermal environment coupling coefficient value, gas environment interference coefficient value and gas electrical environment safety coefficient value transmitted by the data analysis module into the gas scenario simulation data calculation model to obtain a gas scenario simulation comprehensive evaluation index value; the gas scenario simulation data calculation model is specifically expressed as: Among them, A represents the calculated comprehensive evaluation index value of gas scenario simulation, α i represents the gas flow complexity coefficient value calculated for the i-th time, β i represents the gas stability coefficient value calculated for the i-th time, γ i represents the gas energy efficiency coefficient value calculated for the i-th time, represents the value of the gas thermal environment coupling coefficient calculated for the i-th time, Indicates the minimum value of the calculated gas thermal environment coupling coefficient, Indicates the maximum value of the calculated gas thermal environment coupling coefficient, λ i Indicates the gas environment interference coefficient value calculated for the i-th time, λ min Indicates the minimum value of the calculated gas environment interference coefficient, λ max Indicates the maximum value of the calculated gas environment interference coefficient, χ i represents the value of the gas-electricity environmental safety factor calculated for the i-th time, χ min represents the minimum value of the calculated gas-electricity environmental safety factor, χ max Indicates the calculated maximum value of the gas-electricity environmental safety factor, i indicates starting from the i-th number, and n indicates ending at the n-th number.

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