Intelligent LED lighting system control method based on quantum algorithm

Abstract

The invention provides an intelligent LED lighting system control method based on a quantum algorithm, and relates to the field of LED lighting system control. The method comprises the following steps of 1, collecting data of environment parameters, user requirements and LED lamp working conditions in real time; 2, establishing a multi-objective optimization model of the lighting system, and mapping the model into a quantum calculation model; 3, inputting the quantum calculation model into a quantum processor, and solving an optimal solution by using a quantum optimization algorithm; and 4, decoding the optimal solution into an LED control instruction, and executing illumination regulation and control. Potential exponential acceleration is provided through a quantum algorithm, so that complex lighting scheduling optimization can be completed in an extremely short time, real-time dynamic light environment regulation and control which are difficult to achieve by a traditional method are achieved, and through global optimal solution search, it is ensured that all comfort requirements are met, energy consumption of the whole lighting system reaches the minimum, and the energy consumption of the whole lighting system reaches the minimum. And the energy efficiency of the system is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of LED lighting system control, in particular to an intelligent LED lighting system control method based on quantum algorithm. BACKGROUND

[0002] The existing intelligent LED lighting system control method mostly adopts classical optimization algorithm, such as particle swarm optimization algorithm or convex optimization algorithm. These methods are effective when dealing with small-scale or linear problems, but when facing complex, dynamic, multi-objective optimization problems containing a large number of lamp arrays, multi-user personalized needs and real-time environmental light parameters (such as natural light intensity), the calculation efficiency of classical algorithm will decrease significantly, and even it is difficult to find a global optimal solution. This leads to energy waste or inability to provide the best lighting environment. SUMMARY

[0003] (I) Technical problems solved In view of the deficiencies of the prior art, the present application provides an intelligent LED lighting system control method based on quantum algorithm, which solves the performance bottleneck problem of traditional methods when dealing with complex intelligent lighting scenes.

[0004] (II) Technical solutions In order to achieve the above purpose, the present application is realized by the following technical solutions: an intelligent LED lighting system control method based on quantum algorithm, comprising the following steps: Step 1: Real-time acquisition of environmental parameters, user needs and LED lamp working conditions data; Step 2: Establishing a multi-objective optimization model of the lighting system, and mapping the model into a quantum computing model; Step 3: Inputting the quantum computing model into a quantum processor, and using quantum optimization algorithm to solve the optimal solution; Step 4: Decoding the optimal solution into LED control instructions, and executing lighting control.

[0005] Preferably, the acquisition of environmental parameters includes detecting environmental brightness changes, obtaining temperature and humidity data of the surrounding environment, detecting pollutant concentration in the air and detecting the presence of human or objects in the environment to assist in determining lighting needs.

[0006] Preferably, the multi-objective optimization model aims to minimize system energy consumption and maximize user comfort.

[0007] Preferably, the quantum computing model is a QUBO model, and the calculation formula is: [f(\mathbf{x})=\sum_{i}Q_{ii}x_i+\sum_{i<j}Q_{ij}x_ix_j] Wherein: (Q_{ii}) is the coefficient of a single variable (x_i), representing the independent term of that variable; (Q_{ij}) is the coefficient of the interaction term between variables (x_i) and (x_j).

[0008] Preferably, the quantum optimization algorithm utilizes quantum superposition and quantum interference effects to search the optimal solution space in parallel; the quantum superposition allows qubits to exist simultaneously in multiple states, enabling the quantum system to explore multiple possible solutions at the same time, and the calculation formula is as follows: [|q\rangle=\alpha|0\rangle+\beta|1\rangle] Where (|q\rangle) is a qubit, (\alpha) and (\beta) are complex coefficients, satisfying |\alpha|^2+|\beta|^2=1; Preferably, the quantum interference effect allows different paths to interfere with each other, thereby increasing the probability of some solutions and suppressing the probability of other solutions, guiding the quantum algorithm to converge toward the optimal solution. The calculation formula is as follows: [|\psi\rangle=\alpha_1e^{i\phi_1}|x_1\rangle+\alpha_2e^{i\phi_2}|x_2\rangle+\dots]; (e^{i\phi_i}) is the phase factor caused by the phase difference of different paths.

[0009] Preferably, the optimal decoding is to decode the quantum bit string, adjust the PWM signal and transmit it wirelessly via Zigbee to achieve remote control of the brightness and color temperature of the LED lamps.

[0010] (III) Beneficial Effects This invention provides a control method for an intelligent LED lighting system based on quantum algorithms. It offers the following advantages: 1. Real-time optimization capability: Quantum algorithms provide potential exponential speedup, enabling complex lighting scheduling optimization to be completed in a very short time, achieving real-time dynamic light environment control that is difficult to achieve with traditional methods.

[0011] 2. Significantly reduced energy consumption: By searching for the global optimal solution, the energy consumption of the entire lighting system is minimized while meeting all comfort requirements, thus improving the system's energy efficiency.

[0012] 3. Improved light quality and comfort: The algorithm can comprehensively consider multiple dimensions such as illuminance, color temperature, and color rendering index to provide a lighting environment that is closer to natural light (such as sunlight) and more in line with human circadian rhythms, thereby improving the user experience.

[0013] 4. Filling a technological gap: This invention introduces cutting-edge quantum computing technology into the specific engineering application field of intelligent lighting control, providing a brand-new approach and implementation path for solving complex optimization problems in the Internet of Things and smart cities. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating the intelligent LED lighting system control method based on quantum algorithms proposed in this invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Example: like Figure 1 As shown, this embodiment of the invention provides a control method for an intelligent LED lighting system based on quantum algorithms, including the following steps: Step 1: Collect real-time data on environmental parameters, user needs, and LED lighting fixture operating conditions; Step 2: Establish a multi-objective optimization model for the lighting system and map the model to a quantum computing model; Step 3: Input the quantum computing model into the quantum processor and use quantum optimization algorithms to solve for the optimal solution; Step 4: Convert the optimal decoder into LED control commands and execute the lighting adjustment; Collecting environmental parameters includes detecting changes in ambient brightness, acquiring temperature and humidity data of the surrounding environment, detecting the concentration of pollutants in the air, and detecting the presence of people or objects in the environment to help determine lighting needs.

[0017] The multi-objective optimization model aims to minimize system energy consumption and maximize user comfort.

[0018] The quantum computing model is the QUBO model, and the calculation formula is: [f(\mathbf{x})=\sum_{i}Q_{ii}x_i+\sum_{i <j}Q_{ij}x_ix_j] in: (Q_{ii}) is the coefficient of a single variable (x_i), representing the independent term of that variable; (Q_{ij}) is the coefficient of the interaction term between variables (x_i) and (x_j).

[0019] From the above: Objective function 1: Minimize energy consumption (E = \sum_{i=1}^{n} P_i x_i) Where (P_i) is the power of the (i)th light fixture, (x_i) is the on-off state of the light fixture (0 means off, 1 means on).

[0020] Objective function 2: Maximize comfort (C = \sum_{i=1}^{n} w_i \cdot L_i) Where (L_i) is the brightness of the (i)th light fixture, (w_i) is the comfort weight of the light fixture.

[0021] Objective function 3: System stability (S = \sum_{i=1}^{n} \left| T_i - T_{max} \right|) Where (T_i) is the operating temperature of the (i)th light fixture, (T_{max}) is the maximum safe temperature, and (T_i \leq T_{max}) is guaranteed.

[0022] Objective function 4: Environmental adaptability (A = \sum_{i=1}^{n} \left| L_i - L_{desired} \right|) Where (L_i) is the actual brightness of the (i)th light fixture, (L_{desired}) is the target brightness set by the user.

[0023] By combining the above objective functions by weighting, we can get a total optimization objective: QUBO model mapping: The form of QUBO model is: [\text{QUBO} = \sum_{i} Q_{ii} x_i + \sum_{i < j} Q_{ij} x_i x_j] Where (x_i) is a binary decision variable, representing the on-off state of the light fixture (0 or 1).

[0024] (Q_{ii}) is the single coefficient, (Q_{ij}) is the quadratic coefficient, both of which are used to describe the linear and quadratic terms in the objective function.

[0025] Mapping of objective functions Convert the above objective functions into the binary optimization form required by the QUBO model: Minimize energy consumption (objective function 1) The form of objective function 1 is: [E=\sum_{i=1}^{n}P_ix_i] Mapped to the QUBO model, the corresponding linear term is: [\sum_{i=1}^{n}P_ix_i\quad\text{(corresponding to linear terms in QUBO)}] Therefore, the corresponding coefficients in QUBO are: [Q_{ii}=P_i\quad\text{(for all (i))}].

[0026] Maximizing comfort (Objective function 2) The form of objective function 2 is: [C=\sum_{i=1}^{n}w_i\cdotL_i] This term can also be linearized into a QUBO linear term. When (L_i) and (x_i) are linearly related, comfort can be mapped as: [Q_{ii}=w_i] At this point, the QUBO coefficient reflects the comfort weight of each luminaire.

[0027] System stability (objective function 3) The objective function for system stability is: [S=\sum_{i=1}^{n}\left|T_i-T_{max}\right|] To avoid overheating, a stability variable (x_i) can be introduced to control the on / off state of the light fixture, preventing excessively high temperatures. This variable can be transformed into a constraint and mapped to a quadratic term in the QUBO model, representing a penalty for excessively high temperatures.

[0028] Environmental adaptability (objective function 4) The objective function for environmental adaptability is: [A=\sum_{i=1}^{n}\left|L_i-L_{desired}\right|] The difference between the target brightness (L_{desired}) and the actual brightness (L_i) can be represented as a quadratic term in the QUBO model. Quantization can also be achieved by setting appropriate weights.

[0029] 4. Constraint Handling Constraints can be handled by introducing a penalty term. A penalty term can be introduced as follows: [\text{Penalty}=\lambda\sum_{i}(T_i-T_{max})^2\cdot(1-x_i)] (T_i\leqT_{max}) represents the temperature of the lamp. When (x_i=0), the penalty term is zero; when (x_i=1), a penalty is imposed when the temperature exceeds the maximum limit, thus guiding the system to avoid this situation. Quantum optimization algorithms utilize quantum superposition and quantum interference effects to search the optimal solution space in parallel. Quantum superposition allows qubits to exist simultaneously in multiple states, enabling the quantum system to explore multiple possible solutions at the same time. The calculation formula is as follows: [|q\rangle=\alpha|0\rangle+\beta|1\rangle] Where (|q\rangle) is a qubit, (\alpha) and (\beta) are complex coefficients, satisfying |\alpha|^2+|\beta|^2=1; Quantum interference allows different paths to interfere with each other, thereby increasing the probability of some solutions and suppressing the probability of others, guiding the quantum algorithm to converge toward the optimal solution. The calculation formula is as follows: [|\psi\rangle=\alpha_1e^{i\phi_1}|x_1\rangle+\alpha_2e^{i\phi_2}|x_2\rangle+\dots]; (e^{i\phi_i}) is the phase factor caused by the phase difference of different paths.

[0030] The optimal decoding method involves decoding the quantum bit string, adjusting the PWM signal, and transmitting it wirelessly via Zigbee to remotely control the brightness and color temperature of LED lights.

[0031] Working principle: Objective function 1: Minimize energy consumption (E=\sum_{i=1}^{n}P_ix_i) Where (P_i) is the power of the (i)th lamp and (x_i) is the on / off state of the lamp (0 means off, 1 means on).

[0032] Objective function 2: Maximize comfort (C=\sum_{i=1}^{n}w_i\cdotL_i) Where (L_i) is the brightness of the (i)th lamp and (w_i) is the comfort weight of the lamp.

[0033] Objective function 3: System stability (S=\sum_{i=1}^{n}\left|T_i-T_{max}\right|) where (T_i) is the operating temperature of the (i)-th luminaire, (T_{max}) is the maximum safe temperature, ensuring (T_i \leq T_{max}).

[0034] Objective function 4: Environmental adaptability (A = \sum_{i=1}^{n} \left| L_i - L_{desired} \right|) where (L_i) is the actual brightness of the (i)-th luminaire, (L_{desired}) is the target brightness set by the user.

[0035] By combining the above objective functions with weights, we can obtain a total optimization objective: 2. QUBO model mapping In quantum computing, the QUBO model is often used to represent optimization problems. The form of the QUBO model is: [\text{QUBO} = \sum_{i} Q_{ii} x_i + \sum_{i<j} Q_{ij} x_i x_j] where (x_i) is a binary decision variable, representing the on-off state of the luminaire (0 or 1).

[0036] (Q_{ii}) is the single-item coefficient, (Q_{ij}) is the quadratic coefficient, both of which are used to describe the linear and quadratic terms in the objective function.

[0037] 3. Mapping of objective functions We convert the objective functions proposed earlier into the binary optimization form required by the QUBO model. Here's how to map each objective function: 1) Minimize energy consumption (Objective function 1) The form of objective function 1 is: [E = \sum_{i=1}^{n} P_i x_i] Its mapping to the QUBO model corresponds to the linear term: [\sum_{i=1}^{n} P_i x_i \quad \text{(corresponding to the linear term in QUBO)}] Therefore, the corresponding coefficient in QUBO is: [Q_{ii} = P_i \quad \text{(for all (i))}] And there is no quadratic term (because this is a linear objective).

[0038] 2) Maximize comfort (Objective function 2) The form of objective function 2 is: [C=\sum_{i=1}^{n}w_i\cdotL_i] This can also be linearized as a linear term of QUBO. Assuming that (L_i) is linearly related to (x_i) (the switch state of the lamp), the comfort can be mapped as: [Q_{ii}=w_i] The QUBO coefficients at this time reflect the comfort weights of each lamp.

[0039] 3) System stability (objective function 3) The objective function of system stability is: [S=\sum_{i=1}^{n}\left|T_i-T_{max}\right|] To avoid overheating problems, the lamp switch state can be controlled by introducing a stability variable (x_i) to prevent excessive temperature. This can be converted into a constraint condition and mapped into a quadratic term in the QUBO model, representing the penalty when the temperature is too high.

[0040] 4) Environmental adaptability (objective function 4) The objective function of environmental adaptability is: [A=\sum_{i=1}^{n}\left|L_i-L_{desired}\right|] The difference between the target brightness (L_{desired}) and the actual brightness (L_i) can be represented as a quadratic term in the QUBO model. This part can also be quantified by setting appropriate weights.

[0041] 4. Constraint handling QUBO model does not directly support constraints, but constraints can be handled by introducing a penalty term. For example, if we need to ensure that the temperature of some lamps (T_i\leqT_{max}), we can introduce a penalty term: [\text{Penalty}=\lambda\sum_{i}(T_i-T_{max})^2\cdot(1-x_i)] When (x_i=0), the penalty term is zero; when (x_i=1), the temperature exceeds the maximum limit and will be penalized, thereby guiding the system to avoid such situations.

[0042] Example two: The difference between this embodiment and example one is: (More than a few examples can be written appropriately) While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A method for controlling an intelligent LED lighting system based on quantum algorithms, characterized in that, The method comprises the following steps: Step 1: Real-time acquisition of environmental parameters, user needs and LED lamp working conditions; Step 2: Establish a multi-objective optimization model of the lighting system and map the model to a quantum computing model; Step 3: Input the quantum computing model into a quantum processor and use a quantum optimization algorithm to solve the optimal solution; Step 4: Decode the optimal solution into LED control instructions and perform lighting control.

2. The quantum algorithm based intelligent LED lighting system control method of claim 1, wherein: The acquisition of environmental parameters includes detecting changes in ambient brightness, obtaining temperature and humidity data from the surrounding environment, detecting pollutant concentrations in the air, and detecting the presence of humans or objects in the environment to assist in determining lighting needs.

3. The quantum algorithm based intelligent LED lighting system control method of claim 1, wherein: The multi-objective optimization model aims to minimize system energy consumption and maximize user comfort.

4. The quantum algorithm based intelligent LED lighting system control method of claim 1, wherein: The quantum computing model is a QUBO model, and the calculation formula is: [f(\mathbf{x})=\sum_{i}Q_{ii}x_i+\sum_{i<j}Q_{ij}x_ix_j] Where: (Q_{ii}) is the coefficient of a single variable (x_i), representing the self-term of the variable; (Q_{ij}) is the interaction term coefficient between variables (x_i) and (x_j).

5. The quantum algorithm based intelligent LED lighting system control method of claim 1, wherein: The quantum optimization algorithm uses quantum superposition and quantum interference effects to search for optimal solution space in parallel; the quantum superposition allows quantum bits to exist simultaneously in multiple states, enabling the quantum system to explore multiple possible solutions simultaneously, with the calculation formula being: [|q\rangle=\alpha|0\rangle+\beta|1\rangle]; Where (|q\rangle) is a quantum bit, (\alpha) and (\beta) are complex coefficients, and |\alpha|^2+|\beta|^2=1.

6. The quantum algorithm based intelligent LED lighting system control method of claim 5, wherein: The quantum interference effect allows different paths to interfere with each other, thereby enhancing the probability of some solutions and suppressing the probability of other solutions, guiding the quantum algorithm to converge to the optimal solution, with the calculation formula being: [|\psi\rangle=\alpha_1e^{i\phi_1}|x_1\rangle+\alpha_2e^{i\phi_2}|x_2\rangle+\dots]; (e^{i\phi_i}) is a phase factor caused by the phase difference of different paths.

7. The quantum algorithm based intelligent LED lighting system control method of claim 1, wherein: The optimal solution is decoded into a decoded quantum bit string, adjusts the PWM signal and transmits it wirelessly through Zigbee to achieve remote control of the brightness and color temperature of the LED lamp.

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