Self-adaptive dimming control method based on energy supply prediction

Abstract

The invention discloses a self-adaptive dimming control method and system based on energy supply prediction. According to the method, the health state and the current electric quantity of an energy storage unit are evaluated in real time, and the energy storage state of a system is divided into a preset level; meanwhile, external environment prediction data are received and analyzed, and are converted into expected supply scene identifiers; a unique global dimming mode is determined and activated by querying a two-dimensional strategy matrix based on the two dimensions of the current energy reserve level and the expected supply scene identifier. In the mode, an associated dynamic dimming formula is loaded to plan the brightness change of the whole illumination period, and real-time sensing data is fused in the execution process for fine tuning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy management, and particularly to an adaptive dimming control method based on energy supply prediction. BACKGROUND

[0002] The present application relates to the technical field of energy management, and particularly to an adaptive dimming control method based on energy supply prediction. Under the current development background of new energy and Internet of Things technology, solar street lamps have been widely used due to their green energy-saving and cable-free advantages. The core challenge of such systems lies in how to achieve the longest stable lighting and the optimal energy utilization efficiency under the condition of limited and unstable energy supply. For this purpose, the existing technology usually adopts an energy-saving control strategy based on the real-time state of the energy storage unit (such as a battery), that is, by monitoring the voltage or estimated remaining power (SOC) of the battery, when the energy reserve decreases to a certain preset threshold, the corresponding energy-saving action will be triggered, such as reducing the output power of the lamp in different time periods. This control mode based on current state feedback improves the endurance of the system to a certain extent through simple automated logic and constitutes the basic framework of current intelligent lighting energy management.

[0003] However, the above-mentioned traditional control method still has deficiencies in intelligent and refined management. For example, Chinese patent document CN112483986A discloses a solar street lamp intelligent control method, which judges the power level of the battery by real-time detection of its voltage, and selects one of the multiple preset working modes for execution in combination with the obtained future weather information. Specifically, each working mode pre-plans a segmented and constant-power lighting scheme for the entire night, for example, when the power is sufficient and the weather is good, a mode of full power in the first half of the night and half power in the second half of the night is adopted; while when the power is low or continuous rainy days are predicted, it may switch to a low-power mode throughout the night. This scheme introduces weather prediction as an auxiliary reference for decision-making, which has made some progress compared to the technology that does not consider future supply at all, and reflects the attempt of the existing technology to improve the forward-looking nature of energy management.

[0004] Although the prior art represented by the above patent document takes into account the future energy supply, it is still essentially within the reactive control framework, and its decision-making mechanism is relatively simple and fixed, resulting in several deep technical problems to be solved. First, its decision-making integration is low, and it cannot systematically quantify and matrix the two core dimensions of current reserves and future supply. Its control logic is usually based on rough condition judgment (such as high power and good weather) to select a fixed working mode, rather than directly mapping to an optimal and global dimming strategy through an accurate two-dimensional index, which makes the control precision and optimality degree not high. Secondly, the accuracy of its energy state evaluation is insufficient. The existing technology generally directly uses the terminal voltage of the battery as the power judgment basis, but this voltage value is easily disturbed by factors such as load start-stop and environmental temperature change, resulting in misjudgment, which may trigger false modes at inappropriate times, affecting lighting effects or causing energy waste. Finally, its control output mode is fixed. The preset working mode is usually composed of several fixed and stepped power segments, which cannot generate a smooth and dynamic brightness curve throughout the night, and it is difficult to integrate real-time environmental perception data (such as people and vehicle flow) for immediate fine-tuning, and lacks a self-adaptive management mechanism for long-term health status of the battery. Therefore, the field needs a new control method that can deeply integrate future energy supply with current energy storage state, achieve high-precision state evaluation, and make fine and adaptive decisions based on multi-dimensional input, to realize predictive planning and efficient use of energy throughout the cycle.

[0005] Based on the defects disclosed in the above-mentioned comparative documents, the key technical problems to be solved by the present application can be summarized as follows: the existing solar street lamps generally only consider the current energy remaining, without energy planning, and do not judge the future energy supply according to the predicted future weather; at the same time, the brightness adjustment has only a few fixed gears, and does not consider the aging decay at all, resulting in a shortened overall life of the street lamp. SUMMARY

[0006] In view of the above existing problems, the present application is proposed.

[0007] The purpose of the present application is to overcome the shortcomings of the prior art and provide an adaptive dimming control method based on energy supply prediction. In order to solve the four core defects of the prior art: first, the current energy storage and future supply prediction cannot be integrated; second, the energy judgment is easily disturbed by temperature and load, leading to state misjudgment; third, the control mode is fixed, and it cannot respond smoothly and dynamically to environmental changes; fourth, there is a lack of health management of the energy storage unit, leading to premature aging of the equipment.

[0008] To solve the above technical problems, the present application provides the following technical solutions: In a first aspect, the present application provides an adaptive dimming control method based on energy supply prediction, which comprises the following steps, Step 1: classifying the energy reserve state of the current system by detecting the electrical energy of the energy storage unit, and mapping it to the most suitable one of the preset multiple classified energy reserve levels; Step 2: receiving and analyzing external environment prediction data, and converting the predicted lighting conditions into an expected supply scenario identifier; Step 3: based on the current energy reserve level and the obtained expected supply scenario identifier, indexing in a two-dimensional calculation matrix built into the lighting device control system to directly determine and activate a unique corresponding pre-configured global dimming mode; Step 4: according to the activated global dimming mode, loading a time- segmented dynamic dimming formula associated therewith, which sets a non-constant, time-varying brightness pattern for the entire lighting period; Step 5: during the execution of the brightness pattern, dynamically fine-tuning the brightness pattern by fusing real-time environment perception data, and conditionally judging and responding to externally triggered brightness enhancement requests according to the permissions set by the currently activated global dimming mode, thereby achieving predictive planning of energy and real-time adaptation to environmental changes.

[0009] As a preferred scheme of the adaptive dimming control method based on energy supply prediction of the present application, wherein: the step of classifying the energy reserve state of the current system and mapping it to the preset classified energy reserve levels comprises: The real-time working temperature of the energy storage unit is obtained through a temperature sensing component, and a plurality of voltage samples are obtained through a voltage sampling circuit, thereby generating a stable voltage value excluding load interference; According to the obtained real-time working temperature, a set of applicable voltage thresholds is dynamically selected and activated from the stored multiple sets of sample voltage thresholds corresponding to different temperature intervals; The stable voltage value is compared with the set of activated voltage thresholds with preset specificity, and the specificity defines voltage intervals that are mutually non-overlapping and have buffer boundaries when the state switches, thereby stably mapping the stable voltage value to one of the energy reserve levels.

[0010] As a preferred scheme of the adaptive dimming control method based on energy supply prediction of the present application, wherein: the step of converting the predicted lighting conditions into a predefined expected supply scenario identifier comprises: Receiving a pre-encoding encapsulating environment prediction information and a data packet corresponding to data integrity check information through a communication interface; After performing the integrity check on the data packet and confirming the data is valid, a prediction code representing expected environmental conditions in a future control period is extracted from the data packet, the prediction code and an expected supply scenario identification are defined as a deterministic association mapping rule, and the prediction code can be converted into a uniquely corresponding expected supply scenario identification.

[0011] As a preferred scheme of the adaptive dimming control method based on energy supply prediction, the step of selecting and applying a mode according to the energy reserve level and the expected supply scenario identification comprises: According to a preset decision logic, a lighting control mode uniquely corresponding to each preset combination of the energy reserve level and the expected supply scenario identification has been established; According to the current input energy reserve level and the expected supply scenario identification, the uniquely corresponding lighting control mode is directly determined, and the lighting device is adjusted to the corresponding operating state according to the definition of the operating parameters of the lighting device in the mode.

[0012] As a preferred scheme of the adaptive dimming control method based on energy supply prediction, the step of obtaining a hierarchical energy reserve level representing the current available energy reserve in real time comprises: At least one state parameter of the energy storage unit is monitored in real time, a continuous energy state value representing the current available energy is determined based on the at least one state parameter, and the continuous energy state value is mapped to a preset energy reserve division interval to obtain a hierarchical energy reserve level corresponding to the energy reserve division interval.

[0013] As a preferred scheme of the adaptive dimming control method based on energy supply prediction, the step of predicting an expected supply scenario identification representing the future energy supply situation in real time comprises: At least one type of environmental parameter reflecting the availability of external energy sources is obtained, and the environmental parameter is analyzed based on a preset prediction model to determine an expected supply scenario identification representing the energy supply trend in a future period.

[0014] As a preferred scheme of the adaptive dimming control method based on energy supply prediction, the step of selecting and applying a mode according to the energy reserve level and the expected supply scenario identification comprises: From a mode set containing multiple preset modes, a target mode is selected according to the combination of the energy reserve level and the expected supply scenario identification, and at least one operating parameter of the lighting device is adjusted based on the target mode.

[0015] As a preferred scheme of the adaptive dimming control method based on energy supply prediction of the present application, wherein: the method comprises a manual intervention and automatic recovery mechanism: Upon receiving a preset user intervention instruction, interrupting the currently executing mode, and causing the lighting device to switch to a preset operation mode independent of the energy reserve level and the expected supply scenario identification, and after a preset recovery condition is met, automatically recovering the step of selecting and applying the mode according to the energy reserve level and the expected supply scenario identification.

[0016] As a preferred scheme of the adaptive dimming control method based on energy supply prediction of the present application, wherein: the method comprises a battery state of health adaptive management procedure for prolonging the life cycle of the energy storage unit: At least one historical operating parameter of the energy storage unit is obtained to evaluate its state of health, and based on the evaluated state of health, at least one adjustment from the following two groups is performed: adjusting the correspondence between the energy reserve level and the expected supply scenario identification to the specific mode, adjusting at least one charge and discharge management limit value of the energy storage unit.

[0017] As a preferred scheme of the adaptive dimming control method based on energy supply prediction of the present application, wherein: the method comprises an expected supply scenario adaptive correction procedure based on local historical data: An actual supply trend is determined based on historical energy input data of the energy collection unit, and the actual supply trend is compared with the expected supply scenario identification, so as to correct the expected supply scenario identification when the actual supply trend does not match the expected indicated by the expected supply scenario identification.

[0018] The present application has the following advantages: compared with the prior art, the present application can effectively improve the reliability of the system, effectively prolong the service life of the core components, maximize the energy utilization efficiency, realize fine management, and deploy the core intelligent decision logic locally on the terminal device, so as to have a certain autonomous ability and reduce the dependence on the central server and the real-time communication network. Even in remote areas with network interruption or no signal, the energy using unit can still operate independently and intelligently. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0020] Figure 1 Flowchart for adaptive dimming control method based on energy replenishment prediction.

[0021] Figure 2 Flowchart for energy reserve state classification.

[0022] Figure 3 Flowchart for expected replenishment scenario identification.

[0023] Figure 4 Flowchart for execution, fusion and fine-tuning. DETAILED DESCRIPTION

[0024] In order to make the above objectives, features and advantages of the present application more apparent, specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0025] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details given herein. In other instances, well-known methods have not been described in detail in order to avoid unnecessarily obscuring the present application. Accordingly, it will be appreciated that the present application is not limited to the embodiments disclosed herein, and that the embodiments are not to be construed as limiting the present application.

[0026] Secondly, one embodiment or embodiments as referred to herein means a specific feature, structure, or characteristic that is included in at least one implementation of the present application. Therefore, appearing multiple times in various places in this specification in reference to one embodiment does not necessarily refer to the same embodiment, nor does it necessarily exclude other embodiments from the scope of the application.

[0027] Embodiment 1 Reference Figure 1 and Figure 2 For the first embodiment of the present application, the embodiment provides an adaptive dimming control method based on energy replenishment prediction, comprising: By detecting the electrical characteristics of the energy storage unit, the energy reserve state of the current system is classified and mapped to a specific level in the preset multiple classified energy reserve levels; Wherein, the energy storage unit can be a lithium iron phosphate battery pack with a nominal voltage of 3.2V and a capacity of 10Ah. The maximum charging current is limited to 0.5C, the maximum discharging current is 1C, and the safe working temperature range is -10°C to 50°C.

[0028] The hierarchical energy reserve level purposefully divides the continuously changing actual energy state (for example, any one precise value of battery power from 0% to 100%) in the energy storage unit into several limited, non-continuous, and well-defined levels or intervals. For example, when the power is less than 30%, the level is empty; when the power is 30%<= power <60%, the level is alert; when the power is 60%<= power <90%, the level is balanced; and when the power is greater than 90%, the level is abundant, so that different energy reserves can be divided into different levels.

[0029] Embodiment 2 Referring to Figure 1 and Figure 4 For the second embodiment of the present application, it includes: the embodiment provides a kind of receiving and analyzing external environment prediction data, and the predicted light condition is converted into an expected supply scene identification; Based on the current energy reserve level and the obtained expected supply scene identification, index in a two-dimensional calculation matrix embedded in the lighting device control system, directly determine and activate the only corresponding, pre-configured global dimming mode; Wherein, an expected supply scene identification (for example, sunny, rainy, cloudy, etc.), two-dimensional calculation matrix is the two dimensions (row dimension, column dimension) of the matrix, and each cell in the matrix stores a specific operating mode. The specific implementation is as follows table: The operating mode specifies at least one set of operating parameters that the energy unit should follow in the next working period. The global dimming mode is the factory default setting (performance reference) of the entire intelligent control system. The main function can make the work instruction accurate and avoid unreasonable instruction.

[0030] Embodiment 3 Referring to Figure 1 For the third embodiment of the present application, it includes: according to the activated global dimming mode, load a time segment dynamic dimming formula associated with it, which sets a non-constant, time-varying brightness mode for the entire lighting period; In the process of executing the brightness mode, real-time fusion of environment perception data, dynamic fine-tuning of reference brightness, and according to the permission set by the currently activated global dimming mode, the brightness enhancement request triggered externally is responded with constraint, so as to realize predictive planning of energy, and then adapt to environmental changes in real time.

[0031] The time-segmented dynamic dimming formula is the final instruction set generated by the whole energy management method for directly controlling the energy-consuming unit (e.g. LED lighting module) to actually run in a complete working cycle (e.g. a night). It is a dynamically generated, non-fixed execution scheme. Its essence is a mapping relationship table of time and power. The brightness mode is a preset function or data sequence that describes how the lighting brightness (or its directly related parameters such as power) changes over time.

[0032] The final brightness of the lighting device is composed of the reference brightness and a real-time calculated adjustment amount The formula is , The calculation of the adjustment amount is as follows: : proportional gain coefficient, determines the system's response sensitivity to the deviation of ambient illumination. For example, It can be set to 0.8.

[0033] : target ambient illumination, a preset value, indicating the ideal brightness (e.g. 20 ) that the lighting area hopes to achieve.

[0034] : the current ambient illumination value detected by the ambient light sensor in real time.

[0035] Boundary conditions: the calculated must be limited within a reasonable range set by the current mode, for example .

[0036] The main purpose of its design is to make the device meet the user's needs under ideal conditions while also showing the best performance of the device. Predictive planning is not only based on the current state, but also takes future expectations into account (for example, when the device decides the running mode of the current working cycle, it must also search for two problems: How much resources does the device have now, which is determined by detecting the current energy reserve level; How much supply can the device get in the future, which is obtained by analyzing external information (such as weather forecast) or internal model to retrieve the expected energy supply scenario.

[0037] Combining the search answers to these two problems to make a comprehensive decision is the essence of predictive planning) This can allow the device to foresee and avoid future energy shortages.

[0038] Embodiment 4 Referring to Figure 1 and Figure 2 For the fourth embodiment of the present application, the specific steps of classifying and mapping the energy reserve status of the current system to the preset energy reserve levels include: The real-time working temperature of the energy storage unit is obtained by the temperature sensing component, and a plurality of voltage samples are obtained by the voltage sampling circuit, so as to generate a stable voltage value excluding load interference; According to the obtained real-time working temperature, a group of applicable voltage thresholds for the current voltage thresholds are dynamically selected and activated from the stored plurality of samples corresponding to different temperature intervals; The stable voltage value is compared with the activated group of voltage thresholds with preset specificity, and the specificity is that each energy reserve level defines a voltage interval that is mutually exclusive and has a buffer boundary when the state switches, so as to stably map the stable voltage value to one of the energy reserve levels.

[0039] In order to avoid the instantaneous jump of voltage reading caused by load mutation (such as LED lamp starting), which leads to the wrong evaluation of energy level, the moving average filtering algorithm is used in this embodiment to generate a stable voltage value excluding load interference The controller continuously collects the original voltage value of the energy storage unit (such as lithium battery) at a preset frequency (for example, 10 Hz) .

[0040] The moving average filtering algorithm is: ; Wherein, is the stable voltage value calculated at (the current time).

[0041] is the original voltage value collected at the past (the past time) sampling period.

[0042] N is the number of sampling points. For example, when N is 5, N is 5; The original voltage value at the current time is 20V; The original voltage value one minute ago is 21V; The original voltage value two minutes ago is 22V; The original voltage value three minutes ago is 12V (interfered); The original voltage value of the first four minutes is 19V.

[0043] Plug into the formula: V If only the worst voltage of the first three minutes is considered, it is easy to cause the system to misjudge as low voltage (12V is much lower than 20V), but by calculating the average voltage of the last five minutes as 18.8V, the gap compared with the other four minutes of voltage value is smaller, and the anti-interference ability is enhanced.

[0044] The voltage threshold refers to the boundary of energy level switching in a system without specificity, which is defined by a single voltage value. (For example, simply define higher than 12.5V as high energy, and lower than 12.5V as medium energy). The inherent defect of this design is that when the stable voltage value of the energy storage module is exactly at its physical limit or fluctuates slightly around 12.5V due to slight disturbances (such as temperature changes, slight self-discharge), the following situation may occur: the voltage drops from 12.51V to 12.49V, and the system judges the level to switch from high to medium.

[0045] Then the voltage may rise to 12.51V due to some disturbance, and the system judges the level to switch back from medium to high. This rapid and repeated level switching near the boundary point is called state jitter.

[0046] It will cause the entire control system to make frequent and meaningless mode adjustments (for example, the light flickers between two brightness modes), which not only consumes the controller's computing resources, but also causes the device to run unstable. In order to eliminate the above problems, the invention introduces specificity.

[0047] The core idea of specificity is that the threshold for entering a new state is higher than the threshold for exiting the state. Specifically, two voltage values can be defined for the boundary between two adjacent energy levels (for example, high and medium). (For example, rising threshold: only when the voltage rises from low to high and exceeds this value, the system will switch from medium energy to high energy. Falling threshold: only when the voltage falls from high to low and is lower than this value, the system will switch from high energy to medium energy.) The key is that the rising threshold is set higher than the falling threshold. The voltage difference between them (rising threshold - falling threshold) forms a specific region or insensitive area, which is specificity.

[0048] Taking a 12V solar street lamp system as an example, assume that we divide the battery energy reserve into three levels: L1 (low), L2 (medium), and L3 (high).

[0049] The specific voltage threshold can be set as follows: Boundary between L2 and L3: rising edge threshold (only when the charging voltage rises above 12.7V, it is upgraded from L2 to L3) falling edge threshold (only when the discharging voltage drops below 12.5V, it is downgraded from L3 to L2) Specificity: .

[0050] With this design, when the voltage fluctuates between (for example, 12.5V to 12.7V), the system state will stably remain in its current state (L2 or L3), without any switching, thus eliminating the possibility of state jitter.

[0051] Embodiment 5 Referring to Figure 1 and Figure 3 For the fifth embodiment of the present application, the step of converting the predicted lighting conditions into a predefined expected replenishment scenario identifier includes: Receiving a pre-compiled data packet encapsulating environmental prediction information through a communication interface, and corresponding data integrity check information; After performing integrity check on the data packet and confirming the data is valid, extract the prediction code representing the expected environmental conditions in the future control period from it, and define the mapping rule of the deterministic association between the prediction code and the expected replenishment scenario identifier, and then convert the prediction code into the unique corresponding expected replenishment scenario identifier.

[0052] According to a preset decision logic, for each preset combination of energy reserve level and expected replenishment scenario identifier, a unique corresponding lighting control mode has been established, so that according to the current input energy reserve level and expected replenishment scenario identifier, the unique corresponding lighting control mode is directly determined, and according to the definition of the lighting device operating parameters in the mode, the lighting device is executed corresponding operating state adjustment.

[0053] Among them, the data packet of data integrity check information is a data transmission unit with self-detection capability. Its core purpose is to ensure that the core instruction (i.e. mode index) received by the energy unit is 100% accurate and undamaged, so as to ensure the reliable operation of the decision-making of the whole distributed intelligent control system. (such as applied to intelligent street lamps, distributed photovoltaic power stations or Internet of Things device clusters) Currently, the central controller and the remote energy unit basically communicate through wireless (such as LoRa, Zigbee, NB-IoT, Wi-Fi) or wired (such as PLC, RS485) channels.

[0054] These channels are all subject to electromagnetic interference, signal attenuation, network congestion, and other factors, which can cause errors in data transmission, resulting in incorrect program execution patterns. For example, the mode index sent by the central controller is binary 01 (representing a conservative mode).

[0055] In transmission, due to interference, the data received by the energy-consuming unit becomes 10 (representing an aggressive mode). This causes the energy-consuming unit to incorrectly execute the high-energy aggressive mode when its power is insufficient, which can cause the device to shut down prematurely in the late night, losing security functions.

[0056] Conversely, it can also cause the device to enter the extreme energy-saving mode too early when the power is sufficient, affecting normal use.

[0057] Embodiment 6 Referring to Figure 1 and Figure 4 For the sixth embodiment of the present application, a step of obtaining a hierarchical energy reserve level representing the current available energy reserve in real time is included: Real-time monitoring of at least one state parameter of the energy storage unit, determining a continuous energy state value representing the current available energy based on at least one state parameter, and mapping the continuous energy state value to a plurality of preset energy reserve division intervals to obtain a hierarchical energy reserve level corresponding to the energy reserve division interval.

[0058] Obtain at least one type of environmental parameter that can reflect the availability of external energy sources, and analyze the environmental parameters based on a preset prediction model to determine an expected supply scenario identifier representing the energy supply trend in a future period.

[0059] From a mode set containing a plurality of preset modes, a target mode is selected according to the combination of the energy reserve level and the expected supply scenario identifier, and at least one operating parameter of the lighting device is adjusted based on the target mode.

[0060] Among them, the mode set contains a plurality of (for example, 3 to 10) pre-designed complete work plans for different working conditions. Each independent mode specifies in detail how the energy-consuming unit should act in a complete work cycle. The core purpose of this design is to simplify and transform the complex dynamic programming problem that needs real-time calculation into two efficient links: lookup decision and plan execution: Lookup decision: A two-dimensional calculation matrix quickly queries and outputs a unique mode index according to the current energy reserve and the future energy supply expectation.

[0061] Pre-arranged execution: the controller of the energy consuming unit finds the corresponding pre-arranged plan in the local plan set according to the received mode index, and strictly executes the instructions of the pre-arranged plan.

[0062] This architecture reduces the requirement of the computing power of the terminal controller, while ensuring the rapidity of response and the determinacy of behavior.

[0063] Embodiment 7 Embodiment 7, with reference to Figure 1 and Figure 2 For the seventh embodiment of the present application, a mechanism of manual intervention and automatic recovery includes: Upon receiving a preset user intervention instruction, interrupting the currently executing mode, and making the lighting device switch to a preset operation mode independent of the energy reserve level and the expected supply scenario identifier, and after satisfying a preset recovery condition, automatically recovering the step of selecting and applying the mode according to the energy reserve level and the expected supply scenario identifier.

[0064] The preset recovery condition is a set of special rules for forced exit and state reset designed for the extreme survival mode. The core purpose is to solve the problem of the system being locked or stuck after entering the extreme survival mode, and to ensure that the device can automatically recover to the normal intelligent control mode driven by the two-dimensional calculation matrix after the energy situation is substantially improved.

[0065] The extreme survival mode is a mode prepared by the system when it encounters low power or bad weather expectations. Once in this mode, the energy consumption of the energy consuming unit is minimized (for example, completely turned off or only providing weak lighting). This can make the energy consumption of the device itself almost zero. In this state, if the standard two-dimensional calculation matrix is continued to be used for decision-making, problems may occur. (For example, even if the weather turns good and the energy storage module starts charging, due to the extremely low initial power, its energy reserve level may still be in the deficit level for a long time) At this time, if the query result of the two-dimensional calculation matrix is still the extreme survival mode, the system will not be able to exit this state, and will be in a locked state of being afraid to use although there is electricity to charge in. This will cause the device to be in a low-function state for a long time when it can actually recover to work, affecting the usability and user experience of the system. In order to solve this problem, the present application introduces a preset recovery condition.

[0066] This is a set of hard standards with higher priority than the two-dimensional calculation matrix for judging whether the extreme survival mode can be exited, and the recovery condition must be strict enough. For example, when and only when the controller of the energy consuming unit is in the extreme survival mode state, it will first check whether these recovery conditions are met at each decision-making period (for example, every morning).

[0067] If the condition is met: the controller will force clear the flag of the extreme survival mode, and skip the matrix query result which may still point to the extreme survival at this time, and directly restore to the normal decision-making process which is fully taken over by the two-dimensional calculation matrix.

[0068] If the condition is not met: the controller continues to maintain the extreme survival mode, and waits for the next check period.

[0069] The specific implementation is as follows: Example 8 Reference Figure 1 and Figure 2 For the eighth embodiment of the present application, a battery health state adaptive management procedure aiming to extend the full life cycle life of the energy storage unit includes: At least one historical operating parameter of the energy storage unit is obtained to evaluate the health state thereof, and based on the evaluated health state, at least one adjustment selected from the following two groups is performed: adjusting the correspondence between the energy reserve level and the expected supply scenario identification to a specific mode; adjusting at least one charge and discharge management limit value of the energy storage unit.

[0070] An actual supply trend is determined based on historical energy input data of the energy collection unit, and the actual supply trend is compared with the expected supply scenario identification, so as to correct the expected supply scenario identification when the actual supply trend does not match the expected supply scenario identification indicated by the expected supply scenario identification.

[0071] Among them, the charge and discharge management limit value is mainly divided into two types of charge management limit value and discharge management limit value. The charge management limit value is used to ensure the safety and efficiency of the energy supplement process, and is beneficial to the health maintenance of the energy storage module. Although many charging processes are automatically managed by standard solar controllers (such as MPPT or PWM), the mode of the present application can optimize and select the key parameters of these processes. These limit values can include: maximum charging current limit value: this limit value is set according to the specifications and current temperature of the energy storage module, to prevent battery overheating or damage caused by excessive charging current.

[0072] Charging cut-off voltage and floating voltage limit value: these voltage limit values define different stages of the charging process. When the battery voltage reaches the charging cut-off voltage limit value, the system switches from the constant current charging mode of large current to the constant voltage charging mode; When the charging current is reduced to a certain extent, the voltage will be maintained at a lower floating voltage limit value to supplement the self-discharge and maintain the full state of the battery, which can effectively prevent overcharging.

[0073] Balancing charge trigger condition: for some types of batteries (such as lead-acid batteries), inconsistency between individual cells may occur after long-term use.

[0074] The mode of the present application can contain a condition limit for triggering balancing charge. For example, when the system is in energy surplus state for a number of consecutive periods (i.e. consecutive sunny days), the charge management limit corresponding to the selected mode will contain an instruction to perform balancing charge, and the controller will perform a special maintenance charge on the battery pack using a slightly higher voltage to restore its capacity and consistency.

[0075] Discharge management limit is mainly used to fine-tune the energy consumption process, ensuring that the power consumption of the load (such as LED lights) in different modes meets the overall planning of the system.

[0076] Output power limit: this directly specifies the maximum power that the load is allowed to reach during operation. For example, the output power limit of a strong energy-saving mode may be set to 30% of the rated power, while the limit of a performance priority mode may be set to 90%.

[0077] Minimum discharge cutoff threshold: this is a protective limit, usually expressed in terms of voltage or state of charge (SOC) of the energy storage module. Regardless of the current mode the system is executing, once the energy storage state reaches this limit, the controller will forcibly cut off power to the load. Mainly to prevent deep over-discharge of the energy storage module, so as to protect the health of the device and prolong its cycle life.

[0078] Example 9 Referring to Figure 1 , Figure 2 , Figure 3 and Figure 4 , for the ninth embodiment of the present application, an expected replenishment scenario adaptive correction procedure based on local historical data includes: determining an actual replenishment trend based on historical energy input data of the energy collection unit, and comparing the actual replenishment trend with the expected replenishment scenario identifier to correct the expected replenishment scenario identifier when the actual replenishment trend does not match the expected replenishment scenario indicated by the expected replenishment scenario identifier.

[0079] Among them, the actual replenishment trend is one of the core mechanisms for the present application to achieve deep intelligence and adaptive optimization. It is essentially a personalized and dynamically updated local energy replenishment profile formed by long-term and continuous monitoring and analysis of the energy acquisition history of the energy-using unit.

[0080] The system can record the actual energy input of the light sensor during the day and compare it with the expected value from the weather forecast. If the actual supply is much lower than expected for several consecutive days (for example, the weather forecast is consistently inaccurate), the system can temporarily modify the expected supply scenario identification, for example, downgrade a good to general, to make better energy planning.

[0081] Its main role is to serve as a realistic calibration factor to modify the externally obtained, more macro and universal future energy supply expectations (such as weather forecasts), so that the final decision is based on the real situation of the unique micro-environment of each energy-consuming unit. (For example, spatial error: weather forecasts are regional and cannot reflect the micro-environment of a specific installation point. For example, a solar street lamp installed in an open square and a street lamp installed in a small alley with high building shading, even if the weather forecast is sunny, the actual light energy acquisition of the whole day may differ by several times.

[0082] State decay: over time, the hardware itself will change. Solar panels will have their photoelectric conversion efficiency reduced due to factors such as dust accumulation, bird droppings blocking, aging, etc.

[0083] Installation differences: even if the same model of street lamp, the orientation and inclination during installation are slightly different, it will cause differences in the efficiency of receiving solar energy.

[0084] These factors will cause errors between prediction and reality. If the system cannot perceive and adapt to this error, the accuracy of its decision-making will decrease significantly over time. The actual supply trend is designed to make up for this error.

[0085] The specific implementation is as follows: In summary, the present application can effectively improve the reliability of the system, effectively prolong the service life of its core components, maximize energy utilization efficiency, and achieve fine-grained management by constructing a two-dimensional calculation matrix based on future energy supply expectations and current energy storage status, and combining with the self-adaptive correction mechanism of historical actual supply data compared with the prior art. The core intelligent decision-making logic is deployed locally on the terminal device, which has a certain degree of autonomy and reduces dependence on central servers and real-time communication networks. Even in remote areas with network interruptions or no signal, energy-consuming units can still operate independently and intelligently.

[0086] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced, without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A method of adaptive dimming control based on energy supply prediction, characterized in that, The method realizes the dimming control of the lighting device in a preset control period through the following steps: Step 1: Grade the energy reserve state of the current system by detecting the electrical energy in the energy storage unit, and map it to the most suitable one of the preset multiple graded energy reserve levels; Step 2: Receive and analyze external environment prediction data, and convert the predicted lighting conditions into an expected supply scenario identifier; Step 3: Based on the current energy reserve level and the expected supply scenario identifier obtained, index in a two-dimensional calculation matrix embedded in the lighting device control system to determine and activate a unique corresponding pre-configured global dimming mode; Step 4: According to the activated global dimming mode, load a time segmented dynamic dimming formula associated with it, which sets a non-constant, time-varying brightness mode for the entire lighting period; Step 5: In the process of executing the brightness mode, real-time fusion of environmental perception data is performed to dynamically fine-tune the brightness mode, and conditional judgment response is performed on externally triggered brightness enhancement requests according to the permissions set by the currently activated global dimming mode, thereby realizing predictive planning of energy and real-time adaptation to environmental changes.

2. The adaptive dimming control method based on the energy replenishment prediction according to claim 1, characterized in that, The step of grading the energy reserve state of the current system and mapping it to the preset multiple graded energy reserve levels includes: Obtain the real-time working temperature of the energy storage unit through the temperature sensing component, and obtain multiple voltage samples through the voltage sampling circuit, thus generating a stable voltage value that excludes load interference; According to the obtained real-time working temperature, dynamically select and activate a set of voltage thresholds suitable for the current temperature interval from the stored multiple sets of samples; Compare the stable voltage value with the set of activated voltage thresholds with preset specificity, which defines non-overlapping voltage intervals for each energy reserve level with buffer boundaries when the state switches, thereby stably mapping the stable voltage value to one of the energy reserve levels.

3. The adaptive dimming control method based on the energy replenishment prediction of claim 1, wherein, The step of converting the predicted lighting conditions into a predefined expected supply scenario identifier includes: After receiving a pre-encoding encapsulating environmental prediction information and corresponding data integrity check information data packet through the communication interface, performing integrity check on the data packet and confirming data validity, extract the prediction code representing the expected environmental conditions in the future control period from it, define the prediction code and the expected supply scenario identifier as a deterministic association mapping rule, and then convert the prediction code into the unique corresponding expected supply scenario identifier.

4. The adaptive dimming control method based on the energy replenishment prediction of claim 1, wherein, The step of selecting and applying the mode according to the energy reserve level and the expected supply scenario identifier includes: According to a preset decision logic, the said decision logic identifies each preset combination of the said energy reserve level and the said expected supply scenario, and has established a unique corresponding lighting control mode for each preset combination, directly determines the unique corresponding said lighting control mode according to the current input said energy reserve level and said expected supply scenario, and performs corresponding operation state adjustment on the said lighting device according to the definition of the said lighting device operation parameters contained in the mode.

5. The adaptive dimming control method based on the energy replenishment prediction of claim 1, wherein, The step of obtaining a hierarchical said energy reserve level representing the current available said energy reserve in real time is specifically: Real-time monitoring of at least one state parameter of the energy storage unit, determining a continuous energy state value representing the current available energy based on the at least one state parameter, and mapping the said continuous energy state value to a plurality of preset said energy reserve division intervals to obtain a hierarchical said energy reserve level corresponding to the said energy reserve division interval.

6. The adaptive dimming control method based on the energy replenishment prediction of claim 1, wherein, The step of predicting a said expected supply scenario representing the future energy supply situation in real time is specifically: Obtaining at least one type of environmental parameter capable of reflecting the availability of external energy sources, and analyzing the said environmental parameter based on a preset prediction model to determine the said expected supply scenario representing the energy supply trend in a future period.

7. The adaptive dimming control method based on the energy replenishment prediction of claim 1, wherein, The step of selecting and applying a mode according to the said energy reserve level and the said expected supply scenario is specifically: From a mode set containing a plurality of preset modes, a target mode is selected according to the combination of the said energy reserve level and the said expected supply scenario, and at least one operation parameter of the said lighting device is adjusted based on the said target mode.

8. The adaptive dimming control method based on the energy replenishment prediction of claim 1, wherein, The method includes a manual intervention and automatic recovery mechanism: Upon receiving a preset user intervention instruction, interrupt the currently executing mode, and make the said lighting device switch to a preset operation mode independent of the said energy reserve level and the said expected supply scenario, and after meeting a preset recovery condition, automatically resume the step of selecting and applying a mode according to the said energy reserve level and the said expected supply scenario.

9. The adaptive dimming control method based on the energy replenishment prediction of claim 1, wherein, The method includes a battery health state adaptive management procedure for prolonging the life cycle of the said energy storage unit: Obtaining at least one historical operation parameter of the said energy storage unit to evaluate its health state, and based on the evaluated health state, performing at least one adjustment selected from the following two groups: Adjusting the correspondence between the said energy reserve level and the said expected supply scenario to a specific mode; Adjusting at least one charge and discharge management limit value of the said energy storage unit.

10. The adaptive dimming control method based on the energy replenishment prediction of claim 1, wherein, The method includes a said expected supply scenario adaptive correction procedure based on local historical data: Determine an actual supply trend based on the historical energy input data of the energy collection unit, and compare the said actual supply trend with the said expected supply scenario to correct the said expected supply scenario when the actual supply trend does not match the expected indicated by the said expected supply scenario.

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