Urban intelligent lighting wireless monitoring method and system

Abstract

The invention discloses an urban intelligent lighting wireless monitoring method and system, and relates to the technical field of intelligent lighting. The method comprises the following steps: acquiring data of lighting equipment and a multi-dimensional sensor, setting the lighting equipment as nodes, setting a dynamic coupling coefficient between the nodes as an edge weight, and constructing a lighting topology model; performing quantum state feature mapping on the multi-dimensional sensor data, constructing a topological manifold tensor, calculating a topological invariant through manifold decomposition, and generating an abnormal alarm when the topological invariant is greater than a set threshold value; and when an abnormal alarm is detected, obtaining all adjacent lighting devices of an abnormal node, constructing a target function, solving to obtain power and lighting brightness of the adjacent lighting devices, and performing adjustment. Through combination of quantum state feature mapping, topological flow decomposition and dynamic coupling coefficients, multi-dimensional sensor data are precisely processed, high-precision monitoring, energy management optimization and dynamic dimming are realized, and meanwhile, stable operation of the system is ensured through global optimization and an intelligent repair mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent lighting, and specifically provides an urban intelligent lighting wireless monitoring method and system. Background Art

[0002] With the acceleration of the urbanization process, the construction of smart cities has become an important direction for improving urban management efficiency and optimizing resource allocation. As an important part of smart cities, intelligent lighting is gradually replacing traditional lighting systems. Traditional lighting systems have problems such as energy waste, inconvenient management, and difficult maintenance. In contrast, intelligent lighting systems can achieve automated and intelligent management of lighting devices by introducing advanced sensor technologies, communication technologies, and intelligent control systems, thereby achieving the goals of energy conservation, improved lighting quality, and system stability.

[0003] In existing intelligent lighting systems, most systems only adjust lighting based on single-sensor data. However, this method cannot fully consider changes in environmental factors, dynamic cooperation between devices, and the global stability of the system. For example, traditional systems fail to make full use of cooperation and communication between nodes, resulting in inefficient use of resources and difficulty in timely detecting faults.

[0004] Currently, although there are some intelligent lighting methods based on sensor data and environmental information, the following problems still exist: insufficient monitoring accuracy. Most traditional systems only manage lighting based on simple sensor data, without considering the complex interactions between nodes, resulting in low monitoring accuracy and inability to adapt to dynamically changing environments; energy management and dimming problems. Although intelligent dimming can improve the energy efficiency of lighting systems, existing technologies often do not consider the dynamic changes in environmental factors, and lighting control is too simple to respond in real time to changes in the environment and traffic flow; multi-source data fusion problems. Multi-source data often cannot be effectively fused, resulting in low data utilization efficiency and lack of comprehensive perception and response capabilities for complex environments; weak system anomaly detection and repair capabilities. Existing intelligent lighting systems often do not have a comprehensive anomaly detection mechanism, unable to detect abnormal nodes in the system in real time and perform timely repair or adjustment, resulting in poor system stability and reliability. Summary of the Invention

[0005] Based on the above-mentioned disadvantages of the prior art, the purpose of the present invention is to provide an urban intelligent lighting wireless monitoring method and system to solve the above technical problems.

[0006] To achieve the above purpose, the present invention provides the following technical solution: An urban intelligent lighting wireless monitoring method, comprising: S1: Obtain the data of lighting devices and multi-dimensional sensors in the urban intelligent scenario, transmit them to the cloud through the wireless network, set the lighting devices as nodes, set the dynamic coupling coefficient between the nodes as the edge weight, and construct a lighting topology model; S2: Perform quantum state feature mapping on the obtained multi-dimensional sensor data, construct a topological manifold tensor according to the mapped quantum state features, and calculate the topological invariant through manifold decomposition. When the topological invariant is greater than the set threshold, generate an abnormal alarm; S3: When an abnormal alarm is detected, obtain all adjacent lighting devices of the abnormal node, construct an objective function, solve to obtain the power and lighting brightness of the adjacent lighting devices, and make adjustments.

[0007] The present invention is further set that the multi-dimensional sensor data includes power, lighting brightness, working temperature, light intensity, position data, traffic flow, and vibration data.

[0008] The present invention is further set that the calculation logic of the dynamic coupling coefficient between nodes includes: Calculate the initial coupling coefficient according to the spatial distance between nodes; Dynamically adjust the initial coupling coefficient according to the change rate of the difference in light intensity between nodes to generate an adjusted coupling coefficient; Combined with the historical coupling degree, perform weighted dynamic adjustment on the adjusted coupling coefficient to generate a dynamic coupling coefficient.

[0009] The present invention is further set that the calculation logic of the initial coupling coefficient is: , is the initial coupling coefficient between the th and the th nodes, is the th and the th nodes' spatial distance, is the first adjustment coefficient; The calculation logic of the adjusted coupling coefficient is: , is the adjusted coupling coefficient at time, is the change value of the difference in light intensity between the th and the th nodes, is the second adjustment coefficient; The calculation logic of the dynamic coupling coefficient is: , is the dynamic coupling coefficient at time, is the power factor, is the weighting coefficient.

[0010] The present invention is further configured such that step S2 specifically includes: Normalize the collected multi-dimensional sensor data, and map the normalized multi-dimensional sensor data to the quantum state space through a preset quantum state feature mapping function to obtain the quantum state features of the nodes; Calculate the topological connection operator of the adjacent nodes, and construct a topological manifold tensor according to the mapped quantum state features and the topological connection operator; Perform manifold decomposition on the topological manifold tensor to calculate the topological invariant. When the topological invariant is greater than the set threshold, generate an anomaly alarm.

[0011] The present invention is further configured such that the preset quantum state feature mapping function is: , is the quantum state feature of the th node, is the cardinality of the quantum state representation, is the eigenvalue of the quantum state, is the quantum phase factor, is the tensor product operation, is the preset quantum ground state, is the th node corresponding to the ground state feature data; The calculation logic of the topological connection operator is: , is the th and the th node's topological connection operator, and are the th and the th node's position vectors, is the scale parameter of the spatial distance, and are the Pauli matrices; The topological manifold tensor is: , is the topological manifold tensor; The calculation logic of the manifold decomposition is: , is the outside of the manifold, is the inside of the manifold, is the topological invariant.

[0012] The present invention is further configured such that step S3 specifically includes: When an anomaly alarm is detected, according to the lighting topology model, obtain all adjacent lighting devices directly connected to the abnormal node; Obtain the multi-dimensional sensor data and dynamic coupling coefficients of all adjacent nodes; Construct an objective function based on multi-dimensional sensor data and dynamic coupling coefficients, and calculate and solve through an optimization algorithm to obtain the power and illumination brightness of each adjacent lighting device for adjustment.

[0013] The present invention is further configured such that the construction logic of the objective function is as follows: , is the objective function, is the adjacent node of the th node, is the th node's power, is the th node's illumination brightness, is the rd and th nodes' dynamic coupling coefficient, , and are weight coefficients.

[0014] The present invention is further configured such that when solving the objective function through an optimization algorithm, the optimization algorithm includes the gradient descent method, the genetic algorithm, and the particle swarm optimization.

[0015] The present invention also provides an urban intelligent lighting wireless monitoring system for the above-mentioned urban intelligent lighting wireless monitoring method. The system includes: Model construction module: Obtain lighting devices and multi-dimensional sensor data in the urban intelligent scenario, transmit them to the cloud through a wireless network, set the lighting devices as nodes, and set the dynamic coupling coefficient between nodes as edge weights to construct a lighting topology model; Abnormal alarm module: Perform quantum state feature mapping on the obtained multi-dimensional sensor data, construct a topological manifold tensor based on the mapped quantum state features, and calculate topological invariants through manifold decomposition. When the topological invariant is greater than the set threshold, generate an abnormal alarm; Adjacent adjustment module: When an abnormal alarm is detected, obtain all adjacent lighting devices of the abnormal node, construct an objective function, solve to obtain the power and illumination brightness of the adjacent lighting devices, and perform adjustment.

[0016] The present invention provides a method and system for wireless monitoring of urban intelligent lighting. The method obtains data of lighting devices and multi-dimensional sensors in the urban intelligent scenario, transmits them to the cloud through a wireless network, sets the lighting devices as nodes, sets the dynamic coupling coefficient between the nodes as the edge weight, and constructs a lighting topology model; performs quantum state feature mapping on the obtained multi-dimensional sensor data, constructs a topological manifold tensor based on the mapped quantum state features, and calculates topological invariants through manifold decomposition. When the topological invariant is greater than the set threshold, an abnormal alarm is generated; when an abnormal alarm is detected, all adjacent lighting devices of the abnormal node are obtained, an objective function is constructed, and the power and lighting brightness of the adjacent lighting devices are solved and adjusted. The beneficial effects generated include: 1. Improve monitoring accuracy: By combining quantum state feature mapping and topological flow solution decomposition, it can accurately process multi-dimensional sensor data from different nodes, effectively capture the mutual influence between nodes, not only consider the spatial distance between nodes, but also reflect the overall state of the system in real time through the calculation of topological invariants, greatly improving the monitoring accuracy, and being able to more accurately identify and locate abnormal states; 2. Optimize energy management and dynamic dimming: Use the dynamic coupling coefficient to accurately adjust the power and illuminance between nodes, and can optimize the lighting brightness in real time according to environmental changes, not only ensuring the maximum energy efficiency of the urban lighting system, but also ensuring the safety of pedestrians and traffic, and meeting the lighting needs under different time and space conditions. In addition, through real-time adjustment, over-illumination and energy waste are avoided, and the energy utilization rate is improved; 3. Achieve global optimization and fault repair: By constructing an objective function, combining dynamic coupling coefficient, power and lighting brightness parameters, calculate the best adjustment plan for adjacent nodes through an optimization algorithm, so as to achieve global optimization, and can dynamically adjust the lighting brightness and power between nodes, ensure stable operation in the face of local failures or anomalies, and prevent the system from crashing through an intelligent repair mechanism.

[0017] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically gives the specific implementation manners of this application. Brief Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. In the drawings: Figure 1Flowchart of a method for wireless monitoring of urban intelligent lighting according to an exemplary embodiment of the present invention; Figure 2 Schematic structural diagram of a wireless monitoring system for urban intelligent lighting according to an exemplary embodiment of the present invention. Detailed implementation manners

[0019] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for explaining the present invention, rather than for limiting the protection scope of the present invention.

[0020] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Therefore, only the components related to the present invention are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0021] In the following description, a large number of details are explored to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0022] Embodiment 1 A method for wireless monitoring of urban intelligent lighting, as Figure 1 shown, includes: S1: Obtain the lighting devices and multi-dimensional sensor data in the urban intelligent scenario, transmit them to the cloud through a wireless network, set the lighting devices as nodes, set the dynamic coupling coefficient between the nodes as the edge weight, and construct a lighting topology model; S2: Perform quantum state feature mapping on the obtained multi-dimensional sensor data, construct a topological manifold tensor according to the mapped quantum state features, and calculate the topological invariant through manifold decomposition. When the topological invariant is greater than the set threshold, generate an abnormal alarm; S3: When an abnormal alarm is detected, obtain all adjacent lighting devices of the abnormal node, construct an objective function, solve to obtain the power and lighting brightness of the adjacent lighting devices, and make adjustments.

[0023] The present invention is further configured such that the multi-dimensional sensor data includes power, illumination brightness, operating temperature, light intensity, position data, traffic flow, and vibration data. Specifically, the power represents the energy consumption of the lighting device. Power is an important parameter for measuring the working intensity of the lighting device and directly affects the energy efficiency and energy management of the system. The power is directly measured by an electric power sensor or can be calculated through the current and voltage of the device. The sensor is integrated with the circuit of the lighting device and transmits the data to the cloud through a wireless network. The illumination brightness represents the intensity of the light emitted by the lighting device and reflects the illumination brightness perceived by the human eye, which is directly related to the visibility and comfort of the environment. The illumination brightness is measured by a light sensor, which is installed on the lighting device or in the environment, monitors the light intensity in real time, and transmits the data to the cloud through a wireless network. The operating temperature represents the temperature of the lighting device during operation and is measured by a temperature sensor. The temperature sensor is installed inside or outside the device, monitors the temperature of the device in real time, and transmits the data wirelessly to the cloud. The light intensity represents the energy of light received per unit area. The light intensity determines the lighting effect of the environment and can be measured by a light sensor and the data is transmitted to the cloud. The position data represents the geographical location of the lighting device and is obtained through a Global Positioning System (GPS) sensor. The GPS module equipped with the device can obtain the geographical location of the device in real time and transmit the data to the cloud through a wireless network. The traffic flow represents the number of vehicles or pedestrians passing through a specific area within a certain period of time, with "number of vehicles per hour" or "number of people per hour" as the unit. The traffic flow is monitored by traffic sensors (such as infrared sensors, video analysis, radar, or magnetic induction sensors). The sensors are usually installed on roads or specific areas. By analyzing traffic data (such as vehicle speed, number of vehicles, number of pedestrians, etc.), the flow data is calculated and transmitted to the cloud in real time. The vibration data represents the vibration condition of the lighting device or the surrounding environment and is measured using an accelerometer or a vibration sensor. The vibration data is obtained through an acceleration sensor or a vibration sensor, which is installed on the structure of the lighting device. The sensor senses the vibration of the device or the environment and converts it into an electrical signal, and then sends the data to the cloud through wireless transmission technology.

[0024] The present invention is further configured such that the calculation logic of the dynamic coupling coefficient between nodes includes: Calculating an initial coupling coefficient based on the spatial distance between nodes; the calculation logic of the initial coupling coefficient is: , is the initial coupling coefficient between the th and the th nodes, is the th and the th nodes, is the first adjustment coefficient; specifically, the initial coupling coefficient is used to measure the The degree of mutual influence or dependence between the th and th nodes. In the above calculation logic, the initial coupling coefficient is affected by the spatial distance between the nodes. Generally, the coupling strength between nodes weakens as the distance increases. By such that when the distance between nodes is larger, the coupling coefficient is smaller, and vice versa. The adjustment coefficient controls the degree of influence of the spatial distance on the coupling coefficient. Adjusting can affect the variation range of the coupling coefficient between nodes at long distances and short distances. It can be set according to the requirements of the actual application scenario to ensure that the adjustment of the coupling coefficient meets the system requirements, and its value range is (0, 1]; dynamically adjusts the initial coupling coefficient according to the change rate of the difference in light intensity between nodes to generate an adjusted coupling coefficient; the calculation logic of the adjusted coupling coefficient is: where is the adjusted coupling coefficient at time ; is the change value of the difference in light intensity between the th and th nodes; is the second adjustment coefficient; specifically, in the above calculation logic, the adjusted coupling coefficient dynamically adjusts the degree of mutual influence between the th and th nodes by analyzing the change rate of the difference in light intensity between nodes. The adjustment takes into account the real-time changing light intensity to ensure that the lighting system can respond to environmental changes in a timely manner, thereby optimizing energy efficiency and lighting quality; the initial coupling coefficient represents the basic coupling strength between the th and th nodes without considering the change in light intensity. The dynamically adjusted coupling coefficient considers the change in the difference in light intensity. The change in the difference in light intensity over time (i.e., ) reflects the real-time change in the collaborative working ability between lighting devices. In an embodiment of the present invention, when the illuminance in a certain area increases significantly, it is necessary to adjust the coupling relationship between adjacent nodes to avoid over-illumination or uneven illumination. By the coefficient controls the influence of light changes on the coupling coefficient. If is larger, the influence of the difference in light intensity on the adjustment of the coupling coefficient is greater; otherwise, the influence is smaller, and its value range is (0.1, 1]; combines the historical coupling degree to perform weighted dynamic adjustment on the adjusted coupling coefficient to generate a dynamic coupling coefficient; the calculation logic of the dynamic coupling coefficient is: is the dynamic coupling coefficient at a moment, is the power factor, is the weighting coefficient; specifically, in the above calculation logic, the dynamic coupling coefficient is weighted and adjusted by combining the historical coupling degree, so as to more accurately represent the th and the th nodes' mutual influence intensity. The goal is to enable the system to adapt to the long-term change trend according to the change of the historical coupling degree between nodes, and improve the system's response ability to factors such as local faults and light changes. The power factor controls the influence strength of the adjustment coupling coefficient on the dynamic coupling coefficient. It determines the response strength of the coupling coefficient at the current moment to the historical coupling degree, and its value range is (0, 1]. The weighting coefficient is used to control the weighted influence of the historical coupling degree on the dynamic coupling coefficient, and affects the role degree of historical data in the current coupling calculation. Its value range is [0, 1]; through the weighted dynamic adjustment of the historical coupling degree of the initial coupling coefficient, the dynamic coupling coefficient can be adaptively adjusted according to the past and current system states, so as to ensure the adaptability and instant response ability of the lighting system to long-term changes. It not only optimizes the lighting quality and energy management, but also improves the stability, flexibility and adaptive ability of the system, and is applicable to the changeable urban lighting environment.

[0025] The present invention is further set as follows. Step S2 specifically includes: Normalize the collected multi-dimensional sensor data, and map the normalized multi-dimensional sensor data to the quantum state space through a preset quantum state feature mapping function to obtain the quantum state features of the nodes; the preset quantum state feature mapping function is: , is the quantum state feature of the th node, is the cardinality of the quantum state representation, is the eigenvalue of the quantum state, is the quantum phase factor, is the tensor product operation, is the preset quantum ground state, is the th node corresponding to the ground state characteristic data; specifically, in the above calculation logic, the normalization processing of multi-dimensional sensor data and the quantum state feature mapping are used in combination to map the multi-dimensional data in the urban intelligent lighting system to the quantum state space. Through this method, the data characteristics of the nodes can be processed at the quantum level, and efficient analysis and decision-making can be carried out based on the advantages of quantum computing; The mapping representation in the quantum space of all data features of a node (including power, illumination brightness, operating temperature, light intensity, location data, traffic flow, and vibration data), the cardinality of the quantum state representation Indicates the number of ground states in the quantum state space, determines the dimension of the quantum state, and the eigenvalues of the quantum state Indicates the ground state The associated weight or importance. The eigenvalues are used to adjust the contribution of the ground state in the quantum state; Is a preset quantum ground state, representing the predefined basic quantum state of the system. The quantum ground state is a quantum state that describes the basic properties of the system and is a commonly used tool in quantum computing; Is the th node corresponding to the ground state The characteristic data, representing the measurement data of the node in the ground state Under. The characteristic data of each node will be mapped according to different ground states Through quantum state feature mapping and tensor product operations, multi-dimensional sensor data is mapped into the quantum state space to achieve efficient high-dimensional data processing and analysis. The quantum computing framework can utilize quantum superposition and interference effects for complex calculations, thereby enhancing the computing power and response speed of the system; Calculate the topological connection operator of adjacent nodes, and construct a topological manifold tensor based on the mapped quantum state features and the topological connection operator; The calculation logic of the topological connection operator is: , Is the th and the th node's topological connection operator, And Are the th and the th node's position vectors, Is the scale parameter of the spatial distance, And Are Pauli matrices; The topological manifold tensor is: , Is the topological manifold tensor; Specifically, the above calculation logic involves the calculation of the topological connection operator and its application in the topological manifold tensor. The goal is to construct a topological structure representation between nodes through the relationship between quantum state features and node positions. In the intelligent lighting system, this method can accurately reflect the spatial and quantum correlations between different lighting devices, and then perform dynamic regulation and optimization; The topological connection operator describes the spatial and quantum correlations between two nodes. It defines the relationship between nodes through the physical positions (spatial distances) and quantum operations (Pauli matrices) between nodes. This operator reflects the coupling strength caused by the distance and environmental factors between nodes; Represents the th and the The influence of the spatial distance between nodes on the coupling strength. The closer the distance, the larger the value of the topological connection operator, indicating a stronger association between them; the farther the distance, the weaker the correlation. is the tensor product of Pauli matrices, which represents the interaction between qubits between nodes in quantum computing and can introduce the complexity and synergy effects of quantum computing; the topological manifold tensor is calculated through the action of quantum states and topological connection operators, reflecting the coupling strength and interaction between nodes. In the intelligent lighting system, the topological manifold tensor helps the system capture the spatial correlation and quantum characteristics between nodes, making the cooperation between nodes more precise and flexible; furthermore, the Pauli matrix and are basic operators used to describe the interaction between qubits in quantum computing, is used to control the phase flip of qubits, is used for the flip operation of qubits, that is, to change the state of the qubit from to , and vice versa; the tensor product of Pauli matrices represents combining two quantum operators together to describe the interaction between multiple qubits in a quantum system; Performing manifold decomposition on the topological manifold tensor to calculate topological invariants. When the topological invariant is greater than the set threshold, an anomaly alarm is generated; the calculation logic of manifold decomposition is: , is the outside of the manifold, is the inside of the manifold, is the topological invariant; specifically, in the above calculation logic, the topological manifold tensor calculates the topological invariant through manifold decomposition, which is used to evaluate the topological relationship between nodes. By decomposing the topological manifold, global topological features are extracted, and the system state is monitored through the calculated topological invariant. When the topological invariant exceeds the set threshold, an anomaly alarm is triggered, which can efficiently detect abnormal patterns in the system, especially in large-scale and complex environment lighting systems; the outside of the manifold represents the relevant characteristics related to the system boundary or edge, describing the topological characteristics of the outside, and the inside of the manifold represents the topological characteristics inside the system, which is used to describe the interaction between each node, is the topological invariant, representing the global topological characteristics of the manifold, which do not change with local changes inside the system, so it is used for anomaly detection. The value of the topological invariant is usually between 0 and 1. If If the value is greater than a set threshold (0.7 or 0.8 in a feasible embodiment of the present invention), it is considered that an abnormality has occurred. Manifold decomposition is a technique for decomposing complex high-dimensional data or topological structures into simpler parts. It is widely used in data science, machine learning, graph theory, and topology in physics. The core idea of manifold decomposition is to decompose the manifold (i.e., high-dimensional data space) into multiple simple substructures by mathematical methods, and then extract the global or local features of the system. This is a prior art and will not be described in detail here. In a feasible embodiment of the present invention, the manifold decomposition is Hodge decomposition.

[0026] The present invention is further configured that step S3 specifically includes: When an abnormal alarm is detected, all adjacent lighting devices directly connected to the abnormal node are obtained according to the lighting topology model; Obtain multi-dimensional sensor data and dynamic coupling coefficients of all adjacent nodes; The objective function is constructed based on the multi-dimensional sensor data and the dynamic coupling coefficient. The power and lighting brightness of each adjacent lighting device are calculated and solved through the optimization algorithm, and then adjusted. The construction logic of the objective function is: , is the objective function, For the The neighboring nodes of a node, For the The power of a node, For the The lighting brightness of each node, For the and The dynamic coupling coefficient between nodes is , and Specifically, the above calculation logic constructs the objective function through multi-dimensional sensor data and dynamic coupling coefficient, and uses the optimization algorithm to calculate the power of each adjacent lighting device. and lighting brightness , and then make adjustments. The purpose of the objective function is to reasonably adjust the power distribution between nodes while ensuring the high efficiency of the system and optimizing the lighting brightness, so as to achieve the best energy efficiency and comfort; the objective function The power, lighting brightness and coupling coefficient between nodes are comprehensively considered by weighted summation. The power, brightness and coupling coefficient of each adjacent node play a role in the optimization process. The objective function will ultimately determine how to adjust the power and brightness of each node to achieve the system optimization goal; through the optimization of the objective function, the energy consumption of the lighting system can be significantly reduced. During the optimization process, the system will try to select nodes with lower power consumption while ensuring that the required lighting brightness is met, thereby maximizing energy efficiency.

[0027] The present invention is further configured such that when solving the objective function through an optimization algorithm, the optimization algorithm includes the gradient descent method, the genetic algorithm, and the particle swarm optimization. Specifically, in the present invention, the optimization algorithm is used to solve the objective function, thereby optimizing the power and illumination brightness of each node in the intelligent lighting system. To efficiently solve this optimization problem, the present invention optionally uses three common optimization algorithms: the gradient descent method, the genetic algorithm, and the particle swarm optimization. The above-mentioned optimization algorithms each have their own advantages and disadvantages and can play their respective advantages in different optimization environments to ensure that the objective function can be efficiently solved; the gradient descent method is an iterative optimization method based on gradient information, aiming to continuously adjust the system parameters and update along the gradient direction of the objective function (i.e., the direction in which the function value decreases fastest) to finally find the minimum value of the objective function; the gradient descent method is applicable to the case where the objective function has continuity and differentiability and has good convergence for most optimization problems; the gradient descent method is very suitable for dealing with high-dimensional optimization problems, especially for large-scale data sets, and can quickly find local optimal solutions; the genetic algorithm is an optimization algorithm that simulates the process of natural evolution. It explores the solution space of the problem by simulating the processes of "natural selection" and "inheritance". The algorithm first randomly generates a population of initial solutions, and then through genetic operations such as selection, crossover, and mutation, continuously evolves better solutions. The key idea of the genetic algorithm is to find the optimal solution in the solution space by simulating the process of natural selection. In practical applications, the representation of the solution is usually a chromosome (i.e., the encoding of parameters), and through continuous selection, crossover, and mutation, the optimal solution to the problem is searched. In intelligent lighting, the genetic algorithm can select the best energy efficiency and lighting quality scheme by simulating different lighting device configurations and power regulations; the genetic algorithm can effectively perform global search, avoid falling into local optimal solutions, and is suitable for solving complex optimization problems; the genetic algorithm can handle various forms of objective functions, including complex problems such as non-linear, multi-modal, and discrete, and is very suitable for complex multi-objective optimization tasks; the particle swarm optimization algorithm (PSO) is a heuristic optimization algorithm that simulates the foraging behavior of bird flocks. The algorithm simulates the flight behavior of a group of "particles" in the solution space, where each particle represents a potential solution. During the search process, the particles update their positions based on their own historical optimal positions and the historical optimal positions of all particles; the particle swarm optimization algorithm searches through group cooperation. Each particle represents a solution and updates its position in the solution space based on its historical optimal position and the global optimal position. The particle swarm searches for the optimal solution to the problem through multiple iterations.In a smart lighting system, PSO can be used to optimize parameters such as the power and brightness of lighting devices, ensuring the energy efficiency and lighting quality of the system under different time and space conditions; PSO can avoid local optima and quickly converge to the global optimal solution through the cooperation of all particles, and is suitable for complex high-dimensional optimization problems; compared with other optimization algorithms, PSO has higher computational efficiency and is especially suitable for multi-objective optimization problems. By adopting gradient descent method, genetic algorithm or particle swarm optimization, the optimal solution can be found in complex lighting adjustment problems, realizing efficient and intelligent lighting control. Each algorithm has its unique advantages and can be flexibly selected according to different scenario requirements, ultimately improving the performance, stability and energy efficiency of the smart lighting system.

[0028] Embodiment 2 Please refer to Figure 2 , an exemplary urban smart lighting wireless monitoring system for the above-mentioned urban smart lighting wireless monitoring method, the system includes: Model construction module: Obtain the lighting devices and multi-dimensional sensor data in the urban smart scenario, transmit them to the cloud through wireless network, set the lighting devices as nodes, set the dynamic coupling coefficient between nodes as edge weights, and construct a lighting topology model; Abnormal alarm module: Perform quantum state feature mapping on the obtained multi-dimensional sensor data, construct a topological manifold tensor according to the mapped quantum state features and calculate topological invariants through manifold decomposition. When the topological invariant is greater than the set threshold, generate an abnormal alarm; Adjacent adjustment module: When an abnormal alarm is detected, obtain all adjacent lighting devices of the abnormal node, construct an objective function, solve for the power and lighting brightness of the adjacent lighting devices, and make adjustments.

[0029] It should be noted that the above-mentioned urban smart lighting wireless monitoring system provided by the above embodiment and the above-mentioned urban smart lighting wireless monitoring method belong to the same concept. The specific ways in which each module and unit perform operations have been described in detail in the method embodiment, and will not be repeated here. The above-mentioned urban smart lighting wireless monitoring system provided by the above embodiment can, in actual application, allocate the above functions to different functional modules according to needs, that is, divide the internal structure of the system into different functional modules to complete all or part of the functions described above. This is not limited here either.

[0030] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0031] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be understood specifically with reference to the context before and after.

[0032] In this application, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0033] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0034] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0035] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0036] In the several embodiments provided in this application, it should be understood that the disclosed systems can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0037] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0038] In addition, the functional units in the various embodiments of this application can be integrated in one processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0039] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0040] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A wireless monitoring method for urban intelligent lighting, characterized in that, Including: S1: Obtain the data of lighting devices and multi-dimensional sensors in the urban intelligent scenario, transmit them to the cloud through wireless network, set the lighting devices as nodes, set the dynamic coupling coefficient between nodes as edge weights, and construct a lighting topology model; S2: Perform quantum state feature mapping on the obtained multi-dimensional sensor data, construct a topological manifold tensor according to the mapped quantum state features, and calculate topological invariants through manifold decomposition. When the topological invariant is greater than the set threshold, generate an anomaly alarm; S3: When an anomaly alarm is detected, obtain all adjacent lighting devices of the abnormal node, construct an objective function, solve to obtain the power and lighting brightness of the adjacent lighting devices, and make adjustments.

2. The wireless monitoring method for urban intelligent lighting according to claim 1, characterized in that The multi-dimensional sensor data includes power, lighting brightness, working temperature, light intensity, location data, traffic flow, and vibration data.

3. The wireless monitoring method for urban intelligent lighting according to claim 1, characterized in that The calculation logic of the dynamic coupling coefficient between nodes includes: Calculate the initial coupling coefficient according to the spatial distance between nodes; Dynamically adjust the initial coupling coefficient according to the change rate of the difference in light intensity between nodes to generate an adjusted coupling coefficient; Perform weighted dynamic adjustment on the adjusted coupling coefficient in combination with the historical coupling degree to generate a dynamic coupling coefficient.

4. A method for wireless monitoring of urban intelligent lighting according to claim 1, characterized in that, The calculation logic of the initial coupling coefficient is as follows: , is the initial coupling coefficient between the th and the th nodes, is the spatial distance between the th and the th nodes, is the first adjustment coefficient; The calculation logic for adjusting the coupling coefficient is as follows: , is the adjusted coupling coefficient at time is the change value of the light intensity difference between the th and the th nodes; is the second adjustment coefficient. The calculation logic of the dynamic coupling coefficient is as follows: , is the dynamic coupling coefficient at time is the power factor, and is the weighting coefficient.

5. A wireless monitoring method for urban intelligent lighting according to claim 1, characterized in that, Step S2 specifically includes: Normalize the collected multi-dimensional sensor data, and map the normalized multi-dimensional sensor data to the quantum state space through a preset quantum state feature mapping function to obtain the quantum state features of the nodes; Calculate the topological connection operator of adjacent nodes, and construct a topological manifold tensor according to the mapped quantum state features and the topological connection operator; Perform manifold decomposition on the topological manifold tensor to calculate topological invariants. When the topological invariant is greater than the set threshold, generate an anomaly alarm.

6. The method for wireless monitoring of urban intelligent lighting according to claim 5, characterized in that, The preset quantum state feature mapping function is as follows: , is the quantum state feature of the th node, is the cardinality of the quantum state representation, is the eigenvalue of the quantum state, is the quantum phase factor, is the tensor product operation, is the preset quantum ground state, is the th node corresponding to the ground state feature data; The calculation logic of the topological connection operator is as follows: , is the topological connection operator between the th and the th nodes, and are the position vectors of the th and the th nodes, is the scale parameter of the spatial distance, and are Pauli matrices; The topological manifold tensor is: , is the topological manifold tensor; The calculation logic of manifold decomposition is as follows: , is outside the manifold, is inside the manifold, is a topological invariant.

7. A method for wireless monitoring of urban intelligent lighting according to claim 1, characterized in that, Step S3 specifically includes: When an anomaly alarm is detected, according to the lighting topology model, obtain all adjacent lighting devices directly connected to the abnormal node; Obtain the multi-dimensional sensor data and dynamic coupling coefficients of all adjacent nodes; Construct an objective function according to the multi-dimensional sensor data and dynamic coupling coefficients, and calculate and solve to obtain the power and lighting brightness of each adjacent lighting device through an optimization algorithm for adjustment.

8. The wireless monitoring method for urban intelligent lighting according to claim 7, characterized in that The construction logic of the objective function is as follows: , is the objective function, is the adjacent node of the th node, is the power of the th node, is the illumination brightness of the th node, is the th and the th nodes' dynamic coupling coefficient, , and are the weight coefficients.

9. The method for wireless monitoring of urban intelligent lighting according to claim 8, characterized in that When solving the objective function through an optimization algorithm, the optimization algorithms include the gradient descent method, genetic algorithm, and particle swarm optimization.

10. A wireless monitoring system for urban intelligent lighting, which is used to implement the wireless monitoring method for urban intelligent lighting described in any one of claims 1-9, and is characterized in that, Including: Model construction module: Obtain the data of lighting devices and multi-dimensional sensors in the urban intelligent scenario, transmit them to the cloud through wireless network, set the lighting devices as nodes, set the dynamic coupling coefficient between nodes as edge weights, and construct a lighting topology model; Anomaly alarm module: Perform quantum state feature mapping on the obtained multi-dimensional sensor data, construct a topological manifold tensor according to the mapped quantum state features, and calculate topological invariants through manifold decomposition. When the topological invariant is greater than the set threshold, generate an anomaly alarm; Adjacent adjustment module: When an anomaly alarm is detected, obtain all adjacent lighting devices of the abnormal node, construct an objective function, solve to obtain the power and lighting brightness of the adjacent lighting devices, and make adjustments.

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