Control power distribution system of urban road solar street lamp

Abstract

The invention relates to the technical field of solar street lamp control power distribution, in particular to an urban road solar street lamp control power distribution system which comprises a global power distribution strategy unit, an independent power distribution strategy unit and a storage battery protection unit. When the global power distribution strategy unit is in the first illumination demand mode, coordinating the electrical load in the area, then collecting environmental data, predicting future illumination change in combination with historical environmental data and brightness records, calculating a dimming demand and generating a global power distribution strategy; when the independent power distribution strategy unit is in the second lighting demand mode, the independent power distribution strategy is generated by detecting the speed and direction of the vehicle and the pedestrian and combining the dimming safety step length to determine the position of the pre-adjusted street lamp; the storage battery protection unit generates a health prediction curve by diagnosing the health state of the battery, and formulates a battery pack load dispersion strategy in combination with the current load demand. By optimizing a power distribution strategy and battery management, power consumption is reduced, and the service life of the solar street lamp system is prolonged.

Description

Technical Field

[0001] The invention relates to the technical field of solar street lamp control and power distribution, in particular to a control and power distribution system for solar street lamps on urban roads. Background Art

[0002] As an environmentally friendly and efficient lighting solution, solar street lights use the photovoltaic effect to convert sunlight into electrical energy through solar panels. The electrical energy is managed by a controller to store and release it, and ultimately drives LED lamps for lighting. In order to ensure lighting stability and safety, urban road solar street lights currently mainly adopt a mains complementary design, and distribute power to street lights through timing control and photosensitive control.

[0003] Existing technologies optimize the power distribution management of solar street lights from three perspectives, including power control, dynamic adjustment and data monitoring. For example, the power distribution method that combines solar photovoltaic components with external smart microgrids improves the utilization rate of solar energy and reduces dependence on municipal electricity; for example, through image acquisition and data analysis, the number and speed of people and vehicles on the road are monitored in real time, the brightness of street lights is adjusted dynamically, and unnecessary energy consumption is reduced; for example, by collecting and processing data such as voltage and current, the switch of street lights is controlled according to real-time data, and fault detection is combined to ensure power supply safety and intelligent management.

[0004] Existing technologies have improved the efficiency and stability of solar street light distribution management to a certain extent, but there are still some shortcomings. The existing solar street light system's power distribution coordination ignores the problem of imbalance between power production and utilization in high- and low-load areas, and the utilization rate of solar energy resources needs to be improved; at the same time, the frequent switching and instantaneous changes in the brightness of lamps will also aggravate the wear and aging of power storage equipment and hardware equipment. How to reduce power consumption while extending the service life of the solar street light system is a problem that needs to be solved urgently.

[0005] In order to improve the energy utilization efficiency of the solar street light system on urban roads and enhance the long-term operation stability of the system, the present invention proposes a control and power distribution system for the solar street light on urban roads. Summary of the invention

[0006] The purpose of the present invention is to provide a control and distribution system for solar street lights on urban roads, which reduces power consumption and prolongs the service life of the solar street light system by optimizing the power distribution strategy and battery management. In the first lighting demand mode, the global power distribution strategy unit collects environmental data to calculate the dimming demand, combines historical environmental data and brightness records to predict future lighting changes, and coordinates the power load in the area to generate a global power distribution strategy; in the second lighting demand mode, the independent power distribution strategy unit generates an independent power distribution strategy by detecting the speed and direction of vehicles and pedestrians and determining the pre-adjustment of the street light position in combination with the dimming safety step; the battery protection unit generates a health prediction curve by diagnosing the battery health status, and formulates a battery group load distribution strategy in combination with the current load demand.

[0007] To achieve the above object, the present invention provides a control and distribution system for solar street lamps on urban roads, comprising:

[0008] Global power distribution strategy unit: In the first lighting demand mode, coordinate the power load demand of solar street lights in multiple regions and calculate the first dimming demand; collect environmental data to calculate the second dimming demand; predict future lighting changes based on historical environmental data and brightness adjustment records, and calculate the third dimming demand; comprehensively generate a global power distribution strategy based on the first dimming demand, the second dimming demand and the third dimming demand;

[0009] Independent power distribution strategy unit: In the second lighting demand mode, the speed and direction of vehicles and pedestrians passing by are detected, and the position of the street lights that need to be pre-adjusted is calculated based on the safe step length of the solar street light dimming, and an independent power distribution strategy is generated according to the street light position.

[0010] Battery protection unit: obtains the battery pack parameter data of the solar street light battery and diagnoses the current health status of the battery pack; obtains the predicted health status curve of the battery pack based on the historical battery pack parameter data; obtains the current load demand of the solar street light and generates a battery pack load distribution strategy based on the current health status of the battery pack and the predicted health status within a certain time step.

[0011] Furthermore, the process of acquiring the first dimming requirement includes:

[0012] The solar street light system of the urban road is divided into multiple sub-areas according to the regional use; for each sub-area, the density of human and vehicle activities is calculated by the number of people and vehicles in the detection area and the detection time;

[0013] Calculate the brightness requirement of the sub-area according to the density of human and vehicle activities, and calculate the power load of each sub-area in combination with the number of solar street lamps in the sub-area;

[0014] For each of the sub-areas, calculating the difference between the average power load of the sub-area and that of all sub-areas;

[0015] The first dimming requirement is calculated according to the preset regional dimming priority of each sub-area and the difference in the corresponding electrical loads.

[0016] Furthermore, the process of acquiring the second dimming requirement includes:

[0017] For each sub-area, collect the environmental data, including the light intensity L env and air quality A env According to the environmental data, the formula L is used req-2 =f(L env ,A env ) calculates the second dimming requirement; wherein f(·) is a dimming requirement calculation function.

[0018] Furthermore, the process of obtaining the third dimming requirement includes:

[0019] Collect historical environmental data and street light brightness adjustment records, and input them into the GRU network to obtain the environmental dimming relationship;

[0020] Acquire future weather forecast data, and obtain the third dimming requirement through the ambient dimming relationship.

[0021] Furthermore, the process of acquiring the global power distribution strategy includes:

[0022] The first dimming requirement L of each sub-area is considered req-1 , the second dimming requirement L req-2 And the third dimming requirement L req-3 , generate the sub-area comprehensive dimming demand S sub ;

[0023]

[0024] Wherein, ω1, ω2 and ω3 are the dynamic weights of the first dimming requirement, the second dimming requirement and the third dimming requirement, respectively. is the sth sudden influencing factor X s The weight of, S is the number of the sudden influencing factors;

[0025] The global power distribution strategy is generated according to the comprehensive dimming requirements of all sub-areas.

[0026] Furthermore, the process of acquiring the independent power distribution strategy includes:

[0027] Capture vehicles and pedestrians within the radiation range of the solar street lamps, and obtain the travel speed and direction of the vehicles and pedestrians;

[0028] Set the safe step length of street light brightness adjustment according to the maximum power of the street light and the tolerance of the lamp hardware, and calculate the time required for the solar street light to adjust from the current brightness to the target brightness; combine the dimming time and the speed of the vehicle or pedestrian to calculate the position of the street light that needs to be adjusted;

[0029] The solar street lamps that need to be pre-adjusted are determined according to the street lamp positions, and the independent power distribution strategy is generated.

[0030] Furthermore, the process of obtaining the current health state and predicted health state curve of the battery pack includes:

[0031] Obtaining battery pack parameter data of the solar street light battery, including voltage, current and temperature, analyzing the health status of the battery pack according to the voltage change rate, current fluctuation and temperature change, and generating a health score of the battery pack;

[0032] The historical parameter data, working data and historical health score of the battery pack in the past period of time are collected and input into the GRU network to obtain the battery pack health relationship function; according to the real-time battery pack parameter data, the predicted health status curve of the battery pack is obtained through the battery pack health relationship function.

[0033] Furthermore, the process of acquiring the battery pack load distribution strategy includes:

[0034] Acquire the current load demand of the solar street lamp; the load demand is generated by the global power distribution strategy and the independent power distribution strategy;

[0035] For the i-th battery in the battery pack, its current health score H battery The predicted health status curve H within a certain time step ΔT predict-i Perform weighted calculation to obtain the load rating L loadability-i ;

[0036]

[0037] Wherein, λ1 and λ2 are weights of the current health score and the predicted health status curve within the certain time step;

[0038] sorting the battery groups in descending order according to the loadability scores;

[0039] The load demand is distributed according to the ranking; the specific distribution calculation formula is:

[0040]

[0041] Among them, P load-i is the distributed load of the ith battery group, P load-totalis the load requirement of the solar street light, L loadability-j is the jth group of batteries, and m is the number of battery groups of the solar street light.

[0042] Furthermore, it also includes a solar street light power distribution priority mechanism;

[0043] Obtain lighting demand and demand urgency based on real-time traffic density and real-time environmental conditions, and calculate the lighting priority score; obtain power saving demand based on real-time load demand and power supply conditions, and calculate the power saving priority score; calculate the battery protection priority score based on the real-time battery health score and the real-time battery health status prediction curve;

[0044] A comprehensive priority score is obtained according to the lighting priority score, the power saving priority score and the battery protection priority score.

[0045] Furthermore, the process of obtaining the comprehensive priority score is as follows:

[0046] The lighting priority score P pri-light , power saving priority score P pri-energy and battery protection priority score P pri-battery , get the comprehensive priority score P pri-final ;

[0047] P pri-final =γ1×P pri-light +γ2×P pri-energy +γ3×P pri-battery ;

[0048] Wherein, γ1, γ2 and γ3 are dynamic weight coefficients of the lighting priority score, the power saving priority score and the battery protection priority score respectively;

[0049] According to the final comprehensive priority score P pri-final Develop real-time power distribution strategies.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. The present invention first coordinates the power load between regions according to the power demand of different regions to achieve overall reasonable distribution; then collects environmental data, including real-time light intensity and air quality, to calculate the current dimming demand; finally, based on historical environmental data and brightness adjustment records, predicts future light changes and optimizes street light brightness in advance; this is different from the prior art method that relies on timing and automatic adjustment of light intensity. The present invention combines dynamic data adjustment and regional load distribution to avoid excessive lighting of low-load areas in high-energy consumption mode and reduce power waste; at the same time, the way of optimizing power resource allocation improves the energy utilization efficiency of urban road solar street light systems and provides technical support for the long-term stable operation of urban road solar street light systems.

[0052] 2. The present invention captures vehicles and pedestrians within the radiation range of street lamps, detects their speed and direction, and calculates the position of street lamps that need to be pre-dimmed in combination with the pre-set solar street lamp brightness adjustment safety step length, ensuring that the street lamp brightness is adjusted before the vehicle or pedestrian arrives. This method gradually pre-dimmes the brightness of the street lamp so that it can smoothly transition to the target brightness before the vehicle or pedestrian arrives, thus solving the current shock and accelerated aging of the lamp that may be caused by the instantaneous dimming method in the prior art. This method not only reduces the hardware loss of the lamp and extends the service life of the solar street lamp system, but also realizes a virtuous cycle between energy saving and equipment durability in low energy consumption mode.

[0053] 3. The present invention first obtains the voltage, current and other data of the solar street light battery pack, analyzes the current health status of the battery pack and generates a health score; then, combined with historical parameter data, the GRU network is used to predict the future health status of the battery pack and generate a health prediction curve; based on the current health score and the health prediction curve within a certain time step, each group of batteries is scored for load capacity; load demand is allocated according to the score to avoid excessive use of batteries with poor health and prevent damage to the battery caused by deep discharge. The present invention not only improves the utilization efficiency of the battery by reasonably allocating load demand, but also extends the overall life of the battery pack, thereby improving the long-term stability of the urban road solar street light system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic diagram of a control and power distribution system for a solar street light on a city road provided by an embodiment of the present invention;

[0055] Figure 2 A schematic diagram of the structure of a control and power distribution system for a solar street light on a city road provided by an embodiment of the present invention;

[0056] Figure 3 A schematic diagram of a battery protection unit flow chart provided by an embodiment of the present invention;

[0057] Figure 4 A schematic diagram of the structure of a solar street light power distribution priority mechanism provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] The present invention provides a control and distribution system for solar street lamps on urban roads. The specific system flow and system structure refer to Figure 1 and Figure 2 The present invention will describe the specific implementation method through the control distribution strategy of the urban road solar energy system in the first lighting demand mode and the second lighting demand mode. In addition, the specific implementation method of the power distribution priority mechanism of the urban road solar street lamp will also be described.

[0060] Embodiment 1

[0061] As an implementation of the present invention, this embodiment describes a specific implementation of the control power distribution strategy of the urban road system under the first lighting demand mode. Under the first lighting demand mode, the density and flow of people and vehicles in the area are at a high level, so a global power distribution strategy is adopted, including:

[0062] Coordinate the power load demand of solar street lights in multiple regions and calculate the first dimming demand; collect environmental data to calculate the second dimming demand; predict future lighting changes based on historical environmental data and brightness adjustment records, and calculate the third dimming demand; combine the first dimming demand, the second dimming demand and the third dimming demand to generate a global power distribution strategy.

[0063] Furthermore, the process of acquiring the first dimming requirement includes:

[0064] The urban road solar street light system is divided into multiple sub-areas according to regional usage;

[0065] Sys light →{C1,C2,...C k ,...,C n};

[0066] Among them, C k Represents the kth sub-area, which contains several solar street lights.

[0067] For each sub-region C k ,pass Calculate the density of human and vehicle activities D traffic-k ; Among them, N people-k and N vehicles-k Sub-area C k In the detection area A area-k And the number of people and vehicles within the detection time t.

[0068] Calculate sub-area C based on the density of human and vehicle activities k Brightness requirement L demand-k ;

[0069] L demand-k =D traffic-k ×L base-k ;

[0070] Among them, L base-k This is the minimum brightness level set according to the area usage.

[0071] Using P req-k =β1×L demand-k +β2×N light-k Calculate sub-area C k The power load P req-k , where N light-k Sub-region C k β1 and β2 are weight factors, which respectively represent the impact of brightness demand and number of street lights on power load.

[0072] For each sub-region C k , through the formula Calculate the difference ΔP between the power load of this sub-area and the average power load of all K sub-areas req-k ;

[0073] According to the preset regional dimming priority P of each sub-area prior-k , combined with the phase applied load difference ΔP req-k , calculate the first dimming requirement:

[0074]

[0075] Among them, θ is the weight factor, which indicates the influence of load difference on dimming demand.

[0076] This embodiment divides the urban road solar street light system into multiple sub-areas according to their uses, calculates the brightness requirements and power load of each sub-area based on the density of human and vehicle activities, and dispatches power from low-load sub-areas to high-load sub-areas; this method reduces energy consumption while meeting the lighting needs of different areas, and optimizes the coordination and use of power resources.

[0077] Furthermore, the light intensity and air quality data of the area where the solar street lights are located are collected through sensors and detection instruments, and then the second dimming demand is calculated; among them, the air quality data mainly refers to the concentration of inhalable particulate matter, and the corresponding situation is haze weather.

[0078] According to the light intensity L env and air quality A env , through L req-2 =f(L env ,A env ) Calculate the first dimming requirement L req-2 ; Where f(·) is the dimming demand calculation function; specifically:

[0079]

[0080] Among them, ε and ξ are constants used to smooth the influence of different factors; β3 and β4 are the dynamic adjustment weights of light intensity and air quality, respectively, which are adjusted according to actual conditions.

[0081] This embodiment collects the light intensity and air quality of each sub-area in real time and calculates the second dimming demand, so that the brightness adjustment of the street lamps in the sub-areas is more targeted, ensuring that the street lamps provide sufficient lighting under various environmental conditions.

[0082] Furthermore, the process of obtaining the third dimming requirement includes:

[0083] Collect historical environmental data and street light brightness adjustment records, as shown below:

[0084]

[0085] This historical record can be described as: at timestamp t, the light intensity L env (lux) exceeds the threshold and the concentration of inhalable particles in the air reaches A env , trigger, the solar street light at time t adjust The original brightness is adjusted from L1 to L2.

[0086] The historical environmental data and street lamp brightness adjustment records are input into the GRU network to obtain the environmental dimming relationship; the process of obtaining the environmental dimming relationship includes:

[0087] Extracting feature vectors from the historical environmental data and the street lamp brightness adjustment records and the target output Y t = L2, constructed as a structured data set D = {(X t ,Y t )}, where each sample corresponds to a record. t ,Yt )} is input into GRU for calculation to obtain the ambient dimming relationship function.

[0088] Loss = Γ(Y,Y pred )ΛY pred =f gru (X)&W←W-η▽Loss;

[0089] Among them, Γ is the loss function, Y is the true value, and Y pred For through f gru (X) The calculated prediction value, η is the learning rate, and ΔLoss represents the gradient of the loss function with respect to the network weight W;

[0090] The ambient dimming relationship function g(·) is:

[0091]

[0092] in, δ1, δ2 and δ3 are parameters, which are fitted by the training data.

[0093] Get future weather forecast data through the weather APIX future , including light intensity and air quality index, the third dimming requirement L is obtained by using the ambient dimming relationship function g(·) req-3 .

[0094] L req-3 =g(X future );

[0095] This embodiment combines historical environmental data and future weather forecasts, and uses the GRU network to establish an environmental dimming relationship model, which can provide a more scientific dimming strategy for street lamps. This method can pre-dispatch power according to the predicted environmental conditions; ensure that the lighting intensity is adjusted in advance under low visibility conditions such as rain or haze, thereby increasing road safety.

[0096] The first dimming requirement L of each sub-area is considered req-1 , the second dimming requirement L req-2 And the third dimming requirement L req-3 , generate the sub-area comprehensive dimming demand S sub ;

[0097]

[0098] Wherein, ω1, ω2 and ω3 are the dynamic weights of the first dimming requirement, the second dimming requirement and the third dimming requirement, respectively. is the sth sudden influencing factor X sThe weight of S is the number of the sudden influencing factors. The sudden influencing factors refer to sudden events that require additional brightness lighting, including traffic accidents, sudden weather changes, emergencies, natural disasters, etc.

[0099] The global power distribution strategy is generated according to the comprehensive dimming requirements of all sub-areas; the specific process is to sort the comprehensive dimming requirements of all sub-areas in descending order, and dispatch the power resources of the regional solar street light system according to the sorting.

[0100] This embodiment generates a global power distribution strategy by integrating the first, second and third dimming requirements of each sub-area to ensure coordinated distribution of power. This comprehensive approach avoids the possibility of power overload or shortage in a certain power area, improves the stability of the urban lighting system, and reduces energy waste.

[0101] Further, refer to Figure 3 , Figure 3 A schematic diagram of a battery protection unit flow provided in this embodiment. In the first lighting demand mode, the lighting load demand of different sub-areas is obtained through the global power distribution strategy, and the lighting load demand is evenly distributed to the solar street lights in the sub-areas; in order to reasonably distribute the load demand carried by each solar street light, the health status of the solar street light battery group is first obtained, including:

[0102] Collect parameter data of the ith battery pack from time point t1 to t2, including current I, voltage V and temperature T, calculate its voltage change rate, current fluctuation and temperature change, analyze the health status of the battery pack, and generate the health score H of the ith battery pack battery-i ;

[0103]

[0104] Among them, ρ V , I and ρ T are the weights of voltage change rate, current fluctuation and temperature change rate, which are adjusted according to the specific system configuration; σ I and μ I are the standard deviation and mean of the current, respectively.

[0105] Furthermore, historical parameter data, working data and historical health scores of the battery pack in the past period of time are collected and input into the GRU network to obtain the battery pack health relationship function; according to the real-time battery pack parameter data, the predicted health state curve of the battery pack is obtained through the battery pack health relationship function. The specific process includes:

[0106] Collect historical parameter data, historical working data and historical health score H of the battery pack in the past period of time his(t), constructed as a battery pack historical health data set; historical parameter data includes voltage data V his (t), current data I his (t) and temperature data T his (t); The historical working data includes the charge and discharge conditions D charge and load data L his (t).

[0107] The battery pack historical health data set is input into the GRU network to learn the changing trend of the battery health status, and the battery pack health relationship function is output; the specific process is:

[0108] For the historical health data set D2 = {[V his (t),I his (t),T his (t),D charge ,L his (t]}, using a sliding window method, the time series data is divided into multiple samples; the samples are input into the GRU network for training to obtain the battery pack health relationship function;

[0109]

[0110] in, δ4 and δ5 are parameters, which are fitted by the training data; ε I is a constant to avoid zero values ​​in logarithmic calculations.

[0111] Continuously collect battery pack parameter data, obtain the health score of the battery pack in the future through the battery pack health relationship function, and combine them into a battery health prediction curve for the battery pack;

[0112] H predict ={H future (t1),H future (t2),...,H future (t q )};

[0113] This embodiment can timely diagnose the health status of the battery pack by monitoring and analyzing parameters such as voltage, current and temperature of the solar street light battery pack; the generated health score helps to identify potential problems in advance and perform pre-emptive maintenance; at the same time, the future health status of the battery pack is predicted with the help of historical health records, providing data support for the formulation of subsequent load distribution strategies.

[0114] Furthermore, the current load demand of the solar street lamp is obtained, and a battery pack load distribution strategy is generated according to the current health state of the battery pack and the predicted health state within a certain time step. The specific process includes:

[0115] For the i-th battery in the battery pack, its current health score H battery-i The predicted health status curve H within a certain time step ΔT predict-i Perform weighted calculation to obtain the load rating L loadability-i ;

[0116]

[0117] Wherein, λ1 and λ2 are weights of the current health score and the predicted health status curve within the certain time step;

[0118] The battery groups are sorted in descending order according to the loadable scores; the load requirements are sorted according to the loadable scores to distribute the load;

[0119]

[0120] Among them, P load-total is the load demand of the solar street light, P load-i is the distributed load of the ith battery group, L loadability-j is the jth group of batteries, and m is the number of battery groups of the solar street light.

[0121] It should be noted that, in the first lighting demand mode, when the power storage capacity of the solar street light system is insufficient to support the lighting demand, the power supply mode of the system is switched to mains power supply.

[0122] This embodiment first obtains the load demand of the solar street light, and then calculates the load score of each battery group according to the health status of the solar street light battery group and the predicted health status curve; the load demand is sorted according to the load score to distribute the load. This method can prevent overload or failure of certain battery groups with crossed health states, improve the working efficiency of the battery group, and achieve more efficient load distribution.

[0123] Embodiment 2

[0124] As an embodiment of the present invention, refer to Figure 4 This embodiment describes a specific implementation method of the control power distribution strategy of the urban road system under the second lighting demand mode. Under the second lighting demand mode, the density and flow of people and vehicles in the area are at a low level, so an independent power distribution strategy is adopted, including:

[0125] The speed and direction of vehicles and pedestrians are detected, and the position of street lights that need to be pre-adjusted is calculated based on the safe step length of solar street light dimming, and an independent power distribution strategy is generated based on the street light position.

[0126] Furthermore, by deploying image acquisition equipment and sensor equipment on solar street lights, vehicles or pedestrians passing on urban roads can be captured, and the moving direction and speed of the targets can be detected.

[0127] Set the safe step length ΔL of street light brightness adjustment according to the maximum power of the street light and the tolerance of the lamp hardware adj , and calculate the solar street light from the current brightness L current Adjust to target brightness L desired Time required adj ;

[0128]

[0129] Combined dimming time t adj and the speed v of the vehicle or pedestrian, estimate the street light position x that needs to be adjusted adj ;

[0130] x adj =x current +v·t adj ;

[0131] Among them, x current is the current position of the solar street light, that is, the position of the street light for vehicle and pedestrian detection.

[0132] According to the pre-adjusted street lamp position, the solar street lamp that needs to be pre-adjusted is determined, and an independent power distribution strategy is generated. Specifically, the street lamp position x adj The corresponding solar street light will adjust the street light brightness to the target value with a safe step length before the target is reached; when x adj The location detects that the vehicle or pedestrian has left x adj The area radiated by the position detection device is the street lamp position x adj The corresponding solar street light will be increased from the target brightness L in a safe step. desired Adjust to original brightness.

[0133] This embodiment first captures the dynamic information of vehicles and pedestrians within the radiation range of the street lamp, determines the position of the pre-adjusted street lamp in combination with the safe step length of the street lamp brightness adjustment, and increases the street lamp brightness in advance before pedestrians or vehicles arrive for safe lighting. While ensuring the lighting needs, this method avoids the accelerated loss of the lamp hardware caused by instantaneous dimming of solar street lamps and prolongs the service life of the equipment.

[0134] Further, in the second lighting demand mode, the load demand of the pre-regulated street lamp is calculated by the independent power distribution strategy;

[0135] P load (x adj )=P min +2·P step +PLdesired ;

[0136] Among them, P load (x adj ) is the load demand of pre-adjusted street lamps, P min is the load requirement corresponding to the minimum brightness level; P step is the load demand during brightness adjustment; P Ldesired The load requirement for continuous target brightness.

[0137] Get street light position x adj The battery pack parameter data corresponding to the solar street light battery includes voltage, current and temperature. The health status of the battery pack is analyzed according to the voltage change rate, current fluctuation and temperature change to generate a health score of the battery pack; the predicted health status curve of the battery pack is obtained through the battery pack health prediction model.

[0138] Calculating a loadability score of a battery pack according to the load demand of the pre-regulated street lamp generated by the independent power distribution strategy;

[0139] The load demands are sorted according to the loadable scores for load distribution.

[0140] In the second lighting demand mode, when the power storage of the pre-adjusted solar street lamp is insufficient to support the lighting demand, the power supply mode of the solar street lamp is switched to mains power supply.

[0141] Embodiment 3

[0142] As an implementation mode of the present invention, this embodiment describes a specific implementation mode of the power distribution priority mechanism of solar street lamps on urban roads. Figure 4 , Figure 4 A schematic diagram of the solar street light power distribution priority mechanism structure provided by an embodiment of the present invention. According to different actual application scenarios, the priorities of lighting demand, power saving demand and battery protection are often in dynamic change. Therefore, in order to meet the specific needs in the actual application scenario, it is necessary to flexibly judge the importance of the three to formulate a priority strategy, and execute the corresponding control power distribution strategy according to the comprehensive priority.

[0143] Furthermore, the power distribution priority mechanism for urban road solar street lights includes:

[0144] Develop lighting priority strategies for solar street lights on urban roads;

[0145] Depending on traffic density and environmental conditions, L rt =α1×D traffic-rt +α2×L env-rt Get the current lighting demand L rt ; Among them, D traffic-rtis the current traffic density, L env-rt is the current environmental condition; α1 and α2 reflect the impact of traffic density and environmental conditions on lighting demand; then, calculate the real-time demand urgency U urgency ;

[0146]

[0147] Among them, L history is the lighting requirement under the same mode in the historical records; D traffic-rt is the current traffic density, D history-traffic is the traffic density under the same mode in the historical records; φ1 and φ2 are used to balance the impact of lighting demand and traffic density on the urgency of real-time demand; the historical records include historical experience and historical record data.

[0148] pass Get the current lighting priority score; where and Used to balance lighting needs with the impact of urgency;

[0149] According to the current load demand and power supply situation, through E demand =φ3×P load +φ4×C energy Calculate power saving requirements; where E demand To save power, P load is the load demand, C energy is the power supply situation, φ1 and φ2 reflect the impact of load demand and power supply situation on the degree of power saving demand;

[0150] pass Get the current power saving priority score; and Used to balance the impact of power saving needs and load demand;

[0151] Calculate the battery protection priority score P based on the current battery health score and health prediction curve pri-battery ;

[0152]

[0153] in, and Used to balance the impact of battery health score and health prediction curve.

[0154] This embodiment calculates the priority scores of lighting, power saving and battery protection based on the specific conditions of the actual application scenario, taking into account the real-time environmental conditions, traffic density, load demand, power supply and battery health, and provides a data basis for the subsequent implementation of the power distribution strategy.

[0155] The lighting priority score P pri-light , power saving priority score P pri-energy and battery protection priority score P pri-battery , get the comprehensive priority score P pri-final ;

[0156] P pri-final =γ1×P pri-light +γ2×P pri-energy +γ3×P pri-battery ;

[0157] Among them, γ1, γ2 and γ3 are dynamic weight coefficients of the lighting priority score, the power saving priority score and the battery protection priority score respectively.

[0158] According to the final comprehensive priority score P pri-final Develop real-time power distribution strategies.

[0159] This embodiment can control power distribution according to the specific conditions of the actual scene by formulating a lighting priority mechanism for solar street lights; this method improves the flexibility of the control and distribution system for urban road solar street lights proposed by the present invention, ensures the efficient operation of the system in a changing environment, and enhances the emergency response capability of the system.

[0160] The control and power distribution system of a solar street light on an urban road provided by the embodiment of the present invention performs intelligent regulation on the brightness and power load of street lights under different lighting demand modes, thereby improving the energy utilization rate of solar street lights and extending the service life of the solar street light system. In the first lighting demand mode, by analyzing environmental data and the flow of people and vehicles, the dimming demand is accurately generated to optimize the utilization efficiency of electric energy. In the second lighting demand mode, by analyzing the speed and direction of people and vehicles, the position of the street lights is pre-adjusted and dimming is performed in advance, thereby avoiding the loss of batteries and lamps caused by frequent switching and instantaneous dimming in the prior art; finally, the health status of the battery is monitored in real time, and the future health strategy of the battery is predicted based on historical data to reasonably disperse the load demand generated in the first lighting demand mode and the second lighting demand mode. The present invention not only alleviates the problem of excessive battery loss caused by frequent dimming in the prior art, but also improves the power saving effect and equipment life of solar street lights, and realizes more environmentally friendly and efficient urban lighting management.

[0161] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A control and distribution system for solar street lights on urban roads, characterized in that: include: Global power distribution strategy unit: In the first lighting demand mode, coordinate the power load demand of solar street lights in multiple regions and calculate the first dimming demand; Collect environmental data to calculate the second dimming demand; predict future lighting changes based on historical environmental data and brightness adjustment records, and calculate the third dimming demand; combine the first dimming demand, the second dimming demand, and the third dimming demand to generate a global power distribution strategy; Independent power distribution strategy unit: In the second lighting demand mode, the speed and direction of vehicles and pedestrians passing by are detected, and the position of the street lights that need to be pre-adjusted is calculated based on the street light dimming safety step length, and an independent power distribution strategy is generated according to the street light position; Battery protection unit: obtain parameter data of solar street light battery pack, diagnose the current health status of the battery pack; obtain the predicted health status curve of the battery pack based on historical battery pack data; obtain the current load demand of the street light, and generate a battery pack load distribution strategy based on the current health status and the predicted health status curve within a certain time step; The load distribution strategy includes: for each battery group in the battery pack, performing weighted calculation on its current health score and the predicted health status curve within a certain time step to obtain the load score of each battery group; The current load demands are sorted in descending order of the loadable scores to perform load distribution.

2. A control and distribution system for solar street lights on urban roads according to claim 1, characterized in that: The process of obtaining the first dimming requirement includes: The solar street light system of the urban road is divided into multiple sub-areas according to the regional use; for the sub-areas, the density of human and vehicle activities is calculated by the number of people and vehicles in the detection area and the detection time; Calculate the brightness requirement of the sub-area according to the density of human and vehicle activities, and calculate the power load of the sub-area in combination with the number of solar street lamps in the sub-area; For each of the sub-areas, the difference between the power load of the sub-area and the average power load of all sub-areas is calculated; and according to the preset regional dimming priority of each of the sub-areas and the corresponding power load difference, the first dimming demand is calculated.

3. A control and distribution system for solar street lights on urban roads according to claim 1, characterized in that: The process of obtaining the second dimming requirement includes: For each sub-area, collect the environmental data, including the light intensity L env and air quality A env According to the environmental data, the formula L is used req-2 =f(L env ,A env ) calculates the second dimming requirement; wherein f(·) is a dimming requirement calculation function.

4. A control and distribution system for solar street lights on urban roads according to claim 1, characterized in that: The process of obtaining the third dimming requirement includes: Collect historical environmental data and street light brightness adjustment records, and input them into the GRU network to obtain the environmental dimming relationship; Acquire future weather forecast data, and obtain the third dimming requirement through the ambient dimming relationship.

5. The control and distribution system for solar street lights on urban roads according to claim 1 is characterized in that: The process of obtaining the global power distribution strategy includes: The first dimming requirement L of all sub-areas is integrated req-1 , the second dimming requirement L req-2 And the third dimming requirement L req-3 , generate the sub-area comprehensive dimming demand S sub ; Wherein, ω1, ω2 and ω3 are the dynamic weights of the first dimming requirement, the second dimming requirement and the third dimming requirement, respectively. is the sth sudden influencing factor X s The weight of, S is the number of the sudden influencing factors; The global power distribution strategy is generated according to the comprehensive dimming requirements of all sub-areas.

6. A control and power distribution system for solar street lights on urban roads according to claim 1, characterized in that: The process of obtaining the independent power distribution strategy includes: Capture vehicles and pedestrians within the radiation range of the solar street lamps, and obtain the travel speed and direction of the vehicles and pedestrians; The safe step length of the street light brightness adjustment is set according to the maximum power of the street light and the tolerance of the lamp hardware, and the time required for the solar street light to be adjusted from the current brightness to the target brightness is calculated; the position of the street light that needs to be adjusted is calculated and obtained by combining the required time and the travel speed; The solar street lamps that need to be pre-adjusted are determined according to the street lamp positions, and the independent power distribution strategy is generated.

7. A control and distribution system for solar street lights on urban roads according to claim 1, characterized in that: The process of acquiring the current health state of the battery pack and the predicted health state curve includes: Obtaining battery pack parameter data of the solar street light battery, including voltage, current and temperature, analyzing the health status of the battery pack according to the voltage change rate, current fluctuation and temperature change, and generating a health score of the battery pack; Collect historical parameter data, working data and historical health scores of the battery pack over a period of time, and input them into the GRU network to obtain the battery pack health relationship function; according to the real-time battery pack parameter data, obtain the predicted health status curve of the battery pack through the battery pack health relationship function.

8. The control and distribution system for solar street lights on urban roads according to claim 1 is characterized in that: The process of obtaining the battery pack load distribution strategy includes: Acquire the current load demand of the solar street lamp; the load demand is generated by the global power distribution strategy and the independent power distribution strategy; For the i-th battery in the battery pack, its current health score H battery-i The predicted health status curve H within a certain time step ΔT predict-i Perform weighted calculation to obtain the load rating L loadability-i ; Wherein, λ1 and λ2 are weights of the current health score and the predicted health status curve within the certain time step; sorting the battery groups in descending order according to the loadability scores; The load demand is distributed according to the ranking; the specific distribution calculation formula is: Among them, P load-i is the distributed load of the ith battery group, P load-total is the load requirement of the solar street light, L loadability-j is the jth group of batteries, and m is the number of battery groups of the solar street light.

9. A control and power distribution system for solar street lights on urban roads according to claim 1, characterized in that: It also includes a priority mechanism for power distribution to solar street lights; Obtain lighting demand and demand urgency based on real-time traffic density and real-time environmental conditions, and calculate lighting priority scores; obtain power saving demand based on real-time load demand and power supply conditions, and calculate power saving priority scores; Calculate the battery protection priority score based on the real-time battery health score and the real-time battery health status prediction curve; A comprehensive priority score is obtained according to the lighting priority score, the power saving priority score and the battery protection priority score.

10. A control and power distribution system for solar street lights on urban roads according to claim 9, characterized in that: The calculation process of the comprehensive priority score is: The lighting priority score P pri-light , the power saving priority score P pri-energy and the battery protection priority score P pri-battery , calculate the comprehensive priority score P pri-final ; P pri-final =γ1×P pri-light +γ2×P pri-energy +γ3×P pri-battery ; Wherein, γ1, γ2 and γ3 are dynamic weight coefficients of the lighting priority score, the power saving priority score and the battery protection priority score respectively; According to the comprehensive priority score P pri-final Develop real-time power distribution strategies.

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