Street lamp intelligent monitoring device and remote control system

Abstract

The invention discloses a street lamp intelligent monitoring device and a remote control system, and particularly relates to the technical field of intelligent street lamp remote control, and the street lamp intelligent monitoring device comprises a remote control platform, a street lamp intelligent controller connected with the remote control platform through a communication network, and a user terminal. The strategy management module is configured to generate and issue a lighting control strategy; the data monitoring and analyzing module is used for analyzing the running state data and the energy consumption data and generating fault diagnosis information; the fault alarm and operation and maintenance module is used for receiving fault diagnosis information reported by the street lamp intelligent controller, generating an alarm and triggering an operation and maintenance process; and the user terminal module is used for accessing the remote control platform, checking the state of the street lamp intelligent controller, configuring a control strategy and receiving alarm information. Through a strategy management module, a data monitoring and analysis module, a fault alarm and operation and maintenance module and a user terminal module, the problems of single control strategy, poor collaboration, low fault diagnosis precision and efficiency and the like are solved.

Description

Technical Field

[0001] This invention relates to the field of intelligent street light remote control technology, and more specifically, to an intelligent street light monitoring device and remote control system. Background Technology

[0002] With the advancement of smart city construction, streetlights, as an important component of urban infrastructure, directly impact public safety, energy consumption, and operational efficiency through their intelligent management level. As a core part of urban lighting, the coverage and number of streetlights continue to expand, leading to increasingly higher demands for the intelligence and efficiency of streetlight control systems. Currently, existing streetlight control systems are mainly divided into two categories: traditional manual control and simple automatic control.

[0003] Existing intelligent remote control systems for streetlights can meet daily needs, but they still have some shortcomings in actual use: First, the control strategies are simplistic and lack coordination. Existing solutions mostly rely on timing or simple light control, resulting in a single strategy type and a lack of intelligent linkage based on multiple conditions such as motion detection. Furthermore, when multiple strategies coexist, there is a lack of conflict resolution mechanisms, leading to unintelligent system response and limited energy efficiency optimization. Second, the accuracy and efficiency of fault diagnosis are low. Fault judgment relies heavily on static threshold comparison of a single parameter, failing to combine data preprocessing, multi-dimensional trend analysis, machine learning, and correlation analysis with energy consumption data, resulting in a high rate of false fault diagnosis, inaccurate root cause location, and delayed operation and maintenance response. Third, there is insufficient data value mining. There is a lack of in-depth and systematic analysis and integration of collected operating status data and energy consumption data, making it impossible to effectively identify potential risks or hidden waste caused by unreasonable strategy configuration, resulting in insufficient overall system optimization capabilities. Fourth, the automation level of the operation and maintenance process is low. The operation and maintenance process from fault alarm to work order generation, dispatch, processing, and verification has not formed an effective closed loop, relying heavily on manual intervention, resulting in high operation and maintenance costs and low efficiency.

[0004] Therefore, there is an urgent need for a street light intelligent monitoring system that can achieve refined intelligent control, has high-precision fault diagnosis capabilities, and can realize automated closed-loop operation and maintenance processes to solve problems such as single control strategies and poor coordination, low accuracy and efficiency of fault diagnosis, insufficient data value mining, and low degree of automation in operation and maintenance processes. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a street light intelligent monitoring device and remote control system, which solves the problems mentioned in the background art through the following solutions.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a remote control system, comprising: a remote control platform, a street light intelligent controller connected to the remote control platform via a communication network, and a user terminal; The strategy management module is configured to generate and distribute lighting control strategies, which include timing strategies, illuminance strategies, and motion detection strategies. Data monitoring and analysis module: configured to receive the operating status data and energy consumption data of the street light intelligent controller, analyze the operating status data and energy consumption data, and generate fault diagnosis information; The fault alarm and maintenance module is configured to receive fault diagnosis information reported by the street light intelligent controller, generate alarms, and trigger maintenance procedures. The street light intelligent controller is deployed at each street light node and includes: C1: Receives and executes the lighting control strategy issued by the strategy management module, and controls the switching or dimming of the streetlights. C2: Collects the operating status data and energy consumption data of the streetlights and uploads them to the data monitoring and analysis module through the communication network; C3: Monitors the fault status of the streetlights under its jurisdiction, and generates fault diagnosis information when a fault is detected, and reports it to the fault alarm and maintenance module; User terminal module: Used to access the remote control platform, view the status of the street light intelligent controller, configure control strategies, and receive alarm information.

[0007] Preferably, the aforementioned intelligent street light monitoring device is the core terminal equipment of the street light remote control system. Deployed at each street light node, it undertakes the key functions of command execution, data acquisition, and fault monitoring. Through communication networks, it collaborates with the remote control platform and user terminals to achieve intelligent management of street lights. Its core functions include: firstly, accurately responding to control strategies by receiving three types of lighting strategies—timing, illuminance, and motion detection—issued by the remote control platform, and realizing on-demand start / stop and dynamic brightness adjustment of street lights through relay switch control and 0-10V dimming signal output.

[0008] The technical effects and advantages of this invention are as follows: 1. To address the issue of low fault diagnosis accuracy in existing technologies, this invention uses a data monitoring and analysis module to perform preprocessing such as data cleaning, noise reduction, and standardization on operating status data and energy consumption data. It also comprehensively utilizes various technical means such as threshold analysis, historical trend analysis, K-means clustering anomaly pattern recognition, energy consumption baseline comparison, and strategy correlation analysis to achieve accurate fault location and root cause analysis. 2. This invention solves the problems of single strategy and poor coordination in the prior art by setting up a multi-dimensional lighting control strategy that includes timing strategy, illuminance strategy and motion detection strategy, and innovatively introducing a strategy conflict arbitration and priority mechanism. 3. This invention, through a fault alarm and maintenance module, realizes full-process automation from fault information reception and verification, hierarchical alarm push, automatic work order generation, intelligent work order dispatch to result verification and information archiving, establishing an automated and closed-loop maintenance process, effectively reducing maintenance costs and improving response speed. 4. This invention not only satisfies fault diagnosis, but also reveals potential problems such as lamp aging and unreasonable strategy configuration through the fusion analysis of operating status and energy consumption data. It can also proactively generate optimization suggestions, deeply explore the value of data, and provide data-driven decision support for the continuous optimization of the system. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the overall system structure of the present invention.

[0010] Figure 2 This is a schematic diagram of the strategy management module structure of the present invention.

[0011] Figure 3 This is a schematic diagram of the data monitoring and analysis module structure of the present invention.

[0012] Figure 4 This is a schematic diagram of the fault alarm and maintenance module structure of the present invention.

[0013] Figure 5 This is a schematic diagram of the user terminal module structure of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] refer to Figures 1-5 The remote control system shown includes: a remote control platform, a street light intelligent controller connected to the remote control platform via a communication network, and a user terminal; The strategy management module is configured to generate and distribute lighting control strategies, which include timing strategies, illuminance strategies, and motion detection strategies. Data monitoring and analysis module: configured to receive the operating status data and energy consumption data of the street light intelligent controller, analyze the operating status data and energy consumption data, and generate fault diagnosis information; The fault alarm and maintenance module is configured to receive fault diagnosis information reported by the street light intelligent controller, generate alarms, and trigger maintenance procedures. The street light intelligent controller is deployed at each street light node and includes: C1: Receives and executes the lighting control strategy issued by the strategy management module, and controls the switching or dimming of the streetlights. C2: Collects the operating status data and energy consumption data of the streetlights and uploads them to the data monitoring and analysis module through the communication network; C3: Monitors the fault status of the streetlights under its jurisdiction, and generates fault diagnosis information when a fault is detected, and reports it to the fault alarm and maintenance module; User terminal module: Used to access the remote control platform, view the status of the street light intelligent controller, configure control strategies, and receive alarm information.

[0016] The strategy management module is configured to generate and distribute lighting control strategies, which include timing strategies, illuminance strategies, and motion detection strategies. A101: Strategy Generation and Configuration Users log in to the remote control platform via a mobile app. In the policy management interface, they can choose to create a new lighting control policy or modify an existing one, and set parameters for the lighting control policy. Timed strategy configuration: Configuration 1, set fixed on / off times for streetlights, such as turning on the lights at 18:00 and turning them off at 06:00 in the morning; Configuration 2, set on / off times for streetlights based on sunrise and sunset time offsets, such as turning on the lights 10 minutes after sunset and turning them off 30 minutes before sunrise; Configuration 3, set on / off times for streetlights according to different dates, such as weekdays, weekends, and holidays. Illuminance Strategy Configuration: Configuration 1, Illuminance Threshold: Illuminance below 500 lux activates lighting (L1=500 lux), illumination above 800 lux deactivates lighting (L2=800 lux); activation threshold L1 and deactivation threshold L2 are in lux. Configuration 2, Brightness Mapping Rule: Brightness is dynamically adjusted based on intermediate illuminance thresholds. For example, street light brightness gradually increases when illumination L≤650 lux and gradually decreases when L>650 lux. Configuration 3, Illuminance Threshold Calibration: Calibration is divided into automatic calibration, which uses the historical data of the light sensor for the past 3 days and calculates the average as the illuminance threshold benchmark; and manual calibration, where the user directly inputs the illuminance threshold. Motion detection strategy configuration: Configuration 1, detection sensitivity is divided into 1 to 5 levels, with level 1 being the lowest and only detecting fast-moving targets, and level 5 being the highest and detecting slow-moving targets; Configuration 2, detection time period restriction, such as enabling detection from 19:00 to 06:00 in the morning to avoid invalid motion detection triggering during the day; Configuration 3, anti-accidental touch rule, brightness mapping is triggered only after two consecutive detections of moving targets, with an interval of ≤1 second.

[0017] A102: Policy Binding and Scope Specifying Bind the configured policy to one or more streetlights. The policy binding methods include: by geographical region, by streetlight model or group, by specifying the unique device ID of the streetlight, and by setting the policy effective time period. The system automatically checks for conflicts among multiple policies bound to the same street light. For example, a timed policy may require the light to be turned off at 21:00, while a motion detection policy may require the light to be turned on at 21:05 due to the detection of a pedestrian. Users can preset priority rules through their user terminals. The priority order includes: motion detection policy, illuminance policy, and timed policy. The system generates conflict-free policy execution logic based on the preset priority order. A103: Policy Issuance and Transmission The strategy compilation and encapsulation process compiles the user-configured lighting control strategy, which has undergone strategy conflict arbitration, into a data packet that the street light intelligent controller can recognize and execute. The data packet is then encapsulated, and its contents include: street light device ID, strategy type, trigger condition, execution action, effective time, and priority. Data packet delivery: Encapsulated data packets are delivered to the target smart street light controller via the LoRaWAN communication network. Delivery methods can be divided into: real-time delivery, which delivers new configurations or modified policies immediately; and batch delivery, which updates policies in batches during periods of low network load in the early morning. Strategy execution and feedback: The smart street light controller monitors the data transmitted by the local clock, illuminance sensor, and motion sensor in real time according to the stored lighting control strategy; when the strategy configuration condition is triggered, the smart street light controller immediately executes the corresponding control action, drives the relay to perform street light switching operation, and outputs 0 to 10V signal to dim the street light; After executing policy actions, the smart street light controller can upload execution logs, such as timestamps, street light device IDs, trigger policy types, and executed actions, as part of the operating status data to the data monitoring and analysis module.

[0018] The data monitoring and analysis module is configured to receive the operating status data and energy consumption data of the street light intelligent controller, analyze the operating status data and energy consumption data, and generate fault diagnosis information. B101: Data Reception and Analysis The data monitoring and analysis module receives data packets uploaded by the street light intelligent controller through a preset communication interface and performs batch parsing of the data packets: The receiving mechanism receives data uploaded by the smart street light controller in real time. The module listens to the corresponding LoRaWAN gateway port 6379 and triggers the data packet receiving process. Batch reception: For street light nodes within the area, a caching and batch retrieval mechanism is adopted. The gateway caches street light node data within 5 minutes and retrieves batch data from the gateway every 5 minutes to reduce the frequency of network interaction. MQTT protocol parsing: Based on the MQTT communication protocol of the street light intelligent controller, the MQTT parser is invoked to extract data fields from the data packets. These data fields include: Operational status data: Street light nodes Collection timestamp Street light intelligent controller power supply voltage Current Streetlight temperature Light sensor data collection values Motion detection sensor status, 0 for no target, 1 for target; controller operating status, For normal This is an anomaly; the policy execution status includes the currently active policy type and street light brightness value. Energy consumption data: The cumulative power consumption of streetlights during the collection period. Average power of streetlights Power factor ; Verification of parsing results: Data packet integrity is verified via CRC check; if the check fails, a data retransmission command is sent to the corresponding street light intelligent controller, with a maximum of 3 retries. If the retries fail, the street light node is marked. This indicates a data transmission error.

[0019] B102: Data Preprocessing Data preprocessing includes data cleaning, noise reduction, and standardization. It involves preprocessing the parsed data packets to remove outliers and correct biases, providing high-quality data for subsequent analysis. Data cleaning and missing value handling: If a data field is missing in a single data packet, interpolation is used to complete it based on the average of the previous 5 historical data collected for that street light node; if a data field is missing in 10 consecutive collections, the sensor of that street light node is marked as faulty and enters the subsequent anomaly analysis process. Outlier removal, based on Extreme outliers are excluded in principle. Under a normal distribution, data that exceeds the mean ± 3 times the standard deviation is considered outlier, such as voltage > 250V or voltage < 180V, current > 5A or current < 0.1A. Outlier information is recorded, including street light node, collection timestamp, and outlier data field value. For data noise reduction, such as illuminance values ​​and lamp temperatures, a sliding window mean filtering algorithm is used. The window size is set to 5 acquisition cycles, and the mean value of the data within the window is calculated as the filter value to reduce fluctuations caused by environmental interference. Data standardization involves converting data of different dimensions into a unified standard format using the Z-Score standardization formula. Standardize the operating status data and energy consumption data, among which This is the average of historical data. Standard deviation is used to convert numerical data such as voltage, current, temperature, and energy consumption into standard deviation. The standard value of the interval.

[0020] B103: Multi-dimensional Analysis of Operation Status Data The power supply voltage of the street light intelligent controller is preset with thresholds based on standardized operating status data through the user terminal. The normal range is The warning threshold is or The abnormal threshold is ; Current The normal range is The warning threshold is or The abnormal threshold is ; Streetlight temperature The normal range is The warning threshold is The abnormal threshold is ; Power factor The normal range is The warning threshold is The abnormal threshold is If the data is within the warning threshold range, it is marked as a status warning; if it is within the abnormal threshold range, it is marked as a status abnormal. Historical trend analysis extracts historical data of the same monitoring indicator for the current street light node over the past 7 days, generating trend curves such as voltage and temperature change trends; and calculates the trend slope, such as the temperature trend slope. If the temperature exceeds the preset threshold and the current temperature is close to the warning threshold, it will be marked as an abnormal trend. Based on the K-means clustering machine learning algorithm, feature combination is performed on the operating status data, including voltage, current, temperature, and policy execution status, to identify abnormal patterns. Feature extraction: Extract features such as voltage standard deviation, current fluctuation coefficient, average temperature, and strategy execution switching frequency for each acquisition cycle; Cluster analysis involves inputting a feature vector into a pre-trained K-means model, which outputs the cluster to which the feature vector belongs. The clusters include normal clusters and abnormal clusters. Abnormal mode determination: If the feature vectors of three consecutive acquisition cycles all belong to the abnormal cluster, the mode is determined to be abnormal, such as the controller frequently switching strategies, abnormal voltage and current fluctuation synchronization, or controller hardware failure.

[0021] B104: Energy Consumption Data Analysis By combining historical data, regional benchmark data, and lighting control strategies, in-depth analysis of energy consumption data is conducted to pinpoint energy consumption anomalies. Energy consumption baselines are established for individual streetlights based on energy consumption data from the same streetlight node over the past 30 days. This excludes extreme weather events, such as rain or heavy downpours leading to extremely low illuminance, and special events, such as frequent motion detection triggers due to regional activities. The daily average energy consumption baseline is then calculated. Regional benchmark: Select the same street light nodes within the same region and calculate the average energy consumption over the past 30 days. , as a regional energy consumption benchmark; Energy consumption anomaly detection, individual anomaly detection, and calculation of the current street light node's daily energy consumption. Compared with individual baseline deviation rate ,like This is then determined to be an individual with abnormal energy consumption; For excessive energy consumption, Because the energy consumption is too low; Regional anomaly detection, calculating the daily energy consumption of the current street light node. Compared with regional benchmarks deviation rate ,like If the energy consumption of a certain area is significantly higher than that of other street light nodes in the same area, it is determined that the lights are aging or the strategy configuration is unreasonable. Policy correlation analysis involves accessing currently active policy data from the policy management module to analyze the correlation between energy consumption and policy execution. If the highlighted periods of the timing strategy perfectly match the peak energy consumption periods, and the deviation rate is... If the energy consumption matches the strategy, then it is determined that there is no anomaly. If the trigger frequency of the motion detection strategy is higher than the historical average, and the energy consumption deviation rate is... If the abnormal energy consumption is determined to be caused by frequent triggering of the motion detection strategy, optimization suggestions for adjusting the motion detection sensitivity will be generated. If the activation threshold L1 of the illuminance strategy is set too low, it will lead to prolonged lighting duration and increased energy consumption deviation rate. This will generate optimized suggestions for adjusting the illuminance activation threshold.

[0022] B105: Fusion Analysis of Operating Status and Energy Consumption Data By integrating the operational status analysis results of B103 with the energy consumption analysis results of B104, cross-dimensional correlations are uncovered, and deep-seated problems that cannot be detected by single-dimensional analysis are identified. Association rule matching, preset association rule library, rule 1, abnormal operating voltage, abnormal power consumption exceeding 120% of rated power, suspected power supply overvoltage causing lamp overload; Rule 2: Operating temperature continues to rise, power factor is less than 0.8, suspected poor heat dissipation of lamps and aging circuits; Rule 3: Frequent switching between execution states of the running strategy and excessive energy consumption. The suspected conflict between strategy coordination rules led to invalid lighting; B106: Fault Level Determination Based on the degree of impact of the fault on the operation of the streetlights, the faults are divided into three levels: Emergency Fault Level 1: Directly causes streetlights to malfunction, such as abnormal power supply voltage causing the lights to go out, or controller hardware failure, which must be resolved within 1 hour; Serious fault level 2: Affects lighting effect or poses safety hazards, such as overheating of lamps or excessive energy consumption leading to overload, and needs to be dealt with within 4 hours; Minor fault level 3: Does not affect basic lighting, only has room for optimization, such as unreasonable strategy configuration or slight deviation of sensor data, and needs to be handled within 24 hours; B107: Diagnostic Information Output and Feedback The structured fault diagnosis information is categorized and output according to the receiving object, triggering subsequent processing flows and receiving feedback results: Output to the fault alarm and operation and maintenance module, push fault diagnosis information in real time, prioritize push to level 1 and level 2 faults, and push level 3 faults in batches to trigger operation and maintenance processes, such as automatically generating operation and maintenance work orders and matching operation and maintenance personnel; receive processing status feedback from the fault alarm and operation and maintenance module, such as work orders have been dispatched and faults have been repaired, and update the processing status of fault diagnosis information. Output to the policy management module, push diagnostic information and optimization suggestions for policy configuration inappropriateness, trigger the automatic policy adjustment process, such as reducing motion detection sensitivity, adjusting light intensity threshold, or push policy adjustment prompts to user terminals for manual confirmation by the user. Output to user terminals, push alarm information according to fault level, level 1 fault pop-up prompt, level 2 fault message notification, level 3 fault recorded in report; provide fault details query interface, support users to view fault diagnosis information, processing progress, and historical fault records. The diagnostic results are optimized and feedback is provided. If the maintenance personnel report that the fault diagnosis is incorrect, such as the actual fault being a sensor fault but the fault being diagnosed as a controller fault, the module records the error case, updates the fault rule base, and adds the case to the model training set to retrain the fault classification model and improve the accuracy of subsequent diagnoses.

[0023] The fault alarm and maintenance module is configured to receive fault diagnosis information reported by the street light intelligent controller, generate an alarm, and trigger the maintenance process. The street light intelligent controller is deployed at each street light node and includes: C1: Receives and executes the lighting control strategy issued by the strategy management module, and controls the switching or dimming of the streetlights. C2: Collects the operating status data and energy consumption data of the streetlights and uploads them to the data monitoring and analysis module through the communication network; C3: Monitors the fault status of the streetlights under its jurisdiction, and generates fault diagnosis information when a fault is detected, and reports it to the fault alarm and maintenance module; The fault diagnosis information reception and verification process involves receiving data, monitoring the communication network in real time, and receiving fault diagnosis information reported by each street light node controller. The information must include core fields such as street light node, fault type, occurrence time, fault description, and confidence level. Validity is verified through data format verification, node legality verification, and information integrity verification to filter valid fault information. Invalid information is marked, stored in the log, and fed back to the controller for re-reporting. The system generates and pushes tiered alarms, classifying faults into levels 1 to 3 based on their severity. Alarms are generated by generating differentiated alarm information according to the fault level, including the location of street light nodes, fault type, and processing priority. Emergency faults are accompanied by GIS location coordinates. Emergency faults are pushed to maintenance personnel's terminals via pop-up windows and SMS messages, serious faults are pushed to the APP and platform alarms, and minor faults are recorded in the platform alarm list and alarm information is pushed to user terminals simultaneously. The operation and maintenance process is automatically triggered and scheduled, and work orders are generated. Work orders are automatically created based on alarm information, and associated with fault details, node location, and historical operation and maintenance records. Intelligent work order dispatching combines the location, skill tags, and current load of operation and maintenance personnel to match the best personnel through algorithms and push work orders to their terminals. Process tracking triggers a real-time update mechanism for work order status, which is synchronized to the remote control platform and user terminals. The system features closed-loop operation and maintenance results and information archiving. Results feedback includes receiving processing results uploaded by maintenance personnel, such as fault causes, repair measures, and on-site photos, and synchronizing them to the data monitoring and analysis module to verify the street light recovery status. Alarm termination automatically terminates the alarm status and pushes a fault-resolved notification to relevant terminals after confirming the street light has returned to normal. Information archiving involves archiving fault information, maintenance work orders, and processing results by street light node and time index to form an operation and maintenance file, supporting subsequent fault analysis and prevention.

[0024] The user terminal module is used to access the remote control platform, view the status of the street light intelligent controller, configure control strategies, and receive alarm information; D101: Check street light status User login and authentication: Users open a web browser or mobile app, enter the platform address, and log in using the assigned username and password. The system performs identity verification and permission checks. Enter the status monitoring interface. On the main interface or in the navigation menu, select the device monitoring or map overview option to view the status information. View the distribution of all streetlights on the electronic map and quickly understand the overall status through color indicators, such as green for normal, gray for offline, and red for fault. Click on a single streetlight icon to bring up a details box and view its operating status data, including voltage, current, power, brightness, energy consumption, and basic information such as device ID, location, and model. D102: Configure Control Strategy Enter the strategy management section. In the main interface navigation menu, select strategy management or smart control to edit the strategy. Click "Add Strategy" or select an existing strategy to edit. Set policy parameters and select policy type: timed policy, illuminance policy, motion detection policy, or a combination thereof; configure rules: set specific time, illuminance threshold, or lighting duration after triggering; specify execution devices: select a single street light, group, or area to bind the policy to them; Save and distribute: Click Save or Save and distribute, and the system will send the policy to the remote control platform, where the policy management module will compile and distribute it to the corresponding smart street light controller. D103: Receive alarm information Automatic alarm push: When a fault alarm or an alarm is generated by the operation and maintenance module, the system will automatically push information to the user terminals that have been logged in or subscribed. Reception methods: In-app messages: unread alerts are generated in the message center of the app or web application; Push notifications: brief alert information is displayed on the phone's lock screen or notification bar.

[0025] To view and handle alarms, users can click on a notification or enter the alarm center to view the alarm list; they can view alarm details, such as the faulty device, location, time, and suggested handling solutions; users with the appropriate permissions can confirm, reassign, or close alarms.

[0026] A smart street light monitoring device is the core terminal equipment of the street light remote control system. It is deployed at each street light node and undertakes the key functions of command execution, data acquisition, and fault monitoring. It achieves intelligent management of street lights by working together with the remote control platform and user terminals through a communication network. Its core functions include: First, precise response to control strategies, receiving three types of lighting strategies from the remote control platform: timing, illuminance, and motion detection, and controlling them through relay switches and outputting 0-10V dimming signals to realize the on-demand start and stop of streetlights and dynamic adjustment of brightness; Second, it comprehensively collects key data, captures real-time street light operation status data (power supply voltage, current, temperature, sensor status, etc.) and energy consumption data (cumulative power consumption, average power, etc.), and uploads them to the platform through the LoRaWAN communication network and MQTT protocol after standardized processing. Third, it actively monitors and reports faults, monitors the streetlights and its own operating status in real time, generates diagnostic data containing node information and fault type when a fault is detected, and promptly pushes it to the fault alarm and operation and maintenance module to trigger a closed-loop operation and maintenance process. At the same time, it supports policy execution log feedback, providing basic support for platform data analysis and policy optimization.

[0027] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other. In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A remote control system, characterized in that, include: Remote control platform, street light intelligent controller connected to the remote control platform via a communication network, and user terminal; The strategy management module is configured to generate and distribute lighting control strategies, which include timing strategies, illuminance strategies, and motion detection strategies. Data monitoring and analysis module: configured to receive the operating status data and energy consumption data of the street light intelligent controller, analyze the operating status data and energy consumption data, and generate fault diagnosis information; The fault alarm and maintenance module is configured to receive fault diagnosis information reported by the street light intelligent controller, generate alarms, and trigger maintenance procedures. The street light intelligent controller is deployed at each street light node and includes: C1: Receives and executes the lighting control strategy issued by the strategy management module, and controls the switching or dimming of the streetlights. C2: Collects the operating status data and energy consumption data of the streetlights and uploads them to the data monitoring and analysis module through the communication network; C3: Monitors the fault status of the streetlights under its jurisdiction, and generates fault diagnosis information when a fault is detected, and reports it to the fault alarm and maintenance module; User terminal module: Used to access the remote control platform, view the status of the street light intelligent controller, configure control strategies, and receive alarm information.

2. The remote control system according to claim 1, characterized in that, The strategy management module generates lighting control strategies including: Timing strategy configuration: Set fixed on / off times for streetlights, set on / off times based on sunrise and sunset time offsets, and set on / off times for streetlights according to different date types; Illuminance strategy configuration: Set illuminance start threshold L1 and stop threshold L2. Lighting is activated when the illuminance is below L1 and turned off when the illuminance is above L2. Set brightness mapping rules based on the illuminance median threshold to dynamically adjust the street light brightness. Motion detection strategy configuration: Divide the detection sensitivity into 1 to 5 levels. Level 1 only detects fast-moving targets, and level 5 detects slow-moving targets; set detection time period limits; set anti-accidental touch rules, and only trigger brightness mapping when two consecutive moving targets are detected with an interval of ≤1 second.

3. A remote control system according to claim 2, characterized in that, The lighting control policy issuance by the policy management module includes: The lighting control strategy that has undergone strategy conflict arbitration is compiled into a data packet that can be recognized by the street light smart controller and encapsulated. The data packet includes the street light device ID, strategy type, trigger condition, execution action, effective time, and priority. Data packets are sent to the target street light smart controller via the LoRaWAN communication network in real time or in batches. Batch sending is carried out during the early morning when the network load is low. After the smart street light controller executes the policy action, it uploads the execution log, which includes timestamps, street light device IDs, trigger policy types, and executed actions, as part of the running status data to the data monitoring and analysis module.

4. A remote control system according to claim 1, characterized in that, The data monitoring and analysis module includes: Data cleaning: When a data field is missing in a single data packet, it is filled by interpolation using the average of the previous 5 historical data collected for that street light node; if a data field is missing in 10 consecutive collections, the sensor of that street light node is marked as faulty. Outlier removal: Based on the principle of normal distribution, extreme outlier data exceeding the mean ± 3 standard deviations are removed, and outlier information is recorded, including street light node, collection timestamp, and outlier data field value; Data noise reduction: For sensor data with large fluctuations such as illuminance and lamp temperature, a sliding window mean filtering algorithm with a window size of 5 acquisition cycles is used to calculate the filtered value; Data standardization: The Z-Score standardization formula is used to convert numerical data such as voltage, current, temperature, and energy consumption into standard values ​​within a unified range.

5. A remote control system according to claim 1, characterized in that, The data monitoring and analysis module analyzes the operational status data: Threshold analysis involves setting normal ranges, warning thresholds, and abnormal thresholds for power supply voltage, current, street light temperature, and power factor through user terminals. Data within the warning threshold range is marked as a state warning, and data within the abnormal threshold range is marked as a state abnormal. Historical trend analysis extracts historical data of the same monitoring indicator for the current street light node over the past 7 days to generate a trend curve, calculates the trend slope, and marks it as an abnormal trend if the slope is greater than a preset threshold and the current indicator is close to the warning threshold. Abnormal pattern recognition is based on the K-means clustering machine learning algorithm. It extracts features such as voltage standard deviation, current fluctuation coefficient, temperature mean, and strategy execution switching frequency for each acquisition cycle. These features are then input into a pre-trained K-means model to obtain clusters. If the feature vectors of three consecutive acquisition cycles all belong to the abnormal clusters, the pattern is determined to be abnormal.

6. A remote control system according to claim 1, characterized in that, The data monitoring and analysis module analyzes the energy consumption data: Energy consumption baselines are established, including individual baselines and regional benchmarks. The individual baseline is the average daily energy consumption of the same street light node over the past 30 days after excluding the impact of extreme weather and special events. The regional benchmark is the average energy consumption of the same street light nodes in the same region over the past 30 days. Energy consumption anomaly detection: Calculate the deviation rate between the current street light node's daily energy consumption and the individual baseline. If the deviation rate exceeds ±20%, it is judged as an individual energy consumption anomaly. Calculate the deviation rate between the current street light node's daily energy consumption and the regional benchmark. If the deviation rate exceeds ±30%, it is judged as a regional energy consumption anomaly. The strategy correlation analysis calls the data of the currently effective strategy to analyze the correlation between energy consumption and strategy execution. If the trigger frequency of the motion detection strategy is higher than the historical average and the energy consumption deviation rate is >20%, optimization suggestions for adjusting the motion detection sensitivity are generated. If the illuminance strategy activation threshold L1 is set too low, resulting in prolonged lighting duration and energy consumption deviation rate >20%, optimization suggestions for adjusting the illuminance activation threshold are generated.

7. A remote control system according to claim 6, characterized in that, The data monitoring and analysis module generates fault diagnosis information: The fault information is divided into three levels: Level 1 emergency fault, which directly causes the street lights to stop working and needs to be dealt with within 1 hour; Level 2 serious fault, which affects the lighting effect or poses a safety hazard and needs to be dealt with within 4 hours; and Level 3 minor fault, which does not affect basic lighting and only has room for optimization and needs to be dealt with within 24 hours.

8. A remote control system according to claim 1, characterized in that, The fault alarm and maintenance module includes: Fault diagnosis information reception and verification: Real-time monitoring of the communication network to receive fault diagnosis information; graded alarm generation and push: Generating differentiated alarm information according to fault level; automatic triggering and scheduling of operation and maintenance processes: Automatically creating operation and maintenance work orders based on alarm information, updating work order status in real time and synchronizing with the remote control platform and user terminals; closed-loop operation and maintenance results and information archiving: Receiving processing results uploaded by operation and maintenance personnel and synchronizing with the data monitoring and analysis module to verify the street light restoration status.

9. A remote control system according to claim 1, characterized in that, The user terminal includes: View street light status: After logging in with a username and password and passing the permission verification, users can view the distribution of all street lights and their status indicated by color on the electronic map. Clicking on a single street light icon will allow users to view its operating status data and basic information. Configure control policies: Enter the policy management interface, select the policy type and configure specific rules, specify the execution device, save and send to the remote control platform; Receive alarm information: Receive alarm information via in-app messages or push notifications. Click on the notification or enter the alarm center to view the alarm list and details. Users with the corresponding permissions can confirm, forward, or close alarms.

10. A smart street light monitoring device, characterized in that, It stores program instructions that are executed by a processor to use a remote control system as described in any one of claims 1-9.

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