A method, system and storage medium for detecting dirt on the light outlet of a navigation light fixture

Abstract

This application relates to the field of navigational lighting, and more particularly to a method, system, and storage medium for detecting contamination at the light outlet of a navigational lighting fixture. The method comprises real-time visual capture of the light outlet surface of the navigational lighting fixture to obtain visual data; determining contamination data by comparing the visual data with preset visual threshold data; and comparing the contamination data with preset contamination threshold data. If the contamination data exceeds the threshold data, a corresponding alarm or indication is generated to a worker. This application improves maintenance efficiency.

Description

Technical Field

[0001] The present application relates to the field of navigation aid lamps, and in particular to a method and system for detecting dirt on the light outlet of a navigation aid lamp and a storage medium thereof. Background Art

[0002] Navigation lights play a vital role in airports and other aviation venues. There are one on each side of the runway, which plays a key role in the approach and landing of aircraft. They are the guiding signs for aircraft landing, especially for the safety of aircraft flying at night.

[0003] However, after long-term use, scale and dirt may accumulate on the light outlet surface of the navigation lights, causing the light of the navigation light station to be scattered or blocked, thereby affecting the lighting effect and recognition of the lights. The number of navigation light stations in airports is extremely large. If manual inspection is carried out, it will be extremely wasteful and have little effect. Summary of the Invention

[0004] In order to improve the problem of dirt accumulation on the light outlet surface of a navigation lamp and difficulty in timely detection, the present application provides a method, system and storage medium for detecting dirt on the light outlet of a navigation lamp.

[0005] This application provides a method, system, and storage medium for detecting contamination at the light outlet of a navigation light fixture, which employ the following technical solutions:

[0006] A method for detecting dirt on a light outlet of a navigation light fixture, comprising:

[0007] Real-time visual capture of the light outlet surface of the navigation light to obtain visual data;

[0008] Determining dirt data based on the visual data and preset visual threshold data;

[0009] The dirtiness data is compared with preset dirtiness threshold data, and if the dirtiness data exceeds the dirtiness threshold data, a corresponding alarm is generated or an indication is given to the staff.

[0010] By adopting the above technical solution, the degree of dirt on the light outlet surface of the navigation light can be judged by judging the light intensity data, image recognition data, etc. If it is judged that the degree of dirt affects the navigation effect, a corresponding alarm is output to the staff to remind them to clean it, thereby realizing automatic detection and automatic reminder, reducing the probability of staff running in vain during regular cleaning, so that the staff can work effectively every time they find the corresponding navigation light, improving work efficiency, improving maintenance efficiency, and reducing maintenance costs.

[0011] Optionally, the visual capture of the light outlet surface of the navigation light to obtain visual data includes:

[0012] Perform visual image recognition on the light outlet surface of the navigation light to obtain a recognition image;

[0013] Analyzing and processing the recognition image to obtain recognition image data;

[0014] The recognition difference data is determined by comparing the recognition image data with the preset recognition image threshold data, and the recognition difference data is compared with the preset allowable difference threshold data. If the recognition difference data exceeds the allowable difference threshold data, a corresponding alarm is generated or an indication is given to the staff.

[0015] By adopting the above technical solution, foreign matter on the surface of the light outlet of the navigation light is identified through image recognition. When the impact of the foreign matter on the light intensity output by the navigation light exceeds the allowable range, that is, the recognition difference data exceeds the allowable difference threshold data, the staff will be reminded to clean it up.

[0016] Optionally, the visual capture of the light outlet surface of the navigation light to obtain visual data includes:

[0017] Emitting a detection light to the light outlet surface of the navigation light, wherein the light intensity of the emitted detection light is the light intensity corresponding to the preset emission light intensity data;

[0018] Receiving reflected light reflected from the light outlet surface of the navigation light, and detecting the light intensity of the reflected light to obtain reflected light intensity data;

[0019] Determine reflection value data using the emitted light intensity data and the reflected light intensity data;

[0020] The reflection value data is compared with the preset reflection threshold data. If the reflection value data exceeds the reflection threshold data, a corresponding alarm is generated or an indication is given to the staff.

[0021] By adopting the above technical solution, the reflection value is detected to determine the loss of light intensity caused by dirt on the light outlet surface of the navigation light. If the loss is too large, resulting in a too low reflection value, that is, the reflection value data exceeds the reflection threshold data, the staff will be reminded to clean the light outlet surface of the navigation light. This method has high sensitivity and accuracy, can effectively detect the contamination of the light outlet surface, and is not affected by the background. It can effectively detect the object to be detected and is not easily affected by external interference.

[0022] Optionally, the visual capture of the light outlet surface of the navigation light to obtain visual data includes:

[0023] Receive the light entering from the light outlet of the navigation light and detect the light intensity data;

[0024] The light intensity difference data is determined by comparing the detected light intensity data with the preset reference light intensity data, and the light intensity difference data is compared with the preset light intensity difference threshold data. If the light intensity difference data exceeds the light intensity difference threshold data, a corresponding alarm is generated or an indication is given to the staff.

[0025] By adopting the above technical solution, the condition of the light outlet is judged by using a device with photosensitivity to detect the light intensity under normal circumstances and the difference in light intensity that is weakened after the surface of the light outlet of the navigation lamp is adhered to mud. This method also has high sensitivity and accuracy, can effectively detect the contamination of the light outlet surface, and issue early warning signals in time, thereby improving the maintenance efficiency and reliability of the navigation lamp.

[0026] Optionally, determining the dirt data by comparing the visual data with preset visual threshold data includes:

[0027] Preset the fluctuation table data of accumulated water;

[0028] Collecting external wind force to obtain external wind force data, reading preset flight status data, and determining wind force fluctuation data based on the external wind force data and the flight status data;

[0029] Determining water accumulation fluctuation data by using the wind force fluctuation data and the water accumulation fluctuation table data;

[0030] Determine whether the reflected light is affected by the accumulation water fluctuations by using the reflected light intensity data and the accumulation water fluctuation data; if so, determine reflected restored light intensity data by using the reflected light intensity data and the accumulation water fluctuation data; the reflected restored light intensity data is light intensity data after removing the influence of the accumulation water fluctuations on the light; and determine the reflection value data by using the reflected restored light intensity data and the emitted light intensity data to participate in the data analysis and processing;

[0031] Whether the reflected light is affected by the water accumulation fluctuation is determined by the detected light intensity data and the water accumulation fluctuation data. If so, the detected restored light intensity data is determined by the detected light intensity data and the water accumulation fluctuation data. The detected restored light intensity data is the light intensity data after removing the influence of the water accumulation fluctuation on the light. The detected restored light intensity data replaces the detected light intensity data and is compared and judged with the reference value light intensity data to participate in data analysis and processing.

[0032] By adopting the above technical solution, if there is water on the surface of the light outlet of the navigation lamp, and the accumulated water produces ripples under the blowing of the wind, then no matter whether the light is entering or emitting, the constantly changing depth and surface angle of the accumulated water when it fluctuates will cause scattering of light, thereby causing deviation in the judgment of light intensity and deviation in the judgment of the degree of dirtiness on the light outlet surface of the navigation lamp. Therefore, it is necessary to remove the error caused by the fluctuation of accumulated water from the detection of light intensity data, and then calculate the light intensity data to obtain a more accurate degree of dirtiness on the light outlet surface of the navigation lamp, thereby reducing the influence of the fluctuation of accumulated water on the light intensity detection and improving accuracy.

[0033] Optionally, determining the dirt data by comparing the visual data with preset visual threshold data includes:

[0034] Preset shadow time threshold data and shadow threshold data, collect the variation of the recognition image data or the reflected light intensity data or the detected light intensity data, and obtain variation data;

[0035] Determine variation detection data by using the shadow time threshold data and the variation data;

[0036] Determine change count data by using the change amount detection data and the shadow threshold data, and record the number of times the change amount detection data exceeds the shadow threshold data by using the change count data;

[0037] If the time data between adjacent change times data is less than the preset interval time threshold data, the corresponding change amount data is removed so that the corresponding recognition image data or the reflected light intensity data or the detected light intensity data does not participate in the data analysis and processing.

[0038] By adopting the above technical solution, when an airplane flies by or large impurities float by, the obstruction of light may affect the detected light intensity, eliminating the influence of shadows on the detected light intensity data, and improving the accuracy of detecting the degree of dirtiness on the light outlet surface of the navigation light.

[0039] Optionally, determining the dirt data by using the visual data and preset visual threshold data further includes:

[0040] Determine the area data and the position data of the obstruction by using the shadow threshold data and the recognition image data;

[0041] If the time data of the obstruction position data and the obstruction area data exceeds the preset shadow determination threshold data, and the obstruction area data has not changed, and the obstruction position data changes continuously within the time corresponding to the shadow time threshold data, a corresponding alarm or instruction is generated to the staff.

[0042] By adopting the above technical solution, the detection of solid obstructions is improved, so that mud and solid obstructions can be distinguished. For example, if mud and other sticky objects stick to the surface of the light outlet of the navigation light, the staff needs to carry special instruments to clean it, but if it is a solid obstruction, it only needs to be removed, which improves work efficiency. In the case of non-solid obstructions, the staff is reminded to carry special instruments to clean it, and if it is a solid obstruction, any staff member can be notified, that is, the staff closest to the corresponding navigation light can be directly notified to remove the obstruction, which is convenient and quick.

[0043] Optionally, a system comprising:

[0044] The lamp body is waterproof, dustproof, weather-resistant and corrosion-resistant;

[0045] A prism, serving as a light outlet for focusing light, is provided on the lamp body;

[0046] A light source module is used to provide light energy for navigation assistance;

[0047] a dirt detection module, configured to detect dirt on the prism and obtain a detection signal;

[0048] The processing module receives the detection signal, processes the detection result into a data signal, and outputs a corresponding result signal;

[0049] The upper monitoring module receives the result signal and outputs an alarm signal if the result signal received indicates that the degree of dirtiness of the prism has exceeded a preset threshold.

[0050] By adopting the above technical solution, the lamp body, prism and light source module are used for navigation assistance, the dirt detection module is used to detect the surface of the prism, the processing module is used to process and calculate the detection signal, and then the upper monitoring module is used to remotely inform the staff, which is convenient and fast, so that the staff can perform effective cleaning every time, reduce the probability of empty running during scheduled cleaning, and improve maintenance efficiency.

[0051] Optionally, the dirt detection module includes an optical detection submodule, a reflection detection submodule, or a photosensitivity detection submodule, the optical detection submodule is used to perform visual image recognition and judgment on the prism, the reflection detection submodule is used to detect and judge the reflection value of the prism, and the photosensitivity detection submodule is used to detect and judge the light intensity of the prism.

[0052] By adopting the above technical solution, the optical detection submodule is used to realize the detection and judgment of image recognition, the reflection detection submodule is used to realize the detection and judgment of the reflection value of infrared light, and the photosensitive detection submodule is used to realize the detection and judgment of the light intensity in the current state and normal conditions. Different submodules are used to realize different emphasis directions, or they are used simultaneously to greatly improve the recognition accuracy.

[0053] Optionally, a computer-readable storage medium stores a computer program that can be loaded and executed by a processor.

[0054] By adopting the above technical solution, the storage and calling of computer programs are realized through storage media.

[0055] In summary, this application includes at least one of the following beneficial technical effects:

[0056] 1. Improved work efficiency, improved maintenance efficiency, and reduced maintenance costs.

[0057] 2. Reduce the impact of accumulated water on light intensity and improve accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a flow chart of a method for detecting dirt on the light outlet of a navigation light in Example 1 of the present application.

[0059] Figure 2 This is a schematic diagram of the exploded structure of a navigation light outlet dirt detection system in Example 1.

[0060] Figure 3 It is a schematic diagram of the exploded structure of the highlight processing module.

[0061] Figure 4 This is a schematic diagram of the module for detecting dirt.

[0062] Figure 5 This is a module diagram of the dirt detection module.

[0063] Figure 6 This is a schematic diagram of the reflection value detection process of Example 2.

[0064] Figure 7 3 is a flow chart of light intensity detection in Example 3.

[0065] Figure 8 It is a schematic flow chart of Example 4.

[0066] Figure 9 It is a schematic flow chart of Example 5.

[0067] Figure 10 It is a schematic flow chart of Example 6.

[0068] Figure 11 It is a schematic flow chart of Example 7.

[0069] Explanation of the accompanying drawings: 1. lamp body; 11. prism; 12. light source module; 2. dirt detection module; 21. optical detection submodule; 22. reflection detection submodule; 23. photosensitivity detection submodule; 3. processing module; 4. upper monitoring module. DETAILED DESCRIPTION

[0070] The following is combined with Figure 1-11 This application is described in further detail.

[0071] Example 1 of the present application discloses a method, system and storage medium for detecting dirt on the light outlet of a navigation light. Figure 1 The methods for detecting dirt on the light outlet of navigation lights include:

[0072] S1. Perform real-time visual image recognition on the light outlet surface of the navigation light to obtain a recognition image;

[0073] S11, analyzing and processing the recognition image to obtain recognition image data;

[0074] S12, determining recognition difference data by recognizing the image data and preset recognition image threshold data;

[0075] S13: Compare and judge the recognition difference data with the preset allowable difference threshold data. If the recognition difference data exceeds the allowable difference threshold data, generate a corresponding alarm or indicate it to the staff.

[0076] In this embodiment, visual image recognition is visual capture, the recognition image is visual data, the recognition image data and the recognition difference data are dirt data, the preset allowable difference threshold data is the preset dirt threshold data, and visual image recognition can be implemented using an optical sensor (such as a camera).

[0077] Real-time image recognition of the light outlet surface of the navigation aid light is performed using an optical sensor or camera to obtain a recognition image, such as a photo or video. The recognition image is then converted into recognition image data that can be processed by a processor. Specifically, the photo is converted into code data for the processor to recognize, analyze, and process. A staff member pre-stores a photo of a clean light outlet surface in a database. The photo is then converted into recognition image threshold data. The processor then calculates the recognition image data against the recognition image threshold data in the database. The calculation includes, but is not limited to, the color depth difference between the recognition image data and the recognition image threshold data, as well as the range of different colors. The difference in light transmittance between the recognition image and the clean light outlet surface of the navigation aid light is calculated, i.e., the recognition difference data. If the recognition difference data exceeds the allowable difference threshold data, it indicates that the light outlet surface of the navigation aid light is dirty, affecting the navigation aid effect. An alarm signal or indication is generated and sent to staff. This can be sent to a monitoring center (console or host) or to a staff member's terminal (mobile phone or host). Data and signal transmission can be conducted via wires or remotely communicated via wireless transmission. The alarm signal sent to the staff includes the identification code or positioning data of the sending end to determine which navigation light outlet surface is dirty.

[0078] For example: Initialize the recognition image threshold data of the clean surface of the light outlet of the navigation light to 0, and set the allowable difference threshold data to 2. If the recognition image data is 1, then the recognition image data - recognition image threshold data = 1 < the allowable difference threshold data, then continue to recognize; and if the recognition image data is 3, then the recognition image data - recognition image threshold data = 3 > the allowable difference threshold data, then output an alarm or instruction to the staff.

[0079] A processor can include a central processing unit (CPU) or MPU, or a host system built around a CPU or MPU, including hardware and software. Once a meter has a processor, it can be freely controlled through programming, allowing it to operate as desired. The processor can control local measurement transmission, remote measurement transmission, and remote communications through internal protocols. Internal protocols generally refer to all protocols that enable intercommunication or links within the same metering instrument or system, including some or all of the following: human-computer interaction protocols, software / hardware (interface) protocols, chip bus (C-Bus) protocols, and internal bus (I-Bus) protocols. With the advancement of integrated circuit technology, some protocols that were considered external bus (E-Bus) protocols have also become internal protocols after the external bus (E-Bus) has been integrated into the chip.

[0080] Telecommunications come in a variety of forms and structures, including WiFi modules, 3G modules, 4G modules, and 5G modules. These modules utilize the resources of linked networks to provide telecommunications or remote control functions. Linked networks generally refer to general or private networks, such as those used by the public, within enterprises, or in homes. Commonly used linked networks include wired networks, wireless networks, and satellite networks, which can be composed of any one of these, two of these, or a combination of all three.

[0081] The implementation principle of a method for detecting dirt on the light outlet of a navigation lamp in an embodiment of the present application is as follows: real-time image recognition is performed on the surface of the light outlet of the navigation lamp through an optical sensor or a camera to obtain a recognition image, and then the recognition image is converted into recognition image data that can be processed by a processor. The staff stores a photo of the clean surface of the light outlet of the navigation lamp in a database in advance. At this time, the photo is converted into recognition image threshold data. The processor calculates and processes the recognition image data and the recognition image threshold data in the database to obtain recognition difference data. The calculation range of the recognition difference data includes but is not limited to the color depth difference between the recognition image data and the recognition image threshold data, and the range size of different colors. The recognition difference data is obtained by comprehensive calculation. If the recognition difference data exceeds the allowable difference threshold data, a corresponding alarm signal will be generated and output to the staff to remind them to clean up.

[0082] System, reference Figure 2 and Figure 3 and Figure 4 , including a lamp body 1, a prism 11, a light source module 12, a dirt detection module 2, a processing module 3 and an upper monitoring module 4. The lamp body 1 is used to protect the internal electronic components. The lamp body 1 is usually made of waterproof, dustproof, weather-resistant and corrosion-resistant materials. The prism 11 is embedded in the side wall of the lamp body 1. The prism 11 serves as a light outlet for focusing light. The light source module 12 is installed in the lamp body 1. The light source module 12 is used to provide light energy for navigation, and the light emitted by the light source module 12 is emitted to the outside through the prism 11 to aid navigation. The dirt detection module 2 is used to detect dirt on the prism 11 and obtain a detection signal. The processing module 3 is used to receive the detection signal, process the detection result as a data signal, and output the corresponding result signal. The processing module 3 includes a processor, a database and a circuit board. The upper monitoring module 4 is used to remotely receive the result signal. If a result signal indicating that the degree of dirt on the prism 11 has exceeded a preset threshold is received, an alarm signal is output.

[0083] Reference Figure 5The dirt detection module 2 includes an optical detection submodule 21 or a reflection detection submodule 22 or a photosensitivity detection submodule 23. The optical detection submodule 21 is used to perform visual image recognition and judgment on the prism 11, the reflection detection submodule 22 is used to detect and judge the reflection value of the prism 11, and the photosensitivity detection submodule 23 is used to detect and judge the light intensity of the prism 11. In this embodiment, the optical detection submodule 21 can adopt an optical sensor or a camera with image recognition function, the reflection detection submodule 22 can adopt a limited reflection type sensor, which includes a transmitter and a receiver, and the photosensitivity detection submodule 23 can adopt a photosensitive device, such as a photosensor, a photoresistor, etc.

[0084] Computer-readable storage media stores computer programs that can be loaded and executed by a processor. Examples of computer-readable storage media include USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.

[0085] Example 2:

[0086] The difference from Example 1 is that Figure 6 ,include:

[0087] S2. emitting a detection light to the light outlet surface of the navigation light in real time, wherein the light intensity of the emitted detection light is the light intensity corresponding to the preset emission light intensity data;

[0088] S21, receiving reflected light reflected from the light outlet surface of the navigation light, and detecting the light intensity of the reflected light to obtain reflected light intensity data;

[0089] S22, determining reflection value data using the emitted light intensity data and the reflected light intensity data;

[0090] S23 , comparing the reflection value data with preset reflection threshold data, and if the reflection value data exceeds the reflection threshold data, generating a corresponding alarm or indicating it to a staff member.

[0091] In this embodiment, the reflected light intensity data is visual data, the reflection value data is dirt data, and the reflection threshold data is dirt threshold data. Infrared rays of a specific frequency are emitted by the transmitter of the reflective sensor, and the reflected infrared rays of the specific frequency are received by the receiver of the reflective sensor.

[0092] For example: Set the emission light intensity data to 1 and the reflection value threshold to 0.5;

[0093] If the reflected light intensity data is 0.8, the reflected light intensity data = reflected light intensity data / emitted light intensity data = 0.8 / 1 = 0.8>0.5, so the reflected light value data>reflection value threshold. This means that the dirtiness of the light outlet surface of the navigation light has not affected the light intensity of the navigation light.

[0094] If the reflected light intensity data is 0.4, the reflected light intensity data = reflected light intensity data / emitted light intensity data = 0.4 / 1 = 0.4 < 0.5, so the reflected light value data < the reflected light threshold. This indicates that the dirtiness of the light outlet surface of the navigation light has affected the light intensity of the navigation light, and a corresponding alarm signal is output to the staff to remind them to clean it.

[0095] Example 3:

[0096] The difference from Example 1 is that Figure 7 ,include:

[0097] S3, receiving light entering from the light outlet of the navigation light, and detecting and obtaining light intensity data;

[0098] S31, determining light intensity difference data by comparing the detected light intensity data with preset reference light intensity data;

[0099] S32 . Compare and determine the light intensity difference data with a preset light intensity difference threshold data. If the light intensity difference data exceeds the light intensity difference threshold data, generate a corresponding alarm or indicate it to a staff member.

[0100] In this embodiment, the detected light intensity data is visual data, the reference value light intensity data is visual threshold data, the light intensity difference data is dirt data, the light intensity difference threshold data is dirt threshold data, and the reference value light intensity data is the light intensity when the surface of the light outlet of the navigation lamp is in a clean state.

[0101] For example: Set the reference light intensity data to 1, and the light intensity difference threshold data to 0.5;

[0102] If the detected light intensity data is 0.8, the light intensity difference data = the reference light intensity data - the detected light intensity data = 1-0.8 = 0.2 < 0.5, and the light intensity difference data < the light intensity difference threshold data. Therefore, the light intensity difference data is within the allowable range. This indicates that the degree of dirt on the light outlet surface of the navigation light has not affected the light intensity of the navigation light.

[0103] If the detected light intensity data is 0.4, the light intensity difference data = the reference light intensity data - the detected light intensity data = 1-0.8 = 0.6>0.5, the light intensity difference data> the light intensity difference threshold data, so the light intensity difference data is outside the allowable range. This indicates that the degree of dirt on the light outlet surface of the navigation light has affected the light intensity of the navigation light, and a corresponding alarm signal is output to the staff to remind them to clean it.

[0104] Example 4:

[0105] The difference from Example 1 is that Figure 8 The three methods for detecting dirt on the light outlet of a navigation aid lamp in Example 1, Example 2 and Example 3 can be applied to navigation aid lamps at the same time.

[0106] Example 5:

[0107] The difference from Example 4 is that Figure 9 ,include:

[0108] Preset the fluctuation table data of accumulated water;

[0109] S4. Collect external wind force to obtain external wind force data, read preset flight status data, and determine wind force fluctuation data based on the external wind force data and the flight status data;

[0110] S41, determining water accumulation fluctuation data by using the wind fluctuation data and the water accumulation fluctuation table data;

[0111] S42. Determine whether the reflected light is affected by the accumulation water fluctuation based on the reflected light intensity data and the accumulation water fluctuation data. If so, determine reflected restored light intensity data based on the reflected light intensity data and the accumulation water fluctuation data. The reflected restored light intensity data is light intensity data after the influence of the accumulation water fluctuation on the light is removed. The reflected restored light intensity data and the emitted light intensity data are used to determine the reflection value data for data analysis and processing.

[0112] S43. Determine whether the reflected light is affected by the water accumulation fluctuation through the detected light intensity data and the water accumulation fluctuation data. If so, determine the detected restored light intensity data through the detected light intensity data and the water accumulation fluctuation data. The detected restored light intensity data is the light intensity data after removing the influence of the water accumulation fluctuation on the light. The detected restored light intensity data replaces the detected light intensity data and is compared with the reference value light intensity data to participate in data analysis and processing.

[0113] In this embodiment, step S42 and step S43 are performed simultaneously. The position and speed of the aircraft can be estimated through the flight status data, so as to calculate the impact of the wind force on the surface of the light outlet of the navigation light. Combined with the external wind force data, the wind force on the surface of the light outlet of the navigation light is obtained, that is, the wind force fluctuation data. Then, the water fluctuation table data is looked up through the wind force fluctuation data to obtain what kind of fluctuations will be caused by the accumulated water under the influence of this wind force. That is, if there is water on the surface of the light outlet of the navigation light, then under the influence of the wind force corresponding to the wind force fluctuation data, the accumulated water will produce fluctuations, that is, the accumulated water fluctuation data. Then, in the data calculation of the reflected light intensity data and the detected light intensity data, the influence of the light intensity fluctuation caused by the accumulated water fluctuation needs to be removed to ensure the accuracy of the data.

[0114] For example: suppose the water fluctuation table data is (wind force, fluctuation) = (1, sinα), where α is time;

[0115] If the detected external wind data is 1 and there are no aircraft passing by in a short period of time, the flight status data is 0, and the wind fluctuation data is 1. The table shows that the water accumulation fluctuation data is sinα, and the detected reflected light intensity data is 0.8sinα. Since the fluctuation ratio of the reflected light intensity data over time matches the water accumulation fluctuation data, there is water on the light outlet surface of the navigation light. After excluding the influence of water accumulation, the reflected restored light intensity data is obtained = reflected light intensity data / water accumulation fluctuation data = 0.8. The reflected restored light intensity data replaces the original reflected light intensity data for calculation;

[0116] If the detected external wind force data is 1.5, there is an airplane passing through, and the wind force generated by the airplane is estimated to be -0.5 (the wind force direction is opposite to the direction of the external natural wind, and the wind force is 0.5). The wind force fluctuation data is 1. The table shows that the water accumulation fluctuation data is sinα, and the detected detection light intensity data is 0.4sinα. Since the fluctuation ratio of the detection light intensity data over time matches the water accumulation fluctuation data, there is water accumulation on the light outlet surface of the navigation light. After excluding the influence of water accumulation, the detection recovery light intensity data = detection light intensity data / water accumulation fluctuation data = 0.4 is obtained. The detection recovery light intensity data replaces the original detection light intensity data for calculation.

[0117] Example 6:

[0118] The difference from Example 5 is that Figure 10 ,include:

[0119] S5. Preset shadow time threshold data and shadow threshold data, collect the variation of the recognition image data or the reflected light intensity data or the detected light intensity data, and obtain variation data;

[0120] S51, determining change detection data by using the shadow time threshold data and the change data;

[0121] S52. Determine the number of change data through the change detection data and the shadow threshold data, and record the number of times the change detection data exceeds the shadow threshold data through the change number data; if the time data between adjacent change number data is less than the preset interval time threshold data, remove the corresponding change data so that the corresponding recognition image data or the reflected light intensity data or the detected light intensity data does not participate in the data analysis and processing.

[0122] This embodiment can be further optimized based on Example 5. If large debris such as leaves or paper float by, or if an airplane flies by, and its shadow covers the light outlet surface of the passing navigation light, the leaves or airplane will block the sun, and the shadow will cause the light on the light outlet surface of the navigation light to drop suddenly, thereby affecting the recognition image data, reflected light intensity data, or detected light intensity data, and affecting the data calculation and judgment results.

[0123] For example: Set the shadow time threshold data to 0.1s, set the shadow threshold data to 0.5, and set the interval time threshold data to 1s;

[0124] If the collected recognition image data or reflected light intensity data or detected light intensity data drops from 0.8 to 0.2 within 0.11s, for example, within 0s-0.01s, the data drops from 0.8 to 0.79, and within 0.01s-0.11s, the data drops from 0.79 to 0.2, the obtained change data is (0.11s, 0.8-0.2), and the corresponding change data is intercepted according to the shadow time threshold data, that is, the data change within a continuous 0.1s time period is intercepted from 0.11s, and the change detection data is 0.79-0.2=0.57>0.5, that is, the change detection data is greater than the shadow threshold data, so it is judged that the light intensity at this time is blocked by the shadow, and the change number data is increased by 1, that is, the change number data is 0+1=1;

[0125] Subsequently, if the collected recognition image data or reflected light intensity data or detected light intensity data increases from 0.2 to 0.8 within 0.1s, the obtained change data is (0.1s, 0.2-0.8), and the obtained change detection data 0.8-0.2=0.6>0.5, that is, the change detection data is greater than the shadow threshold data, so it is judged that the light intensity at this time is that the shadow occlusion has disappeared, the change number data is 1+1=2, and if the time interval between the two change number data is less than the interval time threshold data, that is, the light intensity data drops and rises within one second, it is judged to be a short-term shadow occlusion. At this time, the data of the process of the light intensity data dropping from 0.8 to 0.2 and then rising to 0.8 is removed from the calculation, so that the corresponding recognition image data or reflected light intensity data or detected light intensity data does not participate in the data analysis and processing.

[0126] Example 7:

[0127] The difference from Example 6 is that Figure 11 ,include:

[0128] S6. Determine the area data and position data of the obstruction by using the shadow threshold data and the recognition image data;

[0129] S61. If the time data of the obstruction position data and the obstruction area data exceeds the preset shadow determination threshold data, and the obstruction area data has not changed, and the obstruction position data has changed continuously within the time corresponding to the shadow time threshold data, a corresponding alarm or instruction is generated to the staff.

[0130] This embodiment is used to identify solid impurities dropped on the light outlet surface of the navigation light on the basis of the sixth embodiment.

[0131] For example: suppose the shadow determination threshold data is 60s, and the shadow threshold data is 0.5. In the recognized image data, when the adjacent color depth difference exceeds the shadow threshold data, that is, the light intensity data is 0.5 less than the light intensity data of the surrounding area, the area is determined to be an obstruction, and the area and position of the obstruction are determined according to the position where the light intensity data changes, and the obstruction area data and obstruction position data are obtained. The obstruction position data is recorded according to a preset coordinate system, and the obstruction position data has not moved out to the surface of the light outlet of the navigation light within the time of the shadow determination threshold data, that is, the obstruction still remains on the surface of the light outlet of the navigation light within 60s. If the obstruction position data changes, but the change of the obstruction position data is continuous, then it is still determined that the obstruction remains on the surface of the light outlet of the navigation light, and the obstruction area data remains unchanged, that is, the obstruction is a solid impurity rather than a soluble impurity, and a corresponding alarm signal is output to the staff to remind them to clean it up.

[0132] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A method for detecting dirt on the light outlet of a navigation light, characterized in that: include: Real-time visual capture of the light outlet surface of the navigation light to obtain visual data; Determining dirt data based on the visual data and preset visual threshold data; By comparing the dirtiness data with the preset dirtiness threshold data, if the dirtiness data exceeds the dirtiness threshold data, a corresponding alarm is generated or an indication is given to the staff; The method further comprises: Emitting a detection light to the light outlet surface of the navigation light, wherein the light intensity of the emitted detection light is the light intensity corresponding to the preset emission light intensity data; Receiving reflected light reflected from the light outlet surface of the navigation light, and detecting the light intensity of the reflected light to obtain reflected light intensity data; Determine reflection value data using the emitted light intensity data and the reflected light intensity data; By comparing the reflection value data with the preset reflection threshold data, if the reflection value data exceeds the reflection threshold data, a corresponding alarm is generated or an indication is given to the staff; The method further comprises: Receive the light entering from the light outlet of the navigation light and detect the light intensity data; Determining light intensity difference data by comparing the detected light intensity data with preset reference light intensity data, performing a comparison and judgment on the light intensity difference data with preset light intensity difference threshold data, and generating a corresponding alarm or indicating to a staff member if the light intensity difference data exceeds the light intensity difference threshold data; The calculation of the reflected light intensity data and the detected light intensity data includes: Preset the fluctuation table data of accumulated water; Collecting external wind force to obtain external wind force data, reading preset flight status data, and determining wind force fluctuation data based on the external wind force data and the flight status data; Determining water accumulation fluctuation data by using the wind force fluctuation data and the water accumulation fluctuation table data; Determine whether the reflected light is affected by the accumulation water fluctuations by using the reflected light intensity data and the accumulation water fluctuation data; if so, determine reflected restored light intensity data by using the reflected light intensity data and the accumulation water fluctuation data; the reflected restored light intensity data is light intensity data after removing the influence of the accumulation water fluctuations on the light; and determine the reflection value data by using the reflected restored light intensity data and the emitted light intensity data to participate in the data analysis and processing; Whether the reflected light is affected by the water accumulation fluctuation is determined by the detected light intensity data and the water accumulation fluctuation data. If so, the detected restored light intensity data is determined by the detected light intensity data and the water accumulation fluctuation data. The detected restored light intensity data is the light intensity data after removing the influence of the water accumulation fluctuation on the light. The detected restored light intensity data replaces the detected light intensity data and is compared and judged with the reference value light intensity data to participate in data analysis and processing.

2. A method for detecting dirt on the light outlet of a navigation light according to claim 1, characterized in that: The method further comprises: Perform visual image recognition on the light outlet surface of the navigation light to obtain a recognition image; Analyzing and processing the recognition image to obtain recognition image data; The recognition difference data is determined by comparing the recognition image data with the preset recognition image threshold data, and the recognition difference data is compared with the preset allowable difference threshold data. If the recognition difference data exceeds the allowable difference threshold data, a corresponding alarm is generated or an indication is given to the staff.

3. A method for detecting dirt on the light outlet of a navigation light according to claim 2, characterized in that: The method further comprises: Preset shadow time threshold data and shadow threshold data, collect the variation of the recognition image data or the reflected light intensity data or the detected light intensity data, and obtain variation data; Determine variation detection data by using the shadow time threshold data and the variation data; Determine change count data by using the change amount detection data and the shadow threshold data, and record the number of times the change amount detection data exceeds the shadow threshold data by using the change count data; If the time data between adjacent change times data is less than the preset interval time threshold data, the corresponding change amount data is removed so that the corresponding recognition image data or the reflected light intensity data or the detected light intensity data does not participate in the data analysis and processing.

4. The method for detecting dirt on the light outlet of a navigation light according to claim 3, characterized in that: The method further comprises: Determine the area data and the position data of the obstruction by using the shadow threshold data and the recognition image data; If the time data of the obstruction position data and the obstruction area data exceeds the preset shadow judgment threshold data, and the obstruction area data has not changed, and the obstruction position data changes continuously within the time corresponding to the shadow time threshold data, a corresponding alarm or instruction is generated to the staff.

5. A system, according to the method for detecting contamination of the light outlet of a navigation light according to claim 1, characterized in that: include: The lamp body (1) is used to be waterproof, dustproof, weather-resistant and corrosion-resistant; A prism (11), serving as a light outlet and used for focusing light, is disposed on the lamp body (1); A light source module (12) is used to provide light energy for navigation assistance; A dirt detection module (2) is used to detect dirt on the prism (11) and obtain a detection signal; The processing module (3) receives the detection signal, processes the detection result into a data signal, and outputs a corresponding result signal; The upper monitoring module (4) receives the result signal and outputs an alarm signal if the result signal received indicates that the degree of contamination of the prism (11) has exceeded a preset threshold value.

6. The system according to claim 5, characterized in that: The dirt detection module (2) comprises an optical detection submodule (21) or a reflection detection submodule (22) or a photosensitive detection submodule (23); the optical detection submodule (21) is used to perform visual image recognition and judgment on the prism (11); the reflection detection submodule (22) is used to detect and judge the reflection value of the prism (11); and the photosensitive detection submodule (23) is used to detect and judge the light intensity of the prism (11).

7. A computer-readable storage medium, characterized in that The computer program is stored which can be loaded by a processor and executes the method for detecting dirt on the light outlet of a navigation light according to any one of claims 1 to 4.

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