Multivariable intelligent monitoring method and system for aviation obstruction beacon
Through the multivariate intelligent monitoring method, multiple monitoring variables of aviation obstacle lights are used to construct a monitoring matrix, the maximum eigenvalue of the covariance matrix is calculated, fault determination and lighting adjustment are carried out, and problems such as rough power consumption management, single diagnosis information, and lag in fault discovery in the existing aviation obstacle light management are solved, and efficient and fine aviation obstacle light management is achieved.
Patent Information
- Application Number
- CN202510594239.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-09
- Publication Date
- 2025-06-06
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The existing aviation obstacle light management methods have problems such as rough power consumption management, single diagnosis information, lag in fault discovery, and low accuracy in fault judgment.
Multivariable intelligent monitoring method is adopted to obtain the primary monitoring variable of the aviation obstacle light and calculate the secondary monitoring variable, build the monitoring matrix and calculate the maximum eigenvalue of the covariance matrix, and perform fault determination and light brightness adjustment.
It realizes comprehensive real-time monitoring and grid-based fine management of the working status of aviation obstacle lights, improves the accuracy of fault judgment and power utilization, and ensures that the aviation obstacle lights are operating normally at all times.
Smart Images
Figure CN120103772A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of aviation obstruction light monitoring, and in particular, relates to a multivariable intelligent monitoring method and system for aviation obstruction lights. Background Art
[0002] With the vigorous development of aviation, how to ensure flight safety has become the research focus of aviation industry technicians. Aviation obstruction lights, as important safety warning equipment in the field of low-altitude flight, play an important role in the safe operation of aircraft. The management of traditional aviation obstruction lights mostly relies on manual inspections, which have problems such as blind spots in monitoring, low inspection efficiency, high labor costs, and easy misreading and omissions. The above problems not only make it impossible to grasp the working status of many aviation obstruction lights in real time, but also cause serious lags from the occurrence to the discovery of faults, posing a potential threat to low-altitude aircraft during the failure of the lamps. In addition, all lamps are operated uniformly with fixed power, which also causes a lot of electricity to be wasted.
[0003] In addition, when it is necessary to troubleshoot aviation obstruction lights, maintenance personnel often lack a systematic and scientific diagnostic mechanism and can only adopt a rigid method of checking and verifying one by one. Especially in scenarios such as complex airports and high-rise buildings with a large number of lamps, it is very time-consuming and laborious to locate the fault point.
[0004] Therefore, in order to meet the current development needs of high efficiency, energy saving and safety, the industry is in urgent need of a new technical solution to achieve all-round real-time monitoring and grid-based fine management of the working status of aviation obstruction lights. Summary of the invention
[0005] The present invention provides an intelligent monitoring and management technology for aviation obstruction lights to solve the technical problems of rough power consumption management, single diagnostic information, delayed fault detection and low fault judgment accuracy in current aviation obstruction lights.
[0006] In order to achieve the above object, the present invention provides a multivariable intelligent monitoring method for aviation obstruction lights, comprising the following steps: S1: The monitoring system obtains primary monitoring variables of multiple aviation obstruction lights, wherein the primary monitoring variables include ambient brightness, operating voltage, operating brightness, operating current and lampshade temperature, wherein the corresponding secondary monitoring variables are calculated for the operating voltage, operating brightness and operating current respectively; S2: Use the same secondary monitoring variables of all aviation obstruction lights as sub-columns to construct a monitoring matrix, calculate the covariance matrix of each sub-column, and calculate the maximum eigenvalue of each covariance matrix; S3: Initialize the characteristic threshold and the normal operating temperature range of the lampshade. When all the maximum characteristic values are greater than or less than their corresponding characteristic thresholds, perform a unified fault determination on all aviation obstruction lights based on whether any real-time lampshade temperature falls within the normal operating temperature range of the lampshade. Otherwise, proceed to the next step. S4: Calculate the fault coefficient of each data in the sub-column corresponding to the maximum characteristic value greater than the characteristic threshold, sort the fault coefficients, and make single fault determinations on the aviation obstruction lights corresponding to each fault coefficient in sequence based on whether the corresponding real-time lampshade temperature falls within the normal operating temperature range of the lampshade; S5: For aviation obstruction lights marked as normal, the light brightness is adaptively adjusted regularly based on the current time period and real-time ambient brightness.
[0007] Furthermore, the step S1 comprises: The monitoring system obtains the primary monitoring variables of multiple aviation obstruction lights, and the primary monitoring variables include the ambient brightness EBRI , Working voltage U , Working brightness BRI , Working current I and lampshade temperature TEM ; Each diagnostic cycle t Perform a fault diagnosis on the aviation obstruction light. t In the monitoring system, each primary monitoring variable is obtained separately data, among which M Indicates the name of the first-level monitoring variable. ; For the working voltage, working brightness and working current, the corresponding secondary monitoring variables are calculated respectively, which are: (1) From the working voltage U Calculate the voltage fluctuation rate , specifically: ; in, i Indicates the sequence number of the data in the same level monitoring variable. Indicates that the monitoring system is in the diagnosis cycle t The number of working voltage data obtained within; (2) From the working current I Calculate the current deviation rate , specifically: ; in, Indicates that the monitoring system is in the diagnosis cycle t The amount of data on the working current obtained within max {} indicates the maximum value operation. min {} indicates the minimum value operation; (3) Working brightness BRI Calculate the average working brightness , specifically: ; in, Indicates that the monitoring system is in the diagnosis cycle t The amount of data obtained for the working brightness within.
[0008] It should be noted that step S1 calculates the secondary monitoring variables through the primary monitoring variables, turning the directly obtained single feature into a continuous feature with time effect. Compared with the primary monitoring variables which are only real-time values, the secondary monitoring variables can better reflect the state changes of various variables of the aviation obstruction lights during the diagnosis cycle, thereby laying a good foundation for accurately identifying faulty aviation obstruction lights.
[0009] Furthermore, the step S2 comprises: Constructing a monitoring matrix based on secondary monitoring variables R , the monitoring matrix includes sub-columns , and , , , ,in N Indicates the number of monitored aviation obstruction lights. Indicates the serial number of the aviation obstruction light; Calculate the covariance matrix of each sub-column, specifically: (1) Calculating sub-columns The mean of all data in ,in ; (2) Calculating sub-columns The mean deviation of each data in ,in , all mean deviations constitute a N The determinant of ×1 ; (3) Calculating sub-columns The covariance matrix of ,in , T Represents the transpose operation; (4) Calculate the covariance matrix , and extract the maximum eigenvalue ,Right now , and .
[0010] It should be noted that the eigenvalue can reflect the degree of discreteness of the covariance matrix in a certain direction. Therefore, the larger the eigenvalue, the greater the degree of discreteness. Therefore, by obtaining the maximum eigenvalue, the maximum degree of discreteness of the covariance matrix can be known, which makes it easier to determine whether the aviation obstruction light is faulty.
[0011] Furthermore, the step S3 comprises: Initialize the normal operating temperature range of the lampshade ; Initialize feature thresholds , , , respectively compare and , and , and The size relationship of When all maximum eigenvalues are greater than or less than their corresponding characteristic thresholds, obtain the real-time lampshade temperature of any aviation obstruction light ,if , then all aviation obstruction lights are marked as normal, otherwise all aviation obstruction lights are marked as faulty; When one or two maximum eigenvalues are greater than their corresponding eigenvalue thresholds, step S4 is executed.
[0012] Furthermore, the step S4 comprises: The largest eigenvalue greater than the eigenthreshold The corresponding sub-column Define as an abnormal sub-column, calculate the abnormal sub-column The failure coefficient of each data , specifically: ; in, Indicates abnormal sub-column Middle Failure coefficient of each data; extract The lampshade temperature of the aviation obstruction light corresponding to the maximum failure coefficient The lampshade temperature of the aviation obstruction light corresponding to the minimum failure coefficient ; when When the aviation obstruction light corresponding to the minimum fault coefficient is marked as faulty, the real-time lampshade temperature of the aviation obstruction light corresponding to each fault coefficient is obtained in order from small to large. ,if , then mark the aviation obstruction light as normal, otherwise mark the aviation obstruction light as faulty, until all faulty aviation obstruction lights are found; when When the aviation obstruction light corresponding to the maximum fault coefficient is marked as faulty, the real-time lampshade temperature of the aviation obstruction light corresponding to each fault coefficient is obtained in order from large to small. ,if , then mark the aviation obstruction light as normal, otherwise mark the aviation obstruction light as faulty, until all faulty aviation obstruction lights are found.
[0013] It should be noted that the fault coefficient calculated in step S4 is essentially the deviation ratio of the abnormal sub-column. If there is a problem in a sub-column, there must be a faulty light in it, but it is not certain whether only one light is faulty or only one light is normal, and it is also uncertain whether the deviation value of the faulty light is too large or too small. For example, when only one light is normal, the fault coefficient of the normal light is the largest. At this time, if the corresponding real-time lampshade temperature is normal, then the light with the smallest fault coefficient must be faulty. Starting comparison from the side with the smallest fault coefficient can improve comparison efficiency. When only one light is faulty, although its fault coefficient is the largest, its lampshade temperature is abnormal. At this time, the lampshade temperature of the aviation obstruction light corresponding to the smallest fault coefficient is normal, that is, comparison should be started from the side with the largest fault coefficient. Step S4 improves diagnostic efficiency by running the maximum and minimum fault coefficient determination processes in parallel.
[0014] Furthermore, the step S5 comprises: Initialize the standard brightness value of aviation obstruction lights , Brightness adjustment cycle of aviation obstruction lights T 0 Night time T 1 , early morning hours T 2 , daytime T 3 , Evening T 4 And the ambient brightness threshold , , and ,and ; For aviation obstruction lights marked as normal, each interval T 0 Adjust the light brightness once, the adjustment rules are as follows: During the night time T 1 When the brightness of the aviation obstruction light is adjusted to ; When it is not night time, get the real-time ambient brightness ;and In the early morning hours T 2 or evening hours T 4 When the reference brightness of the aviation obstruction light is adjusted to ,if , then increase the brightness based on the reference brightness ,if , then increase the brightness based on the reference brightness ,if , no additional adjustment is made; During daytime T 3 When the base brightness of the aviation obstruction light is adjusted to 0, if , then increase the brightness based on the reference brightness ,if , then increase the brightness based on the reference brightness ,if , then increase the brightness based on the reference brightness ,if , then increase the brightness based on the reference brightness ,if , no additional adjustments are made.
[0015] On the other hand, the present invention also provides a multivariable intelligent monitoring system for aviation obstruction lights, comprising: Data acquisition module: The monitoring system acquires the primary monitoring variables of multiple aviation obstruction lights, wherein the primary monitoring variables include ambient brightness, operating voltage, operating brightness, operating current and lampshade temperature, wherein the corresponding secondary monitoring variables are calculated for the operating voltage, operating brightness and operating current respectively; Unified fault judgment module: construct a monitoring matrix with the same secondary monitoring variables of all aviation obstruction lights as sub-columns, calculate the covariance matrix of each sub-column, and calculate the maximum eigenvalue of each covariance matrix; initialize the characteristic threshold and the normal operating temperature range of the lampshade. When all the maximum eigenvalues are greater than or less than their corresponding characteristic thresholds, perform unified fault judgment on all aviation obstruction lights based on whether any real-time lampshade temperature falls within the normal operating temperature range of the lampshade, otherwise proceed to the next step; Single fault judgment module: calculates the fault coefficient of each data in the sub-column corresponding to the maximum characteristic value greater than the characteristic threshold, sorts the fault coefficients, and makes single fault judgments on the aviation obstruction lights corresponding to each fault coefficient in sequence based on whether the corresponding real-time lampshade temperature falls within the normal operating temperature range of the lampshade; Light adjustment module: For aviation obstruction lights marked as normal, the light brightness is regularly and adaptively adjusted based on the current time period and real-time ambient brightness.
[0016] The beneficial effects brought by the technical solution of the present invention include at least: 1. The present invention uses multi-variable information to realize intelligent monitoring of aviation obstruction lights. The monitoring system obtains the primary monitoring variables of the aviation obstruction lights. In order to improve the characteristic of the monitoring variables, the secondary monitoring variables associated with the time period are further calculated from the primary monitoring variables. Compared with the traditional method, this solution can improve the accuracy of fault judgment by monitoring and processing multi-variable information, and avoid fault judgment errors caused by the failure of a single piece of information. In addition, multi-variable monitoring can further improve the richness of information, thereby improving the fault tolerance level of the monitoring solution; 2. The present invention uses secondary monitoring variables to construct a monitoring matrix, and calculates the covariance matrix and maximum eigenvalue of each sub-column. The maximum eigenvalue is used to determine whether the corresponding secondary monitoring variable has abnormal data. In order to improve the judgment efficiency of this method, a rapid diagnosis scenario for all faults and all normal conditions of all aviation obstruction lights is designed. Compared with traditional methods, this scheme makes full use of the similarity of the same variable between the same aviation obstruction lights, and realizes rapid diagnosis through difference comparison, saving computing power costs.
[0017] 3. For the scenario of partial aviation obstruction light failure, the present invention ensures that all faulty aviation obstruction lights are found by calculating the fault coefficient of each data in the sub-column corresponding to the abnormal characteristic value and traversing the corresponding aviation obstruction lights of all fault coefficients. The accuracy of fault identification is improved by combining the overall differences of multiple aviation obstruction lights and the differences of individual aviation obstruction lights for screening. 4. The present invention realizes all-round real-time monitoring and grid-based fine management of the working status of aviation obstruction lights, and realizes fault diagnosis and rapid positioning through multi-dimensional variables, ensuring that aviation obstruction lights are always in normal operation. In addition, through intelligent control means, the brightness of normal aviation obstruction lights is regularly and adaptively adjusted, which not only flexibly adapts to specific weather conditions, but also saves a lot of electricity, reflecting the economic value of this solution. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] Figure 1 A flowchart of a multivariable intelligent monitoring method for aviation obstruction lights provided in Example 1 of the present invention; DETAILED DESCRIPTION
[0019] The present invention is further described below in conjunction with the accompanying drawings, but the present invention is not limited in any way. Any changes or substitutions made based on the teachings of the present invention belong to the protection scope of the present invention.
[0020] Example 1
[0021] like Figure 1As shown, this embodiment provides a multivariable intelligent monitoring method for aviation obstruction lights, including the following steps: S1: Obtain the primary monitoring variables of multiple aviation obstruction lights and calculate the corresponding secondary monitoring variables: The monitoring system obtains the primary monitoring variables of multiple aviation obstruction lights, and the primary monitoring variables include the ambient brightness EBRI , Working voltage U , Working brightness BRI , Working current I and lampshade temperature TEM , the unit of lampshade temperature is lux; Each diagnostic cycle t Perform a fault diagnosis on the aviation obstruction light. t In the monitoring system, each primary monitoring variable is obtained separately data, among which M Indicates the name of the first-level monitoring variable. ; In this embodiment, the diagnosis cycle t 1 hour; For the working voltage, working brightness and working current, the corresponding secondary monitoring variables are calculated respectively, which are: (1) From the working voltage U Calculate the voltage fluctuation rate , specifically: ; in, i Indicates the sequence number of the data in the same level monitoring variable. Indicates that the monitoring system is in the diagnosis cycle t The number of data of the working voltage obtained in this embodiment is =60; (2) From the working current I Calculate the current deviation rate , specifically: ; in, Indicates that the monitoring system is in the diagnosis cycle t The amount of data of the working current obtained within, in this embodiment, =3600, max {} indicates the maximum value operation. min {} indicates the minimum value operation; (3) Working brightness BRI Calculate the average working brightness , specifically: ; in, Indicates that the monitoring system is in the diagnosis cycle t The amount of data of working brightness acquired within the time period, in this embodiment, =60.
[0022] S2: Use the same secondary monitoring variables of all aviation obstruction lights as sub-columns to construct a monitoring matrix, calculate the covariance matrix of each sub-column, and calculate the maximum eigenvalue of each covariance matrix: Constructing a monitoring matrix based on secondary monitoring variables R , the monitoring matrix includes sub-columns , and , , , ,in N Indicates the number of monitored aviation obstruction lights. In this embodiment, N =4, Indicates the serial number of the aviation obstruction light and ; Calculate the covariance matrix of each sub-column, specifically: (1) Calculating sub-columns The mean of all data in ,in ; (2) Calculating sub-columns The mean deviation of each data in ,in , all mean deviations form a 4×1 determinant ; (3) Calculating sub-columns The covariance matrix of ,in , T Represents the transpose operation; (4) Calculate the covariance matrix , and extract the maximum eigenvalue ,Right now , and Since calculating the eigenvalues of a matrix is a conventional mathematical operation, it will not be described in detail in the present invention.
[0023] S3: Initialize the characteristic threshold and the normal operating temperature range of the lampshade. When all the maximum characteristic values are greater than or less than their corresponding characteristic thresholds, perform a unified fault judgment on all aviation obstruction lights based on whether any real-time lampshade temperature falls within the normal operating temperature range of the lampshade. Otherwise, proceed to the next step: Initialize the normal operating temperature range of the lampshade ; Initialize feature thresholds , and , respectively compare and , and , and The size relationship of When all maximum eigenvalues are greater than or less than their corresponding characteristic thresholds, obtain the real-time lampshade temperature of any aviation obstruction light ,if , then all aviation obstruction lights are marked as normal, otherwise all aviation obstruction lights are marked as faulty; When one or two maximum eigenvalues are greater than their corresponding eigenvalue thresholds, step S4 is executed.
[0024] S4: Calculate the fault coefficient of each data in the sub-column corresponding to the maximum characteristic value greater than the characteristic threshold, sort the fault coefficients, and make single fault judgments on the aviation obstruction lights corresponding to each fault coefficient in sequence based on whether the corresponding real-time lampshade temperature falls within the normal operating temperature range of the lampshade: The largest eigenvalue greater than the eigenthreshold The corresponding sub-column Define as an abnormal sub-column, calculate the abnormal sub-column The failure coefficient of each data , specifically: ; in, Indicates abnormal sub-column Middle Failure coefficient of each data; extract The lampshade temperature of the aviation obstruction light corresponding to the maximum failure coefficient The lampshade temperature of the aviation obstruction light corresponding to the minimum failure coefficient ; when When the maximum fault coefficient is 0, it indicates that the state of the aviation obstruction light corresponding to the maximum fault coefficient is normal. However, due to the presence of abnormal data in the sub-column, the most likely possibility is that the aviation obstruction light corresponding to the minimum fault coefficient has a fault. Mark it and check the relationship between the lampshade temperature of the corresponding aviation obstruction light and the normal operating temperature range of the lampshade one by one in the order of the fault coefficient from small to large, that is, obtain the real-time lampshade temperature of the aviation obstruction light corresponding to each fault coefficient in sequence. ,if , then mark the aviation obstruction light as normal, otherwise mark the aviation obstruction light as faulty, until all faulty aviation obstruction lights are found; when , it indicates that the state of the aviation obstruction light corresponding to the minimum fault coefficient is normal. Referring to the above operation, mark the aviation obstruction light corresponding to the maximum fault coefficient as faulty, and obtain the real-time lampshade temperature of the aviation obstruction lights corresponding to each fault coefficient in order from large to small. ,if , then mark the aviation obstruction light as normal, otherwise mark the aviation obstruction light as faulty, until all faulty aviation obstruction lights are found.
[0025] S5: For aviation obstruction lights marked as normal, the light brightness is regularly and adaptively adjusted based on the current time period and real-time ambient brightness: Initialize the standard brightness value of aviation obstruction lights , Brightness adjustment cycle of aviation obstruction lights T 0 Night time T 1 , early morning hours T 2 , daytime T 3 , Evening T 4 And the ambient brightness threshold , , and ,and ; In this embodiment, the brightness adjustment cycle T 0 1 hour, night time T 1 19:00-5:00 the next day, early morning period T 2 5:00-7:00, daytime hours T 3 7:00-17:00, evening time T 4 17:00-19:00; For aviation obstruction lights marked as normal, adjust the light brightness every 1 hour. The adjustment rules are as follows: During the night time T 1 When the brightness of the aviation obstruction light is adjusted to ; When it is not night time, get the real-time ambient brightness ; In the early morning hours T 2 or evening hoursT 4 When the reference brightness of the aviation obstruction light is adjusted to 0.5 ,if , then increase the base brightness by 0.5 ,if , then increase the base brightness by 0.25 ,if , no additional adjustment is made; During daytime T 3 When the base brightness of the aviation obstruction light is adjusted to 0, if , then increase the brightness based on the reference brightness ,if , then increase the brightness based on the reference brightness ,if , then increase the brightness based on the reference brightness ,if , then increase the brightness based on the reference brightness ,if , no additional adjustments are made.
[0026] Example 2 This embodiment provides a multivariable intelligent monitoring system for aviation obstruction lights, including the following modules: Data acquisition module: The monitoring system acquires the primary monitoring variables of multiple aviation obstruction lights, wherein the primary monitoring variables include ambient brightness, operating voltage, operating brightness, operating current and lampshade temperature, wherein the corresponding secondary monitoring variables are calculated for the operating voltage, operating brightness and operating current respectively; Unified fault judgment module: construct a monitoring matrix with the same secondary monitoring variables of all aviation obstruction lights as sub-columns, calculate the covariance matrix of each sub-column, and calculate the maximum eigenvalue of each covariance matrix; initialize the characteristic threshold and the normal operating temperature range of the lampshade. When all the maximum eigenvalues are greater than or less than their corresponding characteristic thresholds, perform unified fault judgment on all aviation obstruction lights based on whether any real-time lampshade temperature falls within the normal operating temperature range of the lampshade, otherwise proceed to the next step; Single fault judgment module: calculates the fault coefficient of each data in the sub-column corresponding to the maximum characteristic value greater than the characteristic threshold, sorts the fault coefficients, and makes single fault judgments on the aviation obstruction lights corresponding to each fault coefficient in sequence based on whether the corresponding real-time lampshade temperature falls within the normal operating temperature range of the lampshade; Light adjustment module: For aviation obstruction lights marked as normal, the light brightness is regularly and adaptively adjusted based on the current time period and real-time ambient brightness; The multivariable intelligent monitoring method for aviation obstruction lights in Example 1 is implemented through the above system.
[0027] As used herein, the word "preferred" is intended to be used as an example, instance, or illustration. Any aspect or design described herein as "preferred" is not necessarily to be construed as being more advantageous than other aspects or designs. On the contrary, the use of the word "preferred" is intended to present concepts in a specific way. The term "or" as used in this application is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless otherwise specified or clear from the context, "X uses A or B" means any one of the naturally included permutations. That is, if X uses A; X uses B; or X uses both A and B, then "X uses A or B" is satisfied in any of the foregoing examples.
[0028] Moreover, although the present disclosure has been shown and described with respect to one or implementations, those skilled in the art will think of equivalent variations and modifications based on the reading and understanding of this specification and the accompanying drawings. The present disclosure includes all such modifications and variations, and is limited only by the scope of the appended claims. In particular, with respect to the various functions performed by the above-mentioned components (such as elements, etc.), the terms used to describe such components are intended to correspond to any component (unless otherwise indicated) that performs the specified function of the component (such as it is functionally equivalent), even if the structure is not equivalent to the disclosed structure of the function in the exemplary implementation of the present disclosure shown herein. In addition, although the specific features of the present disclosure have been disclosed with respect to only one of several implementations, such features can be combined with one or other features of other implementations that may be desired and advantageous for a given or specific application. Moreover, insofar as the terms "including", "having", "containing" or their variations are used in specific embodiments or claims, such terms are intended to be included in a manner similar to the term "comprising".
[0029] The functional units in the embodiments of the present invention may be integrated into a processing module, or each unit may exist physically separately, or multiple or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. The above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc. The above-mentioned devices or systems may execute the storage method in the corresponding method embodiment.
[0030] To sum up, the above embodiment is an implementation mode of the present invention, but the implementation mode of the present invention is not limited by the embodiment. Any other changes, modifications, substitutions, combinations, and simplifications that deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.
Claims
1. A multivariable intelligent monitoring method for aviation obstruction lights, characterized in that: The following steps are involved: S1: The monitoring system obtains primary monitoring variables of multiple aviation obstruction lights, wherein the primary monitoring variables include ambient brightness, operating voltage, operating brightness, operating current and lampshade temperature, wherein the corresponding secondary monitoring variables are calculated for the operating voltage, operating brightness and operating current respectively; S2: Use the same secondary monitoring variables of all aviation obstruction lights as sub-columns to construct a monitoring matrix, calculate the covariance matrix of each sub-column, and calculate the maximum eigenvalue of each covariance matrix; S3: Initialize the characteristic threshold and the normal operating temperature range of the lampshade. When all the maximum characteristic values are greater than or less than their corresponding characteristic thresholds, perform a unified fault determination on all aviation obstruction lights based on whether any real-time lampshade temperature falls within the normal operating temperature range of the lampshade. Otherwise, proceed to the next step. S4: Calculate the fault coefficient of each data in the sub-column corresponding to the maximum characteristic value greater than the characteristic threshold, sort the fault coefficients, and make single fault determinations on the aviation obstruction lights corresponding to each fault coefficient in sequence based on whether the corresponding real-time lampshade temperature falls within the normal operating temperature range of the lampshade; S5: For aviation obstruction lights marked as normal, the light brightness is adaptively adjusted regularly based on the current time period and real-time ambient brightness.
2. The multivariable intelligent monitoring method for aviation obstruction lights according to claim 1 is characterized in that: The step S1 comprises: The monitoring system obtains the primary monitoring variables of multiple aviation obstruction lights, and the primary monitoring variables include the ambient brightness EBRI , Working voltage U , Working brightness BRI , Working current I and lampshade temperature TEM ; Each diagnostic cycle t Perform a fault diagnosis on the aviation obstruction light. t In the monitoring system, each primary monitoring variable is obtained separately data, among which M Indicates the name of the first-level monitoring variable. ; For the working voltage, working brightness and working current, the corresponding secondary monitoring variables are calculated respectively, which are: (1) From the working voltage U Calculate the voltage fluctuation rate , specifically: ； in, i Indicates the sequence number of the data in the same level monitoring variable. Indicates that the monitoring system is in the diagnosis cycle t The number of working voltage data obtained within; (2) From the working current I Calculate the current deviation rate , specifically: ； in, Indicates that the monitoring system is in the diagnosis cycle t The amount of data on the working current obtained within max {} indicates the maximum value operation. min {} indicates the minimum value operation; (3) Working brightness BRI Calculate the average working brightness , specifically: ； in, Indicates that the monitoring system is in the diagnosis cycle t The amount of data obtained for the working brightness within.
3. The multivariable intelligent monitoring method for aviation obstruction lights according to claim 2 is characterized in that: The step S2 comprises: Constructing a monitoring matrix based on secondary monitoring variables R , the monitoring matrix includes sub-columns , and , , , ,in N Indicates the number of monitored aviation obstruction lights. Indicates the serial number of the aviation obstruction light; Calculate the covariance matrix of each sub-column, specifically: (1) Calculating sub-columns The mean of all data in ,in ; (2) Calculating sub-columns The mean deviation of each data in ,in , all mean deviations constitute a N The determinant of ×1 ; (3) Calculating sub-columns The covariance matrix of ,in , T Represents the transpose operation; (4) Calculate the covariance matrix , and extract the maximum eigenvalue ,Right now , and .
4. The multivariable intelligent monitoring method for aviation obstruction lights according to claim 3 is characterized in that: The step S3 comprises: Initialize the normal operating temperature range of the lampshade ; Initialize feature thresholds , and , respectively compare and , and , and The size relationship of When all maximum eigenvalues are greater than or less than their corresponding characteristic thresholds, obtain the real-time lampshade temperature of any aviation obstruction light ,if , then all aviation obstruction lights are marked as normal, otherwise all aviation obstruction lights are marked as faulty; When one or two maximum eigenvalues are greater than their corresponding eigenvalue thresholds, step S4 is executed.
5. The multivariable intelligent monitoring method for aviation obstruction lights according to claim 4 is characterized in that: The step S4 comprises: The largest eigenvalue greater than the eigenthreshold The corresponding sub-column Define as an abnormal sub-column, calculate the abnormal sub-column The failure coefficient of each data , specifically: ； in, Indicates abnormal sub-column Middle Failure coefficient of each data; extract The lampshade temperature of the aviation obstruction light corresponding to the maximum failure coefficient The lampshade temperature of the aviation obstruction light corresponding to the minimum failure coefficient ; when When the aviation obstruction light corresponding to the minimum fault coefficient is marked as faulty, the real-time lampshade temperature of the aviation obstruction light corresponding to each fault coefficient is obtained in order from small to large. ,if , then mark the aviation obstruction light as normal, otherwise mark the aviation obstruction light as faulty, until all faulty aviation obstruction lights are found; when When the aviation obstruction light corresponding to the maximum fault coefficient is marked as faulty, the real-time lampshade temperature of the aviation obstruction light corresponding to each fault coefficient is obtained in order from large to small. ,if , then mark the aviation obstruction light as normal, otherwise mark the aviation obstruction light as faulty, until all faulty aviation obstruction lights are found.
6. The multivariable intelligent monitoring method for aviation obstruction lights according to claim 5, characterized in that: The step S5 comprises: Initialize the standard brightness value of aviation obstruction lights , Brightness adjustment cycle of aviation obstruction lights T 0. Night time T 1. Early morning hours T 2. Daytime T 3. Evening T 4 and the ambient brightness threshold , , and ,and ; For aviation obstruction lights marked as normal, each interval T 0 Adjust the light brightness once, the adjustment rules are as follows: During the night time T 1, adjust the brightness of the aviation obstruction lights to ; When it is not night time, get the real-time ambient brightness ;and In the early morning hours T 2 or evening time T At 4 o'clock, adjust the reference brightness of the aviation obstruction lights to ,if , then increase the brightness based on the reference brightness ,if , then increase the brightness based on the reference brightness ,if , no additional adjustment is made; During daytime T 3, adjust the base brightness of the aviation obstruction light to 0. , then increase the brightness based on the reference brightness ,if , then increase the brightness based on the reference brightness ,if , then increase the brightness based on the reference brightness ,if , then increase the brightness based on the reference brightness ,if , no additional adjustments are made.
7. A multivariable intelligent monitoring system for aviation obstruction lights, characterized in that: include: Data acquisition module: The monitoring system acquires the primary monitoring variables of multiple aviation obstruction lights, wherein the primary monitoring variables include ambient brightness, operating voltage, operating brightness, operating current and lampshade temperature, wherein the corresponding secondary monitoring variables are calculated for the operating voltage, operating brightness and operating current respectively; Unified fault judgment module: Use the same secondary monitoring variables of all aviation obstruction lights as sub-columns to construct a monitoring matrix, calculate the covariance matrix of each sub-column, and calculate the maximum eigenvalue of each covariance matrix; Initialize the characteristic threshold and the normal operating temperature range of the lampshade. When all the maximum characteristic values are greater than or less than their corresponding characteristic thresholds, perform a unified fault determination on all aviation obstruction lights based on whether any real-time lampshade temperature falls within the normal operating temperature range of the lampshade. Otherwise, proceed to the next step. Single fault judgment module: calculates the fault coefficient of each data in the sub-column corresponding to the maximum characteristic value greater than the characteristic threshold, sorts the fault coefficients, and makes single fault judgments on the aviation obstruction lights corresponding to each fault coefficient in sequence based on whether the corresponding real-time lampshade temperature falls within the normal operating temperature range of the lampshade; Light adjustment module: For aviation obstruction lights marked as normal, the light brightness is regularly and adaptively adjusted based on the current time period and real-time ambient brightness; To realize the multivariable intelligent monitoring method for aviation obstruction lights as described in any one of claims 1-6.