Charging and discharging control method of solar street lamp and intelligent control system

Abstract

The invention relates to the technical field of solar street lamp charging and discharging, in particular to a solar street lamp charging and discharging control method and an intelligent control system, and the method comprises the steps: building a same-batch street lamp temperature sequence matching model, and screening battery packs with similar working conditions; constructing a double-mean voltage sequence comparison mechanism, and calculating an abnormal evaluation index; and implementing a dynamic threshold grading response and municipal power supply switching strategy. By identifying the abnormal state of the solar cell, the charging and discharging strategy of the solar cell is optimized, and the service life of the cell is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of solar street lamp charging and discharging, and in particular to a solar street lamp charging and discharging control method and an intelligent control system. Background Art

[0002] As a typical application combining green energy and intelligent lighting, solar street lights have made significant technological progress in recent years. Their core system consists of photovoltaic power generation modules, battery modules, control modules and lamps, and achieves efficient energy utilization through strategies such as light control, time control, and charge and discharge management.

[0003] The effectiveness of solar street light charge and discharge management strategies directly impacts the lifespan of battery modules and the overall system performance. Currently, mainstream technologies for solar street light charge and discharge management rely on voltage and current thresholds, but fail to fully consider the degradation effects of dynamic ambient temperature changes on battery performance. Existing systems also rely on a single voltage mean comparison for anomaly detection, ignoring the changing trends and discrete characteristics of voltage sequences. This can lead to misjudgments of healthy batteries and missed detection of potential faults due to environmental noise. Summary of the Invention

[0004] The purpose of the present invention is to improve the recognition of abnormal conditions of solar cells, optimize the charging and discharging strategy of solar cells, and extend the service life of batteries.

[0005] In order to achieve the above objectives, the present invention provides a charging and discharging control method and an intelligent control system for a solar street lamp.

[0006] In the first aspect, the present application provides a charging and discharging control method for solar street lamps, including the steps of: establishing a temperature sequence matching model for street lamps in the same batch, screening battery packs with similar working conditions; constructing a dual-mean voltage sequence comparison mechanism, calculating an abnormality evaluation index; and implementing a dynamic threshold graded response and municipal power supply switching strategy.

[0007] Optionally, the method of establishing a temperature sequence matching model for street lamps in the same batch and screening battery packs with similar working conditions includes the following steps: constructing a distributed temperature field monitoring network; dynamically constructing a temperature difference sequence based on the collected temperature sequence, and calculating the temperature difference dispersion; setting the time window length; modeling the working condition matching coefficient based on the temperature difference dispersion; setting the working condition matching coefficient threshold, and dynamically clustering groups with the same working condition based on the working condition matching coefficient.

[0008] Optionally, the working condition matching coefficient expression is:

[0009] Where r (a,b) Indicates the working condition matching coefficient of the two solar cells; Indicates the ratio of the span of time periods s1 and s2 to days; It is expressed as the average dispersion of the temperature difference between the a-th and b-th solar cells around the mean.

[0010] Optionally, the working condition matching coefficient expression also includes 1+0.05ΔT amb Factor, where ΔT amb Indicates the change in ambient temperature; 0.05 is the temperature sensitivity coefficient; 1 is the baseline correction amount.

[0011] Optionally, the construction of a dual-mean voltage sequence comparison mechanism and calculation of anomaly evaluation index includes the steps of: establishing a dual-channel voltage acquisition system; constructing a voltage mean sequence; and calculating an anomaly evaluation index based on the voltage mean sequence comparison.

[0012] Optionally, the abnormal evaluation index expression is:

[0013] u 1 =PPMCC{{V a},{V 1}}×tanh(∑|V a -V 1 |)

[0014] u 2 =PPMCC{{V a},{V 2}}×tanh(∑|V a -V 2 |)

[0015] u=u 1 -u 2

[0016] Where, u is the abnormal evaluation index of the a-th solar cell, u 1 is the first mean value sequence and the first state abnormality degree of the a-th solar cell, u 2 is the second mean value sequence and the second state abnormality degree of the a-th solar cell, V a is the voltage sequence of the ath solar cell from time s2 to time s1, V 1 is the first mean sequence, V 2 is the second mean sequence.

[0017] Optionally, the working condition matching coefficient threshold θ r =0.15, if r (a,b) >θ r , it is determined that a and b belong to different working condition groups; otherwise they are classified into the same group.

[0018] Optionally, the clustering algorithm adopts the DBSCAN clustering algorithm, and the input features include: daily average temperature fluctuation amplitude, nighttime cooling rate, temperature gradient directionality index, and irradiance-temperature coupling coefficient.

[0019] Optionally, the span of the time periods s1 and s2 is converted into a ratio of days; a dynamic time window is used, i.e. The maximum matching period is limited to 48 hours.

[0020] The beneficial effect is that by setting a dynamic time window, interference from historical data can be effectively prevented.

[0021] On the second aspect, the present application provides an intelligent charging and discharging control system for solar street lights, which adopts the following technical solutions, including a data acquisition module, a power management module, a core control module, an actuator module, a communication and monitoring module, and a fault handling module; the data acquisition module is used to collect the temperature and charging voltage of solar cells in real time, and transmit them to the core control module through the communication and monitoring module. The core control module establishes a temperature sequence matching model for street lights in the same batch, screens battery groups with similar working conditions, constructs a dual-mean voltage sequence comparison mechanism, and calculates an abnormality evaluation index; the power management module provides basic power supply requirements for solar street lights; the actuator module and the fault handling module both communicate with the core control module through the communication and monitoring module. The actuator module is used to realize the normal lighting function of the solar street light and perform municipal power supply switching; the fault handling module is used to judge abnormalities and perform three-level response protection.

[0022] Compared with the prior art, the charge and discharge control method and intelligent control system for solar street lamps provided by the embodiment of the present invention have the following advantages:

[0023] 1. Dynamically construct a temperature difference sequence through a distributed temperature field monitoring network, and combine it with a set time window algorithm to suppress historical data interference. Introduce the ambient temperature compensation factor 1+0.05ΔT in the working condition matching coefficient formula amb , keeping the temperature difference between batteries in the same operating condition group within a smaller range, significantly improving measurement accuracy. Combined with the DBSCAN-MK clustering algorithm to screen groups with similar operating conditions, it effectively suppresses the "barrel effect" and significantly extends the cycle life of the battery pack.

[0024] 2. The dual-channel voltage acquisition system is combined with Kalman filtering to eliminate noise, and the steady-state accuracy of the voltage mean sequence reaches ±0.05V. Abnormal evaluation index u=u 1 -u 2 By using the Pearson correlation coefficient (PPMCC) and the hyperbolic tangent function (tanh) weighted calculation, it is possible to distinguish between individual battery degradation and group fluctuations, improving detection sensitivity and thus enhancing latent fault warnings. The dynamic threshold grading response trigger time is shortened, significantly improving compared to traditional PI control. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of a method for controlling the charging and discharging of a solar street lamp according to an embodiment of the present invention.

[0026] Figure 2 This is a flow chart of a method for establishing a temperature sequence matching model for street lamps in the same batch and screening battery packs with similar working conditions.

[0027] Figure 3 This is a flow chart of a method for constructing a dual-mean voltage sequence comparison mechanism and calculating anomaly evaluation index in the present invention. DETAILED DESCRIPTION

[0028] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0029] The present application discloses a method for controlling the charging and discharging of a solar street lamp. Figure 1 , including steps S1 to S3, specifically as follows:

[0030] Example 1

[0031] S1: Establish a temperature sequence matching model for street lamps in the same batch and screen battery packs with similar working conditions.

[0032] The battery temperature sequence is a set of temperatures of solar cells in the same batch; the same working condition refers to the situation where the temperatures of the solar battery groups are the same or within a certain range.

[0033] By processing the temperatures of multiple solar cells, battery groups with the same operating conditions can be screened out to provide accurate and reliable basic data for subsequent charging and discharging strategies.

[0034] In the above Figure 1 In the embodiment shown, in step S1, the battery temperature sequence is matched with the battery pack screening under the same working condition, such as Figure 2 As shown, the following steps are included:

[0035] S101: Build a distributed temperature field monitoring network.

[0036] The distributed temperature field monitoring network collects temperature data in real time through multi-node sensors, which can accurately identify the temperature of solar cells and provide key basic data for the charging and discharging strategies of solar street lights.

[0037] Specifically, a high-precision NTC sensor array with a 5-point matrix layout (four corners + geometric center) is deployed on the back panel of each solar panel.

[0038] In one embodiment, the NTC sensor sampling frequency is ≥10Hz, and data is uploaded to an edge computing node (Raspberry Pi CM4) via the LoRaWAN protocol. The edge node also incorporates a built-in LSTM temperature prediction model to predict temperature trends for the next 15 minutes. The NTC sensor system has a built-in temperature drift compensation algorithm, meeting an operating range of -40°C to 85°C and a temperature resolution of ±0.1°C. The NTC sensor adopts an IP68 protection rating and is coated with 3W / mK thermally conductive silicone to ensure a contact thermal resistance of <0.1°C / W. Dual sensors are deployed at each corner, using active / standby dual sensor technology. If the temperature difference at a single point exceeds ±1.5°C three times consecutively, an alarm is triggered and the backup data source is switched.

[0039] S102: Dynamically construct a temperature difference sequence based on the collected temperature sequence, and calculate the temperature difference dispersion.

[0040] Specifically, the temperature difference sequence of the two batteries (a and b) in the time period s1 and s2 is generated in real time to reflect the spatiotemporal distribution of the environment or working condition differences. The expression is:

[0041] Example: If a period of time h i If the temperature remains high, the shadow of the high-rise building may cause uneven heating of battery a; if the temperature is high during a certain period of time, i If there is a sudden change, the redundant sensor can be triggered to switch, the temperature data can be retransmitted, and the temperature difference sequence can be recalculated.

[0042] The expression of average dispersion is: Where, Indicates the average temperature at the time s1 and s2.

[0043] S103: Standardize the time window length.

[0044] Convert the span of time periods s1 and s2 into a ratio of days Suppress the interference of short-term fluctuations on grouping.

[0045] For example, a 12-hour window corresponds to 0.5, which reduces the weight of instantaneous temperature change, suppresses cloud cover interference, and suppresses short-term fluctuations.

[0046] S104: Modeling the operating condition matching coefficient based on the temperature difference dispersion.

[0047] After obtaining the temperature sequence of the temperature monitoring module installed on each solar street light, taking the solar cell of the a-th solar street light as an example, calculate the operating condition matching coefficient of the a-th solar street light and the b-th solar street light:

[0048]

[0049] Where r(a,b) is the operating condition matching coefficient of the ath and bth solar cells; is the average dispersion of the temperature difference between the a-th and b-th solar cells around the mean. The higher the dispersion, the more dissimilar the working conditions of the a-th solar street light are to the b-th solar street light.

[0050] S105: Setting a threshold value of the operating condition matching coefficient, and dynamically clustering groups with the same operating condition according to the operating condition matching coefficient.

[0051] Set the threshold θ r =0.15, if r (a,b) >θ r , it is determined that a and b belong to different working condition groups; otherwise they are classified into the same group.

[0052] The clustering algorithm is DBSCAN clustering or K-Means clustering. The clustering algorithm can be a K-means clustering algorithm or a DBSCAN clustering algorithm. The K-means algorithm requires the number of clusters to be specified in advance, but in practical applications, the number of clusters cannot be accurately set, and the accuracy of the clustering results is low. Therefore, this application prefers the DBSCAN clustering algorithm, which can automatically identify the number of clusters. The clustering process is prior art and will not be described in detail here. After clustering is completed, multiple clusters are obtained.

[0053] Specifically, the DBSCAN-MK algorithm is used to cluster all streetlights and output battery packs with similar temperature characteristics. Input features include: daily average temperature fluctuation amplitude (using FFT to extract the main frequency component), nighttime cooling rate (calculated from the linear regression slope), temperature gradient directionality index (2D thermal imaging data), and irradiance-temperature coupling coefficient (derived from simulations using the photovoltaic simulation software PSIM9.3). In one embodiment, the cluster radius ε = 0.3, the minimum number of samples MinPts = 5, and the abnormal temperature gradient alarm trigger condition is MD > 0.5 for three consecutive windows. The grouping is updated every 24 hours to adapt to seasonal changes.

[0054] S2: Construct a dual-mean voltage sequence comparison mechanism and calculate the anomaly evaluation index.

[0055] Since the working environment of solar street lights is outdoors and the working environment is relatively complex, the battery life of multiple solar cells under the same working conditions is inconsistent. Therefore, the voltage changes during the charging process are not completely consistent, but the difference in voltage changes is within a certain range. That is, if the voltage change of a solar cell in a solar cell group under the same working conditions is far away from the voltage change of the rest of the solar cells, it means that the solar cell has a fault and needs to be connected to the municipal power supply to ensure that the solar cell does not have an abnormal voltage accident.

[0056] In the above Figure 1 In the embodiment shown, in step S2, a dual mean voltage sequence comparison mechanism is constructed, and the calculation of the abnormality evaluation index includes the following steps: Figure 3 As shown, the following steps are included:

[0057] S201: Establish a dual-channel voltage acquisition system.

[0058] Build a voltage synchronous sampling system, including:

[0059] Specifically: the main channel is a Fluke 435II power quality analyzer (accuracy ±0.1%), which collects the charging voltage waveform in real time;

[0060] Calibration channel: HIOKI PW3390 high-precision power meter (1MHz sampling rate), data fused by Kalman filtering to eliminate phase error <0.1°.

[0061] S202: Constructing a voltage mean value sequence.

[0062] For n batteries in the same working condition group, extract the charging voltage sequence of time periods s1 and s2, and calculate the first mean value sequence V 1 and the first mean sequence V 2 .

[0063] (w i Dynamic adjustment based on health level)

[0064] Health weight: When health is >90%, w=1.2; when health is <80%, w=0.8.

[0065] Use Jackknife resampling to generate V 2 Eliminate outlier interference.

[0066] Among them, the first mean sequence V 1 is the population mean of the target battery a; the first mean sequence V 2 It is the corrected mean after excluding a, which is used to eliminate the impact of a's own abnormality on the overall benchmark.

[0067] S203: Calculate an abnormality evaluation index based on voltage mean sequence comparison.

[0068] Calculate the abnormality evaluation index of the a-th solar cell, where the abnormality evaluation index satisfies the following polynomial:

[0069] u 1 =PPMCC{{V a},{V 1}}×tanh(∑|V a -V 1 |)

[0070] u 2 =PPMCC{{V a},{V 2}}×tanh(∑|V a -V 2 |)

[0071] u=u 1 -u 2

[0072] Where, u is the abnormal evaluation index of the a-th solar cell, u 1 is the first mean value sequence and the first state abnormality degree of the a-th solar cell, u 2 is the second mean value sequence and the second state abnormality degree of the a-th solar cell, V a is the voltage sequence of the ath solar cell from time s2 to time s1, V 1 is the first mean sequence, V 2 is the second mean sequence.

[0073] PPMCC{{V a},{V 1}} is the similarity between the first mean sequence and the voltage sequence of the a-th solar cell. The larger the value, the more similar the voltage sequence of the a-th solar cell is to the first mean sequence representing the whole. tanh(∑|V a -V 1 The smaller the difference between the first mean sequence and the voltage sequence of the a-th solar cell, the larger the value, indicating that the voltage sequence of the a-th solar cell is more similar to the overall numerical value of the first mean sequence representing the whole. 1 -u 2 It can express the degree of influence of the voltage sequence of the a-th solar cell on the overall mean sequence. The larger the value, the higher the abnormality of the a-th solar cell and the further away it is from the overall state of the solar cell.

[0074] When the abnormal evaluation index of the a-th solar cell is greater than the preset abnormal evaluation index threshold, it indicates that the a-th solar cell has an abnormal state and needs to be isolated from charging or discharging to protect the battery and notify relevant personnel for maintenance to extend the battery life.

[0075] S3: Implement dynamic threshold graded response and municipal power supply switching strategy.

[0076] Set up a three-level response strategy, specifically:

[0077] 1) Level 1 warning (u > θ1 = 1.8), when the abnormal evaluation index u exceeds 1.8, the system triggers the temperature difference compensation type MPPT algorithm. This algorithm dynamically adjusts the step size of the maximum power point tracking (MPPT) by collecting battery temperature (from the NTC sensor array) and irradiance data in real time. For example, when the temperature rises by 1°C, the open circuit voltage (Voc) of the silicon-based battery drops by about 210 mV, and the algorithm will correct the MPPT operating point in real time according to the temperature compensation formula V OC (T) = V OC (25°C) + K V ×(T - 25°C) (K V = -210 mV / °C) to avoid power loss caused by temperature fluctuations.

[0078] Function: Suppress voltage fluctuations caused by local shading or environmental temperature differences, improve the charging efficiency by 5% - 10%, response time < 30 s, and ensure fast adjustment.

[0079] 2) Level 2 response (θ1 < u ≤ θ2 = 2.5), when the abnormal evaluation index u is in the range of (θ1 < u ≤ θ2 = 2.5), activate the AC impedance spectrum detection (frequency range 100 Hz - 100 kHz), and detect hidden faults such as battery internal resistance and capacity attenuation by injecting a sinusoidal perturbation signal and measuring the battery impedance response. For example, a sudden 10% increase in internal resistance may indicate electrolyte dry-out or plate corrosion.

[0080] Function: Quantify the battery health state (SOH), with an accuracy of ±1 mΩ, can give an early warning of faults with a capacity attenuation > 20% 38 days in advance, and avoid thermal runaway caused by over-discharge.

[0081] 3) Level 3 protection (u > θ2 = 2.5)

[0082] Trigger the Vishay SCT3030KL MOSFET solid-state switch (on-resistance ≤ 2 mΩ), cut off the abnormal battery circuit, and synchronously start the municipal power supply switchover. The MOSFET adopts ZVS (zero voltage switching) technology, with a switching delay < 20 ms and THD < 3%. Prevent the battery from overvoltage and spontaneous combustion, ensure system safety, and there is no perceived power-off during the switching process.

[0083] Example 2

[0084] The difference between the embodiment of this application and the previous embodiment is that in step S103: a dynamic time window is adopted, that is Limit the longest matching interval to 48 hours to prevent interference from historical data.

[0085] The difference between the embodiment of this application and the above embodiment is that in S104: according to the temperature difference dispersion, a dynamic correction factor 1 + 0.05ΔT is added to the operating condition matching coefficient in the operating condition matching coefficient modeling amb, specifically:

[0086]

[0087] Where, 1 + 0.05ΔT amb It is a dynamic correction factor for ambient temperature changes, which dynamically adjusts key parameters according to ambient temperature fluctuations to improve the system's adaptability and accuracy to temperature disturbances. amb Indicates the change in ambient temperature (e.g., temperature rise or fall due to diurnal temperature differences or sudden weather changes); 0.05 is the temperature sensitivity coefficient, reflecting the system's response to ambient temperature changes. Similarly, different base values ​​are selected based on the battery type, such as 0.08 for silicon-based batteries and 0.03 for thin-film batteries. This application uses 0.05 as an example. 1 is the baseline correction value, ensuring that the minimum correction factor is 1 (i.e., no parameter adjustment is made when there is no temperature change).

[0088] Specifically, 1+0.05ΔT amb The factor is used to dynamically modify the threshold. In one embodiment, the operating condition matching threshold modification, θ r =0.15×(1+0.05ΔT amb ). When the ambient temperature changes greatly (such as on rainy days ΔT amb >0), automatically relax the threshold (θ r Increase), to avoid misjudging the battery pack working condition due to temperature fluctuations

[0089] In sunny scenes, the temperature change is small (ΔT amb ≈0), the correction factor is close to 1, and the system maintains a high-precision threshold (such as θ r =0.15). In rainy or hot scenes, the temperature fluctuates greatly (ΔT amb The correction factor dynamically adjusts the parameters to avoid misjudgment due to thermal expansion, material deformation, etc.

[0090] Example: A battery's temperature rises abnormally due to partial shading (ΔT amb = +5°C), the traditional method may misjudge it as a fault, and the voltage threshold is adjusted by the correction factor: V threshold =V base (1+0.05×5)=1.25V base If the actual voltage is within the corrected threshold range, it is determined to be a normal temperature change, not a battery failure.

[0091] 2)1+0.05ΔT amb Factors are also used to compensate for temperature sensitive parameters.

[0092] The open circuit voltage (Voc) of silicon-based solar cells decreases linearly with increasing temperature (about -210mV / ℃). The voltage measurement value V is calibrated by the correction factor. comp=V measured ×(1+0.05ΔT amb ).

[0093] Example: In winter, the temperature difference between day and night is large (ΔT amb =-10℃), the battery capacity detection value is low, through temperature compensation of the capacity detection value, C real =C measured ×(1+0.05×10)=1.5C measured , thus avoiding misjudging battery aging due to low temperature.

[0094] The embodiment of the present application also discloses an intelligent charging and discharging system for solar street lights, which adopts the above-mentioned charging and discharging control method for solar street lights, including a data acquisition module, a power management module, a core control module, an actuator module, a communication and monitoring module, and a fault handling module; wherein the data acquisition module is used to collect the temperature and charging voltage of solar cells in real time, and transmit them to the core control module through the communication and monitoring module, and the core control module establishes a temperature sequence matching model for street lights in the same batch, screens battery groups with similar working conditions, constructs a dual-mean voltage sequence comparison mechanism, and calculates an abnormality evaluation index; the power management module provides basic power supply requirements for solar street lights; the actuator module and the fault handling module both communicate with the core control module through the communication and monitoring module, and the actuator module is used to realize the normal lighting function of the solar street light and perform municipal power supply switching; the fault handling module is used to judge abnormalities and perform three-level response protection.

[0095] Specifically, the data acquisition module includes a temperature acquisition unit and a voltage acquisition unit. The temperature acquisition unit uses a five-point matrix NTC thermistor array to collect the temperature of each solar cell in real time. The voltage acquisition unit uses a dual-channel architecture to collect the charging voltage of each solar cell in real time.

[0096] Specifically, the core control module includes an edge computing unit and an anomaly evaluation model. The edge computing unit hardware platform is equipped with an STM32 microcontroller with integrated ADC, PWM, and UART interfaces. It can calculate the battery temperature difference dispersion and dynamic threshold. The anomaly evaluation model is used to perform dual-modal analysis of the collected voltage and calculate the anomaly index.

[0097] Specifically, the actuator module includes a municipal power supply switching unit, which switches the solar cell power supply voltage to the municipal power supply through the LLC resonance + phase-shifted full-bridge composite circuit architecture, Vishay SCT3030KL MOSFET and ADPLL phase-locked loop.

[0098] The working process of the present invention is as follows: a distributed temperature field monitoring network is constructed by deploying a 5-point NTC sensor matrix on the backplane of the solar panel. After dynamic temperature difference sequence generation and discreteness calculation of the collected temperature data, operating condition matching coefficient modeling and dynamic clustering are performed to obtain solar cell groups with the same operating conditions.

[0099] A dual-channel voltage acquisition system is constructed to collect voltage data of solar cell groups with the same working conditions, and the charging voltage of solar cells is analyzed through the abnormal evaluation index. It can be determined whether the current solar voltage is abnormal, and the charging strategy of solar cells with abnormal voltage can be adjusted accordingly through a three-level response strategy.

[0100] In summary, the present invention provides a charge and discharge control method and intelligent control system for solar street lamps. The method dynamically constructs a temperature difference sequence through a distributed temperature field monitoring network and combines a set time window algorithm to suppress historical data interference. The ambient temperature compensation factor 1+0.05ΔT is introduced into the working condition matching coefficient formula. amb , keeping the temperature difference between batteries in the same operating condition group within a smaller range, significantly improving measurement accuracy. Combined with the DBSCAN-MK clustering algorithm to screen groups with similar operating conditions, it effectively suppresses the "barrel effect" and significantly extends the cycle life of the battery pack.

[0101] The dual-channel voltage acquisition system is combined with Kalman filtering to eliminate noise, and the steady-state accuracy of the voltage mean sequence reaches ±0.05V. Abnormal evaluation index u=u 1 -u 2 By using the Pearson correlation coefficient (PPMCC) and the hyperbolic tangent function (tanh) weighted calculation, it is possible to distinguish between individual battery degradation and group fluctuations, improving detection sensitivity and thus enhancing latent fault warnings. The dynamic threshold grading response trigger time is shortened, significantly improving compared to traditional PI control.

[0102] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary counting personnel in this technical field, several improvements and substitutions can be made without departing from the counting principle of the present invention. These improvements and substitutions should also be regarded as the scope of protection of the present invention.

Claims

1. A method for controlling the charge and discharge of a solar street light, characterized in that: The method includes the following steps: establishing a temperature sequence matching model for street lamps in the same batch and screening battery packs with similar working conditions; constructing a dual-mean voltage sequence comparison mechanism and calculating anomaly evaluation index; and implementing a dynamic threshold graded response and municipal power supply switching strategy.

2. The charge and discharge control method of a solar street lamp according to claim 1, characterized in that: The method of establishing a temperature sequence matching model for street lamps of the same batch and screening battery packs with similar working conditions includes the following steps: constructing a distributed temperature field monitoring network; dynamically constructing a temperature difference sequence based on the collected temperature sequence and calculating the temperature difference dispersion; and setting the time window length; Based on the temperature difference dispersion, the working condition matching coefficient model is established; the working condition matching coefficient threshold is set, and dynamic clustering of the same working condition groups is performed based on the working condition matching coefficient.

3. The charge and discharge control method of a solar street lamp according to claim 2, characterized in that: The working condition matching coefficient expression is: Where r (a,b) Indicates the working condition matching coefficient of the two solar cells; Indicates the ratio of the span of time periods s1 and s2 to days; It is expressed as the average dispersion of the temperature difference between the a-th and b-th solar cells around the mean.

4. The charge and discharge control method of a solar street lamp according to claim 3, characterized in that: The working condition matching coefficient expression also includes 1+0.05ΔT amb Factor, where ΔT amb Indicates the change in ambient temperature; 0.05 is the temperature sensitivity coefficient; 1 is the baseline correction amount.

5. The charge and discharge control method of a solar street lamp according to claim 4, characterized in that: The method of constructing a dual-mean voltage sequence comparison mechanism and calculating anomaly evaluation index comprises the steps of: establishing a dual-channel voltage acquisition system; constructing a voltage mean sequence; and calculating anomaly evaluation index based on the voltage mean sequence comparison.

6. The charge and discharge control method of a solar street lamp according to claim 5, characterized in that: The expression of abnormal evaluation index is: at 1 =PPMCC{{V a },{V 1 }}×tanh(∑|V a -V 1 |) at 2 =PPMCC{{V a },{V 2 }}×tanh(∑|V a -V 2 |) in=in 1 -in 2 Where, u is the abnormal evaluation index of the a-th solar cell, u 1 is the first mean value sequence and the first state abnormality degree of the a-th solar cell, u 2 is the second mean value sequence and the second state abnormality degree of the a-th solar cell, V a is the voltage sequence of the ath solar cell from time s2 to time s1, V 1 is the first mean sequence, V 2 is the second mean sequence.

7. The charge and discharge control method of a solar street lamp according to claim 6, characterized in that: Working condition matching coefficient threshold θ r =0.15, if r (a,b) >θ r , it is determined that a and b belong to different working condition groups; otherwise they are classified into the same group.

8. The charge and discharge control method of a solar street lamp according to claim 7, characterized in that: The clustering algorithm adopts the DBSCAN clustering algorithm, and the input features include: daily average temperature fluctuation amplitude, nighttime cooling rate, temperature gradient directionality index and irradiance-temperature coupling coefficient.

9. The charge and discharge control method of a solar street lamp according to claim 3, characterized in that: The span of the time periods s1 and s2 is converted into a ratio of days; a dynamic time window is used, i.e. The maximum matching period is limited to 48 hours.

10. The intelligent control system for charging and discharging of solar street lights is characterized by: The charging and discharging control method of the solar street lamp according to any one of claims 1 to 9 comprises a data acquisition module, a power management module, a core control module, an actuator module, a communication and monitoring module, and a fault processing module; the data acquisition module is used to collect the temperature and charging voltage of the solar cell in real time, and transmit the data to the core control module through the communication and monitoring module; the core control module establishes a temperature sequence matching model for the same batch of street lamps, screens battery packs with similar working conditions, constructs a dual-mean voltage sequence comparison mechanism, and calculates an abnormality evaluation index; the power management module provides basic power supply requirements for the solar street lamp; the actuator module and the fault processing module both communicate with the core control module through the communication and monitoring module; the actuator module is used to realize the normal lighting function of the solar street lamp and perform municipal power supply switching; The fault processing module is used to determine abnormalities and perform three-level response protection.

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