Digital twin system based on online gas monitoring data and diffusion model

Abstract

This invention relates to the field of gas monitoring technology and discloses a digital twin system based on online gas monitoring data and a diffusion model. The system includes: an X-axis microwave generator deployed along the X-axis of the monitoring area, and a Y-axis microwave generator deployed along the Y-axis of the monitoring area; both the X-axis and Y-axis microwave generators are at the same horizontal height and emit microwave rays with the same characteristic properties; the intersection of the microwave rays emitted by the X-axis and Y-axis microwave generators forms a collection area. This invention uses the intersection of X / Y-axis microwave rays to form a discrete collection area, allowing the system to independently analyze the gas diffusion state at specific points within the monitoring area. This method can accurately locate anomalies, avoid misjudgments caused by general monitoring, and particularly solve the problem of indistinguishable diffusion behavior due to density differences in mixed gases.

Description

Technical Field

[0001] This invention relates to the field of gas monitoring technology, and in particular to a digital twin system based on online gas monitoring data and diffusion models. Background Technology

[0002] Current gas monitoring technologies generally adopt a holistic area detection method, which makes it difficult to distinguish the independent diffusion behavior of components of different densities (such as volatile substances like formaldehyde) in a gas mixture. This leads to fuzzy leak source location and early warning thresholds that rely on experience. In particular, the diffusion rate differentiation caused by density differences (such as rapid diffusion of light gases and local deposition of heavy gases) makes it impossible for traditional methods to accurately quantify the diffusion status at each point, resulting in misjudgment of leaks and delayed response.

[0003] Existing patents disclose CN118091054A, a hazardous gas online monitoring system and method, and CN119961696A, an AI-powered intelligent source tracing data processing method. These existing patents cannot accurately distinguish the independent diffusion behavior in mixed gases caused by density differences (such as rapid diffusion of light gases and local deposition of heavy gases), and therefore cannot accurately locate anomalies or identify leak sources. Summary of the Invention

[0004] This invention provides a digital twin system based on online gas monitoring data and diffusion models to solve existing technical problems, addressing the issue that different gases have different diffusion states due to their different densities.

[0005] To solve the above-mentioned technical problems, according to one aspect of the present invention, more specifically a digital twin system based on online gas monitoring data and diffusion models, includes:

[0006] An X-axis microwave generator is deployed along the X-axis of the monitoring area, and a Y-axis microwave generator is deployed along the Y-axis of the monitoring area.

[0007] Both the X-axis microwave generator and the Y-axis microwave generator are at the same horizontal height and emit microwave rays with the same characteristic properties.

[0008] The intersection of the microwave rays emitted by the X-axis microwave generator and the microwave rays emitted by the Y-axis microwave generator forms the acquisition area;

[0009] Temperature monitoring units are deployed along the Z-axis of the monitoring area to monitor temperature changes in the area being monitored.

[0010] After each temperature change data collection in the collection area is completed, a temporary air velocity monitoring module is deployed, and real-time air velocity monitoring is performed through this air velocity monitoring module.

[0011] A diffusion model is constructed based on the changes in air velocity and temperature, as well as the changes in characteristic attributes in the data collection area, and a diffusion level is established based on the diffusion coefficient output by the diffusion model.

[0012] Different warning thresholds are defined based on different diffusion levels and diffusion requirements for different gases.

[0013] Furthermore, the Z-axis height of the X-axis microwave generating end and the Y-axis microwave generating end is the same, and the microwave rays emitted by the X-axis microwave generating end and the Y-axis microwave generating end are all at the same Z-axis height.

[0014] Furthermore, the microwave rays emitted by the X-axis microwave generating end and the Y-axis microwave generating end both have 180º degrees of freedom.

[0015] Furthermore, the temperature monitoring unit can be located at the lowest or highest point of the monitoring area, and there can be one or more temperature monitoring units.

[0016] Furthermore, in microwave rays with the same characteristic attributes, these characteristic attributes include wavelength, frequency, and amplitude.

[0017] Furthermore, the specific steps for establishing the diffusion model are as follows:

[0018] (1) Record the air velocity of the currently monitored collection area into the sample data;

[0019] (2) Divide the proportion of the air velocity in the sample data according to the magnitude of the air velocity in the collection area;

[0020] (3) Establish a temperature change model based on the relationship between the proportion of air velocity in the collection area and the temperature change in the collection area;

[0021] (4) Establish an attribute change model based on the relationship between the proportion of air velocity in the collection area and the change in the characteristic properties of the emitted microwave rays;

[0022] (5) Construct a diffusion model based on the correlation between the temperature change model and the property change model.

[0023] Furthermore, the formula for calculating the diffusion coefficient output by the diffusion model is as follows:

[0024] ;

[0025] In the formula, c represents the diffusion coefficient output by the diffusion model in the acquisition area; m represents the change in amplitude in the microwave ray; and T represents the temperature change in the acquisition area.

[0026] Furthermore, the change in the characteristic attribute refers to the change in only the amplitude attribute of the microwave rays emitted from the X-axis microwave generator and the Y-axis microwave generator.

[0027] Furthermore, the different warning thresholds are established based on the diffusion coefficient of the corresponding gas in the diffusion model output and the actual situation of whether the corresponding gas has leaked or diffused.

[0028] The digital twin system based on online gas monitoring data and diffusion models provided by this invention achieves the following advantages compared to existing technologies:

[0029] 1. This invention uses X / Y axis microwave rays to form a discrete acquisition area. The system can independently analyze the gas diffusion state at specific points within the monitoring area. This method can accurately locate abnormal points and avoid misjudgments caused by general monitoring. In particular, it solves the problem of indistinguishable diffusion behavior caused by density differences in mixed gases.

[0030] 2. This invention combines a Z-axis temperature monitoring unit with a temporarily deployed air velocity monitoring module, enabling the system to simultaneously acquire multi-dimensional dynamic parameters such as temperature changes, air velocity, and microwave amplitude changes in the acquisition area. This collaborative acquisition mechanism provides high-precision input data for the diffusion model, significantly enhancing its ability to characterize the diffusion behavior of gases with different densities.

[0031] 3. This invention objectively classifies diffusion levels using the diffusion coefficient output by the diffusion model and establishes graded warning thresholds based on the leakage characteristics of different gases. This mechanism realizes the transformation from "empirical thresholds" to "model-driven thresholds," improving the accuracy of early warnings and the efficiency of source tracing.

[0032] 4. This invention addresses the differentiation in diffusion rates caused by density differences among different gases. The system constructs independent diffusion models for each location by discretizing the acquisition area, avoiding interference from gas mixing. This allows for the differentiation and identification of diffusion paths between light and heavy gases, directly contributing to leak source location and ventilation optimization.

[0033] 5. By adjusting the microwave characteristic properties and diffusion model parameters, the system of this invention can be adapted to different gases and environments (such as indoor / industrial areas). Meanwhile, the flexibility in the location (lowest point / highest point) and number (single or multiple) of the temperature monitoring units enhances deployment adaptability in complex spaces. Attached Figure Description

[0034] Figure 1 This is a schematic diagram of the spatial location of the present invention;

[0035] Figure 2 This is a graph showing the relationship between the diffusion coefficient c and the change in wave amplitude m in this invention;

[0036] Figure 3 This is a graph showing the relationship between the diffusion coefficient c and the temperature change T in this invention.

[0037] Figure 4 This is a schematic diagram illustrating the diffusion levels of formaldehyde gas indoors in this invention. Detailed Implementation

[0038] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] Example 1

[0040] like Figure 1 As shown, a digital twin system based on online gas monitoring data and a diffusion model deploys an X-axis microwave generator along the X-axis of the monitoring area and a Y-axis microwave generator along the Y-axis. Both the X-axis and Y-axis microwave generators are at the same horizontal height and emit microwave rays with the same characteristic properties. The Z-axis heights of the X-axis and Y-axis microwave generators are the same, and the microwave rays emitted by both are at the same Z-axis height. The microwave rays emitted by both the X-axis and Y-axis microwave generators have 180º degrees of freedom. The intersection of the microwave rays emitted by the X-axis and Y-axis microwave generators forms the acquisition area. A temperature monitoring unit is deployed along the Z-axis of the monitoring area to monitor temperature changes within that acquisition area. By forming a discrete acquisition area through the intersection of the X / Y-axis microwave rays, independent analysis of the gas diffusion state at specific locations within the monitoring area is achieved. Compared to traditional overall area monitoring, this system can accurately locate anomalies (such as leak sources) and avoid misjudgments caused by general monitoring.

[0041] Temperature monitoring units can be located at the lowest or highest point of the monitoring area, and there can be one or more temperature monitoring units. After each temperature change monitoring of the area is completed, a temporary air velocity monitoring module is deployed for real-time air velocity monitoring. This method, which uses the intersection of X / Y axis microwave rays to form a precise acquisition area, combined with Z-axis temperature monitoring and a temporary air velocity module, has the core advantage of achieving differentiated and precise monitoring of gas diffusion. Through microwave ray cross-positioning technology, the system refines the monitoring area into discrete acquisition points, avoiding the shortcomings of traditional methods that provide general monitoring of mixed gases. At the same time, by combining dynamic and coordinated acquisition of temperature and air velocity, independent diffusion models are established for gases of different densities (such as volatile substances like formaldehyde), directly solving the technical bottleneck of "indistinguishable diffusion behavior due to differences in gas density." This provides high-resolution data support for leak tracing (such as identifying volatile accumulation points and vent locations) and risk classification and early warning.

[0042] The above-described method allows for the detection of specific gas diffusion within a monitoring area, rather than monitoring the general sum of gases. Furthermore, because different gases have different densities, their diffusion coefficients vary, leading to different diffusion levels and different diffusion states indicating leaks. Therefore, this method effectively addresses the issue of varying diffusion states caused by differences in gas density.

[0043] Example 2

[0044] like Figure 1 As shown, a diffusion model is constructed based on the changes in air velocity and temperature in the collection area, as well as the changes in characteristic attributes, and the diffusion level is set based on the diffusion coefficient output by the diffusion model; among microwave rays with the same characteristic attributes, these characteristic attributes include wavelength attribute, frequency attribute, and amplitude attribute.

[0045] The specific steps for establishing the diffusion model are as follows:

[0046] (1) Record the air velocity in the current monitoring and collection area into the sample data.

[0047] (2) The proportion of the air velocity in the sample data is determined based on the magnitude of the air velocity in the collection area.

[0048] For example, if 100 sample data are collected, and the air velocity in a certain collection area exceeds the air velocity in the other 50 sample data, then it can be used to represent that the diffusion coefficient c=50% of the collection area is output by the diffusion model.

[0049] (3) Establish a temperature change model based on the relationship between the proportion of air velocity in the collection area and the temperature change in the collection area.

[0050] A mathematical model is established to determine the relationship between the diffusion coefficient c and the change in wave amplitude m (e.g.) Figure 2 As shown in the figure (where the red dots represent the distribution of the 100 collected sample data), then:

[0051] ;

[0052] In Formula 1 above, k represents an empirical constant for adjusting the sensitivity of the model.

[0053] (4) Establish an attribute change model based on the relationship between the proportion of air velocity in the collection area and the change in the characteristic properties of the emitted microwave rays.

[0054] A mathematical model was established to investigate the relationship between the diffusion coefficient c and the temperature change T (e.g., Figure 3 As shown in the figure (where the blue dots represent the distribution of the 100 collected sample data), then:

[0055] ;

[0056] In Formula 2 above, k represents an empirical constant for adjusting the sensitivity of the above model.

[0057] (5) Construct a diffusion model based on the correlation between the temperature change model and the property change model.

[0058] A diffusion model is established to establish the relationship between the diffusion coefficient c and the changes in wave amplitude m and temperature T (which can be achieved through...). Figure 2 , 3 Knowing that the amplitude change *m* and the temperature change *T* are positively correlated, and combining this with the characteristic relationship from Formulas 1 and 2 above, the formula for calculating the diffusion coefficient output by the diffusion model is:

[0059] ;

[0060] In the formula, c represents the diffusion coefficient output by the diffusion model in the acquisition area; m represents the change in amplitude in the microwave rays (the change in characteristic attribute is the change in amplitude attribute only in the microwave rays emitted from the X-axis microwave generator and the Y-axis microwave generator); T represents the temperature change in the acquisition area.

[0061] Explanation of the above diffusion model:

[0062] The change in amplitude m in microwave radiation is:

[0063] .

[0064] The temperature change T in the data collection area is:

[0065] .

[0066] Examples of the above diffusion models:

[0067] For example, the diffusion of formaldehyde in the air was monitored using the W-band with a wavelength of 4.1 mm and a frequency of 72.8 GHz. The initial amplitude of the emitted microwave rays was set to 2 V / m.

[0068] The monitored area is located at (19, 30) on the X and Y axes. The amplitude change in the microwave radiation is set to m = 0.60 (adjusted amplitude is 5V / m). The temperature change in the monitored area is set to T = 0.269 (the monitored temperature after amplitude adjustment is 43℃, the initial amplitude was 36℃, and the ambient temperature was 26℃). Therefore:

[0069] ;

[0070] Based on the above calculations, it can be seen that after formaldehyde monitoring in the monitoring area with X and Y axis coordinates of (19, 30) in this space, the diffusion coefficient at that location is 49.0%. Furthermore, comparing multiple sets of data, we have:

[0071] Table 1 Formaldehyde diffusion coefficients monitored in the monitoring area

[0072]

[0073] The data above shows that the diffusion coefficient c is larger closer to coordinates (19, 30), indicating that there may be windows or air vents near coordinates (19, 30). Conversely, the diffusion coefficient c is smaller closer to coordinates (11, 15), indicating that there may be paint, volatile substances, etc. near coordinates (11, 15).

[0074] Example 3

[0075] like Figure 4 As shown, different warning thresholds are defined based on different diffusion levels and diffusion requirements for different gases. These different warning thresholds are established according to the diffusion coefficient of the corresponding gas output by the diffusion model and the actual situation regarding whether a leak or diffusion of the corresponding gas has occurred. Therefore:

[0076] Table 2. Formaldehyde diffusion coefficient and volatile matter concentration at the monitoring location in the monitoring area.

[0077]

[0078] Based on the above data, it can be seen that different diffusion levels are classified according to whether the object is near volatiles or near the air outlet (e.g., Figure 4 In the diagram, the purple area has a lower diffusion level, while the yellow area has a higher diffusion level.

[0079] Furthermore, a threshold for determining the source of a leak or volatile substance can be established based on this diffusion coefficient c. For example, in determining formaldehyde gas, when c < 42.3%, it indicates that the sampling area is close to volatile substances, thus providing a basis for locating the source of the volatile substances. Conversely, when c > 49.0%, it indicates that the sampling area is close to an air outlet, thus aiding in locating the leak.

[0080] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A digital twin system based on online gas monitoring data and diffusion models, characterized in that, Including: An X-axis microwave generator is deployed along the X-axis of the monitoring area, and a Y-axis microwave generator is deployed along the Y-axis of the monitoring area. Both the X-axis microwave generator and the Y-axis microwave generator are at the same horizontal height and emit microwave rays with the same characteristic properties. The intersection of the microwave rays emitted by the X-axis microwave generator and the microwave rays emitted by the Y-axis microwave generator forms the acquisition area; Temperature monitoring units are deployed along the Z-axis of the monitoring area to monitor temperature changes in the area being monitored. After each temperature change data collection in the collection area is completed, a temporary air velocity monitoring module is deployed, and real-time air velocity monitoring is performed through this air velocity monitoring module. A diffusion model is constructed based on the changes in air velocity and temperature, as well as the changes in characteristic attributes in the data collection area, and a diffusion level is established based on the diffusion coefficient output by the diffusion model. Different warning thresholds are defined based on different diffusion levels and diffusion requirements for different gases. Among microwave rays with the same characteristic attributes, these characteristic attributes include wavelength, frequency, and amplitude. The specific steps for establishing the diffusion model are as follows: (1) Record the air velocity of the currently monitored collection area into the sample data; (2) Divide the proportion of the air velocity in the sample data according to the magnitude of the air velocity in the collection area; (3) Establish a temperature change model based on the relationship between the proportion of air velocity in the collection area and the temperature change in the collection area; (4) Establish an attribute change model based on the relationship between the proportion of air velocity in the collection area and the change in the characteristic properties of the emitted microwave rays; (5) Construct a diffusion model based on the correlation between the temperature change model and the property change model; The formula for calculating the diffusion coefficient output by the diffusion model is as follows: ； In the formula, c represents the diffusion coefficient output by the diffusion model in the acquisition area; m represents the change in amplitude in the microwave ray; and T represents the temperature change in the acquisition area.

2. The digital twin system based on online gas monitoring data and diffusion model according to claim 1, characterized in that: The X-axis microwave generating end and the Y-axis microwave generating end have the same Z-axis height, and the microwave rays emitted by the X-axis microwave generating end and the Y-axis microwave generating end are all at the same Z-axis height.

3. The digital twin system based on online gas monitoring data and diffusion model according to claim 1, characterized in that: The microwave rays emitted by the X-axis microwave generator and the Y-axis microwave generator both have 180º degrees of freedom.

4. The digital twin system based on online gas monitoring data and diffusion model according to claim 1, characterized in that: The temperature monitoring unit can be located at the lowest or highest point of the monitoring area, and there can be one or more temperature monitoring units.

5. The digital twin system based on online gas monitoring data and diffusion model according to claim 1, characterized in that: The change in the characteristic attribute refers to the change in only the amplitude attribute of the microwave rays emitted from the X-axis microwave generator and the Y-axis microwave generator.

6. The digital twin system based on online gas monitoring data and diffusion model according to claim 1, characterized in that: The different warning thresholds are set based on the diffusion coefficient of the corresponding gas in the diffusion model output and the actual situation of whether the corresponding gas has leaked or diffused.

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