Vehicle-mounted camera cleaning method, device, equipment, medium and product

Abstract

The invention discloses a vehicle-mounted camera cleaning method, device and equipment, a medium and a product. The method comprises the steps of obtaining multi-source cleaning sensing data, wherein the multi-source cleaning sensing data at least comprises optical reflectivity of multi-wavelength light collected by a multi-spectral optical sensor integrated on a vehicle-mounted camera, inter-electrode impedance collected by a micro-impedance electrode integrated on the vehicle-mounted camera, current vehicle speed and environment temperature; determining a dirt type and a pollution degree based on the optical reflectivity and the inter-electrode impedance through a dirt recognition algorithm; through a cleaning decision algorithm, based on the dirt type, the pollution degree, the environment temperature and the current vehicle speed, cleaning execution parameters are generated; and based on the cleaning execution parameters, a micro-vibration membrane, a vortex generator and an annular piezoelectric array which are integrated on the vehicle-mounted camera are started in sequence according to a time sequence, and non-contact cleaning of the vehicle-mounted camera is carried out. According to the scheme, non-contact efficient and accurate cleaning adaptive to various types of dirt can be achieved.

Description

Technical Field

[0001] This invention relates to the field of intelligent vehicle technology, and in particular to a method, apparatus, device, medium, and product for cleaning vehicle-mounted cameras. Background Technology

[0002] As the core role of vehicle cameras in autonomous driving and assisted driving becomes more prominent, the cleaning effect of their lenses directly affects the accuracy of image recognition (such as lane line recognition, obstacle detection, etc.). Therefore, it is necessary to clean vehicle cameras to ensure image clarity and driving safety.

[0003] Existing vehicle camera cleaning methods can be achieved through physical cleaning brushes. While the brush head can remove some dust by wiping the lens back and forth, the brush head material is prone to wear and tear over long-term use, and the cleaning effect decreases with each use. Furthermore, the hard brush head is prone to friction with the lens surface when the vehicle vibrates, causing scratches on the coating and permanently affecting image quality. Alternatively, a pure high-pressure gas jet solution can be used, which relies on compressed air to remove dirt. However, it is not effective at cleaning sticky dirt such as oil stains and cannot meet the cleaning needs of various cleaning conditions. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, medium, and product for cleaning vehicle-mounted cameras. This solution enables efficient and precise cleaning of vehicle-mounted cameras that is contactless and adaptable to various types of dirt.

[0005] In a first aspect, embodiments of the present invention provide a method for cleaning an in-vehicle camera, comprising:

[0006] Acquire multi-source clean sensing data, which includes at least the optical reflectivity of multi-wavelength light collected by a multispectral optical sensor integrated on the vehicle camera, the inter-electrode impedance collected by a micro-impedance electrode integrated on the vehicle camera, the current vehicle speed, and the ambient temperature.

[0007] The type and degree of contamination are determined by a contaminant identification algorithm based on the optical reflectivity and the inter-electrode impedance.

[0008] A cleaning decision algorithm is used to generate cleaning execution parameters based on the type of dirt, the degree of pollution, the ambient temperature, and the current vehicle speed.

[0009] Based on the cleaning execution parameters, the micro-vibration membrane, eddy current generator, and ring piezoelectric array integrated on the vehicle camera are activated sequentially in a time sequence to perform contactless cleaning of the vehicle camera.

[0010] Secondly, embodiments of the present invention provide a vehicle-mounted camera cleaning device, comprising:

[0011] The perception data acquisition module is used to acquire multi-source cleaning perception data, which includes at least the optical reflectivity of multi-wavelength light collected by the multispectral optical sensor integrated on the vehicle camera, the inter-electrode impedance collected by the micro-impedance electrode integrated on the vehicle camera, the current vehicle speed, and the ambient temperature.

[0012] A dirt identification module is used to determine the type and degree of dirt based on the optical reflectivity and the inter-electrode impedance using a dirt identification algorithm.

[0013] The cleaning decision module is used to generate cleaning execution parameters based on the type of dirt, the degree of pollution, the ambient temperature, and the current vehicle speed using a cleaning decision algorithm.

[0014] The cleaning execution module is used to sequentially activate the micro-vibration membrane, eddy current generator, and ring piezoelectric array integrated on the vehicle camera according to the cleaning execution parameters, so as to perform non-contact cleaning of the vehicle camera.

[0015] Thirdly, embodiments of the present invention provide an electronic device, including:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the method as described in the first aspect.

[0019] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions that cause a processor to execute the method described in the first aspect.

[0020] Fifthly, embodiments of the present invention provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements the method described in the first aspect.

[0021] The technical solution of this invention involves acquiring multi-source cleaning sensing data, which includes at least the optical reflectivity of multi-wavelength light collected by a multispectral optical sensor integrated on an in-vehicle camera, the inter-electrode impedance collected by a micro-impedance electrode integrated on the in-vehicle camera, the current vehicle speed, and the ambient temperature. A dirt identification algorithm determines the type and degree of dirt based on the optical reflectivity and the inter-electrode impedance. A cleaning decision algorithm generates cleaning execution parameters based on the dirt type, the degree of pollution, the ambient temperature, and the current vehicle speed. Based on the cleaning execution parameters, a micro-vibration membrane, an eddy current generator, and a ring piezoelectric array integrated on the in-vehicle camera are sequentially activated to perform contactless cleaning of the in-vehicle camera. This solution utilizes the differences in reflectivity of multi-wavelength light for different types of dirt, combined with the acquisition of inter-electrode impedance, to determine the type and degree of dirt. It then combines the ambient temperature and current vehicle speed to generate cleaning execution parameters matching the current cleaning conditions. Based on these parameters, the combination of the micro-vibration membrane, the eddy current generator, and the ring piezoelectric array enables coordinated cleaning through sound waves, airflow, and micro-vibration. This solution enables efficient and precise cleaning of vehicle-mounted cameras that are contactless and adaptable to various types of dirt.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of a vehicle-mounted camera cleaning method according to Embodiment 1 of the present invention;

[0025] Figure 2 This is a flowchart of a vehicle-mounted camera cleaning method according to Embodiment 2 of the present invention;

[0026] Figure 3 This is a schematic diagram of the structure of a vehicle-mounted camera cleaning device according to Embodiment 3 of the present invention;

[0027] Figure 4 This is a schematic diagram of the structure of an electronic device that implements an embodiment of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., used in this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] Example 1

[0031] Figure 1 This is a flowchart of a vehicle-mounted camera cleaning method according to Embodiment 1 of the present invention. This embodiment is applicable to situations involving the cleaning of vehicle-mounted cameras. The method can be executed by a vehicle-mounted camera cleaning device, which can be implemented in software and / or hardware and integrated into an electronic device. Furthermore, the electronic device includes, but is not limited to, computers, laptops, servers, vehicles, etc.

[0032] like Figure 1 As shown, the method includes:

[0033] S110. Acquire multi-source cleaning perception data, wherein the multi-source cleaning perception data includes at least the optical reflectivity of multi-wavelength light collected by a multispectral optical sensor integrated on the vehicle camera, the inter-electrode impedance collected by a micro-impedance electrode integrated on the vehicle camera, the current vehicle speed, and the ambient temperature.

[0034] Multi-source cleaning sensing data can be sensing data collected from multiple sources for cleaning analysis, including optical reflectivity of multi-wavelength light, inter-electrode impedance, current vehicle speed, and ambient temperature.

[0035] In this embodiment of the invention, a device for collecting multi-source cleaning sensing data can be integrated into the relevant components of the vehicle-mounted camera, specifically including:

[0036] A multispectral optical sensor is embedded at the edge of the vehicle camera lens, such as four optical sensors arranged in a ring. The optical sensor is flush with the lens plane to avoid obstructing the image. The optical sensor is a 4-channel sensor with wavelengths of 400nm / 550nm / 800nm / 1100nm. The reason for this arrangement is that different wavelengths of light have different reflectivities for different types of dirt (such as water stains, oil stains, dust, and frost). For example, water stains have a reflectivity of >80% for 550nm light, while oil stains have a reflectivity of <50% for 800nm ​​light.

[0037] Micro-impedance electrodes are embedded inside the coating layer (antireflective coating) of the automotive camera lens, such as eight micro-impedance electrodes embedded in a ring. The reason for this setting is that the impedance between electrodes may be different for different types of dirt, such as frost impedance >100kΩ, oil resistance 5kΩ~20kΩ, and dust resistance >50kΩ, which are used for cross-verification with multispectral data to help verify the type of dirt.

[0038] A temperature and humidity sensor is integrated into the printed circuit board of the vehicle camera to obtain the ambient temperature.

[0039] In this step, the optical reflectivity of multi-wavelength light can be obtained through a multispectral optical sensor, the inter-electrode impedance can be obtained through a micro-impedance electrode, the ambient temperature can be obtained through a temperature and humidity sensor, and the current vehicle speed can be obtained by communicating with the vehicle electronic control unit through the controller area network bus interface, thus obtaining multi-source clean perception data.

[0040] S120. Using a dirt identification algorithm, the type and degree of dirt are determined based on the optical reflectivity and the inter-electrode impedance.

[0041] The dirt identification algorithm can be an algorithm that can identify the type and degree of dirt, and it can be pre-trained based on a convolutional neural network (CNN). There are no restrictions here.

[0042] In this step, a feature vector is constructed based on the optical reflectivity of multi-wavelength light and the inter-electrode impedance. The constructed feature vector is then input into a pre-trained lightweight CNN model, which outputs the type of dirt and the degree of contamination. The output dirt type can be any one of water stains, oil stains, dust, or frost, and the output contamination degree can be any one of light, moderate, or heavy.

[0043] In the dirt identification algorithm, the initial dirt type can be determined by comparing the optical reflectivity of multi-wavelength light with the reflectivity threshold of each dirt type; then, by combining the results of comparing the impedance between electrodes with the impedance threshold of each dirt type, the initial dirt type is cross-validated, and the final determined dirt type is output.

[0044] In dirt detection algorithms, the output of the dirt level is determined by the transmittance loss rate, such as light pollution <5%, moderate pollution 5%~15%, and heavy pollution >15%. The transmittance loss rate is directly related to optical reflectance. For the same lens medium, reflectance and transmittance satisfy an optical conservation relationship. Dirt adhesion causes reflectance to shift towards a specific spectral range (e.g., oil stains increase infrared light reflectance), while transmittance decreases (the transmittance loss rate increases). Based on this, the CNN model establishes a mapping relationship between "optical reflectance + micro-impedance" and the transmittance loss rate through training, ultimately classifying the dirt level using a loss rate threshold.

[0045] S130. Based on the type of dirt, the degree of pollution, the ambient temperature and the current vehicle speed, a cleaning execution parameter is generated using a cleaning decision algorithm.

[0046] A cleaning decision algorithm can be an algorithm that decides whether to perform cleaning and generates cleaning execution parameters. Cleaning execution parameters are the parameters used by the cleaning execution device to perform cleaning. The cleaning execution device may include a micro-vibrating diaphragm, an eddy current generator, and a ring piezoelectric array integrated on an onboard camera. The micro-vibrating diaphragm generates high-frequency micro-vibrations, the eddy current generator generates high-speed swirling airflow, and the ring piezoelectric array generates high-frequency acoustic vibrations.

[0047] In this step, the cleaning decision algorithm may include a cleaning decision part, a vortex airflow intensity determination part, a sound wave output power determination part, a vibration frequency determination part, an amplitude determination part, and a cleaning duration determination part.

[0048] Specifically, the cleaning decision-making section determines the cleaning priority based on the type of dirt, the degree of contamination, and the current vehicle speed. When the cleaning priority indicates that cleaning is required, the calculation of cleaning execution parameters is triggered. The vortex airflow intensity determination section determines the vortex airflow intensity based on the degree of contamination and the ambient temperature. The sound wave output power determination section determines the sound wave output power based on the type of dirt. The vibration frequency determination section determines the vibration frequency of the micro-vibrating diaphragm based on the material of the micro-vibrating diaphragm. The amplitude determination section determines the amplitude of the micro-vibrating diaphragm based on the type of dirt and the degree of contamination. The cleaning duration determination section determines the cleaning duration based on the type of dirt and the degree of contamination. The vortex airflow intensity, sound wave output power, vibration frequency, amplitude, and cleaning duration are used as cleaning execution parameters.

[0049] S140. Based on the cleaning execution parameters, the micro-vibration membrane, eddy current generator and ring piezoelectric array integrated on the vehicle camera are activated in sequence to perform contactless cleaning of the vehicle camera.

[0050] In this step, a pulse width modulation (PWM) signal is output according to the logic of "first prevent adhesion → then peel off → finally decompose". The PWM signal controls the micro-vibration membrane to start first (e.g., start at 0 seconds) to prevent dirt residue after cleaning, i.e., to prevent secondary adhesion of dirt; controls the eddy current generator to start after a first set time (e.g., start after 0.5 seconds) to mainly peel off loose dirt; controls the annular piezoelectric array to start after a second set time (e.g., start after 1 second) to decompose stubborn dirt; according to the cleaning time in the cleaning execution parameters, non-contact cleaning of the vehicle camera is achieved through the synergy of sound waves, airflow and micro-vibration.

[0051] The micro-vibrating diaphragm is fixed to the outside of the lens by a metal bracket and uses a giant magnetostrictive material. A miniature electromagnetic coil (to control vibration) is connected to the edge of the diaphragm. The micro-vibrating diaphragm operates based on the vibration frequency and amplitude specified in the cleaning execution parameters.

[0052] Six vortex generators are arranged in a ring around the outer edge of the annular piezoelectric array. A miniature centrifugal air pump generates a high-speed swirling airflow, with the airflow direction at a 15° angle to the lens surface to prevent direct airflow from splashing contaminants. The vortex generators operate based on the vortex airflow intensity specified in the cleaning execution parameters.

[0053] A ring-shaped piezoelectric array surrounds the outer ring of the lens (diameter 25mm~50mm, adaptable to different camera sizes), using piezoelectric ceramic material and operating at a frequency of 40kHz~100kHz (adjustable). It breaks down sticky dirt through high-frequency acoustic vibration. The ring-shaped piezoelectric array operates based on the acoustic output power in the cleaning execution parameters.

[0054] It should be noted that the core function of PWM is to adjust the output of the cleaning execution device through the duty cycle, which is calculated from the cleaning execution parameters of the corresponding device.

[0055] Optionally, the ring piezoelectric array can adopt a pulse working mode, such as sound waves cycling on and off at 200ms / 100ms instead of continuous operation, to reduce energy consumption; at the same time, it integrates a vehicle vibration energy acquisition unit (installed on the camera bracket, which converts vibration into electrical energy through piezoelectric elements to power the sensor, reducing vehicle power consumption).

[0056] The technical solution of this invention involves acquiring multi-source cleaning sensing data, which includes at least the optical reflectivity of multi-wavelength light collected by a multispectral optical sensor integrated on an in-vehicle camera, the inter-electrode impedance collected by a micro-impedance electrode integrated on the in-vehicle camera, the current vehicle speed, and the ambient temperature. A dirt identification algorithm determines the type and degree of dirt based on the optical reflectivity and the inter-electrode impedance. A cleaning decision algorithm generates cleaning execution parameters based on the dirt type, the degree of pollution, the ambient temperature, and the current vehicle speed. Based on the cleaning execution parameters, a micro-vibration membrane, an eddy current generator, and a ring piezoelectric array integrated on the in-vehicle camera are sequentially activated to perform contactless cleaning of the in-vehicle camera. This solution utilizes the differences in reflectivity of multi-wavelength light for different types of dirt, combined with the acquisition of inter-electrode impedance, to determine the type and degree of dirt. It then combines the ambient temperature and current vehicle speed to generate cleaning execution parameters matching the current cleaning conditions. Based on these parameters, the combination of the micro-vibration membrane, the eddy current generator, and the ring piezoelectric array enables coordinated cleaning through sound waves, airflow, and micro-vibration. This solution enables efficient and precise cleaning of vehicle-mounted cameras that are contactless and adaptable to various types of dirt.

[0057] Example 2

[0058] Figure 2 This is a flowchart of a vehicle-mounted camera cleaning method according to Embodiment 2 of the present invention. This embodiment is based on Embodiment 1 above, further refining the cleaning execution parameters generated by the cleaning decision algorithm based on the type of dirt, the degree of contamination, the ambient temperature, and the current vehicle speed, such as... Figure 2 As shown, the method includes:

[0059] S110. Acquire multi-source cleaning perception data, wherein the multi-source cleaning perception data includes at least the optical reflectivity of multi-wavelength light collected by a multispectral optical sensor integrated on the vehicle camera, the inter-electrode impedance collected by a micro-impedance electrode integrated on the vehicle camera, the current vehicle speed, and the ambient temperature.

[0060] S120. Using a dirt identification algorithm, the type and degree of dirt are determined based on the optical reflectivity and the inter-electrode impedance.

[0061] S131. Using a cleaning decision algorithm, the type of dirt, the degree of pollution, and the current vehicle speed are converted into corresponding mapping function values ​​and then weighted and summed to obtain the cleaning priority.

[0062] Cleaning priority P can be determined by the following formula:

[0063] P=a1×y(L)+ a2×f(T) + a3×g(V).

[0064] Where a1, a2, and a3 are the pollution degree weight, the pollutant type weight, and the vehicle speed weight, respectively, and can be taken as 0.5, 0.3, and 0.2, respectively, without any limitation here.

[0065] y(L) is the pollution level mapping function, where L is the pollution level. The values ​​of y(L) corresponding to low, medium, and severe pollution levels can be 0.8, 1.0, and 1.2, respectively.

[0066] f(T) is a pollution type mapping function, where T is the pollution type. The values ​​of f(T) corresponding to pollution types such as water stains, oil stains, dust, and frost can be 0.6, 1.0, 0.4, and 0.8, respectively.

[0067] g(V) is the vehicle speed mapping function, where V is the vehicle speed. The values ​​of g(V) corresponding to V < 30, 30 ≤ V ≤ 100, and V > 100 can be 0.5, 1.0, and 0.8, respectively.

[0068] S132. If the cleaning priority exceeds the cleaning threshold, the vortex airflow intensity is determined based on the product of the reference airflow intensity, the mapping function value of the pollution degree conversion, and the ambient temperature compensation function value corresponding to the ambient temperature.

[0069] In this step, if the cleaning priority exceeds the cleaning threshold (e.g., 0.7), the calculation of cleaning execution parameters will be triggered; otherwise, the calculation of cleaning execution parameters will not be triggered.

[0070] The intensity w of the vortex airflow can be determined by the following formula:

[0071] w = w0×y(L)×h(Ta).

[0072] Where w0 is the reference airflow intensity, such as 20m / s, which is not limited here; y(L) is the pollution degree mapping function; h(Ta) is the ambient temperature compensation function, which is used to correct the influence of ambient temperature on the cleaning effect of vortex airflow and avoid the effect decay caused by temperature. Ta is the ambient temperature. The h(Ta) values ​​corresponding to Ta < -5℃, -5℃ ≤ Ta ≤ 40℃, and Ta > 40℃ can be 1.2 (low temperature heating airflow compensation), 1.0 (normal temperature no compensation), and 0.9 (high temperature load reduction to prevent equipment overheating).

[0073] S133. Determine the sound output power based on the product of the reference sound power and the sound power correction function value corresponding to the type of dirt.

[0074] The acoustic output power Pac can be determined by the following formula:

[0075] Pac = P0 × k(T).

[0076] Where P0 is the reference sound wave power, such as 10W, which is not limited here; k(T) is the sound wave power correction function, which is used to adjust the reference sound wave power according to the characteristics of dirt to improve the cleaning effect of stubborn dirt; T is the type of dirt. The values ​​of k(T) corresponding to the types of dirt, such as water stains, oil stains, dust, and frost, can be 1.0, 1.5 (oil stains are sticky and require increased power), 1.0, and 1.2 (frost is hard and requires increased power).

[0077] S134. Based on the material of the micro-vibration membrane, determine the vibration frequency of the micro-vibration membrane.

[0078] The vibration frequency of the micro-vibration diaphragm is determined by the material of the micro-vibration diaphragm used, and is not limited here.

[0079] S135. Based on the type of dirt and the degree of contamination, determine the cleaning time and the amplitude of the micro-vibration membrane.

[0080] In practical applications, a mapping relationship can be established in advance between the type and degree of dirt and the cleaning time, as well as the mapping relationship between the cleaning time and the amplitude of the micro-vibration membrane. Thus, the cleaning time and the amplitude of the micro-vibration membrane can be determined by the type and degree of dirt.

[0081] For example, moderate contamination plus sticky oil requires a moderate amplitude (8μm) to prevent secondary adhesion of dirt without affecting camera imaging due to excessive amplitude.

[0082] Optionally, the cleaning time and the amplitude of the micro-vibration membrane can also be determined by the degree of contamination, but this is not limited here.

[0083] S136. Based on the vortex airflow intensity, the acoustic output power, the vibration frequency, the amplitude, and the cleaning duration, generate cleaning execution parameters.

[0084] The vortex airflow intensity, acoustic output power, vibration frequency, amplitude, and cleaning duration are used as cleaning execution parameters.

[0085] S140. Based on the cleaning execution parameters, the micro-vibration membrane, eddy current generator and ring piezoelectric array integrated on the vehicle camera are activated in sequence to perform contactless cleaning of the vehicle camera.

[0086] In one embodiment, prior to acquiring the multi-source cleaning sensing data, the method further includes:

[0087] When the vehicle is powered on, the multispectral optical sensor, the micro-impedance electrode, and the temperature and humidity sensor operate with low power consumption and monitor cleaning sensing trigger information in real time.

[0088] When the cleaning sensing trigger information meets the cleaning sensing trigger conditions, the multispectral optical sensor, the micro-impedance electrode, and the temperature and humidity sensor are operated in high-frequency mode.

[0089] When the vehicle is powered on, the multispectral optical sensor operates once every 5 seconds, the micro-impedance electrode operates once every 10 seconds, and the temperature and humidity sensor operates once every 1 second, achieving low-power data acquisition. Meanwhile, all cleaning execution devices are powered off, and the microcontroller unit is in sleep-wake mode, monitoring cleaning perception trigger information and determining whether the information meets the cleaning perception trigger conditions. The cleaning perception trigger information refers to information related to cleaning perception triggering, and the cleaning perception trigger conditions can be any conditions that trigger cleaning perception; this is not limited here.

[0090] Optionally, the cleaning perception trigger information includes at least the cleaning perception transmittance loss rate, vehicle navigation information, and cleaning command trigger information; the cleaning perception trigger condition includes at least one of the following: the cleaning perception transmittance loss rate exceeds the loss rate threshold, the vehicle navigation information indicates a severe weather forecast, and the cleaning command trigger information indicates that cleaning is triggered.

[0091] Among them, the light transmittance loss rate for cleaning perception can be obtained through the light transmittance monitoring unit. The loss rate threshold can be the lowest loss rate at which cleaning perception is not required, which is not limited here. The vehicle navigation information can be read from the vehicle through the microcontroller unit. The vehicle navigation information can indicate severe weather forecasts, such as rain or sandstorms. The cleaning instruction trigger information can be information that the driver actively triggers to indicate that cleaning is required, or it can be information that the vehicle electronic control unit triggers to indicate that cleaning is required when it detects a decrease in image recognition accuracy. There is no limitation here, as long as it can indicate the triggering of cleaning.

[0092] When the cleaning sensing trigger information meets any of the cleaning sensing trigger conditions, the multispectral optical sensor, micro-impedance electrode, and temperature and humidity sensor are switched to high-frequency mode. Simultaneously, the cleaning execution device is powered and preheated, and the dirt recognition algorithm and cleaning decision algorithm are loaded into the microcontroller's memory, ready to acquire and process multi-source cleaning sensing data. It should be noted that the dirt recognition algorithm and cleaning decision algorithm can be upgraded and updated online.

[0093] In one embodiment, the method further includes:

[0094] During the non-contact cleaning process, if the ambient temperature is lower than the temperature threshold, the low-temperature heater configured in the air inlet pipe of the vortex generator is activated.

[0095] This means that a low-temperature heater is integrated into the air inlet of the eddy current generator, which starts when the temperature is low (such as ambient temperature < -5℃) to avoid airflow condensation.

[0096] Optionally, a low-temperature antifreeze coating (such as polytetrafluoroethylene) can be applied to the surface of the annular piezoelectric array and the micro-vibration membrane to prevent the components from freezing.

[0097] In one embodiment, the method further includes:

[0098] Once the contactless cleaning is completed, obtain the light transmittance loss rate to verify the cleaning process.

[0099] If the light transmittance loss rate during the cleaning verification is lower than the verification threshold, then the cleaning is satisfactory; otherwise, the cleaning execution parameters are adjusted for a second cleaning.

[0100] This means that after cleaning is completed, the cleaning effect is verified. The light transmittance loss rate during cleaning verification is obtained through the light transmittance monitoring unit. The light transmittance loss rate during cleaning verification is the same as the light transmittance loss rate obtained during the cleaning effect verification. If the light transmittance loss rate during cleaning verification is lower than the verification threshold, the cleaning is considered successful and the system returns to standby mode; otherwise, the cleaning execution parameters are adjusted for a second cleaning, such as increasing the cleaning time, increasing the intensity of the vortex airflow, or increasing the sound wave output power.

[0101] Optionally, the input parameters (i.e., multi-source cleaning perception data), cleaning execution parameters, and cleaning results for each cleaning can be recorded to form a dataset. When the vehicle is connected to the network, the dataset is uploaded to the cloud, and federated learning is used to update the parameters of the dirt recognition algorithm and the cleaning decision algorithm to improve the accuracy and energy efficiency of subsequent cleaning.

[0102] The technical solutions of the embodiments of the present invention have the following advantages:

[0103] Non-contact cleaning reduces lens scratch rate to 0% and ensures no permanent damage to image quality after cleaning. Specifically, it employs a completely non-contact method combining sound waves, airflow, and micro-vibration. All cleaning actions do not directly contact the lens surface, avoiding scratches to the coating caused by friction. At the same time, the micro-vibration film prevents secondary adhesion of dirt, further protecting the lens.

[0104] Multimodal collaboration ensures superior cleaning performance across all scenarios, achieving a cleaning rate of over 90% for special contaminants such as oil and frost. Specifically, different cleaning execution parameters are designed for different contaminant characteristics. Meanwhile, sound waves primarily break down the molecular binding forces of sticky oil, vortex airflow primarily peels off dust / water stains, micro-vibration prevents residue, and a low-temperature heater prevents condensation, forming a complete "decomposition-peeling-residue prevention" cleaning process that is adaptable to various types of contaminants and extreme temperatures.

[0105] Uninterrupted imaging and low-cost integration with advanced driver assistance systems. Specifically, the cleaning time is shorter than the decision cycle of the advanced driver assistance system, and the vibration frequency of the piezoelectric array exceeds the interference range of the camera's imaging frame rate, resulting in no image blurring or interruption. No additional camera is required; it can be directly integrated into the existing camera, reducing costs.

[0106] Embedded integration ensures high reliability and vibration resistance. Specifically, the entire cleaning execution device is embedded and integrated into the camera housing and bracket, with no exposed pipes or components; it uses automotive-grade materials, and its vibration resistance level meets vehicle vibration standards, making it suitable for bumpy roads, with a vibration failure rate far lower than that of aftermarket add-on systems.

[0107] Intelligent and adaptive, with high energy efficiency and low power consumption. Specifically, at the software level, it adopts dirt recognition and cleaning decision algorithms to achieve adaptive cleaning rather than over-cleaning; at the hardware level, it adopts pulse working mode (non-continuous power supply) and vibration energy harvesting (supplementing sensor power), which significantly reduces energy consumption and adapts to the range requirements of new energy vehicles.

[0108] Example 3

[0109] Figure 3 This is a schematic diagram of a vehicle-mounted camera cleaning device according to Embodiment 3 of the present invention. This embodiment is applicable to situations requiring vehicle-mounted camera cleaning, such as... Figure 3 As shown, the specific structure of the device includes:

[0110] The perception data acquisition module 31 is used to acquire multi-source cleaning perception data, which includes at least the optical reflectivity of multi-wavelength light collected by the multispectral optical sensor integrated on the vehicle camera, the inter-electrode impedance collected by the micro-impedance electrode integrated on the vehicle camera, the current vehicle speed, and the ambient temperature.

[0111] The dirt identification module 32 is used to determine the type and degree of dirt based on the optical reflectivity and the inter-electrode impedance using a dirt identification algorithm.

[0112] Cleaning decision module 33 is used to generate cleaning execution parameters based on the type of dirt, the degree of pollution, the ambient temperature and the current vehicle speed through a cleaning decision algorithm;

[0113] The cleaning execution module 34 is used to sequentially activate the micro-vibration membrane, eddy current generator and ring piezoelectric array integrated on the vehicle camera according to the cleaning execution parameters, so as to perform contactless cleaning of the vehicle camera.

[0114] The vehicle-mounted camera cleaning device provided in this embodiment acquires multi-source cleaning perception data through a perception data acquisition module. This multi-source cleaning perception data includes at least the optical reflectivity of multi-wavelength light collected by a multispectral optical sensor integrated on the vehicle-mounted camera, the inter-electrode impedance collected by a micro-impedance electrode integrated on the vehicle-mounted camera, the current vehicle speed, and the ambient temperature. A dirt identification module uses a dirt identification algorithm to determine the type and degree of dirt based on the optical reflectivity and the inter-electrode impedance. A cleaning decision module uses a cleaning decision algorithm to generate cleaning execution parameters based on the type of dirt, the degree of dirt, the ambient temperature, and the current vehicle speed. A cleaning execution module, based on the cleaning execution parameters, sequentially activates the micro-vibration membrane, eddy current generator, and ring piezoelectric array integrated on the vehicle-mounted camera to perform contactless cleaning of the vehicle-mounted camera. This solution utilizes the differences in reflectivity of multi-wavelength light for different types of contaminants, combined with the acquisition of inter-electrode impedance, to determine the type and degree of contamination. It then combines ambient temperature and current vehicle speed to generate cleaning execution parameters tailored to the current cleaning conditions. Based on these parameters, a combination of a micro-vibration diaphragm, an eddy current generator, and a ring piezoelectric array enables coordinated cleaning through sound waves, airflow, and micro-vibrations. In short, this solution achieves efficient and precise cleaning of vehicle-mounted cameras that are contactless and adaptable to various types of contaminants.

[0115] Furthermore, the cleaning decision module 33 is specifically used for:

[0116] The cleaning decision algorithm converts the type of dirt, the degree of pollution, and the current vehicle speed into corresponding mapping function values, and then performs a weighted summation to obtain the cleaning priority.

[0117] When the cleaning priority exceeds the cleaning threshold, the vortex airflow intensity is determined based on the product of the baseline airflow intensity, the mapping function value of the pollution degree conversion, and the ambient temperature compensation function value corresponding to the ambient temperature.

[0118] The sound output power is determined by multiplying the reference sound power and the sound power correction function value corresponding to the type of dirt.

[0119] The vibration frequency of the micro-vibration membrane is determined based on its material.

[0120] Based on the type of contaminant and the degree of contamination, the cleaning duration and the amplitude of the micro-vibration membrane are determined;

[0121] Cleaning execution parameters are generated based on the vortex airflow intensity, the acoustic output power, the vibration frequency, the amplitude, and the cleaning duration.

[0122] Furthermore, the device also includes:

[0123] The power-on monitoring module is used to enable the multispectral optical sensor, the micro-impedance electrode, and the temperature and humidity sensor to operate at low power consumption when the vehicle is powered on, before acquiring multi-source cleaning sensing data, and to monitor cleaning sensing trigger information in real time.

[0124] The high-frequency operation module is used to enable the multispectral optical sensor, the micro-impedance electrode, and the temperature and humidity sensor to operate in high-frequency mode when the cleaning sensing trigger information meets the cleaning sensing trigger conditions.

[0125] Furthermore, the cleaning perception trigger information includes at least the cleaning perception transmittance loss rate, vehicle navigation information, and cleaning command trigger information;

[0126] The cleaning perception triggering conditions include at least one of the following: the cleaning perception transmittance loss rate exceeds the loss rate threshold, the vehicle navigation information indicates a severe weather forecast, and the cleaning command triggering information indicates that cleaning is triggered.

[0127] Furthermore, the device also includes:

[0128] A low-temperature heating module is used to activate the low-temperature heater configured in the air inlet pipe of the eddy current generator if the ambient temperature is lower than the temperature threshold during the non-contact cleaning process.

[0129] Furthermore, the device also includes:

[0130] The first verification module is used to obtain the light transmittance loss rate after the contactless cleaning is completed.

[0131] The second verification module is used to determine whether cleaning meets the standard if the light transmittance loss rate during the cleaning verification is lower than the verification threshold; otherwise, the cleaning execution parameters are adjusted for a second cleaning.

[0132] The vehicle camera cleaning device provided in this embodiment of the invention can perform the vehicle camera cleaning method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of performing the method.

[0133] Example 4

[0134] Figure 4This is a schematic diagram of the structure of an electronic device implementing embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as smartphones and other similar computing devices. The electronic device can also be a vehicle. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0135] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 performs various appropriate actions and processes based on the computer programs stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0136] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0137] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the vehicle camera cleaning method.

[0138] In some embodiments, the vehicle camera cleaning method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the vehicle camera cleaning method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the vehicle camera cleaning method by any other suitable means (e.g., by means of firmware).

[0139] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0140] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0141] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0142] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0143] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0144] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0145] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and no limitation is imposed herein.

[0146] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for cleaning a vehicle-mounted camera, characterized in that, include: Acquire multi-source clean sensing data, which includes at least the optical reflectivity of multi-wavelength light collected by a multispectral optical sensor integrated on the vehicle camera, the inter-electrode impedance collected by a micro-impedance electrode integrated on the vehicle camera, the current vehicle speed, and the ambient temperature. The type and degree of contamination are determined by a contaminant identification algorithm based on the optical reflectivity and the inter-electrode impedance. A cleaning decision algorithm is used to generate cleaning execution parameters based on the type of dirt, the degree of pollution, the ambient temperature, and the current vehicle speed. Based on the cleaning execution parameters, the micro-vibration membrane, eddy current generator, and ring piezoelectric array integrated on the vehicle camera are activated sequentially in a time sequence to perform contactless cleaning of the vehicle camera.

2. The method according to claim 1, characterized in that, A cleaning decision algorithm generates cleaning execution parameters based on the type of contaminant, the degree of contamination, the ambient temperature, and the current vehicle speed, including: The cleaning decision algorithm converts the type of dirt, the degree of pollution, and the current vehicle speed into corresponding mapping function values, and then performs a weighted summation to obtain the cleaning priority. When the cleaning priority exceeds the cleaning threshold, the vortex airflow intensity is determined based on the product of the baseline airflow intensity, the mapping function value of the pollution degree conversion, and the ambient temperature compensation function value corresponding to the ambient temperature. The sound output power is determined by multiplying the reference sound power and the sound power correction function value corresponding to the type of dirt. The vibration frequency of the micro-vibration membrane is determined based on its material. Based on the type of contaminant and the degree of contamination, the cleaning duration and the amplitude of the micro-vibration membrane are determined; Cleaning execution parameters are generated based on the vortex airflow intensity, the acoustic output power, the vibration frequency, the amplitude, and the cleaning duration.

3. The method according to claim 1, characterized in that, Before acquiring the multi-source cleaning sensing data, the method further includes: When the vehicle is powered on, the multispectral optical sensor, the micro-impedance electrode, and the temperature and humidity sensor operate with low power consumption and monitor cleaning sensing trigger information in real time. When the cleaning sensing trigger information meets the cleaning sensing trigger conditions, the multispectral optical sensor, the micro-impedance electrode, and the temperature and humidity sensor are operated in high-frequency mode.

4. The method according to claim 3, characterized in that, The cleaning perception trigger information includes at least the cleaning perception transmittance loss rate, vehicle navigation information, and cleaning command trigger information; The cleaning perception triggering conditions include at least one of the following: the cleaning perception transmittance loss rate exceeds the loss rate threshold, the vehicle navigation information indicates a severe weather forecast, and the cleaning command triggering information indicates that cleaning is triggered.

5. The method according to claim 1, characterized in that, Also includes: During the non-contact cleaning process, if the ambient temperature is lower than the temperature threshold, the low-temperature heater configured in the air inlet pipe of the vortex generator is activated.

6. The method according to claim 1, characterized in that, Also includes: Once the contactless cleaning is completed, obtain the light transmittance loss rate to verify the cleaning process. If the light transmittance loss rate during the cleaning verification is lower than the verification threshold, then the cleaning meets the standard. Otherwise, adjust the cleaning execution parameters to perform a second cleaning.

7. A vehicle-mounted camera cleaning device, characterized in that, include: The perception data acquisition module is used to acquire multi-source cleaning perception data, which includes at least the optical reflectivity of multi-wavelength light collected by the multispectral optical sensor integrated on the vehicle camera, the inter-electrode impedance collected by the micro-impedance electrode integrated on the vehicle camera, the current vehicle speed, and the ambient temperature. A dirt identification module is used to determine the type and degree of dirt based on the optical reflectivity and the inter-electrode impedance using a dirt identification algorithm. The cleaning decision module is used to generate cleaning execution parameters based on the type of dirt, the degree of pollution, the ambient temperature, and the current vehicle speed using a cleaning decision algorithm. The cleaning execution module is used to sequentially activate the micro-vibration membrane, eddy current generator, and ring piezoelectric array integrated on the vehicle camera according to the cleaning execution parameters, so as to perform non-contact cleaning of the vehicle camera.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method as described in any one of claims 1-6.

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