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

Abstract

The invention relates to the technical field of gas monitoring, and discloses a digital twin system based on gas online monitoring data and a diffusion model, which comprises an X-axis microwave generating end deployed on the basis of an X-axis area of a monitoring area, and a Y-axis microwave generating end deployed on the basis of a Y-axis area of the monitoring area; the X-axis microwave generating end and the Y-axis microwave generating end are arranged at the same horizontal height and respectively emit microwave rays with the same characteristic attributes; and an acquisition area is formed at the intersection of the microwave rays emitted by the X-axis microwave generating end and the microwave rays emitted by the Y-axis microwave generating end. The X / Y-axis microwave rays intersect to form a discretization collection area, the system can independently analyze the gas diffusion state of a specific point position in a monitoring area, the method can accurately position an abnormal point, misjudgment caused by general monitoring is avoided, and the problem that diffusion behaviors cannot be distinguished due to density difference in mixed gas is particularly solved.

Description

Technical Field

[0001] The present invention relates to the field of gas monitoring technology, and in particular to a digital twin system based on online gas monitoring data and a diffusion model. Background Art

[0002] Current gas monitoring technologies generally adopt an overall area detection method, which makes it difficult to distinguish the independent diffusion behavior of different density components in the mixed gas (such as volatile substances such as formaldehyde), resulting in unclear leakage source positioning and the early warning threshold relying on experience-based settings; especially due to the diffusion rate differentiation phenomenon caused by density differences (such as rapid diffusion of light gases and local deposition of heavy gases), traditional methods cannot accurately quantify the diffusion state of each point, resulting in misjudgment of leaks and delayed response.

[0003] Existing patents include CN118091054A, a hazardous gas online monitoring system and method, and CN119961696A, an AI-powered traceability data processing method. These patents are unable to accurately distinguish between independent diffusion behaviors in mixed gases caused by density differences (such as rapid diffusion of light gases and localized deposition of heavy gases), and are unable to accurately locate anomalies or identify leak sources. Summary of the Invention

[0004] In order to solve existing technical problems, the present invention provides a digital twin system based on gas online monitoring data and diffusion model, which solves the problem that different gases have different diffusion states due to different densities.

[0005] To solve the above technical problems, according to one aspect of the present invention, more specifically, a digital twin system based on gas online monitoring data and diffusion model includes: Deploy the X-axis microwave generator end based on the X-axis area of ​​the monitoring area, and deploy the Y-axis microwave generator end based on the Y-axis area of ​​the monitoring area; The X-axis microwave generating end and the Y-axis microwave generating end are at the same level and emit microwave rays with the same characteristic properties respectively; The intersection of the microwave rays emitted by the X-axis microwave generating end and the microwave rays emitted by the Y-axis microwave generating end forms a collection area; Deploy a temperature monitoring unit based on the Z-axis region of the monitoring area, wherein the temperature monitoring unit is used to monitor the temperature change in the acquisition area; After each temperature change acquisition of the acquisition area is completed, a temporary air flow rate monitoring module is deployed, and the air flow rate monitoring module is used to perform real-time air flow rate monitoring; A diffusion model is constructed based on the air velocity and temperature changes and characteristic attribute changes in the collection area, and the diffusion level is established based on the diffusion coefficient output by the diffusion model. Different levels of warning thresholds are divided based on different diffusion levels and diffusion requirements for different gases.

[0006] Furthermore, 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 at the same Z-axis height.

[0007] Furthermore, the microwave rays emitted by the X-axis microwave generating end and the Y-axis microwave generating end both have 180 degrees of freedom.

[0008] Furthermore, the temperature monitoring unit may be located at the lowest point or the highest point of the monitoring area, and there may be one or more than two temperature monitoring units.

[0009] Furthermore, in microwave rays with the same characteristic attributes, the characteristic attributes include wavelength attributes, frequency attributes, and amplitude attributes.

[0010] Furthermore, the specific steps of establishing the diffusion model are: (1) Recording the air velocity of the current monitoring area into sample data; (2) Divide the proportion of the air flow rate in the sample data according to the size of the air flow rate in the collection area; (3) Establish a temperature change model based on the relationship between the proportion of air flow rate in the collection area and the temperature change in the collection area; (4) Establishing an attribute change model based on the relationship between the proportion of air flow velocity in the collection area and the change in the characteristic attribute of the emitted microwave rays; (5) Construct a diffusion model based on the correlation between the temperature change model and the property change model.

[0011] Furthermore, the calculation formula of the diffusion coefficient output by the diffusion model is: ; Where c represents the diffusion coefficient of the acquisition area output according to the diffusion model; m represents the change in the amplitude of the microwave ray; and T represents the temperature change in the acquisition area.

[0012] Furthermore, the variation of the characteristic attribute is the variation of only the amplitude attribute of the microwave rays emitted by the X-axis microwave generating end and the Y-axis microwave generating end.

[0013] Furthermore, different levels of warning thresholds are established based on the diffusion coefficient of the corresponding gas output by the diffusion model and the actual situation of whether the corresponding gas leaks or diffuses.

[0014] The digital twin system based on online gas monitoring data and diffusion model provided by the present invention has the following advantages compared with the existing technology: 1. The present invention forms a discrete collection area by crossing X / Y-axis microwave rays. The system can independently analyze the gas diffusion state at specific points in 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 in mixed gases due to density differences.

[0015] 2. This system combines a Z-axis temperature monitoring unit with a temporarily deployed air velocity monitoring module 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 the ability to characterize the diffusion behavior of gases of varying densities.

[0016] 3. This invention objectively categorizes 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 shifts from "empirical thresholds" to "model-driven thresholds," improving both early warning accuracy and traceability efficiency.

[0017] 4. This system addresses the phenomenon of differential diffusion rates due to density differences between gases. By discretizing the acquisition area, the system independently constructs a diffusion model for each point, avoiding interference from gas mixing. This allows the diffusion paths of light and heavy gases to be distinguished, directly serving leak source location and ventilation optimization.

[0018] 5. By adjusting microwave characteristics and diffusion model parameters, the system can adapt to different gases and environments (e.g., indoors and industrial areas). Furthermore, the flexibility of the location (lowest point / highest point) and number (single or multiple) of temperature monitoring units enhances deployment adaptability in complex spaces. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a spatial position schematic diagram of the present invention; Figure 2 is a relationship diagram between the diffusion coefficient c and the change in amplitude m in the present invention; Figure 3 is a relationship diagram between the diffusion coefficient c and the temperature change T in the present invention; Figure 4 Schematic diagram of the diffusion level of formaldehyde gas in the room in the present invention. DETAILED DESCRIPTION

[0020] In order to make the technical solution of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] Example 1

[0022] like Figure 1 As shown in the figure, a digital twin system based on online gas monitoring data and a diffusion model deploys X-axis microwave generators along the X-axis of the monitoring area and Y-axis microwave generators along the Y-axis of the monitoring area. Both X-axis and Y-axis microwave generators are located at the same horizontal height and emit microwave radiation with identical characteristics. The X-axis and Y-axis microwave generators have the same Z-axis height, and the microwave radiation emitted by both X-axis and Y-axis microwave generators is at the same Z-axis height. The microwave radiation emitted by both X-axis and Y-axis microwave generators has 180 degrees of freedom. The intersection of the microwave radiation emitted by the X-axis and Y-axis microwave generators forms a collection area. Temperature monitoring units are deployed along the Z-axis of the monitoring area to monitor temperature changes within that collection area. Discrete collection areas are formed by the intersection of X-axis and Y-axis microwave radiation, enabling independent analysis of the gas diffusion state at specific locations within the monitoring area. Compared to traditional, integrated area monitoring, this system can precisely locate anomalies (such as leak sources) and avoid misjudgments caused by generalized monitoring.

[0023] 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. After each temperature change in the acquisition area is collected, a temporary air velocity monitoring module is deployed, and real-time air velocity monitoring is performed through this air velocity monitoring module. This method, which forms a precise acquisition area through the intersection of X / Y-axis microwave rays, 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 drawbacks of traditional methods of general monitoring of mixed gases. At the same time, combined with the dynamic coordinated acquisition of temperature and air velocity, independent diffusion models are established for gases of different densities (such as volatile substances like formaldehyde), directly resolving the technical bottleneck of "indistinguishable diffusion behavior due to differences in gas density," thereby providing high-resolution data support for leak tracing (such as identifying volatile accumulation points and air outlet locations) and risk classification and early warning.

[0024] The above solution can detect the diffusion of specific gases within the monitoring area, rather than monitoring the total amount of gases. Furthermore, because different gases have different densities, the diffusion coefficients derived from them differ, resulting in different diffusion levels and leak states. Therefore, this solution can address the issue of varying diffusion states due to varying densities of different gases.

[0025] Example 2

[0026] like Figure 1As shown, a diffusion model is constructed based on the air velocity and temperature changes and the changes in characteristic attributes in the collection area, and the diffusion level is established based on the diffusion coefficient output by the diffusion model; in microwave rays with the same characteristic attributes, the characteristic attributes include wavelength attributes, frequency attributes and amplitude attributes.

[0027] The specific steps to establish the diffusion model are: (1) Enter the air velocity in the current monitoring and collection area into the sample data.

[0028] (2) Divide the proportion of the air flow rate in the sample data according to the size of the air flow rate in the collection area.

[0029] For example, when 100 sample data are collected, if the air flow rate in a certain collection area exceeds the air flow rate in the other 50 sample data, then the diffusion coefficient c=50% of the collection area output according to the diffusion model can be used as an alternative.

[0030] (3) A temperature change model is established based on the relationship between the proportion of air flow rate in the collection area and the temperature change in the collection area.

[0031] A mathematical model is established for the relationship between the diffusion coefficient c and the amplitude change m (e.g. Figure 2 As shown in the figure, the red dots are the distribution of the 100 sample data collected), then: ; In the above formula 1, k represents an empirical constant for adjusting the sensitivity of the above model.

[0032] (4) Establish an attribute change model based on the relationship between the proportion of air flow rate in the collection area and the change in the characteristic attributes of the emitted microwave rays.

[0033] A mathematical model is established for the relationship between the diffusion coefficient c and the temperature change T (e.g. Figure 3 As shown in the figure, the blue dots are the distribution of the 100 sample data collected), then: ; In the above formula 2, k represents an empirical constant for adjusting the sensitivity of the above model.

[0034] (5) Construct a diffusion model based on the correlation between the temperature change model and the property change model.

[0035] A diffusion model is established for the relationship between the diffusion coefficient c, the amplitude change m, and the temperature change T (which can be obtained by Figure 2 、 3Knowing that the amplitude change m and temperature change T are positively correlated, and combining the characteristic relationship of the above formula 1 and formula 2, the calculation formula for the diffusion coefficient output by the diffusion model is: ; Where c represents the diffusion coefficient of the acquisition area output according to the diffusion model; m represents the change in the amplitude of the microwave ray (the change in the characteristic attribute is the change in the amplitude of the microwave ray emitted by the X-axis microwave generating end and the Y-axis microwave generating end); and T represents the temperature change in the acquisition area.

[0036] Explanation of the above diffusion model: The change in amplitude m in microwave rays is: .

[0037] The temperature change T in the acquisition area is: .

[0038] Example of the above diffusion model: For example, the W band with a wavelength of 4.1 mm and a frequency of 72.8 GHz is used to monitor the diffusion of formaldehyde in the air, wherein the initial amplitude of the emitted microwave radiation is 2 V / m.

[0039] The coordinates of the monitored acquisition area on the X and Y axes in space are (19, 30). When the amplitude change in the microwave radiation is m = 0.60 (the adjusted amplitude is 5V / m), the temperature change in the acquisition area is T = 0.269 (the monitored temperature after the amplitude adjustment is 43°C, the initial amplitude is 36°C, and the ambient temperature is 26°C). Then we have: ; According to the above calculations, when the formaldehyde monitoring area at the X and Y coordinates (19, 30) in this space is taken, the diffusion coefficient at this location is 49.0%. And by comparing multiple sets of data, we have: Table 1 Formaldehyde diffusion coefficient monitored in the monitoring area

[0040] From the above data, we can see that the closer to the coordinate (19, 30), the larger the diffusion coefficient c is, which means that there may be windows or air vents near the coordinate (19, 30). The diffusion coefficient c is smaller near the coordinate (11, 15), which means that there may be paint, volatile substances, etc. near the coordinate (11, 15).

[0041] Example 3

[0042] like Figure 4 As shown, different levels of warning thresholds are divided based on different diffusion levels and diffusion requirements for different gases. The different levels of warning thresholds are established based on the diffusion coefficient of the corresponding gas output in the diffusion model and the actual situation of whether the corresponding gas is leaking or diffusing. Then there are: Table 2 Formaldehyde diffusion coefficient, volatile matter, and air outlet position monitored in the monitoring area

[0043] Based on the above data, we can know that different diffusion levels are divided according to whether it is close to volatile substances or close to the air outlet (such as Figure 4 Purple areas have lower diffusion levels, while yellow areas have higher diffusion levels).

[0044] Furthermore, the diffusion coefficient c can be used to establish a threshold for determining the source of leaks or volatiles. For example, in the case of formaldehyde gas detection, when c < 42.3%, it indicates that the collection area is close to volatiles, and this threshold can be used to identify the source of volatiles. When c > 49.0%, it indicates that the collection area is close to the air outlet, and this threshold can help locate the leak.

[0045] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A digital twin system based on online gas monitoring data and diffusion model, characterized by: Includes: Deploy the X-axis microwave generator end based on the X-axis area of ​​the monitoring area, and deploy the Y-axis microwave generator end based on the Y-axis area of ​​the monitoring area; The X-axis microwave generating end and the Y-axis microwave generating end are at the same level and emit microwave rays with the same characteristic properties respectively; The intersection of the microwave rays emitted by the X-axis microwave generating end and the microwave rays emitted by the Y-axis microwave generating end forms a collection area; Deploy a temperature monitoring unit based on the Z-axis region of the monitoring area, wherein the temperature monitoring unit is used to monitor the temperature change in the acquisition area; After each temperature change acquisition of the acquisition area is completed, a temporary air flow rate monitoring module is deployed, and the air flow rate monitoring module is used to perform real-time air flow rate monitoring; A diffusion model is constructed based on the air velocity and temperature changes and characteristic attribute changes in the collection area, and the diffusion level is established based on the diffusion coefficient output by the diffusion model. Different levels of warning thresholds are divided based on different diffusion levels and diffusion requirements for different gases.

2. The digital twin system based on gas online monitoring data and diffusion model according to claim 1 is characterized by: 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 at the same Z-axis height.

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

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

5. The digital twin system based on gas online monitoring data and diffusion model according to claim 1 is characterized in that: In microwave rays with the same characteristic attributes, the characteristic attributes include wavelength attributes, frequency attributes, and amplitude attributes.

6. The digital twin system based on gas online monitoring data and diffusion model according to claim 5 is characterized by: The specific steps of establishing the diffusion model are: (1) Recording the air velocity of the current monitoring area into sample data; (2) Divide the proportion of the air flow rate in the sample data according to the size of the air flow rate in the collection area; (3) Establish a temperature change model based on the relationship between the proportion of air flow rate in the collection area and the temperature change in the collection area; (4) Establishing an attribute change model based on the relationship between the proportion of air flow velocity in the collection area and the change in the characteristic attribute of the emitted microwave rays; (5) Construct a diffusion model based on the correlation between the temperature change model and the property change model.

7. The digital twin system based on gas online monitoring data and diffusion model according to claim 6 is characterized by: The calculation formula of the diffusion coefficient output by the diffusion model is: ； Where c represents the diffusion coefficient of the acquisition area output according to the diffusion model; m represents the change in the amplitude of the microwave ray; and T represents the temperature change in the acquisition area.

8. The digital twin system based on gas online monitoring data and diffusion model according to claim 6 is characterized by: The variation of the characteristic attribute is the variation of only the amplitude attribute of the microwave rays emitted by the X-axis microwave generating end and the Y-axis microwave generating end.

9. The digital twin system based on gas online monitoring data and diffusion model according to claim 1 is characterized in that: Different levels of warning thresholds are established based on the diffusion coefficient of the corresponding gas output by the diffusion model and the actual situation of whether the corresponding gas leaks or diffuses.

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