Static identification energy storage system and intelligent static identification

Abstract

The invention belongs to the technical field of energy storage management, and particularly discloses and provides a static identification energy storage system and an intelligent static identifier, and the system comprises an identification energy storage evaluation module, an identification energy storage evaluation module, an energy supply balance judgment module and an energy supply adjustment feedback control terminal. According to the method, the light efficiency conversion fluctuation impact is incorporated into an energy storage attenuation evaluation system, and the effective residual electric quantity considering the actual attenuation characteristic of the equipment is accurately estimated by combining the attenuation correction factor with the real-time illumination intensity and the dynamic charging and discharging data, so that a data footstone closer to the real operation state is provided for subsequent energy consumption analysis; and secondly, based on the functional difference between the main identification area and the auxiliary identification area, in combination with the residual duration of the current time period and the preset identification time period, the unit time power consumption of different areas is finely calculated, a hierarchical energy consumption data model is formed, a quantitative basis is provided for a differential adjustment strategy, and the effectiveness and rationality of brightness adjustment are ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage management, and specifically, relates to a static identification energy storage system and an intelligent static identification. Background Art

[0002] With the development of new energy technologies and the Internet of Things, outdoor static identification energy storage systems have realized independent power supply and intelligent upgrade of identification by integrating energy storage devices and renewable energy. In order to maintain the indication effect and energy-saving balance of the identification, it is necessary to adjust the energy consumption of the identification.

[0003] Currently, the focus of attention between static identification and energy storage usually focuses on improving the energy conversion efficiency to ensure the stability of identification power supply. However, when the external energy state is poor and the energy storage cannot meet the power supply requirements, the overall energy consumption is mainly adjusted to achieve the power supply target, and there are still the following deficiencies: 1. The analysis of the effective electric energy of the energy storage system is insufficient. Currently, the remaining energy of the energy storage system is directly used for analysis, and the energy storage system has attenuation. Even if partial attenuation is considered, it is only combined with the usage time and charge-discharge conditions, without considering the impact of fluctuations in light efficiency conversion, resulting in certain errors in the analysis results.

[0004] 2. Currently, it is a holistic adjustment. Static identification often comes with a main identification area and an auxiliary identification area, and the power consumption and brightness requirement priorities of the two are also different. Currently, targeted adjustment is carried out in combination with the actual energy storage situation, so there is still a certain deviation in the pertinence of the adjustment, and it is impossible to ensure the rationality of display while ensuring energy conservation. Summary of the Invention

[0005] In view of this, in order to solve the problems raised in the above background art, a static identification energy storage system and an intelligent static identification are now proposed.

[0006] The object of the present invention can be achieved by the following technical solutions: In the first aspect of the present invention, a static identification energy storage system is provided. The system includes: an identification energy storage evaluation module that sets an attenuation correction factor based on the charge-discharge data and light energy conversion data of the energy storage terminal carried by the identification component within the current set period, and estimates the effective remaining power based on the current remaining energy storage power, the real-time light intensity, and the attenuation correction factor.

[0007] An identification energy consumption determination module that calculates the estimated total power consumption of the identification based on the remaining time of the current time point and the preset identification period and in combination with the power consumption per unit time of each area of the identification component, and the areas are composed of a main identification area and an auxiliary identification area.

[0008] The energy supply balance judgment module compares the effective remaining power with the estimated total power consumption. If the comparison result is greater than or equal to, it controls the identification component to supply power at the preset brightness. Otherwise, it determines the adjusted identification area and the adjusted brightness of the corresponding adjusted identification area based on the brightness priority of the main identification area and the auxiliary identification area and the brightness - energy consumption mapping relationship, and generates a brightness adjustment strategy accordingly.

[0009] The energy supply adjustment feedback control terminal executes the brightness adjustment strategy and sends an updated power supply instruction to the identification component.

[0010] The second aspect of the present invention provides an intelligent static identifier, which is equipped with an ambient light sensor, an energy storage terminal, a communication component and a photovoltaic component. Among them, the intelligent static identifier is divided into a main identification area and an auxiliary identification area.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) By setting an attenuation correction factor and combining the remaining power and the real - time light intensity to estimate the effective remaining power, the present invention fully considers the impact of light efficiency conversion fluctuations, avoids the analysis error of energy storage attenuation caused by only relying on the usage time and charge - discharge conditions and ignoring the light efficiency fluctuations, and ensures the authenticity and effectiveness of the estimation.

[0012] (2) By dividing into the main identification area and the auxiliary identification area and calculating the power consumption per unit time of each area by combining the remaining time of the current period and the preset identification period, the present invention breaks the traditional extensive mode of overall adjustment, distinguishes the power consumption and brightness requirement priorities of different areas, and forms refined regional energy consumption data.

[0013] (3) By comparing the effective remaining power with the estimated total power consumption and based on the regional brightness priority and the brightness - energy consumption mapping relationship, the present invention dynamically determines the area and brightness to be adjusted, can give priority to ensuring the display requirements of the main identification area, reasonably allocate the energy consumption of the auxiliary area, ensure the rationality of the display of core information under the premise of energy conservation, and achieve the dynamic balance of energy consumption and function. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0015] Figure 1 It is a schematic diagram of the system structure connection of the present invention.

[0016] Figure 2 It is a schematic diagram of the overall implementation step flow of the present invention.

[0017] Figure 3 Schematic diagram of the static identifier components of the present invention Detailed implementation manners

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention

[0019] Please refer to Figure 1 and Figure 2 As shown, the present invention provides a static identifier energy storage system, which includes: an identifier energy storage evaluation module, an identifier energy storage evaluation module, an energy supply balance judgment module, and an energy supply adjustment feedback control terminal

[0020] Among the above, the identifier energy storage evaluation module is respectively connected to the identifier energy storage evaluation module and the energy supply balance judgment module, and the energy supply balance judgment module is connected to the energy supply adjustment feedback control terminal

[0021] The identifier energy storage evaluation module sets an attenuation correction factor based on the charge and discharge data and light energy conversion data of the energy storage terminal carried by the identifier component within the current set period, and estimates the effective remaining power by combining the current remaining energy storage power, the real-time light intensity, and the attenuation correction factor

[0022] Specifically, the charge and discharge data includes, but is not limited to, the depth and rate of each charge and discharge, and the light energy conversion data includes the actual light energy conversion rate of each operating day, etc

[0023] Specifically, the attenuation correction factor includes: A1, the energy storage terminal cycle attenuation factor, which is obtained by normalizing and weighted summing based on the deep discharge ratio, charge and discharge regularity, historical cumulative charge and discharge times, and average charge and discharge rate

[0024] It should be added that the importance of each parameter to the energy storage system can be the deep discharge ratio, charge and discharge regularity, historical cumulative charge and discharge times, and average charge and discharge rate. For example, if the depth has the greatest impact on the battery life, a higher weight can be given. For example, the weight of the deep discharge ratio is 0.4, the weight of the charge and discharge regularity is 0.3, the weight of the average charge and discharge rate is 0.2, and the weight of the historical cumulative charge and discharge times is 0.1

[0025] A2, the photovoltaic module efficiency attenuation factor, which is obtained by weighting the light energy conversion efficiency deviation degree and the MPPT accuracy deviation degree and then inputting them into the Sigmoid function

[0026] Further, the deep discharge ratio is obtained by statistically calculating the ratio of the number of times the discharge depth exceeds the preset warning value to the total number of discharges within a set period. The specific value of the preset warning value for the discharge depth can be 80% DoD.

[0027] Further, the specific statistical process of the charge-discharge regularity is as follows: A11. Calculate the standard deviation and mean of the depth of each charge-discharge respectively to obtain the standard deviation of the charge-discharge depth and the average charge-discharge depth, and record the ratio of the two as the coefficient of variation of the charge-discharge depth.

[0028] A12. Similarly process the rate of each charge-discharge according to the processing method of the charge-discharge depth to obtain the coefficient of variation of the charge-discharge rate.

[0029] A13. Sort the depth of each charge-discharge in chronological order, count the continuous number of times the charge-discharge depth exceeds the preset threshold, and calculate the ratio to the total number of charge-discharges to obtain the charge-discharge depth deviation compensation coefficient. Set the charge-discharge rate deviation compensation coefficient according to the setting method of the charge-discharge depth deviation compensation coefficient.

[0030] A14. Compensate the corresponding coefficient of variation based on the deviation compensation coefficient to obtain the corrected coefficient of variation.

[0031] A15. Based on the preset weight and comprehensively considering the charge-discharge frequency, the corrected coefficient of variation of the charge-discharge depth, and the coefficient of variation of the charge-discharge rate, obtain the charge-discharge regularity through weighted summation.

[0032] It can be understood that the standard deviation reflects the degree of data dispersion, and the mean reflects the central tendency of the data. The ratio of the two, that is, the coefficient of variation, can measure the relative fluctuation degree of the depth and rate. A low coefficient of variation means that the charge-discharge depth and rate are stable and highly regular, while a high coefficient of variation means poor regularity. The stability of these two key parameters in the charge-discharge process can be quantified through the coefficient of variation.

[0033] It should be added that the charge-discharge frequency, depth, and rate are all key factors affecting the regularity of the charge-discharge process. The frequency reflects the frequency of charge-discharge operations, and the depth and rate reflect the intensity and stability of each operation. Therefore, these three parameter indicators are selected for the analysis of the charge-discharge regularity to ensure the representativeness of the analysis results.

[0034] It should also be added that the deviation compensation coefficient obtained by statistically calculating the continuous number of times exceeding the preset threshold and calculating the ratio to the total number can directly reflect the frequency of abnormal working conditions. Abnormal working conditions will accelerate the aging of energy storage equipment and affect its lifespan. Therefore, using this coefficient to correct the coefficient of variation can highlight the impact of abnormal situations on regularity. If the deviation compensation coefficient is high, it means that abnormal working conditions are frequent. Even if the coefficient of variation is low, the actual poor regularity can be reflected after compensation, ensuring the effectiveness and reference value of the subsequent regularity analysis results.

[0035] In a specific embodiment, the minimum and maximum values of the charge-discharge frequency, depth, and rate coefficient of variation can be determined by statistically analyzing historical data or industry standards, and then a reference value range for the charge-discharge frequency, depth, and rate coefficient of variation can be obtained.

[0036] Furthermore, the MPPT accuracy refers to the ability of a photovoltaic system to track and maintain the maximum power output point MPP of a photovoltaic module, which is usually measured by the tracking error, that is, the percentage deviation between the actual power and the theoretical maximum power. The higher the accuracy, the smaller the energy loss, and vice versa.

[0037] Furthermore, the calculation methods of the light energy conversion efficiency deviation degree and the MPPT accuracy deviation degree are the same. Taking the light energy conversion efficiency deviation degree as an example, its specific calculation process includes: A21. Statistically analyze the reference light energy conversion efficiency based on the preset reference light energy conversion efficiency, the cumulative operation duration of the energy storage terminal connected to the photovoltaic module, and the historical average light energy conversion efficiency.

[0038] A22. Calculate the light energy conversion rate change rate and the average actual light energy conversion rate based on the actual light energy conversion rates of each operating day within the current set period.

[0039] Among them, the light energy conversion rate change rate is the ratio of the difference between the actual light energy conversion rates corresponding to the first operating day and the last operating day to the total number of operating days within the set period.

[0040] A23. Statistically analyze the ratio of the number of operating days with the actual light energy conversion efficiency lower than the reference light energy conversion efficiency to the total number of operating days, which is recorded as the proportion of low-efficiency days.

[0041] A23. If any of the following conditions is triggered, use the average of the minimum actual light energy conversion rate and the average actual light energy conversion rate as the analyzed light energy conversion efficiency, otherwise use the average actual light energy conversion rate as the analyzed light energy conversion efficiency. The triggering conditions are:

[0042] 1) The light energy conversion rate change rate is negative and less than the set reference change rate threshold.

[0043] 2) The proportion of low-efficiency days is greater than the preset deviation proportion threshold.

[0044] A24. Calculate the relative deviation value between the deviation efficiency conversion ratio and the reference light energy conversion efficiency to obtain the light energy conversion efficiency deviation degree.

[0045] Understandably, the efficiency of light energy conversion is affected by equipment aging, environmental factors and historical operating conditions, and a single benchmark cannot fully reflect the true performance. The preset benchmark provides an ideal reference, the accumulated operating time is used to correct the natural efficiency decay caused by aging, and the historical average efficiency reflects the actual performance of similar equipment under similar conditions. By combining the three to form a dynamic benchmark, normal decay is avoided as an abnormal deviation, ensuring the accuracy of the reference setting.

[0046] It should be added that the rate of change of the light energy conversion rate can identify whether the efficiency fluctuation trend is rising or falling. When it is negative and the absolute value is large, it indicates that the efficiency is declining rapidly. Based on this, accidental fluctuations and systematic deviations can be distinguished. For example, if the average efficiency meets the standard but the rate of change continues to be negative, it indicates potential performance decline. At the same time, the light energy conversion efficiency is affected by random factors such as weather and equipment maintenance. Single-day inefficiency may be caused by accidental factors, but if the proportion of inefficient days exceeds the preset threshold, such as 30%, it reflects poor efficiency stability or persistence problems. This indicator quantifies the prevalence of inefficient events by frequency, supplementing the frequency risk that the average efficiency cannot reflect. Therefore, the trigger conditions are set based on both.

[0047] Understandably, when the rate of change of the light energy conversion rate is negative and less than the set threshold or the proportion of inefficient days is greater than the preset deviation ratio threshold, it indicates that the efficiency is rapidly declining or inefficiency events occur frequently. At this time, the average of the minimum actual efficiency and the average actual efficiency is used as the analysis efficiency, which can lower the evaluation value and strengthen abnormal warnings. Otherwise, it indicates that the actual light energy conversion efficiency tends to stabilize normal operating conditions. The average actual efficiency can reflect the actual situation, which can make the evaluation results closer to the actual level of risk and enhance the sensitivity of deviation to serious problems.

[0048] Furthermore, the specific calculation formula of the reference light energy conversion efficiency in step A21 is as follows: ,in, Indicates the reference light energy conversion efficiency, Indicates the preset benchmark light energy conversion efficiency, is the historical average light energy conversion efficiency, The cumulative operating time of the photovoltaic modules connected to the energy storage terminal, for, is the annual decay rate, used to describe the natural decay of efficiency over time, is the aging reference operating life, is the aging correction term, which represents the cumulative running time The attenuation of light energy conversion efficiency. The value also changes over time, and its specific value can be determined based on industry experience and test data.

[0049] Specifically, the specific estimation process of the effective remaining power includes: B1. Import the real-time light intensity, the current time point, the total area of the energy storage terminal connected to the photovoltaic module, and the real-time light energy conversion efficiency into the energy calculation model of light energy-electric energy conversion to calculate the predicted remaining light energy conversion amount.

[0050] B2. Calculate the standard deviation of the real-time light intensity, and after normalization, obtain the light intensity fluctuation correction factor. Calculate the comprehensive conversion correction factor through weighted summation of the light intensity fluctuation correction factor and the photovoltaic module efficiency attenuation factor in the attenuation correction factor.

[0051] B3. Correct the predicted remaining light energy conversion amount through the conversion correction factor to obtain the target light energy conversion amount, denoted as .

[0052] B4. Denote the current remaining energy storage power and the energy storage terminal cycle attenuation factor in the attenuation correction factor as and respectively, and calculate the effective remaining power , .

[0053] It should be added that the current time point is denoted as . Compare the real-time light intensity with the lowest reference conversion light intensity of the photovoltaic module connected to the energy storage terminal. Sort the time points lower than the lowest reference conversion light intensity in chronological order, and denote the last time point in the sorting as . The specific representation formula of the energy calculation model of light energy-electric energy conversion is: , represents the light intensity at the th time point, represents the total area of the photovoltaic module connected to the energy storage terminal, represents the light energy conversion efficiency at the th time point.

[0054] It can be understood that the light intensity fluctuates randomly, resulting in unstable power generation. The efficiency of the photovoltaic module will decay due to factors such as aging and temperature. These non-ideal factors need to be corrected in the prediction. By calculating the standard deviation of the light intensity and normalizing it, the fluctuation correction factor is obtained, and then weighted and fused with the module efficiency attenuation factor, which can comprehensively reflect the impact of environmental fluctuations and equipment aging on power generation, and can ensure that the subsequent target light energy conversion amount is closer to the actual working conditions. The normalization process can be processed by min-max normalization, and the same method can also be used for subsequent processing.

[0055] In a specific embodiment, there is cyclic attenuation during the charging and discharging process of the energy storage terminal, that is, the capacity decreases with the number of uses, and this part of the loss needs to be deducted when calculating the available power. By integrating the photovoltaic power generation prediction and the energy storage state, the effective remaining power that can be actually used can be output after considering the battery attenuation.

[0056] In the embodiment of the present invention, the effective remaining power is estimated by setting an attenuation correction factor and combining the remaining power and the real-time light intensity, fully considering the influence of the conversion fluctuation impact of the light efficiency, avoiding the analysis error of the energy storage attenuation caused by only relying on the use time and the charging and discharging conditions and ignoring the light efficiency fluctuation, and ensuring the authenticity and effectiveness of the estimation.

[0057] Further, the target light energy conversion amount obtained by correcting the predicted remaining light energy conversion amount through the conversion correction factor can be obtained by calculating the product of the predicted remaining light energy conversion amount and the conversion correction factor.

[0058] The identification energy consumption determination module calculates the predicted total consumption power of the identification according to the remaining duration between the current time point and the preset identification time period and combines the power consumption per unit time of each area of the identification component. Each area is composed of a main identification area and an auxiliary identification area.

[0059] In the embodiment of the present invention, by dividing according to the main identification area and the auxiliary identification area and combining the remaining duration of the current time period and the preset identification time period, the power consumption per unit time of each area is calculated, breaking the traditional extensive mode of overall adjustment, distinguishing the power consumption and brightness requirement priorities of different areas, and forming refined area energy consumption data.

[0060] It can be understood that first, the preset identification time period is matched based on the current time point, and the remaining duration of this time period is obtained in real time. For example, if the current is the 2nd hour of the night mode and the total duration of the preset night mode is 8 hours, the remaining duration is 6 hours.

[0061] Combined with the power consumption per unit time of the main identification area and the auxiliary identification area in the identification component during the corresponding time period, such as the main area consuming 15W per hour and the auxiliary area consuming 5W per hour in the night mode, the total power consumption of each area during the remaining time period is calculated respectively. The total power consumption is the product of the power consumption per unit hour and the remaining duration. Finally, the power consumption of the two types of areas is added together to form the predicted total consumption power of the identification.

[0062] The energy supply balance judgment module compares the effective remaining power with the predicted total consumption power. If the comparison relationship is greater than or equal to, it controls the identification component to supply power according to the preset brightness. Otherwise, based on the brightness priority of the main identification area and the auxiliary identification area and the brightness - energy consumption mapping relationship, it determines the adjusted identification area and the adjusted brightness of the corresponding adjusted identification area, and generates a brightness adjustment strategy accordingly.

[0063] Specifically, the determination of the adjustment identification area includes: D1. Real-time collection of the ambient brightness of the identification area through an ambient brightness sensor to determine the minimum required brightness threshold of the main identification area, denoted as .

[0064] D2. Based on the brightness - energy consumption mapping relation table, with as the constraint condition, iteratively calculate the adjustable brightness range of the auxiliary identification area.

[0065] D3. Calculate the energy consumption adjustment range of the auxiliary identification area based on the currently set identification brightness, adjustable brightness range, power consumption per unit time corresponding to different identification brightnesses, and the remaining duration of the preset identification period.

[0066] D4. If the difference between the effective remaining power and the estimated total power consumption is within the current energy consumption adjustment range of the auxiliary identification area, take the auxiliary identification area as the adjustment identification area; otherwise, take the main identification area as the adjustment identification area.

[0067] Further, in step D1, the specific determination process of the minimum required brightness threshold of the main identification area is as follows: D11. Calculate the average value of the real-time collected ambient brightness of the identification area to obtain the average ambient brightness , and at the same time, fit the ambient brightness curve based on the ambient brightness, and extract the slope of the curve as the ambient brightness change rate .

[0068] D12. Calculate the identification brightness demand coefficient based on the average ambient brightness and the ambient brightness change rate , , and respectively represent the weights corresponding to the set ambient brightness and ambient brightness change rate, is the natural constant, and are respectively the set reference ambient brightness and ambient brightness change rate.

[0069] It can be understood that this formula dynamically adjusts the identification brightness demand coefficient through the coupled calculation of ambient brightness and change rate, and balances static and dynamic environmental factors through weights to achieve the adaptive adjustment of identification brightness, ensuring visibility while optimizing energy consumption. The weight setting can be combined with empirical data for setting.

[0070] It can also be understood that the relationship between ambient brightness and identification brightness demand is not linearly corresponding. For example, in extremely dark environments, the brightness needs to be significantly increased to be recognized. When the brightness is close to the threshold, a small adjustment can meet the demand. Therefore, an exponential function is used for analysis.

[0071] D13. After that, the safety brightness threshold set in advance is corrected by the brightness demand coefficient to obtain the minimum required brightness threshold for the main identification area.

[0072] It should be added that the specific correction formula for the minimum required brightness threshold is: , is the safety brightness threshold set in advance, is the minimum required brightness threshold for the main identification area.

[0073] Furthermore, in the specific implementation process, in step D2, based on the pre-established brightness - energy consumption mapping relationship table, combined with the functional requirements of the auxiliary identification area, such as the minimum visible brightness threshold and the system energy-saving target, starting from the currently set brightness, iterative calculation is performed downward to determine the lower and upper limits of the adjustable brightness, such as not less than 30% of the rated brightness to ensure basic display and not exceeding the current brightness, forming an adjustable brightness range, for example, 20% - 80%.

[0074] Furthermore, in the specific implementation process, in step D3, based on the adjustable brightness range output by D2, the currently set brightness of the auxiliary identification area is retrieved, such as initially 60%. Each optional brightness value within the range is traversed, such as 60%, 50%, 40%, 30%. According to the mapping relationship table, the corresponding power consumption per unit time is obtained, such as 60% corresponding to 5W / h, 50% corresponding to 4W / h, and then multiplied by the remaining duration of the preset identification period, such as 8 hours, to calculate the total energy consumption corresponding to each brightness. Exemplarily, the total energy consumption at 60% brightness is 40Wh, and the total energy consumption at 30% brightness is 24Wh. Finally, an energy consumption adjustment range of [24Wh - 40Wh] is formed.

[0075] Specifically, the specific establishment of the brightness - energy consumption mapping relationship includes: recording the brightness and power consumption of the main identification area as and respectively. There is a linear relationship between the brightness and power consumption of the main identification area. The specific linear formula is: , represents the preset power consumption growth ratio of the main identification area, is a constant term.

[0076] Record the brightness and power consumption of the auxiliary identification area as and respectively. There is an exponential relationship between the brightness and power consumption of the auxiliary identification area. The specific representation formula is: , is the preset power consumption growth ratio of the auxiliary identification area, is a constant, .

[0077] It should be added that since the main identification area is the core area for information display, high brightness and stability need to be ensured. And linearity indicates that for every certain unit increase in brightness, the power consumption increases at a fixed ratio. For example , when the brightness increases from 500 to 600 , the power consumption increases by 50 W.

[0078] It should be noted that in a specific embodiment, the boundary of the linear relationship between the brightness and power consumption of the main identification area is defined as , which is mainly used to ensure that the minimum visibility requirement is met. For example, the standard stipulates that the brightness of traffic signs under daytime illumination should be greater than or equal to 400 . A higher threshold is set here to cope with extreme weather or long-distance visibility requirements.

[0079] It can also be understood that , which reflects the sensitivity of the brightness of the main identification area to power consumption and is related to the efficiency of the LED drive circuit, covers fixed losses such as the standby power consumption of the controller and sensor, and The specific values of can be obtained through calibration experiments, including: fixing the environmental temperature at 25°C in the laboratory. Gradually adjust the brightness of the main identification area, such as 500 , 600 , …, 1000 . Measure the power consumption value corresponding to each brightness, and fit through linear regression and .

[0080] It should be added that the auxiliary identification area may contain secondary information, and its brightness can be flexibly adjusted according to the environment. The exponential function can better show the actual power consumption relationship.

[0081] The boundary of the linear relationship between the brightness and power consumption of the auxiliary identification area is defined as , similarly and the exponential term can be determined through experiments: in a dynamic lighting environment, that is, simulate day and night changes with a step size of 50 , measure the power consumption in the range of 200 - 800 , take the logarithm of the data and fit the power function to determine and the exponential term , where can also be fixed at a value of 1.2. And characterizes the non-linear power consumption characteristics of the auxiliary identification area and is closely related to display technologies such as LED backlight and electronic ink.

[0082] Specifically, the specific determination process of adjusting the brightness of the adjustment identification area includes: R1. If the adjustment identification area is an auxiliary identification area, the difference between the effective remaining power and the expected total power consumption is recorded as the energy consumption reduction. Based on the power consumption per unit time corresponding to the current identification brightness and the power consumption reduction, the adjusted brightness is obtained through the brightness - energy consumption mapping relationship.

[0083] It can be understood that the adjusted brightness can be derived from the brightness - energy consumption mapping relationship formula, and the derivation process is not shown here.

[0084] R2. If the adjustment identification area is an auxiliary identification area and a main identification area, based on the priority, set the highest adjustment ratio of the main identification area. Based on the highest adjustment ratio of the main identification area and the lowest required brightness threshold of the main identification area, gradually reduce the brightness according to the brightness - energy consumption mapping relationship. After each reduction, calculate the energy consumption change and accumulate the saved energy consumption until the accumulated saved energy consumption reaches or exceeds the lacking power value. At this time, stop the brightness adjustment, and use the brightness of the auxiliary identification area and the main identification area at this time as the corresponding adjusted brightness.

[0085] It should be added that the functional priority of the main identification area is higher than that of the auxiliary identification area, and its brightness needs to be guaranteed first. For example, the quantization model of brightness reduction and energy consumption reduction can ensure that the adjusted energy consumption does not exceed the remaining power. By weighted summation to distribute the remaining power, the functional requirements and energy consumption limits can be balanced. For example, the weight of the main area is 70% and the auxiliary area is 30%.

[0086] It can be understood that the highest adjustment ratio is the difference between 1 and the priority weight. For example, if the priority weight is 0.7, the highest allocation ratio is 0.3.

[0087] It can be understood that when the adjustment identification area involves both the auxiliary identification area and the main identification area, first, based on the high - priority attribute of the main identification area carrying key information, set its highest adjustment ratio, which limits the maximum degree to which the brightness of the main identification area can be reduced to ensure the basic visibility of the core information. Then, combined with the lowest required brightness threshold of the main identification area, based on the pre - established brightness - energy consumption mapping relationship, gradually reduce the brightness of the main identification area in the order from low - priority information to high - priority information.

[0088] It should be added that after each reduction in brightness, accurately calculate the energy consumption change through the brightness - energy consumption mapping relationship and accumulate the saved energy consumption. During this process, continuously monitor the brightness of the main identification area to ensure that it is always not lower than the lowest required brightness threshold. For the auxiliary identification area, also based on the brightness - energy consumption mapping relationship, as long as it meets its own lowest visible brightness requirement, reduce the brightness as much as possible to save energy.

[0089] Repeat the above operations continuously until the cumulative energy consumption saved in the auxiliary identification area and the main identification area reaches or exceeds the lack of power value. At this time, stop the brightness adjustment, and determine the current brightness of the auxiliary identification area and the main identification area as the corresponding adjusted brightness, so as to effectively make up for the lack of power while ensuring the display effect of key information, and achieve the balance between energy consumption and functional requirements.

[0090] In the embodiment of the present invention, by comparing the effective remaining power with the expected total power consumption, and based on the regional brightness priority and the brightness-energy consumption mapping relationship, the area and brightness to be adjusted are dynamically determined, which can give priority to ensuring the display requirements of the main identification area, reasonably allocate the energy consumption of the auxiliary area, ensure the rationality of the core information display under the premise of energy saving, and achieve the dynamic balance between energy consumption and function.

[0091] The energy supply adjustment feedback control terminal executes the brightness adjustment strategy and sends an updated power supply instruction to the identification component.

[0092] Please refer to Figure 3 As shown in the figure, the present invention provides a static identifier. The intelligent static identifier is equipped with an ambient light sensor, an energy storage terminal, a communication component, and a photovoltaic component. Among them, the intelligent static identifier is divided into a main identification area and an auxiliary identification area.

[0093] The parameters in the above formula are all data values after normalization processing. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0094] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0095] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0096] In addition, in each embodiment of the present application, the various functional modules can be integrated in a processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0097] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims described above.

[0098] Finally, the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A static identification energy storage system, characterized in that, The system includes: An identification energy storage evaluation module that sets an attenuation correction factor based on the charge-discharge data and light energy conversion data of the energy storage terminal carried by the identification component within the current set period, and estimates the effective remaining power by combining the current remaining energy storage power, the real-time light intensity, and the attenuation correction factor; An identification energy consumption determination module that calculates the estimated total power consumption of the identification based on the remaining time between the current time point and the preset identification period and combines the power consumption per unit time of each area of the identification component. Each area consists of a main identification area and an auxiliary identification area; An energy supply balance judgment module that compares the effective remaining power with the estimated total power consumption. If the comparison result is greater than or equal to, it controls the identification component to supply power at the preset brightness. Otherwise, it determines the adjusted identification area and the adjusted brightness of the corresponding adjusted identification area based on the brightness priority between the main identification area and the auxiliary identification area and the brightness-energy consumption mapping relationship, and generates a brightness adjustment strategy accordingly; An energy supply adjustment feedback control terminal that executes the brightness adjustment strategy and sends an updated power supply instruction to the identification component.

2. The static identification energy storage system according to claim 1, wherein: The attenuation correction factor includes: An energy storage terminal cycle attenuation factor obtained by performing normalized weighted summation based on the deep discharge ratio, charge-discharge regularity, historical cumulative charge-discharge times, and average charge-discharge rate; A photovoltaic module efficiency attenuation factor obtained by weighting the light energy conversion efficiency deviation degree and the MPPT accuracy deviation degree and then inputting them into the Sigmoid function.

3. The static identification energy storage system according to claim 2, wherein: The specific statistical process of the charge-discharge regularity is as follows: Calculate the standard deviation and the average charge-discharge depth of the charge-discharge depth respectively for each charge-discharge, and record the ratio of the two as the charge-discharge depth coefficient of variation; Similarly process the rate of each charge-discharge according to the processing method of the charge-discharge depth to obtain the charge-discharge rate coefficient of variation; Sort the charge-discharge depths of each charge-discharge in chronological order, count the continuous number of times the charge-discharge depth exceeds the preset threshold, and calculate the ratio with the total number of charge-discharges as the charge-discharge depth deviation compensation coefficient. Set the charge-discharge rate deviation compensation coefficient in the same way as the charge-discharge depth deviation compensation coefficient; Obtain the corrected coefficient of variation by compensating the corresponding coefficient of variation based on the deviation compensation coefficient; Obtain the charge-discharge regularity by weighted summation based on the preset weight and comprehensively considering the charge-discharge frequency, the corrected charge-discharge depth coefficient of variation, and the charge-discharge rate coefficient of variation.

4. The static identification energy storage system according to claim 2, characterized in that: The calculation methods of the light energy conversion efficiency deviation degree and the MPPT accuracy deviation degree are the same. Among them, the specific calculation process of the light energy conversion efficiency deviation degree includes: Statistical reference light energy conversion efficiency based on the preset reference light energy conversion efficiency, the cumulative operation duration of the energy storage terminal connected to the photovoltaic module, and the historical average light energy conversion efficiency; Calculate the light energy conversion rate change rate and the average actual light energy conversion rate based on the actual light energy conversion rate of each operation day within the current set period; Statistical ratio of the number of operation days with actual light energy conversion efficiency lower than the reference light energy conversion efficiency to the total number of operation days, and record it as the proportion of low-efficiency days; If any of the following conditions is triggered, the average of the minimum actual light energy conversion rate and the average actual light energy conversion rate will be used as the analysis of the light energy conversion efficiency; otherwise, the average actual light energy conversion rate will be used as the analysis of the light energy conversion efficiency. The trigger conditions are as follows: 1) The change rate of the light energy conversion rate is negative and less than the set reference change rate threshold; 2) The proportion of low-efficiency days is greater than the preset deviation proportion threshold; Calculate the relative deviation value between the deviation efficiency conversion ratio and the reference light energy conversion efficiency to obtain the light energy conversion efficiency deviation degree.

5. A static identification energy storage system according to claim 1, characterized in that: The specific estimation process of the effective remaining power is as follows: Import the real-time light intensity, the current time point, the total area of the energy storage terminal connected to the photovoltaic module, and the real-time light energy conversion efficiency into the energy calculation model of light energy-electric energy conversion to calculate the expected remaining light energy conversion amount; Calculate the standard deviation of the real-time light intensity and then perform normalization processing to obtain the light intensity fluctuation correction factor. Calculate the comprehensive conversion correction factor by weighted summation of the light intensity fluctuation correction factor and the photovoltaic module efficiency attenuation factor in the attenuation correction factor; The target light energy conversion amount is obtained by correcting the predicted remaining light energy conversion amount with the converted correction factor, denoted as ; Denote the current remaining energy storage power and the energy storage terminal cycle attenuation factor in the attenuation correction factor as and respectively, and calculate the effective remaining power , .

6. A static identification energy storage system according to claim 1, characterized in that: The determination of the adjustment identification area includes: Real-time collect the ambient brightness of the identification area, and determine the lowest required brightness threshold of the main identification area, denoted as ; Based on the brightness - energy consumption mapping relationship table, with as the constraint condition, iteratively calculate the adjustable brightness range of the auxiliary identification area; Calculate the energy consumption adjustment range of the auxiliary identification area based on the current set identification brightness, the adjustable brightness range, the power consumption per unit time corresponding to different identification brightnesses, and the remaining duration of the preset identification period; If the difference between the effective remaining power and the expected total power consumption is within the current energy consumption adjustment range of the auxiliary identification area, the auxiliary identification area will be used as the adjustment identification area; otherwise, the main identification area will be used as the adjustment identification area.

7. The static identification energy storage system according to claim 6, wherein: The specific determination process of the minimum required brightness threshold of the main identification area is as follows: Calculate the average value of the ambient brightness of the identified area collected in real time to obtain the average ambient brightness , and at the same time, fit an ambient brightness curve based on the ambient brightness, and extract the slope of the curve as the ambient brightness change rate ; Calculate the identification brightness demand coefficient based on the average ambient brightness and the ambient brightness change rate , , and respectively represent the weights corresponding to the set ambient brightness and the ambient brightness change rate, is the natural constant, and are respectively the set reference ambient brightness and the ambient brightness change rate; And correct the preset safety brightness threshold through the brightness requirement coefficient to obtain the minimum required brightness threshold of the main identification area.

8. A static identification energy storage system according to claim 1, characterized in that: The specific establishment of the brightness-energy consumption mapping relationship includes: Denote the brightness and power consumption of the main identification area as and respectively. There is a linear relationship between the brightness and power consumption of the main identification area. The specific linear formula is: where represents the preset growth ratio of the power consumption of the main identification area, and is a constant term; Denote the brightness and power consumption of the auxiliary identification area as and respectively. The brightness and power consumption of the auxiliary identification area have an exponential relationship, and the specific expression formula is: , is the preset power consumption growth ratio of the auxiliary identification area, is a constant, .

9. The static identification energy storage system according to claim 6, wherein: The specific determination process of the adjusted brightness of the adjustment identification area includes: If the adjustment identification area is the auxiliary identification area, record the difference between the effective remaining power and the expected total power consumption as the energy consumption reduction amount. Based on the power consumption per unit time corresponding to the current identification brightness and the reduced power consumption, obtain the adjusted brightness through the brightness-energy consumption mapping relationship; If the adjustment identification area is the auxiliary identification area and the main identification area, set the highest adjustment ratio of the main identification area based on the priority. Based on the highest adjustment ratio of the main identification area and the minimum required brightness threshold of the main identification area, gradually reduce the brightness according to the brightness-energy consumption mapping relationship. After each reduction, calculate the energy consumption change amount and accumulate the saved energy consumption until the accumulated saved energy consumption reaches or exceeds the lacking power value. At this time, stop the brightness adjustment, and use the brightnesses of the auxiliary identification area and the main identification area at this time as the corresponding adjusted brightnesses.

10. An intelligent static identifier, characterized in that: The intelligent static identification is equipped with an ambient light sensor, an energy storage terminal, a communication component, and a photovoltaic module. Among them, the intelligent static identification is divided into a main identification area and an auxiliary identification area.

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