CN117293977A - Photovoltaic guardrail power supply management method and system based on Internet of things - Google Patents
Photovoltaic guardrail power supply management method and system based on Internet of things Download PDFInfo
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0063—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with circuits adapted for supplying loads from the battery
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F21—LIGHTING
- F21V—FUNCTIONAL FEATURES OR DETAILS OF LIGHTING DEVICES OR SYSTEMS THEREOF; STRUCTURAL COMBINATIONS OF LIGHTING DEVICES WITH OTHER ARTICLES, NOT OTHERWISE PROVIDED FOR
- F21V33/00—Structural combinations of lighting devices with other articles, not otherwise provided for
- F21V33/0004—Personal or domestic articles
- F21V33/0052—Audio or video equipment, e.g. televisions, telephones, cameras or computers; Remote control devices therefor
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F21—LIGHTING
- F21V—FUNCTIONAL FEATURES OR DETAILS OF LIGHTING DEVICES OR SYSTEMS THEREOF; STRUCTURAL COMBINATIONS OF LIGHTING DEVICES WITH OTHER ARTICLES, NOT OTHERWISE PROVIDED FOR
- F21V33/00—Structural combinations of lighting devices with other articles, not otherwise provided for
- F21V33/0004—Personal or domestic articles
- F21V33/0052—Audio or video equipment, e.g. televisions, telephones, cameras or computers; Remote control devices therefor
- F21V33/0056—Audio equipment, e.g. music instruments, radios or speakers
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- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
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- G09F9/00—Indicating arrangements for variable information in which the information is built-up on a support by selection or combination of individual elements
- G09F9/30—Indicating arrangements for variable information in which the information is built-up on a support by selection or combination of individual elements in which the desired character or characters are formed by combining individual elements
- G09F9/33—Indicating arrangements for variable information in which the information is built-up on a support by selection or combination of individual elements in which the desired character or characters are formed by combining individual elements being semiconductor devices, e.g. diodes
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- G09G3/00—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes
- G09G3/20—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters
- G09G3/22—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters using controlled light sources
- G09G3/30—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters using controlled light sources using electroluminescent panels
- G09G3/32—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix no fixed position being assigned to or needed to be assigned to the individual characters or partial characters using controlled light sources using electroluminescent panels semiconductive, e.g. using light-emitting diodes [LED]
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- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
- H02J13/00022—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission
- H02J13/00026—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission involving a local wireless network, e.g. Wi-Fi, ZigBee or Bluetooth
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- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
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Abstract
The invention relates to the technical field of power supply management, in particular to a photovoltaic guardrail power supply management method and system based on the Internet of things. The method comprises the following steps: acquiring meteorological environment data and calculating actual solar radiation degree so as to acquire actual solar radiation data; analyzing the electric energy conversion rate of the solar panel to obtain photoelectric conversion efficiency, and calculating an electric energy generation value to obtain the electric energy generation value; acquiring advertisement putting data of the Internet of things and performing day and night acousto-optic analysis to acquire day and night acousto-optic data; carrying out cable loss analysis on the energy storage battery to obtain a battery output power correction value; performing battery output power correction on the electric energy circulation data to obtain actual battery output data; and performing day and night power supply analysis according to the actual battery output data and day and night acousto-optic data, so as to obtain a day and night power supply strategy, and transmitting the day and night power supply strategy to the controller to execute a power supply management task. The invention efficiently manages the power supply of the photovoltaic guardrail based on the Internet of things.
Description
Technical Field
The invention relates to the technical field of power supply management, in particular to a photovoltaic guardrail power supply management method and system based on the Internet of things.
Background
Along with the increasing demands of society for clean energy, photovoltaic guardrails become widely used renewable energy power generation devices. However, existing photovoltaic guardrail power supply systems suffer from several drawbacks in terms of management, monitoring, and maintenance. Traditional systems lack real-time and intelligence. It is one of the challenges in the prior art to provide an efficient, intelligent, and reliable photovoltaic guardrail power management system to meet the ever-increasing clean energy demands.
Disclosure of Invention
Based on the above, the present invention is necessary to provide a photovoltaic guardrail power supply management method and system based on the internet of things, so as to solve at least one of the above technical problems.
In order to achieve the above purpose, the photovoltaic guardrail power supply management method based on the Internet of things is applied to a photovoltaic guardrail, and the photovoltaic guardrail comprises a controller, a solar panel, an LED light source module, a WIFI module, a weather environment monitoring module, a monitoring ball machine, an IP sound column, an LED display screen and an energy storage battery, wherein the solar panel, the LED light source module, the WIFI module, the weather environment monitoring module, the monitoring ball machine, the IP sound column, the LED display screen and the energy storage battery are electrically connected with the controller; the photovoltaic guardrail power supply management method based on the Internet of things comprises the following steps of:
Step S1: acquiring weather environment data through a weather environment monitoring module, and calculating actual solar radiation according to the weather environment data so as to acquire actual solar radiation data;
step S2: performing electric energy conversion rate analysis on the solar panel to obtain photoelectric conversion efficiency, and performing electric energy generation value calculation according to actual solar radiation data and the photoelectric conversion efficiency to obtain an electric energy generation value;
step S3: acquiring advertisement putting data of the Internet of things through the WIFI module, and performing day and night acousto-optic analysis according to the advertisement putting data of the Internet of things so as to acquire day and night acousto-optic data;
step S4: acquiring electric energy circulation data through an energy storage battery, and performing cable loss analysis according to the electric energy generation value and the electric energy circulation data so as to acquire a battery output power correction value;
step S5: performing battery output power correction on the electric energy circulation data by using the battery output power correction value so as to obtain actual battery output data;
step S6: and performing day and night power supply analysis according to the actual battery output data and day and night acousto-optic data, so as to obtain a day and night power supply strategy, and transmitting the day and night power supply strategy to the controller to execute a power supply management task.
The invention obtains the meteorological environment data, such as the sunlight intensity, the temperature and the like, through the meteorological environment monitoring module and is used for calculating the actual solar radiation degree in real time. Accurate calculation of actual solar radiance is helpful for more accurate prediction of energy generation of the photovoltaic guardrail system, and energy utilization efficiency is improved. According to actual solar radiation data, the system can dynamically adjust the working state of the system so as to adapt to different meteorological conditions and improve the stability and reliability of the system. And calculating an electric energy generation value by combining the actual solar radiation data with the electric energy conversion rate analysis of the solar panel. By knowing the photoelectric conversion efficiency of the solar panel, the system can optimize the generation of electric energy and ensure that the optimal energy conversion can be realized under various illumination conditions. And acquiring advertisement delivery data of the Internet of things through the WIFI module, and performing day and night acousto-optic analysis. Based on the day and night acousto-optic data, the system can intelligently adjust the lighting equipment, improve the lighting effect and save energy. And acquiring electric energy circulation data through the energy storage battery, analyzing cable loss, and calculating a battery output power correction value. Through cable loss analysis, the system can optimize the transmission path of electric energy, reduce the loss of energy in the transmission process, and improve the energy utilization efficiency of the whole system. And correcting the electric energy circulation data by using the battery output power correction value to obtain actual battery output data. By accurately correcting the output power of the battery, the system can better manage the charge and discharge processes of the energy storage battery, prolong the service life of the battery and improve the reliability of the system. And carrying out day and night power supply analysis according to the actual battery output data and day and night acousto-optic data, and formulating a day and night power supply strategy. The system can intelligently adjust the power supply strategy according to the real-time data, ensure to provide proper energy supply in different time periods, meet the requirements of the photovoltaic guardrail system, and simultaneously reduce the excessive consumption of the energy storage battery.
Optionally, step S1 specifically includes:
step S11: acquiring weather environment data and maximum instantaneous solar radiation data through a weather environment monitoring module;
step S12: solar azimuth data extraction and atmospheric compound data extraction are carried out on meteorological environment data, so that solar azimuth data and atmospheric compound data are obtained;
step S13: acquiring solar panel orientation data through a solar panel, and calculating a solar radiation offset angle according to the solar azimuth data and the solar panel orientation data so as to acquire solar radiation offset data;
step S14: performing solar radiation attenuation analysis according to the atmospheric compound data and the solar azimuth data, so as to obtain solar radiation shielding data;
step S15: and carrying out actual solar radiation analysis on the solar radiation offset data and the solar radiation shielding data so as to obtain actual solar radiation data.
The meteorological environment data acquired by the meteorological environment monitoring module comprises temperature, humidity, wind speed and the like, and maximum instantaneous solar radiation data. This is beneficial for achieving real-time environmental monitoring, providing reference data for the system under current weather conditions. The acquisition of the maximum instantaneous solar radiation data is beneficial to the real-time adjustment of the running state of the system so as to cope with the sunlight change and ensure that the system can fully utilize solar energy under different illumination conditions. Extracting solar azimuth data and atmospheric compound data facilitates more accurate calculation of solar radiation. Azimuth data can tell the system the position of the sun in the sky, while atmospheric compound data provides the effect of the atmosphere on solar radiation. By calculating the solar radiation offset angle, the system can adjust the orientation of the solar panel to maximize the capture of solar energy. This helps to increase the efficiency of the solar panel and the energy yield of the overall system. Analysis of solar radiation attenuation can help the system understand the effect of atmospheric conditions on solar radiation. This is beneficial for predicting the actual lighting conditions of the system, thereby better planning the energy production. By analyzing the solar radiation offset and shade data, the system can more accurately calculate the actual solar radiation. This helps to improve the accuracy of the energy prediction, ensuring that the system is able to fully utilize the available solar energy. The accurate analysis of the actual solar radiation data is beneficial to optimizing an energy prediction algorithm, so that the energy utilization efficiency of the system is improved, and the system is more intelligent and adaptive.
Optionally, step S15 specifically includes:
step S151: performing correlation analysis on the solar radiation offset data and the solar radiation shielding data so as to obtain solar radiation attenuation data;
step S152: calculating the maximum instantaneous solar radiation data and the solar radiation attenuation data through an actual solar radiation degree calculation formula, thereby obtaining an actual solar radiation degree data set;
the actual solar radiance calculation formula specifically comprises:
；
in the method, in the process of the invention,is at->Actual solar irradiance of time, +.>For observing time, < >>For maximum instantaneous solar irradiance +.>Is the base of natural logarithm, +.>Is at->Time of the offset influence coefficient received by solar radiation, < +.>Is at->Time of the solar radiation received a shading coefficient of influence, < ->Is the rate of solar radiation attenuation;
the invention constructs an actual solar radiation degree calculation formula for calculating the maximum instantaneous solar radiation data and the solar radiation attenuation data. The formula fully considers influencing the actual solar radiation degreeObservation time of +.>Maximum instantaneous solar radiance +.>Offset influence coefficient, occlusion influence coefficient and rate of solar radiation attenuation +.>A functional relationship is formed:
；
Wherein,is the maximum instantaneous solar irradiance. This is the maximum of solar radiation that can be observed without any obstruction or offset. />Is the offset coefficient of influence to which solar radiation is subjected. Indicating the effect of solar position changes due to earth rotation or other celestial motion on the radiation experienced by the solar panel. />A shading coefficient of influence received for solar radiation. Is indicated at +.>The effect of the presence of cloud cover on solar radiation. />Is the rate at which solar radiation decays. The decay rate of solar radiation over time is described taking into account possible decay factors such as absorption, scattering, etc. of the atmosphere. />This is the product of the offset and the occlusion effect coefficient. Is indicated at +.>The combined effect of offset and shielding on solar radiation.Is an exponential term describing the rate of solar radiation attenuation over time +.>Attenuation. Over time, if->Is a positive value and the exponential term will decrease, indicating the decay of solar radiation over time. On the contrary, if->Is negative and the exponential term will increase, indicating that the solar radiation increases over time. In the art, the actual solar radiation degree is generally calculated by adopting technical means such as a solar radiation model, a remote sensing technology and the like. By adopting the actual solar radiance calculation formula provided by the invention, the actual solar radiance can be obtained more accurately and rapidly.
Step S153: and carrying out time sequence combination according to the actual solar radiation degree data set and the solar radiation influence data, so as to obtain actual solar radiation data.
By the method, the system can more accurately predict the loss of solar radiation through correlation analysis of the solar radiation offset and the shielding data. This is beneficial to optimizing the design and performance of the energy system to minimize radiation losses and improve energy utilization efficiency. Correlation analysis may provide insight into specific factors in the system that may cause solar radiation loss. This helps to formulate corresponding system adjustment strategies to cope with illumination changes under different environmental conditions. By means of the actual solar radiation degree calculation formula, the system can calculate the actual solar radiation degree more accurately. This helps to improve performance assessment of the energy system, ensuring that the system design and operation can achieve optimal results under different lighting conditions. The calculated actual solar irradiance data can be used to monitor the energy yield of the system in real time. This provides a basis for real-time adjustment of the system operating state to maximize the utilization of solar energy resources. By combining the actual solar irradiance data and the solar irradiance impact data in time sequence, the system may better understand the trend of solar irradiance over different time periods. This facilitates more accurate energy prediction and planning by the system. The time-series combined actual solar radiation data provides detailed information about solar radiation variations to the system, thereby enabling the system to better make optimization decisions. The system can adjust the operation strategy according to the actual situation so as to improve the overall performance.
Optionally, step S3 specifically includes:
step S31: acquiring advertisement putting data of the Internet of things through a WIFI module;
step S32: extracting a poster image and advertisement audio data according to advertisement putting data of the Internet of things, so as to obtain the poster image and the advertisement audio data;
step S33: collecting brightness parameters of the LED display screen, so as to obtain brightness data of the LED display screen; carrying out day and night brightness analysis on the poster image according to the brightness data of the LED display screen so as to obtain day and night poster brightness data;
step S34: collecting audio parameters of the IP voice column, thereby obtaining IP voice column audio data; performing day and night audio analysis on the advertisement audio data according to the IP sound column audio data, thereby obtaining day and night advertisement audio data;
step S35: collecting brightness parameters of the LED light source module to obtain brightness parameters of the LED light source; performing day and night brightness analysis on brightness parameters of the LED light source so as to obtain day and night street lamp brightness data;
step S36: and carrying out time sequence combination on the day and night street lamp brightness data, the day and night poster brightness data and the day and night advertisement audio data, thereby obtaining day and night acousto-optic data.
According to the invention, the advertisement delivery data of the Internet of things is obtained, so that a system can know the advertisement delivery condition in time, and the advertisement delivery condition comprises information such as delivery positions, time intervals and the like. Through analysis of the advertisement delivery data, relevant poster images and advertisement audio data are extracted, and basic data are provided for subsequent analysis and processing. Through gathering the luminance parameter of LED display screen, the system can know the luminance variation of LED display screen to carry out the luminance analysis round clock, help optimizing the display effect of poster image, can reduce the energy consumption of LED display screen simultaneously. By collecting the audio parameters of the IP sound column, the system can acquire audio data and perform day and night audio analysis, and is beneficial to optimizing the playing effect of advertisement audio so as to adapt to the sound characteristics of different environments. Through the brightness parameter acquisition of the LED light source module, the system can know the brightness change of the LED street lamp, is favorable for analyzing the illumination condition of the street lamp, and provides data support for the intelligent illumination system. The system can comprehensively analyze time-varying characteristics of sound and illumination, and provide data support for intelligent advertisement delivery and optimization of a lighting power supply system. This helps to improve advertising effectiveness, energy efficiency, and optimize the power supply system.
Optionally, step S33 specifically includes:
step S331: collecting brightness parameters of the LED display screen, so as to obtain brightness data of the LED display screen;
step S332: carrying out primary and secondary image segmentation according to the poster image so as to obtain a primary image and a secondary image;
step S333: carrying out proper brightness analysis on the main image by using the brightness data of the LED display screen, so as to obtain the brightness data of the main image;
step S334: carrying out secondary image proper brightness analysis on the secondary image by using the LED display screen brightness data so as to obtain secondary image brightness data;
step S335: and carrying out regional day and night brightness strategy analysis on the brightness data of the LED display screen according to the main image brightness data and the secondary image brightness data, thereby obtaining day and night poster brightness data.
According to the invention, the brightness parameters of the LED display screen are acquired, so that the system can acquire the brightness change of the LED display screen in real time, and basic data is provided for subsequent analysis and control. The image segmentation helps to identify and extract the primary and secondary elements in the poster, providing a data basis for different image areas for subsequent luminance analysis. Through the proper brightness analysis of the main image, the system can optimize the display effect of the main image according to the brightness data of the LED display screen, and the visibility and the attraction of the main image are improved. Similar to the primary image, the system can optimize the display effect of the secondary image according to the brightness data of the LED display screen by proper brightness analysis of the secondary image, and the visibility of the secondary image is improved. By analyzing the brightness data of the main image and the secondary image and combining the brightness data of the LED display screen, the system can formulate a day-night brightness strategy of the subareas so as to adapt to the ambient illumination changes of different areas, thereby improving the visual effect of the whole poster. And meanwhile, the energy consumption condition of the LED display screen is optimized.
Optionally, step S332 specifically includes:
performing gray level conversion on the poster image so as to obtain the poster gray level image;
carrying out gray value statistical analysis on the poster gray image so as to obtain a high-frequency gray value, a low-frequency gray value and a medium-frequency gray value;
carrying out image segmentation on the poster gray level image according to the high-frequency gray level value so as to obtain a secondary gray level image;
dividing the poster gray level image according to the low-frequency gray level value, so as to obtain a main gray level image;
carrying out gray value difference calculation on the high-frequency gray value and the low-frequency gray value according to the intermediate-frequency gray value, thereby obtaining a gray value difference;
clustering calculation is carried out on the intermediate frequency gray value, the high frequency gray value and the low frequency gray value based on the gray value difference value, so that a main gradient gray value and a secondary gradient gray value are obtained;
dividing the poster gray image according to the main gray value to obtain a main gray image, and combining the main gray image with the main gray image to obtain a complete main gray image;
carrying out image segmentation on the poster gray level image according to the secondary gray level value so as to obtain a secondary gray level image, and carrying out data combination on the secondary gray level image and the secondary gray level image so as to obtain a complete secondary gray level image;
Performing RGB conversion on the complete main gray image so as to obtain a main image;
the complete secondary gray image is RGB converted to obtain a secondary image.
The invention converts the color image into the gray image, which is helpful to simplify the complexity of image processing, and simultaneously retains the main brightness information of the image, so that the subsequent processing is more efficient. By counting the frequencies of different gray values, the system can know the overall brightness distribution condition of the image, and is beneficial to subsequent image segmentation and brightness adjustment. The high frequency gray values generally correspond to details and textures in the image, and by segmenting out the high frequency portions, secondary information in the image can be extracted, which helps to highlight details of the image. The low-frequency gray value reflects the overall brightness change in the image, and by dividing the low-frequency part, the main information of the image can be extracted, which is helpful for preserving the overall structure of the image. The calculation of the intermediate frequency gray value difference value is helpful for quantifying the structure and detail information of the image and providing more information for subsequent clustering. Areas with different gray value ranges in the image can be identified through clustering, so that the main gradual change and the secondary gradual change parts can be segmented, and the readability and the visibility of the image are improved. The segmentation of the primary gray scale image helps to highlight the important parts of the image, and the merging of the data of the primary gray scale image can restore the complete primary image. The segmentation of the secondary gray scale image helps to emphasize the secondary information in the image, and the merging of the data of the secondary gray scale image can restore the complete secondary image. And converting the complete main gray image into an RGB format, and restoring the color information of the image to obtain a final main image. And converting the complete secondary gray image into an RGB format, and restoring the color information of the image to obtain a final secondary image.
Optionally, step S335 is specifically:
dividing the brightness data of the LED display screen into areas according to the primary image and the secondary image, so as to obtain a primary image display area and a secondary image display area;
acquiring day and night brightness data through a monitoring dome camera, and carrying out brightness division on the day and night brightness data so as to acquire day brightness data, night brightness data and late night brightness data;
performing daytime brightness strategy analysis on the secondary image display area according to the secondary image brightness data and the daytime brightness data, so as to obtain daytime brightness strategy data;
according to the night brightness data, the main image brightness data and the secondary image brightness data, performing night brightness strategy analysis on the main image display area and the secondary image display area, so as to obtain night brightness strategy data;
performing late-night brightness strategy analysis on the main image display area according to the main image brightness data and the late-night brightness data, so as to obtain late-night brightness strategy data;
and carrying out time sequence combination on the daytime brightness strategy data, the night brightness strategy data and the late night brightness strategy data so as to obtain the day and night poster brightness data.
According to the invention, the LED display screen is divided into the areas according to the main image and the secondary image, so that the image content to be displayed in different areas can be determined. The partition can be optimized according to the importance of the image or other factors, so that the LED display screen can display different types of information at the same time, and the efficiency and the flexibility of information transmission are improved. The whole time period is divided into different brightness stages such as daytime, night, late night and the like, so that the brightness of the LED display screen can be adjusted according to the illumination conditions of different time periods, and the definition and the readability of information under different environment illumination conditions are ensured. By analyzing the secondary image brightness data and daytime brightness data, a strategy for the brightness of the LED display screen during daytime can be formulated. This helps to optimise the brightness when displaying the secondary image in a daytime environment, ensuring that the image is clearly visible under intense light. And formulating a night LED display screen brightness strategy based on the night brightness data, the main image brightness data and the secondary image brightness data. This ensures that the LED display screen provides suitable brightness when displaying the primary and secondary images in a darker light, making the image clearly visible and less obtrusive. And determining the brightness strategy of the late-night LED display screen according to the main image brightness data and the late-night brightness data. This helps to maintain proper brightness while displaying the primary image in a late-night environment while saving energy and reducing interference with the surrounding environment. And combining the daytime brightness strategy data, the night brightness strategy data and the late night brightness strategy data according to time sequences to obtain the day and night poster brightness data. The combination can generate an integral brightness strategy, and brightness adjustment is carried out on the LED display screen in different time periods so as to adapt to the requirements of different environments and display contents, and the ornamental value and the information transmission effect are improved.
Optionally, step S4 specifically includes:
step S41: acquiring electric energy circulation data through an energy storage battery;
step S42: performing electric energy conversion loss calculation on the electric energy circulation data so as to obtain electric energy conversion loss data;
step S43: calculating instantaneous input electric energy according to the electric energy conversion loss data and the electric energy circulation data, so as to obtain instantaneous input electric energy data;
step S44: performing cable loss calculation based on the instantaneous input electric energy data and the maximum electric energy generation value, thereby obtaining cable loss data;
step S45: and carrying out output power correction value calculation on the electric energy circulation data and the cable loss data through an output power correction value formula, thereby obtaining a battery output power correction value.
The invention obtains the circulation data of the electric energy in the energy storage battery system. This includes the input and output of electrical energy and the conversion process within the energy storage system. Accurate collection of these data provides the basis for subsequent analysis. By calculating the loss of electrical energy during the conversion process, the efficiency of the energy storage system may be assessed. The acquisition of the electric energy conversion loss data is beneficial to system optimization, reduces energy waste and improves overall efficiency. The instantaneous input power data reflects the real-time power input of the system at different points in time. This is important to understand the dynamic operating conditions of the system and to provide an accurate data basis for subsequent analysis. The cable loss calculation helps to evaluate the energy loss of the electrical energy during transmission due to cable impedance or the like. The cable loss data can be obtained to guide the system design and operation, and the high efficiency of power transmission is ensured. The calculation of the output power correction value allows an accurate assessment of the actual output power of the energy storage battery system taking into account the power conversion loss and the cable loss. This helps to ensure that the system operates at optimum efficiency and provides an accurate reference for the power supply strategy of the system.
Alternatively, the output power correction value formula in step S45 is specifically:
；
in the method, in the process of the invention,for the output power correction value, +.>To correct the term balance parameter->For the output current of the energy storage battery, < >>For the loss factor of the cable>For the rated capacity of the energy storage battery, +.>Is the internal impedance coefficient of the energy storage battery, +.>For error adjustment item, ++>For the temperature of the energy storage cell->For the cable length between the energy storage battery and the power supply appliance, < >>For maximum output power of the energy storage battery, < >>Is the electric energy conversion efficiency of the energy storage battery.
The invention constructs an output power correction value formula for calculating the output power correction value of the electric energy circulation data and the cable loss data. The formula fully considers influencing the output power correction valueCorrection term balance parameter +.>Output current of energy storage battery>Cable loss factor->Rated capacity of energy storage battery>Internal impedance coefficient of energy storage batteryError adjustment item-> Temperature of energy storage battery->The cable length between the energy storage battery and the power supply appliance is +.>Maximum output power of energy storage battery->Electric energy conversion efficiency of the energy storage battery>A functional relationship is formed:
；
wherein,part of which is related to the correction term balance parameter- >Output current of energy storage battery->Cable loss factor->Rated capacity of energy storage battery>And internal impedance coefficient->. Square term +.>And logarithmic term->For adjusting the relationship between output power and battery capacity, output current and internal impedance. />This is the output power +.>Regarding the efficiency of electric energy conversion>Is a second derivative of (c). This term is used to take into account the effect of the power conversion efficiency on the output power and to ensure that the correction takes into account the change in efficiency. />Part contains error adjustment item->Temperature of energy storage cell>And the cube root of the cable length ∈ ->. Indicating the effect of temperature on battery performance and the factors affecting cable length and cable loss. Error adjustment terms are used to correct for other effects not considered in the modelFactors. In the art, the output power correction value is generally calculated by adopting technical means such as electro-mechanical modeling, signal processing and the like. By adopting the output power correction value calculation formula provided by the invention, the output power correction value can be obtained more accurately and rapidly.
Optionally, the specification further provides a photovoltaic guardrail power supply management system based on the internet of things, which is used for executing the photovoltaic guardrail power supply management method based on the internet of things, and the photovoltaic guardrail power supply management system based on the internet of things comprises:
The actual solar radiation degree calculation module is used for acquiring weather environment data through the weather environment monitoring module and calculating the actual solar radiation degree according to the weather environment data so as to acquire actual solar radiation data;
the maximum electric energy generation value calculation module is used for analyzing the electric energy conversion rate of the solar panel so as to obtain photoelectric conversion efficiency, and calculating the electric energy generation value according to actual solar radiation data and the photoelectric conversion efficiency so as to obtain an electric energy generation value;
the day and night acousto-optic analysis module is used for acquiring advertisement putting data of the Internet of things through the WIFI module and carrying out day and night acousto-optic analysis according to the advertisement putting data of the Internet of things so as to acquire day and night acousto-optic data;
the cable loss analysis module is used for acquiring electric energy circulation data through the energy storage battery and carrying out cable loss analysis according to the electric energy generation value and the electric energy circulation data so as to obtain a battery output power correction value;
the battery output power correction module is used for correcting the battery output power of the electric energy circulation data by utilizing the battery output power correction value so as to obtain actual battery output data;
and the day and night power supply analysis module is used for carrying out day and night power supply analysis according to the actual battery output data and the day and night acousto-optic data so as to obtain a day and night power supply strategy, and transmitting the day and night power supply strategy to the controller so as to execute a power supply management task.
The beneficial effects of the invention are as follows: the accurate calculation of the actual solar radiance is beneficial to more accurately predicting the energy generation of the photovoltaic guardrail system, and the energy utilization efficiency is improved. According to actual solar radiation data, the system can dynamically adjust the working state of the system so as to adapt to different meteorological conditions and improve the stability and reliability of the system. Through cable loss analysis, the system can optimize the transmission path of electric energy, reduce the loss of energy in the transmission process, and improve the energy utilization efficiency of the whole system. And correcting the electric energy circulation data by using the battery output power correction value to obtain actual battery output data. By accurately correcting the output power of the battery, the system can better manage the charge and discharge processes of the energy storage battery, prolong the service life of the battery and improve the reliability of the system. The system can intelligently adjust the power supply strategy according to the real-time data, ensure to provide proper energy supply in different time periods, meet the requirements of the photovoltaic guardrail system, and simultaneously reduce the excessive consumption of the energy storage battery.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of a non-limiting implementation, made with reference to the accompanying drawings in which:
Fig. 1 is a schematic flow chart of steps of a photovoltaic guardrail power supply management method based on the internet of things.
Fig. 2 is a detailed step flow chart of step S1 in the present invention.
Fig. 3 is a detailed step flow chart of step S3 in the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.
It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
In order to achieve the above objective, referring to fig. 1 to 3, the present invention provides a photovoltaic guardrail power supply management method based on the internet of things, the method comprises the following steps:
step S1: acquiring weather environment data through a weather environment monitoring module, and calculating actual solar radiation according to the weather environment data so as to acquire actual solar radiation data;
in this embodiment, the weather environment monitoring module is used to obtain weather environment data, such as illumination intensity, temperature and humidity. Based on the data, the actual solar radiation degree is calculated, a meteorological model such as a solar radiation amount calculation formula is adopted, and correction is carried out by combining the actual measurement data, so that accurate actual solar radiation data is obtained.
Step S2: performing electric energy conversion rate analysis on the solar panel to obtain photoelectric conversion efficiency, and performing electric energy generation value calculation according to actual solar radiation data and the photoelectric conversion efficiency to obtain an electric energy generation value;
in this embodiment, the solar panel is subjected to electric energy conversion rate analysis, and the photoelectric conversion efficiency is calculated by measuring parameters such as open-circuit voltage and short-circuit current of the solar panel. And combining actual solar radiation data, using a calculation formula of an electric energy generation value, such as K=o, G, A, wherein K is the electric energy generation value, o is the photoelectric conversion efficiency, G is the solar radiation intensity, and A is the effective area of the solar panel.
Step S3: acquiring advertisement putting data of the Internet of things through the WIFI module, and performing day and night acousto-optic analysis according to the advertisement putting data of the Internet of things so as to acquire day and night acousto-optic data;
in this embodiment, the WIFI module is used to obtain advertisement delivery data of the internet of things, including information such as advertisement playing time slots and frequency. And carrying out day and night acousto-optic analysis according to the data, and obtaining day and night acousto-optic data, such as day lighting requirement, night lighting requirement, sound background and the like, by taking environmental noise and lighting requirement into consideration.
Step S4: acquiring electric energy circulation data through an energy storage battery, and performing cable loss analysis according to the electric energy generation value and the electric energy circulation data so as to acquire a battery output power correction value;
In this embodiment, the energy storage battery is used to obtain the electric energy circulation data, and the charging and discharging processes of the battery are monitored. And carrying out cable loss analysis according to the electric energy generation value and the battery circulation data, and obtaining a battery output power correction value by considering the loss of the electric energy in the conveying process.
Step S5: performing battery output power correction on the electric energy circulation data by using the battery output power correction value so as to obtain actual battery output data;
in the embodiment, the battery output power correction value is utilized to correct the battery output power of the electric energy circulation data, so that the output power of the battery is adjusted, and the actual output is ensured to accord with the expectation. This includes adjusting parameters such as charge rate and discharge rate to improve system efficiency.
Step S6: and performing day and night power supply analysis according to the actual battery output data and day and night acousto-optic data, so as to obtain a day and night power supply strategy, and transmitting the day and night power supply strategy to the controller to execute a power supply management task.
In the embodiment, day and night power supply analysis is performed according to actual battery output data and day and night acousto-optic data. Based on the data, a day and night power supply strategy is formulated, and the working modes of the photovoltaic guardrail in the daytime and at night are determined. And the day and night power supply strategy is transmitted to the controller, the power supply management task is executed, the system can meet the electric energy requirement in different time periods, and meanwhile, the energy utilization rate is maximized.
The invention obtains the meteorological environment data, such as the sunlight intensity, the temperature and the like, through the meteorological environment monitoring module and is used for calculating the actual solar radiation degree in real time. Accurate calculation of actual solar radiance is helpful for more accurate prediction of energy generation of the photovoltaic guardrail system, and energy utilization efficiency is improved. According to actual solar radiation data, the system can dynamically adjust the working state of the system so as to adapt to different meteorological conditions and improve the stability and reliability of the system. And calculating an electric energy generation value by combining the actual solar radiation data with the electric energy conversion rate analysis of the solar panel. By knowing the photoelectric conversion efficiency of the solar panel, the system can optimize the generation of electric energy and ensure that the optimal energy conversion can be realized under various illumination conditions. And acquiring advertisement delivery data of the Internet of things through the WIFI module, and performing day and night acousto-optic analysis. Based on the day and night acousto-optic data, the system can intelligently adjust the lighting equipment, improve the lighting effect and save energy. And acquiring electric energy circulation data through the energy storage battery, analyzing cable loss, and calculating a battery output power correction value. Through cable loss analysis, the system can optimize the transmission path of electric energy, reduce the loss of energy in the transmission process, and improve the energy utilization efficiency of the whole system. And correcting the electric energy circulation data by using the battery output power correction value to obtain actual battery output data. By accurately correcting the output power of the battery, the system can better manage the charge and discharge processes of the energy storage battery, prolong the service life of the battery and improve the reliability of the system. And carrying out day and night power supply analysis according to the actual battery output data and day and night acousto-optic data, and formulating a day and night power supply strategy. The system can intelligently adjust the power supply strategy according to the real-time data, ensure to provide proper energy supply in different time periods, meet the requirements of the photovoltaic guardrail system, and simultaneously reduce the excessive consumption of the energy storage battery.
Optionally, step S1 specifically includes:
step S11: acquiring weather environment data and maximum instantaneous solar radiation data through a weather environment monitoring module;
in this embodiment, the meteorological environment monitoring module collects meteorological environment data including temperature, humidity and the like, and obtains maximum instantaneous solar radiation data for determining a peak value of solar energy input, so as to ensure that the system can cope with high-intensity solar radiation. For example, the maximum instantaneous solar radiation intensity measured with the sensor is 1000W/m.
Step S12: solar azimuth data extraction and atmospheric compound data extraction are carried out on meteorological environment data, so that solar azimuth data and atmospheric compound data are obtained;
in this embodiment, meteorological environment data is processed to extract solar azimuth data and atmospheric compound data. The solar azimuth data comprises the elevation angle and the azimuth angle of the sun, and can be calculated through astronomical algorithm. Atmospheric compound data relates to particulate matter and gaseous components in the atmosphere, and these data have an effect on the degree of transmission of solar radiation.
Step S13: acquiring solar panel orientation data through a solar panel, and calculating a solar radiation offset angle according to the solar azimuth data and the solar panel orientation data so as to acquire solar radiation offset data;
In this embodiment, orientation data of the solar panel is obtained through the solar panel, and solar radiation offset angle is calculated by combining solar azimuth data. This angle represents the deviation of the solar panel from the solar direction, affecting the receiving efficiency of the solar panel. For example, the solar azimuth data is 45 degrees in elevation and 180 degrees in azimuth, the solar panel is oriented south, and the calculated solar radiation offset angle is 15 degrees.
Step S14: performing solar radiation attenuation analysis according to the atmospheric compound data and the solar azimuth data, so as to obtain solar radiation shielding data;
in this example, solar radiation attenuation analysis was performed using atmospheric compound data and solar azimuth data. Attenuation caused by atmospheric compounds and solar radiation shielding data, such as shielding effects of buildings and trees, affect the effective arrival of solar radiation at the surface of the solar panel. For example, from the data analysis, it was determined that solar radiation was blocked by the cloud, resulting in 25% attenuation.
Step S15: and carrying out actual solar radiation analysis on the solar radiation offset data and the solar radiation shielding data so as to obtain actual solar radiation data.
In this embodiment, the solar radiation offset data and the solar radiation shielding data are integrated to perform actual solar radiation analysis. And correcting the maximum instantaneous solar radiation data by considering the solar radiation offset and the shielding factors to obtain actual solar radiation data. This ensures that the system more accurately estimates the solar input, improving the efficiency of the solar panel system. For example, the corrected actual solar radiation data is 800W/m, taking into account various influencing factors of solar radiation.
The meteorological environment data acquired by the meteorological environment monitoring module comprises temperature, humidity, wind speed and the like, and maximum instantaneous solar radiation data. This is beneficial for achieving real-time environmental monitoring, providing reference data for the system under current weather conditions. The acquisition of the maximum instantaneous solar radiation data is beneficial to the real-time adjustment of the running state of the system so as to cope with the sunlight change and ensure that the system can fully utilize solar energy under different illumination conditions. Extracting solar azimuth data and atmospheric compound data facilitates more accurate calculation of solar radiation. Azimuth data can tell the system the position of the sun in the sky, while atmospheric compound data provides the effect of the atmosphere on solar radiation. By calculating the solar radiation offset angle, the system can adjust the orientation of the solar panel to maximize the capture of solar energy. This helps to increase the efficiency of the solar panel and the energy yield of the overall system. Analysis of solar radiation attenuation can help the system understand the effect of atmospheric conditions on solar radiation. This is beneficial for predicting the actual lighting conditions of the system, thereby better planning the energy production. By analyzing the solar radiation offset and shade data, the system can more accurately calculate the actual solar radiation. This helps to improve the accuracy of the energy prediction, ensuring that the system is able to fully utilize the available solar energy. The accurate analysis of the actual solar radiation data is beneficial to optimizing an energy prediction algorithm, so that the energy utilization efficiency of the system is improved, and the system is more intelligent and adaptive.
Optionally, step S15 specifically includes:
step S151: performing correlation analysis on the solar radiation offset data and the solar radiation shielding data so as to obtain solar radiation attenuation data;
in this embodiment, correlation analysis is performed on the solar radiation offset data and the solar radiation shielding data to evaluate the comprehensive influence of the two influencing factors on solar radiation. First, solar radiation offset data (e.g., angular offset of a solar panel) is associated with solar radiation shielding data (e.g., shielding effect of buildings, clouds, etc. on radiation). By this correlation analysis, the degree of radiation attenuation can be quantified, and the degree of the combined effect of two factors on solar radiation under specific conditions can be determined. For example, combining solar radiation shielding at 15 degrees and 30% of the solar radiation offset angle yields a combined radiation attenuation of 20%.
Step S152: calculating the maximum instantaneous solar radiation data and the solar radiation attenuation data through an actual solar radiation degree calculation formula, thereby obtaining an actual solar radiation degree data set;
in this embodiment, an actual solar radiation degree calculation formula is used to calculate the maximum instantaneous solar radiation data and the solar radiation attenuation data, so as to obtain an actual solar radiation degree data set. This calculation takes into account the effects of various factors such as solar radiation offset, solar radiation shielding, etc. on the solar energy input to more accurately estimate the actual received solar radiation level. For example, using the formula, the actual solar irradiance is 800W/m K calculated from the maximum instantaneous solar irradiance data of 1000W/m and the 20% irradiance.
The actual solar radiance calculation formula specifically comprises:
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in the method, in the process of the invention,is at->Actual solar irradiance of time, +.>For observing time, < >>For maximum instantaneous solar irradiance +.>Is the base of natural logarithm, +.>Is at->Time of the offset influence coefficient received by solar radiation, < +.>Is at->Time of the solar radiation received a shading coefficient of influence, < ->Is the rate of solar radiation attenuation;
the invention constructs an actual solar radiation degree calculation formula for calculating the maximum instantaneous solar radiation data and the solar radiation attenuation data. The formula fully considers influencing the actual solar radiation degreeObservation time of +.>Maximum instantaneous solar radiance +.>Offset effectsCoefficient, shading influence coefficient and rate of solar radiation attenuation +.>A functional relationship is formed:
；
wherein,is the maximum instantaneous solar irradiance. This is the maximum of solar radiation that can be observed without any obstruction or offset. />Is the offset coefficient of influence to which solar radiation is subjected. Indicating the effect of solar position changes due to earth rotation or other celestial motion on the radiation experienced by the solar panel. />A shading coefficient of influence received for solar radiation. Is indicated at +. >The effect of the presence of cloud cover on solar radiation. />Is the rate at which solar radiation decays. The decay rate of solar radiation over time is described taking into account possible decay factors such as absorption, scattering, etc. of the atmosphere. />This is the product of the offset and the occlusion effect coefficient. Is indicated at +.>The combined effect of offset and shielding on solar radiation.Is an exponential term describingThe rate of solar radiation attenuation over time +.>Attenuation. Over time, if->Is a positive value and the exponential term will decrease, indicating the decay of solar radiation over time. On the contrary, if->Is negative and the exponential term will increase, indicating that the solar radiation increases over time. In the art, the actual solar radiation degree is generally calculated by adopting technical means such as a solar radiation model, a remote sensing technology and the like. By adopting the actual solar radiance calculation formula provided by the invention, the actual solar radiance can be obtained more accurately and rapidly.
Step S153: and carrying out time sequence combination according to the actual solar radiation degree data set and the solar radiation influence data, so as to obtain actual solar radiation data.
In this embodiment, the actual solar radiation data set and the solar radiation influence data are combined in a time sequence, so that the complete actual solar radiation data is finally obtained. The time sequence combination considers the solar radiation change in different time periods and the change condition of various influencing factors in the time periods. This enables the data to more fully reflect fluctuations and variations in the actual solar radiation. For example, in combination with actual solar irradiance data and corresponding solar irradiance impact data for different time periods, a time-sequential continuous, comprehensive actual solar irradiance data set is formed for energy management and optimization of the system.
By the method, the system can more accurately predict the loss of solar radiation through correlation analysis of the solar radiation offset and the shielding data. This is beneficial to optimizing the design and performance of the energy system to minimize radiation losses and improve energy utilization efficiency. Correlation analysis may provide insight into specific factors in the system that may cause solar radiation loss. This helps to formulate corresponding system adjustment strategies to cope with illumination changes under different environmental conditions. By means of the actual solar radiation degree calculation formula, the system can calculate the actual solar radiation degree more accurately. This helps to improve performance assessment of the energy system, ensuring that the system design and operation can achieve optimal results under different lighting conditions. The calculated actual solar irradiance data can be used to monitor the energy yield of the system in real time. This provides a basis for real-time adjustment of the system operating state to maximize the utilization of solar energy resources. By combining the actual solar irradiance data and the solar irradiance impact data in time sequence, the system may better understand the trend of solar irradiance over different time periods. This facilitates more accurate energy prediction and planning by the system. The time-series combined actual solar radiation data provides detailed information about solar radiation variations to the system, thereby enabling the system to better make optimization decisions. The system can adjust the operation strategy according to the actual situation so as to improve the overall performance.
Optionally, step S3 specifically includes:
step S31: acquiring advertisement putting data of the Internet of things through a WIFI module;
according to the embodiment, the advertisement putting data of the Internet of things are obtained through the WIFI module, and firstly, the WIFI module is installed on devices such as a billboard and the like, and the Internet of things is connected in real time through the WIFI module. The WIFI module is responsible for collecting advertisement delivery data around the equipment, including advertisement types, delivery time periods, audience feedback and the like. For example, the WIFI module scans surrounding WIFI signals, analyzes information of the connection device, and obtains data such as residence time and access frequency of the user device in the range of the advertising board.
Step S32: extracting a poster image and advertisement audio data according to advertisement putting data of the Internet of things, so as to obtain the poster image and the advertisement audio data;
in the embodiment, poster image extraction and advertisement audio data extraction are performed according to advertisement delivery data of the Internet of things. And extracting poster images and advertisement audios of corresponding time intervals from the advertisement putting data through a data processing algorithm. For example, according to the play records of the advertising board, the advertising pictures and sounds in a specific time interval are extracted, and the data is ensured to accurately reflect the presentation forms of the advertisements at different times.
Step S33: collecting brightness parameters of the LED display screen, so as to obtain brightness data of the LED display screen; carrying out day and night brightness analysis on the poster image according to the brightness data of the LED display screen so as to obtain day and night poster brightness data;
in the embodiment, brightness parameters of the LED display screen are acquired, and brightness data of the LED display screen are obtained. And collecting brightness parameters of the LED display screen through equipment such as a light sensor. Based on these parameters, a day and night brightness analysis is performed to obtain day and night poster brightness data. For example, the brightness change of the LED display screen during the day and night is recorded to accurately reflect the visual effect of the billboard.
Step S34: collecting audio parameters of the IP voice column, thereby obtaining IP voice column audio data; performing day and night audio analysis on the advertisement audio data according to the IP sound column audio data, thereby obtaining day and night advertisement audio data;
in this embodiment, audio parameters of the IP sound column are collected, so as to obtain audio data of the IP sound column. And collecting audio parameters played by the IP sound column by using equipment such as an audio sensor. And combining the advertisement putting data to perform day and night audio analysis to acquire day and night advertisement audio data. For example, the performance of advertising audio in different time periods is analyzed according to parameters such as sound intensity and frequency of the sound column. For example, audio during daytime to dusk is 1, while audio during dusk to night (19:00-24:00) is 0.5, and audio during late night (24:00-7:00) is 0. Therefore, the energy consumption can be reduced while the audio effect of the advertisement is not influenced, and the design is more humanized.
Step S35: collecting brightness parameters of the LED light source module to obtain brightness parameters of the LED light source; performing day and night brightness analysis on brightness parameters of the LED light source so as to obtain day and night street lamp brightness data;
in this embodiment, the brightness parameter of the LED light source module is acquired, so as to obtain the brightness parameter of the LED light source. And (3) acquiring brightness parameters of the LED light source module through equipment such as a sensor and the like, and performing day and night brightness analysis to acquire day and night street lamp brightness data. For example, the brightness change of the LED light source in different time periods is recorded to reflect the illumination effect of the street lamp.
Step S36: and carrying out time sequence combination on the day and night street lamp brightness data, the day and night poster brightness data and the day and night advertisement audio data, thereby obtaining day and night acousto-optic data.
In the embodiment, the day and night street lamp brightness data, day and night poster brightness data and day and night advertisement audio data are combined in time sequence. And combining various data according to time sequence through the marks such as time stamps and the like to form a day and night acousto-optic data set. This integrated dataset may provide more comprehensive information for subsequent analysis, such as comparative analysis of advertising effectiveness under different brightness conditions, and assessment of acousto-optic coordination.
According to the invention, the advertisement delivery data of the Internet of things is obtained, so that a system can know the advertisement delivery condition in time, and the advertisement delivery condition comprises information such as delivery positions, time intervals and the like. Through analysis of the advertisement delivery data, relevant poster images and advertisement audio data are extracted, and basic data are provided for subsequent analysis and processing. Through gathering the luminance parameter of LED display screen, the system can know the luminance variation of LED display screen to carry out the luminance analysis round clock, help optimizing the display effect of poster image, can reduce the energy consumption of LED display screen simultaneously. By collecting the audio parameters of the IP sound column, the system can acquire audio data and perform day and night audio analysis, and is beneficial to optimizing the playing effect of advertisement audio so as to adapt to the sound characteristics of different environments. Through the brightness parameter acquisition of the LED light source module, the system can know the brightness change of the LED street lamp, is favorable for analyzing the illumination condition of the street lamp, and provides data support for the intelligent illumination system. The system can comprehensively analyze time-varying characteristics of sound and illumination, and provide data support for intelligent advertisement delivery and optimization of a lighting power supply system. This helps to improve advertising effectiveness, energy efficiency, and optimize the power supply system.
Optionally, step S33 specifically includes:
step S331: collecting brightness parameters of the LED display screen, so as to obtain brightness data of the LED display screen;
in this embodiment, a light sensor is disposed on the LED display screen to monitor the brightness of the display screen in real time. The sensor collects data such as light intensity and the like, and the brightness change of the LED display screen is recorded through proper sampling frequency. The LED display screen brightness data acquisition method can be realized through sensors such as photodiodes, and can be used for ensuring that the brightness data of the LED display screen can be accurately acquired under different ambient illumination conditions.
Step S332: carrying out primary and secondary image segmentation according to the poster image so as to obtain a primary image and a secondary image;
in this embodiment, the posters on the billboard are analyzed using an image processing algorithm, such as Convolutional Neural Network (CNN), or the like. The image is divided into major and minor parts by detecting specific features in the image, such as color, shape, etc. This can be achieved by methods such as calculating color histograms of the image areas, edge detection, etc., ensuring accurate extraction of the primary and secondary images.
Step S333: carrying out proper brightness analysis on the main image by using the brightness data of the LED display screen, so as to obtain the brightness data of the main image;
In this embodiment, the brightness of the main image is adjusted according to the dynamic range of the brightness data of the LED display screen, so that the best display effect is achieved on the LED display screen. This can be achieved by means of an optical model, a feedback control system, etc., ensuring that the main image presents an optimal visual effect under different brightness conditions.
Step S334: carrying out secondary image proper brightness analysis on the secondary image by using the LED display screen brightness data so as to obtain secondary image brightness data;
in this embodiment, the LED display screen luminance data is associated with the secondary image. By a similar method, the brightness of the secondary image is adjusted according to the change of the brightness data of the LED display screen so as to adapt to different environment brightness. This may be achieved by means of an adaptive brightness adjustment algorithm or the like, ensuring that the secondary image is clearly visible under various illumination conditions.
Step S335: and carrying out regional day and night brightness strategy analysis on the brightness data of the LED display screen according to the main image brightness data and the secondary image brightness data, thereby obtaining day and night poster brightness data.
In the embodiment, a regional day and night brightness strategy of the LED display screen is formulated according to brightness data of the main image and the secondary image. For example, the brightness of the LED display screen in a specific area is adjusted according to the brightness requirement of the main image in the area. Also, other regions are adjusted based on the brightness requirements of the secondary image. The LED display screen can be realized by means of an intelligent control system, a brightness mapping function and the like, and the LED display screen is ensured to present the best day and night brightness effect in different areas.
According to the invention, the brightness parameters of the LED display screen are acquired, so that the system can acquire the brightness change of the LED display screen in real time, and basic data is provided for subsequent analysis and control. The image segmentation helps to identify and extract the primary and secondary elements in the poster, providing a data basis for different image areas for subsequent luminance analysis. Through the proper brightness analysis of the main image, the system can optimize the display effect of the main image according to the brightness data of the LED display screen, and the visibility and the attraction of the main image are improved. Similar to the primary image, the system can optimize the display effect of the secondary image according to the brightness data of the LED display screen by proper brightness analysis of the secondary image, and the visibility of the secondary image is improved. By analyzing the brightness data of the main image and the secondary image and combining the brightness data of the LED display screen, the system can formulate a day-night brightness strategy of the subareas so as to adapt to the ambient illumination changes of different areas, thereby improving the visual effect of the whole poster. And meanwhile, the energy consumption condition of the LED display screen is optimized.
Optionally, step S332 specifically includes:
performing gray level conversion on the poster image so as to obtain the poster gray level image;
an example of gradation conversion of a poster image in this embodiment is by converting an original color image into a gradation image. This can be achieved by using standard gray scale conversion formulas, such as y=0.299 r+0.587g+0.114×b, where R, G, B is the red, green, and blue channel values of the color image. This ensures that a single gray scale channel is used for processing in subsequent analysis.
Carrying out gray value statistical analysis on the poster gray image so as to obtain a high-frequency gray value, a low-frequency gray value and a medium-frequency gray value;
the embodiment of performing gray value statistical analysis on the poster gray image in this embodiment includes calculating a gray histogram to obtain gray value ranges of high frequency, low frequency and intermediate frequency. This can be achieved by performing pixel value statistics on the gray image, creating a gray histogram. The statistical result will provide a distribution of gray values in the image, facilitating subsequent image segmentation and processing.
Carrying out image segmentation on the poster gray level image according to the high-frequency gray level value so as to obtain a secondary gray level image;
the embodiment of image division of a poster gray image according to high-frequency gray values in the present embodiment involves dividing the image into corresponding parts according to a high-frequency gray value range defined in advance. And extracting a high-frequency part by applying a threshold value or an image segmentation algorithm to obtain a secondary gray image, wherein the secondary gray image comprises a part with more details in the poster and a background part.
Dividing the poster gray level image according to the low-frequency gray level value, so as to obtain a main gray level image;
the embodiment of image segmentation of the poster gray scale image according to the low frequency gray scale values in this embodiment includes segmenting the image based on the low frequency gray scale value range. The main gray image is obtained by setting a proper threshold value or extracting a low-frequency part by adopting an image segmentation algorithm, wherein the main gray image contains information of a large range such as an integral structure.
Carrying out gray value difference calculation on the high-frequency gray value and the low-frequency gray value according to the intermediate-frequency gray value, thereby obtaining a gray value difference;
in this embodiment, the gray value difference between the high-frequency gray value and the low-frequency gray value is calculated according to the intermediate-frequency gray value. This can be achieved by simple difference calculation or more complex image filtering techniques, etc., to obtain gray value differences for subsequent cluster analysis.
Clustering calculation is carried out on the intermediate frequency gray value, the high frequency gray value and the low frequency gray value based on the gray value difference value, so that a main gradient gray value and a secondary gradient gray value are obtained;
the embodiment of clustering the intermediate frequency gray values, the high frequency gray values and the low frequency gray values based on the gray value difference in the embodiment comprises grouping the intermediate frequency gray values, the high frequency gray values and the low frequency gray values by using a clustering algorithm such as K-means clustering. This ensures that similar gray value features are categorized into the same group, resulting in primary and secondary gradation values.
Dividing the poster gray image according to the main gray value to obtain a main gray image, and combining the main gray image with the main gray image to obtain a complete main gray image;
In this embodiment, the image segmentation of the poster gray image according to the main gradation value is performed by segmenting the image according to a predetermined main gradation value range. The primary gradation image is extracted by setting a threshold or using an image segmentation algorithm and combined with the primary gradation image data to obtain a complete primary gradation image. The gradation portion here refers to a color change portion where the main image smoothly transitions.
Carrying out image segmentation on the poster gray level image according to the secondary gray level value so as to obtain a secondary gray level image, and carrying out data combination on the secondary gray level image and the secondary gray level image so as to obtain a complete secondary gray level image;
the embodiment of the present embodiment wherein the image segmentation of the poster grey scale image according to the secondary gradation grey scale values comprises segmenting the image based on a predefined secondary gradation grey scale value range. The secondary gray scale image is extracted by setting an appropriate threshold or applying an image segmentation algorithm and combined with the secondary gray scale image data to obtain a complete secondary gray scale image. The gradation portion here refers to a color change portion where the main image smoothly transitions.
Performing RGB conversion on the complete main gray image so as to obtain a main image;
an embodiment of RGB conversion of the full primary gray image in this embodiment is by converting the full primary gray image to RGB format. This can generate a primary image with complete color information by copying single channel data in a gray scale image to red, green, and blue channels.
The complete secondary gray image is RGB converted to obtain a secondary image.
The embodiment of RGB conversion of the complete secondary gray scale image in this embodiment is by converting the complete secondary gray scale image into RGB format. Likewise, single channel data in the gray scale image is copied to red, green, and blue channels, creating a secondary image with complete color information.
The invention converts the color image into the gray image, which is helpful to simplify the complexity of image processing, and simultaneously retains the main brightness information of the image, so that the subsequent processing is more efficient. By counting the frequencies of different gray values, the system can know the overall brightness distribution condition of the image, and is beneficial to subsequent image segmentation and brightness adjustment. The high frequency gray values generally correspond to details and textures in the image, and by segmenting out the high frequency portions, secondary information in the image can be extracted, which helps to highlight details of the image. The low-frequency gray value reflects the overall brightness change in the image, and by dividing the low-frequency part, the main information of the image can be extracted, which is helpful for preserving the overall structure of the image. The calculation of the intermediate frequency gray value difference value is helpful for quantifying the structure and detail information of the image and providing more information for subsequent clustering. Areas with different gray value ranges in the image can be identified through clustering, so that the main gradual change and the secondary gradual change parts can be segmented, and the readability and the visibility of the image are improved. The segmentation of the primary gray scale image helps to highlight the important parts of the image, and the merging of the data of the primary gray scale image can restore the complete primary image. The segmentation of the secondary gray scale image helps to emphasize the secondary information in the image, and the merging of the data of the secondary gray scale image can restore the complete secondary image. And converting the complete main gray image into an RGB format, and restoring the color information of the image to obtain a final main image. And converting the complete secondary gray image into an RGB format, and restoring the color information of the image to obtain a final secondary image.
Optionally, step S335 is specifically:
dividing the brightness data of the LED display screen into areas according to the primary image and the secondary image, so as to obtain a primary image display area and a secondary image display area;
in this embodiment, by analyzing the primary image and the secondary image of the LED display screen, an image processing algorithm, such as a segmentation algorithm or an edge detection algorithm, may be used to divide the image on the LED display screen into a primary display area and a secondary display area. This may be achieved by detecting boundaries, color changes or texture features in the image. With this division, luminance data of the primary and secondary image display areas on the LED display screen can be obtained.
Acquiring day and night brightness data through a monitoring dome camera, and carrying out brightness division on the day and night brightness data so as to acquire day brightness data, night brightness data and late night brightness data;
in the embodiment, day and night brightness data of the LED display screen are acquired by using the monitoring dome camera. The brightness information of the LED display screen in different time periods can be obtained by monitoring the picture shot by the dome camera. Dividing the luminance data into three time periods of day, night and late night, an image processing method or threshold setting may be employed for automatic division of luminance.
Performing daytime brightness strategy analysis on the secondary image display area according to the secondary image brightness data and the daytime brightness data, so as to obtain daytime brightness strategy data;
in this embodiment, daytime luminance policy analysis is performed using the secondary image luminance data and the daytime luminance data. By comparing the brightness changes of the secondary images during daytime, a corresponding daytime brightness strategy can be formulated to ensure that the images displayed by the LED display screen are clearly visible during daytime. For example, the brightness of the secondary image display area is properly selected, and the brightness of the primary image display area is 0, so that the energy-saving effect can be improved while the visual effect is not affected.
According to the night brightness data, the main image brightness data and the secondary image brightness data, performing night brightness strategy analysis on the main image display area and the secondary image display area, so as to obtain night brightness strategy data;
in this embodiment, the night brightness policy of the LED display screen is analyzed by using the night brightness data, the primary image brightness data, and the secondary image brightness data. Through knowing the luminance demand of LED display screen at night, can adjust the luminance of display screen to guarantee that the display effect is good at night.
Performing late-night brightness strategy analysis on the main image display area according to the main image brightness data and the late-night brightness data, so as to obtain late-night brightness strategy data;
in this embodiment, the main image brightness data and the late-night brightness data are used to analyze the late-night brightness policy of the LED display screen. According to the use requirement of the LED display screen in late night, the brightness in late night can be adjusted so as to meet specific display requirements. For example, the brightness of the main image display area is properly selected, and the brightness of the secondary image display area is 0, so that the energy-saving effect can be improved while the visual effect is not influenced.
And carrying out time sequence combination on the daytime brightness strategy data, the night brightness strategy data and the late night brightness strategy data so as to obtain the day and night poster brightness data.
In this embodiment, daytime luminance policy data, night luminance policy data, and late-night luminance policy data are time-sequentially combined. The time factors can be considered, a complete day and night poster brightness data management scheme is formulated, and the LED display screen can be ensured to present the optimal brightness effect in different time periods. This may involve time-series interpolation, smoothing or other time-series analysis methods.
According to the invention, the LED display screen is divided into the areas according to the main image and the secondary image, so that the image content to be displayed in different areas can be determined. The partition can be optimized according to the importance of the image or other factors, so that the LED display screen can display different types of information at the same time, and the efficiency and the flexibility of information transmission are improved. The whole time period is divided into different brightness stages such as daytime, night, late night and the like, so that the brightness of the LED display screen can be adjusted according to the illumination conditions of different time periods, and the definition and the readability of information under different environment illumination conditions are ensured. By analyzing the secondary image brightness data and daytime brightness data, a strategy for the brightness of the LED display screen during daytime can be formulated. This helps to optimise the brightness when displaying the secondary image in a daytime environment, ensuring that the image is clearly visible under intense light. And formulating a night LED display screen brightness strategy based on the night brightness data, the main image brightness data and the secondary image brightness data. This ensures that the LED display screen provides suitable brightness when displaying the primary and secondary images in a darker light, making the image clearly visible and less obtrusive. And determining the brightness strategy of the late-night LED display screen according to the main image brightness data and the late-night brightness data. This helps to maintain proper brightness while displaying the primary image in a late-night environment while saving energy and reducing interference with the surrounding environment. And combining the daytime brightness strategy data, the night brightness strategy data and the late night brightness strategy data according to time sequences to obtain the day and night poster brightness data. The combination can generate an integral brightness strategy, and brightness adjustment is carried out on the LED display screen in different time periods so as to adapt to the requirements of different environments and display contents, and the ornamental value and the information transmission effect are improved.
Optionally, step S4 specifically includes:
step S41: acquiring electric energy circulation data through an energy storage battery;
in this embodiment, sensors are used to monitor the voltage and current of the energy storage cells and to collect these data at regular time intervals. Such data may be obtained by a Battery Management System (BMS) or similar monitoring system.
Step S42: performing electric energy conversion loss calculation on the electric energy circulation data so as to obtain electric energy conversion loss data;
in this embodiment, an electric energy conversion efficiency formula is used: electric energy conversion efficiency = actual output electric energy/input electric energy, the energy conversion loss is calculated from the voltage and current data of the battery. For example, the percentage loss may be determined by comparing the actual output energy to the input energy.
Step S43: calculating instantaneous input electric energy according to the electric energy conversion loss data and the electric energy circulation data, so as to obtain instantaneous input electric energy data;
in this embodiment, the current and voltage data are multiplied in real time by the power conversion loss data calculated in the previous step to obtain the instantaneous input power. Instantaneous input power = instantaneous current x instantaneous voltage.
Step S44: performing cable loss calculation based on the instantaneous input electric energy data and the maximum electric energy generation value, thereby obtaining cable loss data;
In the embodiment, parameters such as cable length, sectional area, resistance and the like are taken into consideration to construct a cable loss model. Wherein the cable loss can be expressed by the following formula: cable loss = maximum power generation value-instantaneous input power. And calculating the actual loss of the cable by using the cable loss model and combining the current value in the instantaneous input electric energy data and the maximum electric energy generation value.
Step S45: and carrying out output power correction value calculation on the electric energy circulation data and the cable loss data through an output power correction value formula, thereby obtaining a battery output power correction value.
The calculation formula for the correction value of the output power in this embodiment may include a battery-specific adjustment parameter. And calculating a correction value of the output power by using the electric energy circulation data and the cable loss data and applying a correction formula.
The invention obtains the circulation data of the electric energy in the energy storage battery system. This includes the input and output of electrical energy and the conversion process within the energy storage system. Accurate collection of these data provides the basis for subsequent analysis. By calculating the loss of electrical energy during the conversion process, the efficiency of the energy storage system may be assessed. The acquisition of the electric energy conversion loss data is beneficial to system optimization, reduces energy waste and improves overall efficiency. The instantaneous input power data reflects the real-time power input of the system at different points in time. This is important to understand the dynamic operating conditions of the system and to provide an accurate data basis for subsequent analysis. The cable loss calculation helps to evaluate the energy loss of the electrical energy during transmission due to cable impedance or the like. The cable loss data can be obtained to guide the system design and operation, and the high efficiency of power transmission is ensured. The calculation of the output power correction value allows an accurate assessment of the actual output power of the energy storage battery system taking into account the power conversion loss and the cable loss. This helps to ensure that the system operates at optimum efficiency and provides an accurate reference for the power supply strategy of the system.
Alternatively, the output power correction value formula in step S45 is specifically:
；
in the method, in the process of the invention,for the output power correction value, +.>To correct the term balance parameter->For the output current of the energy storage battery, < >>For the loss factor of the cable>For the rated capacity of the energy storage battery, +.>Is the internal impedance coefficient of the energy storage battery, +.>For error adjustment item, ++>For the temperature of the energy storage cell->For the cable length between the energy storage battery and the power supply appliance, < >>For maximum output power of the energy storage battery, < >>Is the electric energy conversion efficiency of the energy storage battery.
The invention constructs an output power correction value formula for calculating the output power correction value of the electric energy circulation data and the cable loss data. The formula fully considers influencing the output power correction valueCorrection term balance parameter +.>Output current of energy storage battery>Cable loss factor->Rated capacity of energy storage battery>Internal impedance coefficient of energy storage batteryError adjustment item-> Temperature of energy storage battery->The cable length between the energy storage battery and the power supply appliance is +.>Maximum output power of energy storage battery->Electric energy conversion efficiency of the energy storage battery>A functional relationship is formed:
；
wherein,part of which is related to the correction term balance parameter- >Output current of energy storage battery->Cable loss factor->Rated capacity of energy storage battery>And internal impedance coefficient->. Square term +.>And logarithmic term->For adjusting the relationship between output power and battery capacity, output current and internal impedance. />This is the output power +.>Regarding the efficiency of electric energy conversion>Is a second derivative of (c). This term is used to take into account the effect of the power conversion efficiency on the output power and to ensure that the correction takes into account the change in efficiency. />Part contains error adjustment item->Temperature of energy storage cell>And the cube root of the cable length ∈ ->. Indicating the effect of temperature on battery performance and the factors affecting cable length and cable loss. The error adjustment term is used to correct for other influencing factors not considered in the model. In the art, the output power correction value is generally calculated by adopting technical means such as electro-mechanical modeling, signal processing and the like. By adopting the output power correction value calculation formula provided by the invention, the output power correction value can be obtained more accurately and rapidly.
Optionally, the specification further provides a photovoltaic guardrail power supply management system based on the internet of things, which is used for executing the photovoltaic guardrail power supply management method based on the internet of things, and the photovoltaic guardrail power supply management system based on the internet of things comprises:
The actual solar radiation degree calculation module is used for acquiring weather environment data through the weather environment monitoring module and calculating the actual solar radiation degree according to the weather environment data so as to acquire actual solar radiation data;
the maximum electric energy generation value calculation module is used for analyzing the electric energy conversion rate of the solar panel so as to obtain photoelectric conversion efficiency, and calculating the electric energy generation value according to actual solar radiation data and the photoelectric conversion efficiency so as to obtain an electric energy generation value;
the day and night acousto-optic analysis module is used for acquiring advertisement putting data of the Internet of things through the WIFI module and carrying out day and night acousto-optic analysis according to the advertisement putting data of the Internet of things so as to acquire day and night acousto-optic data;
the cable loss analysis module is used for acquiring electric energy circulation data through the energy storage battery and carrying out cable loss analysis according to the electric energy generation value and the electric energy circulation data so as to obtain a battery output power correction value;
the battery output power correction module is used for correcting the battery output power of the electric energy circulation data by utilizing the battery output power correction value so as to obtain actual battery output data;
and the day and night power supply analysis module is used for carrying out day and night power supply analysis according to the actual battery output data and the day and night acousto-optic data so as to obtain a day and night power supply strategy, and transmitting the day and night power supply strategy to the controller so as to execute a power supply management task.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The photovoltaic guardrail power supply management method based on the Internet of things is characterized by being applied to a photovoltaic guardrail, wherein the photovoltaic guardrail comprises a controller, a solar panel, an LED light source module, a WIFI module, a meteorological environment monitoring module, a monitoring ball machine, an IP sound column, an LED display screen and an energy storage battery, wherein the solar panel, the LED light source module, the WIFI module, the meteorological environment monitoring module, the monitoring ball machine, the IP sound column, the LED display screen and the energy storage battery are electrically connected with the controller; the photovoltaic guardrail power supply management method based on the Internet of things comprises the following steps of:
Step S1: acquiring weather environment data through a weather environment monitoring module, and calculating actual solar radiation according to the weather environment data so as to acquire actual solar radiation data;
step S2: performing electric energy conversion rate analysis on the solar panel to obtain photoelectric conversion efficiency, and performing electric energy generation value calculation according to actual solar radiation data and the photoelectric conversion efficiency to obtain an electric energy generation value;
step S3: acquiring advertisement putting data of the Internet of things through the WIFI module, and performing day and night acousto-optic analysis according to the advertisement putting data of the Internet of things so as to acquire day and night acousto-optic data;
step S4: acquiring electric energy circulation data through an energy storage battery, and performing cable loss analysis according to the electric energy generation value and the electric energy circulation data so as to acquire a battery output power correction value;
step S5: performing battery output power correction on the electric energy circulation data by using the battery output power correction value so as to obtain actual battery output data;
step S6: and performing day and night power supply analysis according to the actual battery output data and day and night acousto-optic data, so as to obtain a day and night power supply strategy, and transmitting the day and night power supply strategy to the controller to execute a power supply management task.
2. The method according to claim 1, wherein step S1 is specifically:
step S11: acquiring weather environment data and maximum instantaneous solar radiation data through a weather environment monitoring module;
step S12: solar azimuth data extraction and atmospheric compound data extraction are carried out on meteorological environment data, so that solar azimuth data and atmospheric compound data are obtained;
step S13: acquiring solar panel orientation data through a solar panel, and calculating a solar radiation offset angle according to the solar azimuth data and the solar panel orientation data so as to acquire solar radiation offset data;
step S14: performing solar radiation attenuation analysis according to the atmospheric compound data and the solar azimuth data, so as to obtain solar radiation shielding data;
step S15: and carrying out actual solar radiation analysis on the solar radiation offset data and the solar radiation shielding data so as to obtain actual solar radiation data.
3. The method according to claim 2, wherein step S15 is specifically:
step S151: performing correlation analysis on the solar radiation offset data and the solar radiation shielding data so as to obtain solar radiation attenuation data;
Step S152: calculating the maximum instantaneous solar radiation data and the solar radiation attenuation data through an actual solar radiation degree calculation formula, thereby obtaining an actual solar radiation degree data set;
the actual solar radiance calculation formula specifically comprises:
；
in the method, in the process of the invention,is at->Actual solar irradiance of time, +.>For observing time, < >>For the maximum instantaneous solar irradiance to be the maximum,is the base of natural logarithm, +.>Is at->Time of the offset influence coefficient received by solar radiation, < +.>Is at->Time of the solar radiation received a shading coefficient of influence, < ->Is the rate of solar radiation attenuation;
step S153: and carrying out time sequence combination according to the actual solar radiation degree data set and the solar radiation influence data, so as to obtain actual solar radiation data.
4. A method according to claim 3, wherein step S3 is specifically:
step S31: acquiring advertisement putting data of the Internet of things through a WIFI module;
step S32: extracting a poster image and advertisement audio data according to advertisement putting data of the Internet of things, so as to obtain the poster image and the advertisement audio data;
step S33: collecting brightness parameters of the LED display screen, so as to obtain brightness data of the LED display screen; carrying out day and night brightness analysis on the poster image according to the brightness data of the LED display screen so as to obtain day and night poster brightness data;
Step S34: collecting audio parameters of the IP voice column, thereby obtaining IP voice column audio data; performing day and night audio analysis on the advertisement audio data according to the IP sound column audio data, thereby obtaining day and night advertisement audio data;
step S35: collecting brightness parameters of the LED light source module to obtain brightness parameters of the LED light source; performing day and night brightness analysis on brightness parameters of the LED light source so as to obtain day and night street lamp brightness data;
step S36: and carrying out time sequence combination on the day and night street lamp brightness data, the day and night poster brightness data and the day and night advertisement audio data, thereby obtaining day and night acousto-optic data.
5. The method according to claim 4, wherein step S33 is specifically:
step S331: collecting brightness parameters of the LED display screen, so as to obtain brightness data of the LED display screen;
step S332: carrying out primary and secondary image segmentation according to the poster image so as to obtain a primary image and a secondary image;
step S333: carrying out proper brightness analysis on the main image by using the brightness data of the LED display screen, so as to obtain the brightness data of the main image;
step S334: carrying out secondary image proper brightness analysis on the secondary image by using the LED display screen brightness data so as to obtain secondary image brightness data;
Step S335: and carrying out regional day and night brightness strategy analysis on the brightness data of the LED display screen according to the main image brightness data and the secondary image brightness data, thereby obtaining day and night poster brightness data.
6. The method according to claim 5, wherein step S332 is specifically:
performing gray level conversion on the poster image so as to obtain the poster gray level image;
carrying out gray value statistical analysis on the poster gray image so as to obtain a high-frequency gray value, a low-frequency gray value and a medium-frequency gray value;
carrying out image segmentation on the poster gray level image according to the high-frequency gray level value so as to obtain a secondary gray level image;
dividing the poster gray level image according to the low-frequency gray level value, so as to obtain a main gray level image;
carrying out gray value difference calculation on the high-frequency gray value and the low-frequency gray value according to the intermediate-frequency gray value, thereby obtaining a gray value difference;
clustering calculation is carried out on the intermediate frequency gray value, the high frequency gray value and the low frequency gray value based on the gray value difference value, so that a main gradient gray value and a secondary gradient gray value are obtained;
dividing the poster gray image according to the main gray value to obtain a main gray image, and combining the main gray image with the main gray image to obtain a complete main gray image;
Carrying out image segmentation on the poster gray level image according to the secondary gray level value so as to obtain a secondary gray level image, and carrying out data combination on the secondary gray level image and the secondary gray level image so as to obtain a complete secondary gray level image;
performing RGB conversion on the complete main gray image so as to obtain a main image;
the complete secondary gray image is RGB converted to obtain a secondary image.
7. The method according to claim 6, wherein step S335 is specifically:
dividing the brightness data of the LED display screen into areas according to the primary image and the secondary image, so as to obtain a primary image display area and a secondary image display area;
acquiring day and night brightness data through a monitoring dome camera, and carrying out brightness division on the day and night brightness data so as to acquire day brightness data, night brightness data and late night brightness data;
performing daytime brightness strategy analysis on the secondary image display area according to the secondary image brightness data and the daytime brightness data, so as to obtain daytime brightness strategy data;
according to the night brightness data, the main image brightness data and the secondary image brightness data, performing night brightness strategy analysis on the main image display area and the secondary image display area, so as to obtain night brightness strategy data;
Performing late-night brightness strategy analysis on the main image display area according to the main image brightness data and the late-night brightness data, so as to obtain late-night brightness strategy data;
and carrying out time sequence combination on the daytime brightness strategy data, the night brightness strategy data and the late night brightness strategy data so as to obtain the day and night poster brightness data.
8. The method according to claim 7, wherein step S4 is specifically:
step S41: acquiring electric energy circulation data through an energy storage battery;
step S42: performing electric energy conversion loss calculation on the electric energy circulation data so as to obtain electric energy conversion loss data;
step S43: calculating instantaneous input electric energy according to the electric energy conversion loss data and the electric energy circulation data, so as to obtain instantaneous input electric energy data;
step S44: performing cable loss calculation based on the instantaneous input electric energy data and the maximum electric energy generation value, thereby obtaining cable loss data;
step S45: and carrying out output power correction value calculation on the electric energy circulation data and the cable loss data through an output power correction value formula, thereby obtaining a battery output power correction value.
9. The method of claim 8, wherein the output power correction value formula in step S45 is specifically:
；
In the method, in the process of the invention,for the output power correction value, +.>To correct the term balance parameter->For the output current of the energy storage battery, < >>For the loss factor of the cable>For the rated capacity of the energy storage battery, +.>Is the internal impedance coefficient of the energy storage battery, +.>For error adjustment item, ++>For the temperature of the energy storage cell->For the cable length between the energy storage battery and the power supply appliance, < >>For maximum output power of the energy storage battery, < >>Is the electric energy conversion efficiency of the energy storage battery.
10. The photovoltaic guardrail power supply management system based on the internet of things is characterized by being used for executing the photovoltaic guardrail power supply management method based on the internet of things as claimed in claim 1, and comprises the following steps:
the actual solar radiation degree calculation module is used for acquiring weather environment data through the weather environment monitoring module and calculating the actual solar radiation degree according to the weather environment data so as to acquire actual solar radiation data;
the maximum electric energy generation value calculation module is used for analyzing the electric energy conversion rate of the solar panel so as to obtain photoelectric conversion efficiency, and calculating the electric energy generation value according to actual solar radiation data and the photoelectric conversion efficiency so as to obtain an electric energy generation value;
The day and night acousto-optic analysis module is used for acquiring advertisement putting data of the Internet of things through the WIFI module and carrying out day and night acousto-optic analysis according to the advertisement putting data of the Internet of things so as to acquire day and night acousto-optic data;
the cable loss analysis module is used for acquiring electric energy circulation data through the energy storage battery and carrying out cable loss analysis according to the electric energy generation value and the electric energy circulation data so as to obtain a battery output power correction value;
the battery output power correction module is used for correcting the battery output power of the electric energy circulation data by utilizing the battery output power correction value so as to obtain actual battery output data;
and the day and night power supply analysis module is used for carrying out day and night power supply analysis according to the actual battery output data and the day and night acousto-optic data so as to obtain a day and night power supply strategy, and transmitting the day and night power supply strategy to the controller so as to execute a power supply management task.
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| JP2003018763A (en) * | 2001-06-27 | 2003-01-17 | Nippon Telegr & Teleph Corp <Ntt> | Energy prediction method in solar power generation |
| CN106374555A (en) * | 2016-08-31 | 2017-02-01 | 四川省魅力传媒有限公司 | Intelligent charging system and intelligent charging method |
| CN211183436U (en) * | 2019-12-27 | 2020-08-04 | 湖北省电力勘测设计院有限公司 | Light storage micro-grid system based on energy prediction |
| CN117010167A (en) * | 2023-07-11 | 2023-11-07 | 国网甘肃省电力公司经济技术研究院 | Output power estimation method of photovoltaic power generation system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| JP2003018763A (en) * | 2001-06-27 | 2003-01-17 | Nippon Telegr & Teleph Corp <Ntt> | Energy prediction method in solar power generation |
| CN106374555A (en) * | 2016-08-31 | 2017-02-01 | 四川省魅力传媒有限公司 | Intelligent charging system and intelligent charging method |
| CN211183436U (en) * | 2019-12-27 | 2020-08-04 | 湖北省电力勘测设计院有限公司 | Light storage micro-grid system based on energy prediction |
| CN117010167A (en) * | 2023-07-11 | 2023-11-07 | 国网甘肃省电力公司经济技术研究院 | Output power estimation method of photovoltaic power generation system |
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