A PLC control-based functional detection system for an airbus aircraft ozone converter

Abstract

The application discloses an airbus aircraft ozone converter function detection system based on PLC control and relates to the technical field of equipment function detection. According to historical detection records, the abnormal standard numerical interval under different equipment defect name combinations is obtained, and then a grid space is set according to the number of equipment defect name combinations, each grid space is spliced according to the abnormal standard numerical interval type and interval numerical gap to obtain a state evaluation network, the initial state of inlet air and ozone is set according to the state evaluation network, and the measurement data sample is obtained in the process that the inlet air and ozone pass through the hardware detection end, the measurement data set is intercepted from the measurement data sample through data extraction rules in the process that the measurement data sample is matched with the state evaluation network, and then whether the to-be-tested ozone converter passes the test is judged according to the measurement data set.

Description

Technical Field

[0001] This invention relates to the field of equipment function testing technology, specifically to a PLC-controlled Airbus aircraft ozone converter function testing system. Background Technology

[0002] In the aviation industry, ozone converters on Airbus aircraft are crucial for ensuring air quality and flight safety inside the aircraft. The primary function of an ozone converter is to convert high concentrations of ozone into harmless oxygen, thereby reducing the potential harm of ozone to aircraft equipment and passenger health. However, as aircraft age and operating environments change, ozone converters may experience various functional failures, such as decreased conversion efficiency and abnormal pressure drops, which typically affect normal aircraft operation and passenger comfort.

[0003] Traditional methods for testing the function of ozone converters mainly rely on periodic manual inspections and simple instrument measurements. These methods suffer from low efficiency, poor accuracy, and the inability to monitor in real time. Furthermore, due to the complex operating environment of ozone converters, which is affected by various factors such as inlet airflow, temperature, pressure, and ozone concentration, traditional testing methods struggle to comprehensively and accurately assess the actual operating status of the ozone converter. Therefore, this paper proposes a PLC-controlled functional testing system for an Airbus aircraft ozone converter. Summary of the Invention

[0004] The purpose of this invention is to provide a PLC-controlled functional detection system for an Airbus aircraft ozone converter, in order to address the shortcomings in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A PLC-controlled functional testing system for an Airbus aircraft ozone converter includes a hardware testing terminal and a data testing terminal.

[0007] The hardware detection terminal comprises a flow meter, an ozone generator, an airflow mixer, a temperature sensor, a first ozone concentration sensor, a second ozone concentration sensor, a pressure sensor, a differential pressure sensor, and an ozone converter to be tested.

[0008] The data detection terminal includes an evaluation network construction module, a data conversion and acquisition module, and a detection and evaluation module.

[0009] The assessment network construction module is used to obtain the abnormal standard value range under different combinations of equipment defect names based on historical detection records, and then set the grid space according to the number of combinations of equipment defect names. The grid spaces are spliced ​​together according to the types of abnormal standard value ranges and the difference between the range values ​​to obtain the status assessment network.

[0010] The conversion data acquisition module is used to set the initial state of the inlet air and ozone according to the state assessment network, and to acquire measurement data samples during the process of the inlet air and ozone passing through the hardware detection terminal;

[0011] The detection and evaluation module is equipped with data extraction rules, which are used to extract measurement datasets from the measurement data samples during the matching process between the measurement data samples and the state assessment network, and then determine whether the ozone converter under test passes the test based on the measurement datasets.

[0012] Furthermore, the historical detection records include historical measurement datasets, equipment fault records, and ozone converter models. The equipment fault records include the name of the equipment defect and the time when the equipment fault was discovered. The equipment fault records may contain zero or one or more equipment defect names.

[0013] Furthermore, the process of obtaining the abnormal standard value range under different combinations of equipment defect names based on historical inspection records includes:

[0014] Set up multiple inlet ozone concentration ranges and outlet ozone concentration ranges with numerical boundaries, and arrange and combine each inlet ozone concentration range and outlet ozone concentration range to generate multiple combinations of inlet and outlet ozone concentration ranges.

[0015] Establish a two-dimensional coordinate system and map the same type of detection data from historical monitoring records, excluding ozone concentrations in inlet and outlet air, onto the same two-dimensional coordinate system. Set several time intervals of equal length on the two-dimensional coordinate system and set density two-dimensional frames.

[0016] The density 2D bounding box is traversed along the y-axis from the x-axis to the detection data, and the standard segment intervals for each time period are output based on the traversal results.

[0017] By sequentially connecting the standard segment intervals under each time period, the normal standard value range of each detection data under the corresponding combination of inlet and outlet ozone concentration intervals is obtained.

[0018] Retrieve historical inspection records with the same equipment defect name, match the corresponding normal standard value range with the historical inlet and outlet ozone concentrations recorded in the historical measurement dataset, and set anomaly labels for the data segments based on the matching results;

[0019] All data segments with anomaly labels before the time the equipment failure was discovered, as well as the data segments with anomaly labels in the three time segments thereafter, are retained. This allows us to obtain multiple abnormal standard value ranges for different combinations of equipment defect names under various combinations of inlet and outlet ozone concentration ranges.

[0020] Furthermore, the setup process for the state assessment network includes:

[0021] For each combination of inlet and outlet ozone concentration ranges and ozone converter models, a status assessment network is set up, and based on the number of equipment defect name combination types in historical inspection records, m+1 grid spaces are set up, where m represents the total number of equipment defect name combination types in historical inspection records.

[0022] The state assessment network is cone-shaped, and the grid space storing the normal standard value range under normal test conditions is denoted as the target grid space, and the target grid space is located at the top of the state assessment network;

[0023] Furthermore, the remaining grid spaces are spaced apart from the target grid space based on the types of abnormal standard numerical ranges and the differences between the numerical ranges.

[0024] Each combination of equipment defect names is input into its respective grid space. Then, the normal standard value range of each test data under normal testing conditions and the abnormal standard value range of each test data under different combinations of equipment defect names are input into their respective grid spaces.

[0025] Furthermore, the process of collecting measurement data samples includes:

[0026] The same data acquisition cycle is set for all sensors. The user sets the ozone concentration in the inlet air and the required ozone concentration at the hardware detection end. Then, the output air and ozone are passed through each device at the hardware detection end in sequence. At the same time, the corresponding state assessment network is matched according to the ozone concentration in the inlet air and the required ozone concentration. The inlet air temperature, inlet air pressure and inlet air flow rate are set according to the median value of the normal standard value range contained in the target grid space in the state assessment network.

[0027] The data acquisition module then collects multiple detection data through various sensors and obtains the model of the ozone converter under test from the hardware detection terminal. The detection dataset includes inlet air flow rate, inlet air temperature, inlet air pressure, outlet air ozone concentration, and pressure drop across the ozone converter under test.

[0028] When the data acquisition cycle ends, the conversion data acquisition module integrates and compresses all the data collected by the sensors to generate a measurement data sample.

[0029] Furthermore, the process of matching measurement data samples with the state assessment network includes:

[0030] Whenever a measurement data sample is generated, a corresponding state assessment network is matched according to the model of the ozone converter under test and the ozone concentration in the inlet air and the required ozone concentration in the measurement data sample.

[0031] The measurement data sample is matched with each grid space in the state assessment network. If it is determined that each measurement data is within the corresponding standard value range, the data extraction rule is activated; otherwise, the current measurement sample data is ignored.

[0032] Furthermore, the data extraction rules include:

[0033] Set a data stability period, starting from the start of the data extraction rule. If all the detection data are within the standard value range contained in the grid space within the data stability period, the measurement data sample is judged to be a stable sample. Then, the data segment corresponding to the start and end time period is extracted from the measurement data sample and recorded as the measurement dataset.

[0034] If, within the data stabilization period, it is determined that any detection data is outside the range of standard values ​​contained in the grid space, the data extraction rules are reset until the entire measurement data sample is traversed, wherein the data stabilization period is less than or equal to one-quarter of the data acquisition cycle.

[0035] If a measurement dataset cannot be output based on the measurement data sample, the current measurement data sample is discarded, and the measurement data sample for the next data acquisition cycle is processed.

[0036] When a measurement dataset is output based on a measurement data sample, the measurement dataset is input into the corresponding grid space. If the measurement dataset is not located in the target grid space, the corresponding device is repaired according to the equipment defect name corresponding to the target grid space, and the subsequent measurement dataset is obtained after the repair is completed.

[0037] Furthermore, the process of determining whether the ozone converter under test passes the test based on the measurement dataset includes:

[0038] Repeat the process of capturing measurement datasets and determining device status until it is determined that the measurement dataset is located in the target grid space and more than 10 measurement datasets are continuously output.

[0039] Set the average conversion threshold and pressure drop threshold, and obtain the average conversion efficiency and average pressure drop values ​​based on the inlet and outlet air ozone concentrations and the pressure drop across the ozone converter under test in the measurement dataset.

[0040] If the average conversion efficiency and average pressure drop values ​​corresponding to all measurement datasets are greater than or equal to the average conversion threshold and pressure drop threshold, then the corresponding ozone converter under test is judged to have passed the test; otherwise, it is judged to have failed the test.

[0041] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0042] 1. This invention constructs a state assessment network that comprehensively considers the abnormal standard value range under different combinations of equipment defect names, enabling more accurate determination of the ozone converter's operating status. Simultaneously, it utilizes data extraction rules to extract stable measurement datasets from the measurement data samples, avoiding the impact of data fluctuations on the detection results and improving the accuracy of equipment detection results.

[0043] 2. This invention employs automated data acquisition and analysis methods, reducing manual intervention and improving detection efficiency. Furthermore, by setting reasonable data acquisition cycles and data stabilization periods, measurement datasets can be obtained quickly and accurately, shortening equipment detection time. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0045] Figure 1 This is a system framework diagram of a PLC-controlled Airbus aircraft ozone converter functional detection system according to the present invention.

[0046] Figure 2 This is a schematic diagram of the hardware detection end described in this invention;

[0047] The attached diagram is labeled as follows: Flow meter 1, Ozone generator 2, Airflow mixer 3, Temperature sensor 4, First ozone concentration sensor 5, Pressure sensor 6, Ozone converter under test 7, Differential pressure sensor 8, Second ozone concentration sensor 9. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] A PLC-controlled functional testing system for an Airbus aircraft ozone converter includes a hardware testing terminal and a data testing terminal.

[0050] Please see Figure 2As shown, the hardware detection terminal includes a flow meter 1, an ozone generator 2, an airflow mixer 3, a temperature sensor 4, a first ozone concentration sensor 5, a pressure sensor 6, an ozone converter under test 7, a differential pressure sensor 8, and a second ozone concentration sensor 9.

[0051] The data detection terminal includes an evaluation network construction module, a data conversion and acquisition module, and a detection and evaluation module.

[0052] The assessment network construction module is used to obtain the abnormal standard value range under different combinations of equipment defect names based on historical detection records, and then set the grid space according to the number of combinations of equipment defect names. The grid spaces are spliced ​​together according to the types of abnormal standard value ranges and the difference between the range values ​​to obtain the status assessment network.

[0053] The conversion data acquisition module is used to set the initial state of the inlet air and ozone according to the state assessment network, and to acquire measurement data samples during the process of the inlet air and ozone passing through the hardware detection terminal;

[0054] The detection and evaluation module is equipped with data extraction rules, which are used to extract measurement datasets from the measurement data samples during the matching process between the measurement data samples and the state assessment network, and then determine whether the ozone converter 7 under test passes the test based on the measurement datasets.

[0055] The working principle of the present invention is illustrated below through examples:

[0056] The evaluation network construction module in the data detection terminal obtains the historical detection records generated when the hardware detection terminal detects n ozone converters, where n is a natural number greater than 100;

[0057] It should be noted that all measurement data contained in the historical detection records are obtained by the conversion data acquisition module through data extraction rules;

[0058] The historical detection records include historical measurement datasets, equipment fault records, and ozone converter models. The equipment fault records include the names of the equipment defects (including all equipment names in the hardware detection end except for the ozone converter 7 under test) and the time when the equipment fault was discovered.

[0059] It should be noted that the equipment fault record may contain zero, one, or more equipment defect names;

[0060] Multiple inlet and outlet ozone concentration ranges with numerical boundary connections are set, and the various inlet and outlet ozone concentration ranges are arranged and combined to generate multiple combinations of inlet and outlet ozone concentration ranges. Then, historical test records with blank equipment fault records and ozone converters of the same model are retrieved under each combination of inlet and outlet ozone concentration ranges.

[0061] A two-dimensional coordinate system is established, and all detection data of the same type in historical detection records, except for the ozone concentration in the inlet and outlet air, are mapped onto the same two-dimensional coordinate system. Several time segments of equal length are set on the two-dimensional coordinate system. The time length of each time segment is generally 50ms to 100ms. A density two-dimensional frame is set. The length of the density two-dimensional frame is equal to the length of the time segment in the two-dimensional coordinate system, and the width is set according to the type of detection data. That is, the width of the density two-dimensional frame is different in each two-dimensional coordinate system depending on the type of detection data displayed. For example, if the detection data displayed in the two-dimensional coordinate system is the inlet air temperature, then the width of the density two-dimensional frame is 0.2.

[0062] For any time interval, let the density two-dimensional bounding box traverse the detection data from the x-axis along the y-axis, and obtain the data segment density at each position in real time during the traversal. Then, select the numerical interval with the maximum data segment density as the standard segment interval for the corresponding time interval.

[0063] By sequentially connecting the standard segment intervals under each time period, the normal standard value range of each detection data under the corresponding combination of inlet and outlet ozone concentration intervals is obtained.

[0064] Furthermore, retrieve historical detection records with the same equipment defect name, match the corresponding normal standard value ranges based on the historical inlet and outlet ozone concentrations recorded in the historical measurement dataset, and then compare each normal standard value range with the other historical detection data. If it is determined that the historical detection data is not in the corresponding normal standard value range, then set an anomaly label for the corresponding data segment; otherwise, do not set an anomaly label.

[0065] All data segments with anomaly labels before the time of equipment failure discovery, as well as data segments with anomaly labels in the three time segments thereafter, are retained. Then, by generating normal standard value ranges, multiple abnormal standard value ranges for different equipment defect name combinations are obtained under each combination of inlet and outlet ozone concentration ranges.

[0066] The evaluation network construction module sets up a status evaluation network for each combination of inlet and outlet ozone concentration ranges and ozone converter models, and sets up m+1 grid spaces based on the number of equipment defect name combination types in historical detection records, where m represents the total number of equipment defect name combination types in historical detection records.

[0067] The state assessment network is cone-shaped, and each grid space is distributed on the state assessment network in a "honeycomb" shape. The grid space that stores the normal standard value range under normal test conditions is recorded as the target grid space, and the target grid space is located at the top of the state assessment network, that is, the top of the cone.

[0068] Furthermore, the remaining grid spaces are spaced apart from the target grid space based on the types of abnormal standard numerical ranges and the differences between the numerical ranges.

[0069] Each combination of equipment defect names is input into each grid space. Then, the normal standard value range of each test data under normal test conditions and the abnormal standard value range of each test data under different combinations of equipment defect names are input into each grid space.

[0070] It should be noted that each time the ozone converter 7 under test is tested and the test is completed, a historical test record is generated based on the model of the ozone converter 7 under test and the measurement dataset. Then, the evaluation network construction module updates the status evaluation network based on the latest generated historical test record.

[0071] Furthermore, whenever the evaluation network construction module updates the state evaluation network, it synchronizes the state evaluation network with the detection and evaluation module and the conversion data acquisition module.

[0072] The conversion data acquisition module communicates with all sensors on the hardware detection end and sets the same data acquisition cycle for all sensors. The duration of the data acquisition cycle is generally 5 to 10 seconds.

[0073] Users set the ozone concentration and required ozone concentration in the inlet air at the hardware detection end, and then pass the output air and ozone through each device of the hardware detection end in sequence. At the same time, the corresponding state assessment network is matched according to the ozone concentration and required ozone concentration in the inlet air, and the inlet air temperature, inlet air pressure and inlet air flow rate are set according to the median value of the normal standard value range contained in the target grid space in the state assessment network.

[0074] The data acquisition module then collects multiple detection data through various sensors and obtains the model of the ozone converter 7 under test from the hardware detection end. The detection dataset includes inlet air flow rate, inlet air temperature, inlet air pressure, outlet air ozone concentration, and pressure drop across the ozone converter 7 under test (obtained through the pressure difference between the inlet and outlet mixed gases).

[0075] When the data acquisition cycle ends, the conversion data acquisition module integrates and compresses all the data collected by the sensors to generate a measurement data sample.

[0076] Furthermore, whenever a measurement data sample is generated, the conversion data acquisition module matches the corresponding state assessment network based on the model of the ozone converter 7 under test and the ozone concentration and ozone requirement in the inlet air of the measurement data sample.

[0077] The measurement data sample is matched with each grid space in the state assessment network. If it is determined that each measurement data is within the corresponding standard value range, the data extraction rule is activated; otherwise, the current measurement sample data is ignored.

[0078] The data extraction rules include: setting a data stability period, starting the timer from the start of the data extraction rules, and if all the detection data are within the standard value range contained in the grid space within the data stability period, then the measurement data sample is determined to be a stable sample, and then the data segment corresponding to the start and end time period is extracted from the measurement data sample and recorded as the measurement dataset.

[0079] If, within the data stabilization period, it is determined that any detection data is outside the range of standard values ​​contained in the grid space, the data extraction rules are reset until the entire measurement data sample is traversed, wherein the data stabilization period is less than or equal to one-quarter of the data acquisition cycle.

[0080] If a measurement dataset cannot be output based on the measurement data sample, the current measurement data sample is discarded, and the measurement data sample for the next data acquisition cycle is processed.

[0081] When a measurement dataset is output based on a measurement data sample, the measurement dataset is input into the corresponding grid space. If the measurement dataset is not located in the target grid space, the corresponding device is repaired according to the equipment defect name corresponding to the target grid space, and the subsequent measurement dataset is obtained after the repair is completed.

[0082] Repeat the above process of capturing measurement datasets and determining device status until it is determined that the measurement dataset is located in the target grid space and more than 10 measurement datasets are continuously output.

[0083] Set the average conversion threshold and pressure drop threshold. Based on the inlet and outlet air ozone concentrations in the measurement dataset and the pressure drop across the ozone converter 7 under test, obtain the average conversion efficiency and average pressure drop values. The calculation process for the average conversion efficiency α and the average pressure drop value β is as follows:

[0084] , ,

[0085] in and The average conversion efficiency and average voltage drop of the i-th measurement dataset are represented by Num, where Num represents the number of time intervals within the data stabilization period. , Y and X represent the ozone concentration and mixed gas pressure values ​​of the outlet air in the j-th time interval, respectively, and the set ozone concentration and mixed gas pressure values ​​of the inlet air.

[0086] If the average conversion efficiency and average pressure drop values ​​corresponding to all measurement datasets are greater than or equal to the average conversion threshold and pressure drop threshold, then the corresponding ozone converter 7 under test is judged to have passed the test; otherwise, it is judged to have failed the test.

[0087] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A system for detecting the function of an ozone converter of an Airbus aircraft based on PLC control, characterized in that, Including hardware detection end and data detection end; The hardware detection end contains flow meter, ozone generator, airflow mixer, temperature sensor, ozone concentration sensor, pressure sensor, differential pressure sensor and ozone converter to be tested; The data detection end includes evaluation network construction module, conversion data acquisition module and detection evaluation module; The evaluation network construction module is used for obtaining abnormal standard value intervals under different equipment defect name combinations according to historical detection records, setting grid space according to the number of equipment defect name combinations, splicing each grid space according to the type of abnormal standard value interval and interval value gap, and obtaining a state evaluation network; The historical detection records include historical measurement data set, equipment failure record and ozone converter model, wherein the equipment failure record includes equipment defect name and equipment failure discovery time point, and the equipment defect name included in the equipment failure record exists zero, one or more; The process of obtaining abnormal standard value intervals under different equipment defect name combinations according to historical detection records includes: Setting multiple value boundary connected import ozone concentration intervals and export ozone concentration intervals, arranging and combining each import ozone concentration interval and export ozone concentration interval to generate multiple import and export ozone concentration interval combinations; A two-dimensional coordinate system is established, and the same kind of detection data in the historical detection records except the import and export air ozone concentration is mapped in the same two-dimensional coordinate system. A number of equal-length time sections are set on the two-dimensional coordinate system, and a density two-dimensional frame is set; The density two-dimensional frame is traversed along the y-axis direction from the x-axis, and the standard segment interval under each time section is output according to the traversal result; The standard segment intervals under each time section are connected in turn to obtain the normal standard value interval of each detection data under the corresponding import and export ozone concentration interval combination; The historical detection records of the same equipment defect name are recalled, the normal standard value interval is matched according to the historical import and export ozone concentration recorded in the historical measurement data set, and the data segment is set with abnormal mark according to the matching result; All data segments with abnormal mark before the equipment failure discovery time point and data segments with abnormal mark in the next three time sections are retained, and then multiple abnormal standard value intervals of different equipment defect name combinations under each import and export ozone concentration interval combination are obtained; The conversion data acquisition module is used for setting the initial state of import air and ozone according to the state evaluation network, and obtaining measurement data samples in the process of import air and ozone passing through the hardware detection end; The detection evaluation module is provided with a data extraction rule, which is used to cut out a measurement data set from the measurement data sample in the process of matching the measurement data sample with the state evaluation network, and then judge whether the ozone converter to be tested passes the test according to the measurement data set.

2. The system for detecting the function of the ozone converter of the Airbus aircraft based on the PLC control according to claim 1, characterized in that, The setting process of the state evaluation network includes: One state evaluation network is set for each combination of import and export ozone concentration interval and ozone converter model, and m+1 grid spaces are set according to the number of device defect name combination types in the historical detection record, wherein m represents the total number of device defect name combination types in the historical detection record; The state evaluation network is conical, the grid space storing the normal standard value interval in the normal test state is recorded as the target grid space, and the target grid space is located at the top of the state evaluation network; Further, the remaining grid spaces are set to have a spatial distance from the target grid space according to the types of abnormal standard value intervals and the value interval gap, each device defect name combination is input into each grid space, and the normal standard value interval of each detection data in the normal test state and the abnormal standard value interval of each detection data under different device defect name combinations are input into each grid space.

3. The system for detecting the function of the ozone converter of the Airbus aircraft based on the PLC control according to claim 2, characterized in that, The collection process of the measurement data sample includes: The same data collection period is set for all sensors, the user sets the import air ozone concentration and the ozone required concentration at the hardware detection end, then the output air and ozone pass through each device of the hardware detection end in turn, the corresponding state evaluation network is matched according to the import air ozone concentration and the ozone required concentration, and the import air temperature, import air pressure and import air flow are set according to the middle value of the normal standard value interval contained in the target grid space in the state evaluation network. Further, the conversion data collection module collects multiple detection data through each sensor, and obtains the to-be-tested ozone converter model from the hardware detection end, and the detection data set includes the import air flow, import air temperature, import air pressure, export air ozone concentration and pressure drop of the to-be-tested ozone converter.

4. The system for detecting the function of the ozone converter of the Airbus aircraft based on the PLC control according to claim 3, characterized in that, The process of matching the measurement data sample with the state evaluation network includes: Whenever a measurement data sample is generated, the state evaluation network is matched according to the to-be-tested ozone converter model and the import air ozone concentration and the ozone required concentration in the measurement data sample; Each detection data of the measurement data sample is matched with each grid space in the state evaluation network, if it is judged that each detection data is within the corresponding standard value interval, the data extraction rule is started, otherwise the current measurement sample data is ignored.

5. The system for detecting the function of the ozone converter of the Airbus aircraft based on the PLC control according to claim 4, characterized in that, The data extraction rule includes: A data stable period is set, the time is counted from the start of the data extraction rule, if each detection data is within the standard value interval contained in the grid space within the data stable period, the measurement data sample is judged to be a stable sample, and then a data segment corresponding to the start and end time period is cut from the measurement data sample and recorded as a measurement data set; If it is judged that any detection data is not within the standard value interval contained in the grid space within the data stable period, the data extraction rule is reset until the entire measurement data sample is traversed; If the measurement data set cannot be output according to the measurement data sample, the current measurement data sample is discarded, and the measurement data sample under the next data collection period is collected; When the measurement data set is output according to the measurement data sample, the measurement data set is input into the corresponding grid space, if the measurement data set is not located in the target grid space, the corresponding device is repaired according to the device defect name corresponding to the target grid space, and the subsequent measurement data set is obtained after the repair is completed.

6. The system for detecting the function of the ozone converter of the Airbus aircraft based on the PLC control according to claim 5, characterized in that, The process of judging whether the ozone converter to be tested passes the test according to the measurement data set comprises: The process of intercepting the measurement data set and judging the device state is repeated until it is judged that the measurement data set is located in the target grid space and more than 10 measurement data sets are continuously output; The average conversion threshold and the pressure drop threshold are set, the average conversion efficiency and the average pressure drop value are obtained according to the inlet and outlet air ozone concentration in the measurement data set and the pressure drop of the ozone converter to be tested; If the average conversion efficiency and the average pressure drop value corresponding to all the measurement data sets are greater than or equal to the average conversion threshold and the pressure drop threshold, it is judged that the corresponding ozone converter to be tested passes the test, otherwise it is judged that the test fails.

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