Public lighting energy efficiency management and data analysis system for city updating

Abstract

The invention relates to the technical field of public lighting management and data processing optimization, in particular to a public lighting energy efficiency management and data analysis system oriented to city updating. Comprising a city updating space coupling module, a public lighting equipment acquisition module, a city updating data docking module, a public lighting energy efficiency analysis module, a lighting control strategy generation module, an instruction analysis and issuing module and an updating closed-loop optimization module. According to the invention, through a four-dimensional space binding mechanism of spatial position, transformation time sequence, construction road occupation diversion and old pipe network distribution, in cooperation with full-process automatic time sequence management and control logic, multi-stage and multi-working-condition parallel management and control requirements of an untransformed area, a transformed middle area and a completed area in a city updating project can be synchronously compatible; the system can adapt to dynamic changes such as construction fence adjustment, road occupation range change and block business state reconstruction, and solves the problems that a traditional public lighting management and control system is fixed in management and control range and fixed in strategy and cannot adapt to and update dynamic changes of a scene.

Description

Technical Field

[0001] This invention relates to the field of public lighting management and data processing optimization technology, and in particular to a public lighting energy efficiency management and data analysis system for urban renewal. Background Technology

[0002] As an essential component of municipal infrastructure renovation in urban renewal, existing technical solutions for public lighting systems mainly revolve around energy-saving upgrades to lighting hardware and basic intelligent management and control, focusing on LED lighting equipment replacement, installation of individual lamp controllers, remote switching, and regional energy consumption statistics.

[0003] Urban renewal, as a core application scenario for the iterative upgrading of urban infrastructure, involves the transformation of public lighting systems. This transformation is not simply a matter of replacing lamps and controllers with hardware, but rather a systematic upgrade that requires adaptation to the tiered transformation of the area and dynamic functional reconstruction. However, existing technologies only achieve basic functions such as single-device energy consumption monitoring and single-area remote control, making it difficult for them to be implemented and realize their actual energy efficiency management value in the aforementioned specific scenarios. The following problems also exist in practical applications:

[0004] On the one hand, urban renewal generally adopts a gradient approach, starting with the core area, followed by the old areas, and retaining the unrenovated areas. This has resulted in the coexistence of multiple generations of equipment, including traditional PLC time controllers, first-generation single-lamp controllers, and new-generation intelligent drive modules. The communication protocols of different devices are incompatible, the data sampling frequencies vary greatly, and the metering accuracy is different. Moreover, there is no unified adapter gateway for multi-protocol parallel access, and there is a lack of integrated algorithms for time axis synchronization, accuracy calibration, and error compensation of heterogeneous data. This leads to a gap in the overall energy efficiency data collection of the renewal area, and a large overall deviation rate in energy consumption statistics. This poses a technical risk of distorting the basic data for the subsequent integration and upgrading of the lighting system in the entire area.

[0005] On the other hand, in urban renewal projects, such as transforming old industrial areas into cultural and creative art parks, old streets and alleys into distinctive commercial pedestrian streets, and vacant land into urban nighttime convenience markets, the lighting needs of these reconstructed areas have upgraded from the traditional single requirement of safe illuminance to a complex requirement of safe illuminance, scene atmosphere illuminance, and dynamic illuminance for pedestrian flow. In addition, there are time-sensitive, regional, and sudden lighting characteristics (such as the need for localized high illuminance for nighttime exhibitions in cultural and creative parks, delayed lighting on weekends in commercial pedestrian streets, and temporary lighting needs for convenience markets). However, existing data analysis cannot be adapted to the specific reconstructed areas, resulting in a serious mismatch between lighting strategies and actual needs, and completely failing to meet the dual requirements of energy efficiency management and scene lighting for the functional reconstructed areas of urban renewal.

[0006] Therefore, it is practically necessary to design a public lighting energy efficiency management and data analysis system that adapts to the characteristics of urban renewal gradient transformation and functional reconstruction. Summary of the Invention

[0007] To solve one of the aforementioned technical problems, the present invention adopts the following technical solution: a public lighting energy efficiency management and data analysis system for urban renewal, comprising: an urban renewal spatial coupling module, used to retrieve public lighting facility ledgers and geographic information data, complete the spatial labeling of lighting facilities, block division and renovation sequence confirmation, and realize multi-dimensional spatial binding between lighting facilities and renewal functional blocks.

[0008] The public lighting equipment acquisition module is used to complete the protocol identification and adaptation of the corresponding block lighting equipment based on the spatial binding results, so as to realize the unified acquisition and standardized transmission of lighting operation, energy consumption and environmental perception data.

[0009] The urban renewal data docking module is used to dock with the urban renewal management system to extract planning, construction and operation data, and to establish a dynamic feature library of lighting requirements for renewal scenarios by combining spatial binding results.

[0010] The public lighting energy efficiency analysis module is used to integrate collected data with a demand feature library, build and update scene-specific energy efficiency analysis models, and complete energy consumption anomaly diagnosis and lighting demand matching analysis.

[0011] The lighting management strategy generation module is used to generate regional and phased lighting energy efficiency management strategies based on energy efficiency analysis results and renovation timelines.

[0012] The instruction parsing and distribution module is used to parse the control strategy into control instructions that can be recognized by the corresponding lighting equipment and distribute them to the terminal for execution and control.

[0013] The updated closed-loop optimization module is used to collect feedback data after the strategy is executed, complete the iterative optimization of the system model and parameters, and form a closed-loop control system for the entire update cycle.

[0014] Based on any of the above technical solutions, a further optimization is made to the specific implementation steps of the urban renewal spatial coupling module for performing multi-dimensional spatial binding:

[0015] Retrieve the updated area enclosure range, construction road occupation boundary and legal renovation time sequence from the geographic information system, and simultaneously extract the physical coordinates, pipeline distribution and asset parameter information from the public lighting facility ledger;

[0016] The public lighting facilities are bound to the renewal blocks in terms of basic space, and the control points corresponding to the construction road occupation traffic diversion correction factors are additionally marked, and the lighting facility nodes covered by the old pipeline network are marked.

[0017] This forms a four-dimensional binding relationship that includes spatial location, renovation sequence, road occupation and diversion, and pipeline distribution.

[0018] Based on any of the above technical solutions, the following optimization is made: the data acquisition implementation steps of the public lighting equipment data acquisition module are as follows:

[0019] Based on the four-dimensional binding results of the spatial coupling module, data is collected at different locations for traditional lighting fixtures in the unmodified area, temporary lighting in the area under renovation, and intelligent devices in the area after renovation.

[0020] For lighting facilities covered by old pipelines, voltage and current load fluctuation data, as well as real-time data on dust thickness on the lamp surface and ambient illuminance are collected simultaneously.

[0021] Subsequently, the heterogeneous data from multiple protocols were uniformly converted into the standard format of the public lighting intelligent system interface specification, and the spatiotemporal alignment calibration of three types of data—load fluctuation, construction dust, and timing misalignment—was completed.

[0022] Based on any of the above technical solutions, a further optimization is made: When the public lighting energy efficiency analysis module builds and updates the scene-specific energy efficiency model, a multi-source data preprocessing step is first performed to integrate the collected real-time data on load, dust, and road occupancy, and then the scene-specific energy efficiency benchmark value is calculated using the following formula:

[0023] ;

[0024] In the formula: The predicted energy efficiency baseline value for the i-th update block and the j-th type of lighting facility during time period t, in kW.h;

[0025] This is a matrix representing the inherent characteristics of lighting facilities, with values ​​conforming to urban road lighting design standards.

[0026] The vector of regression coefficients obtained by weighted least squares method; To adjust the temporal weights, values ​​are assigned in stages according to the urban renewal terminology standard. The business format restructuring coefficient is set according to the differentiated planning attributes of the updated blocks; For the shielding factor of construction site fencing; The correction factor for traffic diversion due to construction road occupancy is determined according to the municipal traffic diversion specifications: 1.0 for no road occupancy, 0.85 for partial road occupancy, and 0.7 for full road occupancy. The load fluctuation factor for old pipeline networks is 0.8-0.95 for old pipeline network coverage areas based on line losses, and 1.0 for new pipeline network areas. The illuminance attenuation factor for construction dust is determined by classifying dust concentrations according to lighting measurement methods. The random error term conforms to a normal distribution; after calculation, the baseline value is synchronously stored in the updated scene lighting requirement feature library for subsequent matching degree comparison and anomaly detection.

[0027] Based on any of the above technical solutions, the following optimization is made: The public lighting energy efficiency analysis module performs a lighting demand matching degree analysis step. Based on the obtained energy efficiency benchmark value and combined with the compliance requirements for noise reduction and light limitation during nighttime construction, it first extracts the construction noise monitoring data and the results of the residents' rest time division during time period t. Then, it calculates the dynamic matching degree through the following correlation formula to complete the suitability determination between lighting demand and actual output:

[0028] ;

[0029] In the formula: The matching degree of lighting demand in the i-th updated block during time period t, with a value range of 0-1. The closer the value is to 1, the higher the adaptability. This is a probability calculation function; This represents the actual illuminance output of the block. To update the comprehensive needs of the scenario, covering construction progress, traffic flow, and business operation status; For the energy efficiency benchmark fit correction item, the logical association is achieved by directly referencing the scenario-based energy efficiency benchmark value; This is the temporary construction control coefficient; This refers to the dynamic coefficient of business model iteration. The noise and light limiting factor for nighttime construction is set at 0.6 during quiet nighttime hours, 1.0 during regular construction hours, and 0.75 during residential rest hours.

[0030] Based on any of the above technical solutions, a further optimization is made: the lighting control strategy generation module combines energy efficiency analysis results with four-dimensional space binding data to generate differentiated control strategies according to the following specific rules:

[0031] For unmodified areas, an energy-saving baseline strategy is adopted, limiting the upper limit of basic lighting power and retaining the backup right for manual control of traditional lighting fixtures;

[0032] The renovation of the blocks adopts a dynamic adaptation strategy, which switches the lighting brightness and operating time in real time according to the construction sequence and the scope of road occupation, while simultaneously strengthening the load limiting and control of old pipeline areas.

[0033] Completed blocks employ intelligent optimization strategies, automatically matching business needs and illuminance standards at all times to achieve optimal energy efficiency and lighting comfort.

[0034] Based on any of the above technical solutions, the following optimization is made: For multi-generational lighting equipment, the instruction parsing and distribution module performs the following instruction compatibility parsing and hierarchical distribution process:

[0035] First, the control strategy is converted into a general digital command. Then, for the three types of terminals—traditional lighting fixtures, temporary lighting equipment, and intelligent control equipment—the corresponding communication protocols and command formats are adapted respectively. Traditional lighting fixtures use switch commands, temporary lighting uses time-based control commands, and intelligent equipment uses a composite command of PWM dimming and power control.

[0036] The instructions are issued in a hierarchical broadcast mode, prioritizing emergency control instructions for blocks under renovation, and then pushing optimization instructions for regular blocks, ensuring zero delay in instructions for core construction areas, while avoiding conflicts between multiple concurrent instructions.

[0037] Based on any of the above technical solutions, a further optimization is made: the system relies on the coordinated cooperation of various modules to execute the complete energy efficiency management and data analysis steps that incorporate all implicit factors, as follows:

[0038] S1. The urban renewal spatial coupling module retrieves public lighting facility ledgers and geographic information data to complete four-dimensional spatial binding and mark key control points.

[0039] S2. The public lighting equipment acquisition module completes the data acquisition of multi-generational lighting equipment and synchronously captures load, dust and noise data according to the binding results.

[0040] S3, the urban renewal data docking module docks with the urban renewal management system, extracts full-cycle planning, construction and operation data, and builds a feature library of lighting demand for renewal scenarios that integrates implicit factors;

[0041] S4, the public lighting energy efficiency analysis module sequentially performs energy efficiency benchmark calculation and demand matching degree analysis to complete the accurate diagnosis of energy consumption anomalies;

[0042] S5, the lighting control strategy generation module, combines the results of full factor analysis to generate adaptive control strategies by region and stage;

[0043] S6. The instruction parsing and sending module parses the strategy into device-recognizable instructions and sends them to the corresponding lighting terminal for control.

[0044] S7. Update the closed-loop optimization module to collect strategy execution feedback data, complete the iterative optimization of full-factor model parameters, and form an updated full-cycle closed-loop control.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0046] 1. This invention utilizes a four-dimensional spatial binding mechanism based on spatial location, renovation sequence, construction road occupancy diversion, and distribution of old pipelines. Combined with a fully automated time-series control logic, it can simultaneously meet the multi-stage and multi-condition parallel control needs of unrenovated areas, renovation areas, and completed areas in urban renewal projects. It can adapt to dynamic changes such as adjustments to construction fences, changes in road occupancy, and restructuring of business formats in blocks. It eliminates the need for bulk replacement of old lighting fixtures in advance and the need to build a separate temporary lighting control system. This solves the problems of fixed control scope, rigid strategies, and inability to adapt to dynamic changes in update scenarios in traditional public lighting control systems, significantly improving the system's adaptability and operational stability in complex construction environments.

[0047] 2. This invention comprehensively integrates the implicit influencing factors unique to the renovation scenarios, such as construction road disturbance, load fluctuations in aging pipelines, illuminance attenuation due to dust from lighting fixtures, and noise reduction and light limitation during nighttime construction, into the energy efficiency benchmark calculation and lighting demand matching analysis model. It breaks through the technical limitations of traditional lighting systems that rely solely on basic operating parameters such as power and duration for energy efficiency judgment, significantly improving the accuracy of energy consumption anomaly diagnosis and lighting demand matching. It can effectively avoid excessive lighting, ineffective energy consumption, or insufficient lighting caused by operating condition interference, achieving refined and intelligent energy saving and consumption reduction while ensuring construction safety and traffic flow, and improving the overall energy efficiency management level.

[0048] 3. This invention constructs a full-cycle intelligent management and control system. The system can automatically iteratively correct model parameters and control rules based on actual terminal operation feedback. It can also automatically complete full-factor model calibration at four statutory milestone nodes: project commencement, main structure renovation completion, final acceptance, and formal operation. This enables the system to continuously self-optimize as the project progresses, without accuracy decay or strategy disconnection issues during long-term operation. It eliminates the need for frequent on-site debugging and parameter maintenance, significantly reducing municipal operation and maintenance manpower and management costs, and achieving long-term, stable, and adaptive intelligent energy efficiency management.

[0049] 4. This invention achieves seamless data exchange and real-time linkage across multiple platforms, including public lighting facility ledgers, geographic information systems, and urban renewal management systems, eliminating traditional methods such as manual secondary data entry and data format conversion. It also establishes a scenario-based anomaly grading and tracing logic, accurately distinguishing between scenario-based anomalies such as road occupancy changes, pipeline overload, and excessive dust, and equipment malfunction anomalies. It automatically adjusts control parameters for scenario-based anomalies and precisely triggers maintenance reminders for malfunction anomalies. This significantly reduces ineffective inspections and emergency repair work orders while also addressing public needs such as construction safety, road accessibility, and nighttime noise reduction and light control. The overall benefits are significantly superior to traditional single-objective public lighting management solutions. Attached Figure Description

[0050] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or components are generally identified by similar reference numerals. In the drawings, the elements or components are not necessarily drawn to actual scale.

[0051] Figure 1 This is a connection block diagram of the public lighting energy efficiency management and data analysis system of the present invention.

[0052] Figure 2 The flowchart illustrates the complete energy efficiency management and data analysis steps that incorporate fully implicit factors in the implementation of this invention. Detailed Implementation

[0053] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore merely examples and should not be used to limit the scope of protection of the present invention. The specific structure of the present invention is as follows: Figures 1-2 As shown in the image.

[0054] The public lighting energy efficiency management and data analysis system for urban renewal includes an urban renewal spatial coupling module, a public lighting equipment acquisition module, an urban renewal data docking module, a public lighting energy efficiency analysis module, a lighting control strategy generation module, an instruction parsing and issuance module, and an update closed-loop optimization module.

[0055] The input end of the urban renewal spatial coupling module is connected to the public lighting facility ledger and the geographic information system, respectively, and the output end is connected to the input end of the public lighting equipment acquisition module and the urban renewal data docking module, respectively.

[0056] The input end of the public lighting equipment acquisition module communicates with existing switch control equipment, dimming control equipment, and intelligent lighting control system within the area, while the output end communicates with the first input end of the public lighting energy efficiency analysis module.

[0057] The input end of the urban renewal data docking module is connected to the urban renewal management system, and the output end is connected to the second input end of the public lighting energy efficiency analysis module.

[0058] The public lighting energy efficiency analysis module, lighting control strategy generation module, and instruction parsing and distribution module are connected in sequence. The output of the instruction parsing and distribution module is connected to the control port of the existing lighting control equipment.

[0059] The input end of the updated closed-loop optimization module communicates with the feedback port of the lighting control equipment, and the output end communicates with the input ends of the urban renewal spatial coupling module, the public lighting energy efficiency analysis module, and the lighting management strategy generation module, respectively.

[0060] The urban renewal spatial coupling module is used to retrieve public lighting facility ledgers and geographic information data, complete the spatial labeling of lighting facilities, block division and renovation sequence confirmation, and realize multi-dimensional spatial binding between lighting facilities and renewal functional blocks;

[0061] The public lighting equipment acquisition module is used to complete the protocol identification and adaptation of the corresponding block lighting equipment based on the spatial binding results, so as to realize the unified acquisition and standardized transmission of lighting operation, energy consumption and environmental perception data.

[0062] The urban renewal data docking module is used to dock with the urban renewal management system to extract planning, construction and operation data, and to establish a dynamic feature library of lighting requirements for renewal scenarios by combining spatial binding results.

[0063] The public lighting energy efficiency analysis module is used to integrate collected data with the demand feature library, build and update scene-specific energy efficiency analysis models, and complete energy consumption anomaly diagnosis and lighting demand matching degree analysis.

[0064] The lighting management strategy generation module is used to generate regional and phased lighting energy efficiency management strategies based on energy efficiency analysis results and renovation timelines.

[0065] The instruction parsing and sending module is used to parse the control strategy into control instructions that can be recognized by the corresponding lighting equipment and send them to the terminal for execution and control.

[0066] The updated closed-loop optimization module is used to collect feedback data after the strategy is executed, complete the iterative optimization of the system model and parameters, and form a closed-loop control system for the entire update cycle.

[0067] This solution adopts a modular, layered architecture, consisting of six core layers: Spatial Perception Layer, Data Acquisition Layer, Data Fusion Layer, Intelligent Analysis Layer, Strategy Execution Layer, and Closed-Loop Optimization Layer. The Spatial Perception Layer corresponds to the Urban Renewal Spatial Coupling Module, responsible for defining the underlying spatial boundaries and control points, thus defining the precise control range for the entire system; it is the basic perception layer. The Data Acquisition Layer corresponds to the Public Lighting Equipment Acquisition Module, responsible for capturing multi-source data from terminal devices, providing raw data sources for upper-level analysis. The Data Fusion Layer corresponds to the Urban Renewal Data Integration Module, responsible for breaking down cross-platform data barriers and achieving bidirectional fusion of equipment operating data and project planning data. The Intelligent Analysis Layer corresponds to the Public Lighting Energy Efficiency Analysis Module, belonging to the core algorithm layer, building analysis models based on fused data; it is the core decision-making layer of this solution. The Strategy Execution Layer corresponds to the Lighting Control Strategy Generation Module and the Command Parsing and Issuance Module, responsible for converting analysis results into executable commands to achieve terminal control. The Closed-Loop Optimization Layer corresponds to the Update Closed-Loop Optimization Module, responsible for collecting execution feedback data, reverse-calibrating parameters at each layer, and forming a complete closed-loop process.

[0068] The entire system works by first binding lighting facilities to update blocks through a spatial coupling module, thus clarifying the control objects and scope; then, the acquisition module and data docking module synchronously collect equipment data and project data, and transmit the data to the analysis module after standardization processing; the analysis module builds an energy efficiency model based on known algorithms to complete energy consumption diagnosis and demand matching analysis; subsequently, the strategy module generates an adaptation plan, which is then sent to the terminal devices for execution after instruction parsing; finally, the optimization module collects the execution results and reverse-optimizes the parameters of each module to achieve full-cycle iteration.

[0069] All modules use common and conventional communication methods such as wired Ethernet for industrial IoT and low-power IoT networking. Port connections and protocol adaptations follow common standards in the field of municipal lighting.

[0070] This technical solution enables dynamic adaptation across all urban renewal scenarios, simultaneously supporting both new and old lighting equipment in unrenovated, under-renovation, and completed scenarios. It eliminates the need for phased equipment replacement or the construction of temporary management systems. For renewal-specific scenarios such as road occupancy, fence changes, and business restructuring, it automatically adjusts management boundaries and strategies, resolving the problem of chaotic lighting management in renewal projects. Simultaneously, it achieves cross-platform data interoperability, breaking down data barriers between municipal lighting facility ledgers, geographic information systems, and urban renewal management systems. This avoids the traditional methods of manual secondary data entry and format conversion, achieving real-time synchronization of multi-source data through standardized data integration. This reduces human error and improves data flow efficiency, laying the foundation for accurate energy efficiency analysis. Furthermore, it balances energy efficiency management with construction safety and public needs, ensuring optimized lighting energy efficiency while also considering construction safety, traffic flow, and the needs of surrounding residents.

[0071] Based on any of the above technical solutions, a further optimization is made to the specific implementation steps of the urban renewal spatial coupling module for performing multi-dimensional spatial binding:

[0072] Retrieve the updated area enclosure range, construction road occupation boundary and legal renovation time sequence from the geographic information system, and simultaneously extract the physical coordinates, pipeline distribution and asset parameter information from the public lighting facility ledger;

[0073] The public lighting facilities are bound to the renewal blocks in terms of basic space, and the control points corresponding to the construction road occupation traffic diversion correction factors are additionally marked, and the lighting facility nodes covered by the old pipeline network are marked.

[0074] This forms a four-dimensional binding relationship that includes spatial location, renovation sequence, road occupation and diversion, and pipeline distribution.

[0075] This step addresses the issue of lighting control deviations caused by road encroachment during urban renewal construction, making up for the industry oversight of conventional spatial binding which only considers geographical boundaries, and can be directly implemented by connecting with municipal construction drawings and ledger data.

[0076] The spatial binding process of this solution is divided into three core levels: data retrieval, point calibration, and relationship generation. In the first step, data retrieval, all data sources are legally publicly available or routinely archived data in the municipal field. The fence range and construction road boundary in the geographic information system are obtained based on the legally required survey drawings of the urban renewal project. The renovation sequence is strictly determined according to the project approval documents and construction plan. The physical coordinates, pipeline distribution, and asset parameters in the public lighting facility ledger are all routinely archived data from the municipal lighting operation and maintenance department. The coordinate data adopts the national 2000 geodetic coordinate system, which conforms to the general standards of municipal surveying and mapping, and has no hidden data sources.

[0077] The second step, point calibration, employs known surveying and mapping techniques such as GIS vector layer matching, coordinate correction, and point marking. The calibration of the traffic diversion correction factor for construction road occupation is based on municipal traffic diversion specifications and on-site construction organization design. The old pipeline network coverage nodes are determined based on municipal pipeline survey data. The calibration process does not require non-standard equipment or special processes.

[0078] The third step, the four-dimensional relationship generation stage, logically associates four dimensions: spatial location, renovation sequence, road occupation and diversion, and pipeline distribution, forming quantifiable and callable spatial binding data. The association logic of various dimension parameters is conventional logic, and only the scene-specific diversion, pipeline, and sequence dimensions are updated.

[0079] This solution first establishes a basic spatial database by retrieving publicly available surveying and mapping data and records. Then, it additionally marks easily overlooked diversion points and old pipeline nodes to address renovation and construction issues. Finally, it integrates the four core dimensions to form a precise spatial control boundary that fits the renovation scenario, providing targeted guidance for subsequent data collection and analysis modules.

[0080] Conventional municipal lighting spatial binding only adopts two-dimensional geographical boundary binding, focusing only on the physical correspondence between lighting facilities and administrative regions, completely ignoring the actual scenario constraints such as road occupation during urban renewal projects, phased renovation, and hidden dangers of old pipelines. In contrast, this solution deeply integrates the basic spatial location with three unique dimensions of urban renewal: renovation sequence, road occupation diversion, and pipeline distribution, forming a four-dimensional binding relationship. The characteristics of each dimension do not exist in isolation, but rather support each other and are linked and constrained.

[0081] The spatial location dimension lays the foundation for basic control boundaries, ensuring precise correspondence between lighting facilities and renovation blocks; the renovation timing dimension matches the project construction progress, enabling phased and block-specific differentiated control, avoiding resource waste from unified control across the entire area; the road occupation and traffic diversion dimension pre-marks control points for the unique scenarios of road occupation and traffic diversion during renovation construction, avoiding control deviations caused by construction interference; the pipeline distribution dimension pre-marks old pipeline nodes, providing a preliminary basis for subsequent load control and fault prevention. The synergistic effect of these four dimensions makes spatial binding no longer a simple geographical match, but a dynamic control foundation that fits the entire renovation process.

[0082] This four-dimensional spatial binding technology solution can accurately avoid the control deviation problem caused by construction road occupation. Unlike conventional binding that only focuses on fixed geographical boundaries, it dynamically adjusts the control points according to the construction road occupation range by calibrating the road occupation diversion factor. This ensures that the lighting control during construction is in line with the on-site traffic diversion and construction needs, avoiding mis-control and missed control, and ensuring construction and traffic safety. It also prevents the operation risks of old pipelines in advance by marking weak nodes by the pipeline distribution dimension. This allows subsequent data collection and control modules to focus on the coverage area of ​​old pipelines, limit the load limit in advance, effectively reduce the occurrence rate of pipeline overload, line short circuit and other failures, and reduce the risk of lighting system shutdown during construction. It achieves synchronous linkage between the renovation sequence and spatial control. It automatically switches the control mode of the corresponding block according to the project renovation sequence. Different control logics are adapted to the unrenovated area, the renovation area, and the completed area, which greatly reduces the workload of manual operation and maintenance and adapts to the practical needs of the dynamic progress of urban renewal projects.

[0083] The data on the boundaries of the construction site fences and the road occupancy data in the geographic information system are all legally publicly available surveying and mapping data for urban renewal projects. The ledger of public lighting facilities is a routine archived document for municipal operation and maintenance. Spatial matching and point calibration adopt well-known surveying and mapping processing methods such as GIS vector layer matching and coordinate correction. By following the steps, four-dimensional spatial binding can be completed quickly.

[0084] Based on any of the above technical solutions, the following optimization is made: the data acquisition implementation steps of the public lighting equipment data acquisition module are as follows:

[0085] Based on the four-dimensional binding results of the spatial coupling module, data is collected at different locations for traditional lighting fixtures in the unmodified area, temporary lighting in the area under renovation, and intelligent devices in the area after renovation.

[0086] For lighting facilities covered by old pipelines, voltage and current load fluctuation data, as well as real-time data on dust thickness on the lamp surface and ambient illuminance are collected simultaneously.

[0087] Subsequently, the heterogeneous data from multiple protocols were uniformly converted into the standard format of the public lighting intelligent system interface specification, and the spatiotemporal alignment calibration of three types of data—load fluctuation, construction dust, and timing misalignment—was completed.

[0088] This step addresses the data distortion caused by the coexistence of multiple generations of equipment in the renovation area by taking into account two essential factors on-site: the load of old pipelines and construction dust. This will better adapt to the actual on-site operating environment.

[0089] This data acquisition scheme is divided into three core levels: regional targeted acquisition, multi-dimensional data capture, and standardized data preparation and calibration. The first level, regional acquisition, defines the acquisition range based on the four-dimensional binding results and distinguishes the acquisition objects into three categories: unmodified, under modification, and completed. The acquisition start logic is triggered by block matching. The second level, data acquisition, uses conventional high-precision current and voltage transmitters for power systems to collect voltage and current load data. The sampling frequency follows the general standards for municipal power monitoring. The dust thickness of lamps is collected using general optical scattering sensors for environmental monitoring, and illuminance data is collected using illuminance sensors. The third level, data preparation, involves multi-protocol conversion to adapt to general communication protocols. The conversion logic follows the general interface specifications of the industry. Spatiotemporal alignment calibration adopts the well-known data processing method of BeiDou time synchronization and linear interpolation completion. The calibration benchmark is BeiDou standard time to ensure the consistency of time sequence and spatial correspondence of multi-source data.

[0090] This solution achieves targeted data collection based on four-dimensional binding results, avoiding the waste of resources from indiscriminate collection across the entire domain. For the unique working conditions of the renovation construction, it additionally collects two types of easily overlooked implicit data: load on old pipeline networks and construction dust. Then, through protocol conversion and spatiotemporal calibration, it eliminates the problems of inconsistent data formats and disordered timing among multiple generations of equipment, forming standardized and effective data that can be directly used for analysis.

[0091] Conventional lighting data acquisition systems neglect implicit data specific to updated scenarios such as construction dust and pipeline load, resulting in poor data adaptability and high distortion rates. In contrast, this solution relies on four-dimensional spatial binding results to achieve targeted data acquisition by region and device, deeply linking spatial control features with data acquisition features, thus solving the problem of compatible data acquisition across multiple generations of devices.

[0092] This data collection solution achieves full compatibility with multiple generations of lighting equipment, enabling data collection and subsequent management without replacing traditional lamps in unmodified areas. This significantly reduces the initial renovation costs of urban renewal projects, avoids resource waste, and solves the problem of inconsistent management of coexisting old and new equipment in renewal areas. It accurately captures hidden operating condition data unique to the renewal scenario, simultaneously collecting pipeline load and construction dust parameters, truly reflecting the actual impact of construction interference and pipeline aging on lighting operation. This provides core evidence for subsequent energy efficiency diagnosis and anomaly tracing, avoiding analytical biases caused by missing data.

[0093] This solution works synergistically with the system's overall modular architecture and four-dimensional space binding features. It's not an isolated optimization of data acquisition functions, but rather a solution tailored to the characteristics of multiple devices coexisting and complex operating conditions in update scenarios, achieving a dual improvement in acquisition accuracy and adaptability. Compared to conventional public lighting acquisition systems, this solution simultaneously captures implicit operating condition data unique to construction scenarios, accurately reflecting the actual impact of construction dust and pipeline load on lighting operation, providing reliable on-site data for energy efficiency analysis.

[0094] Based on any of the above technical solutions, a further optimization is made: When the public lighting energy efficiency analysis module builds and updates the scene-specific energy efficiency model, a multi-source data preprocessing step is first performed to integrate the collected real-time data on load, dust, and road occupancy, and then the scene-specific energy efficiency benchmark value is calculated using the following formula:

[0095] ;

[0096] In the formula: The predicted energy efficiency baseline value for the i-th update block and the j-th type of lighting facility during time period t, in kW.h;

[0097] This is a matrix representing the inherent characteristics of lighting facilities, with values ​​conforming to urban road lighting design standards.

[0098] The vector of regression coefficients obtained by weighted least squares method; To adjust the temporal weights, values ​​are assigned in stages according to the urban renewal terminology standard. The business format restructuring coefficient is set according to the differentiated planning attributes of the updated blocks; For the shielding factor of construction site fencing; The correction factor for traffic diversion due to construction road occupancy is determined according to the municipal traffic diversion specifications: 1.0 for no road occupancy, 0.85 for partial road occupancy, and 0.7 for full road occupancy. The load fluctuation factor for old pipeline networks is 0.8-0.95 for old pipeline network coverage areas based on line losses, and 1.0 for new pipeline network areas. The illuminance attenuation factor for construction dust is determined by classifying dust concentrations according to lighting measurement methods. The random error term conforms to a normal distribution; after calculation, the baseline value is synchronously stored in the updated scene lighting requirement feature library for subsequent matching degree comparison and anomaly detection.

[0099] This model is divided into three layers: a data input layer, a weighted calculation layer, and a result output layer. The input and output relationships of each layer are clear: the data input layer receives preprocessed multi-source fusion data, including inherent parameters of lighting facilities, spatially bound dimension data, and implicit data collected on-site; the weighted calculation layer uses the well-known weighted least squares method for regression calculation; and the result output layer generates scenario-based energy efficiency benchmark values, which are directly used for subsequent analysis.

[0100] Regression coefficient vector The weighted least squares method is used to solve the problem. The solution process is based on fitting historical measured energy efficiency data with the feature matrix, following general mathematical statistics standards. The training steps involve collecting historical measured energy consumption data for 3-6 months, dividing the data into training and test sets, and solving for the optimal coefficients by minimizing the sum of squared residuals. The time-series weights are modified. Based on the urban renewal terminology standard, values ​​are assigned to three stages: unrenovated, under renovation, and completed, with 1.0 for unrenovated areas, 0.9 for under renovation areas, and 1.05 for completed areas; Business restructuring coefficient. Based on the updated block planning attributes, commercial blocks are assigned a value of 1.1, residential blocks 0.9, and transportation blocks 1.0, which aligns with the standard practice for adapting municipal lighting business formats; construction road occupation factor Factors related to old pipe networks Dust factors All values ​​are determined based on national standards, industry specifications, and on-site measured data, with clear and quantifiable value ranges.

[0101] This energy efficiency benchmark model is specifically designed for the reconstruction of dynamic scenarios in urban renewal. The algorithm features, front-end spatial binding, and data acquisition technology features support and synergize with each other in terms of functionality. In contrast, conventional public lighting energy efficiency models are static models that only consider inherent parameters such as lighting facility power and operating time. The energy efficiency benchmark value remains unchanged, completely ignoring dynamic constraints such as road occupation during urban renewal construction, aging pipelines, dust interference, and phased renovations. This results in significant calculation deviations and makes it unsuitable for renewal scenarios. This model transforms four-dimensional spatial binding features and multi-dimensional data collection into quantitative correction factors, which are then integrated into a known linear regression model to achieve the transformation from a static model to a dynamic scenario-based model.

[0102] Front-end spatial binding and data acquisition provide real-time dynamic parameters for the model, enabling the model to adapt to operating conditions; the algorithm model transforms multi-dimensional front-end data into quantitative energy efficiency benchmark values, realizing data value transformation and providing core basis for subsequent strategy generation. The two are inseparable.

[0103] Compared to conventional static energy efficiency models, this scenario-based energy efficiency benchmark model relies on dynamic factor collaboration and algorithm optimization for all its effects. It can dynamically and adaptively adjust the energy efficiency benchmark value, unlike the fixed values ​​of conventional models. The benchmark value changes in real time with construction progress, road occupancy, pipeline status, and dust concentration, which is in line with the core characteristics of dynamic construction in urban renewal. It can output accurate benchmark values ​​under different working conditions. It significantly improves the accuracy of energy efficiency calculation by incorporating three types of renewal-specific influencing factors: construction road occupancy, pipeline loss, and dust attenuation. It effectively eliminates the calculation deviation caused by conventional models ignoring on-site working conditions, controls energy efficiency errors within the national standard's allowable range, and avoids misjudgment and omission of abnormal energy consumption. It enables differentiated energy efficiency calibration for multiple blocks, generating exclusive benchmark values ​​for different renewal blocks and different types of lighting facilities. It abandons the extensive model of a uniform energy efficiency standard for the entire area and lays a quantitative foundation for refined management by region and stage.

[0104] Conventional energy efficiency models only consider the inherent parameters of lighting facilities, ignoring the specific constraints of the renewal scenario. In contrast, this algorithm transforms data on road occupancy, pipeline network, temporal characteristics, load, and dust into quantitative factors and integrates them into the model. The algorithm model relies on front-end technical features to obtain real-time dynamic parameters, and the front-end technical features realize the quantitative analysis and value transformation of data through the algorithm model. The two interact and are inseparable. It is specifically adapted to urban renewal scenarios, and through multi-factor weighted correction, the energy efficiency benchmark value is made to fit the actual working conditions on site. The collaboration of various technical features and algorithm features realizes the scenario-based and accurate calculation of energy efficiency, which plays a core role in solving the problem of deviation in energy efficiency management.

[0105] It should be noted that this energy efficiency benchmark model is dynamically corrected through multiple scenario factors. The energy efficiency benchmark value is no longer a fixed value, but is adjusted in real time according to the construction progress and on-site working conditions, which fully conforms to the dynamic changes of urban renewal. The calculation accuracy is greatly improved by incorporating unique influencing factors such as construction road occupation, pipeline loss, and dust attenuation, effectively eliminating the calculation deviation of conventional models and avoiding misjudgment of energy efficiency anomalies.

[0106] Based on any of the above technical solutions, the following optimization is made: The public lighting energy efficiency analysis module performs a lighting demand matching degree analysis step. Based on the obtained energy efficiency benchmark value and combined with the compliance requirements for noise reduction and light limitation during nighttime construction, it first extracts the construction noise monitoring data and the results of the residents' rest time division during time period t. Then, it calculates the dynamic matching degree through the following correlation formula to complete the suitability determination between lighting demand and actual output:

[0107] ;

[0108] In the formula: The matching degree of lighting demand in the i-th updated block during time period t, with a value range of 0-1. The closer the value is to 1, the higher the adaptability. This is a probability calculation function; This represents the actual illuminance output of the block. To update the comprehensive needs of the scenario, covering construction progress, traffic flow, and business operation status; For the energy efficiency benchmark fit correction item, the logical association is achieved by directly referencing the scenario-based energy efficiency benchmark value; This is the temporary construction control coefficient; This refers to the dynamic coefficient of business model iteration. The noise and light limiting factor for nighttime construction is set at 0.6 during quiet nighttime hours, 1.0 during regular construction hours, and 0.75 during residential rest hours.

[0109] This lighting demand matching model serves as a correlation model with the energy efficiency benchmark model, with clear input-output relationships at each level: the data input layer receives three types of core data, namely the energy efficiency benchmark value, actual illuminance and noise data collected on-site, and updated comprehensive demand data for the scenario; the weighted correction layer integrates four types of factors, namely energy efficiency fit, construction management, business iteration, and noise reduction and light limiting, to dynamically correct the probability calculation results; the result output layer generates matching degree values ​​in the 0-1 range, which are intuitive, easy to determine, and directly used for subsequent strategy generation.

[0110] Among them, the Bayesian probability calculation function The conditional probability formula is used, and the prior probability is obtained based on statistical data of historical lighting demand; noise reduction and light limiting factors for nighttime construction are employed. Values ​​are assigned based on three levels: quiet hours (22:00-6:00 the next day), residents' lunch break, and regular construction periods; temporary construction control coefficient. Values ​​are assigned based on the urgency of on-site construction: 1.0 for emergency construction and 0.9 for routine construction; Business model iteration coefficient. Adjustments will be made dynamically based on the progress of the block renovation.

[0111] Conventional lighting demand analysis focuses solely on whether illuminance meets standards, considering only construction or traffic needs while completely ignoring public safety and compliance requirements such as nighttime construction disturbances and residents' rest. This approach results in a narrow analysis and one-sided findings. In contrast, this model directly reuses energy efficiency benchmark values ​​as the core correction term, achieving seamless integration between two algorithm layers and avoiding analytical biases caused by algorithmic disconnect. It also incorporates nighttime noise reduction and light limiting factors into the analysis logic. The model's various technical and algorithmic features mutually support each other: energy efficiency benchmark values ​​provide the core quantitative basis for matching degree calculations; on-site noise and time-period data provide real-time parameters for noise reduction factors; scene demand characteristics provide the foundational data for probability calculations; and matching degree results directly provide the decision-making basis for the strategy generation module. This model deeply integrates public safety compliance requirements with lighting energy efficiency requirements, achieving coordinated management of multiple objectives. The overall concept aligns with the practical characteristics of urban renewal projects located near residential areas and with high public safety requirements.

[0112] This dynamic matching degree analysis scheme achieves a two-way balance between construction needs and public welfare. During nighttime rest and quiet periods, the matching degree threshold is automatically lowered, and the lighting intensity and operating power are reduced simultaneously to avoid glare and noise interference from equipment, while ensuring the necessary lighting needs for construction, taking into account both compliance and practicality. It achieves a precise match between lighting output and actual needs, which not only eliminates energy waste caused by excessive lighting and reduces municipal energy consumption expenditure, but also avoids insufficient lighting affecting construction safety and citizen traffic, thus eliminating safety hazards.

[0113] Based on any of the above technical solutions, a further optimization is made: the lighting control strategy generation module combines energy efficiency analysis results with four-dimensional space binding data to generate differentiated control strategies according to the following specific rules:

[0114] For unmodified areas, an energy-saving baseline strategy is adopted, limiting the upper limit of basic lighting power and retaining the backup right for manual control of traditional lighting fixtures;

[0115] The renovation of the blocks adopts a dynamic adaptation strategy, which switches the lighting brightness and operating time in real time according to the construction sequence and the scope of road occupation, while simultaneously strengthening the load limiting and control of old pipeline areas.

[0116] Completed blocks employ intelligent optimization strategies, automatically matching business needs and illuminance standards at all times to achieve optimal energy efficiency and lighting comfort.

[0117] All three strategies are tied to the transformation timeline, automatically triggering strategy switching without manual intervention, thus fully adapting to the phased implementation rhythm of urban renewal.

[0118] This phased differentiated management strategy is strictly based on the four-dimensional spatial binding results, and is divided into three core phases: unmodified, under renovation, and completed. The management objectives, control methods, and permission settings of each phase strategy are all tailored to the actual on-site needs: The strategy for the unmodified area is designed for compatibility with old equipment, taking into account both basic lighting and energy-saving needs, and retaining manual backup permissions to deal with emergencies; the strategy for the under renovation area is designed for dynamic construction scenarios, with all control parameters linked to construction road occupancy, timing, and pipeline data, and the triggering conditions are quantifiable and measurable; the strategy for the completed area is designed for mature operation scenarios, fully complying with national municipal lighting standards and business requirements, and achieving fully automatic intelligent management. The strategy switching trigger mechanism is a dual trigger of timing nodes and spatial boundaries, and the control algorithm used is a well-known fuzzy control algorithm in the lighting field.

[0119] This solution, taking into account the characteristics of the entire lifecycle of the upgrade project, deeply integrates the phased features bound by four-dimensional space with the quantitative results of energy efficiency analysis, and formulates three differentiated strategies. These strategies are logically complementary and seamlessly connected. The strategy for un-renovated areas addresses the compatibility of old equipment and basic energy conservation issues; the strategy for areas under renovation addresses the challenges of dynamic construction adaptation; and the strategy for completed areas achieves long-term intelligent optimization. The three strategies form a closed loop of full-cycle management and control that supports each other.

[0120] This differentiated management and control strategy precisely matches the management and control logic to the different working conditions at different stages of the renovation project, avoiding the management imbalance caused by a single strategy. It ensures lighting safety during construction and achieves long-term energy saving. The fully automatic time-series switching automatically triggers strategy conversion based on the renovation time sequence nodes, eliminating the need for on-site debugging or remote manual operation by maintenance personnel, which greatly reduces manual maintenance costs and operational errors. The compatible management and control of old and new equipment takes into account the control needs of old traditional lighting fixtures and new intelligent equipment, eliminating the need for batch replacement of equipment in advance, reducing the upfront investment of urban renewal projects and avoiding resource waste.

[0121] Based on any of the above technical solutions, the following optimization is made: For multi-generational lighting equipment, the instruction parsing and distribution module performs the following instruction compatibility parsing and hierarchical distribution process:

[0122] First, the control strategy is converted into a general digital command. Then, for the three types of terminals—traditional lighting fixtures, temporary lighting equipment, and intelligent control equipment—the corresponding communication protocols and command formats are adapted respectively. Traditional lighting fixtures use switch commands, temporary lighting uses time-based control commands, and intelligent equipment uses a composite command of PWM dimming and power control.

[0123] The instructions are issued in a hierarchical broadcast mode, prioritizing emergency control instructions for blocks under renovation, and then pushing optimization instructions for regular blocks, ensuring zero delay in instructions for core construction areas, while avoiding conflicts between multiple concurrent instructions.

[0124] The module is compatible with Modbus, DALI, and direct control protocols, all of which are common standard protocols in the lighting industry. Command format conversion follows industry-standard interface specifications, and the hierarchical priority logic is set based on the core needs of the construction scenario. This command parsing solution, in collaboration with the front-end strategy generation and device acquisition modules, solves the problem of inconsistent management of multiple generations of devices coexisting in the update area.

[0125] Conventional command issuance modules are only compatible with a single type of smart device and cannot be compatible with traditional lamps and temporary lighting. This can easily lead to problems such as command not being recognized and control failure. In contrast, this solution adopts a hierarchical parsing and compatibility approach, customizing exclusive command formats for different devices and optimizing the issuance priority to ensure the timeliness of commands in the core construction area. The overall design is tailored to the complex equipment operating conditions in the update scenario.

[0126] Based on any of the above technical solutions, a further optimization is made: the system relies on the coordinated cooperation of various modules to execute the complete energy efficiency management and data analysis steps that incorporate all implicit factors, as follows:

[0127] S1. The urban renewal spatial coupling module retrieves public lighting facility ledgers and geographic information data to complete four-dimensional spatial binding and mark key control points.

[0128] S2. The public lighting equipment acquisition module completes the data acquisition of multi-generational lighting equipment and synchronously captures load, dust and noise data according to the binding results.

[0129] S3, the urban renewal data docking module docks with the urban renewal management system, extracts full-cycle planning, construction and operation data, and builds a feature library of lighting demand for renewal scenarios that integrates implicit factors;

[0130] S4, the public lighting energy efficiency analysis module sequentially performs energy efficiency benchmark calculation and demand matching degree analysis to complete the accurate diagnosis of energy consumption anomalies;

[0131] S5, the lighting control strategy generation module, combines the results of full factor analysis to generate adaptive control strategies by region and stage;

[0132] S6. The instruction parsing and sending module parses the strategy into device-recognizable instructions and sends them to the corresponding lighting terminal for control.

[0133] S7. Update the closed-loop optimization module to collect strategy execution feedback data, complete the iterative optimization of full-factor model parameters, and form an updated full-cycle closed-loop control.

[0134] Further explanation is needed regarding the energy efficiency benchmark calculation model, which consists of three layers: a data input layer, a weighted calculation layer, and a result output layer. The input layer receives multi-source data collected in steps S2 and S3. The weighted calculation layer employs the well-known weighted least squares algorithm, and the output layer generates standardized energy efficiency benchmark values. The demand matching degree model uses the well-known Bayesian estimation algorithm and is divided into a probability calculation layer, a factor correction layer, and a matching degree output layer. The input-output relationships at each layer are completely clear, and the model training steps and parameter determination are supported by explicit national standards. The transformation time-series weights are also clearly defined. Based on the phased value selection of urban renewal terminology standards, the load fluctuation factor of the aging pipeline network is... Based on measured data of municipal pipeline losses and power transmission standards, the illuminance attenuation factor for construction dust is determined. Based on the lighting measurement method and the dust concentration classification, the noise reduction and light limiting factor for nighttime construction is calculated. It is set in accordance with the environmental noise emission standards for construction site boundaries.

[0135] This entire process is structured around the practical rhythm of the entire lifecycle of urban renewal projects. Step S1 uses four-dimensional spatial binding to accurately anchor the control scope, defining the target boundaries for all subsequent steps and resolving control deviations caused by dynamic changes in construction scenarios. Steps S2 and S3 simultaneously collect and integrate equipment operating data and project planning data, constructing a comprehensive database covering explicit operational data and implicit scenario factors to provide data support for subsequent analysis. Step S4 uses the comprehensive data to complete quantitative energy efficiency analysis, transforming vague energy consumption control into precise numerical judgments. Steps S5 and S6 convert the analysis results into executable terminal instructions, achieving seamless integration from analysis to implementation. Step S7 uses feedback data to iteratively optimize all front-end modules and model parameters, forming a closed loop.

[0136] This full-process timeline is a comprehensive technical reconstruction aimed at addressing the core issues of complex working conditions, long cycles, and significant dynamic changes in urban renewal projects. Existing public lighting energy efficiency management systems generally suffer from modular and decentralized operation, fragmented data flow, and disconnect between upstream and downstream processes. Each module only performs a single function independently, lacking a full-process linkage mechanism, and is unable to adapt to the dynamic needs of urban renewal projects throughout their entire lifecycle, from early planning and mid-term construction to later operation and maintenance.

[0137] This end-to-end time-series management solution achieves fully automated closed-loop operation, eliminating the need for manual intervention in process advancement and node switching. It executes automatically according to a preset time sequence, resolving the cumbersome issues of manual step-by-step operation and data integration required by conventional systems. This significantly reduces the workload of municipal maintenance personnel, making it particularly suitable for the real-world scenarios of long-cycle urban renewal projects and limited maintenance manpower. By solidifying the linkage relationships between modules through time-series logic, the system possesses autonomous process advancement capabilities. It achieves seamless data flow throughout the entire process, from original facility ledgers and geographic information data to terminal equipment operating data, project construction data, and then to quantitative analysis results, control instructions, and feedback optimization data. The entire data flow is standardized, eliminating manual input errors and format conversion distortions, ensuring the accuracy of end-to-end management. It achieves full integration and dynamic adaptation of all implicit factors, incorporating implicit factors unique to renewal scenarios such as construction road occupation, pipeline load, dust interference, and nighttime noise reduction throughout the entire time-series process, rather than applying them only in a single analysis stage. This ensures that each step aligns with the actual on-site conditions, solving the management failure problem caused by conventional systems that only consider explicit parameters and ignore implicit scenario impacts.

[0138] Based on any of the above technical solutions, the following optimization is made: the public lighting energy efficiency analysis module executes energy consumption anomaly classification and handling steps, and establishes a special source tracing step for hidden factors as follows:

[0139] When the actual energy consumption deviates from the energy efficiency benchmark value by more than the national standard allowable threshold, or the lighting demand matching degree is lower than the qualified line, the cause of the abnormality should be investigated first according to the scenario dimension, and the three types of scenario-based abnormalities should be distinguished as construction road occupation change, pipeline overload, and dust thickness exceeding the standard, as well as two types of conventional abnormalities as equipment failure and model parameter deviation.

[0140] Then, differentiated handling plans are generated accordingly. If the road occupancy is changed, the control boundary is adjusted simultaneously; if the pipeline is overloaded, the power is reduced and the flow is limited; if the dust exceeds the standard, the operation and maintenance cleaning reminder is triggered. At the same time, the corresponding implicit factor benchmark values ​​are updated simultaneously.

[0141] Further explanation is needed. First, the energy consumption deviation threshold of this anomaly handling plan strictly refers to the allowable deviation range of public lighting energy consumption specified in the urban road lighting design standard, set at ±5% of the benchmark value. Exceeding this range is considered an energy consumption anomaly. The lighting demand matching degree qualification line is set at 0.8, a value calculated based on the actual operation and maintenance needs of municipal lighting and scenario adaptability, and is a generally reasonable threshold in the industry. Second, the logical hierarchy of anomaly grading and tracing is clear, divided into four levels: anomaly judgment layer, cause investigation layer, handling execution layer, and parameter update layer. The input and output relationships of each level are clear: the anomaly judgment layer inputs the actual energy consumption value, energy efficiency benchmark value, and lighting demand matching degree value generated in step S4, and outputs an anomaly trigger signal; the cause investigation layer inputs the anomaly signal and the data collected from all implicit factors, and outputs the anomaly cause classification result; the handling execution layer inputs the cause classification result and outputs the corresponding differentiated handling instructions; the parameter update layer inputs the handling completion signal and outputs the updated implicit factor benchmark value.

[0142] Among them, abnormal changes in road occupation during construction are identified by comparing real-time road occupation boundary data with initial four-dimensional bound boundary data; if the deviation exceeds 1 meter, it is considered an abnormality. Abnormal overload of pipeline network is identified according to power transmission standards; if the current value exceeds 1.2 times the rated load, it is considered an abnormality. If the dust thickness exceeds 2 mm and the illuminance attenuation exceeds 30%, it is considered an abnormality.

[0143] This anomaly handling process relies on full-process time-series data and a database of all implicit factors. When the system detects abnormal energy consumption or matching degree, it does not directly determine equipment failure. Instead, it first retrieves the scenario implicit factor data collected in steps S2 and S3, and checks each of the five dimensions of construction scenario, pipeline network conditions, environmental interference, equipment status, and model parameters one by one to accurately pinpoint the root cause of the anomaly. Then, it executes corresponding handling measures for different causes. Scenario-based anomalies are automatically adjusted by the linkage of system modules, and routine faults trigger operation and maintenance reminders. After the handling is completed, the baseline values ​​of the corresponding implicit factors are updated synchronously to make the subsequent analysis model more in line with the on-site conditions. No hardware equipment is required throughout the entire process.

[0144] Conventional public lighting systems neglect unique anomalies during urban renewal construction, such as road occupancy changes, pipeline overload, and dust interference, resulting in a high rate of misjudgment, low handling efficiency, and numerous invalid maintenance work orders.

[0145] This invention incorporates implicit factors of the scenario into the anomaly tracing logic, and constructs a two-dimensional hierarchical handling system for routine anomalies and scenario-based anomalies. It deeply binds energy efficiency analysis data, scenario collection data and anomaly handling logic to form a close collaborative relationship. Its core concept is that energy consumption anomalies in urban renewal scenarios are not only caused by equipment failure, but more often by scenario-based problems caused by dynamic changes in construction. Therefore, anomaly handling needs to be in line with the actual construction of the project, rather than simply repairing equipment.

[0146] This anomaly classification and handling solution significantly improves the accuracy of anomaly tracing, incorporates implicit factors in the investigation scope, distinguishes between two different causes: scene interference and hardware failure, avoids misjudging operating condition fluctuations caused by construction as equipment damage, reduces invalid maintenance work orders, and significantly reduces municipal maintenance costs. This effect cannot be achieved by conventional single-dimensional anomaly handling.

[0147] The efficiency of handling anomalies is significantly improved without affecting the progress of urban renewal construction. For scenario-based anomalies, the system adopts an automatic parameter adjustment method, which eliminates the need for maintenance personnel to come to the site for emergency repairs. For example, the control boundary is automatically adjusted for changes in road occupancy, and the power is automatically reduced and the current is limited for pipeline overload. On-site maintenance reminders are only triggered for equipment failure anomalies. The handling process is in line with the construction rhythm, avoiding delays in project construction due to maintenance and emergency repairs, and adapting to the actual needs of tight schedules in renewal projects.

[0148] In addition, it realizes two-way linkage between anomaly handling and system optimization. After each anomaly handling is completed, the baseline value of the corresponding implicit factor is updated synchronously, and the energy efficiency analysis model and demand matching degree model are optimized in reverse. This allows the system to continuously improve itself in the process of anomaly handling, and the accuracy of subsequent anomaly judgment is continuously improved, forming a virtuous cycle, rather than the system state remaining unchanged after a single handling.

[0149] Based on any of the above technical solutions, a further optimization is made: updating the closed-loop optimization module to perform full-cycle calibration steps, and binding the legal milestone nodes of urban renewal to complete the calibration of the full-factor model.

[0150] At the four statutory milestones of project commencement, main structure renovation completion, final acceptance, and formal business operation, the system automatically retrieves the latest official data on road occupation, pipeline renovation, dust control, and noise reduction from the urban renewal management system, recalculates the benchmark values ​​of the four types of implicit factors, and updates the energy efficiency benchmark model, matching degree model, and iteration parameters simultaneously.

[0151] The calibration process is fully aligned with the legal procedures for urban renewal, ensuring that the system adapts to dynamic changes on-site throughout the entire process.

[0152] Further explanation is needed regarding the four legally mandated milestones bound in this scheme. These milestones are all legally mandated milestones commonly used in the urban renewal industry. The model hierarchy and logical relationships for the full-cycle calibration are completely clear, divided into five layers: the node triggering layer, the data retrieval layer, the factor calculation layer, the parameter update layer, and the model iteration layer. The node triggering layer is bound to the four legally mandated milestones. Reaching a milestone triggers a calibration signal. The input is the project timeline and the synchronization signal with the management system, and the output is the calibration start command. The data retrieval layer inputs the calibration command and outputs the latest official surveying, operation, and construction data from the urban renewal management system. The factor calculation layer inputs the latest official data and outputs the recalculated baseline values ​​for the four types of implicit factors. The parameter update layer inputs new factor values ​​and outputs the updated model parameters. The model iteration layer inputs new parameters and outputs the calibrated complete energy efficiency model and matching degree model.

[0153] The construction road occupation factor is calculated based on the official fence survey data of the current period, the pipeline load factor is calculated based on the pipeline renovation completion data and line loss measurement data of the current period, the dust attenuation factor is calculated based on the dust control acceptance data of the current period, and the noise reduction and light limiting factor is calculated based on the official construction noise reduction control data of the current period. The calculation process adopts known linear fitting and standardized calculation methods.

[0154] From a working principle perspective, this full-cycle calibration solution deeply integrates the calibration process with the statutory process of urban renewal. When a project progresses to a statutory milestone, the system automatically triggers the calibration process without manual intervention. It directly retrieves the most authoritative current data from the official management system to replace the initial data from the previous period, recalculates the benchmark values ​​of all implicit factors, and then synchronizes the new parameters to the energy efficiency benchmark model and demand matching model to complete the overall iterative optimization of the model. The optimized parameters are automatically synchronized to all related modules to ensure that the system can operate with the latest operating parameters at different stages of the project. Technical personnel only need to bind the statutory nodes and data interface ports when deploying the system to achieve fully automatic calibration.

[0155] Urban renewal projects are divided into several key phases, and the working conditions and management requirements of each phase are very different. Initial calibration alone cannot meet the full-cycle management requirements. Dynamic calibration must be carried out in conjunction with the project's statutory milestones. This concept deeply integrates the administrative processes and technical management logic of municipal projects.

[0156] This statutory milestone full-cycle calibration solution achieves precise adaptation to all operating conditions throughout the system's lifecycle. For the different operating conditions of the four core stages of urban renewal projects—construction, renovation, acceptance, and operation—it automatically completes parameter and model calibration at statutory milestones. This ensures the system operates with the latest and most site-appropriate parameters at each stage of the project lifecycle, preventing a decrease in control accuracy after stage transitions. For example, after the main renovation is completed, the pipeline network and road occupancy conditions change significantly; the system immediately adapts to the new operating conditions after calibration. It achieves fully automated calibration without manual intervention, automatically binding the calibration trigger mechanism to the project's statutory milestones. Data is directly synchronized from the official management system, and the entire process of calculation, updating, and iteration is automated. Maintenance personnel do not need to manually retrieve data or adjust parameters, significantly reducing the workload of manual maintenance, especially suitable for the limited manpower and professional personnel in municipal projects. The calibration data are all taken from the latest official data of the urban renewal management system, rather than temporary on-site measurements or manually entered data. The data source is authoritative and compliant. The calibrated model parameters and energy efficiency control standards meet the official requirements of the project and simultaneously meet the legal standards for project completion acceptance and energy efficiency assessment, taking into account both technical practicality and administrative compliance. In addition, it can achieve long-term stable operation of the system without repeated modification or redeployment. The full-cycle calibration mechanism allows the system to continuously self-optimize as the project progresses, and the parameters always remain up-to-date. There will be no problems of accuracy decay or adaptation failure due to the extension of the operating time. The system's service life is fully matched with the entire project cycle, which greatly improves the system's economy and practicality.

[0157] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention. For those skilled in the art, any alternative improvements or transformations made to the implementation of the present invention fall within the protection scope of the present invention.

[0158] Any aspects of this invention not described in detail are well-known to those skilled in the art.

Claims

1. A public lighting energy efficiency management and data analysis system for urban renewal, characterized in that, include: The urban renewal spatial coupling module is used to retrieve public lighting facility ledgers and geographic information data, complete the spatial labeling of lighting facilities, block division and renovation sequence confirmation, and realize multi-dimensional spatial binding between lighting facilities and renewal functional blocks; The public lighting equipment acquisition module is used to complete the protocol identification and adaptation of the corresponding block lighting equipment based on the spatial binding results, so as to realize the unified acquisition and standardized transmission of lighting operation, energy consumption and environmental perception data. The urban renewal data docking module is used to dock with the urban renewal management system to extract planning, construction and operation data, and to establish a dynamic feature library of lighting requirements for renewal scenarios by combining spatial binding results. The public lighting energy efficiency analysis module is used to integrate collected data with the demand feature library, build and update scene-specific energy efficiency analysis models, and complete energy consumption anomaly diagnosis and lighting demand matching degree analysis. The lighting management strategy generation module is used to generate regional and phased lighting energy efficiency management strategies based on energy efficiency analysis results and renovation timelines. The instruction parsing and sending module is used to parse the control strategy into control instructions that can be recognized by the corresponding lighting equipment and send them to the terminal for execution and control. The updated closed-loop optimization module is used to collect feedback data after the strategy is executed, complete the iterative optimization of the system model and parameters, and form a closed-loop control system for the entire update cycle.

2. The public lighting energy efficiency management and data analysis system for urban renewal as described in claim 1, characterized in that, The specific implementation steps for multi-dimensional spatial binding in the urban renewal spatial coupling module are as follows: Retrieve the updated area enclosure range, construction road occupation boundary and legal renovation time sequence from the geographic information system, and simultaneously extract the physical coordinates, pipeline distribution and asset parameter information from the public lighting facility ledger; The public lighting facilities are bound to the renewal blocks in terms of basic space, and the control points corresponding to the construction road occupation traffic diversion correction factors are additionally marked, and the lighting facility nodes covered by the old pipeline network are marked. This forms a four-dimensional binding relationship that includes spatial location, renovation sequence, road occupation and diversion, and pipeline distribution.

3. The public lighting energy efficiency management and data analysis system for urban renewal as described in claim 1, characterized in that, The data acquisition steps for the public lighting equipment data acquisition module are as follows: Based on the four-dimensional binding results of the spatial coupling module, data is collected at different locations for traditional lighting fixtures in the unmodified area, temporary lighting in the area under renovation, and intelligent devices in the area after renovation. For lighting facilities covered by old pipelines, voltage and current load fluctuation data, as well as real-time data on dust thickness on the lamp surface and ambient illuminance are collected simultaneously. Subsequently, the heterogeneous data from multiple protocols were uniformly converted into the standard format of the public lighting intelligent system interface specification, and the spatiotemporal alignment calibration of three types of data—load fluctuation, construction dust, and timing misalignment—was completed.

4. The public lighting energy efficiency management and data analysis system for urban renewal as described in claim 1, characterized in that, When building and updating a scene-specific energy efficiency model, the public lighting energy efficiency analysis module first performs a multi-source data preprocessing step, integrating the collected real-time data on load, dust, and road occupancy, and then calculates the scene-specific energy efficiency benchmark value using the following formula: ； In the formula: The predicted energy efficiency baseline value for the i-th update block and the j-th type of lighting facility during time period t; The matrix represents the inherent characteristics of lighting facilities; The vector of regression coefficients obtained by weighted least squares method; To modify the time series weights; This is the business restructuring coefficient; For the shielding factor of construction site fencing; Correction factor for traffic diversion during construction road occupancy; For load fluctuation factors of old pipeline networks; The illuminance attenuation factor for construction dust; The random error term conforms to a normal distribution; After the calculation is completed, the baseline value will be synchronously stored in the updated scene lighting requirement feature library for subsequent matching degree comparison and anomaly detection.

5. The public lighting energy efficiency management and data analysis system for urban renewal as described in claim 4, characterized in that, The public lighting energy efficiency analysis module performs a lighting demand matching degree analysis step. Based on the obtained energy efficiency benchmark value and combined with the compliance requirements for noise reduction and light limitation during nighttime construction, it first extracts the construction noise monitoring data for time period t and the results of the division of residents' rest time. Then, it calculates the dynamic matching degree through the following correlation formula to complete the suitability determination between lighting demand and actual output: ； In the formula: The matching degree of lighting demand in the i-th updated block during time period t, with a value range of 0-1; This is a probability calculation function; This represents the actual illuminance output of the block. To update the comprehensive needs of the scenario, covering construction progress, traffic flow, and business operation status; This is a correction item for energy efficiency benchmark compliance; This is the temporary construction control coefficient; This refers to the dynamic coefficient of business model iteration. Noise reduction and light limiting factor for nighttime construction.

6. The public lighting energy efficiency management and data analysis system for urban renewal as described in claim 1, characterized in that, The lighting control strategy generation module combines energy efficiency analysis results with four-dimensional space binding data to generate differentiated control strategies according to the following specific rules: For unmodified areas, an energy-saving baseline strategy is adopted, limiting the upper limit of basic lighting power and retaining the backup right for manual control of traditional lighting fixtures; The renovation of the blocks adopts a dynamic adaptation strategy, which switches the lighting brightness and operating time in real time according to the construction sequence and the scope of road occupation, while simultaneously strengthening the load limiting and control of old pipeline areas. Completed blocks employ intelligent optimization strategies, automatically matching business needs and illuminance standards at all times to achieve optimal energy efficiency and lighting comfort.

7. The public lighting energy efficiency management and data analysis system for urban renewal according to claim 6, characterized in that, The instruction parsing and distribution module is designed for multi-generational lighting equipment. The instruction compatibility parsing and hierarchical distribution process is as follows: First, the control strategy is converted into a general digital command. Then, for the three types of terminals—traditional lighting fixtures, temporary lighting equipment, and intelligent control equipment—the corresponding communication protocols and command formats are adapted respectively. Traditional lighting fixtures use switch commands, temporary lighting uses time-based control commands, and intelligent equipment uses a composite command of PWM dimming and power control. The instructions are issued in a hierarchical broadcast mode, prioritizing emergency control instructions for blocks under renovation, and then pushing optimization instructions for regular blocks, ensuring zero delay in instructions for core construction areas, while avoiding conflicts between multiple concurrent instructions.

8. The public lighting energy efficiency management and data analysis system for urban renewal as described in claim 1, characterized in that, The system relies on the coordinated operation of its various modules to perform complete energy efficiency management and data analysis steps that incorporate all implicit factors, as follows: S1. The urban renewal spatial coupling module retrieves public lighting facility ledgers and geographic information data to complete four-dimensional spatial binding and mark key control points. S2. The public lighting equipment acquisition module completes the data acquisition of multi-generational lighting equipment and synchronously captures load, dust and noise data according to the binding results. S3, the urban renewal data docking module docks with the urban renewal management system, extracts full-cycle planning, construction and operation data, and builds a feature library of lighting demand for renewal scenarios that integrates implicit factors; S4, the public lighting energy efficiency analysis module sequentially performs energy efficiency benchmark calculation and demand matching degree analysis to complete the accurate diagnosis of energy consumption anomalies; S5, the lighting control strategy generation module, combines the results of full factor analysis to generate adaptive control strategies by region and stage; S6. The instruction parsing and sending module parses the strategy into device-recognizable instructions and sends them to the corresponding lighting terminal for control. S7. Update the closed-loop optimization module to collect strategy execution feedback data, complete the iterative optimization of full-factor model parameters, and form an updated full-cycle closed-loop control.

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