Maintenance methods and systems for solar-powered streetlights

Abstract

A maintenance method and system for solar-powered streetlights are disclosed. The method includes: acquiring detection data of each solar-powered streetlight within a target area over a preset time period, as well as environmental data of the target area; determining, based on the detection data and the environmental data, whether the solar-powered streetlights within the target area are in a shaded state, where the shaded state characterizes the degree to which the solar-powered streetlights within the target area are shaded by trees; if it is determined that the solar-powered streetlights within the target area are in a shaded state, generating maintenance suggestions corresponding to the shaded state. Implementing the technical solution provided in this application can automatically generate maintenance suggestions corresponding to the shaded state, helping maintenance personnel to rationally plan maintenance work, improve maintenance efficiency, and avoid wasting maintenance resources.

Description

Technical Field

[0001] This application relates to a maintenance method and system for solar-powered streetlights, specifically a maintenance method and system for solar-powered streetlights. Background Technology

[0002] Solar-powered streetlights, as a clean and environmentally friendly lighting device, are widely used in highways, municipal roads, and other similar applications. These streetlights convert solar energy into electricity through photovoltaic panels and store it in batteries for nighttime illumination. However, in practical applications, the presence of green belts or protective forests along roadsides can cause trees to grow and shade the photovoltaic panels, leading to reduced power generation efficiency and affecting the normal operation of the streetlights.

[0003] Currently, the maintenance of solar-powered streetlights mainly relies on manual inspections. Maintenance personnel regularly patrol the streetlights, observing the environment around the photovoltaic panels or measuring the power generation to determine if there are any issues with trees obstructing the view. Once a problem is identified, relevant personnel are then assigned to perform maintenance work such as tree trimming.

[0004] However, this manual inspection method has significant limitations. Because solar-powered streetlights are typically widely distributed and numerous, relying solely on manual inspection makes it difficult to promptly detect shading issues. Furthermore, relying solely on manual observation or simple power measurements cannot accurately determine the degree of shading, easily leading to untimely maintenance or wasted maintenance resources. Summary of the Invention

[0005] This application provides a maintenance method and system for solar-powered streetlights, which can automatically generate maintenance suggestions corresponding to the shading status, helping maintenance personnel to rationally plan maintenance work, improve maintenance efficiency, and avoid wasting maintenance resources.

[0006] In a first aspect, this application provides a method for maintaining a solar-powered street light, comprising:

[0007] Acquire detection data of each solar-powered street light in the target area within a preset time period, as well as environmental data of the target area;

[0008] Based on the detection data and the environmental data, it is determined whether the solar-powered streetlights in the target area are in a blocked state. The blocked state is used to characterize the degree to which the solar-powered streetlights in the target area are blocked by trees.

[0009] If it is determined that the solar-powered streetlights in the target area are blocked, maintenance suggestions corresponding to the blocked state are generated.

[0010] Optionally, the detection data includes the actual power generation of the photovoltaic panels in each of the solar-powered streetlights, and the geographical location of each of the solar-powered streetlights; the environmental data includes light intensity; the step of determining whether the solar-powered streetlights in the target area are in a shaded state based on the detection data and the environmental data includes:

[0011] Based on the geographical location and light intensity of each of the solar-powered streetlights, calculate the expected power generation of each of the solar-powered streetlights;

[0012] Based on the difference between the expected power generation and the actual power generation of each of the solar-powered streetlights, it is determined whether the solar-powered streetlights in the target area are in a blocked state.

[0013] If the difference between the expected power generation and the actual power generation of each of the solar-powered streetlights is greater than a preset threshold, then the solar-powered streetlights in the target area are determined to be in a blocked state.

[0014] If the difference between the expected power generation and the actual power generation of each solar-powered street light is less than or equal to a preset threshold, then the solar-powered street lights in the target area are determined to be in an unobstructed state.

[0015] Optionally, the environmental data also includes wind speed, and after determining that the solar-powered streetlights in the target area are blocked, the method further includes:

[0016] Based on the expected and actual power generation of each solar-powered street light, calculate the power fluctuation of the solar-powered street lights in the target area within the preset time period.

[0017] Based on the wind speed, calculate the wind speed fluctuation in the target area within the preset time period;

[0018] Calculate the correlation coefficient between the power fluctuation and the wind speed fluctuation;

[0019] The occlusion state is determined to be either dynamic occlusion or fixed occlusion state based on the correlation coefficient.

[0020] Optionally, generating maintenance suggestions corresponding to the occlusion state includes:

[0021] A heat map is generated based on the geographical location of the solar-powered streetlights that are in the shaded state;

[0022] Maintenance recommendations are generated based on the heatmap.

[0023] Optionally, generating a heat map based on the geographical location of the solar-powered streetlights in the shaded state includes:

[0024] Obtain the degree of shading of the solar-powered streetlights in the shading state;

[0025] Based on the degree of shading and geographical location of each of the solar-powered streetlights that are in a shading state, the target area is divided into multiple clustered regions with different degrees of shading.

[0026] Heatmaps are generated based on the clustered regions.

[0027] Optionally, generating maintenance recommendations based on the heatmap includes:

[0028] If the occlusion state is a fixed occlusion state, then tree pruning suggestions are generated based on the heatmap;

[0029] If the occlusion state is a non-fixed occlusion state, then power dispatching suggestions are generated based on the heat map.

[0030] A second aspect of this application provides a maintenance system for a solar-powered street light, comprising:

[0031] The data acquisition module is used to acquire detection data of each solar-powered street light in the target area within a preset time period, as well as environmental data of the target area;

[0032] The occlusion determination module is used to determine whether the solar-powered streetlights in the target area are in an occlusion state based on the detection data and the environmental data. The occlusion state is used to characterize the degree to which the solar-powered streetlights in the target area are blocked by trees.

[0033] The suggestion generation module is used to generate maintenance suggestions corresponding to the shading state if it is determined that the solar-powered streetlights in the target area are in a shading state.

[0034] A third aspect of this application provides a computer storage medium storing a plurality of instructions adapted for loading by a processor and executing the method steps described above.

[0035] A fourth aspect of this application provides an electronic device, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to execute the above-described method steps.

[0036] A fifth aspect of this application provides a computer program product including a computer program that, when executed by a processor, implements the method steps described above.

[0037] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0038] By acquiring detection data and environmental data from each solar-powered street light within the target area, and based on this data determining the shading status of the street lights, the system can automatically identify shading problems without relying on manual inspections. This overcomes the shortcomings of traditional manual inspection methods, which struggle to detect shading issues in a timely manner. Furthermore, because the judgment process comprehensively considers both detection and environmental data, it can accurately assess the degree of shading, avoiding judgment biases caused by relying solely on manual observation or simple power measurements. In addition, the system can automatically generate maintenance suggestions corresponding to the shading status, helping maintenance personnel to rationally plan maintenance work, improve maintenance efficiency, and avoid wasting maintenance resources. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating a maintenance method for a solar-powered street light provided in an embodiment of this application;

[0040] Figure 2 This is a schematic diagram of the structure of a maintenance system for a solar-powered street light provided in an embodiment of this application;

[0041] Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. Detailed Implementation

[0042] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0043] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0044] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0045] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a maintenance method for a solar-powered street light provided in this application embodiment. This method can be implemented using a computer program, a microcontroller, or run on a solar-powered street light maintenance system based on the von Neumann architecture. The computer program can be integrated into an application or run as a standalone utility application. Specifically, the maintenance method for the solar-powered street light may further include the following steps:

[0046] Step 101: Obtain the detection data of each solar-powered street light in the target area within a preset time period, as well as the environmental data of the target area.

[0047] The target area refers to a specific geographical area where monitoring and maintenance of the obstruction status of solar-powered streetlights are required. In this embodiment, it can be understood as a continuous area along a highway where multiple solar-powered streetlights are installed, with clearly defined geographical boundaries and unified management requirements. For example, the target area could be a 10-kilometer-long section of highway or the area surrounding a transportation hub, in which several solar-powered streetlights are installed as road lighting facilities.

[0048] By dividing the target area, it is easier to determine the number and distribution range of solar-powered streetlights that need to be monitored, thus achieving precise coverage of data collection. Secondly, it helps to rationally arrange the location of environmental monitoring stations and ensure the representativeness of environmental data. Finally, it allows for correlation analysis of the operating status of adjacent solar-powered streetlights to identify regional shading problems.

[0049] The preset duration refers to the continuous time interval for data collection and shading status analysis of solar-powered streetlights. In this embodiment, it can be understood as 24 hours, i.e., a complete solar cycle. The power generation of solar-powered streetlights exhibits a clear periodic characteristic with changes in the solar altitude angle. By collecting data from a complete solar cycle, the power generation status of photovoltaic panels at different times can be comprehensively reflected, and the impact of shading on power generation efficiency can be accurately identified.

[0050] Two types of data need to be collected within a preset time period: detection data and environmental data. Detection data refers to the operating parameters obtained directly from the solar-powered street light equipment itself. In this embodiment, this can be understood as the actual power generation data and geographical location data of each solar-powered street light. The actual power generation data is collected in real time by current and voltage sensors installed at the output end of the photovoltaic panel and calculated based on the product of the current and voltage values. The geographical location data is obtained through the built-in GPS positioning module, including longitude, latitude, and altitude information. This detection data is used to reflect the real-time operating status of the solar-powered street lights.

[0051] Environmental data refers to external environmental parameters that affect the power generation efficiency of solar-powered streetlights. In this embodiment, it can be understood as illuminance data and wind speed data within the target area. Illuminance data is collected by illuminance sensors deployed at environmental monitoring stations and used to calculate the theoretical power generation of the solar-powered streetlights; wind speed data is collected by wind speed sensors and used to analyze whether power fluctuations are caused by dynamic shading due to tree branch swaying.

[0052] Specifically, within each preset duration, the system continuously collects the aforementioned detection data and environmental data at a sampling cycle of 1 minute, thereby forming a time-series data set of 1440 sampling points. This sampling strategy ensures data continuity while capturing short-term power fluctuations. For example, during periods of lower solar altitude angle, such as early morning and evening, tree shading has the most significant impact on power generation efficiency; while at noon, due to the higher solar altitude angle, the impact of shading is relatively smaller. By analyzing data from the complete solar cycle, this time-varying characteristic can be identified.

[0053] By collecting the data described above, we can ensure the accuracy of shading status assessment and promptly identify and address shading issues, thereby improving the overall operational efficiency of the solar-powered street lighting system. Furthermore, this analysis method based on the complete solar cycle provides reliable data support for developing long-term maintenance strategies.

[0054] Step 102: Based on the detection data and environmental data, determine whether the solar-powered streetlights in the target area are in a shading state. The shading state is used to characterize the degree to which the solar-powered streetlights in the target area are blocked by trees.

[0055] The term "shading state" refers to the working state of a solar-powered street light when its photovoltaic panels are blocked by external objects such as trees at a specific time. This state directly affects the power generation efficiency of the photovoltaic panels. In this application embodiment, it can be understood as two types: fixed shading state and dynamic shading state. Fixed shading state refers to the photovoltaic panels being continuously blocked by stationary objects such as leaves, resulting in a long-term lower-than-expected power generation. Dynamic shading state refers to the photovoltaic panels being periodically blocked by objects such as tree branches that sway in the wind, resulting in power generation fluctuating with wind speed.

[0056] In practical applications, the power generation efficiency of solar-powered streetlights is affected by various environmental factors, with tree shading being one of the most significant. Specifically, the system analyzes collected data such as the actual power generation of the photovoltaic panels, geographical location, and environmental data like light intensity to determine whether the solar-powered streetlight is shaded. Shading status, as a quantitative indicator, accurately represents the severity of tree shading of the solar-powered streetlight.

[0057] The above-mentioned judgment methods can not only promptly identify malfunctions in solar-powered streetlights and determine the urgency of maintenance, but also provide a reliable basis for developing targeted maintenance plans, thereby improving maintenance efficiency and ensuring the normal operation of the solar-powered streetlight system.

[0058] Based on the above embodiments, as an optional embodiment, the detection data includes the actual power generation of the photovoltaic panels in each solar-powered street light, and the geographical location of each solar-powered street light; environmental data includes light intensity; step 102 may further include the following steps:

[0059] Step 201: Calculate the expected power generation of each solar-powered street light based on its geographical location and light intensity.

[0060] The expected power generation refers to the theoretical power output that the photovoltaic panel of a solar-powered street light should produce under specific lighting conditions, under ideal operating conditions. In this embodiment, it can be understood as the power generation value calculated based on parameters such as ambient light intensity, photoelectric conversion efficiency, photovoltaic panel area, and solar incidence angle.

[0061] Specifically, before calculating the expected power generation, the collected raw data needs to be preprocessed. Due to equipment failures, communication interruptions, and other reasons, data gaps may occur during data acquisition. The system uses cubic spline interpolation to fill in the missing data. For short-term data gaps, interpolation is performed using data from adjacent time points; for longer-term data gaps, the average of historical data within the same time period is used for filling. This hierarchical filling strategy ensures both data continuity and maximizes data accuracy.

[0062] Based on data completion, since the measurement accuracy and range of different types of sensors vary, directly using the raw data may introduce systematic errors. Therefore, the system normalizes data such as light intensity and power generation, mapping all data to the [0,1] interval.

[0063] To eliminate the impact of sensor measurement noise on the calculation results, the system employs a moving average filter to denoise the normalized data. For example, for a data sequence with a sampling period of 1 minute, a 5-minute time window is selected for moving average, meaning the value of each data point is replaced by the arithmetic mean of five sampling points two minutes before and after it. This processing method effectively suppresses interference from short-term fluctuations without overly smoothing out the actual power changes.

[0064] After data preprocessing, the system calculates the expected power generation based on the cleaned data. The expected power generation is obtained through a preset calculation formula, where ambient light intensity is collected by a light sensor, the photoelectric conversion efficiency is the nominal efficiency value of the photovoltaic panel, the photovoltaic panel area is the actual installed area, and the solar incidence angle is calculated in real-time using an astronomical algorithm based on the geographical location of the solar-powered streetlights and the current time. For example, the preset formula can be expressed as:

[0065] ;

[0066] In the formula, Indicates the expected power generation; Light intensity; The photoelectric conversion efficiency of a photovoltaic panel. The area of ​​the photovoltaic panel. The angle of incidence of the sun can be calculated from geographical location and time.

[0067] By employing the aforementioned data preprocessing and expected power generation calculation methods, not only can the influence of noise and outliers in the raw data be effectively eliminated, ensuring the accuracy of the calculation results, but a reliable reference benchmark can also be provided for subsequent shading status assessment. Furthermore, this standardized data processing workflow facilitates system expansion and maintenance, allowing for flexible adaptation to solar-powered streetlight systems of varying scales.

[0068] Step 202: Based on the difference between the expected power generation and the actual power generation of each solar-powered street light, determine whether the solar-powered street lights in the target area are in a blocked state.

[0069] In practical applications, the shading status of solar-powered streetlights can be determined by the difference between the expected and actual power generation. The expected power generation reflects the photovoltaic panel's power generation capacity under ideal operating conditions, while the actual power generation reflects the actual output under current operating conditions. The difference between the two directly reflects whether the photovoltaic panel is shaded and the severity of the shading.

[0070] Specifically, the system first calculates the difference between the expected power generation and the actual power generation. The power difference is obtained through a preset calculation formula, the expression of which is:

[0071] ;

[0072] In the formula, The difference between the expected power generation and the actual power generation. This represents the actual power generation.

[0073] Step 203: If the difference between the expected power generation and the actual power generation of each solar-powered street light is greater than a preset threshold, then the solar-powered street lights in the target area are determined to be in a blocked state.

[0074] Step 204: If the difference between the expected power generation and the actual power generation of each solar-powered street light is less than or equal to a preset threshold, then the solar-powered street lights in the target area are determined to be in an unobstructed state.

[0075] Specifically, after obtaining the power difference, the system compares it with a preset threshold to determine whether the solar-powered street light is obstructed. For example, the determination rule can be expressed as:

[0076] ;

[0077] In the formula, This is a preset threshold.

[0078] The selection of the preset threshold needs to comprehensively consider the performance parameters of the photovoltaic panel, environmental factors, and historical operating data. Preferably, the preset threshold can be set to 20% of the expected power generation, that is, when the actual power generation is lower than 80% of the expected power generation, shading is determined to have occurred.

[0079] To improve the accuracy of the judgment, the system can also perform time-dimensional analysis on the power difference. Since instantaneous power fluctuations may be caused by transient factors such as cloud cover, the system uses the average power difference within a 5-minute time window for judgment. This processing method can effectively filter out the influence of environmental noise and improve the reliability of the occupancy status judgment. At the same time, the system also records the duration for which the power difference exceeds a preset threshold, which is used to subsequently distinguish between dynamic and fixed occupancy statuses.

[0080] In practical applications, the system's judgment of shading status also needs to consider the impact of weather factors. For example, on cloudy or rainy days, the ambient light intensity is generally low, which may lead to a general decrease in the power of solar-powered streetlights throughout the target area. To address this, the system dynamically adjusts the preset threshold based on light intensity data. For instance, when the ambient light intensity is below 50% of the nominal value, the system correspondingly increases the preset threshold to avoid misjudging power reduction caused by weather factors as shading.

[0081] The aforementioned judgment method can not only accurately identify the shading status of solar-powered streetlights but also quantify the shading status, providing a reliable basis for subsequent maintenance decisions. Furthermore, this power difference-based judgment method is computationally simple and easy to implement, making it suitable for widespread application in large-scale solar-powered streetlight systems. In addition, by adjusting the preset threshold and time window size, this method can flexibly adapt to the needs of different application scenarios, exhibiting good scalability.

[0082] Based on the above embodiments, as an optional embodiment, the environmental data also includes wind speed. After step 203: determining that the solar-powered streetlights in the target area are blocked, the following steps may also be included:

[0083] Step 301: Based on the expected and actual power generation of each solar-powered street light, calculate the power fluctuation of the solar-powered street lights in the target area within a preset time period.

[0084] Power fluctuation refers to the degree to which the actual power generation of a solar-powered street light deviates from its time series mean within a preset time period. Specifically, power fluctuation is quantified using the standard deviation method, reflecting the severity and fluctuation characteristics of power generation changes over time.

[0085] Power fluctuation can be understood as an indicator of the instability of the output power of solar-powered streetlights. When the power generation remains relatively stable, the power fluctuation value is small; when the power generation changes frequently and significantly, the power fluctuation value is large. For example, under clear, unobstructed weather conditions, the power fluctuation of solar-powered streetlights is usually small; however, when there is dynamic obstruction such as tree branches, the periodic movement of the obstruction causes changes in light intensity, resulting in greater fluctuations in power generation.

[0086] Power fluctuation analysis is primarily used to identify and assess the dynamic shading status of solar-powered streetlights. By calculating and monitoring power fluctuation values, the system can distinguish between static and dynamic shading. In particular, when combined with wind speed fluctuation analysis, it can effectively determine whether there are dynamic shading phenomena such as wind-induced tree branch swaying.

[0087] For example, the power fluctuation of solar-powered streetlights in a target area within a preset time period can be calculated using the following formula:

[0088] ;

[0089] In the formula, For power fluctuations, This represents the total length of the time series, i.e., the number of time points recorded within the preset duration. Let be the actual power of the solar-powered street light at time t. This represents the time series mean of solar-powered streetlights.

[0090] Specifically, the system first calculates the time series mean of the actual power over a preset duration. This mean reflects the basic power level and can be used as a benchmark to assess the degree of power deviation. In this embodiment, the time series mean is obtained by taking the arithmetic mean of all power sampling points over the preset duration.

[0091] After obtaining the time series mean, the system calculates the squared deviation of the actual power from the mean at each time point. This squared deviation reflects the degree to which the power deviates from the average level at that time; the larger the deviation, the more significant the power fluctuation. By calculating the squared deviation instead of the simple deviation, the problem of positive and negative deviations canceling each other out can be avoided, while amplifying the impact of large deviations, making drastic fluctuations easier to detect.

[0092] Then, the system sums the squared deviations at all times within the preset time period. This sum comprehensively reflects the power fluctuations throughout the entire time period. To eliminate the influence of time length on the calculation results, the system divides the sum by the number of samples to obtain the average squared deviation. Finally, to ensure that the calculation results have the same dimensions as the original power, the system takes the square root of the average squared deviation to obtain the final standard deviation.

[0093] In practical applications, a larger standard deviation indicates drastic power fluctuations, which are usually related to dynamic shading caused by tree branches swaying in the wind; a smaller standard deviation indicates relatively stable power, which may be in an unshaded or fixed shading state.

[0094] Step 302: Based on wind speed, calculate the wind speed fluctuation in the target area within a preset time period.

[0095] Wind speed fluctuation refers to the degree to which the wind speed within the target area deviates from its time series mean within a preset time period. Specifically, wind speed fluctuation is quantified using the standard deviation method, reflecting the severity and fluctuation characteristics of wind force changes in the target area.

[0096] Wind speed fluctuation can be understood as an indicator of the instability of wind intensity in a target area. When wind force remains relatively stable, the wind speed fluctuation value is small; when wind force changes frequently and significantly, the wind speed fluctuation value is large. For example, under calm or uniform wind conditions, wind speed fluctuation is usually small; however, when gusts occur or wind force is unstable, the wind speed will exhibit greater fluctuations. This fluctuation may cause surrounding obstructions such as tree branches to sway, thereby affecting the power generation efficiency of solar-powered streetlights.

[0097] Wind speed fluctuations are primarily used to predict and assess the impact of wind changes on the dynamic shading of solar-powered streetlights. By calculating and monitoring wind speed fluctuation values, the system can determine whether wind changes are sufficient to cause significant swaying of obstructions such as tree branches. Especially when analyzed in conjunction with power fluctuations, wind speed fluctuation data can help the system more accurately identify wind-induced shading phenomena, providing crucial reference information for the intelligent operation and maintenance and fault diagnosis of solar-powered streetlights.

[0098] For example, the wind speed fluctuation in the target area within a preset time period can be calculated using the following formula:

[0099] ;

[0100] In the formula, For wind speed fluctuations, Let be the wind speed of the solar-powered street light at time t. This represents the time series mean of wind speed.

[0101] The system employs a standard deviation method similar to that used in power fluctuation calculations to quantify wind speed fluctuations. Using a pre-defined formula, the system first obtains the mean wind speed time series over a preset period. This mean reflects the average wind force level during the observation period and can serve as a benchmark for assessing wind speed changes. In practical applications, the system collects wind speed data at fixed time intervals using meteorological sensors installed in the target area.

[0102] After obtaining wind speed data, the system calculates the squared deviation of the actual wind speed at each moment from the mean of the time series. This squared deviation reflects the degree to which the wind speed deviates from the average level at that moment; the larger the deviation, the more significant the wind speed fluctuation. By summing and averaging the squared deviations at all moments and then taking the square root, the final wind speed fluctuation value is obtained. A larger wind speed fluctuation value indicates drastic wind changes, making it easier for obstructions such as tree branches to sway.

[0103] Step 303: Calculate the correlation coefficient between power fluctuation and wind speed fluctuation.

[0104] The correlation coefficient is a quantitative indicator of the degree of linear correlation between power fluctuations and wind speed fluctuations. Specifically, the correlation coefficient is calculated using standardized covariance, and its value ranges from -1 to 1, reflecting the strength and direction of the correlation between the two fluctuations.

[0105] The correlation coefficient can be understood as a statistical measure of the degree of synchronization between power fluctuations and wind speed fluctuations. When the correlation coefficient is close to 1, it indicates a strong positive correlation between the two fluctuations, meaning that power fluctuations also increase as wind speed increases. When the correlation coefficient is close to -1, it indicates a strong negative correlation between the two fluctuations. When the correlation coefficient is close to 0, it indicates that there is essentially no linear correlation between the two fluctuations. For example, when tree branches cause power fluctuations, since the swaying of the branches is mainly affected by wind, power fluctuations and wind speed fluctuations usually show a high positive correlation.

[0106] The correlation coefficient is primarily used to determine whether power fluctuations in solar-powered streetlights are caused by wind-induced shading. By analyzing the magnitude of the correlation coefficient, the system can effectively distinguish between dynamic shading caused by wind-induced tree branch swaying and power fluctuations caused by other reasons. This correlation analysis provides reliable data support for the system to accurately identify shading types, assess the degree of shading impact, and formulate corresponding maintenance strategies.

[0107] For example, the correlation coefficient between power fluctuations and wind speed fluctuations can be calculated using the following formula:

[0108] ;

[0109] In the formula, The correlation coefficient is represented by the product of the standard deviations of power fluctuations and wind speed fluctuations in the denominator, reflecting the magnitude of the fluctuations; the numerator represents the covariance of power fluctuations and wind speed fluctuations, reflecting the synchronicity and directionality of the fluctuations.

[0110] Specifically, the system employs a standardized correlation coefficient calculation method that comprehensively considers two key characteristics: the intensity and synchronicity of fluctuations. In the calculation formula, the denominator is the product of the standard deviations of power fluctuations and wind speed fluctuations. This standardization eliminates dimensional differences between different physical quantities, providing a unified metric for the correlation coefficient. The numerator reflects the synchronous change characteristics of the two fluctuation sequences by calculating their covariance; the sign of the covariance indicates the directional relationship of the fluctuation changes.

[0111] Step 304: Determine whether the occlusion state is dynamic occlusion or fixed occlusion state based on the correlation coefficient.

[0112] After obtaining the correlation coefficients between power fluctuations and wind speed fluctuations, the system needs to further determine the specific type of shading. Since different types of shading will cause power fluctuations to exhibit different characteristic patterns, by analyzing the correlation coefficients and other key parameters, dynamic shading and fixed shading states can be accurately distinguished, thus providing a more targeted basis for subsequent maintenance decisions.

[0113] Specifically, the system first analyzes the correlation coefficient r. When the correlation coefficient is greater than a preset threshold (e.g., r > 0.7), it indicates a significant positive correlation between power fluctuations and wind speed fluctuations, meaning that the power change trend is basically consistent with the wind speed change trend. This situation typically occurs in dynamic shading caused by tree branches or other obstructions swaying in the wind. Increased wind speed exacerbates the swaying of tree branches, leading to periodic changes in the shading state of the photovoltaic panels, ultimately manifesting as periodic power fluctuations. In this case, the system classifies the shading state as dynamic shading.

[0114] Determining whether an event is a fixed shading condition requires considering multiple conditions. First, the system checks if the duration of the power anomaly exceeds a preset fixed shading time threshold. This threshold is typically set to a relatively long period to distinguish between persistent shading and short-term power fluctuations. Second, the system calculates the rate of change of ambient light intensity. When this rate of change is close to zero, it indicates that the ambient light conditions are basically stable, and the power anomaly is not caused by weather changes. Finally, the system also checks if the correlation coefficient r is less than a preset correlation threshold to rule out the possibility of dynamic shading. When all three conditions are met, the system classifies the shading state as fixed shading.

[0115] For example, the judgment condition is: (1) (2) Duration of power anomaly Exceeding the fixed occlusion time threshold : (3) The rate of change of ambient light intensity is close to zero: ;

[0116] Dynamic occlusion only needs to satisfy: .

[0117] By introducing correlation coefficient analysis, the system can accurately identify dynamic shading caused by wind-induced tree branch swaying, which often requires different handling measures than fixed shading. Secondly, by setting reasonable time thresholds and introducing light intensity change rate judgment, the system can effectively distinguish fixed shading from other factors causing power anomalies, avoiding misjudgments. Finally, this judgment method has low computational complexity, making it suitable for application in real-time monitoring systems. It can quickly respond to changes in shading status and adjust maintenance strategies promptly.

[0118] Step 103: If it is determined that the solar-powered streetlights in the target area are blocked, then generate maintenance suggestions corresponding to the blocked state.

[0119] Specifically, based on the shading status information obtained from the aforementioned steps, the system needs to generate corresponding maintenance suggestions to guide maintenance personnel in taking appropriate measures to ensure the normal operation of the solar-powered street lighting system. The generation of maintenance suggestions requires comprehensive consideration of multiple factors such as shading type, shading status, and geographical location, using a systematic analysis method to form an actionable maintenance plan.

[0120] Based on the above embodiments, as an optional embodiment, step 103 may further include the following steps:

[0121] Step 401: Generate a heat map based on the geographical location of the solar-powered streetlights that are in a shaded state.

[0122] In the actual operation and maintenance of solar-powered street lighting systems, shading problems often exhibit obvious spatial clustering characteristics. To help maintenance personnel quickly identify and locate shading issues, the system needs to use an intuitive visualization method to display the spatial distribution of shading status. Heatmaps, as a visualization tool that maps data values ​​to color depth, can effectively express the spatial distribution density and severity of shading problems.

[0123] A heatmap, in this context, refers to a data representation that maps data values ​​to color depths and visualizes them within a spatial coordinate system. In this embodiment, it can be understood as a two-dimensional visualization map generated based on the geographical location and shading status of solar-powered streetlights, used to intuitively display the distribution density, severity, and spatial clustering characteristics of shading problems within a target area.

[0124] Based on the above embodiments, as an optional embodiment, step 401 may further include the following steps:

[0125] Step 501: Obtain the degree of shading of the solar-powered streetlights that are in a shading state.

[0126] Before generating the heatmap, the system first needs to determine the severity of shading for each solar-powered street light. This is the foundational data for subsequent spatial distribution analysis and visualization. The shading degree Di, as a standardized quantitative indicator, accurately reflects the severity of the shading impact on solar-powered street lights by calculating the relative deviation between the actual and expected power generation.

[0127] The degree of shading refers to the deviation between the actual power generation of a solar-powered street light's photovoltaic panel and its expected power generation. In the embodiments of this application, it can be understood as a standardized parameter calculated based on the ratio of expected power generation to actual power generation, used to quantify the severity of the shading effect on the solar-powered street light.

[0128] For example, the formula for calculating the degree of occlusion can be expressed as:

[0129] ;

[0130] In the formula, For the first The degree of obstruction by each street light, with a value range of [value missing]. A larger value indicates a more severe occlusion. For the first The expected power generation of each street light For the first The actual power generation of each street light This represents the total number of streetlights within the target area.

[0131] The above method normalizes the power difference by dividing it by the expected power to obtain the degree of shading. Limited to the [0,1] interval, the dimensional differences between streetlights of different power levels are eliminated; secondly, The value is positively correlated with the severity of occlusion, when When the value is close to 0, it indicates that the actual power generation is close to the expected value, and there is basically no impact from shading. When the value is close to 1, it indicates a serious occlusion problem that needs to be addressed promptly.

[0132] By employing the aforementioned method for calculating shading levels, the system can quantify the shading status of solar-powered streetlights, providing reliable data support for subsequent spatial distribution analysis and maintenance decisions. Furthermore, the standardized processing of the calculation results facilitates color mapping and grading of heatmaps, enhancing the intuitiveness of the visualization. In addition, the versatility of this calculation method makes it applicable to solar-powered streetlight systems of different types and scales, demonstrating significant practical value.

[0133] Step 502: Based on the degree of shading and geographical location of each solar-powered street light that is in a shading state, the target area is divided into multiple clustered regions with different degrees of shading.

[0134] In actual maintenance, the shading problem of solar-powered streetlights often exhibits a clear spatial clustering characteristic, which may be due to common environmental factors. To improve maintenance efficiency, the system needs to identify and locate these concentrated areas of shading problems. Through spatial cluster analysis, streetlights with similar geographical locations and degrees of shading can be grouped together, thereby revealing the spatial distribution pattern of the shading problem.

[0135] In one feasible implementation, the system employs the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm for cluster analysis. Since the shading problem of solar-powered streetlights typically exhibits regional characteristics—for example, a grove of trees may simultaneously affect multiple adjacent streetlights, or undulating terrain may cause widespread shading of streetlights in a specific area—this spatial distribution characteristic aligns perfectly with the density-based clustering principle of the DBSCAN algorithm. The DBSCAN algorithm can not only discover irregularly shaped clusters but also automatically identify and remove outliers, making it particularly suitable for analyzing the shading problem of solar-powered streetlights.

[0136] In the specific implementation process, the system first needs to determine two key parameters: cluster radius ε and minimum number of neighboring points minPts. ε is defined as the maximum distance threshold for determining whether two streetlights constitute a neighbor relationship. This parameter is usually set based on the actual installation spacing of the streetlights, for example, it can be 1.5 times the standard spacing. minPts specifies the minimum number of streetlights required to form an effective cluster. The setting of this parameter needs to balance the effectiveness of clustering and maintenance efficiency, and is usually set to 3-5. Based on these two parameters, the system first calculates the geographical distance between any two streetlights. When the distance is less than ε, the two streetlights are marked as neighbors. Then, the system checks the number of neighbors for each streetlight. If the number of neighbors for a streetlight is not less than minPts, it is marked as a core point. For each core point, the system groups it and all its reachable neighboring streetlights into the same cluster region.

[0137] Using the clustering method described above, the system can effectively identify the spatial distribution characteristics of occlusion problems. For example, when multiple streetlights in a certain area experience occlusion simultaneously, these streetlights will be grouped into the same cluster, indicating that there may be a common source of occlusion in the area; while streetlights marked as noise points may be occluded due to localized causes. This analysis not only helps maintenance personnel quickly locate problem areas but also helps in developing more targeted maintenance strategies. For example, for occlusion problems within a cluster, regional tree trimming or streetlight repositioning may be necessary; while for noise points, only localized treatment may be required. Furthermore, by appropriately adjusting the ε and minPts parameters, the system can flexibly control the granularity of clustering, achieving a good balance between the accuracy of problem localization and maintenance efficiency.

[0138] After obtaining the initial clustering results, the system further analyzes each cluster region. Feature calculation and analysis are performed, among which, , where K represents the number of clusters.

[0139] First, calculate the number of streetlights in the area. The number of streetlights directly reflects the scale of the shading problem, helps maintenance personnel assess the required maintenance resources and workload, and is of great significance for developing a reasonable maintenance plan.

[0140] Specifically, the system iterates through the DBSCAN clustering results, counts the number of streetlights corresponding to each cluster label, and sums up the counts of streetlights belonging to the same cluster to obtain the total number of streetlights in that cluster area. This counting not only reflects the spatial scale of the occlusion problem but also helps maintenance personnel assess the required operation time and equipment quantity.

[0141] Secondly, since maintenance resources for solar-powered street lighting systems are usually limited, priority needs to be given to areas with severe shading. Therefore, calculating and assessing the overall severity of shading in an area is of great importance.

[0142] For example, calculating the average occlusion level can be expressed as: ,in, This represents the average occlusion level corresponding to the j-th cluster region. The average occlusion level index reflects the overall severity of occlusion in the region and helps determine the maintenance priority.

[0143] Then, record the range of occlusion severity: ,in, The minimum and maximum values ​​of the occlusion degree within the j-th cluster region are used to characterize the degree of change in the occlusion problem within the region.

[0144] The range of shading severity reflects the dispersion of shading problems within a region, helping maintenance personnel determine the nature and distribution characteristics of shading sources. For example, if the shading severity range in a certain area is narrow, meaning the maximum and minimum values ​​are similar, it may indicate that streetlights in that area are affected by similar degrees of shading, suggesting the existence of a single primary shading source, such as a relatively uniform forest. Conversely, an area with a wider shading severity range may have multiple shading factors or uneven shading sources, such as scattered trees or buildings. Through this range analysis, maintenance personnel can better understand the spatial characteristics of shading problems, thereby developing more targeted maintenance strategies.

[0145] Finally, calculate the cluster coverage radius. ;in, This represents the maximum distance between two points in the j-th cluster region, used to describe the geographical coverage of the region.

[0146] Cluster coverage radius can characterize the geographical coverage of the problem area, which helps in planning maintenance routes and assessing the geographical span of maintenance work.

[0147] After obtaining the initial clustering results, the system further analyzes each cluster region. Feature calculation and analysis are performed, among which, , where K represents the number of clusters.

[0148] Step 503: Generate a heatmap based on each cluster region.

[0149] After obtaining the basic characteristics of the clustered regions, the system needs to further assess the severity of occlusion problems in each region and display the assessment results through intuitive visualization. To this end, the system introduces a maintenance priority scoring mechanism, which comprehensively quantifies multiple key characteristics of occlusion problems into a single indicator and generates a heat map based on this indicator to help maintenance personnel quickly identify key areas.

[0150] Specifically, the system first calculates the maintenance priority score for each cluster region. As a comprehensive indicator, the priority score needs to consider the scale, severity, and spatial distribution characteristics of the occlusion problem simultaneously.

[0151] For example, the priority score of the j-th cluster region can be calculated using the following formula:

[0152] ;

[0153] In the formula, This represents the priority score of the j-th cluster region. The larger the value, the more severe the occlusion problem in the region, and the more priority it needs to be addressed. This represents the number of streetlights in the j-th cluster region. This represents the average occlusion level of the j-th cluster region. This represents the area of ​​the j-th cluster region. ∝ .

[0154] in, and The larger the value, the more concentrated and severe the occlusion problem is within the hotspot area. The larger the value, the more dispersed the problem is, and the lower its priority. The above scoring formula comprehensively considers the scale, severity, and distribution density of occlusion problems, facilitating the rational allocation of maintenance resources.

[0155] After obtaining priority scores, the system maps these scores to color depths in a heatmap. Specifically, the system first normalizes the priority scores of all clustered regions, mapping the scores to the [0,1] interval. Then, the system uses a preset color mapping scheme, such as a gradient spectrum from green to red, where green represents lower priority regions and red represents higher priority regions. For each clustered region, the system selects the corresponding color value from the color spectrum based on its normalized priority score.

[0156] To generate a continuous and smooth heatmap, the system needs to handle the transition regions between clusters. Here, a kernel density estimation method is used to transform discrete score values ​​into a continuous density distribution. Specifically, the system generates a two-dimensional Gaussian kernel function centered on the location of each streetlight and based on the priority score of its respective cluster. By superimposing the kernel functions of all streetlight locations, a continuous density distribution is formed for the entire target area. This processing method not only smoothly displays the spatial distribution of priorities but also reflects the influence range and intensity gradient of occlusion issues.

[0157] During the heatmap generation process, the system also adds necessary legends and annotations. For example, the legend indicates the correspondence between colors and priority scores, and the key features of each cluster area are marked on the map. This auxiliary information helps maintenance personnel better understand and use the heatmap.

[0158] Step 402: Generate maintenance recommendations based on the heatmap;

[0159] Based on the above embodiments, as an optional embodiment, step 402 may specifically include the following steps:

[0160] Step 601: If the occlusion state is a fixed occlusion state, then generate tree pruning suggestions based on the heatmap;

[0161] Step 602: If the shading state is a non-fixed shading state, then generate power dispatching suggestions based on the heat map.

[0162] During the operation and maintenance of solar-powered street lighting systems in the target area, different types of shading issues require different handling strategies. Fixed shading and dynamic shading correspond to different maintenance needs, and the system needs to generate targeted maintenance recommendations based on heatmap analysis results to ensure the continuous and stable operation of the system.

[0163] When the system detects a fixed shading condition, it indicates that the solar-powered streetlights are primarily obstructed by stationary objects such as leaves. In this case, the system needs to generate tree pruning recommendations. Specifically, the system first extracts the geographical extent and shading characteristics of high-priority areas from a heatmap. Then, based on the average shading level and coverage radius of these areas, the system calculates the recommended pruning area. Simultaneously, the system determines the urgency of pruning based on the average shading level and assigns a corresponding priority level to guide the prioritization of maintenance work.

[0164] For each area requiring trimming, the system generates trimming recommendations that include the geographical coordinates of the area, the number of affected streetlights within the area, the recommended trimming height, and the recommended trimming time. These recommendations are presented in a structured manner for easy understanding and execution by maintenance personnel. The system determines a reasonable trimming height by analyzing parameters such as streetlight installation height and solar incidence angle, and selects an appropriate maintenance time window based on electrical load characteristics.

[0165] When the system detects dynamic shading, since this shading is usually caused by temporary factors such as tree branch swaying, directly pruning the trees may not be economical. In this case, the system generates power dispatch suggestions to reduce the impact of shading by optimizing energy allocation. Specifically, the system first analyzes the spatiotemporal distribution characteristics of dynamically shading areas in the heatmap and assesses the power fluctuation patterns in each area. Based on the average shading degree and power fluctuation characteristics of each clustered region, the system calculates the additional energy compensation requirements.

[0166] The power dispatch recommendations mainly include calculating the expected power deficit in dynamically shaded areas at different times, and predicting periods of high power fluctuation based on historical data. The system identifies potential energy supply sources from adjacent unshaded areas, prioritizing areas that are closer and have a larger power generation surplus. Finally, the system generates a specific dispatch plan that includes information such as energy transmission paths, dispatch time windows, and expected compensation power.

[0167] Through the aforementioned differentiated maintenance suggestion generation mechanism, the system achieves refined management of shading issues. For fixed shading, precise location and reasonable planning of pruning areas can minimize maintenance costs and improve operational efficiency. For dynamic shading, intelligent power dispatching strategies ensure power supply stability in affected areas while avoiding unnecessary tree pruning, thus optimizing resource allocation. Furthermore, this suggestion generation method is highly practical and scalable, allowing for flexible adjustment of parameters and strategies based on actual operation and maintenance needs.

[0168] Reference Figure 2 This application also provides a maintenance system for solar-powered streetlights, comprising:

[0169] The data acquisition module is used to acquire detection data of each solar-powered street light in the target area within a preset time period, as well as environmental data of the target area;

[0170] The occlusion determination module is used to determine whether the solar-powered streetlights in the target area are in an occlusion state based on the detection data and the environmental data. The occlusion state is used to characterize the degree to which the solar-powered streetlights in the target area are blocked by trees.

[0171] The suggestion generation module is used to generate maintenance suggestions corresponding to the shading state if it is determined that the solar-powered streetlights in the target area are in a shading state.

[0172] Based on the above embodiments, as an optional embodiment, the detection data includes the actual power generation of the photovoltaic panels in each of the solar-powered streetlights, and the geographical location of each of the solar-powered streetlights; the environmental data includes light intensity; the shading judgment module is further used to calculate the expected power generation of each of the solar-powered streetlights based on the geographical location and light intensity of each of the solar-powered streetlights; based on the difference between the expected power generation and the actual power generation of each of the solar-powered streetlights, determine whether the solar-powered streetlights in the target area are in a shading state; if the difference between the expected power generation and the actual power generation of each of the solar-powered streetlights is greater than a preset threshold, then it is determined that the solar-powered streetlights in the target area are in a shading state; if the difference between the expected power generation and the actual power generation of each of the solar-powered streetlights is less than or equal to the preset threshold, then it is determined that the solar-powered streetlights in the target area are in an unshading state.

[0173] Based on the above embodiments, as an optional embodiment, the environmental data further includes wind speed and a shading judgment module, which is also used to calculate the power fluctuation of the solar-powered streetlights in the target area within the preset time period based on the expected power generation and actual power generation of each of the solar-powered streetlights; calculate the wind speed fluctuation of the target area within the preset time period based on the wind speed; calculate the correlation coefficient between the power fluctuation and the wind speed fluctuation; and determine whether the shading state is dynamic shading or fixed shading state based on the correlation coefficient.

[0174] Based on the above embodiments, as an optional embodiment, it is suggested that the generation module is further configured to generate a heat map based on the geographical location of the solar-powered streetlights in the shaded state; and generate maintenance suggestions based on the heat map.

[0175] Based on the above embodiments, as an optional embodiment, it is suggested that the generation module is further configured to obtain the degree of shading of the solar-powered streetlights in the shading state; divide the target area into multiple clustered regions with different degrees of shading based on the degree of shading and geographical location of each of the solar-powered streetlights in the shading state; and generate a heat map based on each of the clustered regions.

[0176] Based on the above embodiments, as an optional embodiment, it is suggested that the generation module is further configured to generate tree pruning suggestions based on the heat map if the shading state is a fixed shading state; and generate power dispatching suggestions based on the heat map if the shading state is a non-fixed shading state.

[0177] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0178] This application also provides a computer storage medium that can store multiple instructions. These instructions are adapted to be loaded and executed by a processor as described in the above embodiments for the maintenance method of a solar-powered street light. The specific execution process can be referred to the detailed description of the illustrated embodiments, which will not be repeated here.

[0179] This application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the maintenance methods for solar-powered streetlights provided by the above methods.

[0180] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0181] The communication bus 302 is used to enable communication between these components.

[0182] The user interface 303 may include a display interface and a camera interface. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0183] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0184] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface graphics, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0185] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for maintaining a solar-powered street light.

[0186] exist Figure 3In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and to acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for a maintenance method of a solar-powered street light. When executed by one or more processors 301, the electronic device 300 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0187] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0188] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0189] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0190] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0191] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0192] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0193] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method of maintaining a solar powered street light, characterized in that, The method comprises the following steps: acquiring detection data of each solar power street lamp in a target area within a preset time period and environmental data of the target area; the detection data comprises actual power generation of a photovoltaic panel in each solar power street lamp and geographical position of each solar power street lamp; the environmental data further comprises wind speed; calculating expected power generation of each solar power street lamp based on the geographical position and light intensity of each solar power street lamp; judging whether the solar power street lamp in the target area is in a shielding state based on a difference between the expected power generation and the actual power generation of each solar power street lamp; if the difference between the expected power generation and the actual power generation of each solar power street lamp is greater than a preset threshold, it is determined that the solar power street lamp in the target area is in the shielding state; the shielding state is used to represent a degree of shielding of the solar power street lamp in the target area by trees; the shielding state comprises a fixed shielding state and a dynamic shielding state; the fixed shielding state refers to that the photovoltaic panel is continuously shielded by a stationary object; the dynamic shielding refers to that the photovoltaic panel is periodically shielded by an object; calculating power fluctuation of the solar power street lamp in the target area within the preset time period based on the expected power generation and the actual power generation of each solar power street lamp; the power fluctuation refers to a degree of deviation of the actual power generation of the solar power street lamp from a time series mean value within the preset time period; calculating wind speed fluctuation of the target area within the preset time period based on the wind speed; the wind speed fluctuation refers to a degree of deviation of the wind speed in the target area from a time series mean value within the preset time period; calculating a correlation coefficient of the power fluctuation and the wind speed fluctuation; the correlation coefficient is used to judge whether the power fluctuation of the solar power street lamp is caused by wind-induced shielding; determining the shielding state as the dynamic shielding state or the fixed shielding state according to the correlation coefficient; if it is determined that the solar power street lamp in the target area is in the shielding state, acquiring a shielding degree of the solar power street lamp in the shielding state; the shielding degree refers to a degree of deviation of the actual power generation of the photovoltaic panel of the solar power street lamp from the expected power generation; dividing the target area into a plurality of clustering areas with different shielding degrees based on the shielding degree and the geographical position of each solar power street lamp in the shielding state; generating a heat map based on each clustering area; generating a maintenance suggestion based on the heat map.

2. The solar power street light maintenance method according to claim 1, wherein The method further comprises the following steps: if the difference between the expected power generation and the actual power generation of each solar power street lamp is less than or equal to the preset threshold, it is determined that the solar power street lamp in the target area is in a non-shielding state.

3. The solar power street light maintenance method according to claim 1, wherein The generating of the maintenance suggestion based on the heat map comprises the following steps: if the shielding state is the fixed shielding state, generating a tree pruning suggestion based on the heat map; if the shielding state is the non-fixed shielding state, generating an electric energy scheduling suggestion based on the heat map.

4. A maintenance system for a solar-powered street light for implementing the maintenance method of any one of claims 1 to 3, characterized in that, The method comprises the following steps: a data acquisition module is configured to acquire detection data of each solar power street lamp in a target area within a preset time period and environmental data of the target area; The shielding judgment module is configured to judge whether the solar power street lamp in the target area is in a shielding state based on the detection data and the environment data, the shielding state being used to represent a degree to which the solar power street lamp in the target area is shielded by trees; The suggestion generation module is configured to generate a maintenance suggestion corresponding to the shielding state if it is determined that the solar power street lamp in the target area is in the shielding state.

5. An electronic device, comprising: The electronic device comprises a processor, a memory, a user interface, and a network interface. The memory is configured to store instructions. The user interface and the network interface are configured to communicate with other devices. The processor is configured to execute the instructions stored in the memory, so that the electronic device performs the method according to any one of claims 1-3.

6. A computer storage medium, characterized in that The computer storage medium stores instructions, which, when executed, perform the method according to any one of claims 1-3.

7. A computer program product comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the method according to any one of claims 1-3.

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