Emergency lighting self-service power supply system for transformer substation

Abstract

The invention discloses a substation emergency lighting self-service power supply system, and relates to the technical field of emergency lighting power supply, and the system comprises the following steps: installing a sensor and a communication module, and setting basic parameters; collecting data, dynamically calculating illumination requirements, and compensating visual delay of human eyes; reading battery parameters, and quantifying the aging degree and the residual available capacity of the battery; calculating the power supply priority of each node by combining the emergency exit position based on the adjacent node state; the LED driving current is dynamically adjusted according to the priority, and gradient switching is adopted when the brightness changes in a large range; the low-electric-quantity node automatically initiates help seeking, and the surrounding nodes reduce own brightness in proportion; operation data are uploaded regularly, algorithm parameters are optimized, and a positioning signal is activated to guide maintenance when the battery is seriously lack of electricity. According to the invention, through the double-variable dynamic model coupling the ambient light attenuation compensation factor and the battery health degree attenuation function, the error accumulation of a single electric quantity criterion is eliminated, so that the power supply duration prediction better fits the real working condition.

Description

Technical Field

[0001] The present invention belongs to the technical field of emergency lighting power supply, and in particular relates to a self-service power supply system for emergency lighting in a substation. Background Art

[0002] In the power system, substations are the hub for power transmission and distribution, making them incredibly important. A substation failure can significantly impact electricity consumption and cause losses to residents, factories, and other facilities. Therefore, timely repairs are crucial to minimize the resulting social and economic losses. Therefore, reliable and stable emergency lighting is crucial for substations.

[0003] Existing emergency systems ignore the inhibitory effect of strong ambient light on the human eye's visual adaptability, resulting in a systematic deviation between the rated brightness requirements and the actual physiological visual requirements; at the same time, the nonlinear characteristics of battery capacity decay are disconnected from the changes in electrochemical parameters such as cycle aging and internal resistance increase, causing errors in the capacity estimation model in the middle and late stages of battery life.

[0004] These two factors, combined with the strong light interference unique to substation failures and the high temperature and humidity that accelerate battery aging, produce a synergistic negative effect. This causes the predicted power supply duration to deviate significantly from the actual duration, directly threatening the safety of personnel evacuation and the timeliness of equipment operation. Therefore, the following solution is proposed to address the above issues. Summary of the Invention

[0005] The purpose of the present invention is to provide a self-service power supply system for substation emergency lighting. By constructing a two-variable dynamic model of the ambient light attenuation compensation factor and the battery health attenuation function, it can correct the mapping relationship between lighting demand and power supply capacity in real time, solving the problem of inaccurate power supply duration prediction caused by ignoring the visual adaptation characteristics of the human eye and the nonlinear attenuation of the battery in the existing technology.

[0006] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0007] The present invention provides a self-service power supply system for emergency lighting in a substation. The power supply system includes an environment perception module, an energy management module, a calculation and decision module, a communication networking module, a drive execution module, a central management platform, and terminal lighting equipment. The environment perception module, the calculation and decision module, the drive execution module, the terminal lighting equipment, the communication networking module, and the energy management module are connected in sequence. The output end of the calculation and decision module is unidirectionally connected to the communication networking module, the output end of the communication networking module is unidirectionally connected to the central management platform, the output end of the central management platform is unidirectionally connected to the calculation and decision module, and the output end of the energy management module is unidirectionally connected to the input end of the calculation and decision module.

[0008] The working process of the power supply system is as follows:

[0009] Step S1: System initialization configuration: Install sensors and communication modules, and set basic parameters including brightness threshold, battery warning value, and device communication range;

[0010] Step S2, real-time ambient light monitoring: collect ambient light data every second, dynamically calculate lighting requirements, and use algorithms to compensate for human visual delay under strong light;

[0011] Step S3, battery health assessment: read key battery parameters and use a coupling model to quantify the battery aging degree and remaining available capacity;

[0012] Step S4, dynamic power allocation decision: based on the status of neighboring nodes and the location of the safety exit, calculate the power supply priority of each node;

[0013] Step S5, adaptive power supply execution: dynamically adjust the LED drive current according to the priority, and use gradual switching when the brightness changes over a large range to avoid visual interference;

[0014] Step S6, fault collaborative processing: the low-power node automatically initiates a request for help, and the surrounding nodes proportionally reduce their own brightness to extend the power supply time of the faulty node;

[0015] Step S7, state feedback closed loop: regularly upload operating data and optimize algorithm parameters, and activate positioning signals to guide maintenance when the battery is seriously low on power.

[0016] Furthermore, the environmental perception module is used to collect light intensity, temperature and humidity, and device location data in real time to provide environmental input for the dynamic compensation algorithm;

[0017] The energy management module is used to monitor the battery pack voltage, internal resistance, and temperature, assess its health status, and perform charge and discharge control.

[0018] The calculation and decision module is used to run the dynamic compensation formula and health model to calculate the power supply priority and drive parameters of each node;

[0019] The communication networking module is used to build a low-power Mesh network to achieve state synchronization between nodes and transmit emergency broadcast signals;

[0020] The driver execution module is used to adjust the LED current according to the algorithm output, perform smooth brightness switching and trigger the sound and light alarm;

[0021] The central management platform is used to store historical operating data, provide a remote monitoring interface and parameter OTA update function;

[0022] The terminal lighting equipment is used to receive driving instructions to generate emergency lighting and feedback fault status to the network.

[0023] Furthermore, the system initialization configuration in step S1 specifically includes the following steps:

[0024] Step S11: Install core hardware components in each lighting terminal, including a microprocessor responsible for logic operations, a light sensor for detecting ambient light levels, a two-way communication module for communication between devices, and a lithium iron phosphate battery pack as a backup power supply;

[0025] Step S12: Set the basic parameter values ​​required for system operation, including: the minimum lighting brightness threshold required to ensure the safe evacuation of personnel, the battery capacity decay warning line for early warning of battery performance degradation, and the maximum effective communication distance for stable information exchange between devices; after completing the hardware installation and parameter setting, the system enters the standby operation state.

[0026] Furthermore, the step S2, real-time ambient light monitoring specifically includes the following steps:

[0027] Step S21: continuously sensing the ambient light intensity in the substation area through a light sensor;

[0028] Step S22: In view of the visual adaptation hysteresis of the human eye when transitioning from a bright light environment to a dark environment, a visual compensation mechanism is adopted: when high ambient illumination is detected, the target brightness output of the lighting terminal is automatically increased; the compensation process is based on a nonlinear physiological adaptation model, in which the brightness requirement value of the model is above the basic emergency threshold, and a compensation amount that exponentially decays with the increase of ambient light is superimposed; the decay rate is controlled by a dynamically adjusted optical factor to ensure that personnel can immediately obtain progressively enhanced lighting when transitioning from a bright area to an emergency scene, eliminating blind spots in azimuth identification; the compensated target brightness will serve as the basis for subsequent power allocation.

[0029] Furthermore, the battery health assessment in step S3 specifically includes the following steps:

[0030] Step S31: Read the battery's current voltage, internal resistance, and historical cycle count;

[0031] Step S32: by calculating the voltage components, the difference between the real-time voltage and the minimum operating voltage is proportionally mapped to the reference interval between the nominal voltage and the minimum operating voltage to reflect the current voltage status;

[0032] To address the cycle aging effect, an inverse proportional decay model was constructed using the hyperbolic tangent function, whose input value was 500 divided by the number of cycles plus an offset of 10, so that the score of batteries with a high cycle number decayed nonlinearly;

[0033] The internal resistance part calculates the relative degradation degree of the current internal resistance to the maximum allowable internal resistance;

[0034] Multiplying the above three results generates a battery health coefficient ranging from 0 to 1, which comprehensively represents the real-time usable status of the battery.

[0035] Furthermore, the step S4, dynamic power allocation decision, specifically includes the following steps:

[0036] Step S41: Obtaining a status dataset of all neighboring nodes in real time through the Mesh network. The dataset contains three key parameters: the battery health of each node, the actual distance to the current node, and the brightness requirement value calculated after environmental compensation.

[0037] Step S42: The node performs power supply priority calculation: the product of its own battery health and the brightness requirement value is used as the numerator, and the result is divided by the sum of the distances from all adjacent nodes to the node. The sum reflects the node distribution density.

[0038] Introduce a spatial position weight factor: 1 minus the ratio of the straight-line distance from the node to the nearest safe exit to the system's preset maximum coverage radius;

[0039] Multiplying the above two parts together generates a quantitative power supply priority score, which determines the subsequent energy allocation ratio and ensures that nodes close to the escape path receive a higher power supply weight under high brightness requirements.

[0040] Furthermore, the step S5, wherein the adaptive power supply is performed, specifically comprises the following steps:

[0041] Step S51: Multiply the ratio of the current node's power supply priority to the sum of the priorities of all neighboring nodes in the network by the quotient of the minimum power supply duration required by the regulations and the predicted remaining power supply duration of the current node, and take the smaller value between the calculated result and the value 1; then multiply the smaller value by the maximum allowable current of the LED lamp to determine the actual output drive current value;

[0042] Step S52: When the drive current adjustment amplitude exceeds the threshold of the current current value, the brightness is smoothly switched in a linear gradient manner; when the adjustment amplitude is less than or equal to the threshold, instantaneous brightness switching is immediately performed.

[0043] Furthermore, the step S6, the coordinated fault handling specifically includes the following steps:

[0044] Step S61: When a node detects that its battery health factor is lower than a threshold, it immediately broadcasts an energy assistance signal through the Mesh network;

[0045] Step S62: The neighboring nodes that receive the help signal will initiate a mutual assistance response mechanism: obtain the current power supply priority data of the faulty node and query the highest priority value in the network;

[0046] The brightness adjustment ratio that the node needs to bear is calculated based on the preset initial mutual assistance coefficient multiplied by the ratio of the fault node priority to the highest priority in the entire network;

[0047] Reduce the current driving current value according to this ratio, but keep the reduced current not lower than the lower limit of the LED safe operation;

[0048] This process allows healthy nodes to extend the power supply time of faulty nodes by sacrificing some brightness, and gives priority to protecting high-priority nodes close to safe exits; the mutual assistance coefficient will be dynamically adjusted according to the average health of the network during the system closed-loop update phase.

[0049] Furthermore, the state feedback closed loop in step S7 specifically includes the following steps:

[0050] Step S71: The system automatically uploads the operating data of each node to the central gateway periodically;

[0051] Step S72: Based on the brightness control deviation value in the historical data, the system dynamically adjusts the ambient light attenuation factor. When the error between the actual brightness and the target brightness increases, the adjustment strength of the attenuation factor is increased in proportion to the error to enhance the compensation sensitivity. The mutual assistance coefficient is iteratively optimized using the average battery health of all nodes in the current network. When the overall battery status of the network is good, the mutual assistance response strength is increased; when the average health decreases, the mutual assistance amplitude is reduced to balance the system load.

[0052] Step S73: When it is detected that the remaining power of any node is lower than the preset threshold, the sound and light positioning signal of the node is immediately activated to guide personnel to quickly identify the location of the low-power lighting terminal.

[0053] The present invention has the following beneficial effects:

[0054] 1. This invention eliminates the error accumulation of a single power criterion by coupling the ambient light attenuation compensation factor with the battery health attenuation function in a two-variable dynamic model, making the power supply duration prediction more closely aligned with actual operating conditions. The ambient light compensation algorithm quantifies the human eye's visual adaptation characteristics in alternating light and dark environments, accurately reflecting actual lighting needs. The battery health function integrates real-time electrochemical parameters with historical aging characteristics to construct a multi-dimensional attenuation assessment. This design reduces the uncontrollable risk of emergency lighting interruptions in scenarios with complex lighting changes and battery performance fluctuations in substations.

[0055] 2. The present invention realizes the adaptive redistribution of lighting resources under fault conditions based on a spatial energy scheduling mechanism based on dynamic priority decision-making. The algorithm automatically identifies key locations such as evacuation passages and equipment operation areas by quantifying the coupling relationship between node health, location weight and real-time lighting demand. Under the constraints of network topology, it prioritizes the lighting continuity of high-weight nodes, while avoiding local overconsumption through gradient brightness adjustment. This energy distribution model, which is guided by safety and efficiency, maintains the visual guidance function of key areas under limited energy storage conditions.

[0056] 3. The present invention's gradual lighting control strategy effectively mitigates the human risk of sudden brightness changes. The system automatically selects either linear gradual or instantaneous switching mode based on the rate of change in brightness demand. When large illumination fluctuations are detected, a time-domain smooth transition algorithm is used to control the LED drive current, allowing the human retina sufficient time to adapt. This design dynamically adjusts the compensation intensity based on ambient light perception data, ensuring minimum illumination while avoiding instantaneous blindness caused by strong light stimulation, thereby improving personnel's spatial positioning ability in emergency situations.

[0057] 4. The present invention forms system-level redundant protection based on the distributed collaborative battery mutual assistance mechanism. When the node health deteriorates to a threshold, an energy cooperation request is initiated through the Mesh network. The surrounding nodes proportionally reduce the brightness of non-core areas according to the dynamic priority formula and transfer the released energy to the faulty node. This process uses closed-loop feedback to calibrate the mutual assistance parameters in real time to ensure the overall brightness balance of the system. This decentralized energy redistribution mode can still maintain basic lighting functions in the event of a single point failure, thereby improving the robustness of the system.

[0058] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0060] Figure 1 The figure is a structural diagram of a self-service power supply system for emergency lighting in a substation according to the present invention. DETAILED DESCRIPTION

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0062] See also Figure 1 As shown, the present invention is a self-service power supply system for emergency lighting in a substation. The power supply system includes an environment perception module, an energy management module, a calculation and decision module, a communication networking module, a drive execution module, a central management platform, and terminal lighting equipment. The environment perception module, the calculation and decision module, the drive execution module, the terminal lighting equipment, the communication networking module, and the energy management module are connected in sequence. The output end of the calculation and decision module is unidirectionally connected to the communication networking module, the output end of the communication networking module is unidirectionally connected to the central management platform, the output end of the central management platform is unidirectionally connected to the calculation and decision module, and the output end of the energy management module is unidirectionally connected to the input end of the calculation and decision module.

[0063] The environmental perception module is used to collect real-time data on light intensity, temperature and humidity, and device location, providing environmental input for the dynamic compensation algorithm;

[0064] The energy management module is used to monitor the battery pack voltage, internal resistance, and temperature, assess its health status, and perform charge and discharge control.

[0065] The calculation and decision module is used to run the dynamic compensation formula and health model to calculate the power supply priority and drive parameters of each node;

[0066] The communication networking module is used to build a low-power Mesh network to achieve state synchronization between nodes and transmit emergency broadcast signals;

[0067] The driver execution module is used to adjust the LED current according to the algorithm output, perform smooth brightness switching and trigger the sound and light alarm;

[0068] The central management platform is used to store historical operating data, provide a remote monitoring interface and parameter OTA update function;

[0069] The terminal lighting equipment is used to receive driving instructions to generate emergency lighting and feedback fault status to the network.

[0070] The working process of the power supply system is as follows:

[0071] Step S1: System initialization configuration: Install sensors and communication modules, and set basic parameters including brightness threshold, battery warning value, and device communication range;

[0072] Step S1, system initialization configuration specifically includes the following steps:

[0073] Step S11: Install a microprocessor, a light sensor, a two-way communication module, and a lithium iron phosphate battery pack in each lighting terminal;

[0074] Step S12: Setting basic parameters, including: rated emergency brightness threshold L min =50lux; battery capacity attenuation warning value C warn =70%; topological communication radius R com =30m;

[0075] This step completes hardware deployment and basic parameter settings, installs sensors, communication modules, and battery packs, establishes core parameters such as brightness thresholds, battery warning values, and communication range, and builds the physical and logical framework for system operation.

[0076] Step S2, real-time ambient light monitoring: collect ambient light data every second, dynamically calculate lighting requirements, and use algorithms to compensate for human visual delay under strong light;

[0077] Step S2, real-time ambient light monitoring specifically includes the following steps:

[0078] Step S21: The light sensor acquires the ambient illumination value E with a sampling period of Δt=1s env ;

[0079] Step S22: Calculate the required compensation light intensity based on the collected ambient light level:

[0080]

[0081] The brightness compensation intensity is jointly controlled by the preset visual adaptation coefficient and light attenuation factor to ensure that emergency lighting that meets the physiological needs of the human eye is always provided under the complex lighting conditions of the substation;

[0082] Where, L req Compensate the target brightness, L min is the rated emergency brightness threshold, α is the visual adaptation coefficient, β is the ambient light attenuation factor, E env is the real-time ambient illumination;

[0083] This step uses a light sensor to periodically collect ambient illumination, and uses an exponential decay formula to dynamically calculate and compensate for lighting needs, solving the problem of visual adaptation delay under strong ambient light and ensuring that the lighting brightness meets the physiological needs of the human eye.

[0084] Step S3, battery health assessment: read key battery parameters and use a coupling model to quantify the battery aging degree and remaining available capacity;

[0085] Step S3, battery health assessment specifically includes the following steps:

[0086] Step S31: Read the current battery voltage V and internal resistance R internal 、N number of historical cycles cycle ;

[0087] Step S32: Calculate the health coefficient to comprehensively represent the real-time usable status of the battery:

[0088]

[0089] Where H b is the battery health coefficient, V is the real-time battery voltage, V min is the minimum operating voltage, V nom is the nominal voltage, 500 is the cycle life reference constant, N cycle is the number of historical charge and discharge cycles, 10 is the zero cycle protection offset, R internal is the current internal resistance of the battery, R max is the maximum allowable internal resistance;

[0090] This step collects real-time data such as battery voltage, internal resistance, and cycle count, and uses the formula coupled with the hyperbolic tangent function to quantify the degree of battery aging, evaluate the available energy status, and provide a reliability basis for power supply decisions.

[0091] Step S4, dynamic power allocation decision: based on the status of neighboring nodes and the location of the safety exit, calculate the power supply priority of each node;

[0092] Step S4, dynamic power allocation decision specifically includes the following steps:

[0093] Step S41: Obtaining the state vector of the adjacent node through the Mesh network:

[0094]

[0095] Where S is the set of adjacent node states, is the battery health coefficient of the i-th neighbor node, D (i) is the Euclidean distance from the current node to the i-th neighbor node, is the compensation brightness requirement value of the i-th neighbor node, and n is the total number of valid neighbor nodes within the communication range of the current node;

[0096] Step S42: Calculate the power supply priority of this node and determine the subsequent energy allocation ratio:

[0097]

[0098] Where, P priority Power supply priority for this node, H b is the current node battery health, L reqis the current node brightness requirement, D k is the Euclidean distance to the kth neighbor node, Pos is the two-dimensional coordinate vector of this node, Pos exit is the coordinate vector of the nearest safe exit, R max is the maximum coverage radius of the system, ||·|| is the Euclidean norm;

[0099] This step obtains the status of adjacent nodes based on the Mesh network and generates a spatial collaborative power supply plan by integrating battery health, lighting requirements, and topological distance priority algorithm to prioritize the allocation of lighting resources on critical paths.

[0100] Step S5, adaptive power supply execution: dynamically adjust the LED drive current according to the priority, and use gradual switching when the brightness changes over a large range to avoid visual interference;

[0101] Step S5, the adaptive power supply execution specifically includes the following steps:

[0102] Step S51: Adjust the LED driving current according to the priority:

[0103]

[0104] Where, I drive is the final output drive current, I max is the maximum allowable current of LED, ∑P is the sum of the priorities of neighboring nodes, T req The shortest power supply duration required by regulations, T pred Predict the remaining power supply time for this node;

[0105] Step S52: Execute gradient brightness switching. When ΔI drive When the value is >10%, a 5-second linear gradient is used, otherwise the switch is instantaneous;

[0106] This step dynamically adjusts the LED drive current according to the priority, adopts a gradient switching mechanism to avoid sudden brightness changes that interfere with visual positioning, and achieves real-time matching of power supply intensity with available system resources.

[0107] Step S6, fault collaborative processing: the low-power node automatically initiates a request for help, and the surrounding nodes proportionally reduce their own brightness to extend the power supply time of the faulty node;

[0108] Step S6, fault collaborative processing specifically includes the following steps:

[0109] Step S61: H is detected b <0.4: Initiate energy assistance broadcast;

[0110] Step S62: The neighboring nodes that receive the help signal will start the mutual assistance response mechanism, and the nodes that receive the help will respond according to the formula:

[0111]

[0112] Where, is the adjusted driving current of node j, is the current value of node j, γ is the mutual assistance coefficient, is the priority of the faulty node k, is the highest priority in the current network, j is the node ID that responds to mutual assistance, and k is the faulty node ID that initiates assistance;

[0113] This step triggers the energy mutual assistance protocol for low-health nodes, dynamically transfers the power supply load through the distributed node current regulation formula, builds an energy sharing network between nodes, and improves the overall fault tolerance of the system.

[0114] Step S7, state feedback closed loop: regularly upload operating data and optimize algorithm parameters, and activate positioning signals to guide maintenance when the battery is seriously low on power.

[0115] Step S7, the state feedback closed loop specifically includes the following steps:

[0116] Step S71: Upload the operation log to the central gateway every 60 seconds;

[0117] Step S72: Update the attenuation parameters based on historical data:

[0118] β←β×(1+0.05×ΔL error );

[0119]

[0120] Where β is the ambient light attenuation factor, ΔL error is the brightness control error, γ is the mutual assistance coefficient, is the average battery health of the network;

[0121] Step S73: When C remain When the percentage is <20%, the sound and light positioning signal is activated;

[0122] This step periodically uploads operating data and optimizes environmental compensation parameters based on historical errors. It also adaptively adjusts the mutual assistance coefficient based on battery status. When the battery level is critical, it activates the positioning signal to achieve parameter self-learning and emergency response.

[0123] A specific application of this embodiment is:

[0124] Implementation Background: A 500kV smart substation experienced a main power failure during a storm, and the emergency lighting system was activated. The system includes five lighting nodes: Node 1: Main control room entrance (coordinates (10,15)); Node 2: Equipment area passage (coordinates (30,40)); Node 3: GIS room passage (coordinates (50,25)); Node 4: Emergency Exit A (coordinates (70,5)); Node 5: Emergency Exit B (coordinates (80,45));

[0125] The real-time parameters are shown in the following table:

[0126]

[0127] Implementation steps:

[0128] Step S1: Ambient light compensation calculation (taking node 2 as an example)

[0129] Get ambient light intensity Calculate the brightness requirement after compensation:

[0130] Strong light environment triggers 17.4% brightness compensation to offset visual adaptation delay;

[0131] Step S2: Battery health assessment (taking node 5 as an example)

[0132] Input parameters: V = 3.10V, R internal =0.09Ω,N cycle =150;

[0133] Calculate the health coefficient:

[0134] High cycle count + excessive internal resistance results in health being below the threshold (0.4);

[0135] Step S3: Dynamic power supply decision (node ​​coordination)

[0136] Node 5 broadcasts a fault signal Each node calculates the distance to safety exit B:

[0137]

[0138] Node 4 calculates its own priority:

[0139]

[0140] Proximity to emergency exits receives positional weighting;

[0141] Step S4: Adaptive brightness adjustment

[0142] Node 3 responds to the support request

[0143] Output current is reduced by 2.5% to support faulty nodes;

[0144] Node 5 drives current after receiving support:

[0145] The current is increased to a safe threshold;

[0146] Step S5: Gradient switching and closed-loop update

[0147] Node 4 detects ΔI drive =15%>10% threshold;

[0148] Perform a 5-second linear gradient: I(t) = 0.65 + 0.07t (t = 0 to 5 seconds);

[0149] Update the ambient light parameters at the end of the cycle:

[0150] β←0.02×(1+0.05×0.03)=0.02003;

[0151] γ←0.3×0.81=0.243.

[0152] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0153] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A self-service power supply system for emergency lighting in a substation, characterized in that: The power supply system includes an environmental perception module, an energy management module, a calculation and decision module, a communication networking module, a drive execution module, a central management platform, and a terminal lighting device; the environmental perception module, the calculation and decision module, the drive execution module, the terminal lighting device, the communication networking module, and the energy management module are connected in sequence, the output end of the calculation and decision module is unidirectionally connected to the communication networking module, the output end of the communication networking module is unidirectionally connected to the central management platform, the output end of the central management platform is unidirectionally connected to the calculation and decision module, and the output end of the energy management module is unidirectionally connected to the input end of the calculation and decision module; The working process of the power supply system is as follows: Step S1: System initialization configuration: Install sensors and communication modules, and set basic parameters including brightness threshold, battery warning value, and device communication range; Step S2, real-time ambient light monitoring: collect ambient light data every second, dynamically calculate lighting requirements, and use algorithms to compensate for human visual delay under strong light; Step S3, battery health assessment: Read key battery parameters and use a coupled model to quantify the battery aging degree and remaining available capacity; Step S4, dynamic power allocation decision: Calculate the power supply priority of each node based on the status of neighboring nodes and the location of the safety exit; Step S5, adaptive power supply execution: dynamically adjust the LED driving current according to the priority, and use gradual switching when the brightness changes over a large range to avoid visual interference; Step S6, fault collaborative processing: the low-power node automatically initiates a request for help, and the surrounding nodes proportionally reduce their own brightness to extend the power supply time of the faulty node; Step S7, state feedback closed loop: regularly upload operating data and optimize algorithm parameters, and activate positioning signals to guide maintenance when the battery is seriously low on power.

2. A substation emergency lighting self-service power supply system according to claim 1, characterized in that: The environmental perception module is used to collect light intensity, temperature and humidity, and device location data in real time to provide environmental input for the dynamic compensation algorithm; The energy management module is used to monitor the battery pack voltage, internal resistance, and temperature, assess its health status, and perform charge and discharge control. The calculation and decision module is used to run the dynamic compensation formula and health model to calculate the power supply priority and drive parameters of each node; The communication networking module is used to build a low-power Mesh network to achieve state synchronization between nodes and transmit emergency broadcast signals; The driver execution module is used to adjust the LED current according to the algorithm output, perform smooth brightness switching and trigger the sound and light alarm; The central management platform is used to store historical operating data, provide a remote monitoring interface and parameter OTA update function; The terminal lighting equipment is used to receive driving instructions to generate emergency lighting and feedback fault status to the network.

3. The substation emergency lighting self-service power supply system according to claim 1, characterized in that: The step S1, system initialization configuration specifically includes the following steps: Step S11: Install core hardware components in each lighting terminal, including a microprocessor responsible for logic operations, a light sensor for detecting ambient light levels, a two-way communication module for communication between devices, and a lithium iron phosphate battery pack as a backup power supply; Step S12: Set the basic parameter values ​​required for system operation, including: the minimum lighting brightness threshold required to ensure the safe evacuation of personnel, the battery capacity attenuation warning line for early warning of battery performance degradation, and the maximum effective communication distance for stable information exchange between devices.

4. The substation emergency lighting self-service power supply system according to claim 1, characterized in that: The step S2, real-time ambient light monitoring, specifically includes the following steps: Step S21: continuously sensing the ambient light intensity in the substation area through a light sensor; Step S22: In view of the visual adaptation hysteresis of the human eye when transitioning from a bright light environment to darkness, a visual compensation mechanism is adopted: when high ambient illumination is detected, the target brightness output of the lighting terminal is automatically increased; the compensation process is based on a nonlinear physiological adaptation model, in which the brightness requirement value of the model is above the basic emergency threshold, and a compensation amount that exponentially decays with the increase of ambient light is superimposed; the decay rate is controlled by a dynamically adjusted optical factor to ensure that personnel can immediately obtain progressively enhanced lighting when transitioning from a bright area to an emergency scene, eliminating blind spots in azimuth identification.

5. The substation emergency lighting self-service power supply system according to claim 1, characterized in that: The step S3, battery health assessment, specifically includes the following steps: Step S31: Read the battery's current voltage, internal resistance, and historical cycle count; Step S32: by calculating the voltage components, the difference between the real-time voltage and the minimum operating voltage is proportionally mapped to the reference interval between the nominal voltage and the minimum operating voltage to reflect the current voltage status; To address the cycle aging effect, an inverse proportional decay model was constructed using the hyperbolic tangent function, whose input value was 500 divided by the number of cycles plus an offset of 10, so that the score of batteries with a high cycle number decayed nonlinearly; The internal resistance part calculates the relative degradation degree of the current internal resistance to the maximum allowable internal resistance; Multiply the above three results to comprehensively represent the real-time available status of the battery.

6. The substation emergency lighting self-service power supply system according to claim 1, characterized in that: The step S4, dynamic power allocation decision specifically includes the following steps: Step S41: Obtaining a status dataset of all neighboring nodes in real time through the Mesh network. The dataset contains three key parameters: the battery health of each node, the actual distance to the current node, and the brightness requirement value calculated after environmental compensation. Step S42: Calculate the power supply priority by multiplying the battery health and the required brightness value by the sum of the distances from all adjacent nodes to the node. The sum reflects the node distribution density. Introduce a spatial position weight factor: 1 minus the ratio of the straight-line distance from the node to the nearest safe exit to the system's preset maximum coverage radius; Multiplying the above two parts together generates a quantitative power supply priority score.

7. The substation emergency lighting self-service power supply system according to claim 1, characterized in that: The step S5, wherein the adaptive power supply is executed, specifically comprises the following steps: Step S51: Multiply the ratio of the current node's power supply priority to the sum of the priorities of all neighboring nodes in the network by the quotient of the minimum power supply duration required by the regulations and the predicted remaining power supply duration of the current node, and take the smaller value between the calculated result and the value 1; then multiply the smaller value by the maximum allowable current of the LED lamp to determine the actual output drive current value; Step S52: When the drive current adjustment amplitude exceeds the threshold of the current current value, the brightness is smoothly switched in a linear gradient manner; when the adjustment amplitude is less than or equal to the threshold, instantaneous brightness switching is immediately performed.

8. The substation emergency lighting self-service power supply system according to claim 1, characterized in that: Step S6, fault collaborative processing specifically includes the following steps: Step S61: When a node detects that its battery health factor is lower than a threshold, it immediately broadcasts an energy assistance signal through the Mesh network; Step S62: The neighboring nodes that receive the help signal will initiate a mutual assistance response mechanism: obtain the current power supply priority data of the faulty node and query the highest priority value in the network; The brightness adjustment ratio that the node needs to bear is calculated based on the preset initial mutual assistance coefficient multiplied by the ratio of the fault node priority to the highest priority in the entire network; Reduce the current driving current value according to this ratio, but keep the reduced current not lower than the lower limit of the LED's safe operation.

9. The substation emergency lighting self-service power supply system according to claim 1, characterized in that: The step S7, the state feedback closed loop specifically includes the following steps: Step S71: The system automatically uploads the operating data of each node to the central gateway periodically; Step S72: Based on the brightness control deviation value in the historical data, the system dynamically adjusts the ambient light attenuation factor: when the error between the actual brightness and the target brightness increases, the adjustment intensity of the attenuation factor is increased in proportion to the error to enhance the compensation sensitivity; The mutual assistance coefficient is iteratively optimized using the average battery health of all nodes in the current network: when the overall battery status of the network is good, the mutual assistance response strength is enhanced; when the average health decreases, the mutual assistance amplitude is reduced to balance the system load; Step S73: When it is detected that the remaining power of any node is lower than a preset threshold, the sound and light positioning signal of the node is activated to guide personnel to quickly identify the location of the low-power lighting terminal.

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