An intelligent inspection method, device and equipment for a street lamp lighting system

Abstract

This invention discloses an intelligent inspection method, device, and equipment for a street lighting system, comprising: acquiring distribution cabinet data from each distribution cabinet and aggregating all distribution cabinet data to generate aggregated data; processing the aggregated data to generate a panoramic status overview interface, and activating corresponding target inspection rules for the distribution cabinets to be inspected according to pre-configured inspection rules; automatically triggering a batch inspection process for the distribution cabinets to be inspected according to the target inspection rules, obtaining inspection results, and generating abnormal status information records when abnormal statuses are found in the inspection results; updating the aggregated data with the abnormal status information records, and analyzing the updated aggregated data to generate abnormal trend prediction and early warning and leakage risk assessment and early warning, and optimizing the inspection parameters in the inspection rules for the corresponding distribution cabinets. This reduces reliance on manual inspection, allows for early detection of potential faults and safety risks, and improves the operational reliability and safety of the street lighting system.

Description

Technical Field

[0001] This invention relates to the field of intelligent inspection technology, and in particular to an intelligent inspection method, device, and equipment for a street lighting system. Background Technology

[0002] With the advancement of smart city construction, street lighting systems, as crucial urban infrastructure, are growing in scale, leading to increasingly complex and sophisticated requirements for operation and maintenance management. Currently, the street lighting control systems widely used in the industry are relatively mature in achieving basic functions such as remote switching and brightness adjustment. However, existing technical solutions still face significant challenges in the daily and long-term health status inspection and maintenance phases. Existing solutions heavily rely on scheduled manual inspections within preset time periods, resulting in high labor costs and limitations due to inspection cycles, scope, and subjectivity, making it difficult to achieve real-time, comprehensive control over the operational status of massive, distributed street lighting power distribution facilities. Especially during non-lighting periods, the lack of a systematic inspection mechanism prevents potential faults from being proactively detected. Furthermore, when multiple projects require unified operation and maintenance, the independent deployment of systems by each project necessitates frequent switching between different accounts by maintenance personnel, resulting in cumbersome procedures and hindering centralized status monitoring and collaborative management across projects. Simultaneously, the discovery of potential faults often lags behind their occurrence, lacking predictive judgment capabilities based on multi-dimensional data fusion, leading to passive maintenance responses and an inability to effectively intervene before the impact of faults escalates. These limitations collectively result in significant deficiencies in the timeliness of operation and maintenance response, rationality of resource allocation, and panoramic management capabilities of existing street lighting control systems, making it difficult to meet the urgent needs of modern cities for high reliability and high efficiency in the operation and maintenance of public lighting systems. Summary of the Invention

[0003] In view of this, the purpose of this invention is to propose an intelligent inspection method, device, and equipment for street lighting systems, aiming to solve the problems of excessive reliance on manual labor, lack of intelligent proactive inspection and predictive maintenance capabilities in the operation and maintenance of existing street lighting systems, resulting in low operation and maintenance efficiency, delayed fault detection, and difficulty in timely warning of safety hazards.

[0004] To achieve the above objectives, the present invention provides an intelligent inspection method for a street lighting system, the method comprising:

[0005] Acquire the distribution cabinet data of each distribution cabinet, and aggregate all the distribution cabinet data to generate aggregated data. The distribution cabinet data includes real-time operation data collected by intelligent control equipment deployed in the distribution cabinet, as well as historical inspection data, abnormal fault data, real-time weather data and road section protection level associated with the corresponding distribution cabinet. After processing the aggregated data, a panoramic status overview interface is generated, and the corresponding target inspection rule is activated for the power distribution cabinet to be inspected according to the pre-configured inspection rules. The batch inspection process of the power distribution cabinet to be inspected is automatically triggered according to the target inspection rules to obtain the inspection results, and an abnormal status information record is generated when there is an abnormal status in the inspection results. The abnormal status information is recorded to update the aggregated data, and the updated aggregated data is analyzed to generate abnormal trend prediction and early warning and leakage risk assessment and early warning, and the inspection parameters in the inspection rules of the corresponding distribution cabinet are optimized.

[0006] Preferably, the real-time operating data collected by the intelligent control equipment deployed in the power distribution cabinet includes: The intelligent control equipment, including intelligent circuit controllers, current sensors, and leakage current sensors, deployed in each distribution cabinet, collects the circuit's switch status, leakage current status, and current and voltage parameters as real-time operating data.

[0007] Preferably, the step of activating the corresponding target inspection rule for the distribution cabinet to be inspected according to the pre-configured inspection rules includes: Based on the geographical location of the distribution cabinet to be inspected, the attributes of the road segment it belongs to, and the application scenario matching the current time, the corresponding target inspection rules are activated for the distribution cabinet to be inspected; among them... The inspection rules configure inspection parameters according to different application scenarios and store them as corresponding rule templates. The inspection parameters of the inspection rules include inspection frequency, inspection range, inspection time and inspection content. The application scenarios include daily operation and maintenance scenarios, special holiday scenarios, major event support scenarios and water-prone area scenarios.

[0008] Preferably, the step of analyzing the updated aggregated data to generate abnormal trend prediction and early warning, and leakage risk assessment and early warning, and optimizing the inspection parameters in the inspection rules for the corresponding distribution cabinet, includes: The probability of an anomaly is calculated based on the historical inspection data, abnormal fault data, and real-time operation data in the updated aggregated data. When the probability of an anomaly is higher than the warning threshold, the anomaly trend prediction warning is generated. Based on real-time weather data, the water accumulation characteristics of power distribution cabinets, and the degree of pipeline aging in the updated aggregated data, a leakage risk value is calculated. When the leakage risk value is higher than the risk threshold, a leakage risk assessment and warning is generated. Based on the road section protection level and historical fault frequency in the updated aggregated data, the inspection parameters of the corresponding power distribution cabinets were adjusted and optimized.

[0009] Preferably, the calculation of the probability of anomaly occurrence based on historical inspection data, abnormal fault data, and real-time operation data in the updated aggregated data includes: According to the formula The probability of the anomaly occurring is obtained by calculation; where, This indicates the number of anomalies in the past T days. Indicates the number of days in the statistical period. This indicates the maximum current value in the current cycle. Indicates the rated current value. Indicates weather influencing factors, , , This represents the corresponding weighting coefficient.

[0010] Preferably, the calculation of the leakage risk value based on real-time weather data, the water accumulation attribute of the distribution cabinet, and the aging degree of pipelines in the updated aggregated data includes: According to the formula The leakage risk value is obtained by calculation; where, , , , , This represents the corresponding weighting coefficient. Indicates the factors affecting rainfall. This indicates the number of times the distribution cabinet experienced leakage within the past T days. Indicates the number of days in the statistical period. This indicates the ambient humidity factor of the area where the distribution cabinet is located. Indicates the factors of areas prone to water accumulation. This indicates the pipeline aging factor.

[0011] Preferably, the step of adjusting and optimizing the inspection parameters of the corresponding power distribution cabinet based on the road segment protection level and historical fault frequency in the updated aggregated data includes: Priority scores are calculated based on the importance of the road segment to which the distribution cabinet belongs, historical fault frequency, and segment protection level. These priority scores are then used to determine the corresponding inspection level for the distribution cabinet, where different inspection levels correspond to different inspection frequencies. according to The priority score is calculated, where L represents the road segment importance score, F represents the fault frequency score, and G represents the road segment protection level score.

[0012] Preferably, the step of processing the aggregated data to generate a panoramic status overview interface includes: The aggregated data undergoes processing including data cleaning, format standardization, multi-dimensional correlation analysis, and data classification and integration. Based on the processed data, a panoramic status overview interface is generated and visualized. The panoramic status overview interface displays the switch status, operating parameters, and fault status of the distribution cabinet circuits of all projects.

[0013] To achieve the above objectives, the present invention also provides an intelligent inspection device for a street lighting system, the device comprising: The acquisition unit is used to acquire the data of each power distribution cabinet and aggregate all the data of the power distribution cabinet to generate aggregated data. The data of the power distribution cabinet includes real-time operation data collected by intelligent control equipment deployed in the power distribution cabinet, as well as historical inspection data, abnormal fault data, real-time weather data and road section protection level associated with the corresponding power distribution cabinet. The processing unit is used to process the aggregated data, generate a panoramic status overview interface, and enable the corresponding target inspection rule for the power distribution cabinet to be inspected according to the pre-configured inspection rules. The inspection unit is used to automatically trigger the batch inspection process of the power distribution cabinet to be inspected according to the target inspection rules, obtain the inspection results, and generate an abnormal status information record when there is an abnormal status in the inspection results. The early warning unit is used to update the aggregated data by recording the abnormal status information, and to analyze the updated aggregated data to generate abnormal trend prediction early warning and leakage risk assessment early warning, and to optimize the inspection parameters in the inspection rules of the corresponding distribution cabinet.

[0014] To achieve the above objectives, the present invention also proposes an intelligent inspection device for a street lighting system, comprising a processor, a memory, and a computer program stored in the memory, wherein the computer program is executed by the processor to implement the steps of an intelligent inspection method for a street lighting system as described in the above embodiments.

[0015] To achieve the above objectives, the present invention also proposes a computer-readable storage medium storing a computer program, which is executed by a processor to implement the steps of an intelligent inspection method for a street lighting system as described in the above embodiments.

[0016] Beneficial effects: The above solution, by aggregating heterogeneous data from various distribution cabinets, achieves centralized and unified monitoring of distributed projects, fundamentally breaking the data silos of traditional solutions and significantly improving the overall efficiency of operation and maintenance management. Standardized processing and visualization of the aggregated data generate a one-stop panoramic status overview, enabling managers to monitor the real-time status of all projects seamlessly on a unified interface. This completely avoids the cumbersome operation and information fragmentation caused by frequently switching accounts between multiple systems, greatly improving monitoring efficiency and overall control. By combining automated inspection processes with real-time data analysis and continuously optimizing subsequent strategies using inspection feedback data, the traditional passive, fixed-cycle manual inspection is transformed into a proactive, predictable, and adaptive intelligent operation and maintenance model. This not only significantly reduces reliance on manual inspections and saves labor costs but also enables early detection of potential faults and safety risks, achieving a leap in prevention and improving the operational reliability and safety of the street light system.

[0017] By employing specialized equipment such as intelligent loop controllers, current sensors, and leakage current sensors to collect precise loop-level electrical parameters, the accuracy and professionalism of the collected real-time operational data (such as switch status, leakage current status, and electrical parameters) are ensured, providing a high-quality and reliable data source for subsequent analysis and decision-making. Based on this, the raw aggregated data undergoes preprocessing such as cleaning, standardization, and correlation integration to generate a comprehensive interface that centrally displays switch status, operating parameters, and fault status. This allows managers to quickly and accurately grasp the overall real-time dynamics, achieving visualization and transparency of the operation and maintenance status, and providing an intuitive basis for efficient decision-making. Overall, this approach collectively realizes a paradigm shift in operation and maintenance management from "dispersed manual" to "centralized intelligent," significantly reducing labor costs and improving the coverage and immediacy of status monitoring.

[0018] The defined scenario-based inspection rule configuration and activation mechanism overcomes the shortcomings of traditional fixed inspection strategies, which are rigid and unable to adapt to diverse management needs, greatly enhancing the flexibility and targeting of inspection work. It matches different application scenarios (such as daily operations, holidays, major events, and areas prone to water accumulation) based on geographical location, road segment attributes, and time, and activates corresponding target inspection rules for power distribution cabinets with preset specific inspection frequencies, ranges, times, and content. This makes the inspection work highly flexible and targeted, ensuring that more intensive and comprehensive inspections are automatically executed in critical periods and key areas. This achieves precise allocation and key support of maintenance resources while ensuring efficiency and coverage, significantly improving the scientific nature and efficiency of maintenance work.

[0019] By calculating the probability of anomalies and issuing early trend warnings, proactive prevention is achieved. By comprehensively considering environmental and equipment factors to calculate leakage risk values, accurate assessment and early warning of specific safety hazards are realized, greatly improving safety. By calculating inspection priority scores and dynamically optimizing the inspection parameters of each distribution cabinet accordingly, differentiated operation and maintenance plans can be automatically generated and executed, ensuring that high-importance, high-failure-risk equipment receives more intensive attention. Through these data model-based intelligent decision-making processes, the system possesses self-learning and adaptive optimization capabilities, achieving a leap from "experience-driven" to "data-driven" approaches. This significantly improves the accuracy of fault prediction, the proactivity of risk prevention, and the refinement and intelligence of the overall operation and maintenance strategy. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating an intelligent inspection method for a street lighting system according to an embodiment of the present invention.

[0022] Figure 2 This is a schematic diagram of the structure of an intelligent inspection device for a street lighting system provided in an embodiment of the present invention.

[0023] The realization of the invention's objective, its functional characteristics, and advantages will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to represent selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] The present invention will be described in detail below with reference to the embodiments.

[0026] Reference Figure 1 The diagram shown is a flowchart illustrating an intelligent inspection method for a street lighting system according to an embodiment of the present invention.

[0027] In this embodiment, the method includes: S11, acquire the power distribution cabinet data of each power distribution cabinet, and aggregate all the power distribution cabinet data to generate aggregated data. The power distribution cabinet data includes real-time operating data collected by intelligent control equipment deployed on the power distribution cabinet, as well as historical inspection data, abnormal fault data, real-time weather data and road section protection level associated with the corresponding power distribution cabinet.

[0028] Furthermore, the real-time operating data collected by the intelligent control equipment deployed in the power distribution cabinet includes: The intelligent control equipment, including intelligent circuit controllers, current sensors, and leakage current sensors, deployed in each distribution cabinet, collects the circuit's switch status, leakage current status, and current and voltage parameters as real-time operating data.

[0029] In this embodiment, in each street light project with distributed deployment, each street light distribution cabinet is equipped with intelligent control devices. These devices integrate intelligent circuit controllers, current sensors, leakage current sensors, and environmental sensors. These devices monitor and collect the physical status and electrical parameters of the circuits they are connected to in real time, specifically including the "on / off" switch status of each circuit, the presence or absence of leakage current, and current and voltage values ​​characterizing the load. This dynamically changing real-time operational data constitutes the most direct operational perception of the distribution cabinet and its associated street light circuits. Simultaneously, a lighting monitoring system deployed locally within the project acts as a data hub, continuously receiving and storing real-time operational data reported by the intelligent control devices of all distribution cabinets within its jurisdiction. Furthermore, this lighting monitoring system also maintains historical data assets associated with each distribution cabinet, accumulated over time, including past manual and automatic inspection records (historical inspection data) and details of previously recorded fault events (abnormal fault data). Simultaneously, the system obtains real-time weather information (real-time weather data) for the project area through an interface, and pre-classifies the importance of each road segment served by the distribution cabinet into categories such as main roads, secondary roads, and branch roads, as well as protection levels such as key, routine, and ordinary (segment protection levels). Finally, in the intelligent operation and maintenance management system deployed at the central control center, a unified data access gateway using standardized data interface protocols aggregates all the "distribution cabinet data" scattered across different projects and lighting monitoring systems, covering real-time operation data, historical inspection data, abnormal fault data, real-time weather data, and segment protection levels. This aggregation process breaks down the barriers of isolated data across projects and the need for separate logins in the traditional model, integrating multi-source, heterogeneous data into a unified global data pool, generating aggregated data that can be uniformly accessed in subsequent steps. This lays a solid and complete data foundation for achieving panoramic monitoring and intelligent analysis across projects.

[0030] S12, after processing the aggregated data, a panoramic status overview interface is generated, and the corresponding target inspection rule is activated for the power distribution cabinet to be inspected according to the pre-configured inspection rules.

[0031] Furthermore, the step of processing the aggregated data to generate a panoramic status overview interface includes: The aggregated data undergoes processing including data cleaning, format standardization, multi-dimensional correlation analysis, and data classification and integration. Based on the processed data, a panoramic status overview interface is generated and visualized. The panoramic status overview interface displays the switch status, operating parameters, and fault status of the distribution cabinet circuits of all projects.

[0032] Furthermore, the step of activating the corresponding target inspection rule for the power distribution cabinet to be inspected according to the pre-configured inspection rules includes: Based on the geographical location of the distribution cabinet to be inspected, the attributes of the road segment it belongs to, and the application scenario matching the current time, the corresponding target inspection rules are activated for the distribution cabinet to be inspected; among them... The inspection rules configure inspection parameters according to different application scenarios and store them as corresponding rule templates. The inspection parameters of the inspection rules include inspection frequency, inspection range, inspection time and inspection content. The application scenarios include daily operation and maintenance scenarios, special holiday scenarios, major event support scenarios and water-prone area scenarios.

[0033] In this embodiment, the generated aggregated data undergoes preprocessing and processing, specifically including: performing data cleaning to remove missing values ​​or abnormal data that significantly exceeds reasonable limits generated during transmission; standardizing the format by unifying the data units, timestamp formats, and encoding rules from different projects or devices; conducting multi-dimensional correlation analysis by linking and integrating scattered real-time operating data, historical data, and attribute data based on information such as the distribution cabinet number, the project it belongs to, and the road section it is located on, forming a complete equipment profile; and finally, classifying and integrating the data by storing the processed data according to dimensions such as switch status, operating parameters (such as current and voltage), and fault / abnormal status. Based on this, the system drives its status display module to generate and present a one-stop panoramic status overview interface on a high-resolution unified display screen. This interface uses visualizations such as maps, lists, and charts to centrally and in real-time display the switch status (e.g., using green / red to indicate on / off), the specific values ​​of key operating parameters, and the presence of faults or abnormalities for each distribution cabinet and its circuits under all managed projects. Managers can have a clear understanding of the overall operation and maintenance status without switching between different system accounts.

[0034] Based on this panoramic monitoring, the system's inspection control module dynamically activates corresponding target inspection rules for the distribution cabinets to be inspected, according to pre-set rule templates for different application scenarios. Administrators have pre-configured various scenario-based rule templates in the system, such as: 1) In daily operation and maintenance scenarios, the inspection frequency is set to 60 minutes / time, the inspection scope covers all regular road section distribution cabinets, the inspection time is 24 hours a day, and the inspection content is the status of circuit switches and leakage status. 2) During special holidays (such as Spring Festival and National Day), the frequency is set to 30 minutes / time, the inspection scope is to cover the main roads, commercial areas, and the surrounding areas of scenic spots, the inspection time is 00:00-24:00 during the holidays, and the inspection content is the status of circuit switches, leakage status, and current / voltage parameters. 3) For major events (such as competitions and celebrations), the frequency is set to 15 minutes / time. The inspection scope covers the power distribution cabinets within the event area and the surrounding preset range. The inspection time is from 24 hours before the event to 2 hours after the event. The inspection content includes the status of circuit switches, leakage status, power parameters, and line temperature. 4) In areas prone to water accumulation, the inspection frequency is 45 minutes / time, the inspection scope is the distribution cabinet marked as prone to water accumulation, the inspection time is during rainfall and 12 hours after the rain, and the inspection content is leakage status. When the system is running, it will automatically match and activate the most suitable scenario rule template based on the geographical location of a specific power distribution cabinet (whether it is in a major event area), the road segment attribute (whether it is a main road or a water-prone section), and the current time (whether it is a holiday or a rainy period). This will give the power distribution cabinet a targeted inspection frequency, scope, time and content, realizing the transformation from "one-size-fits-all" inspection to "scenario-based" precise inspection.

[0035] S13, automatically trigger the batch inspection process of the distribution cabinet to be inspected according to the target inspection rules, obtain the inspection results, and generate an abnormal status information record when there is an abnormal status in the inspection results.

[0036] In this embodiment, the intelligent operation and maintenance management platform automatically generates and issues inspection instructions based on the specific target inspection rules enabled for each distribution cabinet. These inspection instructions are accurately sent to the corresponding systems through the communication link between the platform and the local lighting monitoring system of each project. Subsequently, the lighting monitoring system of each project initiates a centralized batch inspection process to the intelligent control equipment of the distribution cabinet to be inspected within its jurisdiction according to the received instructions. This process sends query and control commands to relevant sensors and actuators according to the inspection content set in the inspection rules (such as checking switch status, leakage status, reading current and voltage values, etc.), and collects the returned real-time data as the inspection result.

[0037] The system performs real-time analysis of the returned inspection results. If the data of a power distribution cabinet or its circuit exceeds the preset safety or normal threshold (e.g., leakage signal detected, abnormal switch status, abnormally high or low current or voltage), the system determines that the power distribution cabinet is in an abnormal state. The system automatically generates a structured abnormal state information record. This record not only includes core information such as the type of abnormality (e.g., leakage, open circuit), occurrence time, and specific parameter values, but also associates the equipment metadata of the power distribution cabinet (e.g., equipment number, installation location) and marks its project and circuit. This record is submitted to the platform's database in real time and simultaneously sent to the responsible maintenance personnel through the platform's early warning push module via system interface pop-ups, SMS, or in-application messages, ensuring that abnormal situations are detected as soon as possible. This process achieves a fully automated closed loop from static rule configuration to dynamic task execution, and then to real-time abnormal capture and structured recording, providing an accurate feedback data source for subsequent intelligent analysis and strategy optimization.

[0038] S14, the abnormal status information is recorded to update the aggregated data, and the updated aggregated data is analyzed to generate abnormal trend prediction and early warning and leakage risk assessment and early warning, and the inspection parameters in the inspection rules of the corresponding distribution cabinet are optimized.

[0039] Furthermore, the analysis based on the updated aggregated data generates abnormal trend prediction and early warning, and leakage risk assessment and early warning, and optimizes the inspection parameters in the inspection rules for the corresponding distribution cabinet, including: The probability of an anomaly is calculated based on the historical inspection data, abnormal fault data, and real-time operation data in the updated aggregated data. When the probability of an anomaly is higher than the warning threshold, the anomaly trend prediction warning is generated. Based on real-time weather data, the water accumulation characteristics of power distribution cabinets, and the degree of pipeline aging in the updated aggregated data, a leakage risk value is calculated. When the leakage risk value is higher than the risk threshold, a leakage risk assessment and warning is generated. Based on the road section protection level and historical fault frequency in the updated aggregated data, the inspection parameters of the corresponding power distribution cabinets were adjusted and optimized.

[0040] Furthermore, the probability of anomaly occurrence is calculated based on historical inspection data, abnormal fault data, and real-time operation data in the updated aggregated data, including: According to the formula The probability of the anomaly occurring is obtained by calculation; where, This indicates the number of anomalies in the past T days. Indicates the number of days in the statistical period. This indicates the maximum current value in the current cycle. Indicates the rated current value. Indicates weather influencing factors, , , This represents the corresponding weighting coefficient.

[0041] Furthermore, the leakage risk value is calculated based on real-time weather data, the water accumulation attribute of the distribution cabinet, and the degree of pipeline aging in the updated aggregated data, including: According to the formula The leakage risk value is obtained by calculation; where, , , , , This represents the corresponding weighting coefficient. Indicates the factors affecting rainfall. This indicates the number of times the distribution cabinet experienced leakage within the past T days. Indicates the number of days in the statistical period. This indicates the ambient humidity factor of the area where the distribution cabinet is located. Indicates the factors of areas prone to water accumulation. This indicates the pipeline aging factor.

[0042] Furthermore, the step of adjusting and optimizing the inspection parameters of the corresponding power distribution cabinet based on the road segment protection level and historical fault frequency in the updated aggregated data includes: Priority scores are calculated based on the importance of the road segment to which the distribution cabinet belongs, historical fault frequency, and segment protection level. These priority scores are then used to determine the corresponding inspection level for the distribution cabinet, where different inspection levels correspond to different inspection frequencies. according to The priority score is calculated, where L represents the road segment importance score, F represents the fault frequency score, and G represents the road segment protection level score.

[0043] In this embodiment, the system records the generated abnormal status information as key real-time feedback data and updates it to the aggregated data. Specifically, this record is categorized and incorporated into the database of historical inspection data and abnormal fault data, thereby dynamically refreshing the historical data baseline of the relevant distribution cabinets and ensuring that the data used for analysis always reflects the latest operating status. Based on this updated aggregated data, the system's intelligent analysis module performs intelligent analysis, specifically including: Regarding abnormal trend prediction and early warning: For specific road sections or distribution cabinets, based on historical inspection data, abnormal fault data, and real-time operation data from the updated aggregated data, a built-in prediction model is applied to calculate the probability P of an anomaly occurring within a future period (e.g., 72 hours) using the following formula. abn(Value range [0,1]): In the formula, This refers to the number of leakage current incidents over the past T days, based on historical data. T represents the number of days in the statistical period (default 30 days). max I is the maximum current value for the current cycle obtained from real-time operating data. nom W represents the rated current value, and W is the weather impact factor obtained from environmental data (e.g., 0.1 for sunny days, 0.3 for cloudy days, 0.6 for rainy days, and 0.8 for foggy days). , , Indicates the corresponding weight coefficient ( The weighting coefficient for historical faults, such as a value of 0.5; This represents the weighting coefficient for deviations in operating parameters, such as a value of 0.3; This represents the weighting coefficient for environmental impacts (e.g., a value of 0.2), used to balance the importance of different factors in the prediction. When P... abn When the value is ≥ 0.7 (early warning threshold), the risk of an anomaly is determined to be high, and an anomaly trend prediction early warning is automatically generated and pushed to prompt preventive checks.

[0044] Regarding leakage risk assessment and early warning: For a single distribution cabinet, the system comprehensively considers real-time weather data, the inherent water accumulation characteristics of the distribution cabinet, and static attributes such as the degree of pipeline aging from the updated aggregated data, and calculates the leakage risk value (R) through a risk assessment model. leak ): In the formula, R rain For the rainfall impact factor (e.g., assigned values ​​based on the rainfall in the past 24 hours: 3 for 0-10mm, 6 for 10-20mm, and 10 for >20mm), N leak (T) represents the number of leakage incidents of the distribution cabinet in the past T days, H represents the environmental humidity factor of the area where the distribution cabinet is located (usually the current environmental humidity percentage of the area where the distribution cabinet is located divided by 10), J represents the water accumulation area factor (assigned according to the "whether it is a water accumulation area" mark in the basic attributes of the distribution cabinet: 10 if it is a water accumulation area, 2 otherwise), A represents the pipeline aging factor (assigned according to the service life of the pipeline: 2 for ≤5 years, 5 for 6-10 years, and 10 for >10 years), δ, ε, ζ, η, and θ are the weighting coefficients of rainfall, historical leakage incidents, environmental humidity, water accumulation attribute, and pipeline aging degree, respectively. When the calculated R leak When the risk level is ≥7 (risk threshold), the system determines that the current leakage risk is high, immediately generates and issues a leakage risk assessment warning, and reminds users to pay close attention.

[0045] Regarding the dynamic optimization of inspection parameters: Based on the updated data, the parameters (mainly the inspection frequency) in the inspection rules of the corresponding power distribution cabinet are automatically optimized. The core logic is to calculate a dynamic inspection priority score (S, value range [0,10]) based on the real-time "value" and "risk" of the power distribution cabinet. The calculation formula is: S = 0.4 × L + 0.3 × F + 0.3 × G; where L is the importance score of the road segment, assigned according to the preset classification of the road segment to which the power distribution cabinet belongs in the system (e.g., 10 points for main roads, 7 points for secondary roads, and 4 points for branch roads); F is the fault frequency score, dynamically assigned based on the latest historical fault frequency of the power distribution cabinet (e.g., 10 points for ≥5 faults in the past 30 days, 7 points for 3-4 faults, and so on); G is the road segment protection level score, assigned according to the preset protection level of the road segment (e.g., 10 points for key protection, 7 points for routine protection, and 4 points for ordinary protection). Based on the calculated S value, the power distribution cabinet is automatically classified into different inspection levels (for example: S≥8 is Level 1 inspection, with an inspection frequency of 15 minutes / time; 6≤S<8 is Level 2 inspection, with an inspection frequency of 30 minutes / time; S<6 is Level 3 inspection, with an inspection frequency of 60 minutes / time). This means that a power distribution cabinet located on a main road (high L value) and experiencing frequent recent failures (increased F value) will have its inspection frequency automatically increased, thereby intelligently tilting maintenance resources towards high-risk and high-importance equipment.

[0046] Reference Figure 2 The diagram shown is a structural schematic of an intelligent inspection device for a street lighting system according to an embodiment of the present invention.

[0047] In this embodiment, the device 20 includes: The acquisition unit 21 is used to acquire the power distribution cabinet data of each power distribution cabinet and aggregate all the power distribution cabinet data to generate aggregated data. The power distribution cabinet data includes real-time operation data collected by intelligent control equipment deployed in the power distribution cabinet, as well as historical inspection data, abnormal fault data, real-time weather data and road section protection level associated with the corresponding power distribution cabinet. The processing unit 22 is used to process the aggregated data, generate a panoramic status overview interface, and enable the corresponding target inspection rule for the power distribution cabinet to be inspected according to the pre-configured inspection rules. The inspection unit 23 is used to automatically trigger the batch inspection process of the power distribution cabinet to be inspected according to the target inspection rules, obtain the inspection results, and generate an abnormal status information record when there is an abnormal status in the inspection results. The early warning unit 24 is used to update the aggregated data by recording the abnormal status information, and to analyze the updated aggregated data to generate abnormal trend prediction early warning and leakage risk assessment early warning, and to optimize the inspection parameters in the inspection rules of the corresponding distribution cabinet.

[0048] Each unit module of the device 20 can execute the corresponding steps in the above method embodiment, so the details of each unit module will not be elaborated here. Please refer to the description of the corresponding steps above for details.

[0049] This invention also provides an intelligent inspection device for a street lighting system. The device includes the intelligent inspection apparatus for a street lighting system described above, wherein the intelligent inspection apparatus for the street lighting system can employ… Figure 2 The structure of the embodiment, correspondingly, can be executed Figure 1 The technical solutions of the method embodiments shown are similar in implementation principle and technical effect. For details, please refer to the relevant records in the above embodiments, which will not be repeated here.

[0050] The device includes: a mobile phone, digital camera, or tablet computer, or other device with a camera function; or a device with an image processing function; or a device with an image display function. The device may include components such as a memory, processor, input unit, display unit, and power supply.

[0051] The memory can be used to store software programs and modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store the operating system, applications required for at least one function, etc.; the data storage area can store data created according to the use of the device, etc. In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory can also include a memory controller to provide access to the memory for the processor and input units.

[0052] The input unit can be used to receive input numerical, character, or image information, and to generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function control. Specifically, in addition to a camera, the input unit of this embodiment may also include a touch-sensitive surface (e.g., a touch screen) and other input devices.

[0053] The display unit can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of the device. These graphical user interfaces can be composed of graphics, text, icons, video, and any combination thereof. The display unit may include a display panel, optionally configured as an LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), or other similar display panel. Furthermore, a touch-sensitive surface may cover the display panel. When the touch-sensitive surface detects a touch operation on or near it, it transmits the information to the processor to determine the type of touch event. Subsequently, the processor provides corresponding visual output on the display panel based on the type of touch event.

[0054] This invention also provides a computer-readable storage medium, which may be a computer-readable storage medium included in the memory described in the above embodiments; or it may be a standalone computer-readable storage medium not assembled into a device. The computer-readable storage medium stores at least one instruction, which is loaded and executed by a processor to implement... Figure 1 The method for intelligent inspection of the street lighting system shown is illustrated. The computer-readable storage medium may be a read-only memory, a disk, or an optical disk, etc.

[0055] This invention also provides a computer program product, including a computer program / instructions, which are loaded and executed by a processor to implement... Figure 1 This paper presents an intelligent inspection method for a street lighting system.

[0056] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the device embodiments, equipment embodiments, and storage medium embodiments, since they are basically similar to the method embodiments, the descriptions are relatively simple, and relevant parts can be referred to the descriptions in the method embodiments.

[0057] Furthermore, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0058] The foregoing description illustrates and describes preferred embodiments of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the inventive concept by means of the foregoing teachings or techniques or knowledge in related fields. Any modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. An intelligent inspection method for a street lighting system, characterized in that, The method includes: Acquire the distribution cabinet data of each distribution cabinet, and aggregate all the distribution cabinet data to generate aggregated data. The distribution cabinet data includes real-time operation data collected by intelligent control equipment deployed in the distribution cabinet, as well as historical inspection data, abnormal fault data, real-time weather data and road section protection level associated with the corresponding distribution cabinet. After processing the aggregated data, a panoramic status overview interface is generated, and the corresponding target inspection rule is activated for the power distribution cabinet to be inspected according to the pre-configured inspection rules. The batch inspection process of the power distribution cabinet to be inspected is automatically triggered according to the target inspection rules to obtain the inspection results, and an abnormal status information record is generated when there is an abnormal status in the inspection results. The abnormal status information is recorded to update the aggregated data, and the updated aggregated data is analyzed to generate abnormal trend prediction and early warning and leakage risk assessment and early warning, and the inspection parameters in the inspection rules of the corresponding distribution cabinet are optimized.

2. The intelligent inspection method for a street lighting system according to claim 1, characterized in that, The real-time operating data collected by the intelligent control equipment deployed in the power distribution cabinet includes: The intelligent control equipment, including intelligent circuit controllers, current sensors, and leakage current sensors, deployed in each distribution cabinet, collects the circuit's switch status, leakage current status, and current and voltage parameters as real-time operating data.

3. The intelligent inspection method for a street lighting system according to claim 1, characterized in that, The step of activating the corresponding target inspection rule for the power distribution cabinet to be inspected according to the pre-configured inspection rules includes: Based on the geographical location of the distribution cabinet to be inspected, the attributes of the road segment it belongs to, and the application scenario matching the current time, the corresponding target inspection rules are activated for the distribution cabinet to be inspected; among them... The inspection rules configure inspection parameters according to different application scenarios and store them as corresponding rule templates. The inspection parameters of the inspection rules include inspection frequency, inspection range, inspection time and inspection content. The application scenarios include daily operation and maintenance scenarios, special holiday scenarios, major event support scenarios and water-prone area scenarios.

4. The intelligent inspection method for a street lighting system according to claim 1, characterized in that, The analysis based on the updated aggregated data generates abnormal trend prediction and early warning, and leakage risk assessment and early warning. It also optimizes the inspection parameters in the inspection rules for the corresponding distribution cabinets, including: The probability of an anomaly is calculated based on the historical inspection data, abnormal fault data, and real-time operation data in the updated aggregated data. When the probability of an anomaly is higher than the warning threshold, the anomaly trend prediction warning is generated. Based on real-time weather data, the water accumulation characteristics of power distribution cabinets, and the degree of pipeline aging in the updated aggregated data, a leakage risk value is calculated. When the leakage risk value is higher than the risk threshold, a leakage risk assessment and warning is generated. Based on the road section protection level and historical fault frequency in the updated aggregated data, the inspection parameters of the corresponding power distribution cabinets were adjusted and optimized.

5. The intelligent inspection method for a street lighting system according to claim 4, characterized in that, The probability of anomalies is calculated based on historical inspection data, abnormal fault data, and real-time operation data in the updated aggregated data, including: According to the formula The probability of the anomaly occurring is obtained by calculation; where, This indicates the number of anomalies in the past T days. Indicates the number of days in the statistical period. This indicates the maximum current value in the current cycle. Indicates the rated current value. Indicates weather influencing factors, , , This represents the corresponding weighting coefficient.

6. The intelligent inspection method for a street lighting system according to claim 4, characterized in that, The leakage risk value is calculated based on real-time weather data, the water accumulation attribute of the distribution cabinet, and the aging degree of pipelines in the updated aggregated data, including: According to the formula The leakage risk value is obtained by calculation; where, , , , , This represents the corresponding weighting coefficient. Indicates the factors affecting rainfall. This indicates the number of times the distribution cabinet experienced leakage within the past T days. Indicates the number of days in the statistical period. This indicates the ambient humidity factor of the area where the distribution cabinet is located. Indicates the factors of areas prone to water accumulation. This indicates the pipeline aging factor.

7. The intelligent inspection method for a street lighting system according to claim 4, characterized in that, The process of adjusting and optimizing the inspection parameters of the corresponding power distribution cabinets based on the road segment protection level and historical fault frequency in the updated aggregated data includes: Priority scores are calculated based on the importance of the road segment to which the distribution cabinet belongs, historical fault frequency, and segment protection level. These priority scores are then used to determine the corresponding inspection level for the distribution cabinet, where different inspection levels correspond to different inspection frequencies. according to The priority score is calculated, where L represents the road segment importance score, F represents the fault frequency score, and G represents the road segment protection level score.

8. The intelligent inspection method for a street lighting system according to claim 1, characterized in that, After processing the aggregated data, a panoramic status overview interface is generated, including: The aggregated data undergoes processing including data cleaning, format standardization, multi-dimensional correlation analysis, and data classification and integration. Based on the processed data, a panoramic status overview interface is generated and visualized. The panoramic status overview interface displays the switch status, operating parameters, and fault status of the distribution cabinet circuits of all projects.

9. An intelligent inspection device for a street lighting system, characterized in that, The device includes: The acquisition unit is used to acquire the data of each power distribution cabinet and aggregate all the data of the power distribution cabinet to generate aggregated data. The data of the power distribution cabinet includes real-time operation data collected by intelligent control equipment deployed in the power distribution cabinet, as well as historical inspection data, abnormal fault data, real-time weather data and road section protection level associated with the corresponding power distribution cabinet. The processing unit is used to process the aggregated data, generate a panoramic status overview interface, and enable the corresponding target inspection rule for the power distribution cabinet to be inspected according to the pre-configured inspection rules. The inspection unit is used to automatically trigger the batch inspection process of the power distribution cabinet to be inspected according to the target inspection rules, obtain the inspection results, and generate an abnormal status information record when there is an abnormal status in the inspection results. The early warning unit is used to update the aggregated data by recording the abnormal status information, and to analyze the updated aggregated data to generate abnormal trend prediction early warning and leakage risk assessment early warning, and to optimize the inspection parameters in the inspection rules of the corresponding distribution cabinet.

10. An intelligent inspection device for a street lighting system, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory, wherein the computer program, when executed by the processor, implements the steps of an intelligent inspection method for a street lighting system as described in any one of claims 1 to 8.

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