Greenhouse gas detection method and detection equipment

Abstract

This application relates to a greenhouse gas detection method and device, and relates to the field of greenhouse gas detection technology. The method includes: constructing a modular redundant architecture for detection modules; implementing real-time cross-verification of multiple channels of gas data collected by multiple detection modules using a dynamic weighted fitting algorithm in a data processing module; and filtering available gas data that has passed real-time cross-verification through the data processing module, and deleting abnormal gas data that differs from other gas data. This application can improve the accuracy of gas detection in confined spaces and reduce gas data errors.

Description

Technical Field

[0001] The present application relates to the field of greenhouse gas detection, and in particular to a greenhouse gas detection method and detection equipment. Background Art

[0002] With the deepening of global climate change governance, greenhouse gas monitoring technology has become a vital component of environmental monitoring. The need for accurate monitoring of greenhouse gas concentrations, particularly in confined spaces, is becoming increasingly urgent. This type of monitoring not only helps preserve cultural heritage but also ensures the stability of precision experimental environments. While advances in environmental monitoring technology have significantly enhanced macro-regional greenhouse gas assessment capabilities, monitoring in micro-confined spaces still faces numerous challenges.

[0003] In the prior art, the gas detection system and detection method disclosed in China with publication number CN112033865A uses multiple sensors in a detection unit to perform real-time detection of the collected gas to be tested, then obtains the detection data of the detection module through an information processing module, and processes the detection data in real time to obtain the detection results. The sensor detection data of the first detection unit is also used to compensate for some sensors of the second detection unit.

[0004] In the above-mentioned gas detection system, although there are two detection units in the detection module for real-time collection of the gas to be tested in the 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 fails to effectively eliminate abnormal data, the gas data detected by the second detection unit will also be deviated, and the reliability of the detection data will be greatly reduced, which limits the application effect of the existing technology in high-precision and high-reliability confined space monitoring scenarios. Summary of the Invention

[0005] The first object of this application is to provide a greenhouse gas detection method that can improve the accuracy of gas detection in a confined space and reduce the error of gas data.

[0006] In one aspect, the present application provides a greenhouse gas detection method, which adopts the following technical solution:

[0007] A greenhouse gas detection method, comprising:

[0008] Construct a modular redundant architecture of several detection modules so that several detection modules can detect gas at the same time;

[0009] The dynamic weighted fitting algorithm of the data processing module is used to realize real-time mutual verification of multi-channel gas data collected by multiple detection modules;

[0010] Through the data processing module, the available gas data that have passed real-time mutual verification and the abnormal gas data that are different from other gas data are screened, and all gas data items of the detection module corresponding to the abnormal gas data are set as abnormal gas data and eliminated.

[0011] By adopting the above technical solution, a modular redundant architecture of the detection module is constructed, which enables several detection modules to detect gas at the same time, and generate several gas data to be uploaded to the data processing module for processing, so as to avoid the situation caused by failure of one detection module or data distortion as much as possible; realize real-time mutual verification of multi-channel gas data, which can improve the accuracy and consistency of gas data; delete all gas data of the detection module corresponding to the unavailable gas data in the gas data, reduce the impact of the distortion of one gas data of the detection module on the output of other gas data, further ensure the reliability of gas monitoring results, and reduce the possibility of false alarms.

[0012] In a preferred example, the present application may be further configured as follows: the step of constructing a modular redundant architecture of the detection module includes:

[0013] At least three detection modules are used to construct a distributed measurement network, and the reliability of the gas detection module is calculated based on the reliability of each detection module.

[0014] By adopting the above technical solution, a distributed measurement network is constructed using at least three independent detection modules to achieve cross-validation and fitting of data, thereby reducing the risk of single point failure and improving the reliability of gas data.

[0015] In a preferred example, the present application may be further configured as follows: the step of implementing real-time mutual verification of multi-channel gas data on a plurality of gas data collected by a plurality of detection modules using a dynamic weighted fitting algorithm of the data processing module includes:

[0016] The gas data between the various detection modules are cross-checked and compared through the data processing module;

[0017] After calculating the optimal data of the integrated gas data of each detection module through the weighted fitting algorithm of the data processing module, the optimal data between each detection module are mutually verified.

[0018] By adopting the above technical solution, the comparison of various gas data between the detection modules can determine whether each gas data of the detection module has abnormal data from a microscopic perspective; and through the dynamic weighted fitting algorithm, the gas data detected by each detection module are integrated and cross-checked again, and then from a macroscopic perspective, it can be determined whether all the gas data generated by the detection module as a whole have abnormal data, thereby improving the accuracy of the gas data.

[0019] 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:

[0020] 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;

[0021] Filter gas data with a discreteness exceeding 3σ and set them as maintenance gas data;

[0022] 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;

[0023] 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.

[0024] 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.

[0025] 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:

[0026] 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.

[0027] 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;

[0028] The optimal data between each detection module is mutually verified through the data processing module.

[0029] By adopting the above technical solution, the optimal data obtained can reflect the actual situation of gas concentration. Then, through mutual verification between the optimal data, it is determined whether the overall gas data of each detection module is abnormal, thereby further improving the accuracy and reliability of the overall gas data. When processing multi-source data in complex environments, it shows stronger adaptability and robustness, and is particularly suitable for the high-precision monitoring needs of greenhouse gases in confined spaces.

[0030] In a preferred example, the present application may be further configured as follows: the step of performing mutual verification of the optimal data between each detection module by the data processing module further includes:

[0031] If the optimal data obtained by the data processing module differs from other optimal data by more than a preset value, the optimal data is set as abnormal optimal data, and the data other than the optimal data is set as available optimal data;

[0032] Screening the detection module with abnormal optimal 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;

[0033] A maintenance work order is generated based on the abnormal optimal data in the detection module to locate the faulty component of the detection module.

[0034] By adopting the above technical solution, the optimal data of each detection module are compared and verified in pairs. If there is a situation where the preset value is exceeded, it means that the detection module with the optimal data of other detection modules that exceeds the preset value is faulty. The available optimal data with abnormalities is further screened, and the detection module with abnormal optimal data is located. A maintenance work order is generated for the faulty detection module, which can improve the efficiency of fault detection and repair.

[0035] In a second aspect, the present application provides a greenhouse gas detection device comprising: at least three detection modules for collecting gas data;

[0036] A gas circulation device, used to form a gas circulation in the space to be tested and guide the gas to the at least three detection modules;

[0037] A data processing module is electrically connected to the at least three detection modules, and is configured to perform the following operations:

[0038] a) receiving gas data from the at least three detection modules;

[0039] b) Perform real-time mutual verification on each set of received gas data to identify differences between the gas data;

[0040] c) Based on the mutual verification processing results, all gas data of the detection modules with abnormal gas data are eliminated to obtain usable gas data;

[0041] The communication module is electrically connected to the data processing module and is used to send gas data or alarm information.

[0042] By adopting the above technical solution, several detection modules synchronously perform multi-channel detection of gas components during the process of gas exchange in the gas circulation device in the space to be tested, and then conduct real-time mutual verification of the detected gas data through the data processing module, and eliminate all gas data of the detection modules with abnormal gas data, so as to reduce the situation where a single detection is performed and a detection module fails or is distorted, resulting in errors in the overall gas data. In addition, the gas circulation device can also solve the problem of inaccurate gas data caused by uneven distribution due to gas stratification in the closed space to be tested. The communication module sends alarm information based on the gas data to promptly remind the user to repair the equipment.

[0043] In a preferred example, the present application can be further configured as follows: the gas circulation device is arranged in the space to be tested of the display cabinet base, and the display cabinet platform on the top of the display cabinet base is provided with an air outlet that is connected to the gas circulation device. The gas circulation device includes a micro pump, and the micro pump is arranged on one side of the air outlet of the display cabinet base. A gas diffusion device is provided on the side where the air inlet of the gas circulation device faces the multiple detection modules, and the output port of the gas diffusion device faces the detection ports of the multiple detection modules.

[0044] By adopting the above technical solution, the micro pump of the gas circulation device can actively stir the gas in the enclosed space located on the top of the display cabinet platform, and then the gas diffusion device diffuses the mixed gas, so that the gas entering the detection module is in a fully mixed and uniform state, and the gas data obtained is more accurate and reliable.

[0045] In a preferred example, the present application can be further configured as follows: 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 infrared absorption NDIR technology to detect gas concentrations. At the same time, each detection module is also equipped with electrochemical and semiconductor sensors for monitoring HCHO and TVOC.

[0046] By adopting the above technical solution, each detection module can independently monitor the concentration of greenhouse gases such as CO2 and CH, making the collected gas data diverse and accurate. In addition, the detection module uses infrared absorption NDIR technology to detect gas concentration, which improves measurement accuracy and reduces 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.

[0047] In summary, this application has the following beneficial technical effects:

[0048] 1. By building a modular redundant architecture for detection modules and using no fewer than three independent detection modules to achieve distributed measurement, the risk of data failure in a single detection module is reduced, and the reliability of data detection is improved;

[0049] 2. Use a dynamic weighted fitting algorithm to perform real-time cross-verification of multi-channel gas data, screen valid data and exclude abnormal data to improve gas data precision and measurement accuracy;

[0050] 3. Through the communication module, abnormal gas data is promptly notified to the user end, so that the faulty part can be quickly repaired and maintained. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a schematic diagram of the internal structure of a greenhouse gas detection device in this application.

[0052] Figure 2 This is a schematic diagram of the overall structure of a greenhouse gas detection device in this application.

[0053] Figure 3 This is a flow chart of a greenhouse gas detection method in one embodiment of the present application.

[0054] Figure 4 This is a flowchart of the sub-steps of step S1 in one embodiment of the present application.

[0055] Figure 5 This is a flowchart of the sub-steps of step S2 in one embodiment of the present application.

[0056] Figure 6 This is a flowchart of the sub-steps of step S3 in one embodiment of the present application.

[0057] Figure 7 This is a flowchart of the sub-steps of step S21 in one embodiment of the present application.

[0058] Figure 8 This is a flowchart of the sub-steps of step S20 in one embodiment of the present application.

[0059] Figure numerals: 1. display case platform; 2. display case base; 3. micro pump; 4. gas diffusion device. DETAILED DESCRIPTION

[0060] The following is combined with Figure 1-8 This application is described in further detail.

[0061] It should be noted that all actions of obtaining data or information or data in this application are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization of the corresponding users.

[0062] refer to Figure 1 、 Figure 2 and Figure 3 , a greenhouse gas detection method, specifically comprising:

[0063] S1. Construct a modular redundant architecture of several detection modules so that the several detection modules can detect gas at the same time.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] refer to Figure 1 、 Figure 2 and Figure 4 Furthermore, in one embodiment, step S1 is further divided into the following sub-steps:

[0072] S10. Use at least three detection modules to build a distributed measurement network, and calculate and obtain the reliability of the gas detection module based on the reliability of each detection module.

[0073] Specifically, by connecting multiple detection modules in parallel, even if one module fails, the others can continue to function. This redundant design effectively reduces the risk of system failure due to a single point of failure. Furthermore, if a detection module frequently fails, consider increasing the number of detection modules to improve reliability.

[0074] The reliability of the gas detection module is obtained using the formula:

[0075] R system = 1 - (1 - R module ) n (1);

[0076] 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 connected in parallel.

[0077] 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.

[0078] In one embodiment, the gas diffusion device 4 is arranged vertically, and the detection modules are also arranged vertically. The detection ports face the gas diffusion device 4 and are coplanar with the gas diffusion device 4. Therefore, when the gas diffusion device 4 outputs gas to the gas circulation device, if the gas data of a detection module in a certain position in the vertical arrangement is abnormal, the number of failures in the position where the detection module is located is recorded. Assuming the number of failures of the detection module in a certain position is F, and the total operating time of the detection module is T, the failure rate λ can be calculated using the following formula:

[0079] λ = T / F;

[0080] If λ is lower than the fault alarm value, an alarm message is issued and the gas data of the detection module in the corresponding position is excluded.

[0081] Mapping λ to R module Assume that the reliability of a single detection module R module =0.95, the system uses n=3 detection modules in parallel:

[0082] R system = 1 − (1 − 0.95) 3 ≈0.999875;

[0083] This shows that the reliability of the system has increased from 0.9 of a single module to approximately 0.999875, reducing the risk of single-point failure and improving reliability.

[0084] In addition, reference Figure 1 、 Figure 2 and Figure 5 Furthermore, in one embodiment, step S2 is further divided into the following sub-steps:

[0085] S20: The data processing module compares the gas data between the detection modules.

[0086] Specifically, in this embodiment, each detection module can independently monitor the greenhouse gas concentrations of CO2 and CH4, and each detection module uses infrared absorption NDIR technology to detect gas concentrations. 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 within the display cabinet or enclosed space to ensure that environmental conditions meet preset standards. Therefore, gas data for CO2, CH4, HCHO, and TVOC, as well as gas data such as temperature, humidity, pressure, and particulate matter concentration, can be obtained. Each gas data item exists independently. Each detection module compares and verifies the gas data of the same item detected in pairs to obtain abnormal gas data.

[0087] S21. After calculating the optimal data of the gas data of each detection module by the weighted fitting algorithm of the data processing module, the optimal data of each detection module are cross-verified.

[0088] Specifically, optimal data is the combined data obtained by weighted fitting of all gas data from a detection module. This data provides a macroscopic view of the gas detection performance of the entire detection module. Using the optimal data from each detection module, we conduct a cross-verification to determine if any abnormalities exist in the overall gas data generated by the detection module, thereby improving gas data accuracy.

[0089] In addition, reference Figure 1 、 Figure 2 and Figure 6 Furthermore, in one embodiment, step S3 is further divided into the following sub-steps:

[0090] S30, integrating the fault tree analysis model through the data processing module, and determining whether the discreteness of the gas data in each detection module exceeds 3σ.

[0091] When the gas data dispersion of a detection module exceeds 3σ, all gas data from that module is marked as abnormal. Specifically, the top event is determined as a gas data dispersion exceeding 3σ. Then, the intermediate events that may have led to this top event are analyzed, such as sensor failure in the detection module, environmental interference, or errors during data transmission. The bottom event, the specific cause of the failure, is further determined, such as whether the gas detected by the sensor port is evenly distributed, sensor performance degradation, power supply fluctuations, electromagnetic interference, or signal line damage.

[0092] When constructing a fault tree, logic gates are used to connect these events. For example, if either sensor failure or environmental interference could cause the gas data dispersion to exceed 3σ, an OR gate is used to connect these two intermediate events, sensor failure and environmental interference, to the top event. Furthermore, if both sensor performance degradation and power supply problems could cause sensor failure, an OR gate is used to connect the two bottom events to the sensor failure. In this way, a complete fault tree analysis model is gradually constructed, enabling analysis and diagnosis of abnormal gas data issues within the detection module.

[0093] S31. Filter gas data with a discreteness exceeding 3σ and set them as maintenance gas data.

[0094] Specifically, in the subsequent step of generating a maintenance work order, the maintenance gas data can point to the faulty sensor.

[0095] S32, screening the detection modules 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.

[0096] Specifically, in this embodiment, the communication module adopts a WIFI communication method.

[0097] S33: Generate a maintenance work order based on the maintenance gas data in the detection module, and locate the faulty component of the sensor corresponding to the maintenance gas data.

[0098] Specifically, the sensor pointed to by the maintenance gas data is obtained and located in the set of sensor-related fault events. The fault events obtained in the fault tree analysis model are then matched with the repair plan for the fault in the fault database. A maintenance work order is automatically generated based on information such as the fault event and fault location, thereby optimizing the maintenance process.

[0099] In addition, reference Figure 1 、 Figure 2 and Figure 7 Furthermore, in one embodiment, step S21 is further divided into the following sub-steps:

[0100] S210. Obtain the gas data of each detection module through the data processing module, use the clustering algorithm to cluster the gas data of the detection modules into N clusters, and output N cluster centroids. According to the safety gas data corresponding to the material of the objects in the space to be tested, assign importance coefficients to the cluster centroids respectively.

[0101] Specifically, before clustering, the gas data from each detection module undergoes preprocessing, including data cleaning and normalization, to ensure data quality and consistency. Next, an existing clustering algorithm is selected and its optimal parameters are determined through methods such as cross-validation and silhouette coefficient to ensure the accuracy and stability of the clustering results. Next, the clustering results are combined with the safety gas data corresponding to the materials of the items within the test platform. A dynamic weighted fitting algorithm is used to assign a significance coefficient to each cluster centroid.

[0102] S211 , multiplying the important coefficient of each cluster by the centroid of each cluster and then adding them up through the data processing module to output the optimal data of each detection module.

[0103] Specifically, a clustering algorithm is used to cluster the data obtained from multiple detection modules. After clustering, the gas data from the multiple detection modules can be classified into N clusters, and N cluster centroids are output. Each cluster centroid is assigned an importance coefficient V1, V2, V3, ... to VN. The importance coefficient V is inversely proportional to the sum of the Euclidean distances from each cluster's data to the centroid data. The larger the sum of the Euclidean distances of the clusters, the smaller the assigned importance coefficient. In other words, the greater the cluster sample density, the larger the assigned importance coefficient, and the sum of all importance coefficients is 1. Furthermore, the optimal data output is the result of multiplying each cluster's importance coefficient by the centroid of each cluster, and then adding them together.

[0104] S212: Perform mutual verification on the optimal data between each detection module through the data processing module.

[0105] Specifically, by cross-verifying the optimal data of each detection module obtained through the clustering algorithm, it is possible to determine whether there are any abnormalities in the overall comprehensive gas data of each detection module, further improving the accuracy and reliability of the overall gas data. When processing complex environments, especially multi-source data from several detection modules in confined spaces, it can demonstrate greater adaptability and robustness.

[0106] In addition, reference Figure 1 、 Figure 2 and Figure 8 Furthermore, in one embodiment, step S20 is further divided into the following sub-steps:

[0107] S202. If the optimal data obtained by the data processing module differs from other optimal data by more than a preset value, the optimal data is set as abnormal optimal data, and data other than the optimal data is set as available optimal data.

[0108] Specifically, after quantifying the optimal data, the preset value is the difference between the optimal data, which can be set by the user to control the degree to which the detection module needs maintenance, thereby controlling the cost of maintenance of the detection module.

[0109] S203. If the optimal data obtained by the data processing module differs from other optimal data by more than a preset value, the optimal data is set as abnormal optimal data, and data other than the optimal data is set as available optimal data.

[0110] Specifically, if there is a situation where the value exceeds the preset value, it means that the detection module whose optimal data exceeds the preset value with other detection modules is faulty, and further screening is required for the available and abnormal optimal data.

[0111] S204: Filter the detection modules with abnormal optimal data, start the alarm function of the communication module, and send the alarm information containing the detection module to the user end through the communication module.

[0112] S205: Generate a maintenance work order based on the abnormal optimal data in the detection module to locate the faulty component of the detection module.

[0113] Specifically, the detection module pointed by the optimal abnormal data is obtained and located in the set of fault events related to the detection module. The fault events obtained in the fault tree analysis model process are then matched to the repair plan of the fault in the fault database, and maintenance work orders are automatically generated based on information such as the fault event and fault location. This optimizes the maintenance process and improves the efficiency of fault inspection and repair. At the same time, the orientation of the faulty detection module is recorded to calculate the failure rate and reliability of the detection module at that orientation.

[0114] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0115] Those skilled in the art can clearly understand that for the convenience and conciseness of description, only the above-mentioned functional modules and module divisions are used as examples. In actual applications, the above-mentioned functions can be distributed and completed by different functional modules and modules as needed, that is, the internal structure of the device can be divided into different functional modules or modules to complete all or part of the functions described above.

[0116] refer to Figure 1 and Figure 2 The present application also provides a greenhouse gas detection device comprising at least three detection modules for collecting gas data. The device also includes a gas circulation device for creating a gas circulation within a test space and directing the gas to the at least three detection modules, wherein the test space is sealed.

[0117] Furthermore, a greenhouse gas detection device further includes a data processing module, the data processing module being electrically connected to the at least three detection modules, and the data processing module being configured to perform the following operations:

[0118] Receive gas data from at least three detection modules. Several detection modules simultaneously upload the detected gas data to the data processing module, and synchronously perform multi-channel detection of gas components.

[0119] Perform real-time mutual verification on each set of gas data received to identify the differences between the gas data.

[0120] Based on the mutual verification processing results, all gas data of the detection modules with abnormal gas data are eliminated to obtain available gas data, so as to reduce the situation where a single detection module fails or is distorted, resulting in errors in the overall gas data.

[0121] Furthermore, a greenhouse gas detection device also 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 mutual verification, the communication module can send alarm information based on the abnormal gas data.

[0122] Furthermore, in this embodiment, a gas circulation device is fixedly connected to the test space of the display case base 2, which is sealed. A display case platform 1 for placing items is fixedly connected to the top of the display case base 2, which is also a sealed space. The display case platform 1 has an outlet that is connected to the gas circulation device. The gas circulation device includes a micropump 3, which is arranged on one side of the outlet of the display case base 2. The micropump 3 can achieve gas exchange between the display case base 2 and the display case platform 1, keeping the gas in a flowing state, thereby solving the problem of inaccurate gas data caused by uneven distribution due to gas stratification in the sealed space of the display case platform 1. A gas diffusion device 4 is provided on the side of the gas circulation device's air inlet facing the multiple detection modules, and the output of the gas diffusion device 4 faces the detection ports of the multiple detection modules. In one embodiment, the gas diffusion device 4 can be selected as a diffuser and / or a guide plate, and the output port of the diffuser and / or the guide plate is vertically arranged, and the detection ports of several detection modules are also arranged vertically. Therefore, several detection modules can fully receive the evenly mixed gas, and the pressure of the gas entering each detection module is relatively consistent, so that the difference in the gas data detected by each detection module is smaller, and the gas data is more accurate and reliable.

[0123] Specifically, several detection modules in the gas circulation device perform multi-channel synchronous detection of gas components, thereby reducing the situation where a single detection module fails or is distorted, resulting in errors in the overall gas data.

[0124] Furthermore, the number of detection modules is at least three independent detection modules, each of which can independently monitor the greenhouse gas concentrations of CO2 and CH4, making the collected gas data diverse and accurate. Each detection module uses infrared absorption NDIR technology to detect gas concentration, which improves measurement accuracy and reduces 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 scope of detection and realizing simultaneous monitoring of multiple gas components.

Claims

1. A greenhouse gas detection method, characterized in that: include: Construct a modular redundant architecture of several detection modules so that several detection modules can detect gas at the same time; The dynamic weighted fitting algorithm of the data processing module is used to realize real-time mutual verification of multi-channel gas data collected by multiple detection modules; Through the data processing module, the available gas data that have passed the real-time mutual verification and the abnormal gas data that are different from other gas data are screened, and all the gas data items of the detection module corresponding to the abnormal gas data are set as abnormal gas data and eliminated. The data processing module compares the gas data of the same item detected by each detection module in pairs for mutual verification; after the weighted fitting algorithm of the data processing module is used to calculate the optimal data of the comprehensive several gas data of each detection module, the optimal data between each detection module are mutually verified, and the optimal data is the comprehensive data obtained by the weighted fitting algorithm of all the gas data items of a detection module.

2. The method according to claim 1, characterized in that The step of constructing a modular redundant architecture of multiple detection modules so that the multiple detection modules can detect gas simultaneously includes: At least three detection modules are used to construct a distributed measurement network, and the reliability of the gas detection module is calculated based on the reliability of each detection module.

3. The method according to claim 1, characterized in that The step of screening the available gas data that has passed real-time mutual verification and abnormal gas data that is different from other gas data by the data processing module, 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.

4. The method according to claim 1, wherein 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.

5. The method according to claim 4, characterized in that The step of performing mutual verification of the optimal data between each detection module by the data processing module also includes: If the optimal data obtained by the data processing module differs from other optimal data by more than a preset value, the optimal data is set as abnormal optimal data, and the data other than the optimal data is set as available optimal data; Screening the detection module with abnormal optimal 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 based on the abnormal optimal data in the detection module to locate the faulty component of the detection module.

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