An urban lighting energy efficiency management system based on internet of things technology

Abstract

The present application relates to the technical field of smart city Internet of Things, and discloses a city lighting energy efficiency management system based on Internet of Things technology. A dynamic data topology construction module of the system constructs a dynamic data topology containing real-time energy efficiency interaction relationship between lighting device nodes; an energy efficiency gene factor analysis and decomposition module generates energy efficiency gene factors according to the dynamic data topology and performs multi-scale decomposition to obtain device-level and network-level energy efficiency segments; an energy efficiency situation calculation module matches the device-level energy efficiency segments with a device energy efficiency benchmark library to generate a device energy efficiency state vector, and inputs the network-level energy efficiency segments into an energy efficiency propagation model to deduce a network energy efficiency influence vector; an energy efficiency optimization strategy generation module fuses the device energy efficiency state vector and the network energy efficiency influence vector to generate a comprehensive energy efficiency situation map, identifies weak links of energy efficiency, and generates an energy efficiency optimization strategy set; and a strategy execution and closed-loop update module drives controllable devices to perform energy efficiency adjustment operations according to the strategy set.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart city Internet of Things, in particular to a city lighting energy efficiency management system based on Internet of Things technology. BACKGROUND

[0002] The current city lighting energy efficiency management mainly adopts the way of timing control or simple light-sensitive control. The management of lighting devices in the prior art is mostly based on independent node monitoring, and the energy efficiency interaction relationship between devices is not effectively modeled. The data collection dimension is single, and the dynamic topology structure reflecting real-time energy efficiency interaction cannot be constructed. The energy efficiency analysis granularity is rough, and lacks multi-scale decomposition capability at the device level and network level. The situation assessment method is simple, and the quantitative deduction model of energy efficiency propagation influence is not established. The optimization strategy generation is linearized, and the synergistic effect of device state and network influence cannot be considered comprehensively. The control process is open-loop, and there is no feedback optimization based on actual effect after strategy execution. The existing method needs to solve key technical problems such as dynamic topology construction, multi-scale energy efficiency analysis, network influence deduction and closed-loop optimization.

[0003] The traditional city lighting energy efficiency management system has obvious deficiencies in refinement and self-adaptation. The device network modeling is static, which cannot reflect the real-time energy efficiency interaction relationship. The energy efficiency factor extraction method is simple, and the key feature representation is incomplete. The multi-scale decomposition algorithm has high complexity and low real-time processing efficiency. The device energy efficiency benchmark library is updated with lag, and the matching accuracy is insufficient. The energy efficiency propagation model is idealized, and the influence of network topology structure is not considered. The situation map generation dimension is single, and the identification precision of weak links is limited. The optimization strategy library size is insufficient, and the matching degree of strategy and actual working condition is low. The control instruction execution is rigid, and the device response cooperativity is poor. The data collection frequency is insufficient, and the timeliness of closed-loop update is difficult to guarantee. SUMMARY

[0004] The purpose of the present application is to provide a city lighting energy efficiency management system based on Internet of Things technology to solve the problems raised in the background art.

[0005] To achieve the above purpose, the present application provides a city lighting energy efficiency management system based on Internet of Things technology, which comprises:

[0006] A dynamic data topology construction module is used to construct a dynamic data topology of city lighting network, and the dynamic data topology contains real-time energy efficiency interaction relationship between lighting device nodes;

[0007] An energy efficiency factor analysis and decomposition module is used to generate energy efficiency factors of lighting device groups according to the dynamic data topology, and to perform multi-scale decomposition on the energy efficiency factors to obtain device-level energy efficiency segments and network-level energy efficiency segments;

[0008] An energy efficiency situation calculation module is configured to match the device-level energy efficiency segment with a preset device energy efficiency benchmark library to generate a device energy efficiency state vector, and input the network-level energy efficiency segment into a pre-built energy efficiency propagation model to derive a network energy efficiency influence vector;

[0009] An energy efficiency optimization strategy generation module is configured to fuse the device energy efficiency state vector and the network energy efficiency influence vector to generate a comprehensive energy efficiency situation map, identify energy efficiency weak links based on the comprehensive energy efficiency situation map, and generate an energy efficiency optimization strategy set;

[0010] A strategy execution and closed-loop update module is configured to drive controllable devices in the urban lighting network to perform energy efficiency adjustment operations according to the energy efficiency optimization strategy set, and collect new energy efficiency data after the operations to update the dynamic data topology to form a closed-loop control.

[0011] Preferably, the specific steps of constructing the dynamic data topology of the urban lighting network include:

[0012] The running parameters and environmental parameters of the lighting devices are continuously collected by the Internet of Things sensing nodes;

[0013] A communication connection relationship mapping between the device nodes is established;

[0014] An initial static topology is constructed according to the geographical location attributes and electrical connection attributes of the devices;

[0015] The real-time collected running parameters and environmental parameters are injected into the initial static topology to form a dynamic data topology reflecting the real-time energy flow state.

[0016] Preferably, the specific steps of generating the energy efficiency gene factor of the lighting device group according to the dynamic data topology include:

[0017] The time-series energy consumption data of each device node is extracted from the dynamic data topology;

[0018] The time-series energy consumption data is subjected to characteristic waveform analysis to extract periodic fluctuation characteristics and abnormal fluctuation characteristics;

[0019] The periodic fluctuation characteristics and the abnormal fluctuation characteristics are encoded to generate an energy efficiency gene unit of each device node;

[0020] The energy efficiency gene units of all device nodes are aggregated to form an energy efficiency gene factor representing the overall energy consumption mode of the device group.

[0021] Preferably, the specific steps of multi-scale decomposition of the energy efficiency gene factor include:

[0022] A time scale decomposition algorithm is used to separate a device-level energy efficiency segment reflecting short-term fluctuation characteristics from the energy efficiency gene factor;

[0023] Separating a network-level energy efficiency segment reflecting regional correlation characteristics from the energy efficiency gene factor by using a spatial scale decomposition algorithm;

[0024] Standardizing and marking the device-level energy efficiency segment and the network-level energy efficiency segment.

[0025] Preferably, the specific step of matching the device-level energy efficiency segment with a preset device energy efficiency benchmark library comprises:

[0026] Querying an energy efficiency benchmark curve matching the current device type in the device energy efficiency benchmark library;

[0027] Calculating a shape similarity degree of the device-level energy efficiency segment and the energy efficiency benchmark curve;

[0028] Determining a health degree index and a deviation index of the device according to the shape similarity degree;

[0029] Generating a device energy efficiency state vector by combining the health degree index and the deviation index.

[0030] Preferably, the specific step of inputting the network-level energy efficiency segment into a pre-constructed energy efficiency propagation model comprises:

[0031] The energy efficiency propagation model pre-obtains energy efficiency influence weights between network nodes by training historical data;

[0032] Mapping the network-level energy efficiency segment to an input layer of the energy efficiency propagation model;

[0033] Calculating a spatial propagation path and intensity of energy efficiency fluctuation by a hidden layer of the energy efficiency propagation model;

[0034] Generating a network energy efficiency influence vector representing an influence range of energy efficiency fluctuation in an output layer.

[0035] Preferably, the specific step of fusing the device energy efficiency state vector and the network energy efficiency influence vector comprises:

[0036] Performing spatial coordinate mapping on the device energy efficiency state vector;

[0037] Superimposing and calculating the mapped device energy efficiency state vector and the network energy efficiency influence vector;

[0038] Generating a continuous energy efficiency distribution surface covering the entire lighting area by an interpolation algorithm;

[0039] Converting the continuous energy efficiency distribution surface into a comprehensive energy efficiency situation map in a grid form.

[0040] Preferably, the specific steps of identifying energy efficiency weak links based on the comprehensive energy efficiency situation map comprise:

[0041] Setting an energy efficiency threshold, and regionally segmenting the comprehensive energy efficiency situation map;

[0042] Identifying regions with energy efficiency values lower than the energy efficiency threshold as candidate weak links;

[0043] Analyzing the energy efficiency gradient relationship between the candidate weak links and surrounding regions;

[0044] Confirming the final energy efficiency weak links in combination with the device topology relationship, and generating an energy efficiency optimization strategy set containing adjustment targets and adjustment amounts.

[0045] Preferably, the specific steps of driving controllable devices in the urban lighting network to perform energy efficiency adjustment operations according to the energy efficiency optimization strategy set comprise:

[0046] Analyzing the adjustment instructions in the energy efficiency optimization strategy set, which contain target device identifiers and adjustment parameters;

[0047] Downlinking the adjustment instructions to corresponding controllable devices through an Internet of Things control channel;

[0048] The controllable devices adjust their operating power or working modes according to the received adjustment parameters, and record the downlink time of the adjustment instructions and the device response state.

[0049] Preferably, after completing the energy efficiency adjustment operation, the system collects new operating parameters of the lighting devices through the Internet of Things sensing nodes, updates the dynamic data topology with the new operating parameters, and starts a new round of energy efficiency analysis and optimization process to realize continuous energy efficiency closed-loop management.

[0050] Compared with the prior art, the present application has the following advantages:

[0051] By constructing a dynamic data topology containing real-time energy efficiency interaction relationships between lighting device nodes, the operating state of the urban lighting network is accurately characterized. Based on the topology, energy efficiency factors are generated, and multi-scale decomposition technology is used to separate them into device-level and network-level energy efficiency segments, realizing fine analysis of energy efficiency characteristics.

[0052] The device-level energy efficiency segments are matched with a device energy efficiency benchmark library to generate a device energy efficiency state vector, and the network-level energy efficiency segments are input into an energy efficiency propagation model to derive a network energy efficiency influence vector. By fusing the two vectors, a comprehensive energy efficiency situation map is generated, clearly showing the energy efficiency distribution and weak link positions.

[0053] Based on the comprehensive energy efficiency situation map, the weak links of energy efficiency are identified, and the energy efficiency optimization strategy set including brightness adjustment, switch strategy and other measures is generated. According to the strategy set, the controllable equipment is driven to perform energy efficiency adjustment operation, and new energy efficiency data is collected after operation to update the dynamic data topology, forming a closed loop control.

[0054] The method realizes the fine management of urban lighting energy efficiency through the synergistic effect of dynamic topology construction, multi-scale energy efficiency analysis, situation assessment and closed-loop optimization. Dynamic topology ensures real-time data, multi-scale analysis provides a comprehensive perspective, situation assessment accurately identifies problems, and closed-loop control ensures continuous optimization. The system significantly improves the level of lighting energy efficiency management and energy saving effect. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 The working principle diagram of the urban lighting energy efficiency management system based on the Internet of Things technology is described.

[0056] Figure 2 The flowchart for dynamic data topology construction is described.

[0057] Figure 3 The flowchart for multi-scale decomposition of energy efficiency genetic factors is described.

[0058] Figure 4 The comparison chart of lighting energy efficiency improvement rate of each region is described.

[0059] Figure 5 The total energy efficiency trend chart of urban lighting network under each time scale is described. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0061] Please refer to Figure 1The application provides a city lighting energy efficiency management system based on Internet of Things technology, which comprises a dynamic data topology construction module, an energy efficiency gene factor analysis and decomposition module, an energy efficiency situation calculation module, an energy efficiency optimization strategy generation module and a strategy execution and closed-loop update module. The dynamic data topology construction module is responsible for constructing a city lighting network dynamic data topology reflecting real-time energy efficiency interaction relationship. The energy efficiency gene factor analysis and decomposition module generates energy efficiency gene factors based on the dynamic data topology and performs multi-scale decomposition to obtain device-level and network-level energy efficiency segments. The energy efficiency situation calculation module matches the device-level energy efficiency segments with a device energy efficiency benchmark library to generate a device energy efficiency state vector and inputs the network-level energy efficiency segments into an energy efficiency propagation model to deduce a network energy efficiency influence vector. The energy efficiency optimization strategy generation module generates a comprehensive energy efficiency situation map by fusing the device energy efficiency state vector and the network energy efficiency influence vector, identifies energy efficiency weak links and generates an energy efficiency optimization strategy set. The strategy execution and closed-loop update module drives controllable devices to perform energy efficiency adjustment operations according to the energy efficiency optimization strategy set and collects new data to update the dynamic data topology to form a closed-loop control.

[0062] Embodiment 1: refer to Figure 2 In specific implementation, the dynamic data topology of the city lighting network is constructed by deploying Internet of Things sensing nodes on the street lamps, distribution boxes and key nodes of the line to continuously collect the operating parameters and environmental parameters of the lighting devices. The operating parameters include but are not limited to voltage, current, power factor and real-time energy consumption data, and the environmental parameters include environmental illumination, vehicle flow sensing signals. In specific implementation, the communication connection relationship mapping between device nodes needs to be determined according to the registration relationship and adjacency discovery protocol of the devices in the Internet of Things communication network, to determine the adjacent node set that each device node can directly communicate and interact with, and form a basic mapping table of the communication link between devices. In specific implementation, the initial static topology is constructed according to the geographical location attribute and electrical connection attribute of the devices. The geographical location attribute is derived from the global positioning system module built-in the device or the coordinate information manually input by the background, and the electrical connection attribute is connected by the edge according to the physical wiring diagram of the power cable and the circuit loop distribution relationship. In specific implementation, the real-time collected operating parameters and environmental parameters are injected into the initial static topology. The operating parameters are mapped as dynamic weights on the topology nodes, and the environmental parameters are mapped as dynamic edge attributes affecting the energy flow relationship between nodes, to form a dynamic data topology map reflecting the real-time energy flow state and the interaction relationship between devices.

[0063] It can be understood that extracting the time-series energy consumption data of each device node from the dynamic data topology means that the power consumption value of each lighting device node is recorded continuously from the node attributes of the dynamic data topology according to the preset sampling period, forming an energy consumption data sequence with equal time intervals. It can be understood that the feature waveform analysis of the time-series energy consumption data needs to apply signal processing technology to identify the periodic fluctuation characteristics in the data caused by day-night alternation, weekday and weekend differences, and detect abnormal fluctuation characteristics caused by device failure, sudden weather or special events, and the abnormal fluctuation characteristics are quantified by calculating the standard deviation deviation of the data points from the expected periodic fluctuation. In some embodiments, the periodic fluctuation characteristics and the abnormal fluctuation characteristics are encoded to generate the energy efficiency gene unit of each device node, and the encoding process is to combine the extracted periodic fluctuation characteristic parameters and abnormal fluctuation characteristic parameters into a multi-dimensional feature vector according to a predefined order and format. The vector is the energy efficiency gene unit. In some embodiments, the energy efficiency gene units of all device nodes are aggregated to form an energy efficiency gene factor representing the overall energy consumption mode of the device group, and the aggregation operation is to spatially weight and fuse the energy efficiency gene units of all nodes in the network to form a higher-dimensional comprehensive feature matrix, which can describe the overall energy consumption behavior mode of the entire lighting device network. An optional aggregation formula is as follows:

[0064]

[0065] wherein: represents the final generated overall energy efficiency gene factor, is the total number of lighting device nodes in the network, represents the energy efficiency gene unit of the th device node, is the weight coefficient given to the th node, and the weight coefficient can be set according to the type of the node or its relative importance in the entire lighting network. Optionally, the assignment of the weight coefficient can be determined according to the rated power of the node or its centrality index in the topology structure.

[0066] Embodiment 2: refer to Figure 3In specific implementations, the multi-scale decomposition of the energy efficiency gene is performed by a time-scale decomposition algorithm to separate the device-level energy efficiency segments reflecting short-term fluctuation characteristics from the energy efficiency gene. The time-scale decomposition algorithm can be a filtering technique based on a sliding window, and the window size is set to several minutes to several hours to capture the energy consumption fluctuation details of a single lighting device in a short time scale. In specific implementations, a spatial-scale decomposition algorithm is used to separate the network-level energy efficiency segments reflecting regional correlation characteristics from the energy efficiency gene. The spatial-scale decomposition algorithm performs regional clustering of the energy efficiency gene based on the geographical coordinates or electrical partition information of the device nodes, aggregates the energy consumption patterns of the device nodes belonging to the same geographical region or the same power supply loop, and extracts segments representing the overall energy efficiency characteristics of the region. In specific implementations, the device-level energy efficiency segments and the network-level energy efficiency segments are standardized and labeled. The standardization and labeling process is to attach metadata tags to each decomposed energy efficiency segment. The tag content includes segment type, device or region identification, timestamp, and data scale information, so that subsequent modules can be identified and processed.

[0067] It can be understood that matching the device-level energy efficiency segments with the preset device energy efficiency benchmark library requires first querying the device energy efficiency benchmark library. The device energy efficiency benchmark library is a pre-generated database that stores the benchmark energy consumption curves of different types and different models of lighting devices under standard test conditions. It can be understood that querying the energy efficiency benchmark curve in the device energy efficiency benchmark library that matches the current device type is based on the model code in the device nameplate parameters or device registration information, and an accurate search and retrieval is performed in the device energy efficiency benchmark library. In some embodiments, the shape similarity between the device-level energy efficiency segment and the energy efficiency benchmark curve is calculated using a curve matching algorithm in pattern recognition. This algorithm quantifies the degree of agreement between the device-level energy efficiency segment generated by actual operation and the energy efficiency benchmark curve in ideal state in terms of waveform and trend through dynamic time warping or feature-based similarity measurement method. In some embodiments, the health index and the deviation index of the device are determined according to the shape similarity. The health index is a value between 0 and 1, which is directly obtained by normalizing the shape similarity. The deviation index is defined by calculating the difference amplitude of the device-level energy efficiency segment and the energy efficiency benchmark curve at the key feature points. An optional deviation index calculation formula is represented as:

[0068]

[0069] wherein: represents the calculated deviation index, is the total number of feature points for comparison, represents the value of the device-level energy efficiency segment at the th feature point, represents the value of the energy efficiency benchmark curve at the The value on the feature point. Optionally, the selection of the feature point can be based on the extreme point, the inflection point or the energy consumption value corresponding to the specific timestamp of the energy consumption curve. The health degree index and the deviation degree index are combined to generate a device energy efficiency state vector, which is a multi-dimensional vector including at least a device identifier, a health degree index value, a deviation degree index value and timestamp information. The vector is used to comprehensively represent the energy efficiency state of a single device at a specific time.

[0070] In a specific implementation, the network-level energy efficiency segment is input into a pre-constructed energy efficiency propagation model, which is a prediction model based on a graph neural network structure. The energy efficiency propagation model is pre-trained to obtain energy efficiency influence weights between network nodes through historical data, which includes different node energy consumption fluctuation events and their chain influence records on the network within a certain period of time. In a specific implementation, the network-level energy efficiency segment is mapped to the input layer of the energy efficiency propagation model. The mapping process is to assign the regional energy efficiency indicators contained in the network-level energy efficiency segment to the corresponding neuron nodes of the input layer of the energy efficiency propagation model according to their corresponding network nodes or regional numbers. In a specific implementation, the spatial propagation path and intensity of energy efficiency fluctuation are calculated through the hidden layer of the energy efficiency propagation model. The hidden layer is composed of multiple graph convolution layers. The graph convolution layer performs multi-layer message passing and feature transformation on the energy efficiency fluctuation signal transmitted by the input layer according to the adjacency matrix composed of the pre-trained energy efficiency influence weights, simulating the diffusion process of energy efficiency fluctuation on the network topology. In a specific implementation, the network energy efficiency influence vector representing the influence range of energy efficiency fluctuation is generated in the output layer. Each neuron of the output layer corresponds to a network node or a region. The output value quantifies the influence degree of the energy efficiency fluctuation represented by the initial network-level energy efficiency segment on the node or region. All output values constitute the network energy efficiency influence vector.

[0071] It is understandable that fusing the device energy efficiency status vector and the network energy efficiency influence vector requires spatial coordinate mapping of the device energy efficiency status vector. This spatial coordinate mapping involves querying the latitude and longitude coordinates of the device in the lighting network geographic information system based on the device identifier contained in the device energy efficiency status vector, and binding data such as health indicators and deviation indicators in the device energy efficiency status vector to geographic coordinates. It is also understandable that the mapped device energy efficiency status vector and the network energy efficiency influence vector are superimposed. This superposition calculation, under a unified geographic coordinate system, involves weighted fusion of the device energy efficiency status vector data representing the status of a single device and the network energy efficiency influence vector data representing the regional energy efficiency influence situation. The network energy efficiency influence vector data typically needs to be converted into point data corresponding to the device coordinates using interpolation methods before superposition. In some embodiments, an interpolation algorithm is used to generate a continuous energy efficiency distribution surface covering the entire lighting area. The interpolation algorithm can be Kriging interpolation, using the energy efficiency fusion value of all spatially discrete points after superposition calculation as known sample points to estimate the comprehensive energy efficiency value of any geographical location within the lighting area, thereby forming a continuous two-dimensional energy efficiency distribution surface. In practical implementation, the application of Kriging interpolation first requires using the energy efficiency fusion values ​​of spatially discrete points obtained after superposition calculation as known sample points. These sample points correspond to the geographical coordinates of specific device nodes in the lighting network and their comprehensive energy efficiency values. Kriging interpolation analyzes the spatial autocorrelation characteristics between these sample points and establishes a variogram model to describe the variation of energy efficiency values ​​with geographical distance. Based on this model, an unbiased optimal estimate of the comprehensive energy efficiency value of any unsampled point within the lighting area is then performed, ultimately generating a smooth and continuous two-dimensional energy efficiency distribution surface. This surface accurately reflects the spatial variation trend of the energy efficiency situation in the entire area. In some embodiments, the continuous energy efficiency distribution surface is transformed into a raster-style comprehensive energy efficiency situation map. The transformation process involves dividing the geographical area of ​​the entire lighting region into regular grid cells according to a preset resolution, assigning each grid cell the energy efficiency estimate corresponding to its center point on the continuous energy efficiency distribution surface, and finally generating a raster image where the grid cell values ​​represent the energy efficiency level. An optional formula for superposition calculation is expressed as:

[0072]

[0073] in: Indicates geographic coordinates The fusion energy efficiency value at the location, This indicates the location obtained after spatial coordinate mapping. The scalarized representation of the device energy efficiency state vector at coordinates. This indicates the result obtained after interpolation of the network energy efficiency influence vector. Network influence strength value of coordinates and is a weighted coefficient for adjusting the relative importance of the device's own state and the network's influence. Optionally, the weighted coefficient and can be set by historical data analysis or expert experience, and satisfy the normalization condition of .

[0074] In a specific implementation, the energy efficiency threshold is set based on the identification of weak links in the comprehensive energy efficiency situation map. The energy efficiency threshold is a pre-defined critical value of the energy efficiency level, and its value can be referenced from the historical average energy efficiency level or industry standards. In a specific implementation, the regional segmentation of the comprehensive energy efficiency situation map is performed using the region growing method or threshold segmentation method in image processing. All continuous grid cells in the comprehensive energy efficiency situation map with energy efficiency values below the set energy efficiency threshold are divided into the same candidate region. In a specific implementation, the identification of regions with energy efficiency values below the energy efficiency threshold as candidate weak links is performed by traversing all segmented regions, extracting the geometric center coordinates, area, and average energy efficiency value of each region, and forming a list of candidate weak links. In a specific implementation, the analysis of the energy efficiency gradient relationship between the candidate weak links and the surrounding regions is performed by calculating the difference between the energy efficiency values of each grid point on the boundary of the candidate weak link region and its immediately adjacent external grid points, and using these difference data to calculate the average gradient and direction of energy efficiency change. In a specific implementation, the confirmation of the final energy efficiency weak links in combination with the device topology relationship requires querying the dynamic data topology to check the device nodes contained in the candidate weak link region and their connection relationships. If the region is on the critical path or contains important devices in the topology, it is confirmed as the final weak link.

[0075] It can be understood that generating an energy efficiency optimization strategy set containing adjustment targets and adjustment amounts requires formulating targeted adjustment measures for each final confirmed energy efficiency weak link. The adjustment target explicitly specifies the device or area that needs to be adjusted, and the adjustment amount quantitatively describes the adjustment amplitude. It can be understood that analyzing the adjustment instructions in the energy efficiency optimization strategy set means that the system internally formats the generated energy efficiency optimization strategy set and converts it into a control instruction queue, each instruction containing a target device identifier and an adjustment parameter. In specific implementations, the adjustment instructions are issued to the corresponding controllable devices through an Internet of Things control channel, which can be a narrowband Internet of Things, LoRa, or 4G / 5G wireless network. The instruction issuing process needs to follow a communication protocol to ensure reliable transmission. In specific implementations, the controllable device adjusts its operating power or working mode according to the received adjustment parameter. The adjustment of operating power can be achieved by adjusting the driving current, and the switching of working mode may involve switching from full night light mode to half night light mode or dimming mode. In specific implementations, recording the issuance time of the adjustment instruction and the device response state is a key step in maintaining the operation log of the system. The issuance time is used to mark the operation time, and the device response state is used to confirm whether the instruction is successfully executed. A formula for calculating the energy efficiency gradient amplitude is as follows:

[0076]

[0077] wherein: represents the calculated energy efficiency gradient amplitude, is the total number of grid points located on the boundary of the candidate weak link area, represents the energy efficiency value of the boundary grid point belonging to one side of the candidate weak link area, represents the energy efficiency value of the boundary grid point adjacent to the external area. Optionally, gradient calculation can be directly performed on the comprehensive energy efficiency situation map using edge detection algorithms such as Sobel operator. Referring to Table 1, the structure of an energy efficiency optimization strategy set is shown.

[0078] Table 1: Energy efficiency optimization strategy set table

[0079] Policy Number Adjustment Target Device Identification Adjustment Parameter Type Adjustment Amount Expected Execution Time Window S-2024-001 Lamp_Node_ZH00125 Operating Power Lowered to 70% of rated power 22:00-05:00 the next day S-2024-002 Cabinet_Circuit_A05 Operating Mode Switched to energy-saving mode (interval lighting) 00:00-05:00 the next day S-2024-003 Lamp_Node_ZH00387 Operating Power Increased to 90% of rated power 19:00-21:00

[0080] In some embodiments, the setting of adjustment parameters can be dynamically calculated based on historical operation data of the device and the energy efficiency deviation index. In some embodiments, the determination of the expected execution time window can be combined with historical human and vehicle flow data and environmental illumination data to achieve energy efficiency optimization while ensuring lighting functionality.

[0081] Referring to Figure 4The transverse bar chart intuitively presents the differences in energy efficiency improvement rates of the six target areas, including the mixing area, park area, industrial area, transportation hub, residential area, and commercial area. From the data dimension, the energy efficiency improvement rate of the industrial area is the best among all areas, reflecting its implementation effect in device-level energy efficiency optimization and network-level energy efficiency coordination. The transportation hub has a relatively low improvement rate, reflecting that the area has a high requirement for lighting function continuity and limited flexibility in energy efficiency adjustment. The quantitative comparison of the chart provides a direct basis for identifying weak links in energy efficiency. Combined with dynamic data topology and energy efficiency gene factor analysis, targeted optimization strategies can be further developed for low improvement rate areas, such as adjusting the adjustment parameter type or optimizing the execution time window for the transportation hub area, to improve the energy efficiency improvement range while ensuring lighting demand.

[0082] In a specific implementation, the system collects new operating parameters of the lighting devices after completing the energy efficiency adjustment operation through the Internet of Things sensor nodes, including intelligent electrical measurement modules installed inside the lamps and power monitoring terminals deployed on the distribution box side. These nodes continuously measure operating parameters such as voltage, current, power factor, and active power according to the preset collection period. In a specific implementation, updating the dynamic data topology with new operating parameters replaces the old parameter values of the corresponding device nodes in the dynamic data topology with the latest collected operating parameter values, while checking whether the online state and communication connection relationship of the device nodes have changed, and synchronously refreshing the node attributes and edge relationships in the topology, so that the dynamic data topology always reflects the latest real-time state of the lighting network. In a specific implementation, restarting the new round of energy efficiency analysis and optimization process is to automatically trigger the entry function of the energy efficiency gene factor analysis and decomposition module after the dynamic data topology is updated, and sequentially execute the complete logical sequence of energy efficiency gene factor generation and multi-scale decomposition, energy efficiency situation calculation, energy efficiency optimization strategy generation, and strategy execution and closed-loop update module.

[0083] It can be understood that the implementation of the continuous energy efficiency closed-loop management relies on the cyclic execution of the above process, and the endpoint of each cycle, i.e. the updated dynamic data topology, becomes the starting point of the next cycle. It can be understood that the stability of the closed-loop control is guaranteed by the system preset cycle trigger condition, which can be based on a fixed time interval, such as starting a new cycle every 15 minutes, or based on event-driven, such as starting a new cycle immediately when the change amplitude of the key operating parameter is monitored to exceed the set threshold. In some embodiments, the system maintains a cycle state record table for tracking the execution of each closed-loop management, which includes cycle number, cycle start time stamp, number of device nodes involved in processing, number of energy efficiency optimization strategies generated, and cycle execution result status. In some embodiments, the system performs comparative analysis on the set of energy efficiency optimization strategies generated in continuous multiple cycles, and if it is found that the adjustment strategy for the same energy efficiency weak link is generated in continuous multiple cycles and the adjustment direction is consistent, the system will automatically enlarge the adjustment amount of the link in the next cycle to accelerate the convergence to the optimal energy efficiency state. An event-driven condition formula for determining whether to start a new cycle is represented as:

[0084]

[0085] wherein: is a Boolean variable, taking the value of 1 when the condition is met and a new round of energy efficiency analysis and optimization process needs to be started, and taking the value of 0 when it is not started; is the set of device nodes monitored in the current dynamic data topology; represents the device node the latest collected operating power parameter; represents the operating power parameter used by the device node in the last cycle; is the preset power change rate threshold. Optionally, the power change rate threshold can be set differently according to different device types or network areas.

[0086] Referring to Figure 5The evolution of the total energy efficiency value (relative unit) with time scale in the presented urban lighting energy efficiency management system intuitively reflects the dynamic effect of the closed-loop management of the system. Specifically, in the process of time scale from 15 minutes to 24 hours, the total energy efficiency presents a fluctuation trend of "growth-fall-growth again": the total energy efficiency is about 88 at 15 minutes, rises to 98 at 1 hour, further increases to 129 at 4 hours, reflecting the positive effect of the short-term energy efficiency optimization strategy; falls to 88 at 12 hours, which may correspond to the energy efficiency fluctuation caused by the regional lighting scene switching or the device working condition fluctuation; rises to 138 at 24 hours, indicating that the system realizes the continuous optimization of long-period energy efficiency through closed-loop updating (dynamic data topology refreshing, energy efficiency gene factor re-decomposition, etc.). The core value of the trend chart lies in quantifying the response effect of the energy efficiency management strategy under different time scales: the short-term (15 minutes-4 hours) reflects the immediacy of the device-level adjustment, the medium-term (4 hours-12 hours) embodies the relevance of the network-level energy efficiency propagation, and the long-term (12 hours-24 hours) verifies the convergence of the closed-loop control, providing data support for the subsequent adjustment of the cycle triggering conditions (such as time interval, parameter threshold) of the system.

[0087] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and variations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A city lighting energy efficiency management system based on Internet of Things technology, characterized in that, The system comprises: a dynamic data topology construction module for constructing a dynamic data topology of the urban lighting network, the dynamic data topology containing real-time energy efficiency interaction relationships between lighting device nodes; an energy efficiency gene factor analysis and decomposition module for generating energy efficiency gene factors of a lighting device group according to the dynamic data topology and performing multi-scale decomposition on the energy efficiency gene factors to obtain device-level energy efficiency segments and network-level energy efficiency segments; an energy efficiency situation calculation module for matching the device-level energy efficiency segments with a preset device energy efficiency benchmark library to generate a device energy efficiency state vector and inputting the network-level energy efficiency segments into a pre-constructed energy efficiency propagation model to deduce a network energy efficiency influence vector; an energy efficiency optimization strategy generation module for fusing the device energy efficiency state vector and the network energy efficiency influence vector to generate a comprehensive energy efficiency situation map, identifying energy efficiency weak links based on the comprehensive energy efficiency situation map and generating an energy efficiency optimization strategy set; a strategy execution and closed-loop update module for driving controllable devices in the urban lighting network to perform energy efficiency adjustment operations according to the energy efficiency optimization strategy set and collecting new energy efficiency data after the operations to update the dynamic data topology, forming a closed-loop control; The specific steps of generating energy efficiency gene factors of a lighting device group according to the dynamic data topology comprise: extracting time-series energy consumption data of each device node from the dynamic data topology; performing feature waveform analysis on the time-series energy consumption data to extract periodic fluctuation characteristics and abnormal fluctuation characteristics; encoding the periodic fluctuation characteristics and the abnormal fluctuation characteristics to generate an energy efficiency gene unit of each device node; the encoding process combines the extracted periodic fluctuation characteristic parameters and abnormal fluctuation characteristic parameters into a multi-dimensional feature vector, i.e., an energy efficiency gene unit, according to a predefined order and format; aggregating energy efficiency gene units of all device nodes to form an energy efficiency gene factor representing the overall energy consumption mode of the device group; The specific steps of performing multi-scale decomposition on the energy efficiency gene factor comprise: using a time scale decomposition algorithm to separate a device-level energy efficiency segment reflecting short-term fluctuation characteristics from the energy efficiency gene factor; using a space scale decomposition algorithm to separate a network-level energy efficiency segment reflecting regional correlation characteristics from the energy efficiency gene factor; standardizing and marking the device-level energy efficiency segment and the network-level energy efficiency segment; The specific steps of inputting the network-level energy efficiency segment into a pre-constructed energy efficiency propagation model comprise: the energy efficiency propagation model pre-trains energy efficiency influence weights between network nodes through historical data; mapping the network-level energy efficiency segment to an input layer of the energy efficiency propagation model; calculating a spatial propagation path and intensity of energy efficiency fluctuation through a hidden layer of the energy efficiency propagation model; generating a network energy efficiency influence vector representing an energy efficiency fluctuation influence range in an output layer.

2. The urban lighting energy efficiency management system based on Internet of Things technology according to claim 1, characterized in that, The specific steps of constructing a dynamic data topology of the urban lighting network comprise: continuously collecting running parameters and environmental parameters of lighting devices through Internet of Things sensing nodes; establishing a communication connection relationship mapping between device nodes; constructing an initial static topology according to geographical location attributes and electrical connection attributes of devices; The real-time collected operation parameters and environmental parameters are injected into the initial static topology to form a dynamic data topology reflecting real-time energy flow state.

3. The urban lighting energy efficiency management system based on Internet of Things technology according to claim 2, characterized in that, The specific steps of matching the device-level energy efficiency segment with the preset device energy efficiency benchmark library include: querying the energy efficiency benchmark curve in the device energy efficiency benchmark library that matches the current device type; calculating the shape similarity of the device-level energy efficiency segment and the energy efficiency benchmark curve; determining the health index and deviation index of the device according to the shape similarity; generating a device energy efficiency state vector by combining the health index and the deviation index.

4. The urban lighting energy efficiency management system based on Internet of Things technology according to claim 3, characterized in that, The specific steps of fusing the device energy efficiency state vector and the network energy efficiency influence vector include: spatial coordinate mapping of the device energy efficiency state vector; superposition calculation of the mapped device energy efficiency state vector and the network energy efficiency influence vector; generating a continuous energy efficiency distribution surface covering the entire lighting area through an interpolation algorithm; converting the continuous energy efficiency distribution surface into a comprehensive energy efficiency situation map in grid form.

5. The urban lighting energy efficiency management system based on Internet of Things technology according to claim 4, characterized in that, The specific steps of identifying energy efficiency weak links based on the comprehensive energy efficiency situation map include: setting an energy efficiency threshold to regionally segment the comprehensive energy efficiency situation map; identifying areas with energy efficiency values below the energy efficiency threshold as candidate weak links; analyzing the energy efficiency gradient relationship between the candidate weak links and the surrounding areas; confirming the final energy efficiency weak links in combination with the device topology relationship and generating an energy efficiency optimization strategy set containing adjustment targets and adjustment amounts.

6. The urban lighting energy efficiency management system based on Internet of Things technology according to claim 5, characterized in that, The specific steps of driving the controllable devices in the urban lighting network to perform energy efficiency adjustment operations according to the energy efficiency optimization strategy set include: parsing the adjustment instructions in the energy efficiency optimization strategy set, which contain target device identifiers and adjustment parameters; issuing the adjustment instructions to the corresponding controllable devices through the Internet of Things control channel; the controllable devices adjust their operation power or working mode according to the received adjustment parameters, record the issuance time of the adjustment instructions and the device response state.

7. The urban lighting energy efficiency management system based on Internet of Things technology according to any one of claims 2 to 6, characterized in that, After completing the energy efficiency adjustment operation, the system collects new operation parameters of the lighting devices through the Internet of Things sensing nodes, updates the dynamic data topology with the new operation parameters, and restarts a new round of energy efficiency analysis and optimization process to realize continuous energy efficiency closed-loop management.

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