Civil aviation metrological instrument data management system and method based on intelligent sensor

Abstract

The application discloses a civil aviation metrological instrument data management system and method based on an intelligent sensor, relates to the technical field of civil aviation metrological instrument management, and comprises a data acquisition unit, a decay evaluation unit, a state judgment unit and a management correction unit. The data acquisition unit is used for collecting basic information, actual use state data, environmental parameter data and maintenance data of equipment in real time, performing outlier rejection, missing value filling and time stamp alignment on the collected original data, ensuring data integrity, and storing the basic information, actual use state data, environmental parameter data and maintenance data of the equipment in association according to a time axis to form equipment full life cycle data chains and send the data chains to the decay evaluation unit. The application can dynamically track the actual use state of equipment, quantify the performance decay degree, and dynamically adjust the effective use period according to the performance decay and maintenance conditions, so that the scientificity and safety of civil aviation metrological instrument management are improved.

Description

Technical Field

[0001] This invention relates to the field of civil aviation metrology instrument management technology, specifically to a civil aviation metrology instrument data management system and method based on intelligent sensors. Background Technology

[0002] In the civil aviation sector, the accuracy and reliability of metrological instruments are directly related to flight safety and operational efficiency. These instruments encompass a wide range of types, including sensors, calibration equipment, and testing instruments. They play a crucial role in every aspect of civil aviation operations, from pre-flight performance testing to real-time data monitoring during flight and post-landing equipment maintenance and calibration. Every step relies on accurate and reliable data support from metrological instruments. According to civil aviation regulations, metrological instruments must undergo regular professional verification and be used within their valid service life. Using them beyond their validity period can lead to data distortion or equipment malfunction, thereby causing safety hazards, flight delays, and cancellations, resulting in significant economic losses for airlines and considerable inconvenience for passengers.

[0003] However, traditional civil aviation metrology instrument data management systems only record the initial verification date and fixed validity period of metrology instruments, lacking a dynamic tracking mechanism for the actual usage status of the equipment. In actual operation, the usage frequency and environmental conditions of different metrology instruments vary greatly. Some equipment, due to its critical mission, is used very frequently, or is in harsh working environments such as high temperature, high humidity, and strong vibration for a long time. Its performance will gradually decline with the increase of usage time. However, since the traditional system cannot grasp the actual status of these devices in real time, even if they have not reached the fixed validity period, they may no longer be able to provide accurate and reliable data, thus creating hidden dangers for flight safety. On the other hand, some metrology instruments may be idle for a long time due to work task adjustments or spare parts. According to the fixed validity period management method of the traditional system, even if the performance of these devices is still good, they will be forcibly scrapped when the validity period expires, resulting in a huge waste of resources and increasing the operating costs of airlines. Furthermore, equipment inevitably requires maintenance during actual use. When equipment malfunctions and needs repair, maintenance personnel may replace critical components or fix some hidden faults. These maintenance operations have a significant impact on the remaining lifespan of the equipment. For example, replacing a critical component with a brand new one may extend the remaining lifespan of the equipment; however, if the hidden fault is more serious, although the equipment can resume normal operation after repair, its remaining lifespan may be shortened. However, existing traditional data management systems cannot dynamically update the validity period of equipment based on its maintenance status. This can lead to situations where equipment has already been repaired but the system still manages it according to the original validity period, posing a risk of the equipment being used beyond its expiration date; or the equipment may perform well after repair, but because the system has not updated the validity period in time, the equipment may be prematurely scrapped, further exacerbating the problem of resource waste.

[0004] Therefore, in view of this, the present invention proposes a data management system and method for civil aviation metrology instruments based on intelligent sensors to make up for and improve the deficiencies of the prior art. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a data management system and method for civil aviation metrology instruments based on intelligent sensors, thereby resolving the corresponding technical issues raised in the background section.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is: a civil aviation metrology instrument data management system based on intelligent sensors, including a data acquisition unit, an attenuation assessment unit, a status judgment unit, and a management correction unit; The data acquisition unit is used to collect basic information, actual usage status data, environmental parameter data and maintenance data of the equipment in real time, and store them in association according to the time axis to form a data chain of the entire life cycle of the equipment, and send them to the attenuation assessment unit. The basic information includes the equipment model, serial number, and initial calibration date; the actual usage status data includes cumulative runtime, load intensity, and working mode; the environmental parameter data includes temperature, humidity, vibration, and corrosive gas concentration; and the maintenance data includes fault type, maintenance time, replaced parts, and post-maintenance performance test results. Configure an intelligent sensor network, integrating current sensors, vibration sensors, temperature and humidity sensors, and gas sensors, which are used to collect the equipment's operating current, vibration spectrum, ambient temperature and humidity, and corrosive gas concentration, respectively. Outlier removal, missing value filling, and timestamp alignment are performed on the collected raw data to ensure data integrity. The basic information of the equipment, actual usage status data, environmental parameter data, and maintenance data are stored in association according to the time axis to form a data chain for the entire life cycle of the equipment. The attenuation assessment unit is used to acquire the equipment's full lifecycle data chain, extract the equipment's cumulative usage time, usage frequency, environmental parameter deviation, and maintenance type from the equipment's full lifecycle data chain, construct an equipment performance attenuation assessment model using a weighted scoring method, calculate the equipment performance attenuation score, quantify the actual performance attenuation degree of the equipment based on the equipment performance attenuation score, output the current performance attenuation percentage of the equipment, and send it to the status judgment unit. The cumulative usage time refers to the total working time of the equipment from when it was put into use to the current moment; the usage frequency refers to the number of times the equipment is used per unit time; the environmental parameter deviation refers to the difference between the actual environmental parameters and the standard environmental parameters; and the maintenance type is classified according to the different maintenance contents. The status judgment unit is used to obtain the current performance degradation percentage of the device, compare it with a preset performance threshold, determine the current status of the device based on the comparison result, and generate an adjustment instruction for dynamically updating the effective service life, which is then sent to the management correction unit. The management correction unit is used to obtain adjustment instructions, establish a mapping relationship between the current performance degradation percentage of the equipment and the effective service life adjustment coefficient using a linear model, calculate the effective service life adjustment coefficient, dynamically update the effective service life using a linear correction algorithm, and generate verification task reminders, which are pushed to the person in charge via SMS and email.

[0007] As a preferred embodiment, the specific process for determining the current performance degradation percentage of the output device is as follows: S101. Obtain the device's full lifecycle data chain, and extract the device's cumulative usage time T, usage frequency F, and environmental parameter deviation from the device's full lifecycle data chain. And the repair type Y; The cumulative usage time T is obtained by cumulative recording through a timing sensor; The frequency F is obtained by cumulative recording using a counting sensor; Environmental parameter deviation The environmental parameter deviation is calculated by collecting actual environmental parameters using environmental sensors and comparing them with standard environmental parameters. The calculation formula is: ; Where E represents the actual environmental parameters; These are standard environmental parameters; The repair type Y is determined based on the repair records, and different repair types correspond to different impact coefficients. S102. A weighted scoring method is used to construct a model for evaluating equipment performance degradation, and the equipment performance degradation score S is calculated using the following formula: ; in, Weighting based on cumulative usage time Weighting based on frequency of use Weights for environmental parameter deviations As the weight of the repair type, and ; A scoring function for cumulative usage time. For the scoring function of usage frequency, This is a scoring function for the deviation of environmental parameters. A scoring function for repair types; S103. Based on the equipment performance degradation score S, quantify the actual degree of equipment performance degradation and output the current performance degradation percentage D. The formula is as follows: ; in, This represents the maximum value of the equipment performance degradation score.

[0008] As a preferred method, the specific process for generating adjustment instructions for dynamically updating the effective usage period is as follows: S201. Obtain the current performance degradation percentage of the device; S202. Based on the current performance degradation percentage of the device, combined with a preset performance threshold. The system performs a comparison, determines the current status of the device based on the comparison results, and generates an adjustment instruction to dynamically update the effective usage period as follows: like If the equipment performance is acceptable, the equipment is determined to be in good condition, and an adjustment instruction to extend its effective service life is generated. The adjusted effective service life is... ,in, This refers to the original effective service life of the equipment. It extends the duration; like If this indicates a significant decline in equipment performance, the equipment is deemed to be in poor condition, and an adjustment command to shorten its effective service life is generated. The adjusted effective service life is... ,in, It means shortening the duration.

[0009] As a preferred method, the specific process for generating verification task reminders is as follows: S301. Obtain the adjustment instruction, get the adjusted effective service life, establish the mapping relationship between the current performance degradation percentage of the equipment and the effective service life adjustment coefficient using a linear model, and calculate the effective service life adjustment coefficient. Its formula is: ,in, The attenuation coefficient is, and ; S302. Based on the effective service life correction coefficient, the effective service life is dynamically updated using a linear correction algorithm to obtain the updated next verification time. Its formula is: ,in, The original scheduled next inspection time for the equipment; S303. Based on the updated next verification time, generate a verification task reminder and send the generated verification task reminder to the person in charge according to the preset SMS and email templates.

[0010] Preferably, the verification task reminder includes equipment information and the updated next verification time.

[0011] A method for managing civil aviation metrology instrument data based on intelligent sensors includes the following steps: Step 1: Configure equipment status sensors, environmental monitoring devices, fault recording interfaces and data fusion engines to collect basic equipment information, actual usage status data, environmental parameter data and maintenance data in real time, and store them in association according to the time axis to form a data chain for the entire life cycle of the equipment. Step 2: Obtain the equipment's full lifecycle data chain. Extract the equipment's cumulative usage time, usage frequency, environmental parameter deviation, and maintenance type from the equipment's full lifecycle data chain. Use a weighted scoring method to construct an equipment performance degradation assessment model, calculate the equipment performance degradation score, and quantify the actual performance degradation degree of the equipment based on the equipment performance degradation score, outputting the current performance degradation percentage of the equipment. Step 3: Obtain the current performance degradation percentage of the device, compare it with the preset performance threshold, determine the current status of the device based on the comparison result, and generate an adjustment instruction to dynamically update the effective service life. Step 4: Obtain adjustment instructions, establish a mapping relationship between the current performance degradation percentage of the equipment and the effective service life adjustment coefficient using a linear model, calculate the effective service life adjustment coefficient, dynamically update the effective service life using a linear correction algorithm, and generate a verification task reminder, which is pushed to the person in charge via SMS and email.

[0012] Compared with existing technologies, the beneficial effects of this invention are: by collecting basic equipment information, actual usage status data, environmental parameter data, and maintenance data in real time, and storing them in association according to a timeline, a full lifecycle data chain for the equipment is formed. From this data chain, the cumulative usage time, usage frequency, environmental parameter deviation, and maintenance type are extracted to construct an equipment performance degradation assessment model, calculate the equipment performance degradation score, quantify the actual degree of performance degradation, output the current percentage of performance degradation, compare it against a preset performance threshold, determine the current state of the equipment, generate adjustment instructions for dynamically updating the effective service life, and establish the equipment... The mapping relationship between the current performance degradation percentage and the effective service life adjustment coefficient is used to calculate the effective service life adjustment coefficient. A linear correction algorithm is used to dynamically update the effective service life and generate verification task reminders, which are pushed to the person in charge via SMS and email. This allows for dynamic tracking of the actual usage status of the equipment, quantification of the degree of performance degradation, and dynamic adjustment of the effective service life based on performance degradation and maintenance status. This improves the scientificity and safety of civil aviation metrology instrument management. At the same time, the effective service life of the equipment is dynamically adjusted based on the evaluation results to ensure that the equipment is always in a safe and reliable operating condition. This avoids the safety hazards caused by exceeding the service life and prevents the waste of resources caused by premature scrapping. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the overall structure of a preferred embodiment of the present invention; Figure 2 This is a schematic diagram of the data management system structure shown in this invention. Detailed Implementation

[0014] 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, and 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.

[0015] Embodiments of the present invention: Please refer to Figures 1 to 2 As shown, the civil aviation metrology instrument data management system based on intelligent sensors includes a data acquisition unit, a decay assessment unit, a status judgment unit, and a management correction unit. The data acquisition unit is used to collect basic information, actual usage status data, environmental parameter data and maintenance data of the equipment in real time, and store them in association according to the time axis to form a data chain of the entire life cycle of the equipment, and send them to the attenuation assessment unit. Basic information includes equipment model, serial number, and initial calibration date; actual usage status data includes cumulative runtime, load intensity, and operating mode; environmental parameter data includes temperature, humidity, vibration, and corrosive gas concentration; and maintenance data includes fault type, maintenance time, replaced parts, and post-maintenance performance test results. Configure an intelligent sensor network, integrating current sensors, vibration sensors, temperature and humidity sensors, and gas sensors, which are used to collect the equipment's operating current, vibration spectrum, ambient temperature and humidity, and corrosive gas concentration, respectively. Outlier removal, missing value filling, and timestamp alignment are performed on the collected raw data to ensure data integrity. The basic information of the equipment, actual usage status data, environmental parameter data, and maintenance data are stored in association according to the time axis to form a data chain for the entire life cycle of the equipment. The degradation assessment unit is used to acquire the equipment's full lifecycle data chain, extract the equipment's cumulative usage time, usage frequency, environmental parameter deviation, and maintenance type from the equipment's full lifecycle data chain, construct the equipment performance degradation assessment model using a weighted scoring method, calculate the equipment performance degradation score, quantify the actual performance degradation degree of the equipment based on the equipment performance degradation score, output the current performance degradation percentage of the equipment, and send it to the status judgment unit. Cumulative usage time refers to the total working time of the equipment from when it was put into use to the present moment; usage frequency refers to the number of times the equipment is used per unit time; environmental parameter deviation refers to the difference between actual environmental parameters and standard environmental parameters; and maintenance type is classified according to the different maintenance contents. The status judgment unit is used to obtain the current performance degradation percentage of the device, compare it with the preset performance threshold, judge the current status of the device based on the comparison result, and generate an adjustment instruction for dynamically updating the effective service life, which is then sent to the management correction unit. The management correction unit is used to obtain adjustment instructions, establish a mapping relationship between the current performance degradation percentage of the equipment and the effective service life adjustment coefficient using a linear model, calculate the effective service life adjustment coefficient, dynamically update the effective service life using a linear correction algorithm, and generate verification task reminders, which are pushed to the person in charge via SMS and email. The calibration task reminder includes equipment information and the updated next calibration time.

[0016] The specific process for determining the current performance degradation percentage of the output device is as follows: S101. Obtain the device's full lifecycle data chain, and extract the device's cumulative usage time T, usage frequency F, and environmental parameter deviation from the device's full lifecycle data chain. And the repair type Y; The cumulative usage time T is obtained by cumulative recording through a timing sensor; The frequency F is obtained by cumulative recording using a counting sensor; Environmental parameter deviation The environmental parameter deviation is calculated by collecting actual environmental parameters using environmental sensors and comparing them with standard environmental parameters. The calculation formula is: ; Where E represents the actual environmental parameters; These are standard environmental parameters; The repair type Y is determined based on the repair records, and different repair types correspond to different impact coefficients. S102. A weighted scoring method is used to construct a model for evaluating equipment performance degradation, and the equipment performance degradation score S is calculated using the following formula: ; in, Weighting based on cumulative usage time Weighting for frequency of use Weights for environmental parameter deviations As the weight of the repair type, and ; A scoring function for cumulative usage time. For the scoring function of usage frequency, This is a scoring function for the deviation of environmental parameters. A scoring function for repair types; S103. Based on the equipment performance degradation score S, quantify the actual degree of equipment performance degradation and output the current performance degradation percentage D. The formula is as follows: ; in, This represents the maximum value of the equipment performance degradation score.

[0017] The specific process for generating adjustment instructions that dynamically update the effective usage period is as follows: S201. Obtain the current performance degradation percentage of the device; S202. Based on the current performance degradation percentage of the device, combined with a preset performance threshold. The system performs a comparison, determines the current status of the device based on the comparison results, and generates an adjustment instruction to dynamically update the effective usage period as follows: like If the equipment performance is acceptable, the equipment is determined to be in good condition, and an adjustment instruction to extend its effective service life is generated. The adjusted effective service life is... ,in, This refers to the original effective service life of the equipment. It extends the duration; like If this indicates a significant decline in equipment performance, the equipment is deemed to be in poor condition, and an adjustment command to shorten its effective service life is generated. The adjusted effective service life is... ,in, It means shortening the duration.

[0018] The specific process for generating verification task reminders is as follows: S301. Obtain the adjustment instruction, get the adjusted effective service life, establish the mapping relationship between the current performance degradation percentage of the equipment and the effective service life adjustment coefficient using a linear model, and calculate the effective service life adjustment coefficient. Its formula is: ,in, The attenuation coefficient is, and ; S302. Based on the effective service life correction coefficient, the effective service life is dynamically updated using a linear correction algorithm to obtain the updated next verification time. Its formula is: ,in, The original scheduled next inspection time for the equipment; S303. Based on the updated next verification time, generate a verification task reminder and send the generated verification task reminder to the person in charge according to the preset SMS and email templates.

[0019] A method for managing civil aviation metrology instrument data based on intelligent sensors includes the following steps: Step 1: Configure equipment status sensors, environmental monitoring devices, fault recording interfaces, and data fusion engines to collect basic equipment information, actual usage status data, environmental parameter data, and maintenance data in real time. Perform outlier removal, missing value filling, and timestamp alignment on the collected raw data to ensure data integrity. Then, store the basic equipment information, actual usage status data, environmental parameter data, and maintenance data in association according to the timeline to form a data chain for the entire equipment lifecycle. Step 2: Obtain the equipment's full lifecycle data chain. Extract the equipment's cumulative usage time, usage frequency, environmental parameter deviation, and maintenance type from the equipment's full lifecycle data chain. Use a weighted scoring method to construct an equipment performance degradation assessment model, calculate the equipment performance degradation score, and quantify the actual performance degradation degree of the equipment based on the equipment performance degradation score, outputting the current performance degradation percentage of the equipment. Step 3: Obtain the current performance degradation percentage of the device, compare it with the preset performance threshold, determine the current status of the device based on the comparison result, and generate an adjustment instruction to dynamically update the effective service life. Step 4: Obtain adjustment instructions, establish a mapping relationship between the current performance degradation percentage of the equipment and the effective service life adjustment coefficient using a linear model, calculate the effective service life adjustment coefficient, dynamically update the effective service life using a linear correction algorithm, and generate a verification task reminder, which is pushed to the person in charge via SMS and email.

[0020] By collecting basic equipment information, actual usage status data, environmental parameter data, and maintenance data in real time and storing them in a timeline, a full lifecycle data chain for the equipment is formed. From this data chain, the cumulative usage time, usage frequency, environmental parameter deviations, and maintenance types are extracted to construct an equipment performance degradation assessment model. This model calculates the equipment performance degradation score, quantifies the actual degree of performance degradation, and outputs the current percentage of performance degradation. A comparison is made with a preset performance threshold to determine the current status of the equipment, generating a dynamic update instruction for the effective service life. A mapping relationship is established between the current percentage of performance degradation and the effective service life adjustment coefficient. The effective service life adjustment coefficient is calculated, and a linear correction algorithm is used to dynamically update the effective service life. Verification task reminders are generated and pushed to the responsible person via SMS and email. This allows for dynamic tracking of the actual usage status of the equipment, quantification of performance degradation, and dynamic adjustment of the effective service life based on performance degradation and maintenance conditions. This improves the scientific and safe management of civil aviation metrology instruments. Furthermore, by dynamically adjusting the equipment's service life based on the assessment results, it ensures that the equipment is always in a safe and reliable operating condition, avoiding safety hazards caused by exceeding the service life and preventing resource waste caused by premature scrapping.

[0021] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value.

[0022] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation. In the two embodiments provided in this application, it should be understood that the disclosed apparatus and system can be implemented in other ways; for example, the apparatus embodiments described above are merely illustrative, and the division of modules is merely a logical functional division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed; furthermore, the coupling or direct coupling or communication connection between the shown or discussed mutuals can be through some interfaces, and the indirect coupling or communication connection between the apparatus or modules can be electrical, mechanical or other forms. The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A civil aviation metrology instrument data management system based on intelligent sensors, characterized in that, It includes a data acquisition unit, an attenuation assessment unit, a status judgment unit, and a management correction unit; The data acquisition unit is used to collect basic information, actual usage status data, environmental parameter data and maintenance data of the equipment in real time. It performs outlier removal, missing value filling and timestamp alignment on the collected raw data to ensure data integrity. It also stores the basic information, actual usage status data, environmental parameter data and maintenance data in association according to the time axis to form a data chain of the entire life cycle of the equipment and sends it to the attenuation assessment unit. The attenuation assessment unit is used to acquire the equipment's full lifecycle data chain, extract the equipment's cumulative usage time, usage frequency, environmental parameter deviation, and maintenance type from the equipment's full lifecycle data chain, construct an equipment performance attenuation assessment model using a weighted scoring method, calculate the equipment performance attenuation score, quantify the actual performance attenuation degree of the equipment based on the equipment performance attenuation score, output the current performance attenuation percentage of the equipment, and send it to the status judgment unit. The status judgment unit is used to obtain the current performance degradation percentage of the device, compare it with a preset performance threshold, determine the current status of the device based on the comparison result, and generate an adjustment instruction for dynamically updating the effective service life, which is then sent to the management correction unit. The management correction unit is used to obtain adjustment instructions, establish a mapping relationship between the current performance degradation percentage of the equipment and the effective service life adjustment coefficient using a linear model, calculate the effective service life adjustment coefficient, dynamically update the effective service life using a linear correction algorithm, and generate verification task reminders, which are pushed to the person in charge via SMS and email.

2. The civil aviation metrology instrument data management system based on intelligent sensors according to claim 1, characterized in that, The basic information includes the equipment model, serial number, and initial calibration date. The actual usage status data includes cumulative runtime, load intensity, and working mode. The environmental parameter data includes temperature, humidity, vibration, and corrosive gas concentration. The maintenance data includes fault type, maintenance time, replaced parts, and post-maintenance performance test results. The calibration task reminder includes equipment information and the updated next calibration time.

3. The civil aviation metrology instrument data management system based on intelligent sensors according to claim 2, characterized in that, The specific process for determining the current performance degradation percentage of the output device is as follows: S101. Obtain the device's full lifecycle data chain, and extract the device's cumulative usage time T, usage frequency F, and environmental parameter deviation from the device's full lifecycle data chain. And the maintenance type Y, where E is the actual environmental parameter. These are standard environmental parameters; S102. A weighted scoring method is used to construct a model for evaluating equipment performance degradation, and the equipment performance degradation score S is calculated using the following formula: ； in, , , , All are weights, and ; A scoring function for cumulative usage time; A scoring function for usage frequency; A scoring function for the deviation of environmental parameters; A scoring function for repair types; S103. Based on the equipment performance degradation score S, quantify the actual degree of equipment performance degradation and output the current performance degradation percentage D. The formula is as follows: ； in, This represents the maximum value of the equipment performance degradation score.

4. The civil aviation metrology instrument data management system based on intelligent sensors according to claim 3, characterized in that, The specific process for generating adjustment instructions that dynamically update the effective usage period is as follows: S201. Obtain the current performance degradation percentage of the device; S202. Based on the current performance degradation percentage of the device, combined with a preset performance threshold. The system performs a comparison, determines the current status of the device based on the comparison results, and generates an adjustment instruction to dynamically update the effective usage period as follows: like If the equipment is determined to be in good condition, an adjustment command to extend its effective service life is generated, and the adjusted effective service life is... ,in, This refers to the original effective service life of the equipment. It extends the duration; like If the device is determined to be in poor condition, an adjustment command is generated to shorten its effective service life. The adjusted effective service life is... ,in, It means shortening the duration.

5. The civil aviation metrology instrument data management system based on intelligent sensors according to claim 4, characterized in that, The specific process for generating verification task reminders is as follows: S301. Obtain the adjustment instruction, get the adjusted effective service life, establish the mapping relationship between the current performance degradation percentage of the equipment and the effective service life adjustment coefficient using a linear model, and calculate the effective service life adjustment coefficient. Its formula is: ,in, The attenuation coefficient is, and ; S302. Based on the effective service life correction coefficient, the effective service life is dynamically updated using a linear correction algorithm to obtain the updated next verification time. Its formula is: ,in, The original scheduled next inspection time for the equipment; S303. Based on the updated next verification time, generate a verification task reminder and send the generated verification task reminder to the person in charge according to the preset SMS and email templates.

6. A method for managing civil aviation metrology instrument data based on intelligent sensors, applied to the civil aviation metrology instrument data management system based on intelligent sensors as described in any one of claims 1-5, characterized in that, Includes the following steps: Step 1: Collect basic information, actual usage status data, environmental parameter data, and maintenance data of the equipment in real time. Remove outliers, fill in missing values, and align timestamps on the collected raw data to ensure data integrity. Then, store the basic information, actual usage status data, environmental parameter data, and maintenance data in association according to the timeline to form a data chain for the entire life cycle of the equipment. Step 2: Obtain the equipment's full lifecycle data chain. Extract the equipment's cumulative usage time, usage frequency, environmental parameter deviation, and maintenance type from the equipment's full lifecycle data chain. Use a weighted scoring method to construct an equipment performance degradation assessment model, calculate the equipment performance degradation score, and quantify the actual performance degradation degree of the equipment based on the equipment performance degradation score, outputting the current performance degradation percentage of the equipment. Step 3: Obtain the current performance degradation percentage of the device, compare it with the preset performance threshold, determine the current status of the device based on the comparison result, and generate an adjustment instruction to dynamically update the effective service life. Step 4: Obtain adjustment instructions, establish a mapping relationship between the current performance degradation percentage of the equipment and the effective service life adjustment coefficient using a linear model, calculate the effective service life adjustment coefficient, dynamically update the effective service life using a linear correction algorithm, and generate a verification task reminder, which is pushed to the person in charge via SMS and email.

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