Greenhouse gas detection method and detection equipment

Abstract

The invention relates to a greenhouse gas detection method and detection equipment, and relates to the technical field of greenhouse gas detection, and the method comprises the following steps: constructing a modular redundant architecture of a detection module; multi-channel gas data real-time mutual verification is realized on a plurality of gas data acquired by the plurality of detection modules through a dynamic weighted fitting algorithm of the data processing module; and through a data processing module, available gas data passing real-time mutual verification are screened, and abnormal gas data different from other gas data are deleted. According to the invention, the accuracy of gas detection in the closed space can be improved, and errors of gas data are reduced.

Description

Technical Field

[0001] This application relates to the field of greenhouse gas detection, and particularly to a greenhouse gas detection method and detection device. Background Art

[0002] With the deepening of global climate change governance, greenhouse gas monitoring technology has become an important part of the environmental monitoring field. Especially in enclosed spaces, the demand for accurate monitoring of greenhouse gas concentrations is becoming increasingly urgent. Such monitoring not only helps to protect cultural heritage but also ensures the stability of precision experimental environments. Currently, the development of environmental monitoring technology has significantly improved the greenhouse gas assessment ability of macro regions, but there are still many challenges in monitoring micro enclosed spaces.

[0003] In the prior art, for the gas detection system and detection method with the Chinese publication number CN112033865A, multiple sensors in the detection unit are used to perform real-time detection on the to-be-detected gas collected, and then the detection data of the detection module is obtained through the information processing module, and the detection data is processed in real time to obtain the detection result. The sensing detection data of the first detection unit is also used to compensate for some sensors of the second detection unit.

[0004] In the above gas detection system, although there are two detection units in the detection module of the to-be-detected gas arranged in the real-time collection air duct, when the gas data detected by the first detection unit is distorted, since the data of the second detection unit relies on the detection data of the first detection unit for compensation and abnormal data cannot be effectively eliminated, therefore, the gas data detected by the second detection unit will also have deviation, and the reliability of the detection data will be greatly reduced, which limits the application effect of the prior art in high-precision and high-reliability enclosed space monitoring scenarios. Summary of the Invention

[0005] An object of this application is to provide a greenhouse gas detection method, which can improve the accuracy of gas detection in an enclosed space and reduce the error of gas data.

[0006] In a first aspect, a greenhouse gas detection method provided by this application adopts the following technical solution: A greenhouse gas detection method includes: Construct a modular redundant architecture of several detection modules so that several detection modules detect the gas simultaneously; Implement real-time mutual verification of multi-channel gas data for the several gas data collected by several detection modules through the dynamic weighted fitting algorithm of the data processing module; Through the data processing module, the available gas data that passes the real-time cross-verification and the abnormal gas data that differs from other gas data are screened. All the gas data of the detection module corresponding to the abnormal gas data are set as abnormal gas data and excluded.

[0007] By adopting the above technical solution, constructing a modular redundant architecture of the detection module enables several detection modules to detect gases simultaneously, and at the same time generates several gas data to be uploaded to the data processing module for processing, so as to avoid as much as possible the situation caused by a detection module malfunctioning or data distortion; realizing real-time cross-verification of multi-channel gas data can improve the accuracy and consistency of gas data; deleting all the gas data of the detection module corresponding to the unavailable gas data in the gas data reduces the influence of the distortion of one gas data of the detection module on the output of other gas data, further ensuring the reliability of the gas monitoring result and reducing the possibility of false alarms.

[0008] In a preferred example of the present application, it can be further configured as follows: The steps of constructing the modular redundant architecture of the detection module include: Construct a distributed measurement network using at least three detection modules, and calculate and obtain the reliability of the gas detection module according to the reliability of each detection module.

[0009] By adopting the above technical solution, constructing a distributed measurement network using at least three independent detection modules realizes cross-verification and fitting of data to reduce the risk of single-point failure and improve the reliability of gas data.

[0010] In a preferred example of the present application, it can be further configured as follows: The steps of realizing real-time cross-verification of multi-channel gas data for several gas data collected by several detection modules through the dynamic weighted fitting algorithm of the data processing module include: Mutually verify and compare the gas data items between each detection module through the data processing module; After calculating the optimal data of the combined several gas data of each detection module through the weighted fitting algorithm of the data processing module, mutually verify the optimal data between each detection module.

[0011] By adopting the above technical solution, the comparison of the gas data items between each detection module can separately determine whether there is abnormal data in each gas data item of the detection module from a microscopic perspective; and through the dynamic weighted fitting algorithm, the gas data detected by each detection module are combined and mutually verified again, and from a macroscopic perspective, it can be judged whether there is abnormal data in all the gas data generated by the detection module as a whole, improving the accuracy of gas data.

[0012] In a preferred example, the present application may be further configured as follows: the step of screening, by the data processing module, the available gas data that has passed real-time mutual verification and abnormal gas data that differs from other gas data, and setting all gas data items of the detection module corresponding to the abnormal gas data as abnormal gas data and eliminating them includes: Integrate the fault tree analysis model through the data processing module and determine whether the discreteness of gas data in each detection module exceeds 3σ; Filter gas data with a discreteness exceeding 3σ and set them as maintenance gas data; Screening the detection module with maintenance gas data, starting the alarm function of the communication module, and sending the alarm information containing the detection module to the user end through the communication module; A maintenance work order is generated according to the maintenance gas data in the detection module, and a faulty component of a sensor corresponding to the maintenance gas data is located.

[0013] By adopting the above technical solution, the integrated fault tree analysis model compares and verifies the same gas data between various detection modules, and can then locate the gas data with faults or data distortion in the detection module, thereby improving the accuracy of fault diagnosis. When a single detection module has abnormal gas data, all gas data of the detection module will be excluded. When a detection module has abnormal gas data, all gas data of the entire detection module will be excluded, reducing the impact of the faulty gas data on other gas data in the same detection module, and improving the reliability of the gas detection results; and by automatically generating maintenance work orders, the maintenance process can be optimized.

[0014] In a preferred example, the present application may be further configured as follows: after calculating the optimal data of the gas data of each detection module by the weighted fitting algorithm of the data processing module, the step of mutually verifying the optimal data between each detection module includes: The data processing module obtains the gas data of each detection module, clusters the gas data using a clustering algorithm, classifies the gas data of the detection modules into N clusters, and outputs N cluster centroids. Based on the safety gas data corresponding to the material of the objects in the test space, the cluster centroids are assigned importance coefficients. The data processing module multiplies the important coefficient of each cluster by the centroid of each cluster and then adds them together to output the optimal data of each detection module; The optimal data between each detection module is mutually verified through the data processing module.

[0015] By adopting the above technical solution, the optimal data obtained can reflect the real situation of gas concentration. Through the mutual verification between the optimal data, it is judged whether there is an abnormality in the comprehensive gas data of each detection module as a whole, further improving the accuracy and reliability of the overall gas data. When processing multi-source data in a complex environment, it shows stronger adaptability and robustness, and is especially suitable for the high-precision monitoring requirements of greenhouse gases in confined spaces.

[0016] In a preferred example of the present application, it can be further configured that: the step of mutual verification of the optimal data between each detection module by the data processing module further includes: If the optimal data calculated and obtained by the data processing module has a difference from other optimal data exceeding the preset value, then the optimal data is set as abnormal optimal data, and the data other than the optimal data is set as available optimal data; Screen the detection modules with abnormal optimal data, activate the alarm function of the communication module, and send the alarm information including the detection module to the user terminal through the communication module; Generate a maintenance work order according to the abnormal optimal data in the detection module, and locate the faulty component of the detection module.

[0017] By adopting the above technical solution, the optimal data between each detection module is compared and verified pairwise. If there is a situation exceeding the preset value, it means that the detection module with the optimal data exceeding the preset value compared with the optimal data of other detection modules has a fault. Further screen the available and abnormal optimal data, locate the detection module with abnormal optimal data, and then generate a maintenance work order for the faulty detection module, which can improve the efficiency of fault repair.

[0018] In a second aspect, the present application provides a greenhouse gas detection device including: at least three detection modules for collecting gas data; A gas circulation device for forming a gas circulation in the space to be measured and guiding the gas to the at least three detection modules; A data processing module electrically connected to the at least three detection modules, and the data processing module is configured to perform the following operations: a) Receive gas data from the at least three detection modules; b) Perform real-time mutual verification processing on the received groups of gas data to identify the differences between the gas data; c) Based on the mutual verification processing result, eliminate all the gas data of the detection modules with abnormal gas data to obtain available gas data; A communication module electrically connected to the data processing module for sending gas data or alarm information.

[0019] By adopting the above technical solution, during the process of the gas circulation device exchanging gases in the space to be measured, several detection modules synchronously perform multi-channel detection of gas components, and then the data processing module performs real-time mutual verification on the detected gas data, eliminating all the gas data of the detection modules with abnormal gas data, so as to reduce the situation where when performing a single detection, a failure or distortion of one detection module leads to errors in the overall gas data. Moreover, the gas circulation device can also solve the problem that the gas data is inaccurate due to uneven distribution caused by the gas stratification phenomenon in the closed space to be measured. The communication module sends an alarm message based on the gas data to timely remind the user to repair the equipment.

[0020] In a preferred example of the present application, it can be further configured that: the gas circulation device is arranged in the space to be measured of the showcase base, and an air outlet communicating with the gas circulation device is opened on the showcase platform at the top of the showcase base. The gas circulation device includes a micro pump, and the micro pump is arranged on one side of the air outlet of the showcase base. A gas diffusion device is arranged on the side of the intake port of the gas circulation device facing several detection modules, and the output port of the gas diffusion device faces the detection ports of several detection modules.

[0021] By adopting the above technical solution, the micro pump of the gas circulation device can actively stir the gas in the closed space at the top of the showcase platform, and then the gas diffusion device diffuses the mixed gas, so that the gas entering the detection module is in a state of being fully mixed and uniform, and the obtained gas data is more accurate and reliable.

[0022] In a preferred example of the present application, it can be further configured that: the number of the detection modules is at least three independent detection modules, each detection module can independently monitor the greenhouse gas concentrations of CO2 and CH4, and each detection module uses the NDIR technology of infrared absorption to detect the gas concentration. At the same time, each detection module is also equipped with electrochemical and semiconductor sensors for monitoring HCHO and TVOC.

[0023] By adopting the above technical solution, each detection module can independently monitor the greenhouse gas concentrations such as CO2 and CH, making the collected gas data diverse and accurate; in addition, the detection module uses the NDIR technology of infrared absorption to detect the gas concentration, improving the measurement accuracy and reducing environmental interference; at the same time, each detection module is also equipped with electrochemical and semiconductor sensors for monitoring HCHO and TVOC, further expanding the monitoring range and realizing the simultaneous monitoring of multiple gas components.

[0024] In summary, the present application has the following beneficial technical effects: 1. By constructing a modular redundant architecture for the detection module, using no less than three independent detection modules to achieve distributed measurement, reducing the risk of data failure of a single detection module, and improving the reliability of data detection; 2. By using a dynamic weighted fitting algorithm to perform real-time cross-verification on multi-channel gas data, screening valid data and excluding abnormal data to improve the accuracy and measurement accuracy of gas data; 3. Through the communication module, notify the abnormal gas data to the user terminal in a timely manner so that the faulty part can be quickly repaired and maintained. Description of the Drawings

[0025] Figure 1 It is a schematic diagram of the internal structure of a greenhouse gas detection device in this application.

[0026] Figure 2 It is a schematic diagram of the overall structure of a greenhouse gas detection device in this application.

[0027] Figure 3 It is a flowchart of a greenhouse gas detection method in one embodiment of this application.

[0028] Figure 4 It is a flowchart of the sub-steps of step S1 in one embodiment of this application.

[0029] Figure 5 It is a flowchart of the sub-steps of step S2 in one embodiment of this application.

[0030] Figure 6 It is a flowchart of the sub-steps of step S3 in one embodiment of this application.

[0031] Figure 7 It is a flowchart of the sub-steps of step S21 in one embodiment of this application.

[0032] Figure 8 It is a flowchart of the sub-steps of step S20 in one embodiment of this application.

[0033] Reference signs: 1, showcase platform; 2, showcase base; 3, micro pump; 4, gas diffusion device. Detailed Description of the Invention

[0034] The following is a further detailed description of this application in conjunction with the attached Figure 1-8 drawings.

[0035] It should be noted that all actions of obtaining data or all actions of obtaining information or data in this application are carried out in accordance with the corresponding data protection regulations and policies of the country where the location is located and with the authorization of the corresponding users.

[0036] Refer to Figure 1 、Figure 2 and Figure 3 , a greenhouse gas detection method, specifically comprising: S1. Construct a modular redundant architecture of several detection modules so that the several detection modules can detect gas at the same time.

[0037] Specifically, the modular redundant architecture of the detection module enables the system to maintain a more stable operating state when facing complex detection environments and possible interference factors, reducing problems such as detection interruption or data loss caused by external factors.

[0038] Therefore, in gas detection, by constructing a modular redundant architecture of detection modules, several detection modules can detect gas at the same time. By fusing and processing the gas data of multiple detection modules, the detection characteristics and advantages of each module can be comprehensively considered, the detection results can be further optimized, and the errors and uncertainties that may be caused by single module detection can be reduced. At the same time, several gas data are generated and uploaded synchronously to the data processing module for parallel processing.

[0039] S2. Realize real-time mutual verification of multi-channel gas data for multiple gas data collected by multiple detection modules through the dynamic weighted fitting algorithm of the data processing module.

[0040] Specifically, real-time cross-verification of multi-channel data can promptly identify faults in a particular detection module. If the gas data from a particular detection module differs significantly from that of other detection modules, and the algorithm determines that the difference is outside the normal range, the detection module may be considered faulty.

[0041] The dynamic weighted fitting algorithm comprehensively considers data from multiple detection modules, fusing gas data from different channels through a weighted approach to obtain more accurate gas information for real-time cross-verification. Furthermore, the dynamic weighted fitting algorithm performs fitting processing on the collected gas data to improve its accuracy and consistency.

[0042] S3. Filter the available gas data that have passed real-time mutual verification and the abnormal gas data that are different from other gas data through the data processing module, and set all gas data items of the detection module corresponding to the abnormal gas data as abnormal gas data and eliminate them.

[0043] Specifically, after abnormal gas data appears, it is not counted as abnormal gas data, and only available gas data is used. Several detection modules can still provide valid gas data, thereby improving the accuracy of the overall gas data and reducing the possibility of false alarms. Furthermore, after excluding abnormal gas data, the dynamic weighted fitting algorithm can still analyze the available gas data from the remaining detection modules to assist in fault diagnosis.

[0044] Reference Figure 1 、 Figure 2 and Figure 4 , further, in one embodiment, step S1 is refined into the following sub-steps: S10. Construct a distributed measurement network using at least three detection modules, and calculate and obtain the reliability of the gas detection module according to the reliability of each detection module.

[0045] Specifically, by paralleling multiple detection modules, even if one module fails, the other modules can still continue to work, and the redundant design can effectively reduce the risk of system failure due to a single-point failure. And, if a certain detection module frequently fails, the number of detection modules can be considered increased to improve reliability.

[0046] Among them, to obtain the reliability of the gas detection module, the formula is used: R system = 1 - (1 - R module ) n (1); Among them, R system is the reliability of the gas detection module, R module is the reliability of a single detection module, and n is the number of detection modules in parallel.

[0047] The reliability of a single detection module is determined by the installation position and failure rate of the detection module. Specifically, the detection port of the detection module faces the output port of the gas diffusion device 4.

[0048] In one embodiment, the gas diffusion device 4 is arranged vertically, and several detection modules are also arranged vertically. Several detection ports face the gas diffusion device 4 and are in the same plane as the gas diffusion device 4. Therefore, when the gas diffusion device 4 outputs gas to the gas circulation device, if the gas data of the detection module in a certain azimuth in the vertical arrangement is abnormal, the number of failures of the azimuth where the detection module is located is recorded once. Assume that the number of failures of the detection module in a certain azimuth is F, and the total running time of the detection module is T, then the failure rate λ can be calculated by the following formula: λ = T / F; If λ is lower than the failure alarm value, an alarm message is sent, and the gas data of the detection module in the corresponding azimuth is excluded.

[0049] Map λ to R module . Assume that the reliability R module of a single detection module = 0.95, and the system uses n = 3 detection modules in parallel: R system = 1 − (1 − 0.95) 3 ≈0.999875; This indicates that the reliability of the system has been improved from 0.9 for a single module to approximately 0.999875, reducing the risk of single-point failure and enhancing reliability.

[0050] In addition, referring to Figure 1 、 Figure 2 and Figure 5 , furthermore, in one of the embodiments, step S2 is refined into the following sub-steps: S20. Use the data processing module to cross-verify and compare the various gas data between the detection modules.

[0051] Specifically, in this embodiment, each detection module can independently monitor the greenhouse gas concentrations of CO2 and CH4, and each detection module uses the NDIR technology of infrared absorption to detect the gas concentration. At the same time, each detection module is also equipped with electrochemical and semiconductor sensors for monitoring HCHO and TVOC. In addition, each detection module is equipped with a sub-module that can independently monitor environmental parameters such as temperature, humidity, pressure, and particulate matter concentration in the display cabinet or enclosed space to ensure that the environmental conditions meet the preset standards. Therefore, gas data of CO2, CH4, as well as HCHO and TVOC, and gas data such as temperature, humidity, pressure, and particulate matter concentration can be obtained, and each of the above gas data exists independently. Each detection module cross-verifies the gas data of the same item detected to obtain abnormal gas data.

[0052] S21. After calculating the optimal data of the combined several gas data of each detection module through the weighted fitting algorithm of the data processing module, cross-verify the optimal data between each detection module.

[0053] Specifically, the optimal data is the comprehensive data obtained by applying the weighted fitting algorithm to all the gas data of a detection module, which can reflect the overall gas detection situation of the entire detection module from a macroscopic perspective. Cross-verifying again with the optimal data of each detection module to determine whether there is abnormal data in all the gas data generated by the overall detection module, improving the accuracy of the gas data.

[0054] In addition, referring to Figure 1 、 Figure 2 and Figure 6 , furthermore, in one of the embodiments, step S3 is refined into the following sub-steps: S30. Integrate the fault tree analysis model through the data processing module and determine whether there is a situation where the dispersion of the gas data of each detection module exceeds 3σ.

[0055] Among them, when the gas data dispersion degree of the detection module exceeds 3σ, all the gas data of this module are marked as abnormal. Specifically, first, the top event is determined to be that the gas data dispersion degree exceeds 3σ. Then, the intermediate events that may cause this top event are analyzed, such as the sensor of the detection module fails, is affected by environmental interference, or there is an error during data transmission, etc. Next, the bottom events are further determined, that is, the specific failure reasons, such as whether the gas detected by the detection port of the sensor is evenly distributed, the sensor performance degrades, power fluctuations, electromagnetic interference, signal line damage, etc.

[0056] When constructing the fault tree structure, these events are connected by logic gates. For example, if sensor failure or environmental interference may both cause the gas data dispersion degree to exceed 3σ, then the two intermediate events of sensor failure and environmental interference are connected to the top event by an OR gate. And for the intermediate event of sensor failure, if sensor performance degradation and power problems may both cause sensor failure, then the two bottom events are connected to sensor failure by an OR gate. In this way, a complete fault tree analysis model is gradually constructed, so as to analyze and diagnose the abnormal gas data problems in the detection module.

[0057] S31. Screen the gas data with a dispersion degree exceeding 3σ and set it as the maintenance gas data.

[0058] Specifically, in the subsequent step of generating a maintenance work order, the maintenance gas data can point to the faulty sensor.

[0059] S32. Screen the detection modules with maintenance gas data, start the alarm function of the communication module, and send the alarm information containing the detection module to the user terminal through the communication module.

[0060] Specifically, in this embodiment, the communication module uses the WIFI communication method.

[0061] S33. Generate a maintenance work order according to the maintenance gas data in the detection module and locate the faulty component of the sensor corresponding to the maintenance gas data.

[0062] Specifically, obtain the sensor pointed to by the maintenance gas data, locate it in the set of fault events related to the sensor, and then match the maintenance solution for the fault in the fault database through the fault events obtained during the process of the fault tree analysis model, and automatically generate a maintenance work order according to information such as the fault event and the fault location, optimizing the maintenance process.

[0063] In addition, referring to Figure 1 、 Figure 2 and Figure 7 , further, in one of the embodiments, step S21 is refined into the following sub-steps: S210. Obtain the gas data of each detection module through the data processing module, perform clustering processing on a number of gas data using a clustering algorithm, classify the gas data of several detection modules into N clusters, output the centroids of the N clusters, and assign importance coefficients to the cluster centroids according to the safety gas data corresponding to the material of the items in the space to be measured.

[0064] Specifically, before clustering, preprocess the various gas data of each detection module, including data cleaning, normalization processing, etc., to ensure the quality and consistency of the data. Then, select an existing clustering algorithm, determine the optimal parameters of the clustering algorithm through methods such as cross-validation and silhouette coefficient to ensure the accuracy and stability of the clustering results. Next, combine the safety gas data corresponding to the material of the items in the platform to be measured with the clustering results, and use a dynamic weighted fitting algorithm to assign importance coefficients to each cluster centroid.

[0065] S211. Multiply the importance coefficient of each cluster by the centroid of each cluster through the data processing module and then sum them up to output the optimal data of each detection module.

[0066] Specifically, use a clustering algorithm to perform clustering processing on the data obtained from multiple detection modules. After clustering, the various gas data of multiple detection modules can be classified into N clusters, and the centroids of the N clusters are output. Importance coefficients V1, V2, V3... to VN are assigned to the cluster centroids respectively. The importance coefficient V is inversely proportional to the Euclidean distance sum of the data of each cluster to the centroid data. The larger the Euclidean distance sum of the cluster, the smaller the assigned importance coefficient, that is, the larger the sample density of the cluster, the larger the assigned importance coefficient, and the sum of all importance coefficients is 1. And the output optimal data is the result of multiplying the importance coefficient of each cluster by the centroid of each cluster and then summing them up.

[0067] S212. Perform mutual verification on the optimal data between each detection module through the data processing module.

[0068] Specifically, through the mutual verification between the optimal data of each detection module obtained by the clustering algorithm, it is possible to judge whether there are abnormalities in the overall comprehensive gas data of each detection module, further improving the accuracy and reliability of the overall gas data. When dealing with complex environments, especially when processing multi-source data of several detection modules in a confined space, it can show stronger adaptability and robustness.

[0069] In addition, referring to Figure 1 、 Figure 2 and Figure 8 , further, in one embodiment, step S20 is refined into the following sub-steps: S202. If the optimal data obtained through calculation by the data processing module has a difference from other optimal data exceeding the preset value, then set the optimal data as abnormal optimal data, and set the data other than the optimal data as available optimal data.

[0070] Specifically, after quantifying the optimal data, the preset value is the difference between the optimal data pairwise, which can be set by the user himself to control the degree of maintenance required by the detection module, and thus control the maintenance cost of the detection module.

[0071] S203. If the optimal data obtained through calculation by the data processing module has a difference from other optimal data exceeding the preset value, then set the optimal data as abnormal optimal data, and set the data other than the optimal data as available optimal data.

[0072] Specifically, if there is a situation exceeding the preset value, it means that the detection module with the optimal data exceeding the preset value compared with other detection modules has a fault, and further screen the available and abnormal optimal data.

[0073] S204. Screen the detection modules with abnormal optimal data, activate the alarm function of the communication module, and send the alarm information containing the detection module to the user terminal through the communication module.

[0074] S205. Generate a maintenance work order according to the abnormal optimal data in the detection module, and locate the faulty component of the detection module.

[0075] Specifically, obtain the detection module pointed to by the abnormal optimal data, locate it in the set of fault events related to the detection module, and then match the fault events obtained in the process of the fault tree analysis model to the repair solutions of the faults in the fault database, and automatically generate a maintenance work order according to information such as the fault events and fault locations, which optimizes the maintenance process, improves the efficiency of fault detection and repair, and at the same time records the orientation of the faulty detection module to calculate the failure rate and reliability of the detection modules in this orientation.

[0076] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0077] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above-mentioned functional modules and module divisions are used as examples. In practical applications, the above functions can be allocated to different functional modules and modules according to needs, that is, the internal structure of the device is divided into different functional modules or modules to complete all or part of the functions described above.

[0078] Reference Figure 1 andFigure 2 , an embodiment of the present application further provides a greenhouse gas detection device, including at least three detection modules for collecting gas data. And it further includes a gas circulation device for forming a gas circulation in the space to be measured and guiding the gas to at least three detection modules, and the space to be measured is in a sealed state.

[0079] Furthermore, a greenhouse gas detection device further includes a data processing module. The data processing module is electrically connected to at least three detection modules, and the data processing module is configured to perform the following operations: Receive gas data from at least three detection modules. Several detection modules simultaneously upload the detected gas data to the data processing module to synchronously perform multi-channel detection of gas components.

[0080] Perform real-time cross-verification processing on the received groups of gas data to identify the differences between the gas data.

[0081] Based on the cross-verification processing results, eliminate all the gas data of the detection modules with abnormal gas data to obtain available gas data, so as to reduce the situation where when a single detection is performed, a failure or distortion of one detection module causes errors in the overall gas data.

[0082] Still further, a greenhouse gas detection device further includes a communication module for sending gas data or alarm information. The communication module is electrically connected to the data processing module. When the data processing module obtains abnormal gas data through real-time cross-verification, the communication module can send an alarm information according to the abnormal gas data.

[0083] Further, in this embodiment, the gas circulation device is fixedly connected to the space to be measured in the display cabinet base 2. The display cabinet base 2 is in a sealed state. The top of the display cabinet base 2 is fixedly connected with a display cabinet platform 1 for placing items, and the display cabinet platform 1 is also a sealed space. The display cabinet platform 1 is provided with an air outlet communicating with the gas circulation device. The gas circulation device includes a micro pump 3. The micro pump 3 is arranged on one side of the air outlet of the display cabinet base 2. The micro pump 3 can realize the gas exchange between the inside of the display cabinet base 2 and the display cabinet platform 1, making the gas in a flowing state, thereby solving the problem that the gas data is inaccurate due to uneven distribution caused by the gas stratification phenomenon in the sealed space of the display cabinet platform 1. The air inlet of the gas circulation device is provided with a gas diffusion device 4 facing several detection modules. The output port of the gas diffusion device 4 faces the detection ports of several detection modules. In one embodiment, the gas diffusion device 4 can be selected as a diffuser and / or a flow guide plate, and the output ports of the diffuser and / or the flow guide plate are arranged vertically. The detection ports of several detection modules are also arranged vertically. Therefore, several detection modules can fully receive the uniformly mixed gas, and the pressure of the gas entering each detection module is relatively consistent, making the difference in the gas data detected by each detection module smaller and the gas data more accurate and reliable.

[0084] Specifically, several detection modules perform synchronous detection of multiple channels of gas components in the gas circulation device, thereby reducing the situation where the overall gas data is incorrect due to a single detection module malfunction or distortion.

[0085] Furthermore, the number of detection modules is at least three independent detection modules. Each detection module can independently monitor the greenhouse gas concentrations of CO2 and CH4, making the collected gas data diverse and accurate. Each detection module uses the NDIR technology of infrared absorption to detect the gas concentration, improving the measurement accuracy and reducing environmental interference. At the same time, each detection module is also equipped with electrochemical and semiconductor sensors for monitoring HCHO and TVOC, further expanding the detection range and realizing the simultaneous monitoring of multiple gas components.

Claims

1. A method for detecting greenhouse gases, characterized in that, Including: Constructing a modular redundant architecture of several detection modules so that the several detection modules detect the gas simultaneously; Implementing real-time mutual verification of multi-channel gas data for the several gas data collected by the several detection modules through the dynamic weighted fitting algorithm of the data processing module; Through the data processing module, screening the available gas data that passes the real-time mutual verification and the abnormal gas data that is different from other gas data, setting all the gas data of the detection module corresponding to the abnormal gas data as abnormal gas data and eliminating them.

2. The method according to claim 1, wherein The step of constructing a modular redundant architecture of several detection modules so that the several detection modules detect the gas simultaneously includes: Using at least three detection modules to construct a distributed measurement network, and calculating and obtaining the reliability of the gas detection module according to the reliability of each detection module.

3. The method according to claim 1, characterized in that, The step of implementing real-time mutual verification of multi-channel gas data for the several gas data collected by the several detection modules through the dynamic weighted fitting algorithm of the data processing module includes: Mutually verifying and comparing the gas data between each detection module through the data processing module; After calculating the optimal data of the several gas data of each detection module through the weighted fitting algorithm of the data processing module, mutually verifying the optimal data between each detection module.

4. The method according to claim 1, characterized in that The step of screening the available gas data that passes the real-time mutual verification and the abnormal gas data that is different from other gas data through the data processing module, setting all the gas data of the detection module corresponding to the abnormal gas data as abnormal gas data and eliminating them includes: Integrating a fault tree analysis model through the data processing module and judging whether there is a situation where the dispersion of the gas data of each detection module exceeds 3σ; Screening the gas data with a dispersion exceeding 3σ and setting it as maintenance gas data; Screening the detection modules with maintenance gas data, starting the alarm function of the communication module, and sending the alarm information including the detection module to the user terminal through the communication module; Generating a maintenance work order according to the maintenance gas data in the detection module and locating the faulty component of the sensor corresponding to the maintenance gas data.

5. The method according to claim 3, characterized in that, The step of mutually verifying the optimal data between each detection module after calculating the optimal data of the several gas data of each detection module through the weighted fitting algorithm of the data processing module includes: Obtaining the gas data of each detection module through the data processing module, performing clustering processing on the several gas data by using the clustering algorithm, classifying the gas data of the several detection modules into N clusters, outputting the centroids of the N clusters, and assigning importance coefficients to the cluster centroids according to the safety gas data corresponding to the material of the items in the space to be measured; Multiplying the importance coefficient of each cluster by the centroid of each cluster and then summing up through the data processing module to output the optimal data of each detection module; Mutually verifying the optimal data between each detection module through the data processing module.

6. The method according to claim 5, wherein The step of mutually verifying the optimal data between each detection module through the data processing module further includes: If the optimal data obtained through calculation by the data processing module has a difference from other optimal data exceeding the preset value, then set the optimal data as abnormal optimal data, and set the data other than the optimal data as available optimal data; Screen the detection modules with abnormal optimal data, activate the alarm function of the communication module, and send the alarm information including the detection module to the user terminal through the communication module; Generate a maintenance work order based on the abnormal optimal data in the detection module and locate the faulty components of the detection module.

7. A greenhouse gas detection device, characterized in that It includes: At least three detection modules for collecting gas data; A gas circulation device for forming a gas circulation in the space to be measured and guiding the gas to the at least three detection modules; A data processing module electrically connected to the at least three detection modules, and the data processing module is configured to perform the following operations: a) Receive gas data from the at least three detection modules; b) Perform real-time cross-verification processing on the received groups of gas data to identify the differences between the gas data; c) Based on the cross-verification processing results, eliminate all the gas data of the detection modules with abnormal gas data and obtain available gas data; A communication module electrically connected to the data processing module for sending gas data or alarm information.

8. An apparatus for detecting greenhouse gases according to claim 7, characterized in that, The gas circulation device is arranged in the space to be measured of the display cabinet base (2). An air outlet communicating with the gas circulation device is provided on the display cabinet platform (1) at the top of the display cabinet base (2). The gas circulation device includes a micro pump (3). The micro pump (3) is arranged on one side of the air outlet of the display cabinet base (2). A gas diffusion device (4) is arranged on the intake port side of the gas circulation device facing several detection modules, and the output port of the gas diffusion device (4) faces the detection ports of several detection modules.

9. The greenhouse gas detection device according to claim 7, characterized in that, The number of the detection modules is at least three independent detection modules. Each detection module can independently monitor the greenhouse gas concentrations of CO2 and CH4, and each detection module uses the NDIR technology of infrared absorption to detect the gas concentration. At the same time, each detection module is also equipped with electrochemical and semiconductor sensors for monitoring HCHO and TVOC.

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