CN117279138A - LED control system based on power grid equalization - Google Patents
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Abstract
The invention relates to the technical field of power grids, in particular to an LED control system based on power grid equalization, which comprises: the LED power grid, the power grid balance control device, the LED matrix and the LED control device; the LED matrix comprises a plurality of LEDs, and each LED is connected to different positions in an LED power grid; the power grid balance control device is configured to consider each LED in the LED power grid as a load, collect equipment parameters of each LED, and perform power balance distribution in the LED power grid so that the load balance degree in the LED power grid is highest; the LED control device is configured to perform multi-state control on each LED in the LED matrix. The invention realizes the load balance and the fine brightness control of the LED power grid, and improves the performance, the energy utilization efficiency and the reliability of the LED system.
Description
Technical Field
The present disclosure relates to, but not limited to, the field of power grid technology, and in particular to a power grid equalization based LED control system.
Background
In recent years, with the rapid development of LED (light emitting diode) technology, LEDs are increasingly used in lighting, display and other fields. However, the design and optimization of LED control systems still face a series of challenges, one of which is how to achieve load balancing of the LED grid to ensure that each LED gets the proper power supply and to improve overall efficiency. In order to solve the problems, an LED control system based on power grid balance is provided, and aims to realize load balance distribution of an LED power grid, so that the performance and energy utilization efficiency of the LED system are improved.
In the past LED control systems, problems such as load imbalance, energy waste, and system performance degradation often occur. Traditional load balancing methods are often based on a fixed power distribution strategy, ignoring the different requirements of each LED in the LED grid. In reality, the power requirements of different LEDs will vary according to the location, application scenario and operating state. The traditional method can not dynamically distribute power according to actual demands, so that some LEDs are insufficient in power supply, and other LEDs waste energy. In addition, the prior art lacks an efficient LED control method, and cannot realize fine control on the brightness of the LEDs, so that the visual effect and energy utilization of an LED system are affected.
Disclosure of Invention
The invention provides the LED control system based on power grid balancing, which realizes load balancing and fine brightness control of an LED power grid and improves the performance, energy utilization efficiency and reliability of the LED system.
In order to solve the problems, the technical scheme of the invention is realized as follows:
an LED control system based on grid equalization, comprising: the LED power grid, the power grid balance control device, the LED matrix and the LED control device; the LED matrix comprises a plurality of LEDs, and each LED is connected to different positions in an LED power grid; the power grid balance control device is configured to consider each LED in the LED power grid as a load, collect equipment parameters of each LED, and perform power balance distribution in the LED power grid so that the load balance degree in the LED power grid is highest; the LED control device is configured to perform multi-state control on each LED in the LED matrix, and specifically comprises: representing the initial brightness of the LED using the polymorphism, and representing the brightness information of the LED as a polymorphism |ψ LED >Let n be the number of multi-state bits, corresponding to the number of brightness levels of the LEDs; by applying the polymorphic gate operation, the polymorphism |ψ of an evolving LED LED >To simulate the variation process of the brightness of the LED; initializing a population of particles, each particle representing an angle of evolution θThe method comprises the steps of carrying out a first treatment on the surface of the The position of the particle represents a candidate value of the evolution angle, and the speed represents the searching direction of the particle in the solution space; calculating fitness based on a fitness function for each particle according to the difference between the LED brightness and the target brightness; finding particles with highest fitness from particle swarm, taking the evolution angle as global optimal angle theta global The method comprises the steps of carrying out a first treatment on the surface of the Updating the position and velocity of each particle to find a more optimal solution; applying the evolution angle θ of each particle to the polymorphic gate operation, updating the polymorphism of the LEDs
|ψ LED >The method comprises the steps of carrying out a first treatment on the surface of the Based on updated LED polymorphism |ψ LED >Controlling the brightness value of the LED;
further, the multi-state gate operation expressed by the following formula is applied to simulate the change process of the brightness of the LED:
wherein the method comprises the steps ofIs the i-th multi-bit Pauli-X matrix, theta is the evolution angle, and U (theta) is the evolution result.
Further, the fitness function is expressed using the following formula:
wherein MSE is the mean square error between the LED luminance and the target luminance; fitness is Fitness.
Further, the position and velocity of each particle is updated using the following formula:
where ω is the inertial weight, c 1 And c 2 Is the acceleration coefficient, rand 1 And rand 2 Is between 0 and 1A random number; velocity new For updated speed, velocity old Is the speed before update; θ current Is the current evolution angle; position old To update the pre-Position, position new Is the updated position; θ local Historical optimal evolution angle.
Further, the device parameters include: the number of LEDs M in the LED matrix, the maximum power supply capability P of each LED max The method comprises the steps of carrying out a first treatment on the surface of the Power demand P for each LED demand And an initial power supply P for each LED supply 。
Further, the power grid balance control device regards each LED in the LED power grid as a load, collects the equipment parameters of each LED, and performs power balance distribution in the LED power grid, so that the method for maximizing the load balance degree in the LED power grid comprises the following steps:
step 1: let the initial power demand change value of the ith LED beThe initial power demand change rate of the ith LED is +.>
Step 2: updating the power demand change value of the LED in consideration of the power demand change rate and the historical optimal power demand change value;
step 3: updating the power demand change rate of the LEDs by considering the optimal power demand change values of the LEDs and the adjacent LEDs;
step 4: calculating the power supply of each LED;
step 5: updating a historical optimal power demand change value; updating the optimal power demand change value of the adjacent LEDs;
step 6: correcting the power demand change value of the LED; based on the corrected power demand change value; updating the power demand change rate of the LED again;
step 7: and (3) circularly executing the steps 4 to 6 for set times, and distributing the power in the LED power grid in an equalizing way based on the power supply of the LED obtained through final calculation, so that the load balance degree in the LED power grid is the highest.
Further, in step 2, the following formula is used to update the power demand change value of the LED:
wherein t is a time step;
in step 3, the following formula is used to update the rate of change of the power demand of the LED:
wherein,the rate of change of power demand for the ith LED at time step t+1; the power demand rate of change refers to the rate of change of the power demand of the LED; k is an inertia weight, and the influence degree of the current speed on the power demand change rate at the next moment is controlled; />The power demand change rate of the ith LED at the time step t is the power demand change rate of the last moment; a, a 1 For individual experience weights, representing the degree of attraction of each LED to a historical optimal power demand change value; r is (r) 1 The random number of the individual, introduce the randomness, is used for increasing the diversity of searching; p (P) best,i A historical optimal power demand change value for the ith LED, i.e., an optimal power supply value obtained by the LED in past iterations; />The power demand change value of the ith LED at the time step t is the power demand change value of the last moment; a, a 2 For group experience weights, representing the attraction degree of each LED to the historical optimal power demand change value of the adjacent LEDs; r is (r) 2 For group random number, drawRandom access is performed to increase the diversity of searches; p (P) best,neighbor,i Historical optimal power demand change values for adjacent LEDs of the ith LED, i.e., optimal power supply values obtained in past iterations for adjacent LEDs; />The quantum power demand change value of the ith LED at the time step t is used as a reference value for updating the power demand change rate.
Further, the power supply for each LED is calculated in step 4 using the following formula:
wherein P is supply,i The power supply for the ith LED.
Further, in step 5, the historical optimal power demand change value is updated using the following formula:
if P supply,i >P best,i P is then best,i ＝P supply,i ；
In step 5, the optimal power demand change value of the adjacent LED is updated using the following formula:
if P best,j >P best,neighbor,i P is then best,neighbor,i ＝P best,j 。
Wherein P is best,j The historical optimal power demand change value for the j-th neighboring LED of the i-th LED.
Further, in step 6, the following formula is used to correct the power demand variation value of the LED;
wherein P is supply,j Supplying power to the j-th neighboring LED of the i-th LED;
the rate of change of the power demand of the LED is updated again using the following formula:
the LED control system based on power grid equalization has the following beneficial effects: conventional LED control systems often suffer from load imbalance in power distribution, some LEDs are not powered enough, and other LEDs waste energy. The control system of the present invention achieves balanced distribution of power by treating each LED as a load and dynamically distributing power according to the power demand, supply capacity and initial power supply of each LED. The LED power grid has the advantages that each LED in the LED power grid can obtain proper power supply, so that the overall energy utilization efficiency is improved, the energy waste is reduced, and the service life of the LEDs is prolonged. The invention introduces multi-state representation and multi-state gate operation, so that the brightness control of the LED is finer and more diversified. The initial brightness of the LED is represented through polymorphism, and the change process of the brightness of the LED is simulated by using the multi-state gate operation, so that the multi-state control of the brightness of the LED is realized. The multi-state control method not only can realize finer brightness adjustment, but also can adapt to different application scenes and requirements, and provides richer visual effects. By combining with a particle swarm optimization algorithm, the invention further improves the control precision in the aspect of LED brightness control. And (3) by calculating the fitness function, evolving particles according to the difference between the brightness of the LED and the target brightness, and optimizing the brightness control of the LED. The introduction of the algorithm enables the brightness control of the LED to be more intelligent and adaptive, and effectively improves the performance and visual effect of the LED system. The invention not only ensures that the load balance degree in the LED power grid is the highest, but also realizes the fine control of the brightness of the LED, thereby greatly improving the overall performance of the LED system. The power distribution of load balancing enables each LED to be met on the premise of not wasting energy, and fine brightness control improves the adjustability and adaptability of the LEDs. The LED display device not only improves the visual effect of LED application, but also can save energy cost and reduce energy consumption, and contributes to sustainable development.
Drawings
Fig. 1 is a schematic system structure diagram of an LED control system based on power grid equalization according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present disclosure more clear and obvious, the present disclosure is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present disclosure and are not intended to limit the present disclosure. .
Example 1: referring to fig. 1, a grid equalization based LED control system, comprising: the LED power grid, the power grid balance control device, the LED matrix and the LED control device; the LED matrix comprises a plurality of LEDs, and each LED is connected to different positions in an LED power grid; the power grid balance control device is configured to consider each LED in the LED power grid as a load, collect equipment parameters of each LED, and perform power balance distribution in the LED power grid so that the load balance degree in the LED power grid is highest; the LED control device is configured to perform multi-state control on each LED in the LED matrix, and specifically comprises: representing the initial brightness of the LED using the polymorphism, and representing the brightness information of the LED as a polymorphism |ψ LED >Setting N as the polymorphic bit number and corresponding to the brightness level number of the LEDs; by applying the polymorphic gate operation, the polymorphism |ψ of an evolving LED LED >To simulate the variation process of the brightness of the LED; initializing a particle swarm, wherein each particle represents an evolution angle theta; the position of the particle represents a candidate value of the evolution angle, and the speed represents the searching direction of the particle in the solution space; calculating fitness based on a fitness function for each particle according to the difference between the LED brightness and the target brightness; finding particles with highest fitness from particle swarm, taking the evolution angle as global optimal angle theta global The method comprises the steps of carrying out a first treatment on the surface of the Updating the position and velocity of each particle to find a more optimal solution; applying the evolution angle θ of each particle to the polymorphic gate operation, updating the polymorphism |ψ of the LED LED >The method comprises the steps of carrying out a first treatment on the surface of the Based on updated LED polymorphism |ψ LED >The brightness value of the LED is controlled.
In particular, the LED grid in the system is a power supply network comprising a plurality of LEDs. These L' sThe EDs are organized into a matrix of LEDs, each connected to a different location in the LED grid. This arrangement allows the LEDs to be controlled centrally to achieve adjustment of the overall illumination. The grid-balancing control is a key part of the innovation, which regards each LED in the LED grid as a load. By collecting the equipment parameters of each LED, the control device realizes the balanced distribution of the power. This means that the system adjusts the power distribution according to the power consumption characteristics of each LED to achieve load balancing in the LED grid. This balance helps to improve energy utilization efficiency and reduce energy waste. The LED control device introduces a multi-state control strategy and controls the brightness of the LED through a quantum state. The luminance information of each LED is expressed as a polymorphic quantum state |ψ LED >Where N is the number of multi-state bits, corresponding to the number of brightness levels of the LED. By applying multi-state gate operation, the quantum state |ψ of the LED LED >May evolve to simulate the course of change in LED brightness. This control strategy is more elaborate and tunable than conventional current control methods. In the LED control device, a particle swarm optimization algorithm is introduced to optimize the brightness of the LEDs. Each particle represents an evolution angle θ whose position represents a candidate value for the evolution angle and speed represents the search direction of the particle in the solution space. Evaluating the fitness of each particle by a fitness function, finding the particle with the highest fitness, and taking the evolution angle as a global optimal angle theta global . This approach may help find an optimal LED brightness control scheme.
The multi-state control strategy allows for highly fine-tuning of the brightness of the LEDs. Since the brightness level of the LED can be represented as a multi-state quantum state, continuous and smooth brightness variation can be realized, and different illumination requirements can be satisfied. Compared with the traditional current control method, the multi-state control strategy can realize higher energy efficiency. By accurate brightness adjustment, energy waste can be avoided and unnecessary energy consumption can be reduced to the maximum extent. The polymorphic control strategy has high flexibility and can adapt to different application scenes and illumination requirements. The brightness of the LEDs can be adjusted in real time according to the environmental change so as to meet the demands of users. Due to the superiority of the multi-state control strategy, the brightness of the LED can be responsive to the control signal quickly. This is important in application scenes where a fast adjustment of the brightness is required, such as stage lighting or emergency lighting. The particle swarm optimization algorithm is used for finding the optimal solution of LED brightness control. By constantly optimizing the position and velocity of the particles, the system can find a globally optimal angle, thereby achieving optimal brightness matching.
Example 2: on the basis of the above embodiment, the multi-state gate operation expressed by the following formula is applied to simulate the change process of the brightness of the LED:
wherein the method comprises the steps ofIs the i-th multi-bit Pauli-X matrix, theta is the evolution angle, and U (theta) is the evolution result.
In particular, the method comprises the steps of,is the i-th multi-bit Pauli-X matrix: in quantum computing, pauli-X matrix is a basic quantum gate for performing bit flipping operations of qubits. For the ith multiple bit ++>Indicating the application of the Pauli-X gate to this bit. In the LED control device, this operation corresponds to changing the brightness level of the LED, because the brightness level of the LED can be represented by the ground state of the quantum state.
θ is the evolution angle: θ represents the angle for evolving multi-state gate operation. This angle affects the change in brightness state of the LED made by the multi-state gate operation. By adjusting the value of θ, brightness adjustment of different magnitudes can be achieved.
U (θ) is the evolution result: u (θ) represents the evolution result obtained after the application of the polymorphic gate operation. This result corresponds to a change in the brightness of the LED. Different brightness change processes can be simulated through different theta values, so that accurate brightness control is realized.
In summary, the multi-state gate operation of this formulation achieves analog variation of LED brightness by varying the brightness levels of the LEDs in the LED matrix. By adjusting the evolution angle theta, accurate brightness control can be realized, thereby meeting different lighting requirements. This operation has higher accuracy and adjustability than conventional current regulation methods.
Example 3: on the basis of the above embodiment, the fitness function is expressed using the following formula:
wherein MSE is the mean square error between the LED luminance and the target luminance; fitness is Fitness.
Specifically, MSE is the mean square error between the LED luminance and the target luminance: the mean square error (MeanSquaredError, MSE) is a statistical measure that measures the difference between the actual and predicted values. In LED control, MSE is used to measure the difference between the actual LED brightness and the target brightness. It is expressed as:
LEDbrightness i is the actual brightness of the ith LED, targetbtrightness i Is the target brightness of the ith LED.
Fitness (Fitness) is a measure that indicates how good an individual is in an evolutionary algorithm. Here, the fitness function uses the inverse of the MSE, scaled and transformed by an exponential function. The design intent of this fitness function is to make the fitness inversely proportional to the MSE, i.e., the fitness increases as the MSE decreases. Using the form of an exponential function, it can be ensured that the increase in fitness is nonlinear. The fitness function calculates fitness from the Mean Square Error (MSE) between the actual luminance of the LED and the target luminance. The fitness value increases as the MSE decreases, and the use of an exponential function form makes the fitness value change more pronounced, helping the optimization algorithm to find the best solution faster.
Example 4: on the basis of the above embodiment, the position and velocity of each particle are updated using the following formula:
where ω is the inertial weight, c 1 And c 2 Is the acceleration coefficient, rand 1 And rand 2 A random number between 0 and 1; velocity new For updated speed, velocity old Is the speed before update; θ current Is the current evolution angle; position old To update the pre-Position, position new Is the updated position; θ local Historical optimal evolution angle.
Specifically, ω is the inertial weight: the inertial weight ω is used to balance the effect between the historical motion direction of the particles and the global optimum position. Its value is typically between 0 and 1, which is used to adjust the inertia of the particles. A larger ω may make the particles more prone to maintain their historical motion direction, while a smaller ω may make the particles more prone to move toward a globally optimal position.
c 1 And c 2 Is the acceleration coefficient: acceleration coefficient c 1 And c 2 The degree of attraction of the particles to the individual optimum position and the global optimum position, respectively. They serve to regulate the extent to which particles are affected by both attractive forces. Larger c 1 Will enhance the attraction of the particles to the optimal position of the individual, a larger c 2 The attraction of the particles to the globally optimal location is enhanced.
rand 1 And rand 2 Is a random number between 0 and 1: rand 1 and rand 2 are random numbers uniformly distributed between 0 and 1 for introducing randomness. They are multiplied by the difference of θlocal and θglobal, affecting the speed adjustment of the particles.
θ local Is the optimal evolution angle of history: θ local Representing the optimal angle of evolution in the history of the particle itself, i.eThe best solution obtained in the previous iteration.
θ global Is the global optimum evolution angle: θ global The global optimum evolution angle in the whole particle swarm is represented, namely, the evolution angle of the particle with the highest adaptability in all particles in the particle swarm.
In summary, this formula describes the way in which the particle positions and velocities are updated in the particle swarm optimization algorithm. By taking into account inertial weights, acceleration coefficients, random numbers, and gravities of individual and globally optimal locations, the particles can search for a better solution in the solution space. The position and speed are updated continuously, and the particles gradually tend to the globally optimal solution, so that the brightness control in the LED control device is optimized.
Example 5: on the basis of the above embodiment, the device parameters include: the number of LEDs M in the LED matrix, the maximum power supply capability P of each LED max The method comprises the steps of carrying out a first treatment on the surface of the Power demand P for each LED demand And an initial power supply P for each LED supply 。
Specifically, the number of LEDs M: this parameter represents the number of LEDs in the LED matrix. The number of LEDs in the LED matrix determines the size and capacity of the overall lighting system. In grid-equalization control, the number of LEDs will affect the power distribution and the calculation of load-balancing.
Maximum power supply capability P of each LED max : this parameter represents the maximum power capability that each LED can provide. It reflects the power supply limitation of the LED, i.e. the maximum power that the LED can withstand. In the power balance distribution, considering the maximum power supply capability of the LEDs can ensure that the LEDs do not exceed their bearing range.
Power demand P for each LED demand : this parameter represents the power requirements of each LED, i.e. the power required by the LED to achieve its target brightness. The power requirements are related to the brightness and performance of the LEDs, with greater brightness generally requiring more power supply.
Initial power supply P for each LED supply : this parameter represents the initial power supply of the LED. The initial power supply may be affected by the initial configuration of the system, which may be a preset value or from thereHe controls the strategy. In grid equalization control, the initial power supply will affect the start of load distribution.
These device parameters are used in the grid-equalization control device to calculate the power distribution of the LEDs to achieve load-equalization and energy efficient utilization. By monitoring the power supply and demand of the LEDs, the system can dynamically adjust the power distribution according to the actual situation to ensure a balanced load distribution in the LED grid, thereby optimizing the performance of the LED lighting system.
Example 6: on the basis of the above embodiment, the power grid balance control device regards each LED in the LED power grid as a load, collects the device parameters of each LED, and performs power balance distribution in the LED power grid, so that the method for maximizing the load balance degree in the LED power grid includes:
step 1: let the initial power demand change value of the ith LED beThe initial power demand change rate of the ith LED is +.>The specific process is as follows: for each LEDi: initializing an initial power demand variation value +.>The initial power demand change value may be set according to the application demand or the history data. Initializing an initial power demand rate of change->It may also be set based on application requirements or historical data. The power demand change values and rates are initialized to provide a starting point for the subsequent steps of power balance control. These initial values will affect the subsequent calculation and adjustment process. The initial power demand change value may reflect an initial state of the system, and the initial power demand change rate may be affected by an expected change rate or historical data of the system. The step 1 is used for providing the power balance control processInitial conditions. The initial power demand change value and the change rate are used as initial information, and can be corrected through calculation and adjustment in subsequent iterations. They provide a starting point for the system state, helping the system to gradually go towards a load balancing state.
Step 2: updating the power demand change value of the LED in consideration of the power demand change rate and the historical optimal power demand change value; the specific process is as follows: for each LEDi: using current rate of change of power demandAnd historical optimal power demand variation value +.>Calculating a new power demand change value X ′i . Some update rules may be employed, such as:where α is a trade-off coefficient. The updating of the power demand change value realizes the gradual adjustment of the power demand by taking the historical optimal value and the preset change rate into consideration. This process aims to gradually equalize the power demand variation values to better meet the power distribution requirements of the system. The step 2 serves to guide the power demand change value of the LED toward the equilibrium state. By combining the historical optimal value and the change rate, the system can gradually approach the load balance state, and severe change and oscillation are avoided. This step aids in controlling the stability and convergence of the system.
Step 3: updating the power demand change rate of the LEDs by considering the optimal power demand change values of the LEDs and the adjacent LEDs; the specific process is as follows: for each LEDi: obtaining self optimal power demand change valueObtaining an optimal power demand variation value of adjacent LEDs>May be adjacent toAverage value of LEDs or other related means. Use of the current power demand rate of change +.>And the optimal power demand change values of the self and adjacent LEDs, calculating a new power demand change rate V '' i . Some update rules may be employed, such as: />Where β is a trade-off coefficient. The power demand rate is updated to achieve better load balancing between adjacent LEDs. By introducing information of adjacent LEDs, the system can adjust the power demand change rate of the system according to the change trend of the adjacent LEDs so as to realize more balanced power distribution. The effect of step 3 is to adjust the rate of change of the power demand of the LEDs by the information of the neighboring LEDs, thereby achieving a more balanced load distribution between the neighboring LEDs. This helps to avoid an imbalance situation where some LEDs are over-used in power distribution.
Step 4: calculating the power supply of each LED;
the specific process is as follows: for each LEDi: using updated power demand change value X' i And a power demand change rate V' i And an initial power supply P supply Calculating the power supply amount P of the LED i . Some calculation formulas may be employed, for example: p (P) i ＝P supply +X′ i ·V′ i . The calculation of the power supply is based on the updated power demand change value and rate of change, and the initial power supply. From this information, the power supply actually obtained by each LED can be calculated to meet the variation in its power demand. The function of step 4 is to convert the change and rate of change in power demand into the actual power supply. From this calculation, the system can determine how much power each LED is actually available for subsequent power balancing. In summary, step 4 converts the power demand change value and the influence of the change rate into an actual power supply by calculating them to meet the power demand of the LED. This helps in the power divisionThe power supply to the LEDs is more precisely controlled during the mating process.
Step 5: updating a historical optimal power demand change value; updating the optimal power demand change value of the adjacent LEDs; the historical optimum and the adjacent LED optimum are updated to guide the direction of adjustment of the system in subsequent iterations. By preserving historical optimal values, the system can guide the calculation with past good performance; by updating the optimal values of the neighboring LEDs, the system can make more accurate adjustments based on the information of the neighboring LEDs. The purpose of step 5 is to provide guidance and guidance for the system to approach the load balancing state gradually in subsequent iterations. The preservation of the historical optimum can avoid system jump to an unstable state, while the updating of the optimum of neighboring LEDs helps to achieve better load distribution among neighboring LEDs.
Step 6: correcting the power demand change value of the LED; based on the corrected power demand change value; updating the power demand change rate of the LED again; and correcting the power demand change value of the LED according to the actual power supply condition, and gradually adjusting the system by updating the power demand change rate. This procedure aims to make the system approach the power equilibrium state gradually, and at the same time, make corrections according to the actual situation. The step 6 is used for correcting according to actual conditions so as to more accurately adjust the change value and the change rate of the power demand of the LED. By correcting according to the power supply condition, the system can better meet the requirement of actual load distribution and avoid over distribution or under distribution. In summary, step 6 corrects the power demand change value according to the actual situation, and updates the power demand change rate again according to the corrected value, so as to gradually realize a more accurate load balancing control effect. This helps to optimize the performance and energy utilization of the LED grid.
Step 7: and (3) circularly executing the steps 4 to 6 for set times, and distributing the power in the LED power grid in an equalizing way based on the power supply of the LED obtained through final calculation, so that the load balance degree in the LED power grid is the highest. Step 7 has the effect of gradually approaching the system to the power equilibrium state through multiple iterations. Each iteration can adjust the power demand according to the actual power supply condition, so that the balanced distribution of the load is realized. Through loop iteration, the system can gradually optimize the performance and energy utilization of the LED power grid. In summary, step 7 performs steps 4 to 6 through multiple iterations to achieve balanced power distribution of the LED grid. This process aims to gradually adjust the power demand, gradually balance the load in the LED grid, and optimize the performance of the whole system.
Example 7: based on the above embodiment, the following formula is used in step 2 to update the power demand change value of the LED:
wherein t is a time step;
in step 3, the following formula is used to update the rate of change of the power demand of the LED:
wherein,the rate of change of power demand for the ith LED at time step t+1; the power demand rate of change refers to the rate of change of the power demand of the LED; k is an inertia weight, and the influence degree of the current speed on the power demand change rate at the next moment is controlled; />The power demand change rate of the ith LED at the time step t is the power demand change rate of the last moment; a, a 1 For individual experience weights, representing the degree of attraction of each LED to a historical optimal power demand change value; r is (r) 1 The random number of the individual, introduce the randomness, is used for increasing the diversity of searching; p (P) best,i A historical optimal power demand change value for the ith LED, i.e., an optimal power supply value obtained by the LED in past iterations; />The power demand change value of the ith LED at the time step t is the power demand change value of the last moment; a, a 2 For group experience weights, representing the attraction degree of each LED to the historical optimal power demand change value of the adjacent LEDs; r is (r) 2 Introducing randomness for the group random numbers for increasing the diversity of searching; p (P) best,neighbor,i Historical optimal power demand change values for adjacent LEDs of the ith LED, i.e., optimal power supply values obtained in past iterations for adjacent LEDs; />The quantum power demand change value of the ith LED at the time step t is used as a reference value for updating the power demand change rate.
Specifically, in this formula,indicating the power demand rate of the ith LED at the next time step t+1,/-)>Representing the rate of change of the power demand of the ith LED at the current time step t, k being the inertial weight, a 1 And a 2 Is the empirical weight, r 1 And r 2 Is a random number, P best,i Is the historical optimal power demand change value, P, of the ith LED best,neighbor,i Is the historical optimal power demand change value of adjacent LEDs, < >>Is the power demand change value of the ith LED at the current time step t. The principle of this formula is to update the rate of change of power demand based on a number of factors to achieve finer load balancing control. Wherein the inertial weight k controls the degree of maintenance of the current rate of change, the individual and population experience weights a 1 And a 2 The random number r represents the influence of the historic optimal values of the individual and the population on the current change rate respectively 1 And r 2 Introducing randomness to increase diversity of searchesSex. By combining these factors, the formula implements a strategy that adjusts the rate of change of power demand based on historical and current conditions. The formula in step 3 enables more accurate adjustment of the rate of change of power demand by taking into account experience, randomness and current trends of individuals and groups. This helps the system approach the load-balanced state faster, avoids trapping in locally optimal solutions, and improves the global nature of the search. In summary, these two formulas in embodiment 7 are used to update the power demand change value and the change rate in step 2 and step 3, respectively, and by comprehensively considering the information of history, individuals and groups, a finer and efficient LED grid load balancing control strategy is realized.
Example 8: on the basis of the above embodiment, the power supply for each LED is calculated in step 4 using the following formula:
wherein P is supply,i The power supply for the ith LED.
Specifically, the power supply of each LED at the next time step is calculated by multiplying the ratio of the power demand change value to the total demand change value of each LED by the maximum power supply capability. This calculation ensures that the power supply to each LED is proportional to its proportion in the load. The formula in step 4 is used to calculate the proportion of each LED in the load according to the power demand change value, and apply the proportion to the maximum power supply capacity, thereby obtaining the power supply of each LED in the next time step. This calculation ensures a balanced load, with each LED being supplied with the appropriate power to meet its needs.
Example 9: on the basis of the above embodiment, the historical optimum power demand change value is updated in step 5 using the following formula:
if P supply,i >P best,i P is then best,i ＝P supply,i ；
In step 5, the optimal power demand change value of the adjacent LED is updated using the following formula:
if P best,j >P best,neighbor,i P is then best,neighbor,i ＝P best,j 。
Wherein P is best,j The historical optimal power demand change value for the j-th neighboring LED of the i-th LED.
The historical optimal power demand change values for the LEDs and the historical optimal power demand change values for neighboring LEDs are updated for use in subsequent iterations. If the power supply of an LED is greater than its historical optimal power demand change value, indicating that this power supply is a better choice, the historical optimal value needs to be updated. The historical optimum values of adjacent LEDs are similarly updated to maintain synchronization and transfer of information. The two formulas in step 5 are used to update the historical optimal power demand change value of the LED and the historical optimal power demand change value of the neighboring LED. These updates ensure that the historical optimum of each LED is always the optimum power supply value it obtains, while information is passed between LEDs through the optimum updates of neighboring LEDs, helping to better guide the system to find a globally optimal solution.
Example 10: based on the above embodiment, in step 6, the following formula is used to correct the power demand variation value of the LED;
wherein P is supply,j Supplying power to the j-th neighboring LED of the i-th LED;
the rate of change of the power demand of the LED is updated again using the following formula:
specifically, in these formulas,indicating the power requirement of the ith LED at the next time step t+1Calculate the change value, P demand Representing the power demand of the ith LED, P supply,j Represents the power supply of the j-th neighboring LED of the i-th LED,indicating the power demand rate of the ith LED at the next time step t+1,/-)>Representing the rate of change, k, a, of the power demand of the ith LED at the current time step t 1 、r 1 、a 2 And r 2 Is the weight and random number that has been explained before. The formula in step 6 aims to make it conform to the expected power demand by correcting the power demand variation value. First, the formula uses the ratio between the power demand and the power supply to correct the power demand variation value so as to match the power supply while satisfying the demand. The power demand rate of change is then updated again with historical optimal values for individuals and groups, as well as other weights, to guide the search process toward a more optimal solution. These formulas in step 6 enable correction of the power demand change value and rate of change, and updating again. By correcting the power demand variation value, it is possible to ensure that the power demand of the LED coincides with the demand thereof, and further optimization is performed on the basis of load balancing. And the change rate of the power demand is updated again, so that the searching process is better guided, and the convergence and the efficiency of the algorithm are improved.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, functional modules/units in the apparatus, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
The preferred embodiments of the present disclosure have been described above with reference to the accompanying drawings, and are not thereby limiting the scope of the claims of the present disclosure. Any modifications, equivalent substitutions and improvements made by those skilled in the art without departing from the scope and spirit of the present disclosure shall fall within the scope of the claims of the present disclosure.
Claims (10)
1. Based on electric networkAn equalized LED control system, comprising: the LED power grid, the power grid balance control device, the LED matrix and the LED control device; the LED matrix comprises a plurality of LEDs, and each LED is connected to different positions in an LED power grid; the power grid balance control device is configured to consider each LED in the LED power grid as a load, collect equipment parameters of each LED, and perform power balance distribution in the LED power grid so that the load balance degree in the LED power grid is highest; the LED control device is configured to perform multi-state control on each LED in the LED matrix, and specifically comprises: representing the initial brightness of the LED using the polymorphism, and representing the brightness information of the LED as a polymorphism |ψ LED >Setting N as the polymorphic bit number and corresponding to the brightness level number of the LEDs; by applying the polymorphic gate operation, the polymorphism |ψ of an evolving LED LED > -to simulate the course of change of the LED brightness; initializing a particle swarm, wherein each particle represents an evolution angle theta; the position of the particle represents a candidate value of the evolution angle, and the speed represents the searching direction of the particle in the solution space; calculating fitness based on a fitness function for each particle according to the difference between the LED brightness and the target brightness; finding particles with highest fitness from particle swarm, taking the evolution angle as global optimal angle theta global The method comprises the steps of carrying out a first treatment on the surface of the Updating the position and velocity of each particle to find a more optimal solution; applying the evolution angle θ of each particle to the polymorphic gate operation, updating the polymorphism |ψ of the LED LED >The method comprises the steps of carrying out a first treatment on the surface of the Based on updated LED polymorphism |ψ LED >The brightness value of the LED is controlled.
2. The grid equalization based LED control system of claim 1, wherein the change in LED brightness is simulated using multi-state gate operation expressed by the following equation:
wherein the method comprises the steps ofIs the i-th multi-bit Pauli-X matrix, theta is the evolution angle, and U (theta) is the evolution result.
3. The grid equalization based LED control system of claim 2, wherein the fitness function is expressed using the following formula:
wherein MSE is the mean square error between the LED luminance and the target luminance; fitness is Fitness.
4. A grid equalization based LED control system as defined in claim 3, wherein the position and velocity of each particle is updated using the formula:
where ω is the inertial weight, c 1 And c 2 Is the acceleration coefficient, rand 1 And rand 2 A random number between 0 and 1; velocity new For updated speed, velocity old Is the speed before update; θ current Is the current evolution angle; position old To update the pre-Position, position new Is the updated position; θ local Historical optimal evolution angle.
5. The grid equalization based LED control system of claim 4, wherein said device parameters comprise: the number of LEDs M in the LED matrix, the maximum power supply capability P of each LED max The method comprises the steps of carrying out a first treatment on the surface of the Power demand P for each LED demand And an initial power supply P for each LED supply 。
6. The LED control system based on grid equalization of claim 5, wherein said grid equalization control means regards each LED in the LED grid as a load, collects the device parameters of each LED, performs the power equalization distribution in the LED grid, and the method for maximizing the load balance in the LED grid comprises:
step 1: let the initial power demand change value of the ith LED beThe initial power demand change rate of the ith LED is
Step 2: updating the power demand change value of the LED in consideration of the power demand change rate and the historical optimal power demand change value;
step 3: updating the power demand change rate of the LEDs by considering the optimal power demand change values of the LEDs and the adjacent LEDs;
step 4: calculating the power supply of each LED;
step 5: updating a historical optimal power demand change value; updating the optimal power demand change value of the adjacent LEDs;
step 6: correcting the power demand change value of the LED; based on the corrected power demand change value; updating the power demand change rate of the LED again;
step 7: and (3) circularly executing the steps 4 to 6 for set times, and distributing the power in the LED power grid in an equalizing way based on the power supply of the LED obtained through final calculation, so that the load balance degree in the LED power grid is the highest.
7. The grid equalization based LED control system of claim 6, wherein the power demand change value of the LED is updated in step 2 using the formula:
wherein t is a time step;
in step 3, the following formula is used to update the rate of change of the power demand of the LED:
wherein,the rate of change of power demand for the ith LED at time step t+1; the power demand rate of change refers to the rate of change of the power demand of the LED; k is an inertia weight, and the influence degree of the current speed on the power demand change rate at the next moment is controlled;the power demand change rate of the ith LED at the time step t is the power demand change rate of the last moment; a, a 1 For individual experience weights, representing the degree of attraction of each LED to a historical optimal power demand change value; r is (r) 1 The random number of the individual, introduce the randomness, is used for increasing the diversity of searching; p (P) best,i A historical optimal power demand change value for the ith LED, i.e., an optimal power supply value obtained by the LED in past iterations; />The power demand change value of the ith LED at the time step t is the power demand change value of the last moment; a, a 2 For group experience weights, representing the attraction degree of each LED to the historical optimal power demand change value of the adjacent LEDs; r is (r) 2 Introducing randomness for the group random numbers for increasing the diversity of searching; p (P) best,neighbor,i Historical optimal power demand change values for adjacent LEDs of the ith LED, i.e., optimal power supply values obtained in past iterations for adjacent LEDs; />Quantum power requirement for the ith LED at time step tAnd calculating a change value as a reference value for updating the power demand change rate.
8. The grid equalization based LED control system of claim 7, wherein the power supply to each LED is calculated in step 4 using the formula:
wherein P is supply,i The power supply for the ith LED.
9. The grid equalization based LED control system of claim 8, wherein the historical optimal power demand change value is updated in step 5 using the formula:
if P supply,i >P best,i P is then best,i ＝P supply,i ；
In step 5, the optimal power demand change value of the adjacent LED is updated using the following formula:
if P best,j >P best,neighbor,i P is then best,neighbor,i ＝P best,j 。
Wherein P is best,j The historical optimal power demand change value for the j-th neighboring LED of the i-th LED.
10. The grid equalization based LED control system of claim 9, wherein in step 6, the power demand change value of the LED is corrected using the following formula;
wherein P is supply,j Supplying power to the j-th neighboring LED of the i-th LED;
the rate of change of the power demand of the LED is updated again using the following formula:
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