A solar street lamp control system based on the maximum power point

Abstract

The present invention relates to the technical field of solar street lamps, and specifically to a solar street lamp control system based on the maximum power point. First, obtain the light intensity data, temperature data, ambient flow data, and photovoltaic panel characteristic data; then, perform bisection iteration optimization on the initially predicted maximum power point voltage output by the maximum power point voltage prediction model to obtain the optimal voltage; then, during the charging and discharging process, evaluate the charging capacity, charging loss, and ambient temperature, and use the charging control value to control the charging behavior; evaluate the remaining capacity, ambient temperature, discharge loss, and the ambient flow data, and use the discharge control value to control the discharge behavior; finally, evaluate the battery temperature, energy conversion rate, and aging defects of the photovoltaic panel, compare the abnormal control value with the abnormal control threshold, and if it is higher than the threshold, send feedback abnormal information; the present invention can improve the working efficiency and reliability of the solar street lamp control system.

Description

Technical Field

[0001] The present invention relates to the technical field of solar street lamps, and specifically to a solar street lamp control system based on the maximum power point. Background Art

[0002] A solar street lamp is a lighting device that uses solar energy as its power source. It converts sunlight into electrical energy through a photovoltaic panel installed on the street lamp, then stores the electrical energy in a storage battery, and is controlled by an intelligent charge and discharge controller for use in place of traditional public power street lamps for lighting. Compared with traditional street lamps, solar street lamps do not rely on grid power supply, and have the advantages of low operation and maintenance costs, low installation restrictions, and high independence.

[0003] A solar street lamp is powered by a photovoltaic cell on the photovoltaic panel, and the output power of the photovoltaic cell is related to the operating voltage. Only when it operates at the most suitable voltage will its output power have a unique maximum value; the MPPT controller, as a "maximum power point tracking" solar controller, adjusts the operating state of the electrical module to make the solar photovoltaic panel work with the maximum possible output power.

[0004] Currently, there are still deficiencies in the prior art for the solar street lamp control system based on the maximum power point. On the one hand, the prior art does not consider that factors such as the temperature of the photovoltaic cell, the light intensity, and the manually measured indicators will affect the calculation accuracy of the maximum power point voltage, and lacks effective control of the maximum power point voltage under various environmental conditions; on the other hand, the prior art lacks management and control over the losses and faults of solar street lamps; these abnormal situations will affect the stable operation of solar street lamps at the maximum power point state, thereby reducing the working efficiency and reliability of the solar street lamp control system.

[0005] Therefore, a solar street lamp control system based on the maximum power point is proposed. Summary of the Invention

[0006] The object of the present invention is to provide a solar street lamp control system based on the maximum power point. First, obtain light intensity data, temperature data, environmental flow data, and photovoltaic panel characteristic data; then, perform bisection iteration optimization on the initially predicted maximum power point voltage output by the maximum power point voltage prediction model to obtain the optimal voltage; then, evaluate the charging capacity, charging loss, and environmental temperature during the charging and discharging process, and use the charging control value to control the charging behavior; evaluate the remaining capacity, environmental temperature, discharging loss, and the environmental flow data, and use the discharging control value to control the discharging behavior; finally, evaluate the battery temperature, the energy conversion rate of the photovoltaic panel, and aging defects, compare the abnormal control value with the abnormal control threshold, and if it is higher than the threshold, send feedback abnormal information to relevant personnel; the present invention can improve the working efficiency and reliability of the solar street lamp control system.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A solar street lamp control system based on the maximum power point, comprising: a data acquisition module, a maximum power point voltage control module, a charge and discharge control module, and an abnormal control module;

[0009] The data acquisition module obtains light intensity data, environmental temperature data, and battery temperature data through sensors; obtains environmental flow data and photovoltaic panel characteristic data by using an identification model;

[0010] The maximum power point voltage control module inputs the light intensity data and the environmental temperature data into the maximum power point voltage prediction model to obtain an initially predicted maximum power point voltage; then, takes the initially predicted maximum power point voltage as the midpoint voltage, and adjusts the midpoint voltage by using the bisection method to obtain the optimal voltage;

[0011] The charge and discharge control module stabilizes the output voltage of the photovoltaic panel at the optimal voltage and charges and discharges the photovoltaic battery; evaluates the charging capacity, charging loss, and environmental temperature, and uses the calculated charging control value to control the charging behavior; evaluates the remaining capacity, environmental temperature, discharging loss, and the environmental flow data, and uses the calculated discharging control value to control the discharging behavior;

[0012] The abnormal control module obtains the battery temperature data and the photovoltaic panel characteristic data, evaluates the battery temperature, the energy conversion rate of the photovoltaic panel, and aging defects, compares the calculated abnormal control value with the abnormal control threshold, and if it is higher than the threshold, sends feedback information to relevant personnel.

[0013] Further, the specific implementation process of the data acquisition module obtaining environmental flow data and photovoltaic panel characteristic data by using the identification model includes:

[0014] Obtain the image data collected by the camera;

[0015] Among them, the image data includes: environmental image data and photovoltaic panel image data;

[0016] Further, input the image data into the input layer of the recognition model for feature extraction to obtain image features;

[0017] Further, use the marking layer of the recognition model to obtain the anchor boxes of the image features;

[0018] Further, input the features in the anchor boxes into the recognition layer of the recognition model to obtain the target recognition result;

[0019] Further, input the target recognition result into the output layer of the recognition model to obtain target data; among them, the target data includes: environmental flow data and photovoltaic panel feature data.

[0020] Further, the environmental flow data includes: environmental personnel flow data and environmental vehicle flow data; the photovoltaic panel feature data includes: photovoltaic panel area, photovoltaic panel occlusion area, photovoltaic panel yellowing area, photovoltaic panel cracks, and photovoltaic panel hot spots;

[0021] Further, the specific implementation process of the maximum power point voltage control module using the maximum power point voltage prediction model and the dichotomy method to obtain the optimal voltage at the maximum power point includes:

[0022] Obtain the light intensity data and the environmental temperature data;

[0023] Further, input the light intensity data and the environmental temperature data into the maximum power point voltage prediction model to obtain the preliminary predicted maximum power point voltage;

[0024] Among them, the maximum power point voltage prediction model is a pre-trained network model;

[0025] Further, use the dichotomy method to further optimize and adjust the preliminary predicted maximum power point voltage until the optimal voltage at the maximum power point is obtained.

[0026] Further, the maximum power point voltage prediction model is a gated recurrent neural network model, and the specific training process of this model includes:

[0027] Obtain historical data; among them, the historical data includes: historical light intensity data, historical environmental temperature data, and historical maximum power point voltage;

[0028] Further, clean and normalize the historical data to obtain preprocessed historical data;

[0029] Further, divide the preprocessed historical data into a training set and a validation set according to 8:2;

[0030] Further, input the training set into the maximum power point voltage prediction model for training to obtain an initial maximum power point voltage prediction model;

[0031] Further, input the validation set into the initial maximum power point voltage prediction model to optimize the model parameters and obtain a final maximum power point voltage prediction model.

[0032] Further, the specific process of the maximum power point voltage control module adjusting the midpoint voltage using the bisection method to obtain the optimal voltage includes:

[0033] Obtain a preliminary predicted maximum power point voltage;

[0034] Further, calculate the reference power according to the preliminary predicted maximum power point voltage;

[0035] Further, set the preliminary predicted maximum power point voltage as the midpoint voltage;

[0036] Further, set an optimal voltage estimation interval based on the midpoint voltage and the interval range threshold; wherein, the maximum value of the optimal voltage estimation interval does not exceed the safety voltage, and the minimum value is not lower than 0;

[0037] Further, compare the power within the optimal voltage estimation interval with the reference power, update the optimal voltage estimation interval and the midpoint voltage until the interval range is less than the interval threshold to obtain a final optimal voltage estimation interval;

[0038] Further, set the midpoint voltage of the final optimal voltage estimation interval as the optimal voltage.

[0039] Further, the specific implementation process of the charge and discharge control module evaluating the charge capacity, charge loss, and environmental temperature and controlling the charging behavior using the calculated charge control value includes:

[0040] Obtain charge capacity data, charge loss data, and environmental temperature data;

[0041] Further, compare the charge capacity data, the charge loss data, and the environmental temperature data with the maximum charge capacity, the charge loss threshold, and the environmental temperature threshold respectively to obtain a charge capacity evaluation value, a charge loss evaluation value, and an environmental temperature evaluation value;

[0042] Further, the charging capacity evaluation value, the charging loss evaluation value, and the ambient temperature evaluation value are weighted respectively by the charging capacity control coefficient, the charging loss control coefficient, and the ambient temperature control coefficient, and a charging control value is calculated;

[0043] Further, the charging control value is compared with a charging control threshold. If it exceeds the charging control threshold, the charging behavior of the charge and discharge control module is stopped.

[0044] Further, the specific implementation process of evaluating the remaining capacity, the ambient temperature, the discharge loss, and the ambient flow rate data and using the calculated discharge control value to control the discharge behavior includes:

[0045] Obtain the remaining capacity data, the ambient temperature data, the discharge loss data, and the ambient flow rate data;

[0046] Further, the remaining capacity data, the ambient temperature data, the discharge loss data, and the ambient flow rate data are respectively compared with a safety remaining capacity, an ambient temperature threshold, a discharge loss threshold, and an ambient flow rate threshold to obtain a remaining capacity evaluation value, an ambient temperature evaluation value, a discharge loss evaluation value, and an ambient flow rate evaluation value;

[0047] Further, the remaining capacity evaluation value, the ambient temperature evaluation value, the discharge loss evaluation value, and the ambient flow rate evaluation value are weighted respectively by the remaining capacity control coefficient, the ambient temperature control coefficient, the discharge loss control coefficient, and the ambient flow rate control coefficient, and a discharge control value is calculated;

[0048] Further, the discharge control value is compared with a discharge control threshold. If it exceeds the discharge control threshold, the discharge behavior of the charge and discharge control module is stopped.

[0049] Further, the charging loss data is used to reflect the loss degree during the battery charging process; the charging loss data is obtained by multiplying the charging duration, the charging frequency, and the charging power loss rate; the discharge loss data is used to reflect the loss degree during the battery discharge process; the discharge loss data is obtained by multiplying the discharge duration, the discharge frequency, and the discharge power loss rate;

[0050] Further, the specific implementation process of the anomaly control module evaluating the battery temperature, the energy conversion rate of the photovoltaic panel, and the aging defect and calculating the anomaly control value includes:

[0051] Obtain the battery temperature data, the irradiance data, the photovoltaic panel output power data, and the photovoltaic panel characteristic data;

[0052] Further, the energy conversion rate of the photovoltaic panel is calculated based on the irradiance data, the output power data of the photovoltaic panel, and the area of the photovoltaic panel and the area of the obstacle in the photovoltaic panel characteristic data;

[0053] Further, the battery temperature data, the energy conversion rate, and the defect characteristics in the photovoltaic panel characteristic data are evaluated to obtain a plurality of evaluation scores; wherein, the defect characteristics include: yellowing characteristics, crack characteristics, and hot spot characteristics;

[0054] Further, a comprehensive evaluation is performed based on the plurality of evaluation scores to obtain the abnormal control value.

[0055] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0056] 1. The present invention proposes a maximum power point voltage control function for controlling and adjusting the maximum power point voltage under different environments; this function is realized by relying on the maximum power point voltage control module; wherein, the maximum power point voltage control module includes a prediction unit and an optimization unit; the prediction unit uses the maximum power point voltage prediction model to learn the correlation between light intensity and temperature and the maximum power point voltage, so as to output a preliminary predicted maximum power point voltage; the optimization unit uses the bisection method to adjust the preliminary predicted maximum power point voltage to obtain the optimal voltage; this function can effectively improve the working efficiency and reliability of the solar street lamp control system.

[0057] 2. The present invention proposes a discharge control function for controlling the discharge behavior of the photovoltaic battery under different conditions; this function is realized by relying on the charge and discharge control module; this module first obtains the remaining capacity, ambient temperature, discharge loss, and ambient flow data, and then performs a threshold evaluation on the remaining capacity, the ambient temperature, the discharge loss, and the ambient flow data, and uses the calculated discharge control value to control the discharge behavior; wherein, the discharge loss is related to the discharge duration, discharge frequency, and discharge power loss; this function can effectively improve the working efficiency and reliability of the solar street lamp control system.

[0058] 3. The present invention proposes an abnormal control function for feedback and control of abnormal data of photovoltaic panels; this function is implemented by an abnormal control module; this module first obtains battery temperature data and photovoltaic panel characteristic data, then performs threshold evaluation on the battery temperature, energy conversion rate, and defect characteristics of the photovoltaic panel, and then compares the calculated abnormal control value with the abnormal control threshold. If it is higher than the threshold, feedback abnormal information is sent to relevant personnel; among them, the energy conversion rate is related to irradiance, photovoltaic panel area, shielding area, and output power; this function can make the solar street lamp work stably at the maximum power point state by controlling abnormal data, thereby effectively improving the working efficiency and reliability of the solar street lamp control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a schematic structural diagram of a solar street lamp control system based on the maximum power point of the present invention;

[0060] Figure 2 It is a schematic diagram of the solar street lamp of the present invention;

[0061] Figure 3 It is a schematic structural diagram of the recognition model of the present invention;

[0062] Figure 4 It is a flowchart of the implementation method of a solar street lamp control system based on the maximum power point of the present invention.

[0063] In the figure: 1, sensor; 2, solar photovoltaic panel; 3, camera; 4, street lamp head. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0065] A solar street lamp is a lighting device that uses solar energy as an energy source. It converts sunlight into electrical energy through a photovoltaic panel installed on the street lamp, then stores the electrical energy in a storage battery, and is controlled by an intelligent charge and discharge controller for lighting instead of traditional public power street lamps. Compared with traditional street lamps, solar street lamps do not rely on grid power supply and have the advantages of low maintenance cost, low installation restrictions, and high independence.

[0066] Solar street lights are powered by photovoltaic cells on the photovoltaic panel, and the output power of the photovoltaic cell is related to the working voltage. Only when it works at the most suitable voltage, its output power will have a unique maximum value. As a "maximum power point tracking" solar controller, the MPPT controller adjusts the working state of the electrical module to make the solar photovoltaic panel work with the maximum output power as much as possible.

[0067] Currently, there are still deficiencies in the solar street light control system based on the maximum power point in the existing technology. On the one hand, the existing technology does not consider that factors such as photovoltaic cell temperature, light intensity, and manual measurement indicators will affect the calculation accuracy of the maximum power point voltage, and lacks effective control of the maximum power point voltage under various environmental conditions. On the other hand, the existing technology lacks management and control of the losses and faults of solar street lights, which will reduce the working efficiency and reliability of the solar street light control system.

[0068] Embodiment 1

[0069] In the specific implementation process of the embodiment of the present application, it will be realized by a solar street light control system based on the maximum power point of the present invention. The structural schematic diagram of the system proposed by the present invention is as Figure 1 shown; a solar street light control system based on the maximum power point includes: a data acquisition module, a maximum power point voltage control module, a charge and discharge control module, and an abnormal control module;

[0070] The structure of the solar street light is as Figure 2 shown, including: sensor 1, solar photovoltaic panel 2, camera 3, and street lamp head 4; wherein, the sensor 1 is used to provide sensor data; the solar photovoltaic panel 2 is used for charging and discharging; the camera 3 is used to provide environmental images and photovoltaic panel images; the street lamp head 4 is used for lighting.

[0071] The data acquisition module obtains light intensity data, environmental temperature data, and battery temperature data through sensor 1; and obtains environmental flow data and photovoltaic panel feature data by using an identification model;

[0072] Further, the data acquisition module obtains environmental flow data and photovoltaic panel feature data by using an identification model; wherein, the structure of the identification model is as Figure 3 shown, and the specific implementation process of the model for obtaining data includes:

[0073] Obtain the image data collected by camera 3;

[0074] In this embodiment, the camera 3 is a panoramic camera installed on the street lamp head 4, so that it can not only capture the environmental conditions of the lamp lighting, but also capture the real-time situation of the solar photovoltaic panel 2; the sensor 1 includes a light intensity sensor and a temperature sensor, and is mainly located below the solar photovoltaic panel 2; among them, the positions of the camera 3 and the sensor 1 are as Figure 2 shown.

[0075] Among them, the image data includes: environmental image data and photovoltaic panel image data;

[0076] Further, the image data is input into the input layer of the recognition model to obtain input features;

[0077] Further, the input features are input into the feature extraction layer of the recognition model to obtain image features;

[0078] Further, the anchor box of the image features is obtained by using the marking layer of the recognition model;

[0079] In this embodiment, the number of anchor boxes set is 5; the number of anchor boxes can be flexibly adjusted according to the actual requirements of model accuracy and model calculation overhead.

[0080] Further, the features in the anchor box are input into the recognition layer of the recognition model to obtain the target recognition result;

[0081] Further, the target recognition result is input into the output layer of the recognition model to obtain target data; among them, the target data includes: environmental flow data and photovoltaic panel feature data.

[0082] In this embodiment, the data acquisition module uses a recognition model based on a convolutional neural network to identify and process the collected environmental images and photovoltaic panel images, so as to obtain environmental flow data and photovoltaic panel feature data, which are used to provide data support for the subsequent charge and discharge control module and abnormal control module; at the same time, this model also improves the efficiency of data acquisition, thereby effectively improving the working efficiency of the solar street lamp control system.

[0083] Further, the environmental flow data includes: environmental personnel flow data and environmental vehicle flow data; the photovoltaic panel feature data includes: photovoltaic panel area, photovoltaic panel occlusion area, photovoltaic panel yellowing area, photovoltaic panel cracks and photovoltaic panel hot spots;

[0084] In this embodiment, the environmental personnel flow data and environmental vehicle flow data reflect the flow changes of personnel and vehicles at different times. Therefore, for the photovoltaic cell discharge, on the premise of meeting the discharge conditions, the higher the flow rate in a certain period, the higher the discharge amount and discharge frequency; the photovoltaic panel characteristic data includes both the appearance characteristics of the photovoltaic panel and its own defect characteristics, which provides corresponding data support for the calculation of the energy conversion rate and the abnormal control value; these data can effectively improve the reliability of the solar street lamp control system.

[0085] The maximum power point voltage control module inputs the light intensity data and the environmental temperature data into the maximum power point voltage prediction model to obtain a preliminary predicted maximum power point voltage; then, taking the preliminary predicted maximum power point voltage as the midpoint voltage, the bisection method is used to adjust the midpoint voltage to obtain the optimal voltage.

[0086] Further, the specific implementation process of the maximum power point voltage control module using the maximum power point voltage prediction model and the bisection method to obtain the optimal voltage at the maximum power point includes:

[0087] Obtain the light intensity data and the environmental temperature data.

[0088] Further, input the light intensity data and the environmental temperature data into the maximum power point voltage prediction model to obtain a preliminary predicted maximum power point voltage.

[0089] Among them, the maximum power point voltage prediction model is a pre-trained network model.

[0090] Further, use the bisection method to further optimize and adjust the preliminary predicted maximum power point voltage until the optimal voltage at the maximum power point is obtained.

[0091] In this embodiment, a maximum power point voltage control function is proposed to control and adjust the maximum power point voltage in different environments; this function is realized by relying on the maximum power point voltage control module; among them, the maximum power point voltage control module includes a prediction unit and an optimization unit; the prediction unit uses the maximum power point voltage prediction model to learn the correlation between light intensity, temperature and the maximum power point voltage, so as to output a preliminary predicted maximum power point voltage; the optimization unit uses the bisection method to adjust the preliminary predicted maximum power point voltage to obtain the optimal voltage; this function can effectively improve the working efficiency and reliability of the solar street lamp control system.

[0092] Further, the maximum power point voltage prediction model is a gated recurrent neural network model, and the specific training process of this model includes:

[0093] Obtain historical data; wherein, the historical data includes: historical light intensity data, historical ambient temperature data, and historical maximum power point voltage;

[0094] Further, perform cleaning and normalization processing on the historical data to obtain preprocessed historical data;

[0095] In this embodiment, the reason for performing cleaning processing on the historical data is that the historical data is mixed with duplicate data and invalid data. Therefore, it is necessary to perform a cleaning operation before inputting it into the prediction model to improve the consistency and integrity of the data; at the same time, performing a normalization operation on the cleaned data is to improve the training rate and training stability of the model.

[0096] Further, divide the preprocessed historical data into a training set and a validation set according to 8:2;

[0097] Further, input the training set into the maximum power point voltage prediction model for training to obtain an initial maximum power point voltage prediction model;

[0098] Further, input the validation set into the initial maximum power point voltage prediction model to optimize the model parameters and obtain a final maximum power point voltage prediction model.

[0099] In this embodiment, the prediction model used by the maximum power point voltage control module is pre-trained for the convenience of the real-time operation of the solar street lamp control system; the training process of the maximum power point voltage prediction model includes two stages. In the first stage, the model is initially trained using the training set and the gradient descent algorithm to obtain an initial maximum power point voltage prediction model; in the second stage, the validation set is used to optimize the parameters of the initial maximum power point voltage prediction model to obtain a final maximum power point voltage prediction model; this training process can improve the prediction accuracy of the maximum power point voltage prediction model and further improve the reliability of the solar street lamp control system.

[0100] Further, the specific process of the maximum power point voltage control module using the bisection method to adjust the midpoint voltage to obtain the optimal voltage includes:

[0101] Obtain a preliminary predicted maximum power point voltage;

[0102] Further, calculate the reference power according to the preliminary predicted maximum power point voltage; wherein, the reference power is the product of the preliminary predicted maximum power point voltage and the current;

[0103] Further, set the preliminary predicted maximum power point voltage as the midpoint voltage V0;

[0104] Further, an optimal voltage estimation interval is set based on the midpoint voltage and the interval range threshold; wherein, the maximum value of the optimal voltage estimation interval does not exceed the safety voltage, and the minimum value is not lower than 0;

[0105] The optimal voltage estimation interval in this embodiment is set to [V0 - 5, V0 + 5]; wherein, the range of the optimal voltage estimation interval is not unique and can be dynamically adjusted by relevant personnel in the field according to historical experience summary and actual situations.

[0106] Further, the power within the optimal voltage estimation interval is compared with the reference power, and the optimal voltage estimation interval and the midpoint voltage are updated until the interval range is less than the interval threshold, obtaining the final optimal voltage estimation interval;

[0107] The setting of the interval threshold in this embodiment is not unique, and the setting of the interval threshold is affected by factors such as hardware models and environmental temperatures. Therefore, it can be dynamically adjusted by relevant personnel in the field according to historical experience summary and actual situations.

[0108] Further, the midpoint voltage of the final optimal voltage estimation interval is set as the optimal voltage.

[0109] In this embodiment, the maximum power point voltage control module further optimizes and adjusts the preliminarily predicted maximum power point voltage by using the bisection method, considering that the insufficient amount of historical data will cause a large error between the output result of the maximum power point voltage prediction model and the true result; combining the historical experience summary of relevant staff, using the bisection algorithm to make the model prediction result approach the ideal optimal voltage more quickly, which can improve the working efficiency and reliability of the solar street lamp control system.

[0110] The charge and discharge control module stabilizes the output voltage of the photovoltaic panel at the optimal voltage and charges and discharges the photovoltaic battery; evaluates the charge capacity, charge loss, and environmental temperature, and controls the charging behavior by using the calculated charge control value; evaluates the remaining capacity, environmental temperature, discharge loss, and the environmental flow data, and controls the discharge behavior by using the calculated discharge control value;

[0111] Further, the specific implementation process of the charge and discharge control module evaluating the charge capacity, charge loss, and environmental temperature and controlling the charging behavior by using the calculated charge control value includes:

[0112] Obtain charge capacity data, charge loss data, and environmental temperature data;

[0113] Further, compare the charging capacity data, the charging loss data, and the ambient temperature data with the maximum charging capacity, the charging loss threshold, and the ambient temperature threshold respectively to obtain a charging capacity evaluation value, a charging loss evaluation value, and an ambient temperature evaluation value;

[0114] Further, use a charging capacity control coefficient, a charging loss control coefficient, and an ambient temperature control coefficient to perform weighted operations on the charging capacity evaluation value, the charging loss evaluation value, and the ambient temperature evaluation value respectively, and calculate a charging control value; wherein, the calculation formula of the charging control value is:

[0115]

[0116] α1 + α2 + α3 = 1.0;

[0117] wherein, CDKZ represents the charging control value; M represents the number of control data; α1 represents the charging capacity control coefficient; rl k represents the charging capacity data of the kth data; rl th represents the maximum charging capacity; α2 represents the charging loss control coefficient; sh k represents the charging loss data of the kth data; sh th represents the charging loss threshold; α3 represents the ambient temperature control coefficient; wd k represents the ambient temperature data of the kth data; wd th represents the ambient temperature threshold; wherein, the calculation formula of the charging loss data is:

[0118]

[0119] wherein, sh represents the charging loss data; T cd represents the charging duration; F cd represents the charging frequency; P cd represents the actual output power during charging; P c0 represents the reference output power during charging.

[0120] Further, compare the charging control value with a charging control threshold. If it exceeds the charging control threshold, stop the charging behavior of the charge and discharge control module.

[0121] In this embodiment, the maximum charging capacity, the charging loss threshold, and the ambient temperature threshold are set by relevant personnel in the field according to the actual situation.

[0122] In this embodiment, the charging capacity control coefficient, the charging loss control coefficient, and the environmental temperature control coefficient are respectively set to 0.4, 0.3, and 0.3. Of course, the selection of the control coefficients can be flexibly adjusted according to actual needs.

[0123] In this embodiment, a charging control function is used to control the charging behavior of the photovoltaic cell under different conditions; this function is implemented by relying on the charge and discharge control module; this module first obtains the charging capacity data, the charging loss data, and the environmental temperature data, and then performs threshold evaluation on the charging capacity data, the charging loss data, and the environmental temperature data, and uses the calculated charging control value to control the charging behavior; this function can effectively improve the working efficiency and reliability of the solar street lamp control system.

[0124] Further, the specific implementation process of evaluating the remaining capacity, the environmental temperature, the discharge loss, and the environmental flow data and using the calculated discharge control value to control the discharge behavior includes:

[0125] Obtain the remaining capacity data, the environmental temperature data, the discharge loss data, and the environmental flow data;

[0126] Further, compare the remaining capacity data, the environmental temperature data, the discharge loss data, and the environmental flow data with the safe remaining capacity, the environmental temperature threshold, the discharge loss threshold, and the environmental flow threshold respectively to obtain the remaining capacity evaluation value, the environmental temperature evaluation value, the discharge loss evaluation value, and the environmental flow evaluation value;

[0127] Further, use the remaining capacity control coefficient, the environmental temperature control coefficient, the discharge loss control coefficient, and the environmental flow control coefficient to perform weighted operations on the remaining capacity evaluation value, the environmental temperature evaluation value, the discharge loss evaluation value, and the environmental flow evaluation value respectively, and calculate the discharge control value; where, the calculation formula of the discharge control value is:

[0128]

[0129] β1 + β2 + β3 + β4 = 1.0;

[0130] Among them, FDKZ represents the discharge control value; N represents the number of control data; β1 represents the remaining capacity control coefficient; srl i represents the remaining capacity data of the i-th data; srl th represents the safe remaining capacity; β2 represents the environmental temperature control coefficient; wd i represents the environmental temperature data of the i-th data; wd th represents the environmental temperature threshold; β3 represents the discharge loss control coefficient; fsdi The discharge loss data represented as the i-th data; fsd th The discharge loss threshold represented as such; β4 represents the environmental flow control coefficient; ω represents the weight factor of the environmental flow data, which is set to 0.5 here; rfl i The pedestrian flow represented as the i-th data; cfl i The vehicle flow data represented as the i-th data; fl th The environmental flow threshold represented as such; wherein, the calculation formula of the discharge loss data is:

[0131]

[0132] wherein, fsh represents the discharge loss data; T fd The discharge duration represented as such; F fd The discharge frequency represented as such; P fd The actual output power during discharge represented as such; P f0 The reference output power during discharge represented as such.

[0133] In this embodiment, the safety remaining capacity, the environmental temperature threshold, the discharge loss threshold and the environmental flow threshold are set by relevant personnel in the field according to the actual situation.

[0134] In this embodiment, the remaining capacity control coefficient, the environmental temperature control coefficient, the discharge loss control coefficient and the environmental flow control coefficient are respectively set to 0.3, 0.2, 0.2 and 0.3. Of course, the selection of the control coefficient can be flexibly adjusted according to actual needs.

[0135] Further, compare the discharge control value with the discharge control threshold. If it exceeds the discharge control threshold, stop the discharge behavior of the charge and discharge control module.

[0136] In this embodiment, a discharge control function is proposed to control the discharge behavior of the photovoltaic cell under different conditions; this function is realized by relying on the charge and discharge control module; this module first obtains the remaining capacity, the environmental temperature, the discharge loss and the environmental flow data, and then performs threshold evaluation on the remaining capacity, the environmental temperature, the discharge loss and the environmental flow data, and controls the discharge behavior by using the calculated discharge control value; wherein, the discharge loss is related to the discharge duration, the discharge frequency and the discharge power loss; this function can effectively improve the working efficiency and reliability of the solar street lamp control system.

[0137] In this embodiment, the charging loss data is used to reflect the loss degree during the battery charging process; the charging loss data is obtained by multiplying the charging duration, charging frequency, and charging power loss rate; the discharging loss data is used to reflect the loss degree during the battery discharging process; wherein, the discharging loss data is obtained by multiplying the discharging duration, discharging frequency, and discharging power loss rate.

[0138] The abnormal control module acquires the battery temperature data and the photovoltaic panel characteristic data, evaluates the battery temperature, the energy conversion rate of the photovoltaic panel, and the aging defects, compares the calculated abnormal control value with the abnormal control threshold, and if it is higher than the threshold, sends feedback information to relevant personnel.

[0139] Further, the specific implementation process of the abnormal control value calculated by the abnormal control module evaluating the battery temperature, the energy conversion rate of the photovoltaic panel, and the aging defects includes:

[0140] Acquire the battery temperature data, irradiance data, photovoltaic panel output power data, and photovoltaic panel characteristic data;

[0141] Further, calculate the energy conversion rate of the photovoltaic panel according to the irradiance data, the photovoltaic panel output power data, and the photovoltaic panel area and the area of the obstruction in the photovoltaic panel characteristic data; wherein, the calculation formula of the energy conversion rate is:

[0142]

[0143] wherein, ecr represents the energy conversion rate; P out represents the photovoltaic panel output power data; G ir represents the irradiance data; S g represents the photovoltaic panel area; S z represents the area of the obstruction.

[0144] Further, evaluate the defect characteristics in the battery temperature data, the energy conversion rate, and the photovoltaic panel characteristic data to obtain multiple evaluation scores; wherein, the defect characteristics include: yellowing characteristics, crack characteristics, and hot spot characteristics;

[0145] Further, perform a comprehensive evaluation according to the multiple evaluation scores to obtain the abnormal control value; wherein, the calculation formula of the abnormal control value is:

[0146]

[0147] γ1 + γ2 + γ3 + γ4 + γ5 = 1.0;

[0148] Among them, YCKZ represents the abnormal control value; P represents the number of control data; γ1 represents the battery temperature coefficient; dwd j represents the battery temperature of the j-th data; dwd th represents the battery temperature threshold; γ2 represents the energy conversion coefficient; ecr j represents the energy conversion rate of the j-th data; ecr th represents the energy conversion threshold; γ3 represents the yellowing characteristic coefficient; fh j represents the maximum number of yellowing areas of the j-th data; fhmj (j,j1) represents the area of the j1-th yellowing area of the j-th data; γ4 represents the crack characteristic coefficient; lh j represents the maximum number of cracks of the j-th data; lhcd (j,j2) represents the length of the j2-th crack of the j-th data; lhkd (j,j2) represents the width of the j2-th crack of the j-th data; γ5 represents the hot spot characteristic coefficient; rb j represents the maximum number of hot spots of the j-th data; rbcd (j,j3) represents the length of the j3-th hot spot of the j-th data; rbkd (j,j3) represents the width of the j3-th hot spot of the j-th data.

[0149] In this embodiment, the battery temperature threshold and the energy conversion threshold are set by relevant personnel in the field according to the actual situation.

[0150] In this embodiment, the battery temperature coefficient, the energy conversion coefficient, the yellowing characteristic coefficient, the crack characteristic coefficient, and the hot spot characteristic coefficient are all set to 0.2. Of course, the numerical selection of the coefficient can be flexibly adjusted according to actual needs.

[0151] In this embodiment, an abnormal control function is proposed to feedback and control the abnormal data of the photovoltaic panel; this function is implemented by relying on the abnormal control module; this module first obtains the battery temperature data and the photovoltaic panel characteristic data, then performs threshold evaluation on the battery temperature, the energy conversion rate of the photovoltaic panel, and the defect characteristics, and then compares the calculated abnormal control value with the abnormal control threshold. If it is higher than the threshold, it sends feedback abnormal information to relevant personnel; among them, the energy conversion rate is related to irradiance, photovoltaic panel area, shielding area, and output power; this function can make the solar street lamp work stably at the maximum power point state by controlling the abnormal data, thereby effectively improving the working efficiency and reliability of the solar street lamp control system.

[0152] In this embodiment, a solar street lamp control system based on the maximum power point is proposed to control the voltage, charge and discharge, and abnormal states of solar street lamps. First, obtain the light intensity data, temperature data, environmental flow data, and photovoltaic panel characteristic data; then, perform bisection iteration optimization on the initially predicted maximum power point voltage output by the maximum power point voltage prediction model to obtain the optimal voltage; then, during the charge and discharge process, evaluate the charge capacity, charge loss, and environmental temperature, and use the charge control value to control the charging behavior; evaluate the remaining capacity, environmental temperature, discharge loss, and the environmental flow data, and use the discharge control value to control the discharge behavior; finally, evaluate the battery temperature, energy conversion rate, and aging defects of the photovoltaic panel, compare the calculated abnormal control value with the abnormal control threshold, and if it is higher than the threshold, send feedback abnormal information to relevant personnel; the present invention can improve the working efficiency and reliability of the solar street lamp control system.

[0153] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

[0154] Embodiment 2

[0155] As an implementation manner of the present invention, refer to Figure 4 , the implementation method of the solar street lamp control system based on the maximum power point includes:

[0156] S10. Obtain the light intensity data and temperature data collected by the sensor;

[0157] S20. Input the image data collected by the camera into the recognition model to obtain the environmental flow data and photovoltaic panel characteristic data;

[0158] S30. Input the light intensity data and environmental temperature data into the maximum power point voltage prediction model to obtain the initially predicted maximum power point voltage; use the bisection method to adjust the initially predicted maximum power point voltage to obtain the optimal voltage;

[0159] S40. Keep the photovoltaic panel working under the optimal voltage for charge and discharge, and at the same time evaluate the battery capacity, power loss, environmental temperature, and environmental flow, and use the charge control value and discharge control value to control the charge and discharge behaviors respectively;

[0160] S50. Evaluate the battery temperature, energy conversion rate, and aging defects of the photovoltaic panel, compare the calculated abnormal control value with the abnormal control threshold, and if it is higher than the threshold, send feedback information to relevant personnel.

[0161] For specific illustration, the present invention is described in combination with the following embodiments as follows:

[0162] Connect the data acquisition module of the control system to the output interfaces of the sensors and the recognition model, for receiving light intensity data, ambient temperature data, battery temperature data, ambient flow data, and photovoltaic panel characteristic data;

[0163] Further, the maximum power point voltage control module receives the light intensity data and the ambient temperature data transmitted by the data acquisition module, and inputs the data into the maximum power point voltage prediction model to output a preliminary predicted maximum power point voltage; wherein, the maximum power point voltage prediction model is a gated recurrent neural network model, and the specific training process of this model includes:

[0164] Obtain historical data; wherein, the historical data includes: historical light intensity data, historical ambient temperature data, and historical maximum power point voltage;

[0165] Further, clean and normalize the historical data to obtain preprocessed historical data;

[0166] Further, divide the preprocessed historical data into a training set and a validation set according to 8:2;

[0167] Further, input the training set into the maximum power point voltage prediction model for training to obtain an initial maximum power point voltage prediction model;

[0168] Further, input the validation set into the initial maximum power point voltage prediction model to optimize the model parameters and obtain a final maximum power point voltage prediction model.

[0169] Further, the maximum power point voltage control module uses the preliminary predicted maximum power point voltage as the midpoint voltage, and adjusts the midpoint voltage by the dichotomy method to obtain the optimal voltage; wherein, the specific obtaining process of the optimal voltage includes:

[0170] Obtain the preliminary predicted maximum power point voltage;

[0171] Further, calculate the reference power according to the preliminary predicted maximum power point voltage; wherein, the reference power is the product of the preliminary predicted maximum power point voltage and the current;

[0172] Further, set the preliminary predicted maximum power point voltage as the midpoint voltage V0;

[0173] Further, based on the midpoint voltage and the interval range threshold, set an optimal voltage estimation interval [V0 - 5, V0 + 5]; wherein, the maximum value of the optimal voltage estimation interval does not exceed the safety voltage, and the minimum value is not lower than 0;

[0174] Further, compare the power within the voltage range on both sides of the midpoint voltage with the reference power, and update the optimal voltage estimation interval and the midpoint voltage until the interval range is less than the interval threshold to obtain the final optimal voltage estimation interval;

[0175] Further, set the midpoint voltage of the final optimal voltage estimation interval as the optimal voltage.

[0176] Further, use the charge-discharge control module to control the charge and discharge of the photovoltaic cell; the specific implementation process of this module for evaluating the charge capacity, charge loss, and environmental temperature and controlling the charging behavior using the calculated charge control value includes:

[0177] Obtain charge capacity data, charge loss data, and environmental temperature data;

[0178] Further, compare the charge capacity data, the charge loss data, and the environmental temperature data with the maximum charge capacity, the charge loss threshold, and the environmental temperature threshold respectively to obtain a charge capacity evaluation value, a charge loss evaluation value, and an environmental temperature evaluation value;

[0179] Further, perform weighted operations on the charge capacity evaluation value, the charge loss evaluation value, and the environmental temperature evaluation value using a charge capacity control coefficient, a charge loss control coefficient, and an environmental temperature control coefficient respectively to calculate a charge control value;

[0180] Further, compare the charge control value with a charge control threshold. If it exceeds the charge control threshold, stop the charging behavior of the charge-discharge control module.

[0181] Further, the specific implementation process of the charge-discharge control module for evaluating the remaining capacity, environmental temperature, discharge loss, and the environmental flow rate data and controlling the discharge behavior using the calculated discharge control value includes:

[0182] Obtain remaining capacity data, environmental temperature data, discharge loss data, and environmental flow rate data;

[0183] Further, compare the remaining capacity data, the environmental temperature data, the discharge loss data, and the environmental flow rate data with the safe remaining capacity, the environmental temperature threshold, the discharge loss threshold, and the environmental flow rate threshold respectively to obtain a remaining capacity evaluation value, an environmental temperature evaluation value, a discharge loss evaluation value, and an environmental flow rate evaluation value;

[0184] Further, the remaining capacity evaluation value, the ambient temperature evaluation value, the discharge loss evaluation value, and the ambient flow evaluation value are weighted by the remaining capacity control coefficient, the ambient temperature control coefficient, the discharge loss control coefficient, and the ambient flow control coefficient respectively to calculate a discharge control value; wherein, the calculation formula of the discharge control value is:

[0185]

[0186] β1 + β2 + β3 + β4 = 1.0;

[0187] wherein, FDKZ represents the discharge control value; N represents the number of control data; β1 represents the remaining capacity control coefficient; srl i represents the remaining capacity data of the i-th data; srl th represents the safe remaining capacity for charging; β2 represents the ambient temperature control coefficient; wd i represents the ambient temperature data of the i-th data; wd th represents the ambient temperature threshold; β3 represents the discharge loss control coefficient; fsd i represents the discharge loss data of the i-th data; fsd th represents the discharge loss threshold; β4 represents the ambient flow control coefficient; ω represents the weight factor of the ambient flow data, which is set to 0.5 here; rfl i represents the number of people flow of the i-th data; cfl i represents the vehicle flow data of the i-th data; fl th represents the ambient flow threshold; wherein, the calculation formula of the discharge loss data is:

[0188]

[0189] wherein, fsh represents the discharge loss data; T fd represents the discharge duration; F fd represents the discharge frequency; P fd represents the actual output power during discharge; P f0 represents the reference output power during discharge;

[0190] Further, the discharge control value is compared with the discharge control threshold. If it exceeds the discharge control threshold, the discharge behavior of the charge and discharge control module is stopped.

[0191] Further, the abnormal control module is used to obtain the battery temperature data and the photovoltaic panel characteristic data, evaluate the battery temperature, the energy conversion rate and the aging defects of the photovoltaic panel, compare the calculated abnormal control value with the abnormal control threshold, and if it is higher than the threshold, send feedback information to relevant personnel; wherein, the specific calculation process of the abnormal control value includes:

[0192] Obtain the battery temperature data, irradiance data, photovoltaic panel output power data and photovoltaic panel characteristic data;

[0193] Further, according to the irradiance data, the photovoltaic panel output power data, and the photovoltaic panel area and the obstruction area in the photovoltaic panel characteristic data, calculate the energy conversion rate of the photovoltaic panel; wherein, the calculation formula of the energy conversion rate is:

[0194]

[0195] wherein, ecr represents the energy conversion rate; P out represents the photovoltaic panel output power data; G ir represents the irradiance data; S g represents the photovoltaic panel area; S z represents the obstruction area.

[0196] Further, evaluate the defect characteristics in the battery temperature data, the energy conversion rate and the photovoltaic panel characteristic data to obtain multiple evaluation scores; wherein, the defect characteristics include: yellowing characteristics, crack characteristics and hot spot characteristics;

[0197] Further, perform a comprehensive evaluation based on the multiple evaluation scores to obtain the abnormal control value.

[0198] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A solar street lamp control system based on the maximum power point, characterized in that Including: A data acquisition module, a maximum power point voltage control module, a charge and discharge control module, and an abnormal control module; The data acquisition module obtains light intensity data, ambient temperature data, and battery temperature data through sensors; and obtains ambient flow data and photovoltaic panel characteristic data using an identification model; The maximum power point voltage control module inputs the light intensity data and the ambient temperature data into a maximum power point voltage prediction model to obtain a preliminary predicted maximum power point voltage; then, uses the preliminary predicted maximum power point voltage as the midpoint voltage and adjusts the midpoint voltage using the bisection method to obtain the optimal voltage; The charge and discharge control module stabilizes the output voltage of the photovoltaic panel at the optimal voltage and charges and discharges the photovoltaic battery; evaluates the charge capacity, charge loss, and ambient temperature, and controls the charging behavior using the calculated charge control value; evaluates the remaining capacity, ambient temperature, discharge loss, and the ambient flow data, and controls the discharge behavior using the calculated discharge control value; The specific implementation process of the charge and discharge control module evaluating the charge capacity, the charge loss, and the ambient temperature and controlling the charging behavior using the calculated charge control value includes: Obtaining charge capacity data, charge loss data, and the ambient temperature data; Comparing the charge capacity data, the charge loss data, and the ambient temperature data with a maximum charge capacity, a charge loss threshold, and an ambient temperature threshold respectively to obtain a charge capacity evaluation value, a charge loss evaluation value, and an ambient temperature evaluation value; Performing a weighting operation on the charge capacity evaluation value, the charge loss evaluation value, and the ambient temperature evaluation value using a charge capacity control coefficient, a charge loss control coefficient, and an ambient temperature control coefficient respectively, and calculating the charge control value; Comparing the charge control value with a charge control threshold, and if it exceeds the charge control threshold, stopping the charging behavior of the charge and discharge control module; The specific implementation process of the charge and discharge control module evaluating the remaining capacity, the ambient temperature, the discharge loss, and the ambient flow data and controlling the discharge behavior using the calculated discharge control value includes: Obtaining remaining capacity data, the ambient temperature data, discharge loss data, and the ambient flow data; Comparing the remaining capacity data, the ambient temperature data, the discharge loss data, and the ambient flow data with a safe remaining capacity, the ambient temperature threshold, a discharge loss threshold, and an ambient flow threshold respectively to obtain a remaining capacity evaluation value, the ambient temperature evaluation value, a discharge loss evaluation value, and an ambient flow evaluation value; Performing a weighting operation on the remaining capacity evaluation value, the ambient temperature evaluation value, the discharge loss evaluation value, and the ambient flow evaluation value using a remaining capacity control coefficient, an ambient temperature control coefficient, a discharge loss control coefficient, and an ambient flow control coefficient respectively, and calculating the discharge control value; Compare the discharge control value with the discharge control threshold. If it exceeds the discharge control threshold, stop the discharge behavior of the charge and discharge control module; The abnormal control module obtains the battery temperature data and the photovoltaic panel characteristic data, evaluates the battery temperature, the energy conversion rate and the aging defects of the photovoltaic panel, compares the calculated abnormal control value with the abnormal control threshold, and if it is higher than the threshold, sends feedback information to relevant personnel.

2. The solar street lamp control system based on the maximum power point according to claim 1 is characterized in that, The specific implementation process of the data acquisition module using the recognition model to obtain the environmental flow data and the photovoltaic panel characteristic data includes: Obtain the image data collected by the camera; Among them, the image data includes: environmental image data and photovoltaic panel image data; Input the image data into the input layer of the recognition model for feature extraction to obtain image features; Use the marking layer of the recognition model to obtain the anchor boxes of the image features; Input the features in the anchor boxes into the recognition layer of the recognition model to obtain the target recognition result; Input the target recognition result into the output layer of the recognition model to obtain the target data; among them, the target data includes: environmental flow data and photovoltaic panel characteristic data.

3. The solar street lamp control system based on the maximum power point according to claim 2, characterized in that, The environmental flow data includes: environmental personnel flow data and environmental vehicle flow data; the photovoltaic panel characteristic data includes: photovoltaic panel area, photovoltaic panel occlusion area, photovoltaic panel yellowing area, photovoltaic panel cracks and photovoltaic panel hot spots.

4. A solar street lamp control system based on the maximum power point according to claim 1, characterized in that The specific implementation process of the maximum power point voltage control module using the maximum power point voltage prediction model and the dichotomy method to obtain the optimal voltage at the maximum power point includes: Obtain the light intensity data and the environmental temperature data; Input the light intensity data and the environmental temperature data into the maximum power point voltage prediction model to obtain the preliminary predicted maximum power point voltage; Among them, the maximum power point voltage prediction model is a pre-trained network model; Use the dichotomy method to further optimize and adjust the preliminary predicted maximum power point voltage until the optimal voltage at the maximum power point is obtained.

5. The solar street lamp control system based on the maximum power point according to claim 4, characterized in that, The maximum power point voltage prediction model is a gated recurrent neural network model. The specific training process of this model includes: Obtain historical data; among them, the historical data includes: historical light intensity data, historical environmental temperature data and historical maximum power point voltage; Clean and normalize the historical data to obtain the preprocessed historical data; Divide the preprocessed historical data into a training set and a validation set according to a ratio of 8:2; Input the training set into the maximum power point voltage prediction model for training to obtain the initial maximum power point voltage prediction model; Input the validation set into the initial maximum power point voltage prediction model to optimize the model parameters and obtain the final maximum power point voltage prediction model.

6. The solar street lamp control system based on the maximum power point according to claim 1, wherein The specific process of the maximum power point voltage control module using the dichotomy method to adjust the midpoint voltage to obtain the optimal voltage includes: Obtain the preliminary predicted maximum power point voltage; Calculate the reference power according to the preliminary predicted maximum power point voltage; Set the preliminary predicted maximum power point voltage as the midpoint voltage; Set an optimal voltage estimation interval based on the midpoint voltage and the interval range threshold; wherein, the maximum value of the optimal voltage estimation interval does not exceed the safety voltage, and the minimum value is not lower than 0; Compare the power within the optimal voltage estimation interval with the reference power, and update the optimal voltage estimation interval and the midpoint voltage until the interval range is less than the interval threshold to obtain the final optimal voltage estimation interval; Set the midpoint voltage of the final optimal voltage estimation interval as the optimal voltage.

7. A solar street lamp control system based on the maximum power point according to claim 1, characterized in that, The charging loss data is used to reflect the loss degree during the battery charging process; the charging loss data is calculated from the charging duration, charging frequency, and charging power loss rate; the discharging loss data is used to reflect the loss degree during the battery discharging process; the discharging loss data is calculated from the discharging duration, discharging frequency, and discharging power loss rate.

8. A solar street lamp control system based on the maximum power point according to claim 1, characterized in that The abnormal control module evaluates the battery temperature, the energy conversion rate of the photovoltaic panel, and the aging defects. The specific implementation process of the calculated abnormal control value includes: Obtain the battery temperature data, irradiance data, photovoltaic panel output power data, and photovoltaic panel characteristic data; Calculate the energy conversion rate of the photovoltaic panel according to the irradiance data, the photovoltaic panel output power data, and the photovoltaic panel area and the area of the obstacle in the photovoltaic panel characteristic data; Evaluate the battery temperature data, the energy conversion rate, and the defect characteristics in the photovoltaic panel characteristic data to obtain multiple evaluation scores; wherein, the defect characteristics include: yellowing characteristics, crack characteristics, and hot spot characteristics; Perform a comprehensive evaluation based on the multiple evaluation scores to obtain the abnormal control value.

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