CN117000692A - Intelligent control-based solar street lamp cleaning method - Google Patents

Abstract

The invention discloses a solar street lamp cleaning method based on intelligent control, which comprises the following steps: collecting pollution data about the surface of the solar street lamp by a sensor arranged on the solar street lamp device, wherein the pollution data comprise dust, grease, leaves and bird droppings; transmitting the collected pollution data to a main controller for intelligent analysis, comparing the collected pollution data with a preset pollution threshold value, and determining the cleaning time and the cleaning mode of cleaning the solar street lamp; when the cleaning is determined to be needed, the main controller controls the driving assembly to drive the cleaning device to perform cleaning operation according to the current illumination intensity and the set cleaning time interval. The intelligent cleaning system is adopted to perform cleaning operation according to actual pollution conditions, so that blindness of periodic maintenance is avoided, and labor and time cost are reduced; the regular cleaning of the solar street lamp can prevent the accumulation of pollutants, prolong the service life of the solar street lamp and reduce the frequency of replacement and maintenance.

Description

Intelligent control-based solar street lamp cleaning method

Technical Field

The invention relates to the technical field of street lamp cleaning, in particular to a solar street lamp cleaning method based on intelligent control.

Background

Solar street lamps are used as environment-friendly and energy-saving lighting equipment and are widely applied to public places such as urban roads, parks, squares and the like, however, the working efficiency of the solar street lamps is greatly dependent on the photoelectric conversion efficiency of the solar street lamps, and the surface of the solar street lamps can seriously influence the photoelectric conversion efficiency if being covered by pollutants such as dust, grease, leaves, bird droppings and the like; the current solar street lamp cleaning technology mainly relies on manual periodic cleaning, and the following problems exist in the mode: firstly, the manual cleaning efficiency is low, the labor intensity is high, and the real-time cleaning cannot be realized; secondly, manual cleaning is difficult to adopt different cleaning modes aiming at different pollutants, and the cleaning effect is limited; thirdly, the manual cleaning cannot accurately control the cleaning time, and cleaning can be performed when the illumination intensity is high, so that the photoelectric conversion efficiency of the solar street lamp is affected; in addition, although the existing solar street lamp cleaning systems can automatically clean, the systems usually adopt a mode of timing cleaning or periodic cleaning, and cannot intelligently clean according to the actual pollution condition of the surface of the solar street lamp, so that the cleaning effect and the cleaning efficiency are required to be improved.

The application number is: the invention of CN201510759411 discloses a solar energy component cleaning method, when the pressure sensor detects that the pressure exceeds a set value, automatic cleaning is carried out at the moment, cleaning comprises the steps of turning the solar energy component to enable the front face of the solar energy component to face downwards, vibrating and blowing, cleaning most sand on the surface of the solar energy component, turning back after cleaning is finished, detecting the pressure again, if the pressure value is smaller than the set value, the cleaning is successful, if the pressure value is larger than the set value, the fact that caking which is difficult to clean is formed on the surface of the solar energy component, and at the moment, cleaning cannot be carried out through vibrating and blowing, and information is sent to a server. The defects include: the cleaning mode is only suitable for cleaning larger-particle pollutants such as sand on the surface of the solar module by turning over the solar module and vibrating and blowing, and can not be effectively cleaned for caking or fine pollutants which are difficult to clean; whether the cleaning is successful or not is judged by detecting the pressure value only, and the change of the pressure value is influenced by various factors, such as the structure, the material and the like of the solar module, so that the cleaning effect can not be accurate by only detecting the pressure value; the scheme does not mention a cleaning state and a feedback mechanism of the real-time monitoring solar module, can not acquire cleaned data in time, and can not evaluate and adjust the cleaning effect in real time.

Therefore, an intelligent control solar street lamp cleaning method is urgently needed.

Disclosure of Invention

The invention provides a solar street lamp cleaning method based on intelligent control, which aims to solve the problems that in the prior art, a solar street lamp is used as an environment-friendly and energy-saving lighting device and is widely applied to public places such as urban roads, parks, squares and the like, however, the working efficiency of the solar street lamp is greatly dependent on the photoelectric conversion efficiency of the solar street lamp, and the surface of the solar street lamp can seriously influence the photoelectric conversion efficiency if being covered by pollutants such as dust, grease, leaves, bird droppings and the like; the current solar street lamp cleaning technology mainly relies on manual periodic cleaning, and the following problems exist in the mode: firstly, the manual cleaning efficiency is low, the labor intensity is high, and the real-time cleaning cannot be realized; secondly, manual cleaning is difficult to adopt different cleaning modes aiming at different pollutants, and the cleaning effect is limited; thirdly, the manual cleaning cannot accurately control the cleaning time, and cleaning can be performed when the illumination intensity is high, so that the photoelectric conversion efficiency of the solar street lamp is affected; in addition, although the existing solar street lamp cleaning systems can automatically clean, the systems usually adopt a mode of timing cleaning or periodic cleaning, and cannot intelligently clean according to the actual pollution condition of the surface of the solar street lamp, so that the cleaning effect and the cleaning efficiency are both required to be improved.

In order to achieve the above purpose, the present invention provides the following technical solutions:

the intelligent control-based solar street lamp cleaning method comprises the following steps:

s101: collecting pollution data about the surface of the solar street lamp by a sensor arranged on the solar street lamp device, wherein the pollution data comprise dust, grease, leaves and bird droppings;

s102: transmitting the collected pollution data to a main controller for intelligent analysis, comparing the collected pollution data with a preset pollution threshold value, and determining the cleaning time and the cleaning mode of cleaning the solar street lamp;

s103: when the cleaning is determined to be needed, the main controller controls the driving assembly to drive the cleaning device to perform cleaning operation according to the current illumination intensity and the set cleaning time interval.

Wherein, the step S101 includes:

s1011: collecting data about dust on the surface of the solar street lamp by a photosensitive sensor arranged on the solar street lamp device;

s1012: collecting data about grease on the surface of the solar street lamp through a chemical sensor arranged on the solar street lamp device;

s1013: through shape recognition sensor and the color recognition sensor that set up on solar street lamp device, gather the data about solar street lamp surface leaf and bird's droppings respectively.

Wherein, the step S102 includes:

s1021: the collected pollution data are sent to a main controller for intelligent analysis, and the types of pollutants are determined by identifying and classifying the types of the pollution data;

s1022: comparing the determined pollutant types with a preset pollution threshold value to obtain a comparison result, wherein the comparison result comprises the pollution degree of the surface of the current solar street lamp;

s1023: and according to the comparison result, the main controller determines the cleaning time and the cleaning mode of the solar street lamp.

Wherein, the step S103 includes:

s1031: when the cleaning operation is determined to be required, the main controller starts a cleaning process according to the current illumination intensity and the set cleaning time interval;

s1032: the main controller controls the driving assembly to drive the cleaning device to perform cleaning operation, and the cleaning device is fixed on the left and right profiles and performs cleaning operation by moving on the surfaces of the solar street lamps;

s1033: after cleaning is finished, the intelligent analysis of the sensor and the main controller is performed again to obtain an analysis result, and whether the cleaning operation is finished is judged according to the analysis result.

Wherein, the step S1021 includes:

the collected pollution data are sent to a main controller for intelligent analysis, the main controller identifies the type of the pollution data through a neural network model, wherein dust is identified according to particle size and color, grease is identified according to viscosity and reflection spectrum, leaves are identified according to shape and color, and bird droppings are identified according to shape, color and chemical components; the main controller classifies pollution data according to a preset pollutant cleaning mode, wherein dust is cleaned by adopting a dust collection mode of a cleaning device; cleaning grease by adopting a solvent cleaning mode of a cleaning device; the leaves are cleaned by a cleaning device in a wiping way; the bird droppings are cleaned by adopting a cleaning device and a high-pressure water gun cleaning mode.

Wherein, step S1022 includes:

comparing the determined pollutant types with preset pollution thresholds, wherein the preset pollution thresholds are set according to factors of the materials, the structures and the environmental conditions of the surfaces of the solar street lamps and are stored in a main controller; the main controller calculates the pollution degree of the surface of the current solar street lamp according to the quantity, the type and the distribution condition of the pollutant types; comparing the calculated pollution degree with a preset pollution threshold value to obtain a comparison result, wherein the comparison result comprises that the pollution degree is lower than, equal to or higher than the preset threshold value.

Wherein, the step S1023 comprises:

the main controller determines the pollution degree of the solar street lamp according to a comparison result, wherein the comparison result is obtained by comparing the calculated pollution degree with a preset pollution threshold value; the method comprises the steps that a main controller determines the cleaning time of the solar street lamp according to the pollution degree and combining the use environment of the solar street lamp with the information of weather forecast, wherein when the pollution degree is higher than a preset threshold value and a few days in the future are a sunny day, cleaning is determined in a certain time period of the sunny day; the main controller determines a cleaning mode of the solar street lamp according to the types and the distribution conditions of pollutants, wherein when the pollutants are dust and bird droppings, the cleaning mode of dust collection by using the cleaning device and cleaning by using the high-pressure water gun is determined.

Wherein, the step S1031 includes:

setting a cleaning time interval, wherein the cleaning time interval is set according to actual conditions and comprises specific time per day or every other certain time period; the main controller receives signals of an illumination intensity sensor to acquire the current illumination intensity, wherein the illumination intensity sensor is arranged on the surface of the solar street lamp and is used for monitoring the illumination intensity of the solar street lamp in real time; the main controller judges whether cleaning operation is needed according to the current illumination intensity and the set cleaning time interval, and if the current illumination intensity is lower than a preset threshold value or the set cleaning time interval is reached, the main controller starts the cleaning process.

Wherein, the step S1032 includes:

the method comprises the steps that a main controller analyzes solar street lamp pollution data to obtain analysis data, wherein the analysis data comprise position information of a pollution area in a space to be cleaned, pollution level and positions of objects in the cleaning space, and the main controller determines a cleaning target area based on the analysis data;

the main controller uses a learning model to determine the cleaning strength of the cleaning target area, and determines the position of the cleaning target area in the space to be cleaned and the cleaning priority corresponding to the cleaning strength through the learning model;

The main controller controls the driving assembly to drive the cleaning device to perform cleaning operation, the cleaning device is fixed on the left and right section bars and moves on the surface of the solar street lamp according to the determined cleaning priority to perform cleaning operation.

Wherein, the step S1033 includes:

after cleaning, the main controller acquires cleaned data by receiving signals of a sensor, wherein the sensor comprises an illumination intensity sensor for monitoring the illumination intensity of the solar street lamp in real time; the main controller preprocesses the received cleaned data, including data cleaning and data conversion; the main controller performs intelligent analysis on the preprocessed data by using a pre-trained model to acquire an analysis result; and the main controller judges whether the cleaning operation is finished according to the analysis result, and if the analysis result shows that the illumination intensity of the lamplight reaches the preset threshold value, the main controller judges that the cleaning operation is finished.

Compared with the prior art, the invention has the following advantages:

the intelligent control-based solar street lamp cleaning method comprises the following steps: collecting pollution data about the surface of the solar street lamp by a sensor arranged on the solar street lamp device, wherein the pollution data comprise dust, grease, leaves and bird droppings; transmitting the collected pollution data to a main controller for intelligent analysis, comparing the collected pollution data with a preset pollution threshold value, and determining the cleaning time and the cleaning mode of cleaning the solar street lamp; when the cleaning is determined to be needed, the main controller controls the driving assembly to drive the cleaning device to perform cleaning operation according to the current illumination intensity and the set cleaning time interval. The intelligent cleaning system is adopted to perform cleaning operation according to actual pollution conditions, so that blindness of periodic maintenance is avoided, and labor and time cost are reduced; the regular cleaning of the solar street lamp can prevent the accumulation of pollutants, prolong the service life of the solar street lamp and reduce the frequency of replacement and maintenance.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.

The technical scheme of the invention is further described in detail through the drawings and the embodiments.

Drawings

The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:

FIG. 1 is a flow chart of a solar street lamp cleaning method based on intelligent control in an embodiment of the invention;

FIG. 2 is a flow chart of collecting data about solar street lamp surface pollution in an embodiment of the invention;

FIG. 3 is a flow chart of intelligent analysis of pollution data in an embodiment of the invention.

Detailed Description

The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.

The embodiment of the invention provides a solar street lamp cleaning method based on intelligent control, which comprises the following steps:

s101: collecting pollution data about the surface of the solar street lamp through a sensor arranged on the solar street lamp device;

S102: transmitting the collected pollution data to a main controller for intelligent analysis, comparing the collected pollution data with a preset pollution threshold value, and determining the cleaning time and the cleaning mode of cleaning the solar street lamp;

s103: when the cleaning is determined to be needed, the main controller controls the driving assembly to drive the cleaning device to perform cleaning operation according to the current illumination intensity and the set cleaning time interval.

The working principle of the technical scheme is as follows: the sensor arranged on the solar street lamp device can periodically collect pollution data on the surface of the solar street lamp, including dust, grease, leaves, bird droppings and the like; the collected pollution data can be transmitted to the main controller in a wireless transmission or wired transmission mode, and the main controller can carry out intelligent analysis on the received data, including data processing and algorithm operation; the main controller compares the collected pollution data with a preset pollution threshold value, and the preset pollution threshold value is set according to the cleaning requirement and performance index of the solar street lamp; according to the comparison result of the pollution data and the pollution threshold value, the main controller can determine the cleaning time and the cleaning mode for cleaning the solar street lamp, for example, if the pollution degree exceeds the threshold value, the main controller can judge that the cleaning operation is required; when the cleaning is determined to be needed, the main controller controls the driving assembly to drive the cleaning device to perform cleaning operation according to the current illumination intensity and the set cleaning time interval. The cleaning means may comprise brushes, water jets or other suitable cleaning devices.

The beneficial effects of the technical scheme are as follows: the solar street lamp is cleaned regularly, so that pollutants can be removed effectively, the power generation efficiency of the solar street lamp is improved, and the normal operation of the solar street lamp is ensured; the intelligent cleaning system is adopted to perform cleaning operation according to actual pollution conditions, so that blindness of periodic maintenance is avoided, and labor and time cost are reduced; the regular cleaning of the solar street lamp can prevent the accumulation of pollutants, prolong the service life of the solar street lamp and reduce the frequency of replacement and maintenance; the solar street lamp can be cleaned, so that the light energy conversion efficiency can be improved, the brightness of the street lamp can be increased, and the safety of pedestrians and vehicles can be improved; through the intelligent cleaning system, the energy and water resource consumption in the cleaning process can be reduced, the influence on the environment is reduced, and the aims of energy conservation and emission reduction are fulfilled.

In another embodiment, the step S101 includes:

s1011: collecting data about dust on the surface of the solar street lamp by a photosensitive sensor arranged on the solar street lamp device;

s1012: collecting data about grease on the surface of the solar street lamp through a chemical sensor arranged on the solar street lamp device;

s1013: through shape recognition sensor and the color recognition sensor that set up on solar street lamp device, gather the data about solar street lamp surface leaf and bird's droppings respectively.

The working principle of the technical scheme is as follows: the photosensitive sensor is arranged on the solar street lamp device, the dust degree on the surface of the solar street lamp is judged by sensing the intensity change of light, and the sensor transmits collected dust data to the main controller for processing and analysis; the chemical sensor is arranged on the solar street lamp device, grease on the surface of the solar street lamp is detected through chemical reaction, and the sensor transmits collected grease data to the main controller for processing and analysis; the shape recognition sensor and the color recognition sensor are arranged on the solar street lamp device, the shape recognition sensor recognizes the shape of the leaf, the color recognition sensor recognizes the color of the bird droppings to detect the leaf and the bird droppings on the surface of the solar street lamp, and the sensor transmits the collected leaf and bird droppings data to the main controller for processing and analysis; the main controller receives the data of dust, grease, leaves and bird droppings collected by the sensor, processes and analyzes the data through an intelligent algorithm, and can determine the cleaning time and the cleaning mode of cleaning the solar street lamp according to the change trend of the data and a preset pollution threshold.

Collecting data concerning dust on a surface of a solar street lamp, comprising:

Collecting data about dust on the surface of the solar street lamp by a photosensitive sensor arranged on the solar street lamp device;

determining dust data acquisition constraints (constraints including a range of reflectivity, a rate of change, etc. for screening dust data conforming to the conditions) based on the photosensitive sensor;

extracting second dust data from a preset dust data acquisition scene based on the dust data acquisition constraint condition (dust data which is extracted from the preset dust data acquisition scene according to the dust data acquisition constraint condition and meets the constraint condition);

on-line expansion is carried out on the dust database based on the second dust data;

wherein, based on the photosensitive sensor, confirm dust data acquisition constraint condition, include:

extracting reflectivity information of the solar street lamp surface based on a preset reflectivity extraction template (a template preset according to factors such as materials and structures of the solar street lamp surface and used for extracting reflectivity information of the solar street lamp surface, wherein the reflectivity extraction template comprises reflectivity range information, change rate information and the like corresponding to different characteristic value types);

based on the characteristic value types of the reflectivity information (the reflectivity information is divided into different characteristic value types according to factors such as materials and structures of the solar street lamp surface, each characteristic value type corresponds to a group of preset dust data acquisition constraint conditions and is used for determining dust data meeting the conditions), a corresponding preset dust data acquisition constraint condition generation template is generated, and the dust data acquisition constraint conditions are generated according to the reflectivity information.

Assume that two types of eigenvalues are defined in a preset reflectivity extraction template: the reflectivity range of the characteristic value type A corresponding to the surface of the solar street lamp is 0.2-0.4, and the change rate is 0.1; the characteristic value type B corresponds to the reflectivity range of 0.5-0.7, the change rate is 0.2, and the reflectivity information of the solar street lamp surface is extracted to be 0.3 according to the data acquired by the photosensitive sensor, and belongs to the characteristic value type A; according to preset dust data acquisition constraint conditions corresponding to the characteristic value type A, the constraint conditions of dust data acquisition can be determined to be that the reflectivity ranges from 0.2 to 0.4, the change rate is not more than 0.1, and second dust data meeting the conditions is extracted from a preset dust data acquisition scene according to the constraint conditions.

The beneficial effects of the technical scheme are as follows: by arranging different types of sensors, the pollution data such as dust, grease, leaves, bird droppings and the like on the surface of the solar street lamp can be accurately collected, and accurate pollution condition analysis is provided; the main controller processes and analyzes the data acquired by the sensor through an intelligent algorithm, and can determine the optimal time and mode for cleaning the solar street lamp according to the change trend of the pollution data and a preset pollution threshold value; according to different types of pollution data, cleaning operation can be performed pertinently, so that the cleaning efficiency is improved, and the normal operation of the solar street lamp is ensured; by accurately collecting pollution data and intelligently analyzing, unnecessary cleaning operation can be avoided, waste of energy and water resources is reduced, and the energy-saving and environment-friendly aims are realized; the regular cleaning of the solar street lamp can prevent the accumulation of pollutants, prolong the service life of the solar street lamp and reduce the frequency of replacement and maintenance.

In another embodiment, the step S102 includes:

s1021: the collected pollution data are sent to a main controller for intelligent analysis, and the types of pollutants are determined by identifying and classifying the types of the pollution data;

s1022: comparing the determined pollutant types with a preset pollution threshold value to obtain a comparison result, wherein the comparison result comprises the pollution degree of the surface of the current solar street lamp;

s1023: and according to the comparison result, the main controller determines the cleaning time and the cleaning mode of the solar street lamp.

The working principle of the technical scheme is as follows: the main controller receives pollution data acquired by the sensor, analyzes and processes the data through an intelligent algorithm, and can identify and classify the types of pollutants according to the characteristics and modes of the data; the main controller compares the determined pollutant types with preset pollution thresholds, wherein the preset pollution thresholds are set according to actual conditions and requirements, for example, the dust threshold is 10%, the grease threshold is 5% and the like; by comparing the pollutant types with a preset threshold, the main controller can obtain a comparison result, namely the pollution degree of the surface of the current solar street lamp, for example, if the pollution degree of dust exceeds the preset threshold, the main controller can judge that the pollution degree of the surface of the solar street lamp is high; according to the obtained pollution level, the main controller can determine the cleaning time and the cleaning mode of the solar street lamp according to a preset cleaning strategy, for example, if the pollution level is high, the main controller can set the solar street lamp to be cleaned once a week and adopts an efficient cleaning mode, such as a water spraying device to perform cleaning operation.

The beneficial effects of the technical scheme are as follows: the type of the pollutant can be accurately identified and classified by the intelligent analysis algorithm of the main controller, so that the analysis accuracy of the pollution data is improved; the pollution degree of the surface of the solar street lamp can be obtained in real time by comparing the pollutant types with a preset threshold value, and cleaning measures can be timely taken to keep the efficient operation of the solar street lamp; according to different pollution degrees, personalized cleaning time and cleaning modes can be formulated, the cleaning efficiency is improved, and resources are saved; the regular cleaning of the solar street lamp can prevent the accumulation of pollutants, prolong the service life of the solar street lamp and reduce the frequency of replacement and maintenance; through accurate pollution analysis and cleaning strategies, unnecessary cleaning operations can be reduced, energy sources and water resources are saved, and the energy-saving and environment-friendly aims are achieved.

In another embodiment, the step S103 includes:

s1031: when the cleaning operation is determined to be required, the main controller starts a cleaning process according to the current illumination intensity and the set cleaning time interval;

s1032: the main controller controls the driving assembly to drive the cleaning device to perform cleaning operation, and the cleaning device is fixed on the left and right profiles and performs cleaning operation by moving on the surfaces of the solar street lamps;

S1033: after cleaning is finished, the intelligent analysis of the sensor and the main controller is performed again to obtain an analysis result, and whether the cleaning operation is finished is judged according to the analysis result.

The working principle of the technical scheme is as follows: when determining that cleaning operation is required, the main controller starts a cleaning process according to the current illumination intensity and a set cleaning time interval, and can judge whether cleaning operation is required or not by receiving signals of a sensor, such as signals of an illumination intensity sensor and according to a preset cleaning time interval; the main controller controls the driving assembly to drive the cleaning device to perform cleaning operation, the cleaning device is fixed on the left and right section bars and can move on the surface of the solar street lamp to perform cleaning operation, the driving assembly can be a motor or other suitable driving equipment, and the cleaning device can be driven to perform cleaning operation according to a control signal of the main controller; after cleaning is finished, the cleaning result is confirmed through intelligent analysis of a sensor and a main controller, the sensor can be an illumination intensity sensor, whether the cleaning operation is finished or not can be judged by detecting the illumination intensity of the solar street lamp, and the main controller can conduct intelligent analysis according to signals of the sensor to confirm the cleaning result; therefore, the solar street lamp can be automatically cleaned, the solar street lamp can be kept clean, and the power generation efficiency of the solar street lamp can be improved.

The beneficial effects of the technical scheme are as follows: the main controller automatically starts the cleaning process according to the illumination intensity and the set cleaning time interval without manual intervention, so that the surface of the solar street lamp can be ensured to be always clean, and the solar energy conversion efficiency is improved; the cleaning device is fixed on the left and right section bars and performs cleaning operation by moving on the surface of the solar street lamp, and the cleaning mode can cover the whole surface of the solar street lamp, ensure that dirt and sundries are thoroughly removed, and improve the cleaning effect; after cleaning is finished, the intelligent analysis of the sensor and the main controller is used for obtaining an analysis result, so that the effect of cleaning operation can be known in time, and the thorough cleaning of the surface of the solar street lamp is ensured; by cleaning the solar street lamp regularly, dirt and sundries can be prevented from accumulating, the high-efficiency working state of the solar street lamp is maintained, the solar energy conversion efficiency is improved, and the service life of the solar street lamp is prolonged; the automatic cleaning operation can reduce the frequency and the workload of manual cleaning and reduce the maintenance cost, and meanwhile, the incomplete cleaning or other problems can be found in time by analyzing the cleaning result in real time, so that the maintenance and replacement requirements are reduced; the solar street lamp is cleaned regularly, so that the energy waste can be reduced, the energy utilization efficiency is improved, the energy-saving and environment-friendly aim is realized, and meanwhile, the cleaning agent used in the cleaning operation can be environment-friendly, and the pollution to the environment is reduced.

In another embodiment, the step S1021 includes:

the collected pollution data are sent to a main controller for intelligent analysis, the main controller identifies the type of the pollution data through a neural network model, wherein dust is identified according to particle size and color, grease is identified according to viscosity and reflection spectrum, leaves are identified according to shape and color, and bird droppings are identified according to shape, color and chemical components; the main controller classifies pollution data according to a preset pollutant cleaning mode, wherein dust is cleaned by adopting a dust collection mode of a cleaning device; cleaning grease by adopting a solvent cleaning mode of a cleaning device; the leaves are cleaned by a cleaning device in a wiping way; the bird droppings are cleaned by adopting a cleaning device and a high-pressure water gun cleaning mode.

The working principle of the technical scheme is as follows: the main controller receives pollution data acquired by the sensor, sends the data into the neural network model for intelligent analysis, and the neural network model can identify the characteristics of different pollutants and classify the pollution data through learning and training; the main controller judges whether the pollution data belong to dust according to the identification result of the neural network model, and the identification of the dust can be judged by comparing the characteristics of the pollution data with preset dust characteristics according to the particle size and the color; the main controller judges whether the pollution data belongs to grease according to the identification result of the neural network model, and the identification of the grease can be judged by comparing the characteristics of the pollution data with the preset grease characteristics according to the viscosity and the reflection spectrum; the main controller judges whether the pollution data belongs to leaves according to the identification result of the neural network model, and the identification of the leaves can be judged by comparing the characteristics of the pollution data with preset leaf characteristics according to the shape and the color of the leaves; the main controller judges whether the pollution data belongs to the bird droppings according to the identification result of the neural network model, and the identification of the bird droppings can be judged by comparing the characteristics of the pollution data with preset bird droppings characteristics according to the shape, the color and the chemical components; according to the classification result of the pollutants and a preset cleaning mode, the main controller selects a corresponding cleaning device and the cleaning mode to perform cleaning operation, for example, for dust, the main controller can use the cleaning device to clean in a wiping mode; for grease, cleaning can be performed by adopting a solvent cleaning mode of a cleaning device; for leaves, the leaves can be cleaned in a wiping way by using a cleaning device; for bird droppings, the cleaning device can be used for cleaning in a high-pressure water gun cleaning mode.

The method for intelligent analysis of the collected pollution data comprises the following steps of:

analyzing the data type of the pollution data in the pollution data cluster;

matching the data type with any preset standard pollution data type; standard pollution data types include: dust, grease, leaves, and bird droppings;

if the matching is met, the corresponding pollution data is used as second target pollution data (the second target pollution data is pollution data which is determined to be cleaned after being identified and matched), and a preset second identification characteristic corresponding to the type of the standard pollution data which is met by the matching is obtained; the second identifying feature comprises: particle size and color of dust, viscosity and reflectance spectrum of grease, shape and color of leaves, shape, color and chemical composition of bird droppings;

sending the pollution data into a neural network model in the main controller, and performing intelligent analysis on the second target pollution data by using the second identification characteristics;

if the analysis is met, a preset cleaning mode template corresponding to the matched second identification characteristic is obtained; determining a cleaning mode of the second target pollution data based on the cleaning mode template, and as a third cleaning element (the third cleaning element is a specific cleaning mode determined according to the cleaning mode template);

And the third cleaning element is complementarily integrated into the intelligent cleaning process of the pollutants.

The beneficial effects of the technical scheme are as follows: the types of different pollutants can be accurately identified and classified through intelligent analysis of the main controller and identification of the neural network model, so that analysis accuracy of pollution data is improved; according to the classification result of the pollutants and a preset cleaning mode, the most suitable cleaning device and the most suitable cleaning mode can be selected for cleaning operation, so that the cleaning effect and the cleaning efficiency are improved; the regular cleaning of the solar street lamp can prevent the accumulation of pollutants, maintain the high-efficiency working state of the solar street lamp, improve the solar energy conversion efficiency and prolong the service life of the solar street lamp; the automatic cleaning operation can reduce the frequency and the workload of manual cleaning and reduce the maintenance cost. Meanwhile, through intelligent analysis and confirmation of cleaning results, incomplete cleaning or other problems can be found in time, and maintenance and replacement requirements are reduced.

In another embodiment, the step S1022 includes:

comparing the determined pollutant types with preset pollution thresholds, wherein the preset pollution thresholds are set according to factors of the materials, the structures and the environmental conditions of the surfaces of the solar street lamps and are stored in a main controller; the main controller calculates the pollution degree of the surface of the current solar street lamp according to the quantity, the type and the distribution condition of the pollutant types; comparing the calculated pollution degree with a preset pollution threshold value to obtain a comparison result, wherein the comparison result comprises that the pollution degree is lower than, equal to or higher than the preset threshold value.

The working principle of the technical scheme is as follows: the main controller collects pollution data on the surface of the solar street lamp through the sensor, including the quantity and distribution conditions of pollutants such as dust, grease, leaves, bird droppings and the like; the main controller analyzes the collected pollution data to determine the types and the amounts of pollutants, for example, the shape and the color of leaves are identified through an image processing algorithm, and chemical components of the bird droppings and the like are detected through a chemical analysis instrument; the method comprises the steps that a main controller obtains a preset pollution threshold value related to the surface material, structure and environmental condition of the solar street lamp from a preset pollution threshold value storage unit; the main controller calculates the pollution degree of the surface of the current solar street lamp according to the quantity, the type and the distribution condition of the pollutant types; for example, the calculation may be performed based on the area, concentration, and other indicators of the contaminant; the main controller compares the calculated pollution degree with a preset pollution threshold value, and judges whether the pollution degree of the surface of the current solar street lamp is lower than, equal to or higher than the preset threshold value through numerical comparison; and the main controller determines whether the pollution degree of the surface of the current solar street lamp reaches or exceeds a preset pollution threshold according to the comparison result, and records or displays the comparison result.

Wherein comparing the determined contaminant species with a preset contamination threshold comprises:

acquiring pollutant type information to meet comparison conditions;

if the pollutant type information is acquired, comparing the pollutant type information with a preset pollution threshold, wherein the preset pollution threshold is set according to the material, structure and environmental condition factors of the solar street lamp surface and is stored in the main controller;

wherein, acquire pollutant type information in order to satisfy contrast condition, include:

acquiring a first condition meeting condition (the first condition meeting condition: refers to the condition that the contaminant species information meets the first condition of the comparison condition) that the contaminant species information meets the comparison condition;

constructing a first condition description vector (a first condition description vector: a vector for describing the condition of satisfaction of the first condition, which may include information on the kind, quantity, type, distribution, etc.) of the contaminant;

constructing a second condition meeting condition (the second condition meeting condition refers to the condition that the pollutant type information meets the second condition of the comparison condition) if the pollutant type information meets the comparison condition;

constructing a second condition description vector (a vector for describing the condition of satisfaction of the second condition) of the second condition satisfying condition;

Calculating the vector similarity between the first case description vector and the second case description vector;

taking the difference value between a preset similarity full value (the maximum value of the preset vector similarity for judging whether the vector similarity meets the preset similarity requirement) and the vector similarity as the difference;

if the difference is smaller than or equal to a preset difference threshold, the main controller calculates the pollution degree of the surface of the current solar street lamp according to the number, the type and the distribution condition of the pollutant types;

comparing the calculated pollution degree with a preset pollution threshold value to obtain a comparison result;

the comparison result includes that the pollution level is lower than, equal to or higher than a preset threshold value, and corresponding cleaning or warning processes are executed according to the comparison result.

The beneficial effects of the technical scheme are as follows: the pollution degree of the surface of the solar street lamp can be monitored in real time through analysis of the types and the quantity of the pollutants and calculation and comparison of the pollution degree; by comparing the pollution degree with a preset pollution threshold value, the condition that the pollution degree exceeds the preset threshold value can be found in time, early warning is performed in advance, maintenance is performed, and the normal operation of the solar street lamp is prevented from being influenced by pollution; according to the real-time monitoring and early warning of the pollution degree, cleaning and maintenance can be performed in a targeted manner, and unnecessary maintenance cost and waste of human resources are reduced; through monitoring and contrast to the pollution degree, can adjust clean tactics and frequency according to actual conditions, improve clean effect, keep the high-efficient operating condition of solar street lamp.

In another embodiment, the step S1023 includes:

the main controller determines the pollution degree of the solar street lamp according to a comparison result, wherein the comparison result is obtained by comparing the calculated pollution degree with a preset pollution threshold value; the method comprises the steps that a main controller determines the cleaning time of the solar street lamp according to the pollution degree and combining the use environment of the solar street lamp with the information of weather forecast, wherein when the pollution degree is higher than a preset threshold value and a few days in the future are a sunny day, cleaning is determined in a certain time period of the sunny day; the main controller determines a cleaning mode of the solar street lamp according to the types and the distribution conditions of pollutants, wherein when the pollutants are dust and bird droppings, the cleaning mode of dust collection by using the cleaning device and cleaning by using the high-pressure water gun is determined.

The working principle of the technical scheme is as follows: the main controller collects pollution data on the surface of the solar street lamp through the sensor, including the quantity and distribution conditions of pollutants such as dust, bird droppings and the like; the main controller processes the collected pollution data and determines the types and distribution conditions of pollutants through an image processing algorithm and a chemical analysis method; the method comprises the steps that a main controller obtains a preset pollution threshold value related to the use environment and the material of the solar street lamp from a preset pollution threshold value storage unit; and the main controller compares the calculated pollution degree with a preset pollution threshold value. Judging whether the pollution degree of the surface of the current solar street lamp exceeds a preset pollution threshold value or not through numerical comparison; and determining the pollution degree of the solar street lamp by the main controller according to the comparison result. When the pollution level is higher than a preset threshold value, the pollution level is considered to be higher; when the contamination level is lower than or equal to the preset threshold, the contamination level is considered to be lower.

The main controller determines the pollution degree of the solar street lamp according to the comparison result, and comprises the following steps:

obtaining comparison result information to meet the condition of determining pollution degree;

if the comparison result information is obtained, the main controller determines the pollution degree of the solar street lamp according to the comparison result, wherein the comparison result is obtained by comparing the calculated pollution degree with a preset pollution threshold value;

wherein, obtain the comparison result information in order to satisfy the condition of confirming the pollution degree, include:

acquiring the first condition satisfaction condition of the comparison result information meeting the determined pollution degree;

constructing a first condition description vector of the condition meeting condition;

constructing a second condition meeting condition for determining pollution degree if the comparison result information meets the condition;

constructing a second condition description vector of the second condition meeting condition;

calculating the vector similarity between the first case description vector and the second case description vector;

taking the difference value between the preset similarity full value and the vector similarity as the difference degree;

if the difference is smaller than or equal to a preset difference threshold, the main controller determines the cleaning time of the solar street lamp according to the pollution degree and combining the information of the use environment and weather forecast of the solar street lamp;

When the pollution degree is higher than a preset threshold value and the future days are predicted to be sunny days, cleaning is determined to be performed in a certain time period of sunny days;

the main controller determines a cleaning mode of the solar street lamp according to the types and distribution conditions of pollutants;

when the pollutants are dust and bird droppings, the cleaning device is used for cleaning in a dust collection and high-pressure water gun cleaning mode.

The beneficial effects of the technical scheme are as follows: the pollution degree of the surface of the solar street lamp can be monitored in real time through analyzing the types and the distribution conditions of pollutants and calculating and comparing the pollution degree; according to the determination of the pollution degree, the cleaning time of the solar street lamp can be accurately determined by combining the use environment of the solar street lamp and the information of weather forecast, and when the pollution degree is higher than a preset threshold and the forecast of days is sunny, cleaning is carried out in a certain time period of sunny days so as to ensure the cleaning effect; according to the type and the distribution condition of the pollutants, the cleaning mode of the solar street lamp can be determined, for example, when the pollutants are dust and bird droppings, the cleaning device can be selected for dust collection and high-pressure water gun cleaning, so that the cleaning effect is improved.

In another embodiment, the step S1031 includes:

setting a cleaning time interval, wherein the cleaning time interval is set according to actual conditions and comprises specific time per day or every other certain time period; the main controller receives signals of an illumination intensity sensor to acquire the current illumination intensity, wherein the illumination intensity sensor is arranged on the surface of the solar street lamp and is used for monitoring the illumination intensity of the solar street lamp in real time; the main controller judges whether cleaning operation is needed according to the current illumination intensity and the set cleaning time interval, and if the current illumination intensity is lower than a preset threshold value or the set cleaning time interval is reached, the main controller starts the cleaning process.

The working principle of the technical scheme is as follows: according to practical situations, a specific time of each day or a certain time period may be set as the cleaning time interval, for example, a cleaning operation is set to be performed at 6 a.m. each day, or a cleaning operation is set to be performed once every other week; the main controller transmits the illumination intensity signal on the surface of the solar street lamp to the main controller through the illumination intensity sensor; after receiving the illumination intensity signal, the main controller acquires the illumination intensity value of the current solar street lamp; the main controller compares the current illumination intensity with a set cleaning time interval, and if the current illumination intensity is lower than a preset threshold value or the set cleaning time interval is reached, the main controller judges that cleaning operation is required; when the main controller judges that the cleaning operation is required, the cleaning device is triggered to perform the cleaning operation, for example, a motor or a water spraying device of the cleaning device is started, and the cleaning device is moved to the surface of the solar street lamp to perform the cleaning operation.

Wherein setting the cleaning time interval comprises:

setting a cleaning time interval, wherein the cleaning time interval is set according to actual conditions and comprises specific time per day or every other certain time period;

the main controller receives signals of an illumination intensity sensor to acquire the current illumination intensity, wherein the illumination intensity sensor is arranged on the surface of the solar street lamp and is used for monitoring the illumination intensity of the solar street lamp in real time;

the main controller judges whether cleaning operation is needed or not according to the current illumination intensity and the set cleaning time interval;

wherein, judge whether need clean the operation, include:

acquiring a first condition meeting condition that the current illumination intensity meets a set cleaning time interval;

constructing a first condition description vector of the condition meeting condition;

acquiring a second condition satisfaction condition of a set cleaning time interval if the current illumination intensity is satisfied;

constructing a second condition description vector of the second condition meeting condition;

calculating the vector similarity between the first case description vector and the second case description vector;

taking the difference value between the preset similarity full value and the vector similarity as the difference degree;

if the difference is less than or equal to a preset difference threshold or a set cleaning time interval has been reached, the master controller initiates a cleaning process.

The beneficial effects of the technical scheme are as follows: the solar energy conversion efficiency can be improved and the energy utilization efficiency can be increased by cleaning pollutants on the surface of the solar street lamp regularly; the periodic cleaning of pollutants on the surface of the solar street lamp can prolong the service life of the solar street lamp and reduce the maintenance and replacement frequency; by setting the cleaning time interval according to the actual situation, unnecessary cleaning operation can be avoided, thereby saving cleaning cost and waste of manpower resources; the brightness of the street lamp can be improved by keeping the surface of the solar street lamp clean, and the safety of pedestrians and vehicles is improved; the automatic judgment and the triggering of the cleaning process of the main controller realize the automatic management of the cleaning of the solar street lamp, and reduce the requirements of manual intervention and operation.

In another embodiment, the step S1032 includes:

the method comprises the steps that a main controller analyzes solar street lamp pollution data to obtain analysis data, wherein the analysis data comprise position information of a pollution area in a space to be cleaned, pollution level and positions of objects in the cleaning space, and the main controller determines a cleaning target area based on the analysis data;

the main controller uses a learning model to determine the cleaning strength of the cleaning target area, and determines the position of the cleaning target area in the space to be cleaned and the cleaning priority corresponding to the cleaning strength through the learning model;

The main controller controls the driving assembly to drive the cleaning device to perform cleaning operation, the cleaning device is fixed on the left and right section bars and moves on the surface of the solar street lamp according to the determined cleaning priority to perform cleaning operation.

The working principle of the technical scheme is as follows: the main controller analyzes the collected solar street lamp pollution data and acquires the position information of a pollution area in the space to be cleaned, the pollution level and the position of an object in the cleaning space; based on the analysis data, the main controller determines a cleaning target area, i.e., an area where cleaning operation is required; the main controller uses a pre-trained learning model to determine the cleaning strength of the cleaning target area according to the position and the pollution level of the cleaning target area, and the learning model can be trained according to historical data and experience to accurately determine the cleaning strength of the cleaning target area; the main controller controls the driving assembly to drive the cleaning device to perform cleaning operation. The cleaning device is fixed on the left and right section bars and can move on the surface of the solar street lamp through a motor or other driving modes; according to the determined cleaning priority, the cleaning device moves on the surface of the solar street lamp and sequentially cleans the polluted area according to the priority, and the cleaning device can use tools such as a dust collector, a brush, a high-pressure water gun and the like to perform cleaning operation so as to ensure the cleaning effect.

Wherein determining the cleaning target area cleaning intensity includes:

the main controller determines cleaning strength of the cleaning target area by using a learning model;

determining the position of a cleaning target area in a space to be cleaned and the corresponding cleaning priority according to the cleaning intensity through a learning model;

wherein determining the cleaning strength of the cleaning target area using the learning model includes:

collecting historical cleaning data of all areas in a space to be cleaned, and constructing a historical cleaning data set;

training a learning model based on the historical cleaning data set to obtain a cleaning strength prediction model;

using a cleaning strength prediction model to predict the cleaning strength of each target area in the space to be cleaned, and obtaining predicted cleaning strength;

wherein determining, by the learning model, a position of the cleaning target area in the space to be cleaned and a cleaning priority corresponding to the cleaning intensity, comprises:

generating a cleaning priority mapping table based on the predicted cleaning intensity;

according to the cleaning priority mapping table, sequencing the priority of each target area in the space to be cleaned;

determining a target area with the highest priority as a preferred cleaning target area;

acquiring a specific position of a preferred cleaning target area in a space to be cleaned;

The method comprises the steps of evaluating predicted cleaning strength based on a preset cleaning priority evaluation template to obtain cleaning priority;

extracting a cleaning scheme from a target area with a cleaning priority greater than or equal to a preset cleaning priority threshold;

and integrating all the cleaning schemes to obtain a cleaning scheme recommendation list.

The beneficial effects of the technical scheme are as follows: the accuracy and efficiency of the cleaning operation can be improved by analyzing the solar street lamp pollution data and determining the cleaning target area and the cleaning strength by using a learning model; according to the cleaning priority, the cleaning device moves on the surface of the solar street lamp, and the polluted area is cleaned in sequence according to the priority, so that the polluted area is cleaned to the greatest extent, and the solar energy conversion efficiency is improved; the automatic judgment and control of the main controller are used for realizing the automatic management of the cleaning of the solar street lamp, and reducing the requirements of manual intervention and operation; by accurately determining the cleaning target area and the cleaning strength, unnecessary cleaning operations are avoided, and cleaning cost and waste of human resources are saved.

In another embodiment, the step S1033 includes:

after cleaning, the main controller acquires cleaned data by receiving signals of a sensor, wherein the sensor comprises an illumination intensity sensor for monitoring the illumination intensity of the solar street lamp in real time; the main controller preprocesses the received cleaned data, including data cleaning and data conversion; the main controller performs intelligent analysis on the preprocessed data by using a pre-trained model to acquire an analysis result; and the main controller judges whether the cleaning operation is finished according to the analysis result, and if the analysis result shows that the illumination intensity of the lamplight reaches the preset threshold value, the main controller judges that the cleaning operation is finished.

The working principle of the technical scheme is as follows: the main controller receives the cleaned data signals through sensors such as an illumination intensity sensor and the like, for example, the illumination intensity of the solar street lamp; the main controller performs preprocessing on the received cleaned data, including data cleaning and data conversion, wherein the data cleaning can remove abnormal values or noise, ensure the accuracy and reliability of the data, and the data conversion can convert the original data into a format suitable for intelligent analysis; the main controller performs intelligent analysis on the preprocessed data by using a pre-trained model, for example, a machine learning algorithm is used for analyzing the illumination intensity data of the road lamp so as to obtain an analysis result; the main controller judges whether the cleaning operation is completed according to the analysis result, for example, if the analysis result shows that the illumination intensity of the lamplight reaches a preset threshold value, the main controller judges that the cleaning operation is completed.

The beneficial effects of the technical scheme are as follows: by intelligently analyzing the cleaned data, whether the cleaning operation achieves the expected effect or not can be judged, and the effectiveness of the cleaning operation is ensured; the condition of insufficient illumination intensity can be found in time by intelligently analyzing the cleaned illumination intensity data, so that corresponding measures are taken to improve the energy utilization efficiency; the intelligent analysis and judgment function of the main controller realizes the automatic management of the cleaning operation of the solar street lamp, and reduces the requirements of manual intervention and operation; by judging whether the cleaning operation is finished or not in time, the illumination intensity of the street lamp can reach a preset threshold value, the brightness of the street lamp is improved, and the safety of pedestrians and vehicles is improved.

It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (10)

1. The intelligent control-based solar street lamp cleaning method is characterized by comprising the following steps of:

s101: collecting pollution data about the surface of the solar street lamp by a sensor arranged on the solar street lamp device, wherein the pollution data comprise dust, grease, leaves and bird droppings;

s102: transmitting the collected pollution data to a main controller for intelligent analysis, comparing the collected pollution data with a preset pollution threshold value, and determining the cleaning time and the cleaning mode of cleaning the solar street lamp;

s103: when the cleaning is determined to be needed, the main controller controls the driving assembly to drive the cleaning device to perform cleaning operation according to the current illumination intensity and the set cleaning time interval.

2. The intelligent control-based solar street lamp cleaning method according to claim 1, wherein the step S101 comprises:

s1011: collecting data about dust on the surface of the solar street lamp by a photosensitive sensor arranged on the solar street lamp device;

S1012: collecting data about grease on the surface of the solar street lamp through a chemical sensor arranged on the solar street lamp device;

s1013: through shape recognition sensor and the color recognition sensor that set up on solar street lamp device, gather the data about solar street lamp surface leaf and bird's droppings respectively.

3. The intelligent control-based solar street lamp cleaning method according to claim 1, wherein the step S102 comprises:

s1021: the collected pollution data are sent to a main controller for intelligent analysis, and the types of pollutants are determined by identifying and classifying the types of the pollution data;

s1022: comparing the determined pollutant types with a preset pollution threshold value to obtain a comparison result, wherein the comparison result comprises the pollution degree of the surface of the current solar street lamp;

s1023: and according to the comparison result, the main controller determines the cleaning time and the cleaning mode of the solar street lamp.

4. The intelligent control-based solar street lamp cleaning method according to claim 1, wherein step S103 comprises:

s1031: when the cleaning operation is determined to be required, the main controller starts a cleaning process according to the current illumination intensity and the set cleaning time interval;

S1032: the main controller controls the driving assembly to drive the cleaning device to perform cleaning operation, and the cleaning device is fixed on the left and right profiles and performs cleaning operation by moving on the surfaces of the solar street lamps;

s1033: after cleaning is finished, the intelligent analysis of the sensor and the main controller is performed again to obtain an analysis result, and whether the cleaning operation is finished is judged according to the analysis result.

5. The intelligent control-based solar street lamp cleaning method as claimed in claim 3, wherein the step S1021 comprises:

the collected pollution data are sent to a main controller for intelligent analysis, the main controller identifies the type of the pollution data through a neural network model, wherein dust is identified according to particle size and color, grease is identified according to viscosity and reflection spectrum, leaves are identified according to shape and color, and bird droppings are identified according to shape, color and chemical components; the main controller classifies pollution data according to a preset pollutant cleaning mode, wherein dust is cleaned by adopting a dust collection mode of a cleaning device; cleaning grease by adopting a solvent cleaning mode of a cleaning device; the leaves are cleaned by a cleaning device in a wiping way; the bird droppings are cleaned by adopting a cleaning device and a high-pressure water gun cleaning mode.

6. The intelligent control-based solar street lamp cleaning method according to claim 3, wherein the step S1022 comprises:

comparing the determined pollutant types with preset pollution thresholds, wherein the preset pollution thresholds are set according to factors of the materials, the structures and the environmental conditions of the surfaces of the solar street lamps and are stored in a main controller; the main controller calculates the pollution degree of the surface of the current solar street lamp according to the quantity, the type and the distribution condition of the pollutant types; comparing the calculated pollution degree with a preset pollution threshold value to obtain a comparison result, wherein the comparison result comprises that the pollution degree is lower than, equal to or higher than the preset threshold value.

7. The intelligent control-based solar street lamp cleaning method according to claim 3, wherein the step S1023 comprises:

the main controller determines the pollution degree of the solar street lamp according to a comparison result, wherein the comparison result is obtained by comparing the calculated pollution degree with a preset pollution threshold value; the method comprises the steps that a main controller determines the cleaning time of the solar street lamp according to the pollution degree and combining the use environment of the solar street lamp with the information of weather forecast, wherein when the pollution degree is higher than a preset threshold value and a few days in the future are a sunny day, cleaning is determined in a certain time period of the sunny day; the main controller determines a cleaning mode of the solar street lamp according to the types and the distribution conditions of pollutants, wherein when the pollutants are dust and bird droppings, the cleaning mode of dust collection by using the cleaning device and cleaning by using the high-pressure water gun is determined.

8. The intelligent control-based solar street lamp cleaning method according to claim 4, wherein step S1031 comprises:

setting a cleaning time interval, wherein the cleaning time interval is set according to actual conditions and comprises specific time per day or every other certain time period; the main controller receives signals of an illumination intensity sensor to acquire the current illumination intensity, wherein the illumination intensity sensor is arranged on the surface of the solar street lamp and is used for monitoring the illumination intensity of the solar street lamp in real time; the main controller judges whether cleaning operation is needed according to the current illumination intensity and the set cleaning time interval, and if the current illumination intensity is lower than a preset threshold value or the set cleaning time interval is reached, the main controller starts the cleaning process.

9. The intelligent control-based solar street lamp cleaning method according to claim 4, wherein the step S1032 comprises:

the method comprises the steps that a main controller analyzes solar street lamp pollution data to obtain analysis data, wherein the analysis data comprise position information of a pollution area in a space to be cleaned, pollution level and positions of objects in the cleaning space, and the main controller determines a cleaning target area based on the analysis data;

The main controller uses a learning model to determine the cleaning strength of the cleaning target area, and determines the position of the cleaning target area in the space to be cleaned and the cleaning priority corresponding to the cleaning strength through the learning model;

the main controller controls the driving assembly to drive the cleaning device to perform cleaning operation, the cleaning device is fixed on the left and right section bars and moves on the surface of the solar street lamp according to the determined cleaning priority to perform cleaning operation.

10. The intelligent control-based solar street lamp cleaning method according to claim 4, wherein the step S1033 comprises:

after cleaning, the main controller acquires cleaned data by receiving signals of a sensor, wherein the sensor comprises an illumination intensity sensor for monitoring the illumination intensity of the solar street lamp in real time; the main controller preprocesses the received cleaned data, including data cleaning and data conversion; the main controller performs intelligent analysis on the preprocessed data by using a pre-trained model to acquire an analysis result; and the main controller judges whether the cleaning operation is finished according to the analysis result, and if the analysis result shows that the illumination intensity of the lamplight reaches the preset threshold value, the main controller judges that the cleaning operation is finished.