Gas monitoring management system based on data analysis
By optimizing the deployment and layout of gas monitoring points through a data analysis-based gas monitoring and management system, the problem of excessive resource consumption was solved, the timeliness of early warnings for production safety and quality was improved, and resource consumption was reduced.
Patent Information
- Application Number
- CN202511306210.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-12
- Publication Date
- 2026-02-03
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing technologies fail to control the activation of gas monitoring points in real time based on relevant data during the production process, resulting in excessive resource consumption and an inability to guarantee the timeliness of early warnings for production safety and quality.
A data-driven gas monitoring and management system is adopted, including a steady-state monitoring module, an update execution module, a cluster analysis module, and an interferometry analysis module. By monitoring parameters such as the fluctuation index, the fluctuation complexity index, and the interferometry index, the system optimizes the activation layout of gas monitoring points, reduces resource consumption, and improves the timeliness of early warning.
This approach achieves the goal of reducing resource consumption during gas monitoring while ensuring production safety and quality, and improving the optimization efficiency of monitoring points and the timeliness of early warnings.
Smart Images
Figure CN121456536A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of gas monitoring, and more particularly to a gas monitoring and management system based on data analysis. Background Technology
[0002] In industrial parks, production processes often involve the emission of different types of gases. Real-time monitoring of gas conditions in different areas within the park is crucial for timely adjustments to production processes, ensuring safety and quality. This requires setting up numerous gas monitoring points, such as monitoring points at all fixed gas emission outlets within the industrial park. However, setting too many monitoring points can lead to redundant data and wasted resources. Conversely, setting too few monitoring points compromises the timeliness of safety and quality warnings. Therefore, analyzing relevant data from the actual production process and optimizing the deployment of gas monitoring points in real time is essential to ensure timely warnings while minimizing resource consumption associated with gas monitoring.
[0003] Chinese Patent Publication No. CN119416411A discloses a monitoring point deployment method, equipment, and medium for gas monitoring in industrial parks, relating to the field of carbon monitoring technology. It utilizes a DT triangular mesh division method to create a triangular mesh. Based on the differences in the monitored areas, monitoring points are deployed according to the DT triangular mesh division method. At least one monitoring point is selected within each mesh to monitor carbon dioxide concentration. The deployment points are located in different areas under different prevailing wind directions. However, this solution has the following drawbacks: while ensuring that the set monitoring points can fully monitor the gas state within the industrial park, it fails to control the activation status of the monitoring points in real time based on relevant data from the production process, leading to excessive resource consumption during actual gas monitoring. Summary of the Invention
[0004] To address this issue, the present invention provides a data analysis-based gas monitoring and management system to overcome the problem in existing technologies that fail to control the activation status of monitoring points in real time based on relevant data from the production process, resulting in excessive resource consumption during actual gas monitoring.
[0005] To achieve the above objectives, the present invention provides a gas monitoring and management system based on data analysis, comprising:
[0006] The steady-state monitoring module is used to periodically determine the monitoring status of each normal monitoring point based on the monitoring fluctuation index, and to determine whether to activate and update the monitoring status of each normal monitoring point based on the monitoring status.
[0007] The update execution module is connected to the steady-state monitoring module and is used to update the activation of each normal monitoring point that is in a key monitoring state. It also determines whether to perform floating aggregation analysis or floating interference analysis on each normal monitoring point that is in a key monitoring state based on the floating complexity index and the floating interference index.
[0008] The aggregation analysis module, which is connected to the update execution module, is used to perform floating aggregation analysis, determine the aggregation point combination based on the aggregation coverage index or diffusion overlap index, and determine the analysis parameters on which the optimization coefficient of each monitoring point to be activated for the aggregation point combination is based on the interference overlap parameter. The analysis parameters include the overlap coverage index, the reference diffusion matching index, and the interference attenuation index.
[0009] An interferometry analysis module, which is connected to the update execution module, is used to perform floating interferometry analysis and determine the optimal coefficients of each monitoring point to be activated based on the wind direction correlation index and the diffusion distance index.
[0010] An execution module is enabled, which is connected to both the aggregation analysis module and the interference analysis module, to determine the preferred activation point based on the preferred coefficient and the preferred coverage parameter.
[0011] Furthermore, the routine monitoring points are existing monitoring points whose monitoring correlation coefficient is greater than the preset monitoring correlation coefficient;
[0012] The method for setting the monitoring correlation coefficient is determined based on the key distribution coefficient of the target management plant area, wherein,
[0013] If the key distribution coefficient is greater than the preset key distribution coefficient, the monitoring correlation coefficient of each existing monitoring point is determined based on the point coverage parameter.
[0014] If the key distribution coefficient is less than or equal to the preset key distribution coefficient, the monitoring correlation coefficient of each existing monitoring point is determined based on the radiation coverage parameters.
[0015] The key distribution coefficient is determined based on the key area parameters and the distribution uniformity parameters.
[0016] Furthermore, the monitoring status of the monitoring points includes both key monitoring status and stable monitoring status;
[0017] The routine monitoring points that are under key monitoring are those whose monitoring fluctuation index is greater than the preset monitoring fluctuation index.
[0018] A normal monitoring point that is in a stable monitoring state is a normal monitoring point where the monitoring fluctuation index is less than or equal to the preset monitoring fluctuation index.
[0019] Furthermore, if the floating complexity index or floating interference index of any normal monitoring point under key monitoring is greater than the preset floating complexity index or the floating interference index is greater than the preset floating interference index, then it is determined that the aggregation analysis module performs floating aggregation analysis on the normal monitoring point.
[0020] One type of monitoring point is a normal monitoring point where the floating complexity index is greater than the preset floating complexity index or the floating interference index is greater than the preset floating interference index.
[0021] Furthermore, the combination aggregation coefficient of the aggregation point combination determined for each type of monitoring point is greater than the preset combination aggregation coefficient;
[0022] The method for setting the combined aggregation coefficient is determined based on the interference coincidence parameter.
[0023] If there is a type of monitoring point with an interference coincidence parameter greater than the preset interference coincidence parameter, the combination aggregation coefficient of the aggregation point combination of this type of monitoring point is determined based on the ensemble coverage index.
[0024] If there exists a type of monitoring point where the interference coincidence parameter is less than or equal to the preset interference coincidence parameter, the combination aggregation coefficient of the aggregation point combination of this type of monitoring point is determined based on the diffusion coincidence index.
[0025] Furthermore, if there exists a type of monitoring point with an interference coincidence parameter greater than the preset interference coincidence parameter, the optimal coefficient of each monitoring point to be activated for the aggregation point combination of this type of monitoring point is determined based on the coincidence coverage index and the reference diffusion matching index.
[0026] The preferred coefficients are positively correlated with the overlap coverage index and the reference diffusion matching index, respectively.
[0027] Furthermore, if there exists a type of monitoring point with an interference coincidence parameter less than or equal to a preset interference coincidence parameter, the optimal coefficient of each monitoring point to be activated for the aggregation point combination of this type of monitoring point is determined based on the coincidence coverage index and the interference attenuation index.
[0028] The preferred coefficients are positively correlated with the overlap coverage index and the interference attenuation index, respectively.
[0029] Furthermore, if the floating complexity index of any normal monitoring point under key monitoring is less than or equal to the preset floating complexity index and the floating interference index is less than or equal to the preset floating interference index, then it is determined that the interference analysis module performs floating interference analysis on the normal monitoring point.
[0030] Furthermore, the optimization coefficients of each monitoring point to be activated for the second-class monitoring points are positively correlated with the wind direction correlation index and the diffusion distance index, respectively.
[0031] The second type of monitoring point is a normal monitoring point where the floating complexity index is less than or equal to the preset floating complexity index and the floating interference index is less than or equal to the preset floating interference index.
[0032] Furthermore, the preferred activation point is a preliminary monitoring point with a preferred coverage parameter greater than a preset preferred coverage parameter;
[0033] The preferred coverage parameters are determined based on the key monitoring targets within the associated evaluation range of each pre-monitoring point. The pre-monitoring points are monitoring points to be activated that have a preferred coefficient greater than the preset preferred coefficient.
[0034] Compared with the prior art, the beneficial effects of the present invention are as follows: the technical solution of the present invention determines the monitoring status of each normal monitoring point based on the monitoring fluctuation index, so as to determine whether to activate and update each normal monitoring point. Furthermore, it determines the specific analysis process for activating and updating each normal monitoring point based on the fluctuation complexity index and the fluctuation interference index. This makes the analysis process for determining the preferred activation point more consistent with the actual working scenario, ensuring the effectiveness of the determination result of the preferred activation point. While avoiding excessive resource consumption caused by too many activated gas monitoring points during the gas monitoring process, it also ensures the timeliness of early warning for production safety and production quality in the working environment.
[0035] Furthermore, in this invention, the setting method of the monitoring correlation coefficient is determined based on the key distribution coefficient of the target management park, and the routine monitoring points are determined based on the monitoring correlation coefficient to conduct continuous gas monitoring for the target management park. The key distribution coefficient characterizes the distribution of locations with abnormal gas content risks within the target management park, and the determination method of the targeted monitoring correlation coefficient is determined according to different distribution situations. This ensures that the focus of the setting process of routine monitoring points can meet the actual working scenario, and reduces resource consumption in the gas monitoring process while ensuring the coverage of gas monitoring.
[0036] Furthermore, in this invention, the need for activation and updating of each normal monitoring point is determined based on the monitoring fluctuation index. The monitoring fluctuation index is used to characterize the maximum fluctuation of the content of different types of gases at the location of the normal monitoring point, ensuring the timeliness of activation and updating of the normal monitoring points, thereby ensuring the timeliness of early warning for production safety and production quality in the working environment.
[0037] Furthermore, in this invention, floating aggregation analysis or floating interference analysis is determined for each normal monitoring point based on the floating complexity index and the floating interference index. The floating complexity index and the floating interference index characterize the richness of interference monitoring data at the normal monitoring points and the richness of key monitoring targets that cause fluctuations in interference monitoring data, so as to distinguish the interference situation of different normal monitoring points, and thus determine the focus of the process of updating the points to supplement the gas monitoring process. This invention ensures the effectiveness of the determination results of the preferred activation points.
[0038] Furthermore, this invention performs floating aggregation analysis on normal monitoring points where the floating complexity index is greater than the preset floating complexity index or the floating interference index is greater than the preset floating interference index. It determines the method for determining the aggregation point combination and the method for determining the optimization coefficient based on the interference coincidence parameter. The interference coincidence parameter characterizes the overlap of interference monitoring data between a type of monitoring point and other normal monitoring points, thereby ensuring that the combination aggregation coefficient for determining the aggregation point combination and the focus of the setting process for each monitoring point to be activated are more consistent with the actual working scenario. This ensures the effectiveness of the subsequent analysis of the synergistic effect of multiple interference targets. This invention guarantees the effectiveness of the determination results for the optimal activation points. Attached Figure Description
[0039] Figure 1 This is a module connection diagram of the gas monitoring and management system based on data analysis according to the present invention;
[0040] Figure 2 This is a flowchart illustrating the method for determining the monitoring correlation coefficient based on the key distribution coefficient in this invention.
[0041] Figure 3 This is a flowchart of the present invention for determining floating aggregation analysis or floating interference analysis for each normal monitoring point based on the floating complexity index and the floating interference index.
[0042] Figure 4 This is a flowchart illustrating the method for setting the combined aggregation coefficient based on interference coincidence parameters according to the present invention. Detailed Implementation
[0043] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.
[0044] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.
[0045] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.
[0046] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.
[0047] Please see Figures 1 to 4 As shown, the present invention provides a gas monitoring and management system based on data analysis, comprising:
[0048] The steady-state monitoring module is used to periodically determine the monitoring status of each normal monitoring point based on the monitoring fluctuation index, and to determine whether to enable update analysis for each normal monitoring point based on the monitoring status.
[0049] The update execution module, which is connected to the steady-state monitoring module, is used to perform activation update analysis for normal monitoring points that are in a key monitoring state.
[0050] This invention optimizes the real-time layout of gas monitoring points during the production phase of industrial parks, avoiding excessive resource consumption during actual gas monitoring. The industrial park is designated as the target management park, and several existing monitoring points exist within it. Each existing monitoring point is equipped with a gas monitoring device. Users can configure the location of each existing monitoring point according to actual work needs, such as fixing gas emission outlets. This invention does not impose specific restrictions on the distribution of existing monitoring points. How to configure the location of existing monitoring points is easily understood by those skilled in the art and will not be elaborated here. Each existing monitoring point is equipped with a gas monitoring device to obtain the gas and various gas contents at its corresponding location. The gas monitoring devices at existing monitoring points are fixed in this invention, and there are no specific limitations on the model of the gas monitoring devices installed at each existing monitoring point. However, all gas monitoring devices must be able to complete gas collection and gas content analysis.
[0051] This invention utilizes several activation analysis records. Each activation analysis record documents at least one activation management process for gas monitoring points within the target management area, including monitoring correlation coefficient, effective radiation parameter, key distribution coefficient, monitoring fluctuation index, fluctuation complexity index, fluctuation interference index, interference overlap parameter, diffusion matching coefficient, combination aggregation coefficient, preferred coverage parameter, and preferred coefficient. Each activation analysis record also has a corresponding qualification mark, which indicates whether the activation management quality of gas monitoring points within the target management area meets user requirements. It is understood that users can determine whether the activation management quality of gas monitoring points within the target management area meets their requirements based on self-defined indicators. These self-defined indicators include, but are not limited to, early warning response time and gas monitoring energy consumption. The early warning response time is the average time required from the occurrence of each gas leak or concentration exceedance event to the system issuing an early warning signal. The gas monitoring energy consumption is the total energy consumption of all existing monitoring points within the target management area per unit time.
[0052] Specifically, the routine monitoring points are existing monitoring points whose monitoring correlation coefficient is greater than the preset monitoring correlation coefficient;
[0053] The method for setting the monitoring correlation coefficient is determined based on the key distribution coefficient of the target management park, wherein,
[0054] If the key distribution coefficient is greater than the preset key distribution coefficient, the monitoring correlation coefficient of each existing monitoring point is determined based on the point coverage parameter.
[0055] If the key distribution coefficient is less than or equal to the preset key distribution coefficient, the monitoring correlation coefficient of each existing monitoring point is determined based on the radiation coverage parameters.
[0056] The key distribution coefficient is determined based on the key area parameters and the distribution uniformity parameters.
[0057] The target management area is divided into several sub-areas of equal size. The number of sub-areas can be set by the user according to the actual work scenario. The higher the user's requirements for the activation and management quality of gas monitoring points in the target management area, the larger the number of sub-areas. This invention does not make a specific limit on the number of sub-areas. The key distribution coefficient is the sum of the key area parameter and the distribution uniformity parameter. The key area parameter = the number of key sub-areas in the target management area / the number of sub-areas divided for the target management area. The key sub-area is the sub-area with key monitoring targets. The distribution uniformity parameter = the average number of key monitoring targets in each sub-area of the target management area / the standard deviation of the number of key monitoring targets in each sub-area of the target management area. The key monitoring targets are the locations in the target management area used for gas emission or where there is a risk of gas leakage. Key monitoring targets in this invention include, but are not limited to: reaction vessels / reactors, incinerators, bioreactors, valves, flanges, fixed gas emission outlets, and storage tank interfaces.
[0058] In this invention, a normal setting cycle is applied. The duration of the normal setting cycle can be determined by the user. The higher the user's requirements for the activation and management quality of gas monitoring points in the target management park, the shorter the duration of the normal setting cycle. A normal setting cycle duration of 24 hours is provided. The start time of each normal setting cycle is set for the normal monitoring points in the target management park.
[0059] If the key distribution coefficient is greater than the preset key distribution coefficient, it indicates that the distribution range of key monitoring targets in the target management park is relatively wide and uniform. At this time, when determining the monitoring correlation coefficient, it is necessary to focus on the coverage of the existing key monitoring targets by the normal monitoring points. For a single existing monitoring point, the point coverage parameter = the number of key monitoring targets within the correlation assessment range of the existing monitoring point / the number of key monitoring targets in the target management park. The correlation assessment range is a circular area with the location of the existing monitoring point as the center and the assessment reference distance as the radius. The value of the assessment reference distance can be set by the user according to the actual working scenario. The higher the user's requirements for the activation management quality of gas monitoring points in the target management park, the smaller the value of the assessment reference distance. This invention does not make specific limitations on the assessment reference distance. The determined point coverage parameter is normalized. The monitoring correlation coefficient and the point coverage parameter after normalization are positively correlated. How to perform normalization is a content that is already known to those skilled in the art and will not be elaborated here.
[0060] If the key distribution coefficient is less than or equal to the preset key distribution coefficient, it indicates that the distribution range of key monitoring targets within the target management park is small and relatively concentrated. In this case, determining the monitoring relationship needs to focus on the response of routine monitoring points to gas fluctuations. For a single existing monitoring point, the radiation coverage parameter = the number of effective radiation targets within the associated assessment range of the existing monitoring point / the number of key monitoring targets within the associated assessment range of the existing monitoring point. The effective radiation targets are key monitoring targets with an effective radiation coefficient greater than the preset effective radiation coefficient. For a single key monitoring target, the effective radiation coefficient is the product of the wind direction deviation index and the gas diffusion index. The wind direction deviation index is calculated as the angle between the line segment connecting the existing monitoring point and the location of the key monitoring target and the current wind direction, divided by 360°. The gas diffusion index is calculated as the absolute value of the difference between the gas density stored or emitted by the key monitoring target and the gas density at the location of the key monitoring target, divided by the gas density at the location of the key monitoring target. How to determine the gas diffusion index of the gas stored or emitted by each key monitoring target is a topic already known to those skilled in the art and will not be elaborated here. The determined radiation coverage parameters are normalized, and the monitoring correlation coefficient is positively correlated with the normalized radiation coverage parameters.
[0061] Existing monitoring points with a monitoring correlation coefficient less than or equal to a preset monitoring correlation coefficient are designated as monitoring points to be activated. The values of the preset monitoring correlation coefficient, preset effective radiation parameter, and preset key distribution coefficient can be determined by the user based on the actual work scenario. For example, the user can set these values based on the activation analysis records. The higher the user's requirements for the activation management quality of gas monitoring points within the target management area, the larger the value of the preset monitoring correlation coefficient and the larger the value of the preset effective radiation parameter. This provides a method for determining the value of the preset monitoring correlation coefficient, which includes routine monitoring data within the activation analysis records that meet the user's requirements for the activation management quality of gas monitoring points within the target management area. The minimum value of the monitoring correlation coefficient of the monitoring points is recorded as the preset monitoring correlation coefficient. A method for determining the value of the preset effective radiation parameter is provided. The average value of the effective radiation parameters of each effective radiation target in the activation analysis record that meets the user's activation management quality requirements for gas monitoring points in the target management park is recorded as the preset effective radiation parameter. A method for determining the value of the preset key distribution coefficient is provided. The activation analysis record that determines the monitoring correlation coefficient of each existing monitoring point based on the point coverage parameter is recorded as the distribution reference record. The minimum value of the key distribution coefficient in the distribution reference record that meets the user's activation management quality requirements for gas monitoring points in the target management park is recorded as the preset key distribution coefficient.
[0062] Specifically, the monitoring status of the monitoring points includes key monitoring status and stable monitoring status;
[0063] The routine monitoring points that are under key monitoring are those whose monitoring fluctuation index is greater than the preset monitoring fluctuation index.
[0064] A normal monitoring point that is in a stable monitoring state is a normal monitoring point where the monitoring fluctuation index is less than or equal to the preset monitoring fluctuation index.
[0065] In this invention, a cyclical routine assessment period is applied. The duration of the routine assessment period can be determined by the user. The higher the user's requirements for the activation and management quality of gas monitoring points within the target management area, the shorter the routine assessment period. One possible routine assessment period is 1 minute. At the end of each routine assessment period, the monitoring fluctuation index of each routine monitoring point is detected to determine the monitoring status of each point. This invention also employs a cyclical gas monitoring period, the duration of which can be determined by the user. The user's requirements for the activation and management quality of gas monitoring points within the target management area... The higher the quantity requirement, the shorter the gas monitoring cycle. A gas monitoring cycle of 10 seconds is provided. At the start of each gas monitoring cycle, each activated monitoring point in the target management park performs a gas collection and analysis task to obtain various gas monitoring data at the location of each activated monitoring point. The categories of gas monitoring data include: gas concentration and gas percentage of each category of gas. Users can set the categories included in the gas monitoring data according to actual needs. It can also include monitoring data on equipment status and environmental parameters. This is content that is easy for those skilled in the art to understand and will not be elaborated here.
[0066] If the current time is the end of a normal assessment cycle, the monitoring fluctuation index of each normal monitoring point is detected. For a single normal monitoring point, the monitoring fluctuation index is the maximum value of the data fluctuation of each gas monitoring data corresponding to that normal monitoring point. For a single gas monitoring data, the data fluctuation = (maximum value of the value obtained by each gas monitoring cycle for that gas monitoring data in the normal assessment cycle - average value of the value obtained by each gas monitoring cycle for that gas monitoring data in the previous normal assessment cycle) / average value of the value obtained by each gas monitoring cycle for that gas monitoring data in the previous normal assessment cycle. The value of the preset monitoring fluctuation index can be determined by the user according to the actual working scenario. For example, the user can set it according to the activation analysis record. A method for setting the value of the preset monitoring fluctuation index is provided, which is the average value of the monitoring fluctuation index of each normal monitoring point in the activation analysis record that meets the user's activation management quality requirements for gas monitoring points in the target management park and is recorded as the preset monitoring fluctuation index.
[0067] Specifically, if the floating complexity index or floating interference index of any normal monitoring point under key monitoring is greater than the preset floating complexity index or the floating interference index is greater than the preset floating interference index, then it is determined that the aggregation analysis module performs floating aggregation analysis on the normal monitoring point.
[0068] One type of monitoring point is a normal monitoring point where the floating complexity index is greater than the preset floating complexity index or the floating interference index is greater than the preset floating interference index.
[0069] Specifically, for a single routine monitoring point under key monitoring status, the floating complexity index is the number of interfering monitoring data items present at the routine monitoring point, the interfering monitoring data is gas monitoring data with a data floating index greater than the preset monitoring floating index, and the floating interference index is the number of different interference targets present in each interfering monitoring data item present at the routine monitoring point. For a single interfering monitoring data item, if the gas stored or emitted by any key monitoring target within the associated assessment range of the routine monitoring point corresponding to the interfering monitoring data item contains a gas type corresponding to the interfering monitoring data item, then the key monitoring target is recorded as the interference target of the interfering monitoring data item.
[0070] For a single routine monitoring point under key monitoring status, if the fluctuation complexity index or the fluctuation interference index of the routine monitoring point is greater than the preset fluctuation complexity index or the fluctuation interference index is greater than the preset fluctuation interference index, it indicates that the routine monitoring point has a large number of interference monitoring data or a relatively rich number of key monitoring targets that can cause fluctuations in interference monitoring data. In the process of updating the routine monitoring point to supplement the gas monitoring process, a fluctuation aggregation analysis is performed on such routine monitoring points to take into account the synergistic effect between different key monitoring targets during the point update process.
[0071] The values of the preset floating complexity index and the preset floating interference index can be determined by the user according to the actual working scenario. For example, the user can set them according to the activation analysis records. A method for determining the value of the preset floating complexity index is provided, in which the activation analysis records for floating aggregation analysis of normal monitoring points are recorded as analysis reference records, and the average value of the floating complexity index of each normal monitoring point in the analysis reference records that meet the user's activation management quality requirements for gas monitoring points in the target management park is recorded as the preset floating complexity index. A method for determining the value of the preset floating interference index is provided, in which the average value of the floating interference index of each normal monitoring point in the analysis reference records that meet the user's activation management quality requirements for gas monitoring points in the target management park is recorded as the preset floating interference index.
[0072] Specifically, the combination aggregation coefficient of the aggregation point combination determined for each type of monitoring point is greater than the preset combination aggregation coefficient;
[0073] The method for setting the combined aggregation coefficient is determined based on the interference coincidence parameter.
[0074] If there is a type of monitoring point with an interference coincidence parameter greater than the preset interference coincidence parameter, the combination aggregation coefficient of the aggregation point combination of this type of monitoring point is determined based on the ensemble coverage index.
[0075] If there exists a type of monitoring point where the interference coincidence parameter is less than or equal to the preset interference coincidence parameter, the combination aggregation coefficient of the aggregation point combination of this type of monitoring point is determined based on the diffusion coincidence index.
[0076] Specifically, if there is a type of monitoring point with an interference coincidence parameter greater than the preset interference coincidence parameter, the optimal coefficient of each monitoring point to be activated for the aggregation point combination of this type of monitoring point is determined based on the coincidence coverage index and the reference diffusion matching index.
[0077] The preferred coefficients are positively correlated with the overlap coverage index and the reference diffusion matching index, respectively.
[0078] Specifically, if there is a type of monitoring point whose interference coincidence parameter is less than or equal to the preset interference coincidence parameter, the optimal coefficient of each monitoring point to be activated for the aggregation point combination of this type of monitoring point is determined based on the coincidence coverage index and the interference attenuation index.
[0079] The preferred coefficients are positively correlated with the overlap coverage index and the interference attenuation index, respectively.
[0080] Specifically, for a single Class I monitoring point, the interference coincidence parameter = the number of interference targets that coexist with the Class I monitoring point and all normal monitoring points within its associated assessment range / the number of interference targets of the Class I monitoring point. The value of the preset interference coincidence parameter can be determined by the user based on the actual working scenario. For example, the user can set it based on the activation analysis record. A method for determining the value of the preset interference coincidence parameter is provided, in which the activation analysis record that determines the combination aggregation coefficient based on the set coverage index is recorded as the aggregation reference record, and the average value of the interference coincidence parameter in the aggregation reference record that meets the user's activation management quality requirements for gas monitoring points in the target management park is recorded as the preset interference coincidence parameter.
[0081] For a single Class I monitoring point, the aggregation point combination is a set that includes the Class I monitoring point and several routine monitoring points, and the number of routine monitoring points included is the maximum number that meets the requirements of the aggregation coefficient.
[0082] For a single monitoring point of type I, if the interference overlap parameter of that monitoring point is greater than the preset interference overlap parameter, it indicates that the interference target existing at that monitoring point overlaps with the interference targets corresponding to the interference monitoring data of other normal monitoring points within its associated assessment range. In this case, the division process of the cluster point combination focuses on the overlap of its interference targets to ensure the effectiveness of subsequent analysis of the synergistic effects caused by multiple interference targets. The cluster coverage index = the number of overlapping interference targets corresponding to the cluster point combination / the number of different interference targets existing in the interference monitoring data of each normal monitoring point within the cluster point combination. For a single interference target, if the number of normal monitoring points to which the interference monitoring data corresponding to the interference target belongs within the cluster point combination is greater than the preset overlap judgment parameter, then the interference target is determined to be an overlapping interference target. For any monitoring point to be activated, the overlap coverage index = the number of overlapping interference targets corresponding to the cluster point combination within the associated assessment range of the monitoring point to be activated / the number of different interference targets existing in the interference monitoring data of each normal monitoring point within the cluster point combination. The number of key monitoring targets within the associated assessment range of the monitoring point is defined as follows: the reference diffusion matching index = the number of diffusion matching targets within the associated assessment range of the monitoring point to be activated / the number of overlapping interference targets corresponding to the combination of aggregation points within the associated assessment range of the monitoring point to be activated. For a single overlapping interference target, if the diffusion matching coefficient of the overlapping interference target is greater than the preset diffusion matching coefficient, then the overlapping interference target is recorded as a diffusion matching target. The data fluctuation degree of each interference monitoring data corresponding to the overlapping interference target within the combination of aggregation points is obtained. The diffusion matching coefficient = the absolute value of the difference between the maximum and minimum values of the fluctuation matching index of each interference monitoring data corresponding to the overlapping interference target / the average value of the fluctuation matching index of each interference monitoring data corresponding to the overlapping interference target. For a single interference monitoring data item, the fluctuation matching index = the data fluctuation degree of the interference monitoring data item / the interval distance between the normal monitoring point corresponding to the interference monitoring data item and the location of the overlapping interference target.
[0083] The values of the preset overlap determination parameter and the preset diffusion matching coefficient can be determined by the user according to the actual working scenario. For example, the user can set them according to the activation analysis record. The higher the user's requirements for the activation management quality of gas monitoring points in the target management park, the larger the value of the preset overlap determination parameter and the larger the value of the preset diffusion matching coefficient. A preset overlap determination parameter is provided, which is 10% of the number of normal monitoring points included in the cluster point combination. A method for determining the preset diffusion matching coefficient is provided, which is the minimum value of the diffusion matching coefficient of the diffusion matching target in the activation analysis record that meets the user's requirements for the activation management quality of gas monitoring points in the target management park. The determined set coverage index and cooperative interference parameter are normalized. The set cluster coefficient is positively correlated with the set coverage index after normalization. The preferred coefficient is positively correlated with the cooperative interference parameter after normalization. The cooperative interference parameter is the sum of the overlap coverage index and the reference diffusion matching index.
[0084] For a single monitoring point of type I, if the interference coincidence parameter of that monitoring point is less than or equal to the preset interference coincidence parameter, it indicates that the interference target at that monitoring point is relatively independent from the interference targets corresponding to other interference monitoring data within its associated assessment range. In this case, the process of dividing the cluster point combination focuses on the gas transport trajectory in space to ensure the predictive analysis effect of the subsequent interference target. The diffusion coincidence index is the average value of the wind direction correlation index between each normal monitoring point within the cluster point combination and that monitoring point of type I. For any two existing monitoring points, the wind direction correlation index between the two existing monitoring points = 360° / the angle between the line segment connecting the two existing monitoring points and the current wind direction. For any monitoring point to be activated, the interference attenuation index is the value within the associated assessment range of the monitoring point to be activated. The average value of the interference attenuation amplitude of each interference monitoring data corresponding to the key monitoring target. For a single interference monitoring data, the interference attenuation amplitude = (data fluctuation of the nearest normal monitoring point to the activated monitoring point within the cluster point combination for this interference monitoring data - data fluctuation of the farthest normal monitoring point to the activated monitoring point within the cluster point combination for this interference monitoring data) / data fluctuation of the nearest normal monitoring point to the activated monitoring point within the cluster point combination for this interference monitoring data. The determined diffusion overlap index and diffusion interference parameters are normalized. The clustering coefficient is positively correlated with the normalized diffusion overlap index, and the optimization coefficient is positively correlated with the normalized diffusion interference parameters. The diffusion interference parameters are the sum of the overlap coverage index and the interference attenuation index.
[0085] The value of the preset combination aggregation coefficient can be determined by the user according to the actual working scenario. For example, the user can set it according to the activation analysis record. The higher the user's requirements for the activation management quality of gas monitoring points in the target management park, the larger the value of the preset combination aggregation coefficient. A method for determining the value of the preset combination aggregation coefficient is provided, which is the minimum value of the combination aggregation coefficient of the aggregation point combination in the activation analysis record that meets the user's requirements for the activation management quality of gas monitoring points in the target management park.
[0086] Specifically, if the floating complexity index of any normal monitoring point under key monitoring is less than or equal to the preset floating complexity index and the floating interference index is less than or equal to the preset floating interference index, then it is determined that the interference analysis module performs floating interference analysis on the normal monitoring point.
[0087] Specifically, the optimization coefficients of each monitoring point to be activated for the second-class monitoring points are positively correlated with the wind direction correlation index and the diffusion distance index, respectively.
[0088] The second type of monitoring point is a normal monitoring point where the floating complexity index is less than or equal to the preset floating complexity index and the floating interference index is less than or equal to the preset floating interference index.
[0089] Specifically, for a single routine monitoring point under key monitoring status, if the fluctuation complexity index of the routine monitoring point is less than or equal to the preset fluctuation complexity index and the fluctuation interference index is less than or equal to the preset fluctuation interference index, it indicates that the number of interference monitoring data items at the routine monitoring point is small and the number of key monitoring targets that can cause fluctuations in the interference monitoring data is small. In the process of updating the routine monitoring point to supplement the gas monitoring process, fluctuation interference analysis is performed on such routine monitoring points to ensure that the analysis during the point update process is more targeted.
[0090] For a single Class II monitoring point, the optimization coefficients for each monitoring point to be activated are detected. For a single monitoring point to be activated, the wind direction correlation index and the diffusion distance index between the monitoring point to be activated and the Class II monitoring point are obtained. The diffusion distance index is equal to the distance between two points on any straight line formed by mapping the monitoring point to be activated and the Class II monitoring point to the current wind direction, divided by the evaluation reference distance. The determined interference quality parameters are normalized. The optimization coefficients are positively correlated with the normalized interference quality parameters. The interference quality parameters are the sum of the wind direction correlation index and the diffusion distance index.
[0091] Specifically, the preferred activation point is a preliminary monitoring point with a preferred coverage parameter greater than a preset preferred coverage parameter;
[0092] The preferred coverage parameters are determined based on the key monitoring targets within the associated evaluation range of each pre-monitoring point. The pre-monitoring points are monitoring points to be activated that have a preferred coefficient greater than the preset preferred coefficient.
[0093] Specifically, for a single preliminary monitoring point, the preferred coverage parameter is calculated as: the number of key monitoring targets belonging to other preliminary monitoring points and the associated assessment range of this preliminary monitoring point / the number of key monitoring targets within the associated assessment range of this preliminary monitoring point. The values of the preset preferred coverage parameter and the preset preferred coefficient can be determined by the user based on the actual working scenario. For example, the user can set them based on the activation analysis records. The higher the user's requirements for the activation management quality of gas monitoring points within the target management park, the larger the value of the preset preferred coverage parameter and the larger the value of the preset preferred coefficient. A method for determining the value of the preset preferred coverage parameter is provided, whereby the minimum value of the preferred coverage parameter of the preferred activation point in the activation analysis records that meet the user's activation management quality requirements for gas monitoring points within the target management park is recorded as the preset preferred coverage parameter. Similarly, a method for determining the value of the preset preferred coefficient is provided, where the minimum value of the preferred coefficient of the preliminary monitoring point in the activation analysis records that meet the user's activation management quality requirements for gas monitoring points within the target management park is recorded as the preset preferred coefficient.
[0094] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.
[0095] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A gas monitoring and management system based on data analysis, characterized in that, include: The steady-state monitoring module is used to periodically determine the monitoring status of each normal monitoring point based on the monitoring fluctuation index, and to determine whether to activate and update the monitoring status of each normal monitoring point based on the monitoring status. The update execution module is connected to the steady-state monitoring module and is used to update the activation of each normal monitoring point that is in a key monitoring state. It also determines whether to perform floating aggregation analysis or floating interference analysis on each normal monitoring point that is in a key monitoring state based on the floating complexity index and the floating interference index. The aggregation analysis module, which is connected to the update execution module, is used to perform floating aggregation analysis, determine the aggregation point combination based on the aggregation coverage index or diffusion overlap index, and determine the analysis parameters on which the optimization coefficient of each monitoring point to be activated for the aggregation point combination is based on the interference overlap parameter. The analysis parameters include the overlap coverage index, the reference diffusion matching index, and the interference attenuation index. An interferometry analysis module, which is connected to the update execution module, is used to perform floating interferometry analysis and determine the optimal coefficients of each monitoring point to be activated based on the wind direction correlation index and the diffusion distance index. An execution module is enabled, which is connected to both the aggregation analysis module and the interference analysis module, to determine the preferred activation point based on the preferred coefficient and the preferred coverage parameter.
2. The gas monitoring and management system based on data analysis according to claim 1, characterized in that, The routine monitoring points are existing monitoring points whose monitoring correlation coefficient is greater than the preset monitoring correlation coefficient; The method for setting the monitoring correlation coefficient is determined based on the key distribution coefficient of the target management plant area, wherein, If the key distribution coefficient is greater than the preset key distribution coefficient, the monitoring correlation coefficient of each existing monitoring point is determined based on the point coverage parameter. If the key distribution coefficient is less than or equal to the preset key distribution coefficient, the monitoring correlation coefficient of each existing monitoring point is determined based on the radiation coverage parameters. The key distribution coefficient is determined based on the key area parameters and the distribution uniformity parameters.
3. The gas monitoring and management system based on data analysis according to claim 1, characterized in that, The monitoring status of the points includes key monitoring status and stable monitoring status; The routine monitoring points that are under key monitoring are those whose monitoring fluctuation index is greater than the preset monitoring fluctuation index. A normal monitoring point that is in a stable monitoring state is a normal monitoring point where the monitoring fluctuation index is less than or equal to the preset monitoring fluctuation index.
4. The gas monitoring and management system based on data analysis according to claim 1, characterized in that, If the floating complexity index or floating interference index of any normal monitoring point under key monitoring is greater than the preset floating complexity index or the floating interference index is greater than the preset floating interference index, then it is determined that the aggregation analysis module performs floating aggregation analysis on the normal monitoring point. One type of monitoring point is a normal monitoring point where the floating complexity index is greater than the preset floating complexity index or the floating interference index is greater than the preset floating interference index.
5. The gas monitoring and management system based on data analysis according to claim 4, characterized in that, The combination aggregation coefficients for the determined aggregation point combinations for each type of monitoring point are all greater than the preset combination aggregation coefficients. The method for setting the combined aggregation coefficient is determined based on the interference coincidence parameter. If there is a type of monitoring point with an interference coincidence parameter greater than the preset interference coincidence parameter, the combination aggregation coefficient of the aggregation point combination of this type of monitoring point is determined based on the ensemble coverage index. If there exists a type of monitoring point where the interference coincidence parameter is less than or equal to the preset interference coincidence parameter, the combination aggregation coefficient of the aggregation point combination of this type of monitoring point is determined based on the diffusion coincidence index.
6. The gas monitoring and management system based on data analysis according to claim 5, characterized in that, If there is a type of monitoring point with an interference coincidence parameter greater than the preset interference coincidence parameter, the optimal coefficient of each monitoring point to be activated for the aggregation point combination of this type of monitoring point is determined based on the coincidence coverage index and the reference diffusion matching index. The preferred coefficients are positively correlated with the overlap coverage index and the reference diffusion matching index, respectively.
7. The gas monitoring and management system based on data analysis according to claim 6, characterized in that, If there exists a type of monitoring point with an interference coincidence parameter less than or equal to the preset interference coincidence parameter, the optimal coefficient for the combination of monitoring points to be activated for that type of monitoring point is determined based on the coincidence coverage index and the interference attenuation index. The preferred coefficients are positively correlated with the overlap coverage index and the interference attenuation index, respectively.
8. The gas monitoring and management system based on data analysis according to claim 1, characterized in that, If the floating complexity index of any routine monitoring point under key monitoring is less than or equal to the preset floating complexity index and the floating interference index is less than or equal to the preset floating interference index, then it is determined that the interference analysis module performs floating interference analysis on the routine monitoring point.
9. The gas monitoring and management system based on data analysis according to claim 8, characterized in that, The optimization coefficients of each monitoring point to be activated for the second-class monitoring points are positively correlated with the wind direction correlation index and the diffusion distance index, respectively. The second type of monitoring point is a normal monitoring point where the floating complexity index is less than or equal to the preset floating complexity index and the floating interference index is less than or equal to the preset floating interference index.
10. The gas monitoring and management system based on data analysis according to claim 1, characterized in that, The preferred activation point is a preliminary monitoring point with a preferred coverage parameter greater than the preset preferred coverage parameter; The preferred coverage parameters are determined based on the key monitoring targets within the associated evaluation range of each pre-monitoring point. The pre-monitoring points are monitoring points to be activated that have a preferred coefficient greater than the preset preferred coefficient.
Citation Information
Patent Citations
Monitoring point distribution method and equipment based on industrial park gas monitoring and medium
CN119416411A